From de6908e7195e87b7dfef8c3d1d1d2fc3a8e2410a Mon Sep 17 00:00:00 2001 From: Jeroen Staab Date: Mon, 24 Dec 2018 14:18:38 +0100 Subject: [PATCH 1/8] Added some new models/weights --- image.darknet/R/darknet_models.R | 3 +- .../inst/include/darknet/cfg/yolov2.cfg | 258 ++++++ .../inst/include/darknet/cfg/yolov3.cfg | 789 ++++++++++++++++++ 3 files changed, 1049 insertions(+), 1 deletion(-) create mode 100644 image.darknet/inst/include/darknet/cfg/yolov2.cfg create mode 100644 image.darknet/inst/include/darknet/cfg/yolov3.cfg diff --git a/image.darknet/R/darknet_models.R b/image.darknet/R/darknet_models.R index 7ce0ca1..19825ec 100644 --- a/image.darknet/R/darknet_models.R +++ b/image.darknet/R/darknet_models.R @@ -136,7 +136,8 @@ image_darknet_model <- function(type = c("classify", "detect"), model, weights, labels, resize=TRUE){ if(model %in% c("tiny.cfg", "alexnet.cfg", "darknet.cfg", "vgg-16.cfg", "extraction.cfg", "darknet19.cfg", "darknet19_448.cfg", - "yolo.cfg", "tiny-yolo.cfg", "yolo-voc", "tiny-yolo-voc.cfg")){ + "yolo.cfg", "tiny-yolo.cfg", "yolo-voc", "tiny-yolo-voc.cfg", + "yolov2.cfg", "yolov2-voc.cfg", "yolov3.cfg", "yolov3-voc.cfg")){ model <- system.file(package="image.darknet", "include", "darknet", "cfg", model) } stopifnot(file.exists(model)) diff --git a/image.darknet/inst/include/darknet/cfg/yolov2.cfg b/image.darknet/inst/include/darknet/cfg/yolov2.cfg new file mode 100644 index 0000000..088edf8 --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/yolov2.cfg @@ -0,0 +1,258 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=8 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + + +####### + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[route] +layers=-9 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=64 +activation=leaky + +[reorg] +stride=2 + +[route] +layers=-1,-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=425 +activation=linear + + +[region] +anchors = 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828 +bias_match=1 +classes=80 +coords=4 +num=5 +softmax=1 +jitter=.3 +rescore=1 + +object_scale=5 +noobject_scale=1 +class_scale=1 +coord_scale=1 + +absolute=1 +thresh = .6 +random=1 diff --git a/image.darknet/inst/include/darknet/cfg/yolov3.cfg b/image.darknet/inst/include/darknet/cfg/yolov3.cfg new file mode 100644 index 0000000..938ffff --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/yolov3.cfg @@ -0,0 +1,789 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + From 0255d3cb6ebe66777a1589209518811a38a088f9 Mon Sep 17 00:00:00 2001 From: Jeroen Staab Date: Mon, 24 Dec 2018 14:56:09 +0100 Subject: [PATCH 2/8] Updated src/ with src/ from YOLO repository --- image.darknet/src/activation_kernels.cu | 87 +- image.darknet/src/activation_layer.c | 20 +- image.darknet/src/activation_layer.h | 8 +- image.darknet/src/activations.c | 7 + image.darknet/src/activations.h | 11 +- image.darknet/src/art.c | 77 - image.darknet/src/avgpool_layer.c | 8 +- image.darknet/src/avgpool_layer.h | 8 +- image.darknet/src/avgpool_layer_kernels.cu | 8 +- image.darknet/src/batchnorm_layer.c | 200 +- image.darknet/src/batchnorm_layer.h | 8 +- image.darknet/src/blas.c | 147 +- image.darknet/src/blas.h | 70 +- image.darknet/src/blas_kernels.cu | 408 ++- image.darknet/src/box.c | 168 +- image.darknet/src/box.h | 10 +- image.darknet/src/captcha.c | 364 --- image.darknet/src/cifar.c | 277 -- image.darknet/src/classifier.c | 1167 -------- image.darknet/src/classifier.h | 1 - image.darknet/src/coco.c | 388 --- image.darknet/src/col2im.h | 2 +- image.darknet/src/col2im_kernels.cu | 2 +- image.darknet/src/compare.c | 2 +- image.darknet/src/connected_layer.c | 179 +- image.darknet/src/connected_layer.h | 22 +- image.darknet/src/convolutional_kernels.cu | 271 +- image.darknet/src/convolutional_layer.c | 254 +- image.darknet/src/convolutional_layer.h | 18 +- image.darknet/src/cost_layer.c | 60 +- image.darknet/src/cost_layer.h | 8 +- image.darknet/src/crnn_layer.c | 106 +- image.darknet/src/crnn_layer.h | 12 +- image.darknet/src/crop_layer.c | 10 +- image.darknet/src/crop_layer.h | 4 +- image.darknet/src/crop_layer_kernels.cu | 32 +- image.darknet/src/cuda.c | 22 +- image.darknet/src/cuda.h | 19 +- image.darknet/src/darknet.c | 452 --- image.darknet/src/data.c | 591 +++- image.darknet/src/data.h | 70 +- image.darknet/src/deconvolutional_kernels.cu | 148 +- image.darknet/src/deconvolutional_layer.c | 292 +- image.darknet/src/deconvolutional_layer.h | 29 +- image.darknet/src/demo.c | 416 ++- image.darknet/src/demo.h | 5 +- image.darknet/src/detection_layer.c | 88 +- image.darknet/src/detection_layer.h | 9 +- image.darknet/src/detector.c | 552 ---- image.darknet/src/dice.c | 118 - image.darknet/src/dropout_layer.c | 16 +- image.darknet/src/dropout_layer.h | 8 +- image.darknet/src/dropout_layer_kernels.cu | 12 +- image.darknet/src/gemm.c | 62 +- image.darknet/src/gemm.h | 2 +- image.darknet/src/go.c | 833 ------ image.darknet/src/gru_layer.c | 461 +-- image.darknet/src/gru_layer.h | 14 +- image.darknet/src/im2col.h | 2 +- image.darknet/src/im2col_kernels.cu | 2 +- image.darknet/src/image.c | 561 ++-- image.darknet/src/image.h | 66 +- image.darknet/src/image_opencv.cpp | 135 + image.darknet/src/iseg_layer.c | 225 ++ image.darknet/src/iseg_layer.h | 19 + image.darknet/src/l2norm_layer.c | 63 + image.darknet/src/l2norm_layer.h | 15 + image.darknet/src/layer.c | 3 +- image.darknet/src/layer.h | 272 +- image.darknet/src/list.h | 15 +- image.darknet/src/local_layer.c | 90 +- image.darknet/src/local_layer.h | 12 +- image.darknet/src/logistic_layer.c | 71 + image.darknet/src/logistic_layer.h | 15 + image.darknet/src/lstm_layer.c | 626 ++++ image.darknet/src/lstm_layer.h | 20 + image.darknet/src/matrix.c | 15 + image.darknet/src/matrix.h | 13 +- image.darknet/src/maxpool_layer.c | 24 +- image.darknet/src/maxpool_layer.h | 8 +- image.darknet/src/maxpool_layer_kernels.cu | 24 +- image.darknet/src/network.c | 933 ++++-- image.darknet/src/network.h | 116 +- image.darknet/src/network_kernels.cu | 408 --- image.darknet/src/nightmare.c | 308 -- image.darknet/src/normalization_layer.c | 41 +- image.darknet/src/normalization_layer.h | 8 +- image.darknet/src/option_list.c | 17 + image.darknet/src/option_list.h | 4 - image.darknet/src/parser.c | 448 ++- image.darknet/src/parser.h | 6 +- image.darknet/src/region_layer.c | 396 ++- image.darknet/src/region_layer.h | 12 +- image.darknet/src/reorg_layer.c | 101 +- image.darknet/src/reorg_layer.h | 10 +- image.darknet/src/rnn.c | 492 ---- image.darknet/src/rnn_layer.c | 173 +- image.darknet/src/rnn_layer.h | 14 +- image.darknet/src/rnn_vid.c | 211 -- image.darknet/src/route_layer.c | 21 +- image.darknet/src/route_layer.h | 8 +- image.darknet/src/shortcut_layer.c | 53 +- image.darknet/src/shortcut_layer.h | 9 +- image.darknet/src/softmax_layer.c | 67 +- image.darknet/src/softmax_layer.h | 8 +- image.darknet/src/stb_image.h | 2723 ++++++++++++------ image.darknet/src/stb_image_write.h | 1208 ++++++-- image.darknet/src/super.c | 131 - image.darknet/src/swag.c | 91 - image.darknet/src/tag.c | 153 - image.darknet/src/tree.c | 16 +- image.darknet/src/tree.h | 21 +- image.darknet/src/upsample_layer.c | 106 + image.darknet/src/upsample_layer.h | 15 + image.darknet/src/utils.c | 115 +- image.darknet/src/utils.h | 33 +- image.darknet/src/voxel.c | 169 -- image.darknet/src/writing.c | 150 - image.darknet/src/yolo.c | 355 --- image.darknet/src/yolo_layer.c | 374 +++ image.darknet/src/yolo_layer.h | 19 + 121 files changed, 9734 insertions(+), 10748 deletions(-) delete mode 100644 image.darknet/src/art.c delete mode 100644 image.darknet/src/captcha.c delete mode 100644 image.darknet/src/cifar.c delete mode 100644 image.darknet/src/classifier.c delete mode 100644 image.darknet/src/coco.c delete mode 100644 image.darknet/src/darknet.c delete mode 100644 image.darknet/src/detector.c delete mode 100644 image.darknet/src/dice.c delete mode 100644 image.darknet/src/go.c create mode 100644 image.darknet/src/image_opencv.cpp create mode 100644 image.darknet/src/iseg_layer.c create mode 100644 image.darknet/src/iseg_layer.h create mode 100644 image.darknet/src/l2norm_layer.c create mode 100644 image.darknet/src/l2norm_layer.h create mode 100644 image.darknet/src/logistic_layer.c create mode 100644 image.darknet/src/logistic_layer.h create mode 100644 image.darknet/src/lstm_layer.c create mode 100644 image.darknet/src/lstm_layer.h delete mode 100644 image.darknet/src/network_kernels.cu delete mode 100644 image.darknet/src/nightmare.c delete mode 100644 image.darknet/src/rnn.c delete mode 100644 image.darknet/src/rnn_vid.c delete mode 100644 image.darknet/src/super.c delete mode 100644 image.darknet/src/swag.c delete mode 100644 image.darknet/src/tag.c create mode 100644 image.darknet/src/upsample_layer.c create mode 100644 image.darknet/src/upsample_layer.h delete mode 100644 image.darknet/src/voxel.c delete mode 100644 image.darknet/src/writing.c delete mode 100644 image.darknet/src/yolo.c create mode 100644 image.darknet/src/yolo_layer.c create mode 100644 image.darknet/src/yolo_layer.h diff --git a/image.darknet/src/activation_kernels.cu b/image.darknet/src/activation_kernels.cu index 994e206..4dc5804 100644 --- a/image.darknet/src/activation_kernels.cu +++ b/image.darknet/src/activation_kernels.cu @@ -10,8 +10,8 @@ extern "C" { __device__ float lhtan_activate_kernel(float x) { - if(x < 0) return .001*x; - if(x > 1) return .001*(x-1) + 1; + if(x < 0) return .001f*x; + if(x > 1) return .001f*(x-1.f) + 1.f; return x; } __device__ float lhtan_gradient_kernel(float x) @@ -27,25 +27,26 @@ __device__ float hardtan_activate_kernel(float x) return x; } __device__ float linear_activate_kernel(float x){return x;} -__device__ float logistic_activate_kernel(float x){return 1./(1. + exp(-x));} -__device__ float loggy_activate_kernel(float x){return 2./(1. + exp(-x)) - 1;} +__device__ float logistic_activate_kernel(float x){return 1.f/(1.f + expf(-x));} +__device__ float loggy_activate_kernel(float x){return 2.f/(1.f + expf(-x)) - 1;} __device__ float relu_activate_kernel(float x){return x*(x>0);} -__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} -__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01*x;} -__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1*x;} -__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1*x;} -__device__ float tanh_activate_kernel(float x){return (2/(1 + exp(-2*x)) - 1);} +__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);} +__device__ float selu_activate_kernel(float x){return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x)-1);} +__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;} +__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1f*x;} +__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1f*x;} +__device__ float tanh_activate_kernel(float x){return (2.f/(1 + expf(-2*x)) - 1);} __device__ float plse_activate_kernel(float x) { - if(x < -4) return .01 * (x + 4); - if(x > 4) return .01 * (x - 4) + 1; - return .125*x + .5; + if(x < -4) return .01f * (x + 4); + if(x > 4) return .01f * (x - 4) + 1; + return .125f*x + .5f; } __device__ float stair_activate_kernel(float x) { - int n = floor(x); - if (n%2 == 0) return floor(x/2.); - else return (x - n) + floor(x/2.); + int n = floorf(x); + if (n%2 == 0) return floorf(x/2); + else return (x - n) + floorf(x/2); } @@ -58,19 +59,20 @@ __device__ float linear_gradient_kernel(float x){return 1;} __device__ float logistic_gradient_kernel(float x){return (1-x)*x;} __device__ float loggy_gradient_kernel(float x) { - float y = (x+1.)/2.; + float y = (x+1)/2; return 2*(1-y)*y; } __device__ float relu_gradient_kernel(float x){return (x>0);} __device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);} -__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01;} -__device__ float ramp_gradient_kernel(float x){return (x>0)+.1;} -__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1;} +__device__ float selu_gradient_kernel(float x){return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732);} +__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01f;} +__device__ float ramp_gradient_kernel(float x){return (x>0)+.1f;} +__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1f;} __device__ float tanh_gradient_kernel(float x){return 1-x*x;} -__device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01 : .125;} +__device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01f : .125f;} __device__ float stair_gradient_kernel(float x) { - if (floor(x) == x) return 0; + if (floorf(x) == x) return 0; return 1; } @@ -87,6 +89,8 @@ __device__ float activate_kernel(float x, ACTIVATION a) return relu_activate_kernel(x); case ELU: return elu_activate_kernel(x); + case SELU: + return selu_activate_kernel(x); case RELIE: return relie_activate_kernel(x); case RAMP: @@ -120,6 +124,8 @@ __device__ float gradient_kernel(float x, ACTIVATION a) return relu_gradient_kernel(x); case ELU: return elu_gradient_kernel(x); + case SELU: + return selu_gradient_kernel(x); case RELIE: return relie_gradient_kernel(x); case RAMP: @@ -140,6 +146,41 @@ __device__ float gradient_kernel(float x, ACTIVATION a) return 0; } +__global__ void binary_gradient_array_kernel(float *x, float *dy, int n, int s, BINARY_ACTIVATION a, float *dx) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + int i = id % s; + int b = id / s; + float x1 = x[b*s + i]; + float x2 = x[b*s + s/2 + i]; + if(id < n) { + float de = dy[id]; + dx[b*s + i] = x2*de; + dx[b*s + s/2 + i] = x1*de; + } +} + +extern "C" void binary_gradient_array_gpu(float *x, float *dx, int n, int size, BINARY_ACTIVATION a, float *y) +{ + binary_gradient_array_kernel<<>>(x, dx, n/2, size, a, y); + check_error(cudaPeekAtLastError()); +} +__global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTIVATION a, float *y) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + int i = id % s; + int b = id / s; + float x1 = x[b*s + i]; + float x2 = x[b*s + s/2 + i]; + if(id < n) y[id] = x1*x2; +} + +extern "C" void binary_activate_array_gpu(float *x, int n, int size, BINARY_ACTIVATION a, float *y) +{ + binary_activate_array_kernel<<>>(x, n/2, size, a, y); + check_error(cudaPeekAtLastError()); +} + __global__ void activate_array_kernel(float *x, int n, ACTIVATION a) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -152,13 +193,13 @@ __global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delt if(i < n) delta[i] *= gradient_kernel(x[i], a); } -extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) +extern "C" void activate_array_gpu(float *x, int n, ACTIVATION a) { activate_array_kernel<<>>(x, n, a); check_error(cudaPeekAtLastError()); } -extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta) +extern "C" void gradient_array_gpu(float *x, int n, ACTIVATION a, float *delta) { gradient_array_kernel<<>>(x, n, a, delta); check_error(cudaPeekAtLastError()); diff --git a/image.darknet/src/activation_layer.c b/image.darknet/src/activation_layer.c index 3430dac..b4ba953 100644 --- a/image.darknet/src/activation_layer.c +++ b/image.darknet/src/activation_layer.c @@ -35,29 +35,29 @@ layer make_activation_layer(int batch, int inputs, ACTIVATION activation) return l; } -void forward_activation_layer(layer l, network_state state) +void forward_activation_layer(layer l, network net) { - copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); activate_array(l.output, l.outputs*l.batch, l.activation); } -void backward_activation_layer(layer l, network_state state) +void backward_activation_layer(layer l, network net) { gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); - copy_cpu(l.outputs*l.batch, l.delta, 1, state.delta, 1); + copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1); } #ifdef GPU -void forward_activation_layer_gpu(layer l, network_state state) +void forward_activation_layer_gpu(layer l, network net) { - copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); } -void backward_activation_layer_gpu(layer l, network_state state) +void backward_activation_layer_gpu(layer l, network net) { - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/src/activation_layer.h b/image.darknet/src/activation_layer.h index a09756a..42118a8 100644 --- a/image.darknet/src/activation_layer.h +++ b/image.darknet/src/activation_layer.h @@ -7,12 +7,12 @@ layer make_activation_layer(int batch, int inputs, ACTIVATION activation); -void forward_activation_layer(layer l, network_state state); -void backward_activation_layer(layer l, network_state state); +void forward_activation_layer(layer l, network net); +void backward_activation_layer(layer l, network net); #ifdef GPU -void forward_activation_layer_gpu(layer l, network_state state); -void backward_activation_layer_gpu(layer l, network_state state); +void forward_activation_layer_gpu(layer l, network net); +void backward_activation_layer_gpu(layer l, network net); #endif #endif diff --git a/image.darknet/src/activations.c b/image.darknet/src/activations.c index 0cbb2f5..da1a17a 100644 --- a/image.darknet/src/activations.c +++ b/image.darknet/src/activations.c @@ -16,6 +16,8 @@ char *get_activation_string(ACTIVATION a) return "relu"; case ELU: return "elu"; + case SELU: + return "selu"; case RELIE: return "relie"; case RAMP: @@ -46,6 +48,7 @@ ACTIVATION get_activation(char *s) if (strcmp(s, "loggy")==0) return LOGGY; if (strcmp(s, "relu")==0) return RELU; if (strcmp(s, "elu")==0) return ELU; + if (strcmp(s, "selu")==0) return SELU; if (strcmp(s, "relie")==0) return RELIE; if (strcmp(s, "plse")==0) return PLSE; if (strcmp(s, "hardtan")==0) return HARDTAN; @@ -72,6 +75,8 @@ float activate(float x, ACTIVATION a) return relu_activate(x); case ELU: return elu_activate(x); + case SELU: + return selu_activate(x); case RELIE: return relie_activate(x); case RAMP: @@ -113,6 +118,8 @@ float gradient(float x, ACTIVATION a) return relu_gradient(x); case ELU: return elu_gradient(x); + case SELU: + return selu_gradient(x); case RELIE: return relie_gradient(x); case RAMP: diff --git a/image.darknet/src/activations.h b/image.darknet/src/activations.h index 1c36ff5..9780d2c 100644 --- a/image.darknet/src/activations.h +++ b/image.darknet/src/activations.h @@ -1,12 +1,9 @@ #ifndef ACTIVATIONS_H #define ACTIVATIONS_H +#include "darknet.h" #include "cuda.h" #include "math.h" -typedef enum{ - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN -}ACTIVATION; - ACTIVATION get_activation(char *s); char *get_activation_string(ACTIVATION a); @@ -15,8 +12,8 @@ float gradient(float x, ACTIVATION a); void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta); void activate_array(float *x, const int n, const ACTIVATION a); #ifdef GPU -void activate_array_ongpu(float *x, int n, ACTIVATION a); -void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); +void activate_array_gpu(float *x, int n, ACTIVATION a); +void gradient_array_gpu(float *x, int n, ACTIVATION a, float *delta); #endif static inline float stair_activate(float x) @@ -36,6 +33,7 @@ static inline float logistic_activate(float x){return 1./(1. + exp(-x));} static inline float loggy_activate(float x){return 2./(1. + exp(-x)) - 1;} static inline float relu_activate(float x){return x*(x>0);} static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} +static inline float selu_activate(float x){return (x >= 0)*1.0507*x + (x < 0)*1.0507*1.6732*(exp(x)-1);} static inline float relie_activate(float x){return (x>0) ? x : .01*x;} static inline float ramp_activate(float x){return x*(x>0)+.1*x;} static inline float leaky_activate(float x){return (x>0) ? x : .1*x;} @@ -78,6 +76,7 @@ static inline float stair_gradient(float x) } static inline float relu_gradient(float x){return (x>0);} static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);} +static inline float selu_gradient(float x){return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732);} static inline float relie_gradient(float x){return (x>0) ? 1 : .01;} static inline float ramp_gradient(float x){return (x>0)+.1;} static inline float leaky_gradient(float x){return (x>0) ? 1 : .1;} diff --git a/image.darknet/src/art.c b/image.darknet/src/art.c deleted file mode 100644 index 71d3719..0000000 --- a/image.darknet/src/art.c +++ /dev/null @@ -1,77 +0,0 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" -#include "option_list.h" -#include "blas.h" -#include "classifier.h" -#include - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -image get_image_from_stream(CvCapture *cap); -#endif - - -void demo_art(char *cfgfile, char *weightfile, int cam_index) -{ -#ifdef OPENCV - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - - srand(2222222); - CvCapture * cap; - - cap = cvCaptureFromCAM(cam_index); - - char *window = "ArtJudgementBot9000!!!"; - if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow(window, CV_WINDOW_NORMAL); - cvResizeWindow(window, 512, 512); - int i; - int idx[] = {37, 401, 434}; - int n = sizeof(idx)/sizeof(idx[0]); - - while(1){ - image in = get_image_from_stream(cap); - image in_s = resize_image(in, net.w, net.h); - show_image(in, window); - - float *p = network_predict(net, in_s.data); - - printf("\033[2J"); - printf("\033[1;1H"); - - float score = 0; - for(i = 0; i < n; ++i){ - float s = p[idx[i]]; - if (s > score) score = s; - } - score = score; - printf("I APPRECIATE THIS ARTWORK: %10.7f%%\n", score*100); - printf("["); - int upper = 30; - for(i = 0; i < upper; ++i){ - printf("%c", ((i+.5) < score*upper) ? 219 : ' '); - } - printf("]\n"); - - free_image(in_s); - free_image(in); - - cvWaitKey(1); - } -#endif -} - - -void run_art(int argc, char **argv) -{ - int cam_index = find_int_arg(argc, argv, "-c", 0); - char *cfg = argv[2]; - char *weights = argv[3]; - demo_art(cfg, weights, cam_index); -} - diff --git a/image.darknet/src/avgpool_layer.c b/image.darknet/src/avgpool_layer.c index b6932fe..83034db 100644 --- a/image.darknet/src/avgpool_layer.c +++ b/image.darknet/src/avgpool_layer.c @@ -37,7 +37,7 @@ void resize_avgpool_layer(avgpool_layer *l, int w, int h) l->inputs = h*w*l->c; } -void forward_avgpool_layer(const avgpool_layer l, network_state state) +void forward_avgpool_layer(const avgpool_layer l, network net) { int b,i,k; @@ -47,14 +47,14 @@ void forward_avgpool_layer(const avgpool_layer l, network_state state) l.output[out_index] = 0; for(i = 0; i < l.h*l.w; ++i){ int in_index = i + l.h*l.w*(k + b*l.c); - l.output[out_index] += state.input[in_index]; + l.output[out_index] += net.input[in_index]; } l.output[out_index] /= l.h*l.w; } } } -void backward_avgpool_layer(const avgpool_layer l, network_state state) +void backward_avgpool_layer(const avgpool_layer l, network net) { int b,i,k; @@ -63,7 +63,7 @@ void backward_avgpool_layer(const avgpool_layer l, network_state state) int out_index = k + b*l.c; for(i = 0; i < l.h*l.w; ++i){ int in_index = i + l.h*l.w*(k + b*l.c); - state.delta[in_index] += l.delta[out_index] / (l.h*l.w); + net.delta[in_index] += l.delta[out_index] / (l.h*l.w); } } } diff --git a/image.darknet/src/avgpool_layer.h b/image.darknet/src/avgpool_layer.h index f8329ae..3bd356c 100644 --- a/image.darknet/src/avgpool_layer.h +++ b/image.darknet/src/avgpool_layer.h @@ -11,12 +11,12 @@ typedef layer avgpool_layer; image get_avgpool_image(avgpool_layer l); avgpool_layer make_avgpool_layer(int batch, int w, int h, int c); void resize_avgpool_layer(avgpool_layer *l, int w, int h); -void forward_avgpool_layer(const avgpool_layer l, network_state state); -void backward_avgpool_layer(const avgpool_layer l, network_state state); +void forward_avgpool_layer(const avgpool_layer l, network net); +void backward_avgpool_layer(const avgpool_layer l, network net); #ifdef GPU -void forward_avgpool_layer_gpu(avgpool_layer l, network_state state); -void backward_avgpool_layer_gpu(avgpool_layer l, network_state state); +void forward_avgpool_layer_gpu(avgpool_layer l, network net); +void backward_avgpool_layer_gpu(avgpool_layer l, network net); #endif #endif diff --git a/image.darknet/src/avgpool_layer_kernels.cu b/image.darknet/src/avgpool_layer_kernels.cu index b7e2770..a7eca3a 100644 --- a/image.darknet/src/avgpool_layer_kernels.cu +++ b/image.darknet/src/avgpool_layer_kernels.cu @@ -43,19 +43,19 @@ __global__ void backward_avgpool_layer_kernel(int n, int w, int h, int c, float } } -extern "C" void forward_avgpool_layer_gpu(avgpool_layer layer, network_state state) +extern "C" void forward_avgpool_layer_gpu(avgpool_layer layer, network net) { size_t n = layer.c*layer.batch; - forward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.input, layer.output_gpu); + forward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, net.input_gpu, layer.output_gpu); check_error(cudaPeekAtLastError()); } -extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network_state state) +extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network net) { size_t n = layer.c*layer.batch; - backward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.delta, layer.delta_gpu); + backward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, net.delta_gpu, layer.delta_gpu); check_error(cudaPeekAtLastError()); } diff --git a/image.darknet/src/batchnorm_layer.c b/image.darknet/src/batchnorm_layer.c index b53548b..ebff387 100644 --- a/image.darknet/src/batchnorm_layer.c +++ b/image.darknet/src/batchnorm_layer.c @@ -1,3 +1,4 @@ +#include "convolutional_layer.h" #include "batchnorm_layer.h" #include "blas.h" #include @@ -5,55 +6,67 @@ layer make_batchnorm_layer(int batch, int w, int h, int c) { fprintf(stderr, "Batch Normalization Layer: %d x %d x %d image\n", w,h,c); - layer layer = {0}; - layer.type = BATCHNORM; - layer.batch = batch; - layer.h = layer.out_h = h; - layer.w = layer.out_w = w; - layer.c = layer.out_c = c; - layer.output = calloc(h * w * c * batch, sizeof(float)); - layer.delta = calloc(h * w * c * batch, sizeof(float)); - layer.inputs = w*h*c; - layer.outputs = layer.inputs; - - layer.scales = calloc(c, sizeof(float)); - layer.scale_updates = calloc(c, sizeof(float)); + layer l = {0}; + l.type = BATCHNORM; + l.batch = batch; + l.h = l.out_h = h; + l.w = l.out_w = w; + l.c = l.out_c = c; + l.output = calloc(h * w * c * batch, sizeof(float)); + l.delta = calloc(h * w * c * batch, sizeof(float)); + l.inputs = w*h*c; + l.outputs = l.inputs; + + l.scales = calloc(c, sizeof(float)); + l.scale_updates = calloc(c, sizeof(float)); + l.biases = calloc(c, sizeof(float)); + l.bias_updates = calloc(c, sizeof(float)); int i; for(i = 0; i < c; ++i){ - layer.scales[i] = 1; + l.scales[i] = 1; } - layer.mean = calloc(c, sizeof(float)); - layer.variance = calloc(c, sizeof(float)); + l.mean = calloc(c, sizeof(float)); + l.variance = calloc(c, sizeof(float)); - layer.rolling_mean = calloc(c, sizeof(float)); - layer.rolling_variance = calloc(c, sizeof(float)); + l.rolling_mean = calloc(c, sizeof(float)); + l.rolling_variance = calloc(c, sizeof(float)); - layer.forward = forward_batchnorm_layer; - layer.backward = backward_batchnorm_layer; + l.forward = forward_batchnorm_layer; + l.backward = backward_batchnorm_layer; #ifdef GPU - layer.forward_gpu = forward_batchnorm_layer_gpu; - layer.backward_gpu = backward_batchnorm_layer_gpu; + l.forward_gpu = forward_batchnorm_layer_gpu; + l.backward_gpu = backward_batchnorm_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, h * w * c * batch); + l.delta_gpu = cuda_make_array(l.delta, h * w * c * batch); + + l.biases_gpu = cuda_make_array(l.biases, c); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, c); - layer.output_gpu = cuda_make_array(layer.output, h * w * c * batch); - layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch); + l.scales_gpu = cuda_make_array(l.scales, c); + l.scale_updates_gpu = cuda_make_array(l.scale_updates, c); - layer.scales_gpu = cuda_make_array(layer.scales, c); - layer.scale_updates_gpu = cuda_make_array(layer.scale_updates, c); + l.mean_gpu = cuda_make_array(l.mean, c); + l.variance_gpu = cuda_make_array(l.variance, c); - layer.mean_gpu = cuda_make_array(layer.mean, c); - layer.variance_gpu = cuda_make_array(layer.variance, c); + l.rolling_mean_gpu = cuda_make_array(l.mean, c); + l.rolling_variance_gpu = cuda_make_array(l.variance, c); - layer.rolling_mean_gpu = cuda_make_array(layer.mean, c); - layer.rolling_variance_gpu = cuda_make_array(layer.variance, c); + l.mean_delta_gpu = cuda_make_array(l.mean, c); + l.variance_delta_gpu = cuda_make_array(l.variance, c); - layer.mean_delta_gpu = cuda_make_array(layer.mean, c); - layer.variance_delta_gpu = cuda_make_array(layer.variance, c); + l.x_gpu = cuda_make_array(l.output, l.batch*l.outputs); + l.x_norm_gpu = cuda_make_array(l.output, l.batch*l.outputs); + #ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); + cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); - layer.x_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); - layer.x_norm_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); + #endif #endif - return layer; + return l; } void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) @@ -108,7 +121,7 @@ void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_del for(f = 0; f < filters; ++f){ for(k = 0; k < spatial; ++k){ int index = j*filters*spatial + f*spatial + k; - delta[index] = delta[index] * 1./(sqrt(variance[f]) + .00001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); + delta[index] = delta[index] * 1./(sqrt(variance[f] + .00001f)) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); } } } @@ -119,33 +132,35 @@ void resize_batchnorm_layer(layer *layer, int w, int h) fprintf(stderr, "Not implemented\n"); } -void forward_batchnorm_layer(layer l, network_state state) +void forward_batchnorm_layer(layer l, network net) { - if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); - if(l.type == CONNECTED){ - l.out_c = l.outputs; - l.out_h = l.out_w = 1; - } - if(state.train){ + if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); + if(net.train){ mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean); variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance); - scal_cpu(l.out_c, .9, l.rolling_mean, 1); - axpy_cpu(l.out_c, .1, l.mean, 1, l.rolling_mean, 1); - scal_cpu(l.out_c, .9, l.rolling_variance, 1); - axpy_cpu(l.out_c, .1, l.variance, 1, l.rolling_variance, 1); + scal_cpu(l.out_c, .99, l.rolling_mean, 1); + axpy_cpu(l.out_c, .01, l.mean, 1, l.rolling_mean, 1); + scal_cpu(l.out_c, .99, l.rolling_variance, 1); + axpy_cpu(l.out_c, .01, l.variance, 1, l.rolling_variance, 1); - copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w); copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1); } else { normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w); } scale_bias(l.output, l.scales, l.batch, l.out_c, l.out_h*l.out_w); + add_bias(l.output, l.biases, l.batch, l.out_c, l.out_h*l.out_w); } -void backward_batchnorm_layer(const layer l, network_state state) +void backward_batchnorm_layer(layer l, network net) { + if(!net.train){ + l.mean = l.rolling_mean; + l.variance = l.rolling_variance; + } + backward_bias(l.bias_updates, l.delta, l.batch, l.out_c, l.out_w*l.out_h); backward_scale_cpu(l.x_norm, l.delta, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates); scale_bias(l.delta, l.scales, l.batch, l.out_c, l.out_h*l.out_w); @@ -153,7 +168,7 @@ void backward_batchnorm_layer(const layer l, network_state state) mean_delta_cpu(l.delta, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta); variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta); normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.out_c, l.out_w*l.out_h, l.delta); - if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, state.delta, 1); + if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1); } #ifdef GPU @@ -171,34 +186,86 @@ void push_batchnorm_layer(layer l) cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.c); } -void forward_batchnorm_layer_gpu(layer l, network_state state) +void forward_batchnorm_layer_gpu(layer l, network net) { - if(l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - if(l.type == CONNECTED){ - l.out_c = l.outputs; - l.out_h = l.out_w = 1; - } - if (state.train) { + if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + if (net.train) { +#ifdef CUDNN + float one = 1; + float zero = 0; + cudnnBatchNormalizationForwardTraining(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + l.dstTensorDesc, + l.x_gpu, + l.dstTensorDesc, + l.output_gpu, + l.normTensorDesc, + l.scales_gpu, + l.biases_gpu, + .01, + l.rolling_mean_gpu, + l.rolling_variance_gpu, + .00001, + l.mean_gpu, + l.variance_gpu); +#else fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); - scal_ongpu(l.out_c, .99, l.rolling_mean_gpu, 1); - axpy_ongpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); - scal_ongpu(l.out_c, .99, l.rolling_variance_gpu, 1); - axpy_ongpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); + scal_gpu(l.out_c, .99, l.rolling_mean_gpu, 1); + axpy_gpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); + scal_gpu(l.out_c, .99, l.rolling_variance_gpu, 1); + axpy_gpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); + + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); +#endif } else { normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); } - scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); } -void backward_batchnorm_layer_gpu(const layer l, network_state state) +void backward_batchnorm_layer_gpu(layer l, network net) { + if(!net.train){ + l.mean_gpu = l.rolling_mean_gpu; + l.variance_gpu = l.rolling_variance_gpu; + } +#ifdef CUDNN + float one = 1; + float zero = 0; + cudnnBatchNormalizationBackward(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + &one, + &one, + l.dstTensorDesc, + l.x_gpu, + l.dstTensorDesc, + l.delta_gpu, + l.dstTensorDesc, + l.x_norm_gpu, + l.normTensorDesc, + l.scales_gpu, + l.scale_updates_gpu, + l.bias_updates_gpu, + .00001, + l.mean_gpu, + l.variance_gpu); + copy_gpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1); +#else + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h); backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu); scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); @@ -206,6 +273,7 @@ void backward_batchnorm_layer_gpu(const layer l, network_state state) fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu); fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu); normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); - if(l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); +#endif + if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/src/batchnorm_layer.h b/image.darknet/src/batchnorm_layer.h index 99d1d0f..25a18a3 100644 --- a/image.darknet/src/batchnorm_layer.h +++ b/image.darknet/src/batchnorm_layer.h @@ -6,12 +6,12 @@ #include "network.h" layer make_batchnorm_layer(int batch, int w, int h, int c); -void forward_batchnorm_layer(layer l, network_state state); -void backward_batchnorm_layer(layer l, network_state state); +void forward_batchnorm_layer(layer l, network net); +void backward_batchnorm_layer(layer l, network net); #ifdef GPU -void forward_batchnorm_layer_gpu(layer l, network_state state); -void backward_batchnorm_layer_gpu(layer l, network_state state); +void forward_batchnorm_layer_gpu(layer l, network net); +void backward_batchnorm_layer_gpu(layer l, network net); void pull_batchnorm_layer(layer l); void push_batchnorm_layer(layer l); #endif diff --git a/image.darknet/src/blas.c b/image.darknet/src/blas.c index 31bd86b..9e16044 100644 --- a/image.darknet/src/blas.c +++ b/image.darknet/src/blas.c @@ -1,5 +1,6 @@ #include "blas.h" -#include "math.h" + +#include #include #include #include @@ -54,7 +55,17 @@ void weighted_sum_cpu(float *a, float *b, float *s, int n, float *c) } } -void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) +void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc) +{ + int i; + for(i = 0; i < n; ++i){ + if(da) da[i] += dc[i] * s[i]; + if(db) db[i] += dc[i] * (1-s[i]); + ds[i] += dc[i] * (a[i] - b[i]); + } +} + +void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out) { int stride = w1/w2; int sample = w2/w1; @@ -73,7 +84,7 @@ void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, for(i = 0; i < minw; ++i){ int out_index = i*sample + w2*(j*sample + h2*(k + c2*b)); int add_index = i*stride + w1*(j*stride + h1*(k + c1*b)); - out[out_index] += add[add_index]; + out[out_index] = s1*out[out_index] + s2*add[add_index]; } } } @@ -112,6 +123,27 @@ void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, fl } } +void l2normalize_cpu(float *x, float *dx, int batch, int filters, int spatial) +{ + int b,f,i; + for(b = 0; b < batch; ++b){ + for(i = 0; i < spatial; ++i){ + float sum = 0; + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + sum += powf(x[index], 2); + } + sum = sqrtf(sum); + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + x[index] /= sum; + dx[index] = (1 - x[index]) / sum; + } + } + } +} + + void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial) { int b, f, i; @@ -161,12 +193,48 @@ void fill_cpu(int N, float ALPHA, float *X, int INCX) for(i = 0; i < N; ++i) X[i*INCX] = ALPHA; } +void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i, j; + int index = 0; + for(j = 0; j < B; ++j) { + for(i = 0; i < NX; ++i){ + if(X) X[j*NX + i] += OUT[index]; + ++index; + } + for(i = 0; i < NY; ++i){ + if(Y) Y[j*NY + i] += OUT[index]; + ++index; + } + } +} + +void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i, j; + int index = 0; + for(j = 0; j < B; ++j) { + for(i = 0; i < NX; ++i){ + OUT[index++] = X[j*NX + i]; + } + for(i = 0; i < NY; ++i){ + OUT[index++] = Y[j*NY + i]; + } + } +} + void copy_cpu(int N, float *X, int INCX, float *Y, int INCY) { int i; for(i = 0; i < N; ++i) Y[i*INCY] = X[i*INCX]; } +void mult_add_into_cpu(int N, float *X, float *Y, float *Z) +{ + int i; + for(i = 0; i < N; ++i) Z[i] += X[i]*Y[i]; +} + void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error) { int i; @@ -179,11 +247,43 @@ void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error) } else { error[i] = 2*abs_val - 1; - delta[i] = (diff < 0) ? -1 : 1; + delta[i] = (diff < 0) ? 1 : -1; } } } +void l1_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float diff = truth[i] - pred[i]; + error[i] = fabs(diff); + delta[i] = diff > 0 ? 1 : -1; + } +} + +void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float t = truth[i]; + float p = pred[i]; + error[i] = (t) ? -log(p) : 0; + delta[i] = t-p; + } +} + +void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float t = truth[i]; + float p = pred[i]; + error[i] = -t*log(p) - (1-t)*log(1-p); + delta[i] = t-p; + } +} + void l2_cpu(int n, float *pred, float *truth, float *delta, float *error) { int i; @@ -202,21 +302,50 @@ float dot_cpu(int N, float *X, int INCX, float *Y, int INCY) return dot; } -void softmax(float *input, int n, float temp, float *output) +void softmax(float *input, int n, float temp, int stride, float *output) { int i; float sum = 0; float largest = -FLT_MAX; for(i = 0; i < n; ++i){ - if(input[i] > largest) largest = input[i]; + if(input[i*stride] > largest) largest = input[i*stride]; } for(i = 0; i < n; ++i){ - float e = exp(input[i]/temp - largest/temp); + float e = exp(input[i*stride]/temp - largest/temp); sum += e; - output[i] = e; + output[i*stride] = e; } for(i = 0; i < n; ++i){ - output[i] /= sum; + output[i*stride] /= sum; } } + +void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + int g, b; + for(b = 0; b < batch; ++b){ + for(g = 0; g < groups; ++g){ + softmax(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); + } + } +} + +void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) +{ + int i, j, k, b; + for(b = 0; b < batch; ++b){ + for(k = 0; k < c; ++k){ + for(j = 0; j < h*stride; ++j){ + for(i = 0; i < w*stride; ++i){ + int in_index = b*w*h*c + k*w*h + (j/stride)*w + i/stride; + int out_index = b*w*h*c*stride*stride + k*w*h*stride*stride + j*w*stride + i; + if(forward) out[out_index] = scale*in[in_index]; + else in[in_index] += scale*out[out_index]; + } + } + } + } +} + + diff --git a/image.darknet/src/blas.h b/image.darknet/src/blas.h index 3d6ee7d..707291d 100644 --- a/image.darknet/src/blas.h +++ b/image.darknet/src/blas.h @@ -1,5 +1,7 @@ #ifndef BLAS_H #define BLAS_H +#include "darknet.h" + void flatten(float *x, int size, int layers, int batch, int forward); void pm(int M, int N, float *A); float *random_matrix(int rows, int cols); @@ -8,53 +10,60 @@ void reorg_cpu(float *x, int w, int h, int c, int batch, int stride, int forward void test_blas(); +void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT); +void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT); +void mult_add_into_cpu(int N, float *X, float *Y, float *Z); + void const_cpu(int N, float ALPHA, float *X, int INCX); -void constrain_ongpu(int N, float ALPHA, float * X, int INCX); +void constrain_gpu(int N, float ALPHA, float * X, int INCX); void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); void mul_cpu(int N, float *X, int INCX, float *Y, int INCY); -void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); -void copy_cpu(int N, float *X, int INCX, float *Y, int INCY); -void scal_cpu(int N, float ALPHA, float *X, int INCX); -void fill_cpu(int N, float ALPHA, float * X, int INCX); -float dot_cpu(int N, float *X, int INCX, float *Y, int INCY); -void test_gpu_blas(); -void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); +int test_gpu_blas(); +void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out); void mean_cpu(float *x, int batch, int filters, int spatial, float *mean); void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); -void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial); void scale_bias(float *output, float *scales, int batch, int n, int size); void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta); void variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta); void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta); +void l2normalize_cpu(float *x, float *dx, int batch, int filters, int spatial); void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error); void l2_cpu(int n, float *pred, float *truth, float *delta, float *error); +void l1_cpu(int n, float *pred, float *truth, float *delta, float *error); +void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error); +void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error); void weighted_sum_cpu(float *a, float *b, float *s, int num, float *c); +void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc); -void softmax(float *input, int n, float temp, float *output); +void softmax(float *input, int n, float temp, int stride, float *output); +void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output); +void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out); #ifdef GPU #include "cuda.h" - -void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); -void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); -void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY); -void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); -void scal_ongpu(int N, float ALPHA, float * X, int INCX); -void supp_ongpu(int N, float ALPHA, float * X, int INCX); -void mask_ongpu(int N, float * X, float mask_num, float * mask); -void const_ongpu(int N, float ALPHA, float *X, int INCX); -void pow_ongpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); -void mul_ongpu(int N, float *X, int INCX, float *Y, int INCY); -void fill_ongpu(int N, float ALPHA, float * X, int INCX); +#include "tree.h" + +void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); +void axpy_gpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); +void copy_gpu(int N, float * X, int INCX, float * Y, int INCY); +void copy_gpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); +void add_gpu(int N, float ALPHA, float * X, int INCX); +void supp_gpu(int N, float ALPHA, float * X, int INCX); +void mask_gpu(int N, float * X, float mask_num, float * mask, float val); +void scale_mask_gpu(int N, float * X, float mask_num, float * mask, float scale); +void const_gpu(int N, float ALPHA, float *X, int INCX); +void pow_gpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); +void mul_gpu(int N, float *X, int INCX, float *Y, int INCY); void mean_gpu(float *x, int batch, int filters, int spatial, float *mean); void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial); +void l2normalize_gpu(float *x, float *dx, int batch, int filters, int spatial); void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta); @@ -63,25 +72,34 @@ void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *varianc void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); -void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); +void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void add_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); +void logistic_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error); +void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error); void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error); void l2_gpu(int n, float *pred, float *truth, float *delta, float *error); +void l1_gpu(int n, float *pred, float *truth, float *delta, float *error); +void wgan_gpu(int n, float *pred, float *truth, float *delta, float *error); void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc); void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c); void mult_add_into_gpu(int num, float *a, float *b, float *c); +void inter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT); +void deinter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT); -void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out); +void reorg_gpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out); -void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output); +void softmax_gpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output); +void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t); void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t); -void flatten_ongpu(float *x, int spatial, int layers, int batch, int forward, float *out); +void flatten_gpu(float *x, int spatial, int layers, int batch, int forward, float *out); +void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier); +void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out); #endif #endif diff --git a/image.darknet/src/blas_kernels.cu b/image.darknet/src/blas_kernels.cu index d940176..47e8217 100644 --- a/image.darknet/src/blas_kernels.cu +++ b/image.darknet/src/blas_kernels.cu @@ -53,24 +53,40 @@ void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, check_error(cudaPeekAtLastError()); } -__global__ void add_bias_kernel(float *output, float *biases, int n, int size) +__global__ void add_bias_kernel(float *output, float *biases, int batch, int n, int size) { - int offset = blockIdx.x * blockDim.x + threadIdx.x; - int filter = blockIdx.y; - int batch = blockIdx.z; + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= n*size*batch) return; + int i = index % size; + index /= size; + int j = index % n; + index /= n; + int k = index; - if(offset < size) output[(batch*n+filter)*size + offset] += biases[filter]; + output[(k*n+j)*size + i] += biases[j]; } void add_bias_gpu(float *output, float *biases, int batch, int n, int size) { - dim3 dimGrid((size-1)/BLOCK + 1, n, batch); - dim3 dimBlock(BLOCK, 1, 1); + int num = n*size*batch; - add_bias_kernel<<>>(output, biases, n, size); + add_bias_kernel<<>>(output, biases, batch, n, size); check_error(cudaPeekAtLastError()); } +__global__ void backward_bias_conn_kernel(float *bias_updates, float *delta, int batch, int n) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= n) return; + int b; + float sum = 0; + for(b = 0; b < batch; ++b){ + int i = b*n + index; + sum += delta[i]; + } + bias_updates[index] += sum; +} + __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size) { __shared__ float part[BLOCK]; @@ -91,6 +107,16 @@ __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batc } } +void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) +{ + if(size == 1){ + backward_bias_conn_kernel<<>>(bias_updates, delta, batch, n); + }else{ + backward_bias_kernel<<>>(bias_updates, delta, batch, n, size); + } + check_error(cudaPeekAtLastError()); +} + /* __global__ void dot_kernel(float *output, float scale, int batch, int n, int size, float *delta) { @@ -133,20 +159,16 @@ void dot_error_gpu(layer l) } */ -void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) -{ - backward_bias_kernel<<>>(bias_updates, delta, batch, n, size); - check_error(cudaPeekAtLastError()); -} - __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (index >= N) return; + + float mhat = m[index] / (1.f - powf(B1, t)); + float vhat = v[index] / (1.f - powf(B2, t)); - x[index] = x[index] - (rate * sqrt(1.-pow(B2, t)) / (1.-pow(B1, t)) * m[index] / (sqrt(v[index]) + eps)); - //if(index == 0) printf("%f %f %f %f\n", m[index], v[index], (rate * sqrt(1.-pow(B2, t)) / (1.-pow(B1, t)) * m[index] / (sqrt(v[index]) + eps))); + x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps); } extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) @@ -155,13 +177,27 @@ extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2 check_error(cudaPeekAtLastError()); } +extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t) +{ + scal_gpu(n, B1, m, 1); + scal_gpu(n, B2, v, 1); + axpy_gpu(n, -decay*batch, w, 1, d, 1); + + axpy_gpu(n, (1-B1), d, 1, m, 1); + mul_gpu(n, d, 1, d, 1); + axpy_gpu(n, (1-B2), d, 1, v, 1); + + adam_gpu(n, w, m, v, B1, B2, rate, eps, t); + fill_gpu(n, 0, d, 1); +} + __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial) { int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (index >= N) return; int f = (index/spatial)%filters; - x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .000001f); + x[index] = (x[index] - mean[f])/(sqrtf(variance[f] + .00001f)); } __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) @@ -170,7 +206,7 @@ __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *vari if (index >= N) return; int f = (index/spatial)%filters; - delta[index] = delta[index] * 1./(sqrt(variance[f]) + .000001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); + delta[index] = delta[index] * 1.f/(sqrtf(variance[f] + .00001f)) + variance_delta[f] * 2.f * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); } extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) @@ -192,7 +228,7 @@ __global__ void variance_delta_kernel(float *x, float *delta, float *mean, floa variance_delta[i] += delta[index]*(x[index] - mean[i]); } } - variance_delta[i] *= -.5 * pow(variance[i] + .000001f, (float)(-3./2.)); + variance_delta[i] *= -.5f * powf(variance[i] + .00001f, (float)(-3.f/2.f)); } __global__ void accumulate_kernel(float *x, int n, int groups, float *sum) @@ -224,12 +260,14 @@ __global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, } } + __syncthreads(); + if(id == 0){ mean_delta[filter] = 0; for(i = 0; i < threads; ++i){ mean_delta[filter] += local[i]; } - mean_delta[filter] *= (-1./sqrt(variance[filter] + .000001f)); + mean_delta[filter] *= (-1.f/sqrtf(variance[filter] + .00001f)); } } @@ -252,12 +290,14 @@ __global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, } } + __syncthreads(); + if(id == 0){ variance_delta[filter] = 0; for(i = 0; i < threads; ++i){ variance_delta[filter] += local[i]; } - variance_delta[filter] *= -.5 * pow(variance[filter] + .000001f, (float)(-3./2.)); + variance_delta[filter] *= -.5f * powf(variance[filter] + .00001f, (float)(-3.f/2.f)); } } @@ -274,7 +314,7 @@ __global__ void mean_delta_kernel(float *delta, float *variance, int batch, int mean_delta[i] += delta[index]; } } - mean_delta[i] *= (-1./sqrt(variance[i] + .000001f)); + mean_delta[i] *= (-1.f/sqrtf(variance[i] + .00001f)); } extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) @@ -297,7 +337,7 @@ extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, flo __global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean) { - float scale = 1./(batch * spatial); + float scale = 1.f/(batch * spatial); int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i >= filters) return; int j,k; @@ -313,7 +353,7 @@ __global__ void mean_kernel(float *x, int batch, int filters, int spatial, floa __global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance) { - float scale = 1./(batch * spatial - 1); + float scale = 1.f/(batch * spatial - 1); int j,k; int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i >= filters) return; @@ -321,7 +361,7 @@ __global__ void variance_kernel(float *x, float *mean, int batch, int filters, i for(j = 0; j < batch; ++j){ for(k = 0; k < spatial; ++k){ int index = j*filters*spatial + i*spatial + k; - variance[i] += pow((x[index] - mean[i]), 2); + variance[i] += powf((x[index] - mean[i]), 2); } } variance[i] *= scale; @@ -391,22 +431,22 @@ __global__ void supp_kernel(int N, float ALPHA, float *X, int INCX) } } -__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX) +__global__ void add_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if(i < N) X[i*INCX] *= ALPHA; + if(i < N) X[i*INCX] += ALPHA; } -__global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) +__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if(i < N) X[i*INCX] = ALPHA; + if(i < N) X[i*INCX] *= ALPHA; } -__global__ void mask_kernel(int n, float *x, float mask_num, float *mask) +__global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if(i < n && mask[i] == mask_num) x[i] = mask_num; + if(i < N) X[i*INCX] = ALPHA; } __global__ void copy_kernel(int N, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY) @@ -429,6 +469,35 @@ extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, check_error(cudaPeekAtLastError()); } +__global__ void l2norm_kernel(int N, float *x, float *dx, int batch, int filters, int spatial) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= N) return; + int b = index / spatial; + int i = index % spatial; + int f; + float sum = 0; + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + sum += powf(x[index], 2); + } + sum = sqrtf(sum); + if(sum == 0) sum = 1; + //printf("%f\n", sum); + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + x[index] /= sum; + dx[index] = (1 - x[index]) / sum; + } +} + +extern "C" void l2normalize_gpu(float *x, float *dx, int batch, int filters, int spatial) +{ + size_t N = batch*spatial; + l2norm_kernel<<>>(N, x, dx, batch, filters, spatial); + check_error(cudaPeekAtLastError()); +} + __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, float *mean) { const int threads = BLOCK; @@ -447,6 +516,8 @@ __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, } } + __syncthreads(); + if(id == 0){ mean[filter] = 0; for(i = 0; i < threads; ++i){ @@ -471,10 +542,12 @@ __global__ void fast_variance_kernel(float *x, float *mean, int batch, int filt for(i = 0; i < spatial; i += threads){ int index = j*spatial*filters + filter*spatial + i + id; - local[id] += (i+id < spatial) ? pow((x[index] - mean[filter]), 2) : 0; + local[id] += (i+id < spatial) ? powf((x[index] - mean[filter]), 2) : 0; } } + __syncthreads(); + if(id == 0){ variance[filter] = 0; for(i = 0; i < threads; ++i){ @@ -509,35 +582,35 @@ extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int check_error(cudaPeekAtLastError()); } -extern "C" void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) +extern "C" void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) { - axpy_ongpu_offset(N, ALPHA, X, 0, INCX, Y, 0, INCY); + axpy_gpu_offset(N, ALPHA, X, 0, INCX, Y, 0, INCY); } -extern "C" void pow_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) +extern "C" void pow_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) { pow_kernel<<>>(N, ALPHA, X, INCX, Y, INCY); check_error(cudaPeekAtLastError()); } -extern "C" void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) +extern "C" void axpy_gpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) { axpy_kernel<<>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY); check_error(cudaPeekAtLastError()); } -extern "C" void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY) +extern "C" void copy_gpu(int N, float * X, int INCX, float * Y, int INCY) { - copy_ongpu_offset(N, X, 0, INCX, Y, 0, INCY); + copy_gpu_offset(N, X, 0, INCX, Y, 0, INCY); } -extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY) +extern "C" void mul_gpu(int N, float * X, int INCX, float * Y, int INCY) { mul_kernel<<>>(N, X, INCX, Y, INCY); check_error(cudaPeekAtLastError()); } -extern "C" void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) +extern "C" void copy_gpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) { copy_kernel<<>>(N, X, OFFX, INCX, Y, OFFY, INCY); check_error(cudaPeekAtLastError()); @@ -560,58 +633,82 @@ __global__ void flatten_kernel(int N, float *x, int spatial, int layers, int bat else out[i1] = x[i2]; } -extern "C" void flatten_ongpu(float *x, int spatial, int layers, int batch, int forward, float *out) +extern "C" void flatten_gpu(float *x, int spatial, int layers, int batch, int forward, float *out) { int size = spatial*batch*layers; flatten_kernel<<>>(size, x, spatial, layers, batch, forward, out); check_error(cudaPeekAtLastError()); } -extern "C" void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out) +extern "C" void reorg_gpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out) { int size = w*h*c*batch; reorg_kernel<<>>(size, x, w, h, c, batch, stride, forward, out); check_error(cudaPeekAtLastError()); } -extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask) +__global__ void mask_kernel(int n, float *x, float mask_num, float *mask, float val) { - mask_kernel<<>>(N, X, mask_num, mask); + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n && mask[i] == mask_num) x[i] = val; +} + +extern "C" void mask_gpu(int N, float * X, float mask_num, float * mask, float val) +{ + mask_kernel<<>>(N, X, mask_num, mask, val); + check_error(cudaPeekAtLastError()); +} + +__global__ void scale_mask_kernel(int n, float *x, float mask_num, float *mask, float scale) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n && mask[i] == mask_num) x[i] *= scale; +} + +extern "C" void scale_mask_gpu(int N, float * X, float mask_num, float * mask, float scale) +{ + scale_mask_kernel<<>>(N, X, mask_num, mask, scale); check_error(cudaPeekAtLastError()); } -extern "C" void const_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void const_gpu(int N, float ALPHA, float * X, int INCX) { const_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -extern "C" void constrain_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void constrain_gpu(int N, float ALPHA, float * X, int INCX) { constrain_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void add_gpu(int N, float ALPHA, float * X, int INCX) +{ + add_kernel<<>>(N, ALPHA, X, INCX); + check_error(cudaPeekAtLastError()); +} + +extern "C" void scal_gpu(int N, float ALPHA, float * X, int INCX) { scal_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void supp_gpu(int N, float ALPHA, float * X, int INCX) { supp_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void fill_gpu(int N, float ALPHA, float * X, int INCX) { fill_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -__global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) +__global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; @@ -625,10 +722,11 @@ __global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stri int out_index = i*sample + w2*(j*sample + h2*(k + c2*b)); int add_index = i*stride + w1*(j*stride + h1*(k + c1*b)); - out[out_index] += add[add_index]; + out[out_index] = s1*out[out_index] + s2*add[add_index]; + //out[out_index] += add[add_index]; } -extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) +extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out) { int minw = (w1 < w2) ? w1 : w2; int minh = (h1 < h2) ? h1 : h2; @@ -642,7 +740,7 @@ extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int if(sample < 1) sample = 1; int size = batch * minw * minh * minc; - shortcut_kernel<<>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); + shortcut_kernel<<>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, s1, s2, out); check_error(cudaPeekAtLastError()); } @@ -651,14 +749,14 @@ __global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ float diff = truth[i] - pred[i]; - float abs_val = abs(diff); + float abs_val = fabsf(diff); if(abs_val < 1) { error[i] = diff * diff; delta[i] = diff; } else { error[i] = 2*abs_val - 1; - delta[i] = (diff < 0) ? -1 : 1; + delta[i] = (diff > 0) ? 1 : -1; } } } @@ -669,6 +767,40 @@ extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, fl check_error(cudaPeekAtLastError()); } +__global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float t = truth[i]; + float p = pred[i]; + error[i] = (t) ? -log(p) : 0; + delta[i] = t-p; + } +} + +extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + softmax_x_ent_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + +__global__ void logistic_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float t = truth[i]; + float p = pred[i]; + error[i] = -t*log(p+.0000001) - (1-t)*log(1-p+.0000001); + delta[i] = t-p; + } +} + +extern "C" void logistic_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + logistic_x_ent_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + __global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float *error) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -685,6 +817,38 @@ extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *er check_error(cudaPeekAtLastError()); } +__global__ void l1_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float diff = truth[i] - pred[i]; + error[i] = abs(diff); + delta[i] = (diff > 0) ? 1 : -1; + } +} + +extern "C" void l1_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + l1_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + +__global__ void wgan_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + error[i] = truth[i] ? -pred[i] : pred[i]; + delta[i] = (truth[i] > 0) ? 1 : -1; + } +} + +extern "C" void wgan_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + wgan_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + + __global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float *c) @@ -695,6 +859,46 @@ __global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float * } } +__global__ void deinter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < (NX+NY)*B){ + int b = i / (NX+NY); + int j = i % (NX+NY); + if (j < NX){ + if(X) X[b*NX + j] += OUT[i]; + } else { + if(Y) Y[b*NY + j - NX] += OUT[i]; + } + } +} + +extern "C" void deinter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + deinter_kernel<<>>(NX, X, NY, Y, B, OUT); + check_error(cudaPeekAtLastError()); +} + +__global__ void inter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < (NX+NY)*B){ + int b = i / (NX+NY); + int j = i % (NX+NY); + if (j < NX){ + OUT[i] = X[b*NX + j]; + } else { + OUT[i] = Y[b*NY + j - NX]; + } + } +} + +extern "C" void inter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + inter_kernel<<>>(NX, X, NY, Y, B, OUT); + check_error(cudaPeekAtLastError()); +} + extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c) { weighted_sum_kernel<<>>(num, a, b, s, c); @@ -706,8 +910,8 @@ __global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ if(da) da[i] += dc[i] * s[i]; - db[i] += dc[i] * (1-s[i]); - ds[i] += dc[i] * a[i] + dc[i] * -b[i]; + if(db) db[i] += dc[i] * (1-s[i]); + ds[i] += dc[i] * (a[i] - b[i]); } } @@ -732,36 +936,100 @@ extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c) } -__device__ void softmax_device(int n, float *input, float temp, float *output) +__device__ void softmax_device(float *input, int n, float temp, int stride, float *output) { int i; float sum = 0; float largest = -INFINITY; for(i = 0; i < n; ++i){ - int val = input[i]; + int val = input[i*stride]; largest = (val>largest) ? val : largest; } for(i = 0; i < n; ++i){ - float e = exp(input[i]/temp - largest/temp); + float e = expf(input[i*stride]/temp - largest/temp); sum += e; - output[i] = e; + output[i*stride] = e; } for(i = 0; i < n; ++i){ - output[i] /= sum; + output[i*stride] /= sum; } } -__global__ void softmax_kernel(int n, int offset, int batch, float *input, float temp, float *output) + +__global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset) { - int b = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if(b >= batch) return; - softmax_device(n, input + b*offset, temp, output + b*offset); + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= spatial*batch*groups) return; + int s = id % spatial; + id = id / spatial; + int g = id % groups; + int b = id / groups; + int goff = group_offset[g]*spatial; + int boff = b*stride; + softmax_device(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s); +} + +extern "C" void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier) +{ + int *tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); + int *tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); + /* + static int *tree_groups_size = 0; + static int *tree_groups_offset = 0; + if(!tree_groups_size){ + tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); + tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); + } + */ + int num = spatial*batch*hier.groups; + softmax_tree_kernel<<>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); + check_error(cudaPeekAtLastError()); + cuda_free((float *)tree_groups_size); + cuda_free((float *)tree_groups_offset); +} + +__global__ void softmax_kernel(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= batch*groups) return; + int b = id / groups; + int g = id % groups; + softmax_device(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); } -extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output) +extern "C" void softmax_gpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + softmax_kernel<<>>(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); + check_error(cudaPeekAtLastError()); +} + + +__global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) +{ + size_t i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i >= N) return; + int out_index = i; + int out_w = i%(w*stride); + i = i/(w*stride); + int out_h = i%(h*stride); + i = i/(h*stride); + int out_c = i%c; + i = i/c; + int b = i%batch; + + int in_w = out_w / stride; + int in_h = out_h / stride; + int in_c = out_c; + + int in_index = b*w*h*c + in_c*w*h + in_h*w + in_w; + + + if(forward) out[out_index] += scale * x[in_index]; + else atomicAdd(x+in_index, scale * out[out_index]); +} +extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { - int inputs = n; - int batch = groups; - softmax_kernel<<>>(inputs, offset, batch, input, temp, output); + size_t size = w*h*c*batch*stride*stride; + upsample_kernel<<>>(size, in, w, h, c, batch, stride, forward, scale, out); check_error(cudaPeekAtLastError()); } diff --git a/image.darknet/src/box.c b/image.darknet/src/box.c index 39dea06..8a1772c 100644 --- a/image.darknet/src/box.c +++ b/image.darknet/src/box.c @@ -3,13 +3,98 @@ #include #include -box float_to_box(float *f) +int nms_comparator(const void *pa, const void *pb) { - box b; + detection a = *(detection *)pa; + detection b = *(detection *)pb; + float diff = 0; + if(b.sort_class >= 0){ + diff = a.prob[b.sort_class] - b.prob[b.sort_class]; + } else { + diff = a.objectness - b.objectness; + } + if(diff < 0) return 1; + else if(diff > 0) return -1; + return 0; +} + +void do_nms_obj(detection *dets, int total, int classes, float thresh) +{ + int i, j, k; + k = total-1; + for(i = 0; i <= k; ++i){ + if(dets[i].objectness == 0){ + detection swap = dets[i]; + dets[i] = dets[k]; + dets[k] = swap; + --k; + --i; + } + } + total = k+1; + + for(i = 0; i < total; ++i){ + dets[i].sort_class = -1; + } + + qsort(dets, total, sizeof(detection), nms_comparator); + for(i = 0; i < total; ++i){ + if(dets[i].objectness == 0) continue; + box a = dets[i].bbox; + for(j = i+1; j < total; ++j){ + if(dets[j].objectness == 0) continue; + box b = dets[j].bbox; + if (box_iou(a, b) > thresh){ + dets[j].objectness = 0; + for(k = 0; k < classes; ++k){ + dets[j].prob[k] = 0; + } + } + } + } +} + + +void do_nms_sort(detection *dets, int total, int classes, float thresh) +{ + int i, j, k; + k = total-1; + for(i = 0; i <= k; ++i){ + if(dets[i].objectness == 0){ + detection swap = dets[i]; + dets[i] = dets[k]; + dets[k] = swap; + --k; + --i; + } + } + total = k+1; + + for(k = 0; k < classes; ++k){ + for(i = 0; i < total; ++i){ + dets[i].sort_class = k; + } + qsort(dets, total, sizeof(detection), nms_comparator); + for(i = 0; i < total; ++i){ + if(dets[i].prob[k] == 0) continue; + box a = dets[i].bbox; + for(j = i+1; j < total; ++j){ + box b = dets[j].bbox; + if (box_iou(a, b) > thresh){ + dets[j].prob[k] = 0; + } + } + } + } +} + +box float_to_box(float *f, int stride) +{ + box b = {0}; b.x = f[0]; - b.y = f[1]; - b.w = f[2]; - b.h = f[3]; + b.y = f[1*stride]; + b.w = f[2*stride]; + b.h = f[3*stride]; return b; } @@ -230,79 +315,6 @@ dbox diou(box a, box b) return dd; } -typedef struct{ - int index; - int class; - float **probs; -} sortable_bbox; - -int nms_comparator(const void *pa, const void *pb) -{ - sortable_bbox a = *(sortable_bbox *)pa; - sortable_bbox b = *(sortable_bbox *)pb; - float diff = a.probs[a.index][b.class] - b.probs[b.index][b.class]; - if(diff < 0) return 1; - else if(diff > 0) return -1; - return 0; -} - -void do_nms_obj(box *boxes, float **probs, int total, int classes, float thresh) -{ - int i, j, k; - sortable_bbox *s = calloc(total, sizeof(sortable_bbox)); - - for(i = 0; i < total; ++i){ - s[i].index = i; - s[i].class = classes; - s[i].probs = probs; - } - - qsort(s, total, sizeof(sortable_bbox), nms_comparator); - for(i = 0; i < total; ++i){ - if(probs[s[i].index][classes] == 0) continue; - box a = boxes[s[i].index]; - for(j = i+1; j < total; ++j){ - box b = boxes[s[j].index]; - if (box_iou(a, b) > thresh){ - for(k = 0; k < classes+1; ++k){ - probs[s[j].index][k] = 0; - } - } - } - } - free(s); -} - - -void do_nms_sort(box *boxes, float **probs, int total, int classes, float thresh) -{ - int i, j, k; - sortable_bbox *s = calloc(total, sizeof(sortable_bbox)); - - for(i = 0; i < total; ++i){ - s[i].index = i; - s[i].class = 0; - s[i].probs = probs; - } - - for(k = 0; k < classes; ++k){ - for(i = 0; i < total; ++i){ - s[i].class = k; - } - qsort(s, total, sizeof(sortable_bbox), nms_comparator); - for(i = 0; i < total; ++i){ - if(probs[s[i].index][k] == 0) continue; - box a = boxes[s[i].index]; - for(j = i+1; j < total; ++j){ - box b = boxes[s[j].index]; - if (box_iou(a, b) > thresh){ - probs[s[j].index][k] = 0; - } - } - } - } - free(s); -} void do_nms(box *boxes, float **probs, int total, int classes, float thresh) { diff --git a/image.darknet/src/box.h b/image.darknet/src/box.h index c65589b..dda3e59 100644 --- a/image.darknet/src/box.h +++ b/image.darknet/src/box.h @@ -1,21 +1,13 @@ #ifndef BOX_H #define BOX_H - -typedef struct{ - float x, y, w, h; -} box; +#include "darknet.h" typedef struct{ float dx, dy, dw, dh; } dbox; -box float_to_box(float *f); -float box_iou(box a, box b); float box_rmse(box a, box b); dbox diou(box a, box b); -void do_nms(box *boxes, float **probs, int total, int classes, float thresh); -void do_nms_sort(box *boxes, float **probs, int total, int classes, float thresh); -void do_nms_obj(box *boxes, float **probs, int total, int classes, float thresh); box decode_box(box b, box anchor); box encode_box(box b, box anchor); diff --git a/image.darknet/src/captcha.c b/image.darknet/src/captcha.c deleted file mode 100644 index 3d449b2..0000000 --- a/image.darknet/src/captcha.c +++ /dev/null @@ -1,364 +0,0 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" - -void fix_data_captcha(data d, int mask) -{ - matrix labels = d.y; - int i, j; - for(i = 0; i < d.y.rows; ++i){ - for(j = 0; j < d.y.cols; j += 2){ - if (mask){ - if(!labels.vals[i][j]){ - labels.vals[i][j] = SECRET_NUM; - labels.vals[i][j+1] = SECRET_NUM; - }else if(labels.vals[i][j+1]){ - labels.vals[i][j] = 0; - } - } else{ - if (labels.vals[i][j]) { - labels.vals[i][j+1] = 0; - } else { - labels.vals[i][j+1] = 1; - } - } - } - } -} - -void train_captcha(char *cfgfile, char *weightfile) -{ - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = 1024; - int i = *net.seen/imgs; - int solved = 1; - list *plist; - char **labels = get_labels("/data/captcha/reimgs.labels.list"); - if (solved){ - plist = get_paths("/data/captcha/reimgs.solved.list"); - }else{ - plist = get_paths("/data/captcha/reimgs.raw.list"); - } - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - clock_t time; - pthread_t load_thread; - data train; - data buffer; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.classes = 26; - args.n = imgs; - args.m = plist->size; - args.labels = labels; - args.d = &buffer; - args.type = CLASSIFICATION_DATA; - - load_thread = load_data_in_thread(args); - while(1){ - ++i; - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - fix_data_captcha(train, solved); - - /* - image im = float_to_image(256, 256, 3, train.X.vals[114]); - show_image(im, "training"); - cvWaitKey(0); - */ - - load_thread = load_data_in_thread(args); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - float loss = train_network(net, train); - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); - free_data(train); - if(i%100==0){ - char buff[256]; - sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i); - save_weights(net, buff); - } - } -} - -void test_captcha(char *cfgfile, char *weightfile, char *filename) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - int i = 0; - char **names = get_labels("/data/captcha/reimgs.labels.list"); - char buff[256]; - char *input = buff; - int indexes[26]; - while(1){ - if(filename){ - strncpy(input, filename, 256); - }else{ - //printf("Enter Image Path: "); - //fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input, net.w, net.h); - float *X = im.data; - float *predictions = network_predict(net, X); - top_predictions(net, 26, indexes); - //printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - for(i = 0; i < 26; ++i){ - int index = indexes[i]; - if(i != 0) printf(", "); - printf("%s %f", names[index], predictions[index]); - } - printf("\n"); - fflush(stdout); - free_image(im); - if (filename) break; - } -} - -void valid_captcha(char *cfgfile, char *weightfile, char *filename) -{ - char **labels = get_labels("/data/captcha/reimgs.labels.list"); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - list *plist = get_paths("/data/captcha/reimgs.fg.list"); - char **paths = (char **)list_to_array(plist); - int N = plist->size; - int outputs = net.outputs; - - set_batch_network(&net, 1); - srand(2222222); - int i, j; - for(i = 0; i < N; ++i){ - if (i%100 == 0) fprintf(stderr, "%d\n", i); - image im = load_image_color(paths[i], net.w, net.h); - float *X = im.data; - float *predictions = network_predict(net, X); - //printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - int truth = -1; - for(j = 0; j < 13; ++j){ - if (strstr(paths[i], labels[j])) truth = j; - } - if (truth == -1){ - fprintf(stderr, "bad: %s\n", paths[i]); - return; - } - printf("%d, ", truth); - for(j = 0; j < outputs; ++j){ - if (j != 0) printf(", "); - printf("%f", predictions[j]); - } - printf("\n"); - fflush(stdout); - free_image(im); - if (filename) break; - } -} - -/* - void train_captcha(char *cfgfile, char *weightfile) - { - float avg_loss = -1; - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = 1024; - int i = net.seen/imgs; - list *plist = get_paths("/data/captcha/train.auto5"); - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - clock_t time; - while(1){ - ++i; - time=clock(); - data train = load_data_captcha(paths, imgs, plist->size, 10, 200, 60); - translate_data_rows(train, -128); - scale_data_rows(train, 1./128); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - float loss = train_network(net, train); - net.seen += imgs; - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen); - free_data(train); - if(i%10==0){ - char buff[256]; - sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i); - save_weights(net, buff); - } - } - } - - void decode_captcha(char *cfgfile, char *weightfile) - { - setbuf(stdout, NULL); - srand(time(0)); - network net = parse_network_cfg(cfgfile); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } - char filename[256]; - while(1){ - printf("Enter filename: "); - fgets(filename, 256, stdin); - strtok(filename, "\n"); - image im = load_image_color(filename, 300, 57); - scale_image(im, 1./255.); - float *X = im.data; - float *predictions = network_predict(net, X); - image out = float_to_image(300, 57, 1, predictions); - show_image(out, "decoded"); -#ifdef OPENCV -cvWaitKey(0); -#endif -free_image(im); -} -} - -void encode_captcha(char *cfgfile, char *weightfile) -{ -float avg_loss = -1; -srand(time(0)); -char *base = basecfg(cfgfile); -printf("%s\n", base); -network net = parse_network_cfg(cfgfile); -if(weightfile){ - load_weights(&net, weightfile); -} -printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); -int imgs = 1024; -int i = net.seen/imgs; -list *plist = get_paths("/data/captcha/encode.list"); -char **paths = (char **)list_to_array(plist); -printf("%d\n", plist->size); -clock_t time; -while(1){ - ++i; - time=clock(); - data train = load_data_captcha_encode(paths, imgs, plist->size, 300, 57); - scale_data_rows(train, 1./255); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - float loss = train_network(net, train); - net.seen += imgs; - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen); - free_matrix(train.X); - if(i%100==0){ - char buff[256]; - sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i); - save_weights(net, buff); - } -} -} - -void validate_captcha(char *cfgfile, char *weightfile) -{ - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int numchars = 37; - list *plist = get_paths("/data/captcha/solved.hard"); - char **paths = (char **)list_to_array(plist); - int imgs = plist->size; - data valid = load_data_captcha(paths, imgs, 0, 10, 200, 60); - translate_data_rows(valid, -128); - scale_data_rows(valid, 1./128); - matrix pred = network_predict_data(net, valid); - int i, k; - int correct = 0; - int total = 0; - int accuracy = 0; - for(i = 0; i < imgs; ++i){ - int allcorrect = 1; - for(k = 0; k < 10; ++k){ - char truth = int_to_alphanum(max_index(valid.y.vals[i]+k*numchars, numchars)); - char prediction = int_to_alphanum(max_index(pred.vals[i]+k*numchars, numchars)); - if (truth != prediction) allcorrect=0; - if (truth != '.' && truth == prediction) ++correct; - if (truth != '.' || truth != prediction) ++total; - } - accuracy += allcorrect; - } - printf("Word Accuracy: %f, Char Accuracy %f\n", (float)accuracy/imgs, (float)correct/total); - free_data(valid); -} - -void test_captcha(char *cfgfile, char *weightfile) -{ - setbuf(stdout, NULL); - srand(time(0)); - //char *base = basecfg(cfgfile); - //printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } - char filename[256]; - while(1){ - //printf("Enter filename: "); - fgets(filename, 256, stdin); - strtok(filename, "\n"); - image im = load_image_color(filename, 200, 60); - translate_image(im, -128); - scale_image(im, 1/128.); - float *X = im.data; - float *predictions = network_predict(net, X); - print_letters(predictions, 10); - free_image(im); - } -} - */ -void run_captcha(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5]: 0; - if(0==strcmp(argv[2], "train")) train_captcha(cfg, weights); - else if(0==strcmp(argv[2], "test")) test_captcha(cfg, weights, filename); - else if(0==strcmp(argv[2], "valid")) valid_captcha(cfg, weights, filename); - //if(0==strcmp(argv[2], "test")) test_captcha(cfg, weights); - //else if(0==strcmp(argv[2], "encode")) encode_captcha(cfg, weights); - //else if(0==strcmp(argv[2], "decode")) decode_captcha(cfg, weights); - //else if(0==strcmp(argv[2], "valid")) validate_captcha(cfg, weights); -} - diff --git a/image.darknet/src/cifar.c b/image.darknet/src/cifar.c deleted file mode 100644 index d0ac459..0000000 --- a/image.darknet/src/cifar.c +++ /dev/null @@ -1,277 +0,0 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" -#include "option_list.h" -#include "blas.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -void train_cifar(char *cfgfile, char *weightfile) -{ - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - - char *backup_directory = "/home/pjreddie/backup/"; - int classes = 10; - int N = 50000; - - char **labels = get_labels("data/cifar/labels.txt"); - int epoch = (*net.seen)/N; - data train = load_all_cifar10(); - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - clock_t time=clock(); - - float loss = train_network_sgd(net, train, 1); - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); - } - if(get_current_batch(net)%100 == 0){ - char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); - save_weights(net, buff); - } - } - char buff[256]; - sprintf(buff, "%s/%s.weights", backup_directory, base); - save_weights(net, buff); - - free_network(net); - free_ptrs((void**)labels, classes); - free(base); - free_data(train); -} - -void train_cifar_distill(char *cfgfile, char *weightfile) -{ - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - - char *backup_directory = "/home/pjreddie/backup/"; - int classes = 10; - int N = 50000; - - char **labels = get_labels("data/cifar/labels.txt"); - int epoch = (*net.seen)/N; - - data train = load_all_cifar10(); - matrix soft = csv_to_matrix("results/ensemble.csv"); - - float weight = .9; - scale_matrix(soft, weight); - scale_matrix(train.y, 1. - weight); - matrix_add_matrix(soft, train.y); - - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - clock_t time=clock(); - - float loss = train_network_sgd(net, train, 1); - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); - } - if(get_current_batch(net)%100 == 0){ - char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); - save_weights(net, buff); - } - } - char buff[256]; - sprintf(buff, "%s/%s.weights", backup_directory, base); - save_weights(net, buff); - - free_network(net); - free_ptrs((void**)labels, classes); - free(base); - free_data(train); -} - -void test_cifar_multi(char *filename, char *weightfile) -{ - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(time(0)); - - float avg_acc = 0; - data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin"); - - int i; - for(i = 0; i < test.X.rows; ++i){ - image im = float_to_image(32, 32, 3, test.X.vals[i]); - - float pred[10] = {0}; - - float *p = network_predict(net, im.data); - axpy_cpu(10, 1, p, 1, pred, 1); - flip_image(im); - p = network_predict(net, im.data); - axpy_cpu(10, 1, p, 1, pred, 1); - - int index = max_index(pred, 10); - int class = max_index(test.y.vals[i], 10); - if(index == class) avg_acc += 1; - free_image(im); - printf("%4d: %.2f%%\n", i, 100.*avg_acc/(i+1)); - } -} - -void test_cifar(char *filename, char *weightfile) -{ - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - clock_t time; - float avg_acc = 0; - float avg_top5 = 0; - data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin"); - - time=clock(); - - float *acc = network_accuracies(net, test, 2); - avg_acc += acc[0]; - avg_top5 += acc[1]; - printf("top1: %f, %lf seconds, %d images\n", avg_acc, sec(clock()-time), test.X.rows); - free_data(test); -} - -void extract_cifar() -{ -char *labels[] = {"airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"}; - int i; - data train = load_all_cifar10(); - data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin"); - for(i = 0; i < train.X.rows; ++i){ - image im = float_to_image(32, 32, 3, train.X.vals[i]); - int class = max_index(train.y.vals[i], 10); - char buff[256]; - sprintf(buff, "data/cifar/train/%d_%s",i,labels[class]); - save_image_png(im, buff); - } - for(i = 0; i < test.X.rows; ++i){ - image im = float_to_image(32, 32, 3, test.X.vals[i]); - int class = max_index(test.y.vals[i], 10); - char buff[256]; - sprintf(buff, "data/cifar/test/%d_%s",i,labels[class]); - save_image_png(im, buff); - } -} - -void test_cifar_csv(char *filename, char *weightfile) -{ - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin"); - - matrix pred = network_predict_data(net, test); - - int i; - for(i = 0; i < test.X.rows; ++i){ - image im = float_to_image(32, 32, 3, test.X.vals[i]); - flip_image(im); - } - matrix pred2 = network_predict_data(net, test); - scale_matrix(pred, .5); - scale_matrix(pred2, .5); - matrix_add_matrix(pred2, pred); - - matrix_to_csv(pred); - fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1)); - free_data(test); -} - -void test_cifar_csvtrain(char *filename, char *weightfile) -{ - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - data test = load_all_cifar10(); - - matrix pred = network_predict_data(net, test); - - int i; - for(i = 0; i < test.X.rows; ++i){ - image im = float_to_image(32, 32, 3, test.X.vals[i]); - flip_image(im); - } - matrix pred2 = network_predict_data(net, test); - scale_matrix(pred, .5); - scale_matrix(pred2, .5); - matrix_add_matrix(pred2, pred); - - matrix_to_csv(pred); - fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1)); - free_data(test); -} - -void eval_cifar_csv() -{ - data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin"); - - matrix pred = csv_to_matrix("results/combined.csv"); - fprintf(stderr, "%d %d\n", pred.rows, pred.cols); - - fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1)); - free_data(test); - free_matrix(pred); -} - - -void run_cifar(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - if(0==strcmp(argv[2], "train")) train_cifar(cfg, weights); - else if(0==strcmp(argv[2], "extract")) extract_cifar(); - else if(0==strcmp(argv[2], "distill")) train_cifar_distill(cfg, weights); - else if(0==strcmp(argv[2], "test")) test_cifar(cfg, weights); - else if(0==strcmp(argv[2], "multi")) test_cifar_multi(cfg, weights); - else if(0==strcmp(argv[2], "csv")) test_cifar_csv(cfg, weights); - else if(0==strcmp(argv[2], "csvtrain")) test_cifar_csvtrain(cfg, weights); - else if(0==strcmp(argv[2], "eval")) eval_cifar_csv(); -} - - diff --git a/image.darknet/src/classifier.c b/image.darknet/src/classifier.c deleted file mode 100644 index 586530a..0000000 --- a/image.darknet/src/classifier.c +++ /dev/null @@ -1,1167 +0,0 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" -#include "option_list.h" -#include "blas.h" -#include "assert.h" -#include "classifier.h" -#include "cuda.h" -#include - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -image get_image_from_stream(CvCapture *cap); -#endif - -float *get_regression_values(char **labels, int n) -{ - float *v = calloc(n, sizeof(float)); - int i; - for(i = 0; i < n; ++i){ - char *p = strchr(labels[i], ' '); - *p = 0; - v[i] = atof(p+1); - } - return v; -} - -void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear) -{ - int i; - - float avg_loss = -1; - char *base = basecfg(cfgfile); - printf("%s\n", base); - printf("%d\n", ngpus); - network *nets = calloc(ngpus, sizeof(network)); - - srand(time(0)); - int seed = rand(); - for(i = 0; i < ngpus; ++i){ - srand(seed); -#ifdef GPU - cuda_set_device(gpus[i]); -#endif - nets[i] = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&nets[i], weightfile); - } - if(clear) *nets[i].seen = 0; - nets[i].learning_rate *= ngpus; - } - srand(time(0)); - network net = nets[0]; - - int imgs = net.batch * net.subdivisions * ngpus; - - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - list *options = read_data_cfg(datacfg); - - char *backup_directory = option_find_str(options, "backup", "/backup/"); - char *label_list = option_find_str(options, "labels", "data/labels.list"); - char *train_list = option_find_str(options, "train", "data/train.list"); - int classes = option_find_int(options, "classes", 2); - - char **labels = get_labels(label_list); - list *plist = get_paths(train_list); - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - int N = plist->size; - clock_t time; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.threads = 32; - args.hierarchy = net.hierarchy; - - args.min = net.min_crop; - args.max = net.max_crop; - args.angle = net.angle; - args.aspect = net.aspect; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; - args.size = net.w; - - args.paths = paths; - args.classes = classes; - args.n = imgs; - args.m = N; - args.labels = labels; - args.type = CLASSIFICATION_DATA; - - data train; - data buffer; - pthread_t load_thread; - args.d = &buffer; - load_thread = load_data(args); - - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - time=clock(); - - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data(args); - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - - float loss = 0; -#ifdef GPU - if(ngpus == 1){ - loss = train_network(net, train); - } else { - loss = train_networks(nets, ngpus, train, 4); - } -#else - loss = train_network(net, train); -#endif - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - free_data(train); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); - } - if(get_current_batch(net)%100 == 0){ - char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); - save_weights(net, buff); - } - } - char buff[256]; - sprintf(buff, "%s/%s.weights", backup_directory, base); - save_weights(net, buff); - - free_network(net); - free_ptrs((void**)labels, classes); - free_ptrs((void**)paths, plist->size); - free_list(plist); - free(base); -} - - -/* - void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear) - { - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - if(clear) *net.seen = 0; - - int imgs = net.batch * net.subdivisions; - - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - list *options = read_data_cfg(datacfg); - - char *backup_directory = option_find_str(options, "backup", "/backup/"); - char *label_list = option_find_str(options, "labels", "data/labels.list"); - char *train_list = option_find_str(options, "train", "data/train.list"); - int classes = option_find_int(options, "classes", 2); - - char **labels = get_labels(label_list); - list *plist = get_paths(train_list); - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - int N = plist->size; - clock_t time; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.threads = 8; - - args.min = net.min_crop; - args.max = net.max_crop; - args.angle = net.angle; - args.aspect = net.aspect; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; - args.size = net.w; - args.hierarchy = net.hierarchy; - - args.paths = paths; - args.classes = classes; - args.n = imgs; - args.m = N; - args.labels = labels; - args.type = CLASSIFICATION_DATA; - - data train; - data buffer; - pthread_t load_thread; - args.d = &buffer; - load_thread = load_data(args); - - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - time=clock(); - - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data(args); - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - -#ifdef OPENCV -if(0){ -int u; -for(u = 0; u < imgs; ++u){ - image im = float_to_image(net.w, net.h, 3, train.X.vals[u]); - show_image(im, "loaded"); - cvWaitKey(0); -} -} -#endif - -float loss = train_network(net, train); -free_data(train); - -if(avg_loss == -1) avg_loss = loss; -avg_loss = avg_loss*.9 + loss*.1; -printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); -if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); -} -if(get_current_batch(net)%100 == 0){ - char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); - save_weights(net, buff); -} -} -char buff[256]; -sprintf(buff, "%s/%s.weights", backup_directory, base); -save_weights(net, buff); - -free_network(net); -free_ptrs((void**)labels, classes); -free_ptrs((void**)paths, plist->size); -free_list(plist); -free(base); -} -*/ - -void validate_classifier_crop(char *datacfg, char *filename, char *weightfile) -{ - int i = 0; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - list *options = read_data_cfg(datacfg); - - char *label_list = option_find_str(options, "labels", "data/labels.list"); - char *valid_list = option_find_str(options, "valid", "data/train.list"); - int classes = option_find_int(options, "classes", 2); - int topk = option_find_int(options, "top", 1); - - char **labels = get_labels(label_list); - list *plist = get_paths(valid_list); - - char **paths = (char **)list_to_array(plist); - int m = plist->size; - free_list(plist); - - clock_t time; - float avg_acc = 0; - float avg_topk = 0; - int splits = m/1000; - int num = (i+1)*m/splits - i*m/splits; - - data val, buffer; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - - args.paths = paths; - args.classes = classes; - args.n = num; - args.m = 0; - args.labels = labels; - args.d = &buffer; - args.type = OLD_CLASSIFICATION_DATA; - - pthread_t load_thread = load_data_in_thread(args); - for(i = 1; i <= splits; ++i){ - time=clock(); - - pthread_join(load_thread, 0); - val = buffer; - - num = (i+1)*m/splits - i*m/splits; - char **part = paths+(i*m/splits); - if(i != splits){ - args.paths = part; - load_thread = load_data_in_thread(args); - } - printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time)); - - time=clock(); - float *acc = network_accuracies(net, val, topk); - avg_acc += acc[0]; - avg_topk += acc[1]; - printf("%d: top 1: %f, top %d: %f, %lf seconds, %d images\n", i, avg_acc/i, topk, avg_topk/i, sec(clock()-time), val.X.rows); - free_data(val); - } -} - -void validate_classifier_10(char *datacfg, char *filename, char *weightfile) -{ - int i, j; - network net = parse_network_cfg(filename); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - list *options = read_data_cfg(datacfg); - - char *label_list = option_find_str(options, "labels", "data/labels.list"); - char *valid_list = option_find_str(options, "valid", "data/train.list"); - int classes = option_find_int(options, "classes", 2); - int topk = option_find_int(options, "top", 1); - - char **labels = get_labels(label_list); - list *plist = get_paths(valid_list); - - char **paths = (char **)list_to_array(plist); - int m = plist->size; - free_list(plist); - - float avg_acc = 0; - float avg_topk = 0; - int *indexes = calloc(topk, sizeof(int)); - - for(i = 0; i < m; ++i){ - int class = -1; - char *path = paths[i]; - for(j = 0; j < classes; ++j){ - if(strstr(path, labels[j])){ - class = j; - break; - } - } - int w = net.w; - int h = net.h; - int shift = 32; - image im = load_image_color(paths[i], w+shift, h+shift); - image images[10]; - images[0] = crop_image(im, -shift, -shift, w, h); - images[1] = crop_image(im, shift, -shift, w, h); - images[2] = crop_image(im, 0, 0, w, h); - images[3] = crop_image(im, -shift, shift, w, h); - images[4] = crop_image(im, shift, shift, w, h); - flip_image(im); - images[5] = crop_image(im, -shift, -shift, w, h); - images[6] = crop_image(im, shift, -shift, w, h); - images[7] = crop_image(im, 0, 0, w, h); - images[8] = crop_image(im, -shift, shift, w, h); - images[9] = crop_image(im, shift, shift, w, h); - float *pred = calloc(classes, sizeof(float)); - for(j = 0; j < 10; ++j){ - float *p = network_predict(net, images[j].data); - if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1); - axpy_cpu(classes, 1, p, 1, pred, 1); - free_image(images[j]); - } - free_image(im); - top_k(pred, classes, topk, indexes); - free(pred); - if(indexes[0] == class) avg_acc += 1; - for(j = 0; j < topk; ++j){ - if(indexes[j] == class) avg_topk += 1; - } - - printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); - } -} - -void validate_classifier_full(char *datacfg, char *filename, char *weightfile) -{ - int i, j; - network net = parse_network_cfg(filename); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - list *options = read_data_cfg(datacfg); - - char *label_list = option_find_str(options, "labels", "data/labels.list"); - char *valid_list = option_find_str(options, "valid", "data/train.list"); - int classes = option_find_int(options, "classes", 2); - int topk = option_find_int(options, "top", 1); - - char **labels = get_labels(label_list); - list *plist = get_paths(valid_list); - - char **paths = (char **)list_to_array(plist); - int m = plist->size; - free_list(plist); - - float avg_acc = 0; - float avg_topk = 0; - int *indexes = calloc(topk, sizeof(int)); - - int size = net.w; - for(i = 0; i < m; ++i){ - int class = -1; - char *path = paths[i]; - for(j = 0; j < classes; ++j){ - if(strstr(path, labels[j])){ - class = j; - break; - } - } - image im = load_image_color(paths[i], 0, 0); - image resized = resize_min(im, size); - resize_network(&net, resized.w, resized.h); - //show_image(im, "orig"); - //show_image(crop, "cropped"); - //cvWaitKey(0); - float *pred = network_predict(net, resized.data); - if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1); - - free_image(im); - free_image(resized); - top_k(pred, classes, topk, indexes); - - if(indexes[0] == class) avg_acc += 1; - for(j = 0; j < topk; ++j){ - if(indexes[j] == class) avg_topk += 1; - } - - printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); - } -} - - -void validate_classifier_single(char *datacfg, char *filename, char *weightfile) -{ - int i, j; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(time(0)); - - list *options = read_data_cfg(datacfg); - - char *label_list = option_find_str(options, "labels", "data/labels.list"); - char *leaf_list = option_find_str(options, "leaves", 0); - if(leaf_list) change_leaves(net.hierarchy, leaf_list); - char *valid_list = option_find_str(options, "valid", "data/train.list"); - int classes = option_find_int(options, "classes", 2); - int topk = option_find_int(options, "top", 1); - - char **labels = get_labels(label_list); - list *plist = get_paths(valid_list); - - char **paths = (char **)list_to_array(plist); - int m = plist->size; - free_list(plist); - - float avg_acc = 0; - float avg_topk = 0; - int *indexes = calloc(topk, sizeof(int)); - - for(i = 0; i < m; ++i){ - int class = -1; - char *path = paths[i]; - for(j = 0; j < classes; ++j){ - if(strstr(path, labels[j])){ - class = j; - break; - } - } - image im = load_image_color(paths[i], 0, 0); - image resized = resize_min(im, net.w); - image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h); - //show_image(im, "orig"); - //show_image(crop, "cropped"); - //cvWaitKey(0); - float *pred = network_predict(net, crop.data); - if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1); - - if(resized.data != im.data) free_image(resized); - free_image(im); - free_image(crop); - top_k(pred, classes, topk, indexes); - - if(indexes[0] == class) avg_acc += 1; - for(j = 0; j < topk; ++j){ - if(indexes[j] == class) avg_topk += 1; - } - - printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); - } -} - -void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) -{ - int i, j; - network net = parse_network_cfg(filename); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - list *options = read_data_cfg(datacfg); - - char *label_list = option_find_str(options, "labels", "data/labels.list"); - char *valid_list = option_find_str(options, "valid", "data/train.list"); - int classes = option_find_int(options, "classes", 2); - int topk = option_find_int(options, "top", 1); - - char **labels = get_labels(label_list); - list *plist = get_paths(valid_list); - int scales[] = {224, 288, 320, 352, 384}; - int nscales = sizeof(scales)/sizeof(scales[0]); - - char **paths = (char **)list_to_array(plist); - int m = plist->size; - free_list(plist); - - float avg_acc = 0; - float avg_topk = 0; - int *indexes = calloc(topk, sizeof(int)); - - for(i = 0; i < m; ++i){ - int class = -1; - char *path = paths[i]; - for(j = 0; j < classes; ++j){ - if(strstr(path, labels[j])){ - class = j; - break; - } - } - float *pred = calloc(classes, sizeof(float)); - image im = load_image_color(paths[i], 0, 0); - for(j = 0; j < nscales; ++j){ - image r = resize_min(im, scales[j]); - resize_network(&net, r.w, r.h); - float *p = network_predict(net, r.data); - if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1); - axpy_cpu(classes, 1, p, 1, pred, 1); - flip_image(r); - p = network_predict(net, r.data); - axpy_cpu(classes, 1, p, 1, pred, 1); - if(r.data != im.data) free_image(r); - } - free_image(im); - top_k(pred, classes, topk, indexes); - free(pred); - if(indexes[0] == class) avg_acc += 1; - for(j = 0; j < topk; ++j){ - if(indexes[j] == class) avg_topk += 1; - } - - printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); - } -} - -void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - - list *options = read_data_cfg(datacfg); - - char *name_list = option_find_str(options, "names", 0); - if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list"); - int top = option_find_int(options, "top", 1); - - int i = 0; - char **names = get_labels(name_list); - clock_t time; - int *indexes = calloc(top, sizeof(int)); - char buff[256]; - char *input = buff; - while(1){ - if(filename){ - strncpy(input, filename, 256); - }else{ - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image orig = load_image_color(input, 0, 0); - image r = resize_min(orig, 256); - image im = crop_image(r, (r.w - 224 - 1)/2 + 1, (r.h - 224 - 1)/2 + 1, 224, 224); - float mean[] = {0.48263312050943, 0.45230225481413, 0.40099074308742}; - float std[] = {0.22590347483426, 0.22120921437787, 0.22103996251583}; - float var[3]; - var[0] = std[0]*std[0]; - var[1] = std[1]*std[1]; - var[2] = std[2]*std[2]; - - normalize_cpu(im.data, mean, var, 1, 3, im.w*im.h); - - float *X = im.data; - time=clock(); - float *predictions = network_predict(net, X); - - layer l = net.layers[layer_num]; - for(i = 0; i < l.c; ++i){ - if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]); - } -#ifdef GPU - cuda_pull_array(l.output_gpu, l.output, l.outputs); -#endif - for(i = 0; i < l.outputs; ++i){ - printf("%f\n", l.output[i]); - } - /* - - printf("\n\nWeights\n"); - for(i = 0; i < l.n*l.size*l.size*l.c; ++i){ - printf("%f\n", l.filters[i]); - } - - printf("\n\nBiases\n"); - for(i = 0; i < l.n; ++i){ - printf("%f\n", l.biases[i]); - } - */ - - top_predictions(net, top, indexes); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - for(i = 0; i < top; ++i){ - int index = indexes[i]; - printf("%s: %f\n", names[index], predictions[index]); - } - free_image(im); - if (filename) break; - } -} - -void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - - list *options = read_data_cfg(datacfg); - - char *name_list = option_find_str(options, "names", 0); - if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list"); - if(top == 0) top = option_find_int(options, "top", 1); - - int i = 0; - char **names = get_labels(name_list); - clock_t time; - int *indexes = calloc(top, sizeof(int)); - char buff[256]; - char *input = buff; - int size = net.w; - while(1){ - if(filename){ - strncpy(input, filename, 256); - }else{ - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input, 0, 0); - image r = resize_min(im, size); - resize_network(&net, r.w, r.h); - printf("%d %d\n", r.w, r.h); - - float *X = r.data; - time=clock(); - float *predictions = network_predict(net, X); - if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0); - top_k(predictions, net.outputs, top, indexes); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - for(i = 0; i < top; ++i){ - int index = indexes[i]; - if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root"); - else printf("%s: %f\n",names[index], predictions[index]); - } - if(r.data != im.data) free_image(r); - free_image(im); - if (filename) break; - } -} - - -void label_classifier(char *datacfg, char *filename, char *weightfile) -{ - int i; - network net = parse_network_cfg(filename); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - list *options = read_data_cfg(datacfg); - - char *label_list = option_find_str(options, "names", "data/labels.list"); - char *test_list = option_find_str(options, "test", "data/train.list"); - int classes = option_find_int(options, "classes", 2); - - char **labels = get_labels(label_list); - list *plist = get_paths(test_list); - - char **paths = (char **)list_to_array(plist); - int m = plist->size; - free_list(plist); - - for(i = 0; i < m; ++i){ - image im = load_image_color(paths[i], 0, 0); - image resized = resize_min(im, net.w); - image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h); - float *pred = network_predict(net, crop.data); - - if(resized.data != im.data) free_image(resized); - free_image(im); - free_image(crop); - int ind = max_index(pred, classes); - - printf("%s\n", labels[ind]); - } -} - - -void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer) -{ - int curr = 0; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - list *options = read_data_cfg(datacfg); - - char *test_list = option_find_str(options, "test", "data/test.list"); - int classes = option_find_int(options, "classes", 2); - - list *plist = get_paths(test_list); - - char **paths = (char **)list_to_array(plist); - int m = plist->size; - free_list(plist); - - clock_t time; - - data val, buffer; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.classes = classes; - args.n = net.batch; - args.m = 0; - args.labels = 0; - args.d = &buffer; - args.type = OLD_CLASSIFICATION_DATA; - - pthread_t load_thread = load_data_in_thread(args); - for(curr = net.batch; curr < m; curr += net.batch){ - time=clock(); - - pthread_join(load_thread, 0); - val = buffer; - - if(curr < m){ - args.paths = paths + curr; - if (curr + net.batch > m) args.n = m - curr; - load_thread = load_data_in_thread(args); - } - fprintf(stderr, "Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time)); - - time=clock(); - matrix pred = network_predict_data(net, val); - - int i, j; - if (target_layer >= 0){ - //layer l = net.layers[target_layer]; - } - - for(i = 0; i < pred.rows; ++i){ - printf("%s", paths[curr-net.batch+i]); - for(j = 0; j < pred.cols; ++j){ - printf("\t%g", pred.vals[i][j]); - } - printf("\n"); - } - - free_matrix(pred); - - fprintf(stderr, "%lf seconds, %d images, %d total\n", sec(clock()-time), val.X.rows, curr); - free_data(val); - } -} - - -void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) -{ -#ifdef OPENCV - float threat = 0; - float roll = .2; - - printf("Classifier Demo\n"); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - list *options = read_data_cfg(datacfg); - - srand(2222222); - CvCapture * cap; - - if(filename){ - cap = cvCaptureFromFile(filename); - }else{ - cap = cvCaptureFromCAM(cam_index); - } - - int top = option_find_int(options, "top", 1); - - char *name_list = option_find_str(options, "names", 0); - char **names = get_labels(name_list); - - int *indexes = calloc(top, sizeof(int)); - - if(!cap) error("Couldn't connect to webcam.\n"); - //cvNamedWindow("Threat", CV_WINDOW_NORMAL); - //cvResizeWindow("Threat", 512, 512); - float fps = 0; - int i; - - int count = 0; - - while(1){ - ++count; - struct timeval tval_before, tval_after, tval_result; - gettimeofday(&tval_before, NULL); - - image in = get_image_from_stream(cap); - if(!in.data) break; - image in_s = resize_image(in, net.w, net.h); - - image out = in; - int x1 = out.w / 20; - int y1 = out.h / 20; - int x2 = 2*x1; - int y2 = out.h - out.h/20; - - int border = .01*out.h; - int h = y2 - y1 - 2*border; - int w = x2 - x1 - 2*border; - - float *predictions = network_predict(net, in_s.data); - float curr_threat = 0; - if(1){ - curr_threat = predictions[0] * 0 + - predictions[1] * .6 + - predictions[2]; - } else { - curr_threat = predictions[218] + - predictions[539] + - predictions[540] + - predictions[368] + - predictions[369] + - predictions[370]; - } - threat = roll * curr_threat + (1-roll) * threat; - - draw_box_width(out, x2 + border, y1 + .02*h, x2 + .5 * w, y1 + .02*h + border, border, 0,0,0); - if(threat > .97) { - draw_box_width(out, x2 + .5 * w + border, - y1 + .02*h - 2*border, - x2 + .5 * w + 6*border, - y1 + .02*h + 3*border, 3*border, 1,0,0); - } - draw_box_width(out, x2 + .5 * w + border, - y1 + .02*h - 2*border, - x2 + .5 * w + 6*border, - y1 + .02*h + 3*border, .5*border, 0,0,0); - draw_box_width(out, x2 + border, y1 + .42*h, x2 + .5 * w, y1 + .42*h + border, border, 0,0,0); - if(threat > .57) { - draw_box_width(out, x2 + .5 * w + border, - y1 + .42*h - 2*border, - x2 + .5 * w + 6*border, - y1 + .42*h + 3*border, 3*border, 1,1,0); - } - draw_box_width(out, x2 + .5 * w + border, - y1 + .42*h - 2*border, - x2 + .5 * w + 6*border, - y1 + .42*h + 3*border, .5*border, 0,0,0); - - draw_box_width(out, x1, y1, x2, y2, border, 0,0,0); - for(i = 0; i < threat * h ; ++i){ - float ratio = (float) i / h; - float r = (ratio < .5) ? (2*(ratio)) : 1; - float g = (ratio < .5) ? 1 : 1 - 2*(ratio - .5); - draw_box_width(out, x1 + border, y2 - border - i, x2 - border, y2 - border - i, 1, r, g, 0); - } - top_predictions(net, top, indexes); - char buff[256]; - sprintf(buff, "/home/pjreddie/tmp/threat_%06d", count); - //save_image(out, buff); - - printf("\033[2J"); - printf("\033[1;1H"); - printf("\nFPS:%.0f\n",fps); - - for(i = 0; i < top; ++i){ - int index = indexes[i]; - printf("%.1f%%: %s\n", predictions[index]*100, names[index]); - } - - if(1){ - show_image(out, "Threat"); - cvWaitKey(10); - } - free_image(in_s); - free_image(in); - - gettimeofday(&tval_after, NULL); - timersub(&tval_after, &tval_before, &tval_result); - float curr = 1000000.f/((long int)tval_result.tv_usec); - fps = .9*fps + .1*curr; - } -#endif -} - - -void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) -{ -#ifdef OPENCV - int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697}; - - printf("Classifier Demo\n"); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - list *options = read_data_cfg(datacfg); - - srand(2222222); - CvCapture * cap; - - if(filename){ - cap = cvCaptureFromFile(filename); - }else{ - cap = cvCaptureFromCAM(cam_index); - } - - int top = option_find_int(options, "top", 1); - - char *name_list = option_find_str(options, "names", 0); - char **names = get_labels(name_list); - - int *indexes = calloc(top, sizeof(int)); - - if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL); - cvResizeWindow("Threat Detection", 512, 512); - float fps = 0; - int i; - - while(1){ - struct timeval tval_before, tval_after, tval_result; - gettimeofday(&tval_before, NULL); - - image in = get_image_from_stream(cap); - image in_s = resize_image(in, net.w, net.h); - show_image(in, "Threat Detection"); - - float *predictions = network_predict(net, in_s.data); - top_predictions(net, top, indexes); - - printf("\033[2J"); - printf("\033[1;1H"); - - int threat = 0; - for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){ - int index = bad_cats[i]; - if(predictions[index] > .01){ - printf("Threat Detected!\n"); - threat = 1; - break; - } - } - if(!threat) printf("Scanning...\n"); - for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){ - int index = bad_cats[i]; - if(predictions[index] > .01){ - printf("%s\n", names[index]); - } - } - - free_image(in_s); - free_image(in); - - cvWaitKey(10); - - gettimeofday(&tval_after, NULL); - timersub(&tval_after, &tval_before, &tval_result); - float curr = 1000000.f/((long int)tval_result.tv_usec); - fps = .9*fps + .1*curr; - } -#endif -} - -void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) -{ -#ifdef OPENCV - printf("Classifier Demo\n"); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - list *options = read_data_cfg(datacfg); - - srand(2222222); - CvCapture * cap; - - if(filename){ - cap = cvCaptureFromFile(filename); - }else{ - cap = cvCaptureFromCAM(cam_index); - } - - int top = option_find_int(options, "top", 1); - - char *name_list = option_find_str(options, "names", 0); - char **names = get_labels(name_list); - - int *indexes = calloc(top, sizeof(int)); - - if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow("Classifier", CV_WINDOW_NORMAL); - cvResizeWindow("Classifier", 512, 512); - float fps = 0; - int i; - - while(1){ - struct timeval tval_before, tval_after, tval_result; - gettimeofday(&tval_before, NULL); - - image in = get_image_from_stream(cap); - image in_s = resize_image(in, net.w, net.h); - show_image(in, "Classifier"); - - float *predictions = network_predict(net, in_s.data); - if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1); - top_predictions(net, top, indexes); - - printf("\033[2J"); - printf("\033[1;1H"); - printf("\nFPS:%.0f\n",fps); - - for(i = 0; i < top; ++i){ - int index = indexes[i]; - printf("%.1f%%: %s\n", predictions[index]*100, names[index]); - } - - free_image(in_s); - free_image(in); - - cvWaitKey(10); - - gettimeofday(&tval_after, NULL); - timersub(&tval_after, &tval_before, &tval_result); - float curr = 1000000.f/((long int)tval_result.tv_usec); - fps = .9*fps + .1*curr; - } -#endif -} - - -void run_classifier(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); - int *gpus = 0; - int gpu = 0; - int ngpus = 0; - if(gpu_list){ - printf("%s\n", gpu_list); - int len = strlen(gpu_list); - ngpus = 1; - int i; - for(i = 0; i < len; ++i){ - if (gpu_list[i] == ',') ++ngpus; - } - gpus = calloc(ngpus, sizeof(int)); - for(i = 0; i < ngpus; ++i){ - gpus[i] = atoi(gpu_list); - gpu_list = strchr(gpu_list, ',')+1; - } - } else { - gpu = gpu_index; - gpus = &gpu; - ngpus = 1; - } - - int cam_index = find_int_arg(argc, argv, "-c", 0); - int top = find_int_arg(argc, argv, "-t", 0); - int clear = find_arg(argc, argv, "-clear"); - char *data = argv[3]; - char *cfg = argv[4]; - char *weights = (argc > 5) ? argv[5] : 0; - char *filename = (argc > 6) ? argv[6]: 0; - char *layer_s = (argc > 7) ? argv[7]: 0; - int layer = layer_s ? atoi(layer_s) : -1; - if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); - else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); - else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear); - else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); - else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); - else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); - else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer); - else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights); - else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights); - else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights); - else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights); - else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights); - else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights); -} - - diff --git a/image.darknet/src/classifier.h b/image.darknet/src/classifier.h index 3c89f49..8b13789 100644 --- a/image.darknet/src/classifier.h +++ b/image.darknet/src/classifier.h @@ -1,2 +1 @@ -list *read_data_cfg(char *filename); diff --git a/image.darknet/src/coco.c b/image.darknet/src/coco.c deleted file mode 100644 index 8f3c968..0000000 --- a/image.darknet/src/coco.c +++ /dev/null @@ -1,388 +0,0 @@ -#include - -#include "network.h" -#include "detection_layer.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "box.h" -#include "demo.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"}; - -int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; - -void train_coco(char *cfgfile, char *weightfile) -{ - //char *train_images = "/home/pjreddie/data/voc/test/train.txt"; - //char *train_images = "/home/pjreddie/data/coco/train.txt"; - char *train_images = "data/coco.trainval.txt"; - //char *train_images = "data/bags.train.list"; - char *backup_directory = "/home/pjreddie/backup/"; - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; - data train, buffer; - - - layer l = net.layers[net.n - 1]; - - int side = l.side; - int classes = l.classes; - float jitter = l.jitter; - - list *plist = get_paths(train_images); - //int N = plist->size; - char **paths = (char **)list_to_array(plist); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.n = imgs; - args.m = plist->size; - args.classes = classes; - args.jitter = jitter; - args.num_boxes = side; - args.d = &buffer; - args.type = REGION_DATA; - - args.angle = net.angle; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; - - pthread_t load_thread = load_data_in_thread(args); - clock_t time; - //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ - i += 1; - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data_in_thread(args); - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - - /* - image im = float_to_image(net.w, net.h, 3, train.X.vals[113]); - image copy = copy_image(im); - draw_coco(copy, train.y.vals[113], 7, "truth"); - cvWaitKey(0); - free_image(copy); - */ - - time=clock(); - float loss = train_network(net, train); - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); - if(i%1000==0 || (i < 1000 && i%100 == 0)){ - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - if(i%100==0){ - char buff[256]; - sprintf(buff, "%s/%s.backup", backup_directory, base); - save_weights(net, buff); - } - free_data(train); - } - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - -void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxes, int classes, int w, int h) -{ - int i, j; - for(i = 0; i < num_boxes; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - float bx = xmin; - float by = ymin; - float bw = xmax - xmin; - float bh = ymax - ymin; - - for(j = 0; j < classes; ++j){ - if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]); - } - } -} - -int get_coco_image_id(char *filename) -{ - char *p = strrchr(filename, '_'); - return atoi(p+1); -} - -void validate_coco(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - char *base = "results/"; - list *plist = get_paths("data/coco_val_5k.list"); - //list *plist = get_paths("/home/pjreddie/data/people-art/test.txt"); - //list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - int side = l.side; - - int j; - char buff[1024]; - snprintf(buff, 1024, "%s/coco_results.json", base); - FILE *fp = fopen(buff, "w"); - fprintf(fp, "[\n"); - - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - int t; - - float thresh = .01; - int nms = 1; - float iou_thresh = .5; - - int nthreads = 8; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.type = IMAGE_DATA; - - for(t = 0; t < nthreads; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - time_t start = time(0); - for(i = nthreads; i < m+nthreads; i += nthreads){ - fprintf(stderr, "%d\n", i); - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - pthread_join(thr[t], 0); - val[t] = buf[t]; - val_resized[t] = buf_resized[t]; - } - for(t = 0; t < nthreads && i+t < m; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - char *path = paths[i+t-nthreads]; - int image_id = get_coco_image_id(path); - float *X = val_resized[t].data; - network_predict(net, X); - int w = val[t].w; - int h = val[t].h; - get_detection_boxes(l, w, h, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh); - print_cocos(fp, image_id, boxes, probs, side*side*l.n, classes, w, h); - free_image(val[t]); - free_image(val_resized[t]); - } - } - fseek(fp, -2, SEEK_CUR); - fprintf(fp, "\n]\n"); - fclose(fp); - - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); -} - -void validate_coco_recall(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - char *base = "results/comp4_det_test_"; - list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - int side = l.side; - - int j, k; - FILE **fps = calloc(classes, sizeof(FILE *)); - for(j = 0; j < classes; ++j){ - char buff[1024]; - snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]); - fps[j] = fopen(buff, "w"); - } - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - - float thresh = .001; - int nms = 0; - float iou_thresh = .5; - float nms_thresh = .5; - - int total = 0; - int correct = 0; - int proposals = 0; - float avg_iou = 0; - - for(i = 0; i < m; ++i){ - char *path = paths[i]; - image orig = load_image_color(path, 0, 0); - image sized = resize_image(orig, net.w, net.h); - char *id = basecfg(path); - network_predict(net, sized.data); - get_detection_boxes(l, 1, 1, thresh, probs, boxes, 1); - if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); - - char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - - int num_labels = 0; - box_label *truth = read_boxes(labelpath, &num_labels); - for(k = 0; k < side*side*l.n; ++k){ - if(probs[k][0] > thresh){ - ++proposals; - } - } - for (j = 0; j < num_labels; ++j) { - ++total; - box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h}; - float best_iou = 0; - for(k = 0; k < side*side*l.n; ++k){ - float iou = box_iou(boxes[k], t); - if(probs[k][0] > thresh && iou > best_iou){ - best_iou = iou; - } - } - avg_iou += best_iou; - if(best_iou > iou_thresh){ - ++correct; - } - } - - fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); - free(id); - free_image(orig); - free_image(sized); - } -} - -void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) -{ - image **alphabet = load_alphabet(); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - detection_layer l = net.layers[net.n-1]; - set_batch_network(&net, 1); - srand(2222222); - float nms = .4; - clock_t time; - char buff[256]; - char *input = buff; - int j; - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); - while(1){ - if(filename){ - strncpy(input, filename, 256); - } else { - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input,0,0); - image sized = resize_image(im, net.w, net.h); - float *X = sized.data; - time=clock(); - network_predict(net, X); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms); - draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, alphabet, 80); - save_image(im, "prediction"); - show_image(im, "predictions"); - free_image(im); - free_image(sized); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif - if (filename) break; - } -} - -void run_coco(int argc, char **argv) -{ - char *prefix = find_char_arg(argc, argv, "-prefix", 0); - float thresh = find_float_arg(argc, argv, "-thresh", .2); - int cam_index = find_int_arg(argc, argv, "-c", 0); - int frame_skip = find_int_arg(argc, argv, "-s", 0); - - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5]: 0; - if(0==strcmp(argv[2], "test")) test_coco(cfg, weights, filename, thresh); - else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights); - else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); - else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); - else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, coco_classes, 80, frame_skip, prefix, .5); -} diff --git a/image.darknet/src/col2im.h b/image.darknet/src/col2im.h index 0237497..3fbe053 100644 --- a/image.darknet/src/col2im.h +++ b/image.darknet/src/col2im.h @@ -6,7 +6,7 @@ void col2im_cpu(float* data_col, int ksize, int stride, int pad, float* data_im); #ifdef GPU -void col2im_ongpu(float *data_col, +void col2im_gpu(float *data_col, int channels, int height, int width, int ksize, int stride, int pad, float *data_im); #endif diff --git a/image.darknet/src/col2im_kernels.cu b/image.darknet/src/col2im_kernels.cu index aed2df9..ba45e0f 100644 --- a/image.darknet/src/col2im_kernels.cu +++ b/image.darknet/src/col2im_kernels.cu @@ -41,7 +41,7 @@ __global__ void col2im_gpu_kernel(const int n, const float* data_col, } } -void col2im_ongpu(float *data_col, +void col2im_gpu(float *data_col, int channels, int height, int width, int ksize, int stride, int pad, float *data_im){ // We are going to launch channels * height_col * width_col kernels, each diff --git a/image.darknet/src/compare.c b/image.darknet/src/compare.c index 4fd266c..d2d2b3b 100644 --- a/image.darknet/src/compare.c +++ b/image.darknet/src/compare.c @@ -54,7 +54,7 @@ void train_compare(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%.3f: %f, %f avg, %lf seconds, %d images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if(i%100 == 0){ char buff[256]; diff --git a/image.darknet/src/connected_layer.c b/image.darknet/src/connected_layer.c index b678ed0..353f4e5 100644 --- a/image.darknet/src/connected_layer.c +++ b/image.darknet/src/connected_layer.c @@ -1,4 +1,5 @@ #include "connected_layer.h" +#include "convolutional_layer.h" #include "batchnorm_layer.h" #include "utils.h" #include "cuda.h" @@ -10,10 +11,11 @@ #include #include -connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize) +layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam) { int i; - connected_layer l = {0}; + layer l = {0}; + l.learning_rate_scale = 1; l.type = CONNECTED; l.inputs = inputs; @@ -50,6 +52,14 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.biases[i] = 0; } + if(adam){ + l.m = calloc(l.inputs*l.outputs, sizeof(float)); + l.v = calloc(l.inputs*l.outputs, sizeof(float)); + l.bias_m = calloc(l.outputs, sizeof(float)); + l.scale_m = calloc(l.outputs, sizeof(float)); + l.bias_v = calloc(l.outputs, sizeof(float)); + l.scale_v = calloc(l.outputs, sizeof(float)); + } if(batch_normalize){ l.scales = calloc(outputs, sizeof(float)); l.scale_updates = calloc(outputs, sizeof(float)); @@ -82,10 +92,16 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.output_gpu = cuda_make_array(l.output, outputs*batch); l.delta_gpu = cuda_make_array(l.delta, outputs*batch); - if(batch_normalize){ - l.scales_gpu = cuda_make_array(l.scales, outputs); - l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs); + if (adam) { + l.m_gpu = cuda_make_array(0, inputs*outputs); + l.v_gpu = cuda_make_array(0, inputs*outputs); + l.bias_m_gpu = cuda_make_array(0, outputs); + l.bias_v_gpu = cuda_make_array(0, outputs); + l.scale_m_gpu = cuda_make_array(0, outputs); + l.scale_v_gpu = cuda_make_array(0, outputs); + } + if(batch_normalize){ l.mean_gpu = cuda_make_array(l.mean, outputs); l.variance_gpu = cuda_make_array(l.variance, outputs); @@ -95,8 +111,17 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.mean_delta_gpu = cuda_make_array(l.mean, outputs); l.variance_delta_gpu = cuda_make_array(l.variance, outputs); + l.scales_gpu = cuda_make_array(l.scales, outputs); + l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs); + l.x_gpu = cuda_make_array(l.output, l.batch*outputs); l.x_norm_gpu = cuda_make_array(l.output, l.batch*outputs); +#ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); + cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); +#endif } #endif l.activation = activation; @@ -104,8 +129,12 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT return l; } -void update_connected_layer(connected_layer l, int batch, float learning_rate, float momentum, float decay) +void update_connected_layer(layer l, update_args a) { + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.outputs, momentum, l.bias_updates, 1); @@ -119,63 +148,39 @@ void update_connected_layer(connected_layer l, int batch, float learning_rate, f scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1); } -void forward_connected_layer(connected_layer l, network_state state) +void forward_connected_layer(layer l, network net) { - int i; fill_cpu(l.outputs*l.batch, 0, l.output, 1); int m = l.batch; int k = l.inputs; int n = l.outputs; - float *a = state.input; + float *a = net.input; float *b = l.weights; float *c = l.output; gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); if(l.batch_normalize){ - if(state.train){ - mean_cpu(l.output, l.batch, l.outputs, 1, l.mean); - variance_cpu(l.output, l.mean, l.batch, l.outputs, 1, l.variance); - - scal_cpu(l.outputs, .95, l.rolling_mean, 1); - axpy_cpu(l.outputs, .05, l.mean, 1, l.rolling_mean, 1); - scal_cpu(l.outputs, .95, l.rolling_variance, 1); - axpy_cpu(l.outputs, .05, l.variance, 1, l.rolling_variance, 1); - - copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); - normalize_cpu(l.output, l.mean, l.variance, l.batch, l.outputs, 1); - copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1); - } else { - normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.outputs, 1); - } - scale_bias(l.output, l.scales, l.batch, l.outputs, 1); - } - for(i = 0; i < l.batch; ++i){ - axpy_cpu(l.outputs, 1, l.biases, 1, l.output + i*l.outputs, 1); + forward_batchnorm_layer(l, net); + } else { + add_bias(l.output, l.biases, l.batch, l.outputs, 1); } activate_array(l.output, l.outputs*l.batch, l.activation); } -void backward_connected_layer(connected_layer l, network_state state) +void backward_connected_layer(layer l, network net) { - int i; gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); - for(i = 0; i < l.batch; ++i){ - axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1); - } - if(l.batch_normalize){ - backward_scale_cpu(l.x_norm, l.delta, l.batch, l.outputs, 1, l.scale_updates); - - scale_bias(l.delta, l.scales, l.batch, l.outputs, 1); - mean_delta_cpu(l.delta, l.variance, l.batch, l.outputs, 1, l.mean_delta); - variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.outputs, 1, l.variance_delta); - normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.outputs, 1, l.delta); + if(l.batch_normalize){ + backward_batchnorm_layer(l, net); + } else { + backward_bias(l.bias_updates, l.delta, l.batch, l.outputs, 1); } int m = l.outputs; int k = l.batch; int n = l.inputs; float *a = l.delta; - float *b = state.input; + float *b = net.input; float *c = l.weight_updates; gemm(1,0,m,n,k,1,a,m,b,n,1,c,n); @@ -185,7 +190,7 @@ void backward_connected_layer(connected_layer l, network_state state) a = l.delta; b = l.weights; - c = state.delta; + c = net.delta; if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); } @@ -213,11 +218,11 @@ void statistics_connected_layer(layer l) printf("Scales "); print_statistics(l.scales, l.outputs); /* - printf("Rolling Mean "); - print_statistics(l.rolling_mean, l.outputs); - printf("Rolling Variance "); - print_statistics(l.rolling_variance, l.outputs); - */ + printf("Rolling Mean "); + print_statistics(l.rolling_mean, l.outputs); + printf("Rolling Variance "); + print_statistics(l.rolling_variance, l.outputs); + */ } printf("Biases "); print_statistics(l.biases, l.outputs); @@ -227,7 +232,7 @@ void statistics_connected_layer(layer l) #ifdef GPU -void pull_connected_layer(connected_layer l) +void pull_connected_layer(layer l) { cuda_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs); cuda_pull_array(l.biases_gpu, l.biases, l.outputs); @@ -240,7 +245,7 @@ void pull_connected_layer(connected_layer l) } } -void push_connected_layer(connected_layer l) +void push_connected_layer(layer l) { cuda_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs); cuda_push_array(l.biases_gpu, l.biases, l.outputs); @@ -253,62 +258,70 @@ void push_connected_layer(connected_layer l) } } -void update_connected_layer_gpu(connected_layer l, int batch, float learning_rate, float momentum, float decay) +void update_connected_layer_gpu(layer l, update_args a) { - axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); - scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1); + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + if(a.adam){ + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.inputs*l.outputs, batch, a.t); + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t); + if(l.scales_gpu){ + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t); + } + }else{ + axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1); - if(l.batch_normalize){ - axpy_ongpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); - scal_ongpu(l.outputs, momentum, l.scale_updates_gpu, 1); - } + if(l.batch_normalize){ + axpy_gpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_gpu(l.outputs, momentum, l.scale_updates_gpu, 1); + } - axpy_ongpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); - axpy_ongpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); - scal_ongpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1); + axpy_gpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1); + } } -void forward_connected_layer_gpu(connected_layer l, network_state state) +void forward_connected_layer_gpu(layer l, network net) { - int i; - fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); int m = l.batch; int k = l.inputs; int n = l.outputs; - float * a = state.input; + float * a = net.input_gpu; float * b = l.weights_gpu; float * c = l.output_gpu; - gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n); - if(l.batch_normalize){ - forward_batchnorm_layer_gpu(l, state); - } - for(i = 0; i < l.batch; ++i){ - axpy_ongpu(l.outputs, 1, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1); + gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); + + if (l.batch_normalize) { + forward_batchnorm_layer_gpu(l, net); + } else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1); } - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); } -void backward_connected_layer_gpu(connected_layer l, network_state state) +void backward_connected_layer_gpu(layer l, network net) { - int i; - constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - for(i = 0; i < l.batch; ++i){ - axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1); - } - + constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if(l.batch_normalize){ - backward_batchnorm_layer_gpu(l, state); + backward_batchnorm_layer_gpu(l, net); + } else { + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.outputs, 1); } int m = l.outputs; int k = l.batch; int n = l.inputs; float * a = l.delta_gpu; - float * b = state.input; + float * b = net.input_gpu; float * c = l.weight_updates_gpu; - gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n); + gemm_gpu(1,0,m,n,k,1,a,m,b,n,1,c,n); m = l.batch; k = l.outputs; @@ -316,8 +329,8 @@ void backward_connected_layer_gpu(connected_layer l, network_state state) a = l.delta_gpu; b = l.weights_gpu; - c = state.delta; + c = net.delta_gpu; - if(c) gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n); + if(c) gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); } #endif diff --git a/image.darknet/src/connected_layer.h b/image.darknet/src/connected_layer.h index 23797b1..6727a96 100644 --- a/image.darknet/src/connected_layer.h +++ b/image.darknet/src/connected_layer.h @@ -5,22 +5,18 @@ #include "layer.h" #include "network.h" -typedef layer connected_layer; +layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam); -connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize); - -void forward_connected_layer(connected_layer layer, network_state state); -void backward_connected_layer(connected_layer layer, network_state state); -void update_connected_layer(connected_layer layer, int batch, float learning_rate, float momentum, float decay); -void denormalize_connected_layer(layer l); -void statistics_connected_layer(layer l); +void forward_connected_layer(layer l, network net); +void backward_connected_layer(layer l, network net); +void update_connected_layer(layer l, update_args a); #ifdef GPU -void forward_connected_layer_gpu(connected_layer layer, network_state state); -void backward_connected_layer_gpu(connected_layer layer, network_state state); -void update_connected_layer_gpu(connected_layer layer, int batch, float learning_rate, float momentum, float decay); -void push_connected_layer(connected_layer layer); -void pull_connected_layer(connected_layer layer); +void forward_connected_layer_gpu(layer l, network net); +void backward_connected_layer_gpu(layer l, network net); +void update_connected_layer_gpu(layer l, update_args a); +void push_connected_layer(layer l); +void pull_connected_layer(layer l); #endif #endif diff --git a/image.darknet/src/convolutional_kernels.cu b/image.darknet/src/convolutional_kernels.cu index fcaea03..4a1047b 100644 --- a/image.darknet/src/convolutional_kernels.cu +++ b/image.darknet/src/convolutional_kernels.cu @@ -33,7 +33,7 @@ __global__ void binarize_input_kernel(float *input, int n, int size, float *bina int i = 0; float mean = 0; for(i = 0; i < n; ++i){ - mean += abs(input[i*size + s]); + mean += fabsf(input[i*size + s]); } mean = mean / n; for(i = 0; i < n; ++i){ @@ -55,7 +55,7 @@ __global__ void binarize_weights_kernel(float *weights, int n, int size, float * int i = 0; float mean = 0; for(i = 0; i < size; ++i){ - mean += abs(weights[f*size + i]); + mean += fabsf(weights[f*size + i]); } mean = mean / size; for(i = 0; i < size; ++i){ @@ -70,19 +70,19 @@ void binarize_weights_gpu(float *weights, int n, int size, float *binary) check_error(cudaPeekAtLastError()); } -void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) +void forward_convolutional_layer_gpu(convolutional_layer l, network net) { - fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); if(l.binary){ - binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + binarize_weights_gpu(l.weights_gpu, l.n, l.c/l.groups*l.size*l.size, l.binary_weights_gpu); swap_binary(&l); } if(l.xnor){ - binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + binarize_weights_gpu(l.weights_gpu, l.n, l.c/l.groups*l.size*l.size, l.binary_weights_gpu); swap_binary(&l); - binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); - state.input = l.binary_input_gpu; + binarize_gpu(net.input_gpu, l.c*l.h*l.w*l.batch, l.binary_input_gpu); + net.input_gpu = l.binary_input_gpu; } #ifdef CUDNN @@ -90,74 +90,126 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) cudnnConvolutionForward(cudnn_handle(), &one, l.srcTensorDesc, - state.input, + net.input_gpu, l.weightDesc, l.weights_gpu, l.convDesc, l.fw_algo, - state.workspace, + net.workspace, l.workspace_size, &one, l.dstTensorDesc, l.output_gpu); #else - int i; - int m = l.n; - int k = l.size*l.size*l.c; + int i, j; + int m = l.n/l.groups; + int k = l.size*l.size*l.c/l.groups; int n = l.out_w*l.out_h; for(i = 0; i < l.batch; ++i){ - im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); - float * a = l.weights_gpu; - float * b = state.workspace; - float * c = l.output_gpu; - gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n); + for(j = 0; j < l.groups; ++j){ + float *a = l.weights_gpu + j*l.nweights/l.groups; + float *b = net.workspace; + float *c = l.output_gpu + (i*l.groups + j)*n*m; + float *im = net.input_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w; + + if (l.size == 1){ + b = im; + } else { + im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + } + gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); + } } #endif if (l.batch_normalize) { - forward_batchnorm_layer_gpu(l, state); + forward_batchnorm_layer_gpu(l, net); + } else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); } -void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) +__global__ void smooth_kernel(float *x, int n, int w, int h, int c, int size, float rate, float *delta) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id >= n) return; + + int j = id % w; + id /= w; + int i = id % h; + id /= h; + int k = id % c; + id /= c; + int b = id; + + int w_offset = -(size/2.f); + int h_offset = -(size/2.f); + + int out_index = j + w*(i + h*(k + c*b)); + int l, m; + for(l = 0; l < size; ++l){ + for(m = 0; m < size; ++m){ + int cur_h = h_offset + i + l; + int cur_w = w_offset + j + m; + int index = cur_w + w*(cur_h + h*(k + b*c)); + int valid = (cur_h >= 0 && cur_h < h && + cur_w >= 0 && cur_w < w); + delta[out_index] += valid ? rate*(x[index] - x[out_index]) : 0; + } + } +} + +extern "C" void smooth_layer(layer l, int size, float rate) { - //constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + int h = l.out_h; + int w = l.out_w; + int c = l.out_c; + + size_t n = h*w*c*l.batch; + + smooth_kernel<<>>(l.output_gpu, n, l.w, l.h, l.c, size, rate, l.delta_gpu); + check_error(cudaPeekAtLastError()); +} + +void backward_convolutional_layer_gpu(convolutional_layer l, network net) +{ + if(l.smooth){ + smooth_layer(l, 5, l.smooth); + } + //constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); if(l.batch_normalize){ - backward_batchnorm_layer_gpu(l, state); - //axpy_ongpu(l.outputs*l.batch, -state.net.decay, l.x_gpu, 1, l.delta_gpu, 1); + backward_batchnorm_layer_gpu(l, net); } else { - //axpy_ongpu(l.outputs*l.batch, -state.net.decay, l.output_gpu, 1, l.delta_gpu, 1); + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); } - float *original_input = state.input; + float *original_input = net.input_gpu; - if(l.xnor) state.input = l.binary_input_gpu; + if(l.xnor) net.input_gpu = l.binary_input_gpu; #ifdef CUDNN float one = 1; cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc, - state.input, + net.input_gpu, l.ddstTensorDesc, l.delta_gpu, l.convDesc, l.bf_algo, - state.workspace, + net.workspace, l.workspace_size, &one, l.dweightDesc, l.weight_updates_gpu); - if(state.delta){ + if(net.delta_gpu){ if(l.binary || l.xnor) swap_binary(&l); cudnnConvolutionBackwardData(cudnn_handle(), &one, @@ -167,108 +219,111 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.delta_gpu, l.convDesc, l.bd_algo, - state.workspace, + net.workspace, l.workspace_size, &one, l.dsrcTensorDesc, - state.delta); + net.delta_gpu); if(l.binary || l.xnor) swap_binary(&l); - if(l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); + if(l.xnor) gradient_array_gpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, net.delta_gpu); } #else - int m = l.n; - int n = l.size*l.size*l.c; + int m = l.n/l.groups; + int n = l.size*l.size*l.c/l.groups; int k = l.out_w*l.out_h; - int i; + int i, j; for(i = 0; i < l.batch; ++i){ - float * a = l.delta_gpu; - float * b = state.workspace; - float * c = l.weight_updates_gpu; - - im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); - gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n); - - if(state.delta){ - if(l.binary || l.xnor) swap_binary(&l); - float * a = l.weights_gpu; - float * b = l.delta_gpu; - float * c = state.workspace; - - gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k); - - col2im_ongpu(state.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta + i*l.c*l.h*l.w); - if(l.binary || l.xnor) { - swap_binary(&l); + for(j = 0; j < l.groups; ++j){ + float *a = l.delta_gpu + (i*l.groups + j)*m*k; + float *b = net.workspace; + float *c = l.weight_updates_gpu + j*l.nweights/l.groups; + + float *im = net.input_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w; + float *imd = net.delta_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w; + + im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); + + if (net.delta_gpu) { + if (l.binary || l.xnor) swap_binary(&l); + a = l.weights_gpu + j*l.nweights/l.groups; + b = l.delta_gpu + (i*l.groups + j)*m*k; + c = net.workspace; + if (l.size == 1) { + c = imd; + } + + gemm_gpu(1,0,n,k,m,1,a,n,b,k,0,c,k); + + if (l.size != 1) { + col2im_gpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd); + } + if(l.binary || l.xnor) { + swap_binary(&l); + } } - if(l.xnor) gradient_array_ongpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, state.delta + i*l.c*l.h*l.w); + if(l.xnor) gradient_array_gpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, net.delta_gpu + i*l.c*l.h*l.w); } } #endif } -void pull_convolutional_layer(convolutional_layer layer) +void pull_convolutional_layer(layer l) { - cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.biases_gpu, layer.biases, layer.n); - cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); - if (layer.batch_normalize){ - cuda_pull_array(layer.scales_gpu, layer.scales, layer.n); - cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); - cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); - } - if (layer.adam){ - cuda_pull_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); + cuda_pull_array(l.weights_gpu, l.weights, l.nweights); + cuda_pull_array(l.biases_gpu, l.biases, l.n); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_pull_array(l.scales_gpu, l.scales, l.n); + cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.n); } } -void push_convolutional_layer(convolutional_layer layer) +void push_convolutional_layer(layer l) { - cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.biases_gpu, layer.biases, layer.n); - cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); - if (layer.batch_normalize){ - cuda_push_array(layer.scales_gpu, layer.scales, layer.n); - cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); - cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); - } - if (layer.adam){ - cuda_push_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); + cuda_push_array(l.weights_gpu, l.weights, l.nweights); + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_push_array(l.scales_gpu, l.scales, l.n); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); } } -void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay) +void update_convolutional_layer_gpu(layer l, update_args a) { - int size = layer.size*layer.size*layer.c*layer.n; - axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); - scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); - - if(layer.scales_gpu){ - axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1); - scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1); - } - - if(layer.adam){ - scal_ongpu(size, layer.B1, layer.m_gpu, 1); - scal_ongpu(size, layer.B2, layer.v_gpu, 1); - - axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + if(a.adam){ + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + if(l.scales_gpu){ + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + } + }else{ + axpy_gpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(l.nweights, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(l.nweights, momentum, l.weight_updates_gpu, 1); - axpy_ongpu(size, -(1-layer.B1), layer.weight_updates_gpu, 1, layer.m_gpu, 1); - mul_ongpu(size, layer.weight_updates_gpu, 1, layer.weight_updates_gpu, 1); - axpy_ongpu(size, (1-layer.B2), layer.weight_updates_gpu, 1, layer.v_gpu, 1); + axpy_gpu(l.n, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.n, momentum, l.bias_updates_gpu, 1); - adam_gpu(size, layer.weights_gpu, layer.m_gpu, layer.v_gpu, layer.B1, layer.B2, learning_rate/batch, layer.eps, layer.t+1); - fill_ongpu(size, 0, layer.weight_updates_gpu, 1); - }else{ - axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); - axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); - scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); + if(l.scales_gpu){ + axpy_gpu(l.n, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_gpu(l.n, momentum, l.scale_updates_gpu, 1); + } + } + if(l.clip){ + constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); } } diff --git a/image.darknet/src/convolutional_layer.c b/image.darknet/src/convolutional_layer.c index 37211ab..1fb58b0 100644 --- a/image.darknet/src/convolutional_layer.c +++ b/image.darknet/src/convolutional_layer.c @@ -12,22 +12,17 @@ #include "xnor_layer.h" #endif -#ifndef AI2 -#define AI2 0 -void forward_xnor_layer(layer l, network_state state); -#endif - void swap_binary(convolutional_layer *l) { float *swap = l->weights; l->weights = l->binary_weights; l->binary_weights = swap; - #ifdef GPU +#ifdef GPU swap = l->weights_gpu; l->weights_gpu = l->binary_weights_gpu; l->binary_weights_gpu = swap; - #endif +#endif } void binarize_weights(float *weights, int n, int size, float *binary) @@ -80,23 +75,15 @@ int convolutional_out_width(convolutional_layer l) image get_convolutional_image(convolutional_layer l) { - int h,w,c; - h = convolutional_out_height(l); - w = convolutional_out_width(l); - c = l.n; - return float_to_image(w,h,c,l.output); + return float_to_image(l.out_w,l.out_h,l.out_c,l.output); } image get_convolutional_delta(convolutional_layer l) { - int h,w,c; - h = convolutional_out_height(l); - w = convolutional_out_width(l); - c = l.n; - return float_to_image(w,h,c,l.delta); + return float_to_image(l.out_w,l.out_h,l.out_c,l.delta); } -size_t get_workspace_size(layer l){ +static size_t get_workspace_size(layer l){ #ifdef CUDNN if(gpu_index >= 0){ size_t most = 0; @@ -127,8 +114,8 @@ size_t get_workspace_size(layer l){ if (s > most) most = s; return most; } - #endif - return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float); +#endif + return (size_t)l.out_h*l.out_w*l.size*l.size*l.c/l.groups*sizeof(float); } #ifdef GPU @@ -137,46 +124,62 @@ void cudnn_convolutional_setup(layer *l) { cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetFilter4dDescriptor(l->dweightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); + + cudnnSetFilter4dDescriptor(l->dweightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size); + cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size); + #if CUDNN_MAJOR >= 6 + cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT); + #else cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION); + #endif + + #if CUDNN_MAJOR >= 7 + cudnnSetConvolutionGroupCount(l->convDesc, l->groups); + #else + if(l->groups > 1){ + error("CUDNN < 7 doesn't support groups, please upgrade!"); + } + #endif + cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), l->srcTensorDesc, l->weightDesc, l->convDesc, l->dstTensorDesc, - CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, - 0, + CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, + 2000000000, &l->fw_algo); cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), l->weightDesc, l->ddstTensorDesc, l->convDesc, l->dsrcTensorDesc, - CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, - 0, + CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, + 2000000000, &l->bd_algo); cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), l->srcTensorDesc, l->ddstTensorDesc, l->convDesc, l->dweightDesc, - CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, - 0, + CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, + 2000000000, &l->bf_algo); } #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam) +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam) { int i; convolutional_layer l = {0}; l.type = CONVOLUTIONAL; + l.groups = groups; l.h = h; l.w = w; l.c = c; @@ -189,17 +192,23 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.pad = padding; l.batch_normalize = batch_normalize; - l.weights = calloc(c*n*size*size, sizeof(float)); - l.weight_updates = calloc(c*n*size*size, sizeof(float)); + l.weights = calloc(c/groups*n*size*size, sizeof(float)); + l.weight_updates = calloc(c/groups*n*size*size, sizeof(float)); l.biases = calloc(n, sizeof(float)); l.bias_updates = calloc(n, sizeof(float)); + l.nweights = c/groups*n*size*size; + l.nbiases = n; + // float scale = 1./sqrt(size*size*c); - float scale = sqrt(2./(size*size*c)); - for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1); - int out_h = convolutional_out_height(l); + float scale = sqrt(2./(size*size*c/l.groups)); + //printf("convscale %f\n", scale); + //scale = .02; + //for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1); + for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal(); int out_w = convolutional_out_width(l); + int out_h = convolutional_out_height(l); l.out_h = out_h; l.out_w = out_w; l.out_c = n; @@ -213,12 +222,12 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.backward = backward_convolutional_layer; l.update = update_convolutional_layer; if(binary){ - l.binary_weights = calloc(c*n*size*size, sizeof(float)); - l.cweights = calloc(c*n*size*size, sizeof(char)); + l.binary_weights = calloc(l.nweights, sizeof(float)); + l.cweights = calloc(l.nweights, sizeof(char)); l.scales = calloc(n, sizeof(float)); } if(xnor){ - l.binary_weights = calloc(c*n*size*size, sizeof(float)); + l.binary_weights = calloc(l.nweights, sizeof(float)); l.binary_input = calloc(l.inputs*l.batch, sizeof(float)); } @@ -241,9 +250,12 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.x_norm = calloc(l.batch*l.outputs, sizeof(float)); } if(adam){ - l.adam = 1; - l.m = calloc(c*n*size*size, sizeof(float)); - l.v = calloc(c*n*size*size, sizeof(float)); + l.m = calloc(l.nweights, sizeof(float)); + l.v = calloc(l.nweights, sizeof(float)); + l.bias_m = calloc(n, sizeof(float)); + l.scale_m = calloc(n, sizeof(float)); + l.bias_v = calloc(n, sizeof(float)); + l.scale_v = calloc(n, sizeof(float)); } #ifdef GPU @@ -253,12 +265,16 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int if(gpu_index >= 0){ if (adam) { - l.m_gpu = cuda_make_array(l.m, c*n*size*size); - l.v_gpu = cuda_make_array(l.v, c*n*size*size); + l.m_gpu = cuda_make_array(l.m, l.nweights); + l.v_gpu = cuda_make_array(l.v, l.nweights); + l.bias_m_gpu = cuda_make_array(l.bias_m, n); + l.bias_v_gpu = cuda_make_array(l.bias_v, n); + l.scale_m_gpu = cuda_make_array(l.scale_m, n); + l.scale_v_gpu = cuda_make_array(l.scale_v, n); } - l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); - l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); + l.weights_gpu = cuda_make_array(l.weights, l.nweights); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); l.biases_gpu = cuda_make_array(l.biases, n); l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); @@ -267,10 +283,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); if(binary){ - l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); } if(xnor){ - l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch); } @@ -291,6 +307,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); } #ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normTensorDesc); cudnnCreateTensorDescriptor(&l.srcTensorDesc); cudnnCreateTensorDescriptor(&l.dstTensorDesc); cudnnCreateFilterDescriptor(&l.weightDesc); @@ -305,7 +322,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.workspace_size = get_workspace_size(l); l.activation = activation; - fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); + fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BFLOPs\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, (2.0 * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w)/1000000000.); return l; } @@ -315,8 +332,8 @@ void denormalize_convolutional_layer(convolutional_layer l) int i, j; for(i = 0; i < l.n; ++i){ float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001); - for(j = 0; j < l.c*l.size*l.size; ++j){ - l.weights[i*l.c*l.size*l.size + j] *= scale; + for(j = 0; j < l.c/l.groups*l.size*l.size; ++j){ + l.weights[i*l.c/l.groups*l.size*l.size + j] *= scale; } l.biases[i] -= l.rolling_mean[i] * scale; l.scales[i] = 1; @@ -325,6 +342,7 @@ void denormalize_convolutional_layer(convolutional_layer l) } } +/* void test_convolutional_layer() { convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0); @@ -344,10 +362,10 @@ void test_convolutional_layer() 3,3,3,3,3, 3,3,3,3,3, 3,3,3,3,3}; - network_state state = {0}; - state.input = data; - forward_convolutional_layer(l, state); + //net.input = data; + //forward_convolutional_layer(l); } +*/ void resize_convolutional_layer(convolutional_layer *l, int w, int h) { @@ -424,88 +442,106 @@ void backward_bias(float *bias_updates, float *delta, int batch, int n, int size } } -void forward_convolutional_layer(convolutional_layer l, network_state state) +void forward_convolutional_layer(convolutional_layer l, network net) { - int out_h = convolutional_out_height(l); - int out_w = convolutional_out_width(l); - int i; + int i, j; fill_cpu(l.outputs*l.batch, 0, l.output, 1); if(l.xnor){ - binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); + binarize_weights(l.weights, l.n, l.c/l.groups*l.size*l.size, l.binary_weights); swap_binary(&l); - binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input); - state.input = l.binary_input; + binarize_cpu(net.input, l.c*l.h*l.w*l.batch, l.binary_input); + net.input = l.binary_input; } - int m = l.n; - int k = l.size*l.size*l.c; - int n = out_h*out_w; - - - float *a = l.weights; - float *b = state.workspace; - float *c = l.output; - + int m = l.n/l.groups; + int k = l.size*l.size*l.c/l.groups; + int n = l.out_w*l.out_h; for(i = 0; i < l.batch; ++i){ - im2col_cpu(state.input, l.c, l.h, l.w, - l.size, l.stride, l.pad, b); - gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); - c += n*m; - state.input += l.c*l.h*l.w; + for(j = 0; j < l.groups; ++j){ + float *a = l.weights + j*l.nweights/l.groups; + float *b = net.workspace; + float *c = l.output + (i*l.groups + j)*n*m; + float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w; + + if (l.size == 1) { + b = im; + } else { + im2col_cpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + } + gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + } } if(l.batch_normalize){ - forward_batchnorm_layer(l, state); + forward_batchnorm_layer(l, net); + } else { + add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w); } - add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); - activate_array(l.output, m*n*l.batch, l.activation); + activate_array(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); } -void backward_convolutional_layer(convolutional_layer l, network_state state) +void backward_convolutional_layer(convolutional_layer l, network net) { - int i; - int m = l.n; - int n = l.size*l.size*l.c; - int k = convolutional_out_height(l)* - convolutional_out_width(l); + int i, j; + int m = l.n/l.groups; + int n = l.size*l.size*l.c/l.groups; + int k = l.out_w*l.out_h; - gradient_array(l.output, m*k*l.batch, l.activation, l.delta); - backward_bias(l.bias_updates, l.delta, l.batch, l.n, k); + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); if(l.batch_normalize){ - backward_batchnorm_layer(l, state); + backward_batchnorm_layer(l, net); + } else { + backward_bias(l.bias_updates, l.delta, l.batch, l.n, k); } for(i = 0; i < l.batch; ++i){ - float *a = l.delta + i*m*k; - float *b = state.workspace; - float *c = l.weight_updates; - - float *im = state.input+i*l.c*l.h*l.w; + for(j = 0; j < l.groups; ++j){ + float *a = l.delta + (i*l.groups + j)*m*k; + float *b = net.workspace; + float *c = l.weight_updates + j*l.nweights/l.groups; + + float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w; + float *imd = net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w; + + if(l.size == 1){ + b = im; + } else { + im2col_cpu(im, l.c/l.groups, l.h, l.w, + l.size, l.stride, l.pad, b); + } - im2col_cpu(im, l.c, l.h, l.w, - l.size, l.stride, l.pad, b); - gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); + gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); - if(state.delta){ - a = l.weights; - b = l.delta + i*m*k; - c = state.workspace; + if (net.delta) { + a = l.weights + j*l.nweights/l.groups; + b = l.delta + (i*l.groups + j)*m*k; + c = net.workspace; + if (l.size == 1) { + c = imd; + } - gemm(1,0,n,k,m,1,a,n,b,k,0,c,k); + gemm(1,0,n,k,m,1,a,n,b,k,0,c,k); - col2im_cpu(state.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w); + if (l.size != 1) { + col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd); + } + } } } } -void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate, float momentum, float decay) +void update_convolutional_layer(convolutional_layer l, update_args a) { - int size = l.size*l.size*l.c*l.n; + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.n, momentum, l.bias_updates, 1); @@ -514,9 +550,9 @@ void update_convolutional_layer(convolutional_layer l, int batch, float learning scal_cpu(l.n, momentum, l.scale_updates, 1); } - axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1); - axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1); - scal_cpu(size, momentum, l.weight_updates, 1); + axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(l.nweights, learning_rate/batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(l.nweights, momentum, l.weight_updates, 1); } @@ -524,7 +560,7 @@ image get_convolutional_weight(convolutional_layer l, int i) { int h = l.size; int w = l.size; - int c = l.c; + int c = l.c/l.groups; return float_to_image(w,h,c,l.weights+i*h*w*c); } @@ -558,8 +594,14 @@ image *get_weights(convolutional_layer l) int i; for(i = 0; i < l.n; ++i){ weights[i] = copy_image(get_convolutional_weight(l, i)); - //normalize_image(weights[i]); + normalize_image(weights[i]); + /* + char buff[256]; + sprintf(buff, "filter%d", i); + save_image(weights[i], buff); + */ } + //error("hey"); return weights; } diff --git a/image.darknet/src/convolutional_layer.h b/image.darknet/src/convolutional_layer.h index 970aa10..6c261f5 100644 --- a/image.darknet/src/convolutional_layer.h +++ b/image.darknet/src/convolutional_layer.h @@ -10,31 +10,31 @@ typedef layer convolutional_layer; #ifdef GPU -void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state); -void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state); -void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay); +void forward_convolutional_layer_gpu(convolutional_layer layer, network net); +void backward_convolutional_layer_gpu(convolutional_layer layer, network net); +void update_convolutional_layer_gpu(convolutional_layer layer, update_args a); void push_convolutional_layer(convolutional_layer layer); void pull_convolutional_layer(convolutional_layer layer); void add_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); +void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t); #ifdef CUDNN void cudnn_convolutional_setup(layer *l); #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam); -void denormalize_convolutional_layer(convolutional_layer l); +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); -void forward_convolutional_layer(const convolutional_layer layer, network_state state); -void update_convolutional_layer(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay); +void forward_convolutional_layer(const convolutional_layer layer, network net); +void update_convolutional_layer(convolutional_layer layer, update_args a); image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_weights); void binarize_weights(float *weights, int n, int size, float *binary); void swap_binary(convolutional_layer *l); void binarize_weights2(float *weights, int n, int size, char *binary, float *scales); -void backward_convolutional_layer(convolutional_layer layer, network_state state); +void backward_convolutional_layer(convolutional_layer layer, network net); void add_bias(float *output, float *biases, int batch, int n, int size); void backward_bias(float *bias_updates, float *delta, int batch, int n, int size); @@ -45,8 +45,6 @@ image get_convolutional_weight(convolutional_layer layer, int i); int convolutional_out_height(convolutional_layer layer); int convolutional_out_width(convolutional_layer layer); -void rescale_weights(convolutional_layer l, float scale, float trans); -void rgbgr_weights(convolutional_layer l); #endif diff --git a/image.darknet/src/cost_layer.c b/image.darknet/src/cost_layer.c index 39d2398..2138ff2 100644 --- a/image.darknet/src/cost_layer.c +++ b/image.darknet/src/cost_layer.c @@ -9,9 +9,12 @@ COST_TYPE get_cost_type(char *s) { + if (strcmp(s, "seg")==0) return SEG; if (strcmp(s, "sse")==0) return SSE; if (strcmp(s, "masked")==0) return MASKED; if (strcmp(s, "smooth")==0) return SMOOTH; + if (strcmp(s, "L1")==0) return L1; + if (strcmp(s, "wgan")==0) return WGAN; fprintf(stderr, "Couldn't find cost type %s, going with SSE\n", s); return SSE; } @@ -19,12 +22,18 @@ COST_TYPE get_cost_type(char *s) char *get_cost_string(COST_TYPE a) { switch(a){ + case SEG: + return "seg"; case SSE: return "sse"; case MASKED: return "masked"; case SMOOTH: return "smooth"; + case L1: + return "L1"; + case WGAN: + return "wgan"; } return "sse"; } @@ -70,26 +79,28 @@ void resize_cost_layer(cost_layer *l, int inputs) #endif } -void forward_cost_layer(cost_layer l, network_state state) +void forward_cost_layer(cost_layer l, network net) { - if (!state.truth) return; + if (!net.truth) return; if(l.cost_type == MASKED){ int i; for(i = 0; i < l.batch*l.inputs; ++i){ - if(state.truth[i] == SECRET_NUM) state.input[i] = SECRET_NUM; + if(net.truth[i] == SECRET_NUM) net.input[i] = SECRET_NUM; } } if(l.cost_type == SMOOTH){ - smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output); + smooth_l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output); + }else if(l.cost_type == L1){ + l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output); } else { - l2_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output); + l2_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output); } l.cost[0] = sum_array(l.output, l.batch*l.inputs); } -void backward_cost_layer(const cost_layer l, network_state state) +void backward_cost_layer(const cost_layer l, network net) { - axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, state.delta, 1); + axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, net.delta, 1); } #ifdef GPU @@ -113,17 +124,30 @@ int float_abs_compare (const void * a, const void * b) return (fa > fb) - (fa < fb); } -void forward_cost_layer_gpu(cost_layer l, network_state state) +void forward_cost_layer_gpu(cost_layer l, network net) { - if (!state.truth) return; - if (l.cost_type == MASKED) { - mask_ongpu(l.batch*l.inputs, state.input, SECRET_NUM, state.truth); + if (!net.truth) return; + if(l.smooth){ + scal_gpu(l.batch*l.inputs, (1-l.smooth), net.truth_gpu, 1); + add_gpu(l.batch*l.inputs, l.smooth * 1./l.inputs, net.truth_gpu, 1); } if(l.cost_type == SMOOTH){ - smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu); + smooth_l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } else if (l.cost_type == L1){ + l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } else if (l.cost_type == WGAN){ + wgan_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); } else { - l2_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu); + l2_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } + + if (l.cost_type == SEG && l.noobject_scale != 1) { + scale_mask_gpu(l.batch*l.inputs, l.delta_gpu, 0, net.truth_gpu, l.noobject_scale); + scale_mask_gpu(l.batch*l.inputs, l.output_gpu, 0, net.truth_gpu, l.noobject_scale); + } + if (l.cost_type == MASKED) { + mask_gpu(l.batch*l.inputs, net.delta_gpu, SECRET_NUM, net.truth_gpu, 0); } if(l.ratio){ @@ -133,16 +157,20 @@ void forward_cost_layer_gpu(cost_layer l, network_state state) float thresh = l.delta[n]; thresh = 0; printf("%f\n", thresh); - supp_ongpu(l.batch*l.inputs, thresh, l.delta_gpu, 1); + supp_gpu(l.batch*l.inputs, thresh, l.delta_gpu, 1); + } + + if(l.thresh){ + supp_gpu(l.batch*l.inputs, l.thresh*1./l.inputs, l.delta_gpu, 1); } cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs); l.cost[0] = sum_array(l.output, l.batch*l.inputs); } -void backward_cost_layer_gpu(const cost_layer l, network_state state) +void backward_cost_layer_gpu(const cost_layer l, network net) { - axpy_ongpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, state.delta, 1); + axpy_gpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/src/cost_layer.h b/image.darknet/src/cost_layer.h index a692831..ceb64de 100644 --- a/image.darknet/src/cost_layer.h +++ b/image.darknet/src/cost_layer.h @@ -8,13 +8,13 @@ typedef layer cost_layer; COST_TYPE get_cost_type(char *s); char *get_cost_string(COST_TYPE a); cost_layer make_cost_layer(int batch, int inputs, COST_TYPE type, float scale); -void forward_cost_layer(const cost_layer l, network_state state); -void backward_cost_layer(const cost_layer l, network_state state); +void forward_cost_layer(const cost_layer l, network net); +void backward_cost_layer(const cost_layer l, network net); void resize_cost_layer(cost_layer *l, int inputs); #ifdef GPU -void forward_cost_layer_gpu(cost_layer l, network_state state); -void backward_cost_layer_gpu(const cost_layer l, network_state state); +void forward_cost_layer_gpu(cost_layer l, network net); +void backward_cost_layer_gpu(const cost_layer l, network net); #endif #endif diff --git a/image.darknet/src/crnn_layer.c b/image.darknet/src/crnn_layer.c index 5495880..7dd29f6 100644 --- a/image.darknet/src/crnn_layer.c +++ b/image.darknet/src/crnn_layer.c @@ -48,17 +48,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.input_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0); l.input_layer->batch = batch; l.self_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0); l.self_layer->batch = batch; l.output_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0); l.output_layer->batch = batch; l.output = l.output_layer->output; @@ -81,17 +81,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou return l; } -void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay) +void update_crnn_layer(layer l, update_args a) { - update_convolutional_layer(*(l.input_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer(*(l.self_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer(*(l.output_layer), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.input_layer), a); + update_convolutional_layer(*(l.self_layer), a); + update_convolutional_layer(*(l.output_layer), a); } -void forward_crnn_layer(layer l, network_state state) +void forward_crnn_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -100,17 +100,17 @@ void forward_crnn_layer(layer l, network_state state) fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1); fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1); fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1); - if(state.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1); + if(net.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1); for (i = 0; i < l.steps; ++i) { - s.input = state.input; + s.input = net.input; forward_convolutional_layer(input_layer, s); s.input = l.state; forward_convolutional_layer(self_layer, s); float *old_state = l.state; - if(state.train) l.state += l.hidden*l.batch; + if(net.train) l.state += l.hidden*l.batch; if(l.shortcut){ copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1); }else{ @@ -122,17 +122,16 @@ void forward_crnn_layer(layer l, network_state state) s.input = l.state; forward_convolutional_layer(output_layer, s); - state.input += l.inputs*l.batch; + net.input += l.inputs*l.batch; increment_layer(&input_layer, 1); increment_layer(&self_layer, 1); increment_layer(&output_layer, 1); } } -void backward_crnn_layer(layer l, network_state state) +void backward_crnn_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -168,8 +167,8 @@ void backward_crnn_layer(layer l, network_state state) copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1); if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1); - s.input = state.input + i*l.inputs*l.batch; - if(state.delta) s.delta = state.delta + i*l.inputs*l.batch; + s.input = net.input + i*l.inputs*l.batch; + if(net.delta) s.delta = net.delta + i*l.inputs*l.batch; else s.delta = 0; backward_convolutional_layer(input_layer, s); @@ -195,58 +194,57 @@ void push_crnn_layer(layer l) push_convolutional_layer(*(l.output_layer)); } -void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_crnn_layer_gpu(layer l, update_args a) { - update_convolutional_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.input_layer), a); + update_convolutional_layer_gpu(*(l.self_layer), a); + update_convolutional_layer_gpu(*(l.output_layer), a); } -void forward_crnn_layer_gpu(layer l, network_state state) +void forward_crnn_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); - fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1); - if(state.train) fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); + fill_gpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); + fill_gpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1); + if(net.train) fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1); for (i = 0; i < l.steps; ++i) { - s.input = state.input; + s.input_gpu = net.input_gpu; forward_convolutional_layer_gpu(input_layer, s); - s.input = l.state_gpu; + s.input_gpu = l.state_gpu; forward_convolutional_layer_gpu(self_layer, s); float *old_state = l.state_gpu; - if(state.train) l.state_gpu += l.hidden*l.batch; + if(net.train) l.state_gpu += l.hidden*l.batch; if(l.shortcut){ - copy_ongpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1); + copy_gpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1); }else{ - fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); + fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1); } - axpy_ongpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); - axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); - s.input = l.state_gpu; + s.input_gpu = l.state_gpu; forward_convolutional_layer_gpu(output_layer, s); - state.input += l.inputs*l.batch; + net.input_gpu += l.inputs*l.batch; increment_layer(&input_layer, 1); increment_layer(&self_layer, 1); increment_layer(&output_layer, 1); } } -void backward_crnn_layer_gpu(layer l, network_state state) +void backward_crnn_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -256,25 +254,25 @@ void backward_crnn_layer_gpu(layer l, network_state state) increment_layer(&output_layer, l.steps - 1); l.state_gpu += l.hidden*l.batch*l.steps; for (i = l.steps-1; i >= 0; --i) { - copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); - axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + copy_gpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); - s.input = l.state_gpu; - s.delta = self_layer.delta_gpu; + s.input_gpu = l.state_gpu; + s.delta_gpu = self_layer.delta_gpu; backward_convolutional_layer_gpu(output_layer, s); l.state_gpu -= l.hidden*l.batch; - s.input = l.state_gpu; - s.delta = self_layer.delta_gpu - l.hidden*l.batch; - if (i == 0) s.delta = 0; + s.input_gpu = l.state_gpu; + s.delta_gpu = self_layer.delta_gpu - l.hidden*l.batch; + if (i == 0) s.delta_gpu = 0; backward_convolutional_layer_gpu(self_layer, s); - copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); - if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1); - s.input = state.input + i*l.inputs*l.batch; - if(state.delta) s.delta = state.delta + i*l.inputs*l.batch; - else s.delta = 0; + copy_gpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); + if (i > 0 && l.shortcut) axpy_gpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1); + s.input_gpu = net.input_gpu + i*l.inputs*l.batch; + if(net.delta_gpu) s.delta_gpu = net.delta_gpu + i*l.inputs*l.batch; + else s.delta_gpu = 0; backward_convolutional_layer_gpu(input_layer, s); increment_layer(&input_layer, -1); diff --git a/image.darknet/src/crnn_layer.h b/image.darknet/src/crnn_layer.h index 0da942e..515f378 100644 --- a/image.darknet/src/crnn_layer.h +++ b/image.darknet/src/crnn_layer.h @@ -8,14 +8,14 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize); -void forward_crnn_layer(layer l, network_state state); -void backward_crnn_layer(layer l, network_state state); -void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_crnn_layer(layer l, network net); +void backward_crnn_layer(layer l, network net); +void update_crnn_layer(layer l, update_args a); #ifdef GPU -void forward_crnn_layer_gpu(layer l, network_state state); -void backward_crnn_layer_gpu(layer l, network_state state); -void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_crnn_layer_gpu(layer l, network net); +void backward_crnn_layer_gpu(layer l, network net); +void update_crnn_layer_gpu(layer l, update_args a); void push_crnn_layer(layer l); void pull_crnn_layer(layer l); #endif diff --git a/image.darknet/src/crop_layer.c b/image.darknet/src/crop_layer.c index 11c59b4..3b91852 100644 --- a/image.darknet/src/crop_layer.c +++ b/image.darknet/src/crop_layer.c @@ -10,8 +10,8 @@ image get_crop_image(crop_layer l) return float_to_image(w,h,c,l.output); } -void backward_crop_layer(const crop_layer l, network_state state){} -void backward_crop_layer_gpu(const crop_layer l, network_state state){} +void backward_crop_layer(const crop_layer l, network net){} +void backward_crop_layer_gpu(const crop_layer l, network net){} crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure) { @@ -64,7 +64,7 @@ void resize_crop_layer(layer *l, int w, int h) } -void forward_crop_layer(const crop_layer l, network_state state) +void forward_crop_layer(const crop_layer l, network net) { int i,j,c,b,row,col; int index; @@ -78,7 +78,7 @@ void forward_crop_layer(const crop_layer l, network_state state) scale = 1; trans = 0; } - if(!state.train){ + if(!net.train){ flip = 0; dh = (l.h - l.out_h)/2; dw = (l.w - l.out_w)/2; @@ -94,7 +94,7 @@ void forward_crop_layer(const crop_layer l, network_state state) } row = i + dh; index = col+l.w*(row+l.h*(c + l.c*b)); - l.output[count++] = state.input[index]*scale + trans; + l.output[count++] = net.input[index]*scale + trans; } } } diff --git a/image.darknet/src/crop_layer.h b/image.darknet/src/crop_layer.h index 3aa2d3d..3b5883c 100644 --- a/image.darknet/src/crop_layer.h +++ b/image.darknet/src/crop_layer.h @@ -9,11 +9,11 @@ typedef layer crop_layer; image get_crop_image(crop_layer l); crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure); -void forward_crop_layer(const crop_layer l, network_state state); +void forward_crop_layer(const crop_layer l, network net); void resize_crop_layer(layer *l, int w, int h); #ifdef GPU -void forward_crop_layer_gpu(crop_layer l, network_state state); +void forward_crop_layer_gpu(crop_layer l, network net); #endif #endif diff --git a/image.darknet/src/crop_layer_kernels.cu b/image.darknet/src/crop_layer_kernels.cu index 8a08630..b5b9f55 100644 --- a/image.darknet/src/crop_layer_kernels.cu +++ b/image.darknet/src/crop_layer_kernels.cu @@ -113,9 +113,9 @@ __global__ void levels_image_kernel(float *image, float *rand, int batch, int w, float r3 = rand[8*id + 3]; saturation = r0*(saturation - 1) + 1; - saturation = (r1 > .5) ? 1./saturation : saturation; + saturation = (r1 > .5f) ? 1.f/saturation : saturation; exposure = r2*(exposure - 1) + 1; - exposure = (r3 > .5) ? 1./exposure : exposure; + exposure = (r3 > .5f) ? 1.f/exposure : exposure; size_t offset = id * h * w * 3; image += offset; @@ -131,9 +131,9 @@ __global__ void levels_image_kernel(float *image, float *rand, int batch, int w, } else { shift = 0; } - image[x + w*(y + h*0)] = rgb.x*scale + translate + (rshift - .5)*shift; - image[x + w*(y + h*1)] = rgb.y*scale + translate + (gshift - .5)*shift; - image[x + w*(y + h*2)] = rgb.z*scale + translate + (bshift - .5)*shift; + image[x + w*(y + h*0)] = rgb.x*scale + translate + (rshift - .5f)*shift; + image[x + w*(y + h*1)] = rgb.y*scale + translate + (gshift - .5f)*shift; + image[x + w*(y + h*2)] = rgb.z*scale + translate + (bshift - .5f)*shift; } __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, int c, int h, int w, int crop_height, int crop_width, int train, int flip, float angle, float *output) @@ -141,8 +141,8 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(id >= size) return; - float cx = w/2.; - float cy = h/2.; + float cx = w/2.f; + float cy = h/2.f; int count = id; int j = id % crop_width; @@ -160,11 +160,11 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i float dw = (w - crop_width)*r4; float dh = (h - crop_height)*r5; - flip = (flip && (r6 > .5)); + flip = (flip && (r6 > .5f)); angle = 2*angle*r7 - angle; if(!train){ - dw = (w - crop_width)/2.; - dh = (h - crop_height)/2.; + dw = (w - crop_width)/2.f; + dh = (h - crop_height)/2.f; flip = 0; angle = 0; } @@ -174,17 +174,17 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i float x = (flip) ? w - dw - j - 1 : j + dw; float y = i + dh; - float rx = cos(angle)*(x-cx) - sin(angle)*(y-cy) + cx; - float ry = sin(angle)*(x-cx) + cos(angle)*(y-cy) + cy; + float rx = cosf(angle)*(x-cx) - sinf(angle)*(y-cy) + cx; + float ry = sinf(angle)*(x-cx) + cosf(angle)*(y-cy) + cy; output[count] = bilinear_interpolate_kernel(input, w, h, rx, ry, k); } -extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state) +extern "C" void forward_crop_layer_gpu(crop_layer layer, network net) { cuda_random(layer.rand_gpu, layer.batch*8); - float radians = layer.angle*3.14159265/180.; + float radians = layer.angle*3.14159265f/180.f; float scale = 2; float translate = -1; @@ -195,12 +195,12 @@ extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state) int size = layer.batch * layer.w * layer.h; - levels_image_kernel<<>>(state.input, layer.rand_gpu, layer.batch, layer.w, layer.h, state.train, layer.saturation, layer.exposure, translate, scale, layer.shift); + levels_image_kernel<<>>(net.input_gpu, layer.rand_gpu, layer.batch, layer.w, layer.h, net.train, layer.saturation, layer.exposure, translate, scale, layer.shift); check_error(cudaPeekAtLastError()); size = layer.batch*layer.c*layer.out_w*layer.out_h; - forward_crop_layer_kernel<<>>(state.input, layer.rand_gpu, size, layer.c, layer.h, layer.w, layer.out_h, layer.out_w, state.train, layer.flip, radians, layer.output_gpu); + forward_crop_layer_kernel<<>>(net.input_gpu, layer.rand_gpu, size, layer.c, layer.h, layer.w, layer.out_h, layer.out_w, net.train, layer.flip, radians, layer.output_gpu); check_error(cudaPeekAtLastError()); /* diff --git a/image.darknet/src/cuda.c b/image.darknet/src/cuda.c index 1b51271..48aba6e 100644 --- a/image.darknet/src/cuda.c +++ b/image.darknet/src/cuda.c @@ -5,7 +5,7 @@ int gpu_index = 0; #include "cuda.h" #include "utils.h" #include "blas.h" -#include "assert.h" +#include #include #include @@ -96,6 +96,8 @@ float *cuda_make_array(float *x, size_t n) if(x){ status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); check_error(status); + } else { + fill_gpu(n, 0, x_gpu, 1); } if(!x_gpu) error("Cuda malloc failed\n"); return x_gpu; @@ -128,12 +130,17 @@ float cuda_compare(float *x_gpu, float *x, size_t n, char *s) return err; } -int *cuda_make_int_array(size_t n) +int *cuda_make_int_array(int *x, size_t n) { int *x_gpu; size_t size = sizeof(int)*n; cudaError_t status = cudaMalloc((void **)&x_gpu, size); check_error(status); + if(x){ + status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); + check_error(status); + } + if(!x_gpu) error("Cuda malloc failed\n"); return x_gpu; } @@ -157,4 +164,15 @@ void cuda_pull_array(float *x_gpu, float *x, size_t n) check_error(status); } +float cuda_mag_array(float *x_gpu, size_t n) +{ + float *temp = calloc(n, sizeof(float)); + cuda_pull_array(x_gpu, temp, n); + float m = mag_array(temp, n); + free(temp); + return m; +} +#else +void cuda_set_device(int n){} + #endif diff --git a/image.darknet/src/cuda.h b/image.darknet/src/cuda.h index 29b1eef..a1bc216 100644 --- a/image.darknet/src/cuda.h +++ b/image.darknet/src/cuda.h @@ -1,28 +1,13 @@ #ifndef CUDA_H #define CUDA_H -extern int gpu_index; +#include "darknet.h" #ifdef GPU -#define BLOCK 512 - -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" - -#ifdef CUDNN -#include "cudnn.h" -#endif - void check_error(cudaError_t status); cublasHandle_t blas_handle(); -float *cuda_make_array(float *x, size_t n); -int *cuda_make_int_array(size_t n); -void cuda_push_array(float *x_gpu, float *x, size_t n); -void cuda_pull_array(float *x_gpu, float *x, size_t n); -void cuda_set_device(int n); -void cuda_free(float *x_gpu); +int *cuda_make_int_array(int *x, size_t n); void cuda_random(float *x_gpu, size_t n); float cuda_compare(float *x_gpu, float *x, size_t n, char *s); dim3 cuda_gridsize(size_t n); diff --git a/image.darknet/src/darknet.c b/image.darknet/src/darknet.c deleted file mode 100644 index 6e56072..0000000 --- a/image.darknet/src/darknet.c +++ /dev/null @@ -1,452 +0,0 @@ -#include -#include -#include - -#include "parser.h" -#include "utils.h" -#include "cuda.h" -#include "blas.h" -#include "connected_layer.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top); -extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh); -extern void run_voxel(int argc, char **argv); -extern void run_yolo(int argc, char **argv); -extern void run_detector(int argc, char **argv); -extern void run_coco(int argc, char **argv); -extern void run_writing(int argc, char **argv); -extern void run_captcha(int argc, char **argv); -extern void run_nightmare(int argc, char **argv); -extern void run_dice(int argc, char **argv); -extern void run_compare(int argc, char **argv); -extern void run_classifier(int argc, char **argv); -extern void run_char_rnn(int argc, char **argv); -extern void run_vid_rnn(int argc, char **argv); -extern void run_tag(int argc, char **argv); -extern void run_cifar(int argc, char **argv); -extern void run_go(int argc, char **argv); -extern void run_art(int argc, char **argv); -extern void run_super(int argc, char **argv); - -void average(int argc, char *argv[]) -{ - char *cfgfile = argv[2]; - char *outfile = argv[3]; - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - network sum = parse_network_cfg(cfgfile); - - char *weightfile = argv[4]; - load_weights(&sum, weightfile); - - int i, j; - int n = argc - 5; - for(i = 0; i < n; ++i){ - weightfile = argv[i+5]; - load_weights(&net, weightfile); - for(j = 0; j < net.n; ++j){ - layer l = net.layers[j]; - layer out = sum.layers[j]; - if(l.type == CONVOLUTIONAL){ - int num = l.n*l.c*l.size*l.size; - axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1); - axpy_cpu(num, 1, l.weights, 1, out.weights, 1); - if(l.batch_normalize){ - axpy_cpu(l.n, 1, l.scales, 1, out.scales, 1); - axpy_cpu(l.n, 1, l.rolling_mean, 1, out.rolling_mean, 1); - axpy_cpu(l.n, 1, l.rolling_variance, 1, out.rolling_variance, 1); - } - } - if(l.type == CONNECTED){ - axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1); - axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, out.weights, 1); - } - } - } - n = n+1; - for(j = 0; j < net.n; ++j){ - layer l = sum.layers[j]; - if(l.type == CONVOLUTIONAL){ - int num = l.n*l.c*l.size*l.size; - scal_cpu(l.n, 1./n, l.biases, 1); - scal_cpu(num, 1./n, l.weights, 1); - if(l.batch_normalize){ - scal_cpu(l.n, 1./n, l.scales, 1); - scal_cpu(l.n, 1./n, l.rolling_mean, 1); - scal_cpu(l.n, 1./n, l.rolling_variance, 1); - } - } - if(l.type == CONNECTED){ - scal_cpu(l.outputs, 1./n, l.biases, 1); - scal_cpu(l.outputs*l.inputs, 1./n, l.weights, 1); - } - } - save_weights(sum, outfile); -} - -void speed(char *cfgfile, int tics) -{ - if (tics == 0) tics = 1000; - network net = parse_network_cfg(cfgfile); - set_batch_network(&net, 1); - int i; - time_t start = time(0); - image im = make_image(net.w, net.h, net.c); - for(i = 0; i < tics; ++i){ - network_predict(net, im.data); - } - double t = difftime(time(0), start); - printf("\n%d evals, %f Seconds\n", tics, t); - printf("Speed: %f sec/eval\n", t/tics); - printf("Speed: %f Hz\n", tics/t); -} - -void operations(char *cfgfile) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - int i; - long ops = 0; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; - if(l.type == CONVOLUTIONAL){ - ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w; - } else if(l.type == CONNECTED){ - ops += 2l * l.inputs * l.outputs; - } - } - printf("Floating Point Operations: %ld\n", ops); - printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.); -} - -void oneoff(char *cfgfile, char *weightfile, char *outfile) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - int oldn = net.layers[net.n - 2].n; - int c = net.layers[net.n - 2].c; - scal_cpu(oldn*c, .1, net.layers[net.n - 2].weights, 1); - scal_cpu(oldn, 0, net.layers[net.n - 2].biases, 1); - net.layers[net.n - 2].n = 9418; - net.layers[net.n - 2].biases += 5; - net.layers[net.n - 2].weights += 5*c; - if(weightfile){ - load_weights(&net, weightfile); - } - net.layers[net.n - 2].biases -= 5; - net.layers[net.n - 2].weights -= 5*c; - net.layers[net.n - 2].n = oldn; - printf("%d\n", oldn); - layer l = net.layers[net.n - 2]; - copy_cpu(l.n/3, l.biases, 1, l.biases + l.n/3, 1); - copy_cpu(l.n/3, l.biases, 1, l.biases + 2*l.n/3, 1); - copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + l.n/3*l.c, 1); - copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + 2*l.n/3*l.c, 1); - *net.seen = 0; - save_weights(net, outfile); -} - -void partial(char *cfgfile, char *weightfile, char *outfile, int max) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights_upto(&net, weightfile, max); - } - *net.seen = 0; - save_weights_upto(net, outfile, max); -} - -#include "convolutional_layer.h" -void rescale_net(char *cfgfile, char *weightfile, char *outfile) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int i; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; - if(l.type == CONVOLUTIONAL){ - rescale_weights(l, 2, -.5); - break; - } - } - save_weights(net, outfile); -} - -void rgbgr_net(char *cfgfile, char *weightfile, char *outfile) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int i; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; - if(l.type == CONVOLUTIONAL){ - rgbgr_weights(l); - break; - } - } - save_weights(net, outfile); -} - -void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if (weightfile) { - load_weights(&net, weightfile); - } - int i; - for (i = 0; i < net.n; ++i) { - layer l = net.layers[i]; - if (l.type == CONVOLUTIONAL && l.batch_normalize) { - denormalize_convolutional_layer(l); - } - if (l.type == CONNECTED && l.batch_normalize) { - denormalize_connected_layer(l); - } - if (l.type == GRU && l.batch_normalize) { - denormalize_connected_layer(*l.input_z_layer); - denormalize_connected_layer(*l.input_r_layer); - denormalize_connected_layer(*l.input_h_layer); - denormalize_connected_layer(*l.state_z_layer); - denormalize_connected_layer(*l.state_r_layer); - denormalize_connected_layer(*l.state_h_layer); - } - } - save_weights(net, outfile); -} - -layer normalize_layer(layer l, int n) -{ - int j; - l.batch_normalize=1; - l.scales = calloc(n, sizeof(float)); - for(j = 0; j < n; ++j){ - l.scales[j] = 1; - } - l.rolling_mean = calloc(n, sizeof(float)); - l.rolling_variance = calloc(n, sizeof(float)); - return l; -} - -void normalize_net(char *cfgfile, char *weightfile, char *outfile) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int i; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; - if(l.type == CONVOLUTIONAL && !l.batch_normalize){ - net.layers[i] = normalize_layer(l, l.n); - } - if (l.type == CONNECTED && !l.batch_normalize) { - net.layers[i] = normalize_layer(l, l.outputs); - } - if (l.type == GRU && l.batch_normalize) { - *l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs); - *l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs); - *l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs); - *l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs); - *l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs); - *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs); - net.layers[i].batch_normalize=1; - } - } - save_weights(net, outfile); -} - -void statistics_net(char *cfgfile, char *weightfile) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if (weightfile) { - load_weights(&net, weightfile); - } - int i; - for (i = 0; i < net.n; ++i) { - layer l = net.layers[i]; - if (l.type == CONNECTED && l.batch_normalize) { - printf("Connected Layer %d\n", i); - statistics_connected_layer(l); - } - if (l.type == GRU && l.batch_normalize) { - printf("GRU Layer %d\n", i); - printf("Input Z\n"); - statistics_connected_layer(*l.input_z_layer); - printf("Input R\n"); - statistics_connected_layer(*l.input_r_layer); - printf("Input H\n"); - statistics_connected_layer(*l.input_h_layer); - printf("State Z\n"); - statistics_connected_layer(*l.state_z_layer); - printf("State R\n"); - statistics_connected_layer(*l.state_r_layer); - printf("State H\n"); - statistics_connected_layer(*l.state_h_layer); - } - printf("\n"); - } -} - -void denormalize_net(char *cfgfile, char *weightfile, char *outfile) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if (weightfile) { - load_weights(&net, weightfile); - } - int i; - for (i = 0; i < net.n; ++i) { - layer l = net.layers[i]; - if (l.type == CONVOLUTIONAL && l.batch_normalize) { - denormalize_convolutional_layer(l); - net.layers[i].batch_normalize=0; - } - if (l.type == CONNECTED && l.batch_normalize) { - denormalize_connected_layer(l); - net.layers[i].batch_normalize=0; - } - if (l.type == GRU && l.batch_normalize) { - denormalize_connected_layer(*l.input_z_layer); - denormalize_connected_layer(*l.input_r_layer); - denormalize_connected_layer(*l.input_h_layer); - denormalize_connected_layer(*l.state_z_layer); - denormalize_connected_layer(*l.state_r_layer); - denormalize_connected_layer(*l.state_h_layer); - l.input_z_layer->batch_normalize = 0; - l.input_r_layer->batch_normalize = 0; - l.input_h_layer->batch_normalize = 0; - l.state_z_layer->batch_normalize = 0; - l.state_r_layer->batch_normalize = 0; - l.state_h_layer->batch_normalize = 0; - net.layers[i].batch_normalize=0; - } - } - save_weights(net, outfile); -} - -void visualize(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - visualize_network(net); -#ifdef OPENCV - cvWaitKey(0); -#endif -} - -int main(int argc, char **argv) -{ - //test_resize("data/bad.jpg"); - //test_box(); - //test_convolutional_layer(); - if(argc < 2){ - fprintf(stderr, "usage: %s \n", argv[0]); - return 0; - } - gpu_index = find_int_arg(argc, argv, "-i", 0); - if(find_arg(argc, argv, "-nogpu")) { - gpu_index = -1; - } - -#ifndef GPU - gpu_index = -1; -#else - if(gpu_index >= 0){ - cuda_set_device(gpu_index); - } -#endif - - if (0 == strcmp(argv[1], "average")){ - average(argc, argv); - } else if (0 == strcmp(argv[1], "yolo")){ - run_yolo(argc, argv); - } else if (0 == strcmp(argv[1], "voxel")){ - run_voxel(argc, argv); - } else if (0 == strcmp(argv[1], "super")){ - run_super(argc, argv); - } else if (0 == strcmp(argv[1], "detector")){ - run_detector(argc, argv); - } else if (0 == strcmp(argv[1], "detect")){ - float thresh = find_float_arg(argc, argv, "-thresh", .24); - char *filename = (argc > 4) ? argv[4]: 0; - test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5); - } else if (0 == strcmp(argv[1], "cifar")){ - run_cifar(argc, argv); - } else if (0 == strcmp(argv[1], "go")){ - run_go(argc, argv); - } else if (0 == strcmp(argv[1], "rnn")){ - run_char_rnn(argc, argv); - } else if (0 == strcmp(argv[1], "vid")){ - run_vid_rnn(argc, argv); - } else if (0 == strcmp(argv[1], "coco")){ - run_coco(argc, argv); - } else if (0 == strcmp(argv[1], "classify")){ - predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5); - } else if (0 == strcmp(argv[1], "classifier")){ - run_classifier(argc, argv); - } else if (0 == strcmp(argv[1], "art")){ - run_art(argc, argv); - } else if (0 == strcmp(argv[1], "tag")){ - run_tag(argc, argv); - } else if (0 == strcmp(argv[1], "compare")){ - run_compare(argc, argv); - } else if (0 == strcmp(argv[1], "dice")){ - run_dice(argc, argv); - } else if (0 == strcmp(argv[1], "writing")){ - run_writing(argc, argv); - } else if (0 == strcmp(argv[1], "3d")){ - composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0); - } else if (0 == strcmp(argv[1], "test")){ - test_resize(argv[2]); - } else if (0 == strcmp(argv[1], "captcha")){ - run_captcha(argc, argv); - } else if (0 == strcmp(argv[1], "nightmare")){ - run_nightmare(argc, argv); - } else if (0 == strcmp(argv[1], "rgbgr")){ - rgbgr_net(argv[2], argv[3], argv[4]); - } else if (0 == strcmp(argv[1], "reset")){ - reset_normalize_net(argv[2], argv[3], argv[4]); - } else if (0 == strcmp(argv[1], "denormalize")){ - denormalize_net(argv[2], argv[3], argv[4]); - } else if (0 == strcmp(argv[1], "statistics")){ - statistics_net(argv[2], argv[3]); - } else if (0 == strcmp(argv[1], "normalize")){ - normalize_net(argv[2], argv[3], argv[4]); - } else if (0 == strcmp(argv[1], "rescale")){ - rescale_net(argv[2], argv[3], argv[4]); - } else if (0 == strcmp(argv[1], "ops")){ - operations(argv[2]); - } else if (0 == strcmp(argv[1], "speed")){ - speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0); - } else if (0 == strcmp(argv[1], "oneoff")){ - oneoff(argv[2], argv[3], argv[4]); - } else if (0 == strcmp(argv[1], "partial")){ - partial(argv[2], argv[3], argv[4], atoi(argv[5])); - } else if (0 == strcmp(argv[1], "average")){ - average(argc, argv); - } else if (0 == strcmp(argv[1], "visualize")){ - visualize(argv[2], (argc > 3) ? argv[3] : 0); - } else if (0 == strcmp(argv[1], "imtest")){ - test_resize(argv[2]); - } else { - fprintf(stderr, "Not an option: %s\n", argv[1]); - } - return 0; -} - diff --git a/image.darknet/src/data.c b/image.darknet/src/data.c index 05e5a91..59051b4 100644 --- a/image.darknet/src/data.c +++ b/image.darknet/src/data.c @@ -102,7 +102,7 @@ matrix load_image_paths(char **paths, int n, int w, int h) return X; } -matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) +matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center) { int i; matrix X; @@ -112,7 +112,12 @@ matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, for(i = 0; i < n; ++i){ image im = load_image_color(paths[i], 0, 0); - image crop = random_augment_image(im, angle, aspect, min, max, size); + image crop; + if(center){ + crop = center_crop_image(im, size, size); + } else { + crop = random_augment_image(im, angle, aspect, min, max, size, size); + } int flip = rand()%2; if (flip) flip_image(crop); random_distort_image(crop, hue, saturation, exposure); @@ -122,6 +127,7 @@ matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, show_image(crop, "crop"); cvWaitKey(0); */ + //grayscale_image_3c(crop); free_image(im); X.vals[i] = crop.data; X.cols = crop.h*crop.w*crop.c; @@ -132,14 +138,18 @@ matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, box_label *read_boxes(char *filename, int *n) { - box_label *boxes = calloc(1, sizeof(box_label)); FILE *file = fopen(filename, "r"); if(!file) file_error(filename); float x, y, h, w; int id; int count = 0; + int size = 64; + box_label *boxes = calloc(size, sizeof(box_label)); while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){ - boxes = realloc(boxes, (count+1)*sizeof(box_label)); + if(count == size) { + size = size * 2; + boxes = realloc(boxes, size*sizeof(box_label)); + } boxes[count].id = id; boxes[count].x = x; boxes[count].y = y; @@ -221,7 +231,7 @@ void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, int id; int i; - for (i = 0; i < count && i < 30; ++i) { + for (i = 0; i < count && i < 90; ++i) { x = boxes[i].x; y = boxes[i].y; w = boxes[i].w; @@ -290,6 +300,150 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int free(boxes); } +void load_rle(image im, int *rle, int n) +{ + int count = 0; + int curr = 0; + int i,j; + for(i = 0; i < n; ++i){ + for(j = 0; j < rle[i]; ++j){ + im.data[count++] = curr; + } + curr = 1 - curr; + } + for(; count < im.h*im.w*im.c; ++count){ + im.data[count] = curr; + } +} + +void or_image(image src, image dest, int c) +{ + int i; + for(i = 0; i < src.w*src.h; ++i){ + if(src.data[i]) dest.data[dest.w*dest.h*c + i] = 1; + } +} + +void exclusive_image(image src) +{ + int k, j, i; + int s = src.w*src.h; + for(k = 0; k < src.c-1; ++k){ + for(i = 0; i < s; ++i){ + if (src.data[k*s + i]){ + for(j = k+1; j < src.c; ++j){ + src.data[j*s + i] = 0; + } + } + } + } +} + +box bound_image(image im) +{ + int x,y; + int minx = im.w; + int miny = im.h; + int maxx = 0; + int maxy = 0; + for(y = 0; y < im.h; ++y){ + for(x = 0; x < im.w; ++x){ + if(im.data[y*im.w + x]){ + minx = (x < minx) ? x : minx; + miny = (y < miny) ? y : miny; + maxx = (x > maxx) ? x : maxx; + maxy = (y > maxy) ? y : maxy; + } + } + } + box b = {minx, miny, maxx-minx + 1, maxy-miny + 1}; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); + return b; +} + +void fill_truth_iseg(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + int i = 0; + int j; + image part = make_image(w, h, 1); + while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect); + if(flip) flip_image(sized); + + image mask = resize_image(sized, mw, mh); + truth[i*(mw*mh+1)] = id; + for(j = 0; j < mw*mh; ++j){ + truth[i*(mw*mh + 1) + 1 + j] = mask.data[j]; + } + ++i; + + free_image(mask); + free_image(sized); + free(rle); + } + if(i < num_boxes) truth[i*(mw*mh+1)] = -1; + fclose(file); + free_image(part); +} + +void fill_truth_mask(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + int i = 0; + image part = make_image(w, h, 1); + while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect); + if(flip) flip_image(sized); + box b = bound_image(sized); + if(b.w > 0){ + image crop = crop_image(sized, b.x, b.y, b.w, b.h); + image mask = resize_image(crop, mw, mh); + truth[i*(4 + mw*mh + 1) + 0] = (b.x + b.w/2.)/sized.w; + truth[i*(4 + mw*mh + 1) + 1] = (b.y + b.h/2.)/sized.h; + truth[i*(4 + mw*mh + 1) + 2] = b.w/sized.w; + truth[i*(4 + mw*mh + 1) + 3] = b.h/sized.h; + int j; + for(j = 0; j < mw*mh; ++j){ + truth[i*(4 + mw*mh + 1) + 4 + j] = mask.data[j]; + } + truth[i*(4 + mw*mh + 1) + 4 + mw*mh] = id; + free_image(crop); + free_image(mask); + ++i; + } + free_image(sized); + free(rle); + } + fclose(file); + free_image(part); +} + + void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy) { char labelpath[4096]; @@ -309,6 +463,7 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, float x,y,w,h; int id; int i; + int sub = 0; for (i = 0; i < count; ++i) { x = boxes[i].x; @@ -317,13 +472,16 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, h = boxes[i].h; id = boxes[i].id; - if ((w < .005 || h < .005)) continue; + if ((w < .001 || h < .001)) { + ++sub; + continue; + } - truth[i*5+0] = x; - truth[i*5+1] = y; - truth[i*5+2] = w; - truth[i*5+3] = h; - truth[i*5+4] = id; + truth[(i-sub)*5+0] = x; + truth[(i-sub)*5+1] = y; + truth[(i-sub)*5+2] = w; + truth[(i-sub)*5+3] = h; + truth[(i-sub)*5+4] = id; } free(boxes); } @@ -391,9 +549,10 @@ void fill_truth(char *path, char **labels, int k, float *truth) if(strstr(path, labels[i])){ truth[i] = 1; ++count; + //printf("%s %s %d\n", path, labels[i], i); } } - if(count != 1) printf("Too many or too few labels: %d, %s\n", count, path); + if(count != 1 && (k != 1 || count != 0)) printf("Too many or too few labels: %d, %s\n", count, path); } void fill_hierarchy(float *truth, int k, tree *hierarchy) @@ -428,6 +587,36 @@ void fill_hierarchy(float *truth, int k, tree *hierarchy) } } +matrix load_regression_labels_paths(char **paths, int n, int k) +{ + matrix y = make_matrix(n, k); + int i,j; + for(i = 0; i < n; ++i){ + char labelpath[4096]; + find_replace(paths[i], "images", "labels", labelpath); + find_replace(labelpath, "JPEGImages", "labels", labelpath); + find_replace(labelpath, ".BMP", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPeG", ".txt", labelpath); + find_replace(labelpath, ".Jpeg", ".txt", labelpath); + find_replace(labelpath, ".PNG", ".txt", labelpath); + find_replace(labelpath, ".TIF", ".txt", labelpath); + find_replace(labelpath, ".bmp", ".txt", labelpath); + find_replace(labelpath, ".jpeg", ".txt", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".png", ".txt", labelpath); + find_replace(labelpath, ".tif", ".txt", labelpath); + + FILE *file = fopen(labelpath, "r"); + for(j = 0; j < k; ++j){ + fscanf(file, "%f", &(y.vals[i][j])); + } + fclose(file); + } + return y; +} + matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy) { matrix y = make_matrix(n, k); @@ -445,18 +634,14 @@ matrix load_tags_paths(char **paths, int n, int k) { matrix y = make_matrix(n, k); int i; - int count = 0; + //int count = 0; for(i = 0; i < n; ++i){ char label[4096]; - find_replace(paths[i], "imgs", "labels", label); - find_replace(label, "_iconl.jpeg", ".txt", label); + find_replace(paths[i], "images", "labels", label); + find_replace(label, ".jpg", ".txt", label); FILE *file = fopen(label, "r"); - if(!file){ - find_replace(label, "labels", "labels2", label); - file = fopen(label, "r"); - if(!file) continue; - } - ++count; + if (!file) continue; + //++count; int tag; while(fscanf(file, "%d", &tag) == 1){ if(tag < k){ @@ -465,7 +650,7 @@ matrix load_tags_paths(char **paths, int n, int k) } fclose(file); } - printf("%d/%d\n", count, n); + //printf("%d/%d\n", count, n); return y; } @@ -488,6 +673,195 @@ void free_data(data d) } } +image get_segmentation_image(char *path, int w, int h, int classes) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + image mask = make_image(w, h, classes); + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + image part = make_image(w, h, 1); + while(fscanf(file, "%d %s", &id, buff) == 2){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + or_image(part, mask, id); + free(rle); + } + //exclusive_image(mask); + fclose(file); + free_image(part); + return mask; +} + +image get_segmentation_image2(char *path, int w, int h, int classes) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + image mask = make_image(w, h, classes+1); + int i; + for(i = 0; i < w*h; ++i){ + mask.data[w*h*classes + i] = 1; + } + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + image part = make_image(w, h, 1); + while(fscanf(file, "%d %s", &id, buff) == 2){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + or_image(part, mask, id); + for(i = 0; i < w*h; ++i){ + if(part.data[i]) mask.data[w*h*classes + i] = 0; + } + free(rle); + } + //exclusive_image(mask); + fclose(file); + free_image(part); + return mask; +} + +data load_data_seg(int n, char **paths, int m, int w, int h, int classes, int min, int max, float angle, float aspect, float hue, float saturation, float exposure, int div) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + + d.y.rows = n; + d.y.cols = h*w*classes/div/div; + d.y.vals = calloc(d.X.rows, sizeof(float*)); + + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h); + image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); + + int flip = rand()%2; + if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; + + image mask = get_segmentation_image(random_paths[i], orig.w, orig.h, classes); + //image mask = make_image(orig.w, orig.h, classes+1); + image sized_m = rotate_crop_image(mask, a.rad, a.scale/div, a.w/div, a.h/div, a.dx/div, a.dy/div, a.aspect); + + if(flip) flip_image(sized_m); + d.y.vals[i] = sized_m.data; + + free_image(orig); + free_image(mask); + + /* + image rgb = mask_to_rgb(sized_m, classes); + show_image(rgb, "part"); + show_image(sized, "orig"); + cvWaitKey(0); + free_image(rgb); + */ + } + free(random_paths); + return d; +} + +data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int boxes, int div, int min, int max, float angle, float aspect, float hue, float saturation, float exposure) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + d.y = make_matrix(n, (((w/div)*(h/div))+1)*boxes); + + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h); + image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); + + int flip = rand()%2; + if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; + //show_image(sized, "image"); + + fill_truth_iseg(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, w/div, h/div); + + free_image(orig); + + /* + image rgb = mask_to_rgb(sized_m, classes); + show_image(rgb, "part"); + show_image(sized, "orig"); + cvWaitKey(0); + free_image(rgb); + */ + } + free(random_paths); + return d; +} + +data load_data_mask(int n, char **paths, int m, int w, int h, int classes, int boxes, int coords, int min, int max, float angle, float aspect, float hue, float saturation, float exposure) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + d.y = make_matrix(n, (coords+1)*boxes); + + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h); + image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); + + int flip = rand()%2; + if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; + //show_image(sized, "image"); + + fill_truth_mask(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, 14, 14); + + free_image(orig); + + /* + image rgb = mask_to_rgb(sized_m, classes); + show_image(rgb, "part"); + show_image(sized, "orig"); + cvWaitKey(0); + free_image(rgb); + */ + } + free(random_paths); + return d; +} + data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter, float hue, float saturation, float exposure) { char **random_paths = get_random_paths(paths, n, m); @@ -624,7 +998,7 @@ data load_data_swag(char **paths, int n, int classes, float jitter) d.X.vals = calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*3; - int k = (4+classes)*30; + int k = (4+classes)*90; d.y = make_matrix(1, k); int dw = w*jitter; @@ -673,45 +1047,46 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, in d.y = make_matrix(n, 5*boxes); for(i = 0; i < n; ++i){ image orig = load_image_color(random_paths[i], 0, 0); + image sized = make_image(w, h, orig.c); + fill_image(sized, .5); - int oh = orig.h; - int ow = orig.w; + float dw = jitter * orig.w; + float dh = jitter * orig.h; - int dw = (ow*jitter); - int dh = (oh*jitter); + float new_ar = (orig.w + rand_uniform(-dw, dw)) / (orig.h + rand_uniform(-dh, dh)); + //float scale = rand_uniform(.25, 2); + float scale = 1; - int pleft = rand_uniform(-dw, dw); - int pright = rand_uniform(-dw, dw); - int ptop = rand_uniform(-dh, dh); - int pbot = rand_uniform(-dh, dh); + float nw, nh; - int swidth = ow - pleft - pright; - int sheight = oh - ptop - pbot; + if(new_ar < 1){ + nh = scale * h; + nw = nh * new_ar; + } else { + nw = scale * w; + nh = nw / new_ar; + } - float sx = (float)swidth / ow; - float sy = (float)sheight / oh; + float dx = rand_uniform(0, w - nw); + float dy = rand_uniform(0, h - nh); - int flip = rand()%2; - image cropped = crop_image(orig, pleft, ptop, swidth, sheight); + place_image(orig, nw, nh, dx, dy, sized); - float dx = ((float)pleft/ow)/sx; - float dy = ((float)ptop /oh)/sy; + random_distort_image(sized, hue, saturation, exposure); - image sized = resize_image(cropped, w, h); + int flip = rand()%2; if(flip) flip_image(sized); - random_distort_image(sized, hue, saturation, exposure); d.X.vals[i] = sized.data; - fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy); + + fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, -dx/w, -dy/h, nw/w, nh/h); free_image(orig); - free_image(cropped); } free(random_paths); return d; } - void *load_thread(void *ptr) { //printf("Loading data: %d\n", rand()); @@ -722,12 +1097,20 @@ void *load_thread(void *ptr) if (a.type == OLD_CLASSIFICATION_DATA){ *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); + } else if (a.type == REGRESSION_DATA){ + *a.d = load_data_regression(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); } else if (a.type == CLASSIFICATION_DATA){ - *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.center); } else if (a.type == SUPER_DATA){ *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); } else if (a.type == WRITING_DATA){ *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h); + } else if (a.type == ISEG_DATA){ + *a.d = load_data_iseg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.scale, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + } else if (a.type == INSTANCE_DATA){ + *a.d = load_data_mask(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.coords, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + } else if (a.type == SEGMENTATION_DATA){ + *a.d = load_data_seg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.scale); } else if (a.type == REGION_DATA){ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ @@ -739,6 +1122,9 @@ void *load_thread(void *ptr) } else if (a.type == IMAGE_DATA){ *(a.im) = load_image_color(a.path, 0, 0); *(a.resized) = resize_image(*(a.im), a.w, a.h); + } else if (a.type == LETTERBOX_DATA){ + *(a.im) = load_image_color(a.path, 0, 0); + *(a.resized) = letterbox_image(*(a.im), a.w, a.h); } else if (a.type == TAG_DATA){ *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); } @@ -784,6 +1170,13 @@ void *load_threads(void *ptr) return 0; } +void load_data_blocking(load_args args) +{ + struct load_args *ptr = calloc(1, sizeof(struct load_args)); + *ptr = args; + load_thread(ptr); +} + pthread_t load_data(load_args args) { pthread_t thread; @@ -863,12 +1256,95 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) +data load_data_regression(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) { if(m) paths = get_random_paths(paths, n, m); data d = {0}; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure); + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0); + d.y = load_regression_labels_paths(paths, n, k); + if(m) free(paths); + return d; +} + +data select_data(data *orig, int *inds) +{ + data d = {0}; + d.shallow = 1; + d.w = orig[0].w; + d.h = orig[0].h; + + d.X.rows = orig[0].X.rows; + d.y.rows = orig[0].X.rows; + + d.X.cols = orig[0].X.cols; + d.y.cols = orig[0].y.cols; + + d.X.vals = calloc(orig[0].X.rows, sizeof(float *)); + d.y.vals = calloc(orig[0].y.rows, sizeof(float *)); + int i; + for(i = 0; i < d.X.rows; ++i){ + d.X.vals[i] = orig[inds[i]].X.vals[i]; + d.y.vals[i] = orig[inds[i]].y.vals[i]; + } + return d; +} + +data *tile_data(data orig, int divs, int size) +{ + data *ds = calloc(divs*divs, sizeof(data)); + int i, j; +#pragma omp parallel for + for(i = 0; i < divs*divs; ++i){ + data d; + d.shallow = 0; + d.w = orig.w/divs * size; + d.h = orig.h/divs * size; + d.X.rows = orig.X.rows; + d.X.cols = d.w*d.h*3; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + + d.y = copy_matrix(orig.y); +#pragma omp parallel for + for(j = 0; j < orig.X.rows; ++j){ + int x = (i%divs) * orig.w / divs - (d.w - orig.w/divs)/2; + int y = (i/divs) * orig.h / divs - (d.h - orig.h/divs)/2; + image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[j]); + d.X.vals[j] = crop_image(im, x, y, d.w, d.h).data; + } + ds[i] = d; + } + return ds; +} + +data resize_data(data orig, int w, int h) +{ + data d = {0}; + d.shallow = 0; + d.w = w; + d.h = h; + int i; + d.X.rows = orig.X.rows; + d.X.cols = w*h*3; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + + d.y = copy_matrix(orig.y); +#pragma omp parallel for + for(i = 0; i < orig.X.rows; ++i){ + image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[i]); + d.X.vals[i] = resize_image(im, w, h).data; + } + return d; +} + +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center) +{ + if(m) paths = get_random_paths(paths, n, m); + data d = {0}; + d.shallow = 0; + d.w=size; + d.h=size; + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, center); d.y = load_labels_paths(paths, n, labels, k, hierarchy); if(m) free(paths); return d; @@ -881,7 +1357,7 @@ data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size d.w = size; d.h = size; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure); + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0); d.y = load_tags_paths(paths, n, k); if(m) free(paths); return d; @@ -909,6 +1385,8 @@ data concat_data(data d1, data d2) d.shallow = 1; d.X = concat_matrix(d1.X, d2.X); d.y = concat_matrix(d1.y, d2.y); + d.w = d1.w; + d.h = d1.h; return d; } @@ -962,7 +1440,6 @@ data load_cifar10_data(char *filename) X.vals[i][j] = (double)bytes[j+1]; } } - //translate_data_rows(d, -128); scale_data_rows(d, 1./255); //normalize_data_rows(d); fclose(fp); @@ -985,7 +1462,7 @@ void get_next_batch(data d, int n, int offset, float *X, float *y) for(j = 0; j < n; ++j){ int index = offset + j; memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float)); - memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float)); + if(y) memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float)); } } @@ -1029,7 +1506,6 @@ data load_all_cifar10() fclose(fp); } //normalize_data_rows(d); - //translate_data_rows(d, -128); scale_data_rows(d, 1./255); smooth_data(d); return d; @@ -1113,6 +1589,19 @@ void translate_data_rows(data d, float s) } } +data copy_data(data d) +{ + data c = {0}; + c.w = d.w; + c.h = d.h; + c.shallow = 0; + c.num_boxes = d.num_boxes; + c.boxes = d.boxes; + c.X = copy_matrix(d.X); + c.y = copy_matrix(d.y); + return c; +} + void normalize_data_rows(data d) { int i; diff --git a/image.darknet/src/data.h b/image.darknet/src/data.h index 3f6ef61..781906f 100644 --- a/image.darknet/src/data.h +++ b/image.darknet/src/data.h @@ -2,6 +2,7 @@ #define DATA_H #include +#include "darknet.h" #include "matrix.h" #include "list.h" #include "image.h" @@ -17,93 +18,32 @@ static inline float distance_from_edge(int x, int max) if (dist > 1) dist = 1; return dist; } +void load_data_blocking(load_args args); -typedef struct{ - int w, h; - matrix X; - matrix y; - int shallow; - int *num_boxes; - box **boxes; -} data; - -typedef enum { - CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA -} data_type; - -typedef struct load_args{ - int threads; - char **paths; - char *path; - int n; - int m; - char **labels; - int h; - int w; - int out_w; - int out_h; - int nh; - int nw; - int num_boxes; - int min, max, size; - int classes; - int background; - int scale; - float jitter; - float angle; - float aspect; - float saturation; - float exposure; - float hue; - data *d; - image *im; - image *resized; - data_type type; - tree *hierarchy; -} load_args; - -typedef struct{ - int id; - float x,y,w,h; - float left, right, top, bottom; -} box_label; - -void free_data(data d); - -pthread_t load_data(load_args args); - -pthread_t load_data_in_thread(load_args args); void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); -data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure); data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); -matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); +matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center); +data load_data_regression(char **paths, int n, int m, int classes, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); data load_go(char *filename); -box_label *read_boxes(char *filename, int *n); -data load_cifar10_data(char *filename); -data load_all_cifar10(); data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int out_h); -list *get_paths(char *filename); -char **get_labels(char *filename); void get_random_batch(data d, int n, float *X, float *y); data get_data_part(data d, int part, int total); data get_random_data(data d, int num); -void get_next_batch(data d, int n, int offset, float *X, float *y); data load_categorical_data_csv(char *filename, int target, int k); void normalize_data_rows(data d); void scale_data_rows(data d, float s); void translate_data_rows(data d, float s); void randomize_data(data d); data *split_data(data d, int part, int total); -data concat_data(data d1, data d2); data concat_datas(data *d, int n); void fill_truth(char *path, char **labels, int k, float *truth); diff --git a/image.darknet/src/deconvolutional_kernels.cu b/image.darknet/src/deconvolutional_kernels.cu index d6259fb..8267dcf 100644 --- a/image.darknet/src/deconvolutional_kernels.cu +++ b/image.darknet/src/deconvolutional_kernels.cu @@ -5,6 +5,7 @@ extern "C" { #include "convolutional_layer.h" #include "deconvolutional_layer.h" +#include "batchnorm_layer.h" #include "gemm.h" #include "blas.h" #include "im2col.h" @@ -13,97 +14,126 @@ extern "C" { #include "cuda.h" } -extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state) +extern "C" void forward_deconvolutional_layer_gpu(layer l, network net) { int i; - int out_h = deconvolutional_out_height(layer); - int out_w = deconvolutional_out_width(layer); - int size = out_h*out_w; - int m = layer.size*layer.size*layer.n; - int n = layer.h*layer.w; - int k = layer.c; + int m = l.size*l.size*l.n; + int n = l.h*l.w; + int k = l.c; - fill_ongpu(layer.outputs*layer.batch, 0, layer.output_gpu, 1); + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); - for(i = 0; i < layer.batch; ++i){ - float *a = layer.weights_gpu; - float *b = state.input + i*layer.c*layer.h*layer.w; - float *c = layer.col_image_gpu; + for(i = 0; i < l.batch; ++i){ + float *a = l.weights_gpu; + float *b = net.input_gpu + i*l.c*l.h*l.w; + float *c = net.workspace; - gemm_ongpu(1,0,m,n,k,1,a,m,b,n,0,c,n); + gemm_gpu(1,0,m,n,k,1,a,m,b,n,0,c,n); - col2im_ongpu(c, layer.n, out_h, out_w, layer.size, layer.stride, 0, layer.output_gpu+i*layer.n*size); + col2im_gpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output_gpu+i*l.outputs); } - add_bias_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, size); - activate_array(layer.output_gpu, layer.batch*layer.n*size, layer.activation); + if (l.batch_normalize) { + forward_batchnorm_layer_gpu(l, net); + } else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + } + activate_array_gpu(l.output_gpu, l.batch*l.n*l.out_w*l.out_h, l.activation); } -extern "C" void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state) +extern "C" void backward_deconvolutional_layer_gpu(layer l, network net) { - float alpha = 1./layer.batch; - int out_h = deconvolutional_out_height(layer); - int out_w = deconvolutional_out_width(layer); - int size = out_h*out_w; int i; - gradient_array(layer.output_gpu, size*layer.n*layer.batch, layer.activation, layer.delta_gpu); - backward_bias(layer.bias_updates_gpu, layer.delta, layer.batch, layer.n, size); + //constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + + if(l.batch_normalize){ + backward_batchnorm_layer_gpu(l, net); + } else { + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + } - if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float)); + //if(net.delta_gpu) memset(net.delta_gpu, 0, l.batch*l.h*l.w*l.c*sizeof(float)); - for(i = 0; i < layer.batch; ++i){ - int m = layer.c; - int n = layer.size*layer.size*layer.n; - int k = layer.h*layer.w; + for(i = 0; i < l.batch; ++i){ + int m = l.c; + int n = l.size*l.size*l.n; + int k = l.h*l.w; - float *a = state.input + i*m*n; - float *b = layer.col_image_gpu; - float *c = layer.weight_updates_gpu; + float *a = net.input_gpu + i*m*k; + float *b = net.workspace; + float *c = l.weight_updates_gpu; - im2col_ongpu(layer.delta_gpu + i*layer.n*size, layer.n, out_h, out_w, - layer.size, layer.stride, 0, b); - gemm_ongpu(0,1,m,n,k,alpha,a,k,b,k,1,c,n); + im2col_gpu(l.delta_gpu + i*l.outputs, l.out_c, l.out_h, l.out_w, + l.size, l.stride, l.pad, b); + gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); - if(state.delta){ - int m = layer.c; - int n = layer.h*layer.w; - int k = layer.size*layer.size*layer.n; + if(net.delta_gpu){ + int m = l.c; + int n = l.h*l.w; + int k = l.size*l.size*l.n; - float *a = layer.weights_gpu; - float *b = layer.col_image_gpu; - float *c = state.delta + i*n*m; + float *a = l.weights_gpu; + float *b = net.workspace; + float *c = net.delta_gpu + i*n*m; - gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); } } } -extern "C" void pull_deconvolutional_layer(deconvolutional_layer layer) +extern "C" void pull_deconvolutional_layer(layer l) { - cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.biases_gpu, layer.biases, layer.n); - cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); + cuda_pull_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size); + cuda_pull_array(l.biases_gpu, l.biases, l.n); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size); + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_pull_array(l.scales_gpu, l.scales, l.n); + cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.n); + } } -extern "C" void push_deconvolutional_layer(deconvolutional_layer layer) +extern "C" void push_deconvolutional_layer(layer l) { - cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.biases_gpu, layer.biases, layer.n); - cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); + cuda_push_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size); + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size); + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_push_array(l.scales_gpu, l.scales, l.n); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); + } } -extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay) +void update_deconvolutional_layer_gpu(layer l, update_args a) { - int size = layer.size*layer.size*layer.c*layer.n; + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + if(a.adam){ + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + if(l.scales_gpu){ + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + } + }else{ + axpy_gpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(l.nweights, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(l.nweights, momentum, l.weight_updates_gpu, 1); - axpy_ongpu(layer.n, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); - scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); + axpy_gpu(l.n, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.n, momentum, l.bias_updates_gpu, 1); - axpy_ongpu(size, -decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); - axpy_ongpu(size, learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); - scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); + if(l.scales_gpu){ + axpy_gpu(l.n, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_gpu(l.n, momentum, l.scale_updates_gpu, 1); + } + } } diff --git a/image.darknet/src/deconvolutional_layer.c b/image.darknet/src/deconvolutional_layer.c index fbef9d5..00c0e85 100644 --- a/image.darknet/src/deconvolutional_layer.c +++ b/image.darknet/src/deconvolutional_layer.c @@ -1,52 +1,41 @@ #include "deconvolutional_layer.h" #include "convolutional_layer.h" +#include "batchnorm_layer.h" #include "utils.h" #include "im2col.h" #include "col2im.h" #include "blas.h" #include "gemm.h" + #include #include -int deconvolutional_out_height(deconvolutional_layer l) -{ - int h = l.stride*(l.h - 1) + l.size; - return h; -} -int deconvolutional_out_width(deconvolutional_layer l) -{ - int w = l.stride*(l.w - 1) + l.size; - return w; -} - -int deconvolutional_out_size(deconvolutional_layer l) -{ - return deconvolutional_out_height(l) * deconvolutional_out_width(l); +static size_t get_workspace_size(layer l){ + return (size_t)l.h*l.w*l.size*l.size*l.n*sizeof(float); } -image get_deconvolutional_image(deconvolutional_layer l) +void bilinear_init(layer l) { - int h,w,c; - h = deconvolutional_out_height(l); - w = deconvolutional_out_width(l); - c = l.n; - return float_to_image(w,h,c,l.output); + int i,j,f; + float center = (l.size-1) / 2.; + for(f = 0; f < l.n; ++f){ + for(j = 0; j < l.size; ++j){ + for(i = 0; i < l.size; ++i){ + float val = (1 - fabs(i - center)) * (1 - fabs(j - center)); + int c = f%l.c; + int ind = f*l.size*l.size*l.c + c*l.size*l.size + j*l.size + i; + l.weights[ind] = val; + } + } + } } -image get_deconvolutional_delta(deconvolutional_layer l) -{ - int h,w,c; - h = deconvolutional_out_height(l); - w = deconvolutional_out_width(l); - c = l.n; - return float_to_image(w,h,c,l.delta); -} -deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation) +layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int adam) { int i; - deconvolutional_layer l = {0}; + layer l = {0}; l.type = DECONVOLUTIONAL; l.h = h; @@ -57,82 +46,182 @@ deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, l.stride = stride; l.size = size; + l.nweights = c*n*size*size; + l.nbiases = n; + l.weights = calloc(c*n*size*size, sizeof(float)); l.weight_updates = calloc(c*n*size*size, sizeof(float)); l.biases = calloc(n, sizeof(float)); l.bias_updates = calloc(n, sizeof(float)); - float scale = 1./sqrt(size*size*c); + //float scale = n/(size*size*c); + //printf("scale: %f\n", scale); + float scale = .02; for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal(); + //bilinear_init(l); for(i = 0; i < n; ++i){ - l.biases[i] = scale; + l.biases[i] = 0; } - int out_h = deconvolutional_out_height(l); - int out_w = deconvolutional_out_width(l); + l.pad = padding; - l.out_h = out_h; - l.out_w = out_w; + l.out_h = (l.h - 1) * l.stride + l.size - 2*l.pad; + l.out_w = (l.w - 1) * l.stride + l.size - 2*l.pad; l.out_c = n; l.outputs = l.out_w * l.out_h * l.out_c; l.inputs = l.w * l.h * l.c; - l.col_image = calloc(h*w*size*size*n, sizeof(float)); - l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); - l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float)); + scal_cpu(l.nweights, (float)l.out_w*l.out_h/(l.w*l.h), l.weights, 1); + + l.output = calloc(l.batch*l.outputs, sizeof(float)); + l.delta = calloc(l.batch*l.outputs, sizeof(float)); l.forward = forward_deconvolutional_layer; l.backward = backward_deconvolutional_layer; l.update = update_deconvolutional_layer; - #ifdef GPU - l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); - l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); + l.batch_normalize = batch_normalize; + + if(batch_normalize){ + l.scales = calloc(n, sizeof(float)); + l.scale_updates = calloc(n, sizeof(float)); + for(i = 0; i < n; ++i){ + l.scales[i] = 1; + } + + l.mean = calloc(n, sizeof(float)); + l.variance = calloc(n, sizeof(float)); + + l.mean_delta = calloc(n, sizeof(float)); + l.variance_delta = calloc(n, sizeof(float)); + + l.rolling_mean = calloc(n, sizeof(float)); + l.rolling_variance = calloc(n, sizeof(float)); + l.x = calloc(l.batch*l.outputs, sizeof(float)); + l.x_norm = calloc(l.batch*l.outputs, sizeof(float)); + } + if(adam){ + l.m = calloc(c*n*size*size, sizeof(float)); + l.v = calloc(c*n*size*size, sizeof(float)); + l.bias_m = calloc(n, sizeof(float)); + l.scale_m = calloc(n, sizeof(float)); + l.bias_v = calloc(n, sizeof(float)); + l.scale_v = calloc(n, sizeof(float)); + } + +#ifdef GPU + l.forward_gpu = forward_deconvolutional_layer_gpu; + l.backward_gpu = backward_deconvolutional_layer_gpu; + l.update_gpu = update_deconvolutional_layer_gpu; + + if(gpu_index >= 0){ + + if (adam) { + l.m_gpu = cuda_make_array(l.m, c*n*size*size); + l.v_gpu = cuda_make_array(l.v, c*n*size*size); + l.bias_m_gpu = cuda_make_array(l.bias_m, n); + l.bias_v_gpu = cuda_make_array(l.bias_v, n); + l.scale_m_gpu = cuda_make_array(l.scale_m, n); + l.scale_v_gpu = cuda_make_array(l.scale_v, n); + } + l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); + + l.biases_gpu = cuda_make_array(l.biases, n); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); - l.biases_gpu = cuda_make_array(l.biases, n); - l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); + l.delta_gpu = cuda_make_array(l.delta, l.batch*l.out_h*l.out_w*n); + l.output_gpu = cuda_make_array(l.output, l.batch*l.out_h*l.out_w*n); - l.col_image_gpu = cuda_make_array(l.col_image, h*w*size*size*n); - l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n); - l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); + if(batch_normalize){ + l.mean_gpu = cuda_make_array(0, n); + l.variance_gpu = cuda_make_array(0, n); + + l.rolling_mean_gpu = cuda_make_array(0, n); + l.rolling_variance_gpu = cuda_make_array(0, n); + + l.mean_delta_gpu = cuda_make_array(0, n); + l.variance_delta_gpu = cuda_make_array(0, n); + + l.scales_gpu = cuda_make_array(l.scales, n); + l.scale_updates_gpu = cuda_make_array(0, n); + + l.x_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n); + l.x_norm_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n); + } + } + #ifdef CUDNN + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); + cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); #endif +#endif l.activation = activation; + l.workspace_size = get_workspace_size(l); - fprintf(stderr, "Deconvolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n); + fprintf(stderr, "deconv%5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); return l; } -void resize_deconvolutional_layer(deconvolutional_layer *l, int h, int w) +void denormalize_deconvolutional_layer(layer l) +{ + int i, j; + for(i = 0; i < l.n; ++i){ + float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001); + for(j = 0; j < l.c*l.size*l.size; ++j){ + l.weights[i*l.c*l.size*l.size + j] *= scale; + } + l.biases[i] -= l.rolling_mean[i] * scale; + l.scales[i] = 1; + l.rolling_mean[i] = 0; + l.rolling_variance[i] = 1; + } +} + +void resize_deconvolutional_layer(layer *l, int h, int w) { l->h = h; l->w = w; - int out_h = deconvolutional_out_height(*l); - int out_w = deconvolutional_out_width(*l); - - l->col_image = realloc(l->col_image, - out_h*out_w*l->size*l->size*l->c*sizeof(float)); - l->output = realloc(l->output, - l->batch*out_h * out_w * l->n*sizeof(float)); - l->delta = realloc(l->delta, - l->batch*out_h * out_w * l->n*sizeof(float)); - #ifdef GPU - cuda_free(l->col_image_gpu); + l->out_h = (l->h - 1) * l->stride + l->size - 2*l->pad; + l->out_w = (l->w - 1) * l->stride + l->size - 2*l->pad; + + l->outputs = l->out_h * l->out_w * l->out_c; + l->inputs = l->w * l->h * l->c; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + if(l->batch_normalize){ + l->x = realloc(l->x, l->batch*l->outputs*sizeof(float)); + l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float)); + } + +#ifdef GPU cuda_free(l->delta_gpu); cuda_free(l->output_gpu); - l->col_image_gpu = cuda_make_array(l->col_image, out_h*out_w*l->size*l->size*l->c); - l->delta_gpu = cuda_make_array(l->delta, l->batch*out_h*out_w*l->n); - l->output_gpu = cuda_make_array(l->output, l->batch*out_h*out_w*l->n); + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); + + if(l->batch_normalize){ + cuda_free(l->x_gpu); + cuda_free(l->x_norm_gpu); + + l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs); + l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs); + } + #ifdef CUDNN + cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); #endif +#endif + l->workspace_size = get_workspace_size(*l); } -void forward_deconvolutional_layer(const deconvolutional_layer l, network_state state) +void forward_deconvolutional_layer(const layer l, network net) { int i; - int out_h = deconvolutional_out_height(l); - int out_w = deconvolutional_out_width(l); - int size = out_h*out_w; int m = l.size*l.size*l.n; int n = l.h*l.w; @@ -142,63 +231,80 @@ void forward_deconvolutional_layer(const deconvolutional_layer l, network_state for(i = 0; i < l.batch; ++i){ float *a = l.weights; - float *b = state.input + i*l.c*l.h*l.w; - float *c = l.col_image; + float *b = net.input + i*l.c*l.h*l.w; + float *c = net.workspace; - gemm(1,0,m,n,k,1,a,m,b,n,0,c,n); + gemm_cpu(1,0,m,n,k,1,a,m,b,n,0,c,n); - col2im_cpu(c, l.n, out_h, out_w, l.size, l.stride, 0, l.output+i*l.n*size); + col2im_cpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output+i*l.outputs); + } + if (l.batch_normalize) { + forward_batchnorm_layer(l, net); + } else { + add_bias(l.output, l.biases, l.batch, l.n, l.out_w*l.out_h); } - add_bias(l.output, l.biases, l.batch, l.n, size); - activate_array(l.output, l.batch*l.n*size, l.activation); + activate_array(l.output, l.batch*l.n*l.out_w*l.out_h, l.activation); } -void backward_deconvolutional_layer(deconvolutional_layer l, network_state state) +void backward_deconvolutional_layer(layer l, network net) { - float alpha = 1./l.batch; - int out_h = deconvolutional_out_height(l); - int out_w = deconvolutional_out_width(l); - int size = out_h*out_w; int i; - gradient_array(l.output, size*l.n*l.batch, l.activation, l.delta); - backward_bias(l.bias_updates, l.delta, l.batch, l.n, size); + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + + if(l.batch_normalize){ + backward_batchnorm_layer(l, net); + } else { + backward_bias(l.bias_updates, l.delta, l.batch, l.n, l.out_w*l.out_h); + } + + //if(net.delta) memset(net.delta, 0, l.batch*l.h*l.w*l.c*sizeof(float)); for(i = 0; i < l.batch; ++i){ int m = l.c; int n = l.size*l.size*l.n; int k = l.h*l.w; - float *a = state.input + i*m*n; - float *b = l.col_image; + float *a = net.input + i*m*k; + float *b = net.workspace; float *c = l.weight_updates; - im2col_cpu(l.delta + i*l.n*size, l.n, out_h, out_w, - l.size, l.stride, 0, b); - gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n); + im2col_cpu(l.delta + i*l.outputs, l.out_c, l.out_h, l.out_w, + l.size, l.stride, l.pad, b); + gemm_cpu(0,1,m,n,k,1,a,k,b,k,1,c,n); - if(state.delta){ + if(net.delta){ int m = l.c; int n = l.h*l.w; int k = l.size*l.size*l.n; float *a = l.weights; - float *b = l.col_image; - float *c = state.delta + i*n*m; + float *b = net.workspace; + float *c = net.delta + i*n*m; - gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + gemm_cpu(0,0,m,n,k,1,a,k,b,n,1,c,n); } } } -void update_deconvolutional_layer(deconvolutional_layer l, float learning_rate, float momentum, float decay) +void update_deconvolutional_layer(layer l, update_args a) { + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + int size = l.size*l.size*l.c*l.n; - axpy_cpu(l.n, learning_rate, l.bias_updates, 1, l.biases, 1); + axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.n, momentum, l.bias_updates, 1); - axpy_cpu(size, -decay, l.weights, 1, l.weight_updates, 1); - axpy_cpu(size, learning_rate, l.weight_updates, 1, l.weights, 1); + if(l.scales){ + axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1); + scal_cpu(l.n, momentum, l.scale_updates, 1); + } + + axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1); scal_cpu(size, momentum, l.weight_updates, 1); } diff --git a/image.darknet/src/deconvolutional_layer.h b/image.darknet/src/deconvolutional_layer.h index 2d36e02..b254fb9 100644 --- a/image.darknet/src/deconvolutional_layer.h +++ b/image.darknet/src/deconvolutional_layer.h @@ -7,28 +7,19 @@ #include "layer.h" #include "network.h" -typedef layer deconvolutional_layer; - #ifdef GPU -void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state); -void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state); -void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay); -void push_deconvolutional_layer(deconvolutional_layer layer); -void pull_deconvolutional_layer(deconvolutional_layer layer); +void forward_deconvolutional_layer_gpu(layer l, network net); +void backward_deconvolutional_layer_gpu(layer l, network net); +void update_deconvolutional_layer_gpu(layer l, update_args a); +void push_deconvolutional_layer(layer l); +void pull_deconvolutional_layer(layer l); #endif -deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation); -void resize_deconvolutional_layer(deconvolutional_layer *layer, int h, int w); -void forward_deconvolutional_layer(const deconvolutional_layer layer, network_state state); -void update_deconvolutional_layer(deconvolutional_layer layer, float learning_rate, float momentum, float decay); -void backward_deconvolutional_layer(deconvolutional_layer layer, network_state state); - -image get_deconvolutional_image(deconvolutional_layer layer); -image get_deconvolutional_delta(deconvolutional_layer layer); -image get_deconvolutional_filter(deconvolutional_layer layer, int i); - -int deconvolutional_out_height(deconvolutional_layer layer); -int deconvolutional_out_width(deconvolutional_layer layer); +layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int adam); +void resize_deconvolutional_layer(layer *l, int h, int w); +void forward_deconvolutional_layer(const layer l, network net); +void update_deconvolutional_layer(layer l, update_args a); +void backward_deconvolutional_layer(layer l, network net); #endif diff --git a/image.darknet/src/demo.c b/image.darknet/src/demo.c index 7818bc3..b89efb8 100644 --- a/image.darknet/src/demo.c +++ b/image.darknet/src/demo.c @@ -9,213 +9,339 @@ #include "demo.h" #include -#define FRAMES 3 +#define DEMO 1 #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" -image get_image_from_stream(CvCapture *cap); static char **demo_names; static image **demo_alphabet; static int demo_classes; -static float **probs; -static box *boxes; -static network net; -static image in ; -static image in_s ; -static image det ; -static image det_s; -static image disp = {0}; -static CvCapture * cap; +static network *net; +static image buff [3]; +static image buff_letter[3]; +static int buff_index = 0; +static void * cap; static float fps = 0; static float demo_thresh = 0; -static float demo_hier_thresh = .5; +static float demo_hier = .5; +static int running = 0; -static float *predictions[FRAMES]; +static int demo_frame = 3; static int demo_index = 0; -static image images[FRAMES]; +static float **predictions; static float *avg; +static int demo_done = 0; +static int demo_total = 0; +double demo_time; -void *fetch_in_thread(void *ptr) +detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num); + +int size_network(network *net) { - in = get_image_from_stream(cap); - if(!in.data){ - error("Stream closed."); + int i; + int count = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO || l.type == REGION || l.type == DETECTION){ + count += l.outputs; + } } - in_s = resize_image(in, net.w, net.h); - return 0; + return count; +} + +void remember_network(network *net) +{ + int i; + int count = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO || l.type == REGION || l.type == DETECTION){ + memcpy(predictions[demo_index] + count, net->layers[i].output, sizeof(float) * l.outputs); + count += l.outputs; + } + } +} + +detection *avg_predictions(network *net, int *nboxes) +{ + int i, j; + int count = 0; + fill_cpu(demo_total, 0, avg, 1); + for(j = 0; j < demo_frame; ++j){ + axpy_cpu(demo_total, 1./demo_frame, predictions[j], 1, avg, 1); + } + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO || l.type == REGION || l.type == DETECTION){ + memcpy(l.output, avg + count, sizeof(float) * l.outputs); + count += l.outputs; + } + } + detection *dets = get_network_boxes(net, buff[0].w, buff[0].h, demo_thresh, demo_hier, 0, 1, nboxes); + return dets; } void *detect_in_thread(void *ptr) { + running = 1; float nms = .4; - layer l = net.layers[net.n-1]; - float *X = det_s.data; - float *prediction = network_predict(net, X); - - memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float)); - mean_arrays(predictions, FRAMES, l.outputs, avg); - l.output = avg; - - free_image(det_s); - if(l.type == DETECTION){ - get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0); - } else if (l.type == REGION){ - get_region_boxes(l, 1, 1, demo_thresh, probs, boxes, 0, 0, demo_hier_thresh); - } else { - error("Last layer must produce detections\n"); + layer l = net->layers[net->n-1]; + float *X = buff_letter[(buff_index+2)%3].data; + network_predict(net, X); + + /* + if(l.type == DETECTION){ + get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0); + } else */ + remember_network(net); + detection *dets = 0; + int nboxes = 0; + dets = avg_predictions(net, &nboxes); + + + /* + int i,j; + box zero = {0}; + int classes = l.classes; + for(i = 0; i < demo_detections; ++i){ + avg[i].objectness = 0; + avg[i].bbox = zero; + memset(avg[i].prob, 0, classes*sizeof(float)); + for(j = 0; j < demo_frame; ++j){ + axpy_cpu(classes, 1./demo_frame, dets[j][i].prob, 1, avg[i].prob, 1); + avg[i].objectness += dets[j][i].objectness * 1./demo_frame; + avg[i].bbox.x += dets[j][i].bbox.x * 1./demo_frame; + avg[i].bbox.y += dets[j][i].bbox.y * 1./demo_frame; + avg[i].bbox.w += dets[j][i].bbox.w * 1./demo_frame; + avg[i].bbox.h += dets[j][i].bbox.h * 1./demo_frame; + } + //copy_cpu(classes, dets[0][i].prob, 1, avg[i].prob, 1); + //avg[i].objectness = dets[0][i].objectness; } - if (nms > 0) do_nms(boxes, probs, l.w*l.h*l.n, l.classes, nms); + */ + + if (nms > 0) do_nms_obj(dets, nboxes, l.classes, nms); + printf("\033[2J"); printf("\033[1;1H"); printf("\nFPS:%.1f\n",fps); printf("Objects:\n\n"); + image display = buff[(buff_index+2) % 3]; + draw_detections(display, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes); + free_detections(dets, nboxes); - images[demo_index] = det; - det = images[(demo_index + FRAMES/2 + 1)%FRAMES]; - demo_index = (demo_index + 1)%FRAMES; - - draw_detections(det, l.w*l.h*l.n, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes); + demo_index = (demo_index + 1)%demo_frame; + running = 0; + return 0; +} +void *fetch_in_thread(void *ptr) +{ + free_image(buff[buff_index]); + buff[buff_index] = get_image_from_stream(cap); + if(buff[buff_index].data == 0) { + demo_done = 1; + return 0; + } + letterbox_image_into(buff[buff_index], net->w, net->h, buff_letter[buff_index]); return 0; } -double get_wall_time() +void *display_in_thread(void *ptr) { - struct timeval time; - if (gettimeofday(&time,NULL)){ + int c = show_image(buff[(buff_index + 1)%3], "Demo", 1); + if (c != -1) c = c%256; + if (c == 27) { + demo_done = 1; return 0; + } else if (c == 82) { + demo_thresh += .02; + } else if (c == 84) { + demo_thresh -= .02; + if(demo_thresh <= .02) demo_thresh = .02; + } else if (c == 83) { + demo_hier += .02; + } else if (c == 81) { + demo_hier -= .02; + if(demo_hier <= .0) demo_hier = .0; } - return (double)time.tv_sec + (double)time.tv_usec * .000001; + return 0; } -void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, float hier_thresh) +void *display_loop(void *ptr) { - //skip = frame_skip; + while(1){ + display_in_thread(0); + } +} + +void *detect_loop(void *ptr) +{ + while(1){ + detect_in_thread(0); + } +} + +void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg_frames, float hier, int w, int h, int frames, int fullscreen) +{ + //demo_frame = avg_frames; image **alphabet = load_alphabet(); - int delay = frame_skip; demo_names = names; demo_alphabet = alphabet; demo_classes = classes; demo_thresh = thresh; - demo_hier_thresh = hier_thresh; + demo_hier = hier; printf("Demo\n"); - net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + pthread_t detect_thread; + pthread_t fetch_thread; srand(2222222); + int i; + demo_total = size_network(net); + predictions = calloc(demo_frame, sizeof(float*)); + for (i = 0; i < demo_frame; ++i){ + predictions[i] = calloc(demo_total, sizeof(float)); + } + avg = calloc(demo_total, sizeof(float)); + if(filename){ printf("video file: %s\n", filename); - cap = cvCaptureFromFile(filename); + cap = open_video_stream(filename, 0, 0, 0, 0); }else{ - cap = cvCaptureFromCAM(cam_index); + cap = open_video_stream(0, cam_index, w, h, frames); } if(!cap) error("Couldn't connect to webcam.\n"); - layer l = net.layers[net.n-1]; - int j; - - avg = (float *) calloc(l.outputs, sizeof(float)); - for(j = 0; j < FRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); - for(j = 0; j < FRAMES; ++j) images[j] = make_image(1,1,3); - - boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box)); - probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); - for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float)); - - pthread_t fetch_thread; - pthread_t detect_thread; - - fetch_in_thread(0); - det = in; - det_s = in_s; - - fetch_in_thread(0); - detect_in_thread(0); - disp = det; - det = in; - det_s = in_s; - - for(j = 0; j < FRAMES/2; ++j){ - fetch_in_thread(0); - detect_in_thread(0); - disp = det; - det = in; - det_s = in_s; - } + buff[0] = get_image_from_stream(cap); + buff[1] = copy_image(buff[0]); + buff[2] = copy_image(buff[0]); + buff_letter[0] = letterbox_image(buff[0], net->w, net->h); + buff_letter[1] = letterbox_image(buff[0], net->w, net->h); + buff_letter[2] = letterbox_image(buff[0], net->w, net->h); int count = 0; if(!prefix){ - cvNamedWindow("Demo", CV_WINDOW_NORMAL); - cvMoveWindow("Demo", 0, 0); - cvResizeWindow("Demo", 1352, 1013); + make_window("Demo", 1352, 1013, fullscreen); } - double before = get_wall_time(); - - while(1){ - ++count; - if(1){ - if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); - if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); - - if(!prefix){ - show_image(disp, "Demo"); - int c = cvWaitKey(1); - if (c == 10){ - if(frame_skip == 0) frame_skip = 60; - else if(frame_skip == 4) frame_skip = 0; - else if(frame_skip == 60) frame_skip = 4; - else frame_skip = 0; - } - }else{ - char buff[256]; - sprintf(buff, "%s_%08d", prefix, count); - save_image(disp, buff); - } - - pthread_join(fetch_thread, 0); - pthread_join(detect_thread, 0); - - if(delay == 0){ - free_image(disp); - disp = det; - } - det = in; - det_s = in_s; - }else { - fetch_in_thread(0); - det = in; - det_s = in_s; - detect_in_thread(0); - if(delay == 0) { - free_image(disp); - disp = det; - } - show_image(disp, "Demo"); - cvWaitKey(1); - } - --delay; - if(delay < 0){ - delay = frame_skip; - - double after = get_wall_time(); - float curr = 1./(after - before); - fps = curr; - before = after; + demo_time = what_time_is_it_now(); + + while(!demo_done){ + buff_index = (buff_index + 1) %3; + if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); + if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); + if(!prefix){ + fps = 1./(what_time_is_it_now() - demo_time); + demo_time = what_time_is_it_now(); + display_in_thread(0); + }else{ + char name[256]; + sprintf(name, "%s_%08d", prefix, count); + save_image(buff[(buff_index + 1)%3], name); } + pthread_join(fetch_thread, 0); + pthread_join(detect_thread, 0); + ++count; } } + +/* + void demo_compare(char *cfg1, char *weight1, char *cfg2, char *weight2, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg_frames, float hier, int w, int h, int frames, int fullscreen) + { + demo_frame = avg_frames; + predictions = calloc(demo_frame, sizeof(float*)); + image **alphabet = load_alphabet(); + demo_names = names; + demo_alphabet = alphabet; + demo_classes = classes; + demo_thresh = thresh; + demo_hier = hier; + printf("Demo\n"); + net = load_network(cfg1, weight1, 0); + set_batch_network(net, 1); + pthread_t detect_thread; + pthread_t fetch_thread; + + srand(2222222); + + if(filename){ + printf("video file: %s\n", filename); + cap = cvCaptureFromFile(filename); + }else{ + cap = cvCaptureFromCAM(cam_index); + + if(w){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w); + } + if(h){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h); + } + if(frames){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FPS, frames); + } + } + + if(!cap) error("Couldn't connect to webcam.\n"); + + layer l = net->layers[net->n-1]; + demo_detections = l.n*l.w*l.h; + int j; + + avg = (float *) calloc(l.outputs, sizeof(float)); + for(j = 0; j < demo_frame; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); + + boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box)); + probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); + for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes+1, sizeof(float)); + + buff[0] = get_image_from_stream(cap); + buff[1] = copy_image(buff[0]); + buff[2] = copy_image(buff[0]); + buff_letter[0] = letterbox_image(buff[0], net->w, net->h); + buff_letter[1] = letterbox_image(buff[0], net->w, net->h); + buff_letter[2] = letterbox_image(buff[0], net->w, net->h); + ipl = cvCreateImage(cvSize(buff[0].w,buff[0].h), IPL_DEPTH_8U, buff[0].c); + + int count = 0; + if(!prefix){ + cvNamedWindow("Demo", CV_WINDOW_NORMAL); + if(fullscreen){ + cvSetWindowProperty("Demo", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + } else { + cvMoveWindow("Demo", 0, 0); + cvResizeWindow("Demo", 1352, 1013); + } + } + + demo_time = what_time_is_it_now(); + + while(!demo_done){ +buff_index = (buff_index + 1) %3; +if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); +if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); +if(!prefix){ + fps = 1./(what_time_is_it_now() - demo_time); + demo_time = what_time_is_it_now(); + display_in_thread(0); +}else{ + char name[256]; + sprintf(name, "%s_%08d", prefix, count); + save_image(buff[(buff_index + 1)%3], name); +} +pthread_join(fetch_thread, 0); +pthread_join(detect_thread, 0); +++count; +} +} +*/ #else -void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, float hier_thresh) +void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg, float hier, int w, int h, int frames, int fullscreen) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/image.darknet/src/demo.h b/image.darknet/src/demo.h index c3d6a61..86e4654 100644 --- a/image.darknet/src/demo.h +++ b/image.darknet/src/demo.h @@ -1,7 +1,6 @@ -#ifndef DEMO -#define DEMO +#ifndef DEMO_H +#define DEMO_H #include "image.h" -void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, float hier_thresh); #endif diff --git a/image.darknet/src/detection_layer.c b/image.darknet/src/detection_layer.c index cd98b4b..d0e0194 100644 --- a/image.darknet/src/detection_layer.c +++ b/image.darknet/src/detection_layer.c @@ -5,6 +5,7 @@ #include "box.h" #include "cuda.h" #include "utils.h" + #include #include #include @@ -46,11 +47,11 @@ detection_layer make_detection_layer(int batch, int inputs, int n, int side, int return l; } -void forward_detection_layer(const detection_layer l, network_state state) +void forward_detection_layer(const detection_layer l, network net) { int locations = l.side*l.side; int i,j; - memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); //if(l.reorg) reorg(l.output, l.w*l.h, size*l.n, l.batch, 1); int b; if (l.softmax){ @@ -58,12 +59,12 @@ void forward_detection_layer(const detection_layer l, network_state state) int index = b*l.inputs; for (i = 0; i < locations; ++i) { int offset = i*l.classes; - softmax(l.output + index + offset, l.classes, 1, + softmax(l.output + index + offset, l.classes, 1, 1, l.output + index + offset); } } } - if(state.train){ + if(net.train){ float avg_iou = 0; float avg_cat = 0; float avg_allcat = 0; @@ -77,7 +78,7 @@ void forward_detection_layer(const detection_layer l, network_state state) int index = b*l.inputs; for (i = 0; i < locations; ++i) { int truth_index = (b*locations + i)*(1+l.coords+l.classes); - int is_obj = state.truth[truth_index]; + int is_obj = net.truth[truth_index]; for (j = 0; j < l.n; ++j) { int p_index = index + locations*l.classes + i*l.n + j; l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]); @@ -95,19 +96,19 @@ void forward_detection_layer(const detection_layer l, network_state state) int class_index = index + i*l.classes; for(j = 0; j < l.classes; ++j) { - l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]); - *(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2); - if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; + l.delta[class_index+j] = l.class_scale * (net.truth[truth_index+1+j] - l.output[class_index+j]); + *(l.cost) += l.class_scale * pow(net.truth[truth_index+1+j] - l.output[class_index+j], 2); + if(net.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; avg_allcat += l.output[class_index+j]; } - box truth = float_to_box(state.truth + truth_index + 1 + l.classes); + box truth = float_to_box(net.truth + truth_index + 1 + l.classes, 1); truth.x /= l.side; truth.y /= l.side; for(j = 0; j < l.n; ++j){ int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords; - box out = float_to_box(l.output + box_index); + box out = float_to_box(l.output + box_index, 1); out.x /= l.side; out.y /= l.side; @@ -139,14 +140,14 @@ void forward_detection_layer(const detection_layer l, network_state state) best_index = 0; } } - if(l.random && *(state.net.seen) < 64000){ + if(l.random && *(net.seen) < 64000){ best_index = rand()%l.n; } int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords; int tbox_index = truth_index + 1 + l.classes; - box out = float_to_box(l.output + box_index); + box out = float_to_box(l.output + box_index, 1); out.x /= l.side; out.y /= l.side; if (l.sqrt) { @@ -166,13 +167,13 @@ void forward_detection_layer(const detection_layer l, network_state state) l.delta[p_index] = l.object_scale * (iou - l.output[p_index]); } - l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]); - l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]); - l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]); - l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]); + l.delta[box_index+0] = l.coord_scale*(net.truth[tbox_index + 0] - l.output[box_index + 0]); + l.delta[box_index+1] = l.coord_scale*(net.truth[tbox_index + 1] - l.output[box_index + 1]); + l.delta[box_index+2] = l.coord_scale*(net.truth[tbox_index + 2] - l.output[box_index + 2]); + l.delta[box_index+3] = l.coord_scale*(net.truth[tbox_index + 3] - l.output[box_index + 3]); if(l.sqrt){ - l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]); - l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]); + l.delta[box_index+2] = l.coord_scale*(sqrt(net.truth[tbox_index + 2]) - l.output[box_index + 2]); + l.delta[box_index+3] = l.coord_scale*(sqrt(net.truth[tbox_index + 3]) - l.output[box_index + 3]); } *(l.cost) += pow(1-iou, 2); @@ -216,12 +217,12 @@ void forward_detection_layer(const detection_layer l, network_state state) } } -void backward_detection_layer(const detection_layer l, network_state state) +void backward_detection_layer(const detection_layer l, network net) { - axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); } -void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness) +void get_detection_detections(layer l, int w, int h, float thresh, detection *dets) { int i,j,n; float *predictions = l.output; @@ -234,17 +235,17 @@ void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box int p_index = l.side*l.side*l.classes + i*l.n + n; float scale = predictions[p_index]; int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4; - boxes[index].x = (predictions[box_index + 0] + col) / l.side * w; - boxes[index].y = (predictions[box_index + 1] + row) / l.side * h; - boxes[index].w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w; - boxes[index].h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h; + box b; + b.x = (predictions[box_index + 0] + col) / l.side * w; + b.y = (predictions[box_index + 1] + row) / l.side * h; + b.w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w; + b.h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h; + dets[index].bbox = b; + dets[index].objectness = scale; for(j = 0; j < l.classes; ++j){ int class_index = i*l.classes; float prob = scale*predictions[class_index+j]; - probs[index][j] = (prob > thresh) ? prob : 0; - } - if(only_objectness){ - probs[index][0] = scale; + dets[index].prob[j] = (prob > thresh) ? prob : 0; } } } @@ -252,36 +253,23 @@ void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box #ifdef GPU -void forward_detection_layer_gpu(const detection_layer l, network_state state) +void forward_detection_layer_gpu(const detection_layer l, network net) { - if(!state.train){ - copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); + if(!net.train){ + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); return; } - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); - float *truth_cpu = 0; - if(state.truth){ - int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes); - truth_cpu = calloc(num_truth, sizeof(float)); - cuda_pull_array(state.truth, truth_cpu, num_truth); - } - cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); - network_state cpu_state = state; - cpu_state.train = state.train; - cpu_state.truth = truth_cpu; - cpu_state.input = in_cpu; - forward_detection_layer(l, cpu_state); + cuda_pull_array(net.input_gpu, net.input, l.batch*l.inputs); + forward_detection_layer(l, net); cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs); - free(cpu_state.input); - if(cpu_state.truth) free(cpu_state.truth); } -void backward_detection_layer_gpu(detection_layer l, network_state state) +void backward_detection_layer_gpu(detection_layer l, network net) { - axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); - //copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1); + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); + //copy_gpu(l.batch*l.inputs, l.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/src/detection_layer.h b/image.darknet/src/detection_layer.h index e847a09..1c81853 100644 --- a/image.darknet/src/detection_layer.h +++ b/image.darknet/src/detection_layer.h @@ -7,13 +7,12 @@ typedef layer detection_layer; detection_layer make_detection_layer(int batch, int inputs, int n, int size, int classes, int coords, int rescore); -void forward_detection_layer(const detection_layer l, network_state state); -void backward_detection_layer(const detection_layer l, network_state state); -void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness); +void forward_detection_layer(const detection_layer l, network net); +void backward_detection_layer(const detection_layer l, network net); #ifdef GPU -void forward_detection_layer_gpu(const detection_layer l, network_state state); -void backward_detection_layer_gpu(detection_layer l, network_state state); +void forward_detection_layer_gpu(const detection_layer l, network net); +void backward_detection_layer_gpu(detection_layer l, network net); #endif #endif diff --git a/image.darknet/src/detector.c b/image.darknet/src/detector.c deleted file mode 100644 index 1416c05..0000000 --- a/image.darknet/src/detector.c +++ /dev/null @@ -1,552 +0,0 @@ -#include "network.h" -#include "region_layer.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "box.h" -#include "demo.h" -#include "option_list.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif -static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; - -void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear) -{ - list *options = read_data_cfg(datacfg); - char *train_images = option_find_str(options, "train", "data/train.list"); - char *backup_directory = option_find_str(options, "backup", "/backup/"); - - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - float avg_loss = -1; - network *nets = calloc(ngpus, sizeof(network)); - - srand(time(0)); - int seed = rand(); - int i; - for(i = 0; i < ngpus; ++i){ - srand(seed); -#ifdef GPU - cuda_set_device(gpus[i]); -#endif - nets[i] = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&nets[i], weightfile); - } - if(clear) *nets[i].seen = 0; - nets[i].learning_rate *= ngpus; - } - srand(time(0)); - network net = nets[0]; - - int imgs = net.batch * net.subdivisions * ngpus; - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - data train, buffer; - - layer l = net.layers[net.n - 1]; - - int classes = l.classes; - float jitter = l.jitter; - - list *plist = get_paths(train_images); - //int N = plist->size; - char **paths = (char **)list_to_array(plist); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.n = imgs; - args.m = plist->size; - args.classes = classes; - args.jitter = jitter; - args.num_boxes = l.max_boxes; - args.d = &buffer; - args.type = DETECTION_DATA; - args.threads = 8; - - args.angle = net.angle; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; - - pthread_t load_thread = load_data(args); - clock_t time; - int count = 0; - //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ - if(l.random && count++%10 == 0){ - printf("Resizing\n"); - int dim = (rand() % 10 + 10) * 32; - if (get_current_batch(net)+200 > net.max_batches) dim = 608; - //int dim = (rand() % 4 + 16) * 32; - printf("%d\n", dim); - args.w = dim; - args.h = dim; - - pthread_join(load_thread, 0); - train = buffer; - free_data(train); - load_thread = load_data(args); - - for(i = 0; i < ngpus; ++i){ - resize_network(nets + i, dim, dim); - } - net = nets[0]; - } - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data(args); - - /* - int k; - for(k = 0; k < l.max_boxes; ++k){ - box b = float_to_box(train.y.vals[10] + 1 + k*5); - if(!b.x) break; - printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h); - } - image im = float_to_image(448, 448, 3, train.X.vals[10]); - int k; - for(k = 0; k < l.max_boxes; ++k){ - box b = float_to_box(train.y.vals[10] + 1 + k*5); - printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h); - draw_bbox(im, b, 8, 1,0,0); - } - save_image(im, "truth11"); - */ - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - - time=clock(); - float loss = 0; -#ifdef GPU - if(ngpus == 1){ - loss = train_network(net, train); - } else { - loss = train_networks(nets, ngpus, train, 4); - } -#else - loss = train_network(net, train); -#endif - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - i = get_current_batch(net); - printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); - if(i%1000==0 || (i < 1000 && i%100 == 0)){ -#ifdef GPU - if(ngpus != 1) sync_nets(nets, ngpus, 0); -#endif - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - free_data(train); - } -#ifdef GPU - if(ngpus != 1) sync_nets(nets, ngpus, 0); -#endif - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - - -static int get_coco_image_id(char *filename) -{ - char *p = strrchr(filename, '_'); - return atoi(p+1); -} - -static void print_cocos(FILE *fp, char *image_path, box *boxes, float **probs, int num_boxes, int classes, int w, int h) -{ - int i, j; - int image_id = get_coco_image_id(image_path); - for(i = 0; i < num_boxes; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - float bx = xmin; - float by = ymin; - float bw = xmax - xmin; - float bh = ymax - ymin; - - for(j = 0; j < classes; ++j){ - if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]); - } - } -} - -void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h) -{ - int i, j; - for(i = 0; i < total; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - for(j = 0; j < classes; ++j){ - if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j], - xmin, ymin, xmax, ymax); - } - } -} - -void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h) -{ - int i, j; - for(i = 0; i < total; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - for(j = 0; j < classes; ++j){ - int class = j; - if (probs[i][class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class], - xmin, ymin, xmax, ymax); - } - } -} - -void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile) -{ - int j; - list *options = read_data_cfg(datacfg); - char *valid_images = option_find_str(options, "valid", "data/train.list"); - char *name_list = option_find_str(options, "names", "data/names.list"); - char *prefix = option_find_str(options, "results", "results"); - char **names = get_labels(name_list); - char *mapf = option_find_str(options, "map", 0); - int *map = 0; - if (mapf) map = read_map(mapf); - - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - list *plist = get_paths(valid_images); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - - char buff[1024]; - char *type = option_find_str(options, "eval", "voc"); - FILE *fp = 0; - FILE **fps = 0; - int coco = 0; - int imagenet = 0; - if(0==strcmp(type, "coco")){ - if(!outfile) outfile = "coco_results"; - snprintf(buff, 1024, "%s/%s.json", prefix, outfile); - fp = fopen(buff, "w"); - fprintf(fp, "[\n"); - coco = 1; - } else if(0==strcmp(type, "imagenet")){ - if(!outfile) outfile = "imagenet-detection"; - snprintf(buff, 1024, "%s/%s.txt", prefix, outfile); - fp = fopen(buff, "w"); - imagenet = 1; - classes = 200; - } else { - if(!outfile) outfile = "comp4_det_test_"; - fps = calloc(classes, sizeof(FILE *)); - for(j = 0; j < classes; ++j){ - snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]); - fps[j] = fopen(buff, "w"); - } - } - - - box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); - float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); - for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - int t; - - float thresh = .005; - float nms = .45; - - int nthreads = 4; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.type = IMAGE_DATA; - - for(t = 0; t < nthreads; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - time_t start = time(0); - for(i = nthreads; i < m+nthreads; i += nthreads){ - fprintf(stderr, "%d\n", i); - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - pthread_join(thr[t], 0); - val[t] = buf[t]; - val_resized[t] = buf_resized[t]; - } - for(t = 0; t < nthreads && i+t < m; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - char *path = paths[i+t-nthreads]; - char *id = basecfg(path); - float *X = val_resized[t].data; - network_predict(net, X); - int w = val[t].w; - int h = val[t].h; - get_region_boxes(l, w, h, thresh, probs, boxes, 0, map, .5); - if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms); - if (coco){ - print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h); - } else if (imagenet){ - print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h); - } else { - print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h); - } - free(id); - free_image(val[t]); - free_image(val_resized[t]); - } - } - for(j = 0; j < classes; ++j){ - if(fps) fclose(fps[j]); - } - if(coco){ - fseek(fp, -2, SEEK_CUR); - fprintf(fp, "\n]\n"); - fclose(fp); - } - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); -} - -void validate_detector_recall(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - list *plist = get_paths("data/voc.2007.test"); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - - int j, k; - box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); - float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); - for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - - float thresh = .001; - float iou_thresh = .5; - float nms = .4; - - int total = 0; - int correct = 0; - int proposals = 0; - float avg_iou = 0; - - for(i = 0; i < m; ++i){ - char *path = paths[i]; - image orig = load_image_color(path, 0, 0); - image sized = resize_image(orig, net.w, net.h); - char *id = basecfg(path); - network_predict(net, sized.data); - get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0, .5); - if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms); - - char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - - int num_labels = 0; - box_label *truth = read_boxes(labelpath, &num_labels); - for(k = 0; k < l.w*l.h*l.n; ++k){ - if(probs[k][0] > thresh){ - ++proposals; - } - } - for (j = 0; j < num_labels; ++j) { - ++total; - box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h}; - float best_iou = 0; - for(k = 0; k < l.w*l.h*l.n; ++k){ - float iou = box_iou(boxes[k], t); - if(probs[k][0] > thresh && iou > best_iou){ - best_iou = iou; - } - } - avg_iou += best_iou; - if(best_iou > iou_thresh){ - ++correct; - } - } - - fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); - free(id); - free_image(orig); - free_image(sized); - } -} - -void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh) -{ - list *options = read_data_cfg(datacfg); - char *name_list = option_find_str(options, "names", "data/names.list"); - char **names = get_labels(name_list); - - image **alphabet = load_alphabet(); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - clock_t time; - char buff[256]; - char *input = buff; - int j; - float nms=.4; - while(1){ - if(filename){ - strncpy(input, filename, 256); - } else { - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input,0,0); - image sized = resize_image(im, net.w, net.h); - layer l = net.layers[net.n-1]; - - box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); - float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); - for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes + 1, sizeof(float *)); - - float *X = sized.data; - time=clock(); - network_predict(net, X); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0, hier_thresh); - if (l.softmax_tree && nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms); - else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms); - draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes); - save_image(im, "predictions"); - show_image(im, "predictions"); - - free_image(im); - free_image(sized); - free(boxes); - free_ptrs((void **)probs, l.w*l.h*l.n); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif - if (filename) break; - } -} - -void run_detector(int argc, char **argv) -{ - char *prefix = find_char_arg(argc, argv, "-prefix", 0); - float thresh = find_float_arg(argc, argv, "-thresh", .24); - float hier_thresh = find_float_arg(argc, argv, "-hier", .5); - int cam_index = find_int_arg(argc, argv, "-c", 0); - int frame_skip = find_int_arg(argc, argv, "-s", 0); - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); - char *outfile = find_char_arg(argc, argv, "-out", 0); - int *gpus = 0; - int gpu = 0; - int ngpus = 0; - if(gpu_list){ - printf("%s\n", gpu_list); - int len = strlen(gpu_list); - ngpus = 1; - int i; - for(i = 0; i < len; ++i){ - if (gpu_list[i] == ',') ++ngpus; - } - gpus = calloc(ngpus, sizeof(int)); - for(i = 0; i < ngpus; ++i){ - gpus[i] = atoi(gpu_list); - gpu_list = strchr(gpu_list, ',')+1; - } - } else { - gpu = gpu_index; - gpus = &gpu; - ngpus = 1; - } - - int clear = find_arg(argc, argv, "-clear"); - - char *datacfg = argv[3]; - char *cfg = argv[4]; - char *weights = (argc > 5) ? argv[5] : 0; - char *filename = (argc > 6) ? argv[6]: 0; - if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh); - else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear); - else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); - else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights); - else if(0==strcmp(argv[2], "demo")) { - list *options = read_data_cfg(datacfg); - int classes = option_find_int(options, "classes", 20); - char *name_list = option_find_str(options, "names", "data/names.list"); - char **names = get_labels(name_list); - demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, hier_thresh); - } -} diff --git a/image.darknet/src/dice.c b/image.darknet/src/dice.c deleted file mode 100644 index 2286459..0000000 --- a/image.darknet/src/dice.c +++ /dev/null @@ -1,118 +0,0 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" - -char *dice_labels[] = {"face1","face2","face3","face4","face5","face6"}; - -void train_dice(char *cfgfile, char *weightfile) -{ - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - char *backup_directory = "/home/pjreddie/backup/"; - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = 1024; - int i = *net.seen/imgs; - char **labels = dice_labels; - list *plist = get_paths("data/dice/dice.train.list"); - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - clock_t time; - while(1){ - ++i; - time=clock(); - data train = load_data_old(paths, imgs, plist->size, labels, 6, net.w, net.h); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - - time=clock(); - float loss = train_network(net, train); - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); - free_data(train); - if((i % 100) == 0) net.learning_rate *= .1; - if(i%100==0){ - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i); - save_weights(net, buff); - } - } -} - -void validate_dice(char *filename, char *weightfile) -{ - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - srand(time(0)); - - char **labels = dice_labels; - list *plist = get_paths("data/dice/dice.val.list"); - - char **paths = (char **)list_to_array(plist); - int m = plist->size; - free_list(plist); - - data val = load_data_old(paths, m, 0, labels, 6, net.w, net.h); - float *acc = network_accuracies(net, val, 2); - printf("Validation Accuracy: %f, %d images\n", acc[0], m); - free_data(val); -} - -void test_dice(char *cfgfile, char *weightfile, char *filename) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - int i = 0; - char **names = dice_labels; - char buff[256]; - char *input = buff; - int indexes[6]; - while(1){ - if(filename){ - strncpy(input, filename, 256); - }else{ - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input, net.w, net.h); - float *X = im.data; - float *predictions = network_predict(net, X); - top_predictions(net, 6, indexes); - for(i = 0; i < 6; ++i){ - int index = indexes[i]; - printf("%s: %f\n", names[index], predictions[index]); - } - free_image(im); - if (filename) break; - } -} - -void run_dice(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5]: 0; - if(0==strcmp(argv[2], "test")) test_dice(cfg, weights, filename); - else if(0==strcmp(argv[2], "train")) train_dice(cfg, weights); - else if(0==strcmp(argv[2], "valid")) validate_dice(cfg, weights); -} - diff --git a/image.darknet/src/dropout_layer.c b/image.darknet/src/dropout_layer.c index b1381e6..780554f 100644 --- a/image.darknet/src/dropout_layer.c +++ b/image.darknet/src/dropout_layer.c @@ -35,26 +35,26 @@ void resize_dropout_layer(dropout_layer *l, int inputs) #endif } -void forward_dropout_layer(dropout_layer l, network_state state) +void forward_dropout_layer(dropout_layer l, network net) { int i; - if (!state.train) return; + if (!net.train) return; for(i = 0; i < l.batch * l.inputs; ++i){ float r = rand_uniform(0, 1); l.rand[i] = r; - if(r < l.probability) state.input[i] = 0; - else state.input[i] *= l.scale; + if(r < l.probability) net.input[i] = 0; + else net.input[i] *= l.scale; } } -void backward_dropout_layer(dropout_layer l, network_state state) +void backward_dropout_layer(dropout_layer l, network net) { int i; - if(!state.delta) return; + if(!net.delta) return; for(i = 0; i < l.batch * l.inputs; ++i){ float r = l.rand[i]; - if(r < l.probability) state.delta[i] = 0; - else state.delta[i] *= l.scale; + if(r < l.probability) net.delta[i] = 0; + else net.delta[i] *= l.scale; } } diff --git a/image.darknet/src/dropout_layer.h b/image.darknet/src/dropout_layer.h index 691cfc5..01f94d4 100644 --- a/image.darknet/src/dropout_layer.h +++ b/image.darknet/src/dropout_layer.h @@ -8,13 +8,13 @@ typedef layer dropout_layer; dropout_layer make_dropout_layer(int batch, int inputs, float probability); -void forward_dropout_layer(dropout_layer l, network_state state); -void backward_dropout_layer(dropout_layer l, network_state state); +void forward_dropout_layer(dropout_layer l, network net); +void backward_dropout_layer(dropout_layer l, network net); void resize_dropout_layer(dropout_layer *l, int inputs); #ifdef GPU -void forward_dropout_layer_gpu(dropout_layer l, network_state state); -void backward_dropout_layer_gpu(dropout_layer l, network_state state); +void forward_dropout_layer_gpu(dropout_layer l, network net); +void backward_dropout_layer_gpu(dropout_layer l, network net); #endif #endif diff --git a/image.darknet/src/dropout_layer_kernels.cu b/image.darknet/src/dropout_layer_kernels.cu index 7e51bd5..bd12b67 100644 --- a/image.darknet/src/dropout_layer_kernels.cu +++ b/image.darknet/src/dropout_layer_kernels.cu @@ -14,9 +14,9 @@ __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand if(id < size) input[id] = (rand[id] < prob) ? 0 : input[id]*scale; } -void forward_dropout_layer_gpu(dropout_layer layer, network_state state) +void forward_dropout_layer_gpu(dropout_layer layer, network net) { - if (!state.train) return; + if (!net.train) return; int size = layer.inputs*layer.batch; cuda_random(layer.rand_gpu, size); /* @@ -27,15 +27,15 @@ void forward_dropout_layer_gpu(dropout_layer layer, network_state state) cuda_push_array(layer.rand_gpu, layer.rand, size); */ - yoloswag420blazeit360noscope<<>>(state.input, size, layer.rand_gpu, layer.probability, layer.scale); + yoloswag420blazeit360noscope<<>>(net.input_gpu, size, layer.rand_gpu, layer.probability, layer.scale); check_error(cudaPeekAtLastError()); } -void backward_dropout_layer_gpu(dropout_layer layer, network_state state) +void backward_dropout_layer_gpu(dropout_layer layer, network net) { - if(!state.delta) return; + if(!net.delta_gpu) return; int size = layer.inputs*layer.batch; - yoloswag420blazeit360noscope<<>>(state.delta, size, layer.rand_gpu, layer.probability, layer.scale); + yoloswag420blazeit360noscope<<>>(net.delta_gpu, size, layer.rand_gpu, layer.probability, layer.scale); check_error(cudaPeekAtLastError()); } diff --git a/image.darknet/src/gemm.c b/image.darknet/src/gemm.c index 3003be0..648027f 100644 --- a/image.darknet/src/gemm.c +++ b/image.darknet/src/gemm.c @@ -77,6 +77,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, float *C, int ldc) { int i,j,k; + #pragma omp parallel for for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ register float A_PART = ALPHA*A[i*lda+k]; @@ -93,6 +94,7 @@ void gemm_nt(int M, int N, int K, float ALPHA, float *C, int ldc) { int i,j,k; + #pragma omp parallel for for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ register float sum = 0; @@ -110,6 +112,7 @@ void gemm_tn(int M, int N, int K, float ALPHA, float *C, int ldc) { int i,j,k; + #pragma omp parallel for for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ register float A_PART = ALPHA*A[k*lda+i]; @@ -126,6 +129,7 @@ void gemm_tt(int M, int N, int K, float ALPHA, float *C, int ldc) { int i,j,k; + #pragma omp parallel for for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ register float sum = 0; @@ -165,7 +169,7 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, #include -void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, +void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, float *A_gpu, int lda, float *B_gpu, int ldb, float BETA, @@ -177,24 +181,6 @@ void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, check_error(status); } -void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float BETA, - float *C, int ldc) -{ - float *A_gpu = cuda_make_array(A, (TA ? lda*K:lda*M)); - float *B_gpu = cuda_make_array(B, (TB ? ldb*N : ldb*K)); - float *C_gpu = cuda_make_array(C, ldc*M); - - gemm_ongpu(TA, TB, M, N, K, ALPHA, A_gpu, lda, B_gpu, ldb, BETA, C_gpu, ldc); - - cuda_pull_array(C_gpu, C, ldc*M); - cuda_free(A_gpu); - cuda_free(B_gpu); - cuda_free(C_gpu); -} - #include #include #include @@ -224,7 +210,7 @@ void time_gpu_random_matrix(int TA, int TB, int m, int k, int n) free(c); } -void time_ongpu(int TA, int TB, int m, int k, int n) +void time_gpu(int TA, int TB, int m, int k, int n) { int iter = 10; float *a = random_matrix(m,k); @@ -242,7 +228,7 @@ void time_ongpu(int TA, int TB, int m, int k, int n) int i; clock_t start = clock(), end; for(i = 0; i= m.n){ - m.n *= 2; - m.data = realloc(m.data, m.n*sizeof(char*)); - } - m.data[count] = line; - ++count; - } - printf("%d\n", count); - m.n = count; - m.data = realloc(m.data, count*sizeof(char*)); - return m; -} - -void string_to_board(char *s, float *board) -{ - int i, j; - //memset(board, 0, 1*19*19*sizeof(float)); - int count = 0; - for(i = 0; i < 91; ++i){ - char c = s[i]; - for(j = 0; j < 4; ++j){ - int me = (c >> (2*j)) & 1; - int you = (c >> (2*j + 1)) & 1; - if (me) board[count] = 1; - else if (you) board[count] = -1; - else board[count] = 0; - ++count; - if(count >= 19*19) break; - } - } -} - -void board_to_string(char *s, float *board) -{ - int i, j; - memset(s, 0, (19*19/4+1)*sizeof(char)); - int count = 0; - for(i = 0; i < 91; ++i){ - for(j = 0; j < 4; ++j){ - int me = (board[count] == 1); - int you = (board[count] == -1); - if (me) s[i] = s[i] | (1<<(2*j)); - if (you) s[i] = s[i] | (1<<(2*j + 1)); - ++count; - if(count >= 19*19) break; - } - } -} - -void random_go_moves(moves m, float *boards, float *labels, int n) -{ - int i; - memset(labels, 0, 19*19*n*sizeof(float)); - for(i = 0; i < n; ++i){ - char *b = m.data[rand()%m.n]; - int row = b[0]; - int col = b[1]; - labels[col + 19*(row + i*19)] = 1; - string_to_board(b+2, boards+i*19*19); - boards[col + 19*(row + i*19)] = 0; - - int flip = rand()%2; - int rotate = rand()%4; - image in = float_to_image(19, 19, 1, boards+i*19*19); - image out = float_to_image(19, 19, 1, labels+i*19*19); - if(flip){ - flip_image(in); - flip_image(out); - } - rotate_image_cw(in, rotate); - rotate_image_cw(out, rotate); - } -} - - -void train_go(char *cfgfile, char *weightfile) -{ - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - - char *backup_directory = "/home/pjreddie/backup/"; - - char buff[256]; - float *board = calloc(19*19*net.batch, sizeof(float)); - float *move = calloc(19*19*net.batch, sizeof(float)); - moves m = load_go_moves("/home/pjreddie/backup/go.train"); - //moves m = load_go_moves("games.txt"); - - int N = m.n; - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - clock_t time=clock(); - - random_go_moves(m, board, move, net.batch); - float loss = train_network_datum(net, board, move) / net.batch; - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory,base, epoch); - save_weights(net, buff); - - } - if(get_current_batch(net)%100 == 0){ - char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); - save_weights(net, buff); - } - if(get_current_batch(net)%10000 == 0){ - char buff[256]; - sprintf(buff, "%s/%s_%d.backup",backup_directory,base,get_current_batch(net)); - save_weights(net, buff); - } - } - sprintf(buff, "%s/%s.weights", backup_directory, base); - save_weights(net, buff); - - free_network(net); - free(base); -} - -void propagate_liberty(float *board, int *lib, int *visited, int row, int col, int side) -{ - if (row < 0 || row > 18 || col < 0 || col > 18) return; - int index = row*19 + col; - if (board[index] != side) return; - if (visited[index]) return; - visited[index] = 1; - lib[index] += 1; - propagate_liberty(board, lib, visited, row+1, col, side); - propagate_liberty(board, lib, visited, row-1, col, side); - propagate_liberty(board, lib, visited, row, col+1, side); - propagate_liberty(board, lib, visited, row, col-1, side); -} - - -int *calculate_liberties(float *board) -{ - int *lib = calloc(19*19, sizeof(int)); - int visited[361]; - int i, j; - for(j = 0; j < 19; ++j){ - for(i = 0; i < 19; ++i){ - memset(visited, 0, 19*19*sizeof(int)); - int index = j*19 + i; - if(board[index] == 0){ - if ((i > 0) && board[index - 1]) propagate_liberty(board, lib, visited, j, i-1, board[index-1]); - if ((i < 18) && board[index + 1]) propagate_liberty(board, lib, visited, j, i+1, board[index+1]); - if ((j > 0) && board[index - 19]) propagate_liberty(board, lib, visited, j-1, i, board[index-19]); - if ((j < 18) && board[index + 19]) propagate_liberty(board, lib, visited, j+1, i, board[index+19]); - } - } - } - return lib; -} - -void print_board(float *board, int swap, int *indexes) -{ - //FILE *stream = stdout; - FILE *stream = stderr; - int i,j,n; - fprintf(stream, "\n\n"); - fprintf(stream, " "); - for(i = 0; i < 19; ++i){ - fprintf(stream, "%c ", 'A' + i + 1*(i > 7 && noi)); - } - fprintf(stream, "\n"); - for(j = 0; j < 19; ++j){ - fprintf(stream, "%2d", (inverted) ? 19-j : j+1); - for(i = 0; i < 19; ++i){ - int index = j*19 + i; - if(indexes){ - int found = 0; - for(n = 0; n < nind; ++n){ - if(index == indexes[n]){ - found = 1; - /* - if(n == 0) fprintf(stream, "\uff11"); - else if(n == 1) fprintf(stream, "\uff12"); - else if(n == 2) fprintf(stream, "\uff13"); - else if(n == 3) fprintf(stream, "\uff14"); - else if(n == 4) fprintf(stream, "\uff15"); - */ - if(n == 0) fprintf(stream, " 1"); - else if(n == 1) fprintf(stream, " 2"); - else if(n == 2) fprintf(stream, " 3"); - else if(n == 3) fprintf(stream, " 4"); - else if(n == 4) fprintf(stream, " 5"); - } - } - if(found) continue; - } - //if(board[index]*-swap > 0) fprintf(stream, "\u25C9 "); - //else if(board[index]*-swap < 0) fprintf(stream, "\u25EF "); - if(board[index]*-swap > 0) fprintf(stream, " O"); - else if(board[index]*-swap < 0) fprintf(stream, " X"); - else fprintf(stream, " "); - } - fprintf(stream, "\n"); - } -} - -void flip_board(float *board) -{ - int i; - for(i = 0; i < 19*19; ++i){ - board[i] = -board[i]; - } -} - -void predict_move(network net, float *board, float *move, int multi) -{ - float *output = network_predict(net, board); - copy_cpu(19*19, output, 1, move, 1); - int i; - if(multi){ - image bim = float_to_image(19, 19, 1, board); - for(i = 1; i < 8; ++i){ - rotate_image_cw(bim, i); - if(i >= 4) flip_image(bim); - - float *output = network_predict(net, board); - image oim = float_to_image(19, 19, 1, output); - - if(i >= 4) flip_image(oim); - rotate_image_cw(oim, -i); - - axpy_cpu(19*19, 1, output, 1, move, 1); - - if(i >= 4) flip_image(bim); - rotate_image_cw(bim, -i); - } - scal_cpu(19*19, 1./8., move, 1); - } - for(i = 0; i < 19*19; ++i){ - if(board[i]) move[i] = 0; - } -} - -void remove_connected(float *b, int *lib, int p, int r, int c) -{ - if (r < 0 || r >= 19 || c < 0 || c >= 19) return; - if (b[r*19 + c] != p) return; - if (lib[r*19 + c] != 1) return; - b[r*19 + c] = 0; - remove_connected(b, lib, p, r+1, c); - remove_connected(b, lib, p, r-1, c); - remove_connected(b, lib, p, r, c+1); - remove_connected(b, lib, p, r, c-1); -} - - -void move_go(float *b, int p, int r, int c) -{ - int *l = calculate_liberties(b); - b[r*19 + c] = p; - remove_connected(b, l, -p, r+1, c); - remove_connected(b, l, -p, r-1, c); - remove_connected(b, l, -p, r, c+1); - remove_connected(b, l, -p, r, c-1); - free(l); -} - -int makes_safe_go(float *b, int *lib, int p, int r, int c){ - if (r < 0 || r >= 19 || c < 0 || c >= 19) return 0; - if (b[r*19 + c] == -p){ - if (lib[r*19 + c] > 1) return 0; - else return 1; - } - if (b[r*19 + c] == 0) return 1; - if (lib[r*19 + c] > 1) return 1; - return 0; -} - -int suicide_go(float *b, int p, int r, int c) -{ - int *l = calculate_liberties(b); - int safe = 0; - safe = safe || makes_safe_go(b, l, p, r+1, c); - safe = safe || makes_safe_go(b, l, p, r-1, c); - safe = safe || makes_safe_go(b, l, p, r, c+1); - safe = safe || makes_safe_go(b, l, p, r, c-1); - free(l); - return !safe; -} - -int legal_go(float *b, char *ko, int p, int r, int c) -{ - if (b[r*19 + c]) return 0; - char curr[91]; - char next[91]; - board_to_string(curr, b); - move_go(b, p, r, c); - board_to_string(next, b); - string_to_board(curr, b); - if(memcmp(next, ko, 91) == 0) return 0; - return 1; -} - -int generate_move(network net, int player, float *board, int multi, float thresh, float temp, char *ko, int print) -{ - int i, j; - for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; - - float move[361]; - if (player < 0) flip_board(board); - predict_move(net, board, move, multi); - if (player < 0) flip_board(board); - - - for(i = 0; i < 19; ++i){ - for(j = 0; j < 19; ++j){ - if (!legal_go(board, ko, player, i, j)) move[i*19 + j] = 0; - } - } - - int indexes[nind]; - top_k(move, 19*19, nind, indexes); - if(thresh > move[indexes[0]]) thresh = move[indexes[nind-1]]; - - for(i = 0; i < 19; ++i){ - for(j = 0; j < 19; ++j){ - if (move[i*19 + j] < thresh) move[i*19 + j] = 0; - } - } - - - int max = max_index(move, 19*19); - int row = max / 19; - int col = max % 19; - int index = sample_array(move, 19*19); - - if(print){ - top_k(move, 19*19, nind, indexes); - for(i = 0; i < nind; ++i){ - if (!move[indexes[i]]) indexes[i] = -1; - } - print_board(board, player, indexes); - for(i = 0; i < nind; ++i){ - fprintf(stderr, "%d: %f\n", i+1, move[indexes[i]]); - } - } - - if(suicide_go(board, player, row, col)){ - return -1; - } - if(suicide_go(board, player, index/19, index%19)) index = max; - return index; -} - -void valid_go(char *cfgfile, char *weightfile, int multi) -{ - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - - float *board = calloc(19*19, sizeof(float)); - float *move = calloc(19*19, sizeof(float)); - moves m = load_go_moves("/home/pjreddie/backup/go.test"); - - int N = m.n; - int i; - int correct = 0; - for(i = 0; i = 'A' && c <= 'Z') c = c - 'A'; - if(c >= 'a' && c <= 'z') c = c - 'a'; - if(c >= 8) --c; - r = 19 - r; - fprintf(stderr, "move: %d %d\n", r, c); - - char *swap = two; - two = one; - one = swap; - move_go(board, player, r, c); - board_to_string(one, board); - - printf("=%s \n\n", ids); - print_board(board, 1, 0); - } else if (!strcmp(buff, "genmove")){ - char color[256]; - scanf("%s", color); - int player = (color[0] == 'b' || color[0] == 'B') ? 1 : -1; - - int index = generate_move(net, player, board, multi, .1, .7, two, 1); - if(passed || index < 0){ - printf("=%s pass\n\n", ids); - passed = 0; - } else { - int row = index / 19; - int col = index % 19; - - char *swap = two; - two = one; - one = swap; - - move_go(board, player, row, col); - board_to_string(one, board); - row = 19 - row; - if (col >= 8) ++col; - printf("=%s %c%d\n\n", ids, 'A' + col, row); - print_board(board, 1, 0); - } - - } else if (!strcmp(buff, "p")){ - //print_board(board, 1, 0); - } else if (!strcmp(buff, "final_status_list")){ - char type[256]; - scanf("%s", type); - fprintf(stderr, "final_status\n"); - char *line = fgetl(stdin); - free(line); - if(type[0] == 'd' || type[0] == 'D'){ - FILE *f = fopen("game.txt", "w"); - int i, j; - int count = 2; - fprintf(f, "boardsize 19\n"); - fprintf(f, "clear_board\n"); - for(j = 0; j < 19; ++j){ - for(i = 0; i < 19; ++i){ - if(board[j*19 + i] == 1) fprintf(f, "play black %c%d\n", 'A'+i+(i>=8), 19-j); - if(board[j*19 + i] == -1) fprintf(f, "play white %c%d\n", 'A'+i+(i>=8), 19-j); - if(board[j*19 + i]) ++count; - } - } - fprintf(f, "final_status_list dead\n"); - fclose(f); - FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); - for(i = 0; i < count; ++i){ - free(fgetl(p)); - free(fgetl(p)); - } - char *l = 0; - while((l = fgetl(p))){ - printf("%s\n", l); - free(l); - } - } else { - printf("?%s unknown command\n\n", ids); - } - } else { - char *line = fgetl(stdin); - free(line); - printf("?%s unknown command\n\n", ids); - } - fflush(stdout); - fflush(stderr); - } -} - -void test_go(char *cfg, char *weights, int multi) -{ - network net = parse_network_cfg(cfg); - if(weights){ - load_weights(&net, weights); - } - srand(time(0)); - set_batch_network(&net, 1); - float *board = calloc(19*19, sizeof(float)); - float *move = calloc(19*19, sizeof(float)); - int color = 1; - while(1){ - float *output = network_predict(net, board); - copy_cpu(19*19, output, 1, move, 1); - int i; - if(multi){ - image bim = float_to_image(19, 19, 1, board); - for(i = 1; i < 8; ++i){ - rotate_image_cw(bim, i); - if(i >= 4) flip_image(bim); - - float *output = network_predict(net, board); - image oim = float_to_image(19, 19, 1, output); - - if(i >= 4) flip_image(oim); - rotate_image_cw(oim, -i); - - axpy_cpu(19*19, 1, output, 1, move, 1); - - if(i >= 4) flip_image(bim); - rotate_image_cw(bim, -i); - } - scal_cpu(19*19, 1./8., move, 1); - } - for(i = 0; i < 19*19; ++i){ - if(board[i]) move[i] = 0; - } - - int indexes[nind]; - int row, col; - top_k(move, 19*19, nind, indexes); - print_board(board, color, indexes); - for(i = 0; i < nind; ++i){ - int index = indexes[i]; - row = index / 19; - col = index % 19; - printf("%d: %c %d, %.2f%%\n", i+1, col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100); - } - //if(color == 1) printf("\u25EF Enter move: "); - //else printf("\u25C9 Enter move: "); - if(color == 1) printf("X Enter move: "); - else printf("O Enter move: "); - - char c; - char *line = fgetl(stdin); - int picked = 1; - int dnum = sscanf(line, "%d", &picked); - int cnum = sscanf(line, "%c", &c); - if (strlen(line) == 0 || dnum) { - --picked; - if (picked < nind){ - int index = indexes[picked]; - row = index / 19; - col = index % 19; - board[row*19 + col] = 1; - } - } else if (cnum){ - if (c <= 'T' && c >= 'A'){ - int num = sscanf(line, "%c %d", &c, &row); - row = (inverted)?19 - row : row-1; - col = c - 'A'; - if (col > 7 && noi) col -= 1; - if (num == 2) board[row*19 + col] = 1; - } else if (c == 'p') { - // Pass - } else if(c=='b' || c == 'w'){ - char g; - int num = sscanf(line, "%c %c %d", &g, &c, &row); - row = (inverted)?19 - row : row-1; - col = c - 'A'; - if (col > 7 && noi) col -= 1; - if (num == 3) board[row*19 + col] = (g == 'b') ? color : -color; - } else if(c == 'c'){ - char g; - int num = sscanf(line, "%c %c %d", &g, &c, &row); - row = (inverted)?19 - row : row-1; - col = c - 'A'; - if (col > 7 && noi) col -= 1; - if (num == 3) board[row*19 + col] = 0; - } - } - free(line); - flip_board(board); - color = -color; - } -} - -float score_game(float *board) -{ - FILE *f = fopen("game.txt", "w"); - int i, j; - int count = 3; - fprintf(f, "komi 6.5\n"); - fprintf(f, "boardsize 19\n"); - fprintf(f, "clear_board\n"); - for(j = 0; j < 19; ++j){ - for(i = 0; i < 19; ++i){ - if(board[j*19 + i] == 1) fprintf(f, "play black %c%d\n", 'A'+i+(i>=8), 19-j); - if(board[j*19 + i] == -1) fprintf(f, "play white %c%d\n", 'A'+i+(i>=8), 19-j); - if(board[j*19 + i]) ++count; - } - } - fprintf(f, "final_score\n"); - fclose(f); - FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); - for(i = 0; i < count; ++i){ - free(fgetl(p)); - free(fgetl(p)); - } - char *l = 0; - float score = 0; - char player = 0; - while((l = fgetl(p))){ - fprintf(stderr, "%s \t", l); - int n = sscanf(l, "= %c+%f", &player, &score); - free(l); - if (n == 2) break; - } - if(player == 'W') score = -score; - pclose(p); - return score; -} - -void self_go(char *filename, char *weightfile, char *f2, char *w2, int multi) -{ - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - - network net2 = net; - if(f2){ - net2 = parse_network_cfg(f2); - if(w2){ - load_weights(&net2, w2); - } - } - srand(time(0)); - char boards[300][93]; - int count = 0; - set_batch_network(&net, 1); - set_batch_network(&net2, 1); - float *board = calloc(19*19, sizeof(float)); - char *one = calloc(91, sizeof(char)); - char *two = calloc(91, sizeof(char)); - int done = 0; - int player = 1; - int p1 = 0; - int p2 = 0; - int total = 0; - while(1){ - if (done || count >= 300){ - float score = score_game(board); - int i = (score > 0)? 0 : 1; - if((score > 0) == (total%2==0)) ++p1; - else ++p2; - ++total; - fprintf(stderr, "Total: %d, Player 1: %f, Player 2: %f\n", total, (float)p1/total, (float)p2/total); - int j; - for(; i < count; i += 2){ - for(j = 0; j < 93; ++j){ - printf("%c", boards[i][j]); - } - printf("\n"); - } - memset(board, 0, 19*19*sizeof(float)); - player = 1; - done = 0; - count = 0; - fflush(stdout); - fflush(stderr); - } - //print_board(board, 1, 0); - //sleep(1); - network use = ((total%2==0) == (player==1)) ? net : net2; - int index = generate_move(use, player, board, multi, .1, .7, two, 0); - if(index < 0){ - done = 1; - continue; - } - int row = index / 19; - int col = index % 19; - - char *swap = two; - two = one; - one = swap; - - if(player < 0) flip_board(board); - boards[count][0] = row; - boards[count][1] = col; - board_to_string(boards[count] + 2, board); - if(player < 0) flip_board(board); - ++count; - - move_go(board, player, row, col); - board_to_string(one, board); - - player = -player; - } -} - -void run_go(int argc, char **argv) -{ - //boards_go(); - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *c2 = (argc > 5) ? argv[5] : 0; - char *w2 = (argc > 6) ? argv[6] : 0; - int multi = find_arg(argc, argv, "-multi"); - if(0==strcmp(argv[2], "train")) train_go(cfg, weights); - else if(0==strcmp(argv[2], "valid")) valid_go(cfg, weights, multi); - else if(0==strcmp(argv[2], "self")) self_go(cfg, weights, c2, w2, multi); - else if(0==strcmp(argv[2], "test")) test_go(cfg, weights, multi); - else if(0==strcmp(argv[2], "engine")) engine_go(cfg, weights, multi); -} - - diff --git a/image.darknet/src/gru_layer.c b/image.darknet/src/gru_layer.c index b78e868..b6601d8 100644 --- a/image.darknet/src/gru_layer.c +++ b/image.darknet/src/gru_layer.c @@ -26,7 +26,7 @@ static void increment_layer(layer *l, int steps) #endif } -layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize) +layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam) { fprintf(stderr, "GRU Layer: %d inputs, %d outputs\n", inputs, outputs); batch = batch / steps; @@ -36,39 +36,37 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no l.steps = steps; l.inputs = inputs; - l.input_z_layer = malloc(sizeof(layer)); + l.uz = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_z_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); - l.input_z_layer->batch = batch; + *(l.uz) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uz->batch = batch; - l.state_z_layer = malloc(sizeof(layer)); + l.wz = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.state_z_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); - l.state_z_layer->batch = batch; + *(l.wz) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wz->batch = batch; - - - l.input_r_layer = malloc(sizeof(layer)); + l.ur = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_r_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); - l.input_r_layer->batch = batch; + *(l.ur) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.ur->batch = batch; - l.state_r_layer = malloc(sizeof(layer)); + l.wr = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.state_r_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); - l.state_r_layer->batch = batch; + *(l.wr) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wr->batch = batch; - l.input_h_layer = malloc(sizeof(layer)); + l.uh = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_h_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); - l.input_h_layer->batch = batch; + *(l.uh) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uh->batch = batch; - l.state_h_layer = malloc(sizeof(layer)); + l.wh = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.state_h_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); - l.state_h_layer->batch = batch; + *(l.wh) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wh->batch = batch; l.batch_normalize = batch_normalize; @@ -94,68 +92,80 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no l.backward_gpu = backward_gru_layer_gpu; l.update_gpu = update_gru_layer_gpu; - l.forgot_state_gpu = cuda_make_array(l.output, batch*outputs); - l.forgot_delta_gpu = cuda_make_array(l.output, batch*outputs); - l.prev_state_gpu = cuda_make_array(l.output, batch*outputs); - l.state_gpu = cuda_make_array(l.output, batch*outputs); - l.output_gpu = cuda_make_array(l.output, batch*outputs*steps); - l.delta_gpu = cuda_make_array(l.delta, batch*outputs*steps); - l.r_gpu = cuda_make_array(l.output_gpu, batch*outputs); - l.z_gpu = cuda_make_array(l.output_gpu, batch*outputs); - l.h_gpu = cuda_make_array(l.output_gpu, batch*outputs); + l.forgot_state_gpu = cuda_make_array(0, batch*outputs); + l.forgot_delta_gpu = cuda_make_array(0, batch*outputs); + l.prev_state_gpu = cuda_make_array(0, batch*outputs); + l.state_gpu = cuda_make_array(0, batch*outputs); + l.output_gpu = cuda_make_array(0, batch*outputs*steps); + l.delta_gpu = cuda_make_array(0, batch*outputs*steps); + l.r_gpu = cuda_make_array(0, batch*outputs); + l.z_gpu = cuda_make_array(0, batch*outputs); + l.h_gpu = cuda_make_array(0, batch*outputs); + +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.uz->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uz->out_c, l.uz->out_h, l.uz->out_w); + cudnnSetTensor4dDescriptor(l.uh->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uh->out_c, l.uh->out_h, l.uh->out_w); + cudnnSetTensor4dDescriptor(l.ur->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ur->out_c, l.ur->out_h, l.ur->out_w); + cudnnSetTensor4dDescriptor(l.wz->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wz->out_c, l.wz->out_h, l.wz->out_w); + cudnnSetTensor4dDescriptor(l.wh->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wh->out_c, l.wh->out_h, l.wh->out_w); + cudnnSetTensor4dDescriptor(l.wr->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wr->out_c, l.wr->out_h, l.wr->out_w); +#endif #endif return l; } -void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay) +void update_gru_layer(layer l, update_args a) { - update_connected_layer(*(l.input_layer), batch, learning_rate, momentum, decay); - update_connected_layer(*(l.self_layer), batch, learning_rate, momentum, decay); - update_connected_layer(*(l.output_layer), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.ur), a); + update_connected_layer(*(l.uz), a); + update_connected_layer(*(l.uh), a); + update_connected_layer(*(l.wr), a); + update_connected_layer(*(l.wz), a); + update_connected_layer(*(l.wh), a); } -void forward_gru_layer(layer l, network_state state) +void forward_gru_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; - layer input_z_layer = *(l.input_z_layer); - layer input_r_layer = *(l.input_r_layer); - layer input_h_layer = *(l.input_h_layer); - - layer state_z_layer = *(l.state_z_layer); - layer state_r_layer = *(l.state_r_layer); - layer state_h_layer = *(l.state_h_layer); - - fill_cpu(l.outputs * l.batch * l.steps, 0, input_z_layer.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, input_r_layer.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, input_h_layer.delta, 1); - - fill_cpu(l.outputs * l.batch * l.steps, 0, state_z_layer.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, state_r_layer.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, state_h_layer.delta, 1); - if(state.train) { + layer uz = *(l.uz); + layer ur = *(l.ur); + layer uh = *(l.uh); + + layer wz = *(l.wz); + layer wr = *(l.wr); + layer wh = *(l.wh); + + fill_cpu(l.outputs * l.batch * l.steps, 0, uz.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ur.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, uh.delta, 1); + + fill_cpu(l.outputs * l.batch * l.steps, 0, wz.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wr.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wh.delta, 1); + if(net.train) { fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1); copy_cpu(l.outputs*l.batch, l.state, 1, l.prev_state, 1); } for (i = 0; i < l.steps; ++i) { s.input = l.state; - forward_connected_layer(state_z_layer, s); - forward_connected_layer(state_r_layer, s); + forward_connected_layer(wz, s); + forward_connected_layer(wr, s); - s.input = state.input; - forward_connected_layer(input_z_layer, s); - forward_connected_layer(input_r_layer, s); - forward_connected_layer(input_h_layer, s); + s.input = net.input; + forward_connected_layer(uz, s); + forward_connected_layer(ur, s); + forward_connected_layer(uh, s); - copy_cpu(l.outputs*l.batch, input_z_layer.output, 1, l.z_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, state_z_layer.output, 1, l.z_cpu, 1); + copy_cpu(l.outputs*l.batch, uz.output, 1, l.z_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, wz.output, 1, l.z_cpu, 1); - copy_cpu(l.outputs*l.batch, input_r_layer.output, 1, l.r_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, state_r_layer.output, 1, l.r_cpu, 1); + copy_cpu(l.outputs*l.batch, ur.output, 1, l.r_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, wr.output, 1, l.r_cpu, 1); activate_array(l.z_cpu, l.outputs*l.batch, LOGISTIC); activate_array(l.r_cpu, l.outputs*l.batch, LOGISTIC); @@ -164,34 +174,34 @@ void forward_gru_layer(layer l, network_state state) mul_cpu(l.outputs*l.batch, l.r_cpu, 1, l.forgot_state, 1); s.input = l.forgot_state; - forward_connected_layer(state_h_layer, s); + forward_connected_layer(wh, s); - copy_cpu(l.outputs*l.batch, input_h_layer.output, 1, l.h_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, state_h_layer.output, 1, l.h_cpu, 1); + copy_cpu(l.outputs*l.batch, uh.output, 1, l.h_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, wh.output, 1, l.h_cpu, 1); - #ifdef USET - activate_array(l.h_cpu, l.outputs*l.batch, TANH); - #else - activate_array(l.h_cpu, l.outputs*l.batch, LOGISTIC); - #endif + if(l.tanh){ + activate_array(l.h_cpu, l.outputs*l.batch, TANH); + } else { + activate_array(l.h_cpu, l.outputs*l.batch, LOGISTIC); + } weighted_sum_cpu(l.state, l.h_cpu, l.z_cpu, l.outputs*l.batch, l.output); copy_cpu(l.outputs*l.batch, l.output, 1, l.state, 1); - state.input += l.inputs*l.batch; + net.input += l.inputs*l.batch; l.output += l.outputs*l.batch; - increment_layer(&input_z_layer, 1); - increment_layer(&input_r_layer, 1); - increment_layer(&input_h_layer, 1); + increment_layer(&uz, 1); + increment_layer(&ur, 1); + increment_layer(&uh, 1); - increment_layer(&state_z_layer, 1); - increment_layer(&state_r_layer, 1); - increment_layer(&state_h_layer, 1); + increment_layer(&wz, 1); + increment_layer(&wr, 1); + increment_layer(&wh, 1); } } -void backward_gru_layer(layer l, network_state state) +void backward_gru_layer(layer l, network net) { } @@ -205,191 +215,192 @@ void push_gru_layer(layer l) { } -void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_gru_layer_gpu(layer l, update_args a) { - update_connected_layer_gpu(*(l.input_r_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.input_z_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.input_h_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.state_r_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.state_z_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.state_h_layer), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.ur), a); + update_connected_layer_gpu(*(l.uz), a); + update_connected_layer_gpu(*(l.uh), a); + update_connected_layer_gpu(*(l.wr), a); + update_connected_layer_gpu(*(l.wz), a); + update_connected_layer_gpu(*(l.wh), a); } -void forward_gru_layer_gpu(layer l, network_state state) +void forward_gru_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = {0}; + s.train = net.train; int i; - layer input_z_layer = *(l.input_z_layer); - layer input_r_layer = *(l.input_r_layer); - layer input_h_layer = *(l.input_h_layer); - - layer state_z_layer = *(l.state_z_layer); - layer state_r_layer = *(l.state_r_layer); - layer state_h_layer = *(l.state_h_layer); - - fill_ongpu(l.outputs * l.batch * l.steps, 0, input_z_layer.delta_gpu, 1); - fill_ongpu(l.outputs * l.batch * l.steps, 0, input_r_layer.delta_gpu, 1); - fill_ongpu(l.outputs * l.batch * l.steps, 0, input_h_layer.delta_gpu, 1); - - fill_ongpu(l.outputs * l.batch * l.steps, 0, state_z_layer.delta_gpu, 1); - fill_ongpu(l.outputs * l.batch * l.steps, 0, state_r_layer.delta_gpu, 1); - fill_ongpu(l.outputs * l.batch * l.steps, 0, state_h_layer.delta_gpu, 1); - if(state.train) { - fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1); + layer uz = *(l.uz); + layer ur = *(l.ur); + layer uh = *(l.uh); + + layer wz = *(l.wz); + layer wr = *(l.wr); + layer wh = *(l.wh); + + fill_gpu(l.outputs * l.batch * l.steps, 0, uz.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, ur.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, uh.delta_gpu, 1); + + fill_gpu(l.outputs * l.batch * l.steps, 0, wz.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wr.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wh.delta_gpu, 1); + if(net.train) { + fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1); } for (i = 0; i < l.steps; ++i) { - s.input = l.state_gpu; - forward_connected_layer_gpu(state_z_layer, s); - forward_connected_layer_gpu(state_r_layer, s); + s.input_gpu = l.state_gpu; + forward_connected_layer_gpu(wz, s); + forward_connected_layer_gpu(wr, s); - s.input = state.input; - forward_connected_layer_gpu(input_z_layer, s); - forward_connected_layer_gpu(input_r_layer, s); - forward_connected_layer_gpu(input_h_layer, s); + s.input_gpu = net.input_gpu; + forward_connected_layer_gpu(uz, s); + forward_connected_layer_gpu(ur, s); + forward_connected_layer_gpu(uh, s); + copy_gpu(l.outputs*l.batch, uz.output_gpu, 1, l.z_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wz.output_gpu, 1, l.z_gpu, 1); - copy_ongpu(l.outputs*l.batch, input_z_layer.output_gpu, 1, l.z_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_z_layer.output_gpu, 1, l.z_gpu, 1); + copy_gpu(l.outputs*l.batch, ur.output_gpu, 1, l.r_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wr.output_gpu, 1, l.r_gpu, 1); - copy_ongpu(l.outputs*l.batch, input_r_layer.output_gpu, 1, l.r_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_r_layer.output_gpu, 1, l.r_gpu, 1); + activate_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1); + mul_gpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); + s.input_gpu = l.forgot_state_gpu; + forward_connected_layer_gpu(wh, s); - s.input = l.forgot_state_gpu; - forward_connected_layer_gpu(state_h_layer, s); + copy_gpu(l.outputs*l.batch, uh.output_gpu, 1, l.h_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wh.output_gpu, 1, l.h_gpu, 1); - copy_ongpu(l.outputs*l.batch, input_h_layer.output_gpu, 1, l.h_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_h_layer.output_gpu, 1, l.h_gpu, 1); - - #ifdef USET - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); - #else - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); - #endif + if(l.tanh){ + activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH); + } else { + activate_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); + } weighted_sum_gpu(l.state_gpu, l.h_gpu, l.z_gpu, l.outputs*l.batch, l.output_gpu); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.state_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.state_gpu, 1); - - state.input += l.inputs*l.batch; + net.input_gpu += l.inputs*l.batch; l.output_gpu += l.outputs*l.batch; - increment_layer(&input_z_layer, 1); - increment_layer(&input_r_layer, 1); - increment_layer(&input_h_layer, 1); + increment_layer(&uz, 1); + increment_layer(&ur, 1); + increment_layer(&uh, 1); - increment_layer(&state_z_layer, 1); - increment_layer(&state_r_layer, 1); - increment_layer(&state_h_layer, 1); + increment_layer(&wz, 1); + increment_layer(&wr, 1); + increment_layer(&wh, 1); } } -void backward_gru_layer_gpu(layer l, network_state state) +void backward_gru_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = {0}; + s.train = net.train; int i; - layer input_z_layer = *(l.input_z_layer); - layer input_r_layer = *(l.input_r_layer); - layer input_h_layer = *(l.input_h_layer); + layer uz = *(l.uz); + layer ur = *(l.ur); + layer uh = *(l.uh); - layer state_z_layer = *(l.state_z_layer); - layer state_r_layer = *(l.state_r_layer); - layer state_h_layer = *(l.state_h_layer); + layer wz = *(l.wz); + layer wr = *(l.wr); + layer wh = *(l.wh); - increment_layer(&input_z_layer, l.steps - 1); - increment_layer(&input_r_layer, l.steps - 1); - increment_layer(&input_h_layer, l.steps - 1); + increment_layer(&uz, l.steps - 1); + increment_layer(&ur, l.steps - 1); + increment_layer(&uh, l.steps - 1); - increment_layer(&state_z_layer, l.steps - 1); - increment_layer(&state_r_layer, l.steps - 1); - increment_layer(&state_h_layer, l.steps - 1); + increment_layer(&wz, l.steps - 1); + increment_layer(&wr, l.steps - 1); + increment_layer(&wh, l.steps - 1); - state.input += l.inputs*l.batch*(l.steps-1); - if(state.delta) state.delta += l.inputs*l.batch*(l.steps-1); + net.input_gpu += l.inputs*l.batch*(l.steps-1); + if(net.delta_gpu) net.delta_gpu += l.inputs*l.batch*(l.steps-1); l.output_gpu += l.outputs*l.batch*(l.steps-1); l.delta_gpu += l.outputs*l.batch*(l.steps-1); + float *end_state = l.output_gpu; for (i = l.steps-1; i >= 0; --i) { - if(i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1); + if(i != 0) copy_gpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1); + else copy_gpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.state_gpu, 1); float *prev_delta_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; - copy_ongpu(l.outputs*l.batch, input_z_layer.output_gpu, 1, l.z_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_z_layer.output_gpu, 1, l.z_gpu, 1); - - copy_ongpu(l.outputs*l.batch, input_r_layer.output_gpu, 1, l.r_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_r_layer.output_gpu, 1, l.r_gpu, 1); - - activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); - - copy_ongpu(l.outputs*l.batch, input_h_layer.output_gpu, 1, l.h_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_h_layer.output_gpu, 1, l.h_gpu, 1); - - #ifdef USET - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); - #else - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); - #endif - - weighted_delta_gpu(l.prev_state_gpu, l.h_gpu, l.z_gpu, prev_delta_gpu, input_h_layer.delta_gpu, input_z_layer.delta_gpu, l.outputs*l.batch, l.delta_gpu); - - #ifdef USET - gradient_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH, input_h_layer.delta_gpu); - #else - gradient_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC, input_h_layer.delta_gpu); - #endif - - copy_ongpu(l.outputs*l.batch, input_h_layer.delta_gpu, 1, state_h_layer.delta_gpu, 1); - - copy_ongpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.forgot_state_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); - fill_ongpu(l.outputs*l.batch, 0, l.forgot_delta_gpu, 1); - - s.input = l.forgot_state_gpu; - s.delta = l.forgot_delta_gpu; - - backward_connected_layer_gpu(state_h_layer, s); + copy_gpu(l.outputs*l.batch, uz.output_gpu, 1, l.z_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wz.output_gpu, 1, l.z_gpu, 1); + + copy_gpu(l.outputs*l.batch, ur.output_gpu, 1, l.r_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wr.output_gpu, 1, l.r_gpu, 1); + + activate_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, uh.output_gpu, 1, l.h_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wh.output_gpu, 1, l.h_gpu, 1); + + if(l.tanh){ + activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH); + } else { + activate_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); + } + + weighted_delta_gpu(l.state_gpu, l.h_gpu, l.z_gpu, prev_delta_gpu, uh.delta_gpu, uz.delta_gpu, l.outputs*l.batch, l.delta_gpu); + + if(l.tanh){ + gradient_array_gpu(l.h_gpu, l.outputs*l.batch, TANH, uh.delta_gpu); + } else { + gradient_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC, uh.delta_gpu); + } + + copy_gpu(l.outputs*l.batch, uh.delta_gpu, 1, wh.delta_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1); + mul_gpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); + fill_gpu(l.outputs*l.batch, 0, l.forgot_delta_gpu, 1); + + s.input_gpu = l.forgot_state_gpu; + s.delta_gpu = l.forgot_delta_gpu; + + backward_connected_layer_gpu(wh, s); if(prev_delta_gpu) mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.r_gpu, prev_delta_gpu); - mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.prev_state_gpu, input_r_layer.delta_gpu); - - gradient_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC, input_r_layer.delta_gpu); - copy_ongpu(l.outputs*l.batch, input_r_layer.delta_gpu, 1, state_r_layer.delta_gpu, 1); - - gradient_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, input_z_layer.delta_gpu); - copy_ongpu(l.outputs*l.batch, input_z_layer.delta_gpu, 1, state_z_layer.delta_gpu, 1); - - s.input = l.prev_state_gpu; - s.delta = prev_delta_gpu; - - backward_connected_layer_gpu(state_r_layer, s); - backward_connected_layer_gpu(state_z_layer, s); - - s.input = state.input; - s.delta = state.delta; - - backward_connected_layer_gpu(input_h_layer, s); - backward_connected_layer_gpu(input_r_layer, s); - backward_connected_layer_gpu(input_z_layer, s); - - - state.input -= l.inputs*l.batch; - if(state.delta) state.delta -= l.inputs*l.batch; + mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.state_gpu, ur.delta_gpu); + + gradient_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC, ur.delta_gpu); + copy_gpu(l.outputs*l.batch, ur.delta_gpu, 1, wr.delta_gpu, 1); + + gradient_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, uz.delta_gpu); + copy_gpu(l.outputs*l.batch, uz.delta_gpu, 1, wz.delta_gpu, 1); + + s.input_gpu = l.state_gpu; + s.delta_gpu = prev_delta_gpu; + + backward_connected_layer_gpu(wr, s); + backward_connected_layer_gpu(wz, s); + + s.input_gpu = net.input_gpu; + s.delta_gpu = net.delta_gpu; + + backward_connected_layer_gpu(uh, s); + backward_connected_layer_gpu(ur, s); + backward_connected_layer_gpu(uz, s); + + + net.input_gpu -= l.inputs*l.batch; + if(net.delta_gpu) net.delta_gpu -= l.inputs*l.batch; l.output_gpu -= l.outputs*l.batch; l.delta_gpu -= l.outputs*l.batch; - increment_layer(&input_z_layer, -1); - increment_layer(&input_r_layer, -1); - increment_layer(&input_h_layer, -1); + increment_layer(&uz, -1); + increment_layer(&ur, -1); + increment_layer(&uh, -1); - increment_layer(&state_z_layer, -1); - increment_layer(&state_r_layer, -1); - increment_layer(&state_h_layer, -1); + increment_layer(&wz, -1); + increment_layer(&wr, -1); + increment_layer(&wh, -1); } + copy_gpu(l.outputs*l.batch, end_state, 1, l.state_gpu, 1); } #endif diff --git a/image.darknet/src/gru_layer.h b/image.darknet/src/gru_layer.h index 9e19cee..9067942 100644 --- a/image.darknet/src/gru_layer.h +++ b/image.darknet/src/gru_layer.h @@ -6,16 +6,16 @@ #include "layer.h" #include "network.h" -layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); +layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam); -void forward_gru_layer(layer l, network_state state); -void backward_gru_layer(layer l, network_state state); -void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_gru_layer(layer l, network state); +void backward_gru_layer(layer l, network state); +void update_gru_layer(layer l, update_args a); #ifdef GPU -void forward_gru_layer_gpu(layer l, network_state state); -void backward_gru_layer_gpu(layer l, network_state state); -void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_gru_layer_gpu(layer l, network state); +void backward_gru_layer_gpu(layer l, network state); +void update_gru_layer_gpu(layer l, update_args a); void push_gru_layer(layer l); void pull_gru_layer(layer l); #endif diff --git a/image.darknet/src/im2col.h b/image.darknet/src/im2col.h index f0ddeee..02c4247 100644 --- a/image.darknet/src/im2col.h +++ b/image.darknet/src/im2col.h @@ -7,7 +7,7 @@ void im2col_cpu(float* data_im, #ifdef GPU -void im2col_ongpu(float *im, +void im2col_gpu(float *im, int channels, int height, int width, int ksize, int stride, int pad,float *data_col); diff --git a/image.darknet/src/im2col_kernels.cu b/image.darknet/src/im2col_kernels.cu index d42d600..07b5e67 100644 --- a/image.darknet/src/im2col_kernels.cu +++ b/image.darknet/src/im2col_kernels.cu @@ -45,7 +45,7 @@ __global__ void im2col_gpu_kernel(const int n, const float* data_im, } } -void im2col_ongpu(float *im, +void im2col_gpu(float *im, int channels, int height, int width, int ksize, int stride, int pad, float *data_col){ // We are going to launch channels * height_col * width_col kernels, each diff --git a/image.darknet/src/image.c b/image.darknet/src/image.c index 5a90efd..4a2c6ba 100644 --- a/image.darknet/src/image.c +++ b/image.darknet/src/image.c @@ -10,12 +10,6 @@ #define STB_IMAGE_WRITE_IMPLEMENTATION #include "stb_image_write.h" -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#endif - - int windows = 0; float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} }; @@ -31,6 +25,70 @@ float get_color(int c, int x, int max) return r; } +image mask_to_rgb(image mask) +{ + int n = mask.c; + image im = make_image(mask.w, mask.h, 3); + int i, j; + for(j = 0; j < n; ++j){ + int offset = j*123457 % n; + float red = get_color(2,offset,n); + float green = get_color(1,offset,n); + float blue = get_color(0,offset,n); + for(i = 0; i < im.w*im.h; ++i){ + im.data[i + 0*im.w*im.h] += mask.data[j*im.h*im.w + i]*red; + im.data[i + 1*im.w*im.h] += mask.data[j*im.h*im.w + i]*green; + im.data[i + 2*im.w*im.h] += mask.data[j*im.h*im.w + i]*blue; + } + } + return im; +} + +static float get_pixel(image m, int x, int y, int c) +{ + assert(x < m.w && y < m.h && c < m.c); + return m.data[c*m.h*m.w + y*m.w + x]; +} +static float get_pixel_extend(image m, int x, int y, int c) +{ + if(x < 0 || x >= m.w || y < 0 || y >= m.h) return 0; + /* + if(x < 0) x = 0; + if(x >= m.w) x = m.w-1; + if(y < 0) y = 0; + if(y >= m.h) y = m.h-1; + */ + if(c < 0 || c >= m.c) return 0; + return get_pixel(m, x, y, c); +} +static void set_pixel(image m, int x, int y, int c, float val) +{ + if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return; + assert(x < m.w && y < m.h && c < m.c); + m.data[c*m.h*m.w + y*m.w + x] = val; +} +static void add_pixel(image m, int x, int y, int c, float val) +{ + assert(x < m.w && y < m.h && c < m.c); + m.data[c*m.h*m.w + y*m.w + x] += val; +} + +static float bilinear_interpolate(image im, float x, float y, int c) +{ + int ix = (int) floorf(x); + int iy = (int) floorf(y); + + float dx = x - ix; + float dy = y - iy; + + float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) + + dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) + + (1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) + + dy * dx * get_pixel_extend(im, ix+1, iy+1, c); + return val; +} + + void composite_image(image source, image dest, int dx, int dy) { int x,y,k; @@ -73,6 +131,7 @@ image tile_images(image a, image b, int dx) image get_label(image **characters, char *string, int size) { + size = size/10; if(size > 7) size = 7; image label = make_empty_image(0,0,0); while(*string){ @@ -177,23 +236,36 @@ image **load_alphabet() return alphabets; } -void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes) +void draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes) { - int i; + int i,j; for(i = 0; i < num; ++i){ - int class = max_index(probs[i], classes); - float prob = probs[i][class]; - if(prob > thresh){ - - int width = im.h * .012; - - if(0){ - width = pow(prob, 1./2.)*10+1; - alphabet = 0; + char labelstr[4096] = {0}; + int class = -1; + for(j = 0; j < classes; ++j){ + if (dets[i].prob[j] > thresh){ + if (class < 0) { + strcat(labelstr, names[j]); + class = j; + } else { + strcat(labelstr, ", "); + strcat(labelstr, names[j]); + } + printf("%s: %.0f%%\n", names[j], dets[i].prob[j]*100); } + } + if(class >= 0){ + int width = im.h * .006; - printf("%s: %.0f%%\n", names[class], prob*100); + /* + if(0){ + width = pow(prob, 1./2.)*10+1; + alphabet = 0; + } + */ + + //printf("%d %s: %.0f%%\n", i, names[class], prob*100); int offset = class*123457 % classes; float red = get_color(2,offset,classes); float green = get_color(1,offset,classes); @@ -205,7 +277,8 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, rgb[0] = red; rgb[1] = green; rgb[2] = blue; - box b = boxes[i]; + box b = dets[i].bbox; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); int left = (b.x-b.w/2.)*im.w; int right = (b.x+b.w/2.)*im.w; @@ -219,8 +292,18 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, draw_box_width(im, left, top, right, bot, width, red, green, blue); if (alphabet) { - image label = get_label(alphabet, names[class], (im.h*.03)/10); + image label = get_label(alphabet, labelstr, (im.h*.03)); draw_label(im, top + width, left, label, rgb); + free_image(label); + } + if (dets[i].mask){ + image mask = float_to_image(14, 14, 1, dets[i].mask); + image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h); + image tmask = threshold_image(resized_mask, .5); + embed_image(tmask, im, left, top); + free_image(mask); + free_image(resized_mask); + free_image(tmask); } } } @@ -294,6 +377,54 @@ image image_distance(image a, image b) return dist; } +void ghost_image(image source, image dest, int dx, int dy) +{ + int x,y,k; + float max_dist = sqrt((-source.w/2. + .5)*(-source.w/2. + .5)); + for(k = 0; k < source.c; ++k){ + for(y = 0; y < source.h; ++y){ + for(x = 0; x < source.w; ++x){ + float dist = sqrt((x - source.w/2. + .5)*(x - source.w/2. + .5) + (y - source.h/2. + .5)*(y - source.h/2. + .5)); + float alpha = (1 - dist/max_dist); + if(alpha < 0) alpha = 0; + float v1 = get_pixel(source, x,y,k); + float v2 = get_pixel(dest, dx+x,dy+y,k); + float val = alpha*v1 + (1-alpha)*v2; + set_pixel(dest, dx+x, dy+y, k, val); + } + } + } +} + +void blocky_image(image im, int s) +{ + int i,j,k; + for(k = 0; k < im.c; ++k){ + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)]; + } + } + } +} + +void censor_image(image im, int dx, int dy, int w, int h) +{ + int i,j,k; + int s = 32; + if(dx < 0) dx = 0; + if(dy < 0) dy = 0; + + for(k = 0; k < im.c; ++k){ + for(j = dy; j < dy + h && j < im.h; ++j){ + for(i = dx; i < dx + w && i < im.w; ++i){ + im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)]; + //im.data[i + j*im.w + k*im.w*im.h] = 0; + } + } + } +} + void embed_image(image source, image dest, int dx, int dy) { int x,y,k; @@ -380,6 +511,11 @@ void normalize_image2(image p) free(max); } +void copy_image_into(image src, image dest) +{ + memcpy(dest.data, src.data, src.h*src.w*src.c*sizeof(float)); +} + image copy_image(image p) { image copy = p; @@ -398,145 +534,27 @@ void rgbgr_image(image im) } } -#ifdef OPENCV -void show_image_cv(image p, const char *name) -{ - int x,y,k; - image copy = copy_image(p); - constrain_image(copy); - if(p.c == 3) rgbgr_image(copy); - //normalize_image(copy); - - char buff[256]; - //sprintf(buff, "%s (%d)", name, windows); - sprintf(buff, "%s", name); - - IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); - int step = disp->widthStep; - cvNamedWindow(buff, CV_WINDOW_NORMAL); - //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); - ++windows; - for(y = 0; y < p.h; ++y){ - for(x = 0; x < p.w; ++x){ - for(k= 0; k < p.c; ++k){ - disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255); - } - } - } - free_image(copy); - if(0){ - int w = 448; - int h = w*p.h/p.w; - if(h > 1000){ - h = 1000; - w = h*p.w/p.h; - } - IplImage *buffer = disp; - disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels); - cvResize(buffer, disp, CV_INTER_LINEAR); - cvReleaseImage(&buffer); - } - cvShowImage(buff, disp); - cvReleaseImage(&disp); -} -#endif - -void show_image(image p, const char *name) +int show_image(image p, const char *name, int ms) { #ifdef OPENCV - show_image_cv(p, name); + int c = show_image_cv(p, name, ms); + return c; #else fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name); save_image(p, name); + return -1; #endif } -#ifdef OPENCV - -image ipl_to_image(IplImage* src) -{ - unsigned char *data = (unsigned char *)src->imageData; - int h = src->height; - int w = src->width; - int c = src->nChannels; - int step = src->widthStep; - image out = make_image(w, h, c); - int i, j, k, count=0;; - - for(k= 0; k < c; ++k){ - for(i = 0; i < h; ++i){ - for(j = 0; j < w; ++j){ - out.data[count++] = data[i*step + j*c + k]/255.; - } - } - } - return out; -} - -image load_image_cv(char *filename, int channels) -{ - IplImage* src = 0; - int flag = -1; - if (channels == 0) flag = -1; - else if (channels == 1) flag = 0; - else if (channels == 3) flag = 1; - else { - fprintf(stderr, "OpenCV can't force load with %d channels\n", channels); - } - - if( (src = cvLoadImage(filename, flag)) == 0 ) - { - fprintf(stderr, "Cannot load image \"%s\"\n", filename); - char buff[256]; - sprintf(buff, "echo %s >> bad.list", filename); - system(buff); - return make_image(10,10,3); - //exit(0); - } - image out = ipl_to_image(src); - cvReleaseImage(&src); - rgbgr_image(out); - return out; -} - -image get_image_from_stream(CvCapture *cap) -{ - IplImage* src = cvQueryFrame(cap); - if (!src) return make_empty_image(0,0,0); - image im = ipl_to_image(src); - rgbgr_image(im); - return im; -} - -void save_image_jpg(image p, const char *name) -{ - image copy = copy_image(p); - if(p.c == 3) rgbgr_image(copy); - int x,y,k; - - char buff[256]; - sprintf(buff, "%s.jpg", name); - - IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); - int step = disp->widthStep; - for(y = 0; y < p.h; ++y){ - for(x = 0; x < p.w; ++x){ - for(k= 0; k < p.c; ++k){ - disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255); - } - } - } - cvSaveImage(buff, disp,0); - cvReleaseImage(&disp); - free_image(copy); -} -#endif - -void save_image_png(image im, const char *name) +void save_image_options(image im, const char *name, IMTYPE f, int quality) { char buff[256]; //sprintf(buff, "%s (%d)", name, windows); - sprintf(buff, "%s.png", name); + if(f == PNG) sprintf(buff, "%s.png", name); + else if (f == BMP) sprintf(buff, "%s.bmp", name); + else if (f == TGA) sprintf(buff, "%s.tga", name); + else if (f == JPG) sprintf(buff, "%s.jpg", name); + else sprintf(buff, "%s.png", name); unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char)); int i,k; for(k = 0; k < im.c; ++k){ @@ -544,21 +562,20 @@ void save_image_png(image im, const char *name) data[i*im.c+k] = (unsigned char) (255*im.data[i + k*im.w*im.h]); } } - int success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c); + int success = 0; + if(f == PNG) success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c); + else if (f == BMP) success = stbi_write_bmp(buff, im.w, im.h, im.c, data); + else if (f == TGA) success = stbi_write_tga(buff, im.w, im.h, im.c, data); + else if (f == JPG) success = stbi_write_jpg(buff, im.w, im.h, im.c, data, quality); free(data); if(!success) fprintf(stderr, "Failed to write image %s\n", buff); } void save_image(image im, const char *name) { -#ifdef OPENCV - save_image_jpg(im, name); -#else - save_image_png(im, name); -#endif + save_image_options(im, name, JPG, 80); } - void show_image_layers(image p, char *name) { int i; @@ -566,7 +583,7 @@ void show_image_layers(image p, char *name) for(i = 0; i < p.c; ++i){ sprintf(buff, "%s - Layer %d", name, i); image layer = get_image_layer(p, i); - show_image(layer, buff); + show_image(layer, buff, 1); free_image(layer); } } @@ -574,7 +591,7 @@ void show_image_layers(image p, char *name) void show_image_collapsed(image p, char *name) { image c = collapse_image_layers(p, 1); - show_image(c, name); + show_image(c, name, 1); free_image(c); } @@ -613,6 +630,29 @@ image float_to_image(int w, int h, int c, float *data) return out; } +void place_image(image im, int w, int h, int dx, int dy, image canvas) +{ + int x, y, c; + for(c = 0; c < im.c; ++c){ + for(y = 0; y < h; ++y){ + for(x = 0; x < w; ++x){ + float rx = ((float)x / w) * im.w; + float ry = ((float)y / h) * im.h; + float val = bilinear_interpolate(im, rx, ry, c); + set_pixel(canvas, x + dx, y + dy, c, val); + } + } + } +} + +image center_crop_image(image im, int w, int h) +{ + int m = (im.w < im.h) ? im.w : im.h; + image c = crop_image(im, (im.w - m) / 2, (im.h - m)/2, m, m); + image r = resize_image(c, w, h); + free_image(c); + return r; +} image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect) { @@ -652,6 +692,12 @@ image rotate_image(image im, float rad) return rot; } +void fill_image(image m, float s) +{ + int i; + for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] = s; +} + void translate_image(image m, float s) { int i; @@ -676,9 +722,7 @@ image crop_image(image im, int dx, int dy, int w, int h) float val = 0; r = constrain_int(r, 0, im.h-1); c = constrain_int(c, 0, im.w-1); - if (r >= 0 && r < im.h && c >= 0 && c < im.w) { - val = get_pixel(im, c, r, k); - } + val = get_pixel(im, c, r, k); set_pixel(cropped, i, j, k, val); } } @@ -746,11 +790,44 @@ void composite_3d(char *f1, char *f2, char *out, int delta) for(i = 0; i < c.w*c.h; ++i){ c.data[i] = a.data[i]; } -#ifdef OPENCV - save_image_jpg(c, out); -#else save_image(c, out); -#endif +} + +void letterbox_image_into(image im, int w, int h, image boxed) +{ + int new_w = im.w; + int new_h = im.h; + if (((float)w/im.w) < ((float)h/im.h)) { + new_w = w; + new_h = (im.h * w)/im.w; + } else { + new_h = h; + new_w = (im.w * h)/im.h; + } + image resized = resize_image(im, new_w, new_h); + embed_image(resized, boxed, (w-new_w)/2, (h-new_h)/2); + free_image(resized); +} + +image letterbox_image(image im, int w, int h) +{ + int new_w = im.w; + int new_h = im.h; + if (((float)w/im.w) < ((float)h/im.h)) { + new_w = w; + new_h = (im.h * w)/im.w; + } else { + new_h = h; + new_w = (im.w * h)/im.h; + } + image resized = resize_image(im, new_w, new_h); + image boxed = make_image(w, h, im.c); + fill_image(boxed, .5); + //int i; + //for(i = 0; i < boxed.w*boxed.h*boxed.c; ++i) boxed.data[i] = 0; + embed_image(resized, boxed, (w-new_w)/2, (h-new_h)/2); + free_image(resized); + return boxed; } image resize_max(image im, int max) @@ -793,8 +870,9 @@ image random_crop_image(image im, int w, int h) return crop; } -image random_augment_image(image im, float angle, float aspect, int low, int high, int size) +augment_args random_augment_args(image im, float angle, float aspect, int low, int high, int w, int h) { + augment_args a = {0}; aspect = rand_scale(aspect); int r = rand_int(low, high); int min = (im.h < im.w*aspect) ? im.h : im.w*aspect; @@ -802,15 +880,27 @@ image random_augment_image(image im, float angle, float aspect, int low, int hig float rad = rand_uniform(-angle, angle) * TWO_PI / 360.; - float dx = (im.w*scale/aspect - size) / 2.; - float dy = (im.h*scale - size) / 2.; - if(dx < 0) dx = 0; - if(dy < 0) dy = 0; + float dx = (im.w*scale/aspect - w) / 2.; + float dy = (im.h*scale - w) / 2.; + //if(dx < 0) dx = 0; + //if(dy < 0) dy = 0; dx = rand_uniform(-dx, dx); dy = rand_uniform(-dy, dy); - image crop = rotate_crop_image(im, rad, scale, size, size, dx, dy, aspect); + a.rad = rad; + a.scale = scale; + a.w = w; + a.h = h; + a.dx = dx; + a.dy = dy; + a.aspect = aspect; + return a; +} +image random_augment_image(image im, float angle, float aspect, int low, int high, int w, int h) +{ + augment_args a = random_augment_args(im, angle, aspect, low, high, w, h); + image crop = rotate_crop_image(im, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); return crop; } @@ -824,6 +914,52 @@ float three_way_min(float a, float b, float c) return (a < b) ? ( (a < c) ? a : c) : ( (b < c) ? b : c) ; } +void yuv_to_rgb(image im) +{ + assert(im.c == 3); + int i, j; + float r, g, b; + float y, u, v; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + y = get_pixel(im, i , j, 0); + u = get_pixel(im, i , j, 1); + v = get_pixel(im, i , j, 2); + + r = y + 1.13983*v; + g = y + -.39465*u + -.58060*v; + b = y + 2.03211*u; + + set_pixel(im, i, j, 0, r); + set_pixel(im, i, j, 1, g); + set_pixel(im, i, j, 2, b); + } + } +} + +void rgb_to_yuv(image im) +{ + assert(im.c == 3); + int i, j; + float r, g, b; + float y, u, v; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + r = get_pixel(im, i , j, 0); + g = get_pixel(im, i , j, 1); + b = get_pixel(im, i , j, 2); + + y = .299*r + .587*g + .114*b; + u = -.14713*r + -.28886*g + .436*b; + v = .615*r + -.51499*g + -.10001*b; + + set_pixel(im, i, j, 0, y); + set_pixel(im, i, j, 1, u); + set_pixel(im, i, j, 2, v); + } + } +} + // http://www.cs.rit.edu/~ncs/color/t_convert.html void rgb_to_hsv(image im) { @@ -903,12 +1039,30 @@ void hsv_to_rgb(image im) } } +void grayscale_image_3c(image im) +{ + assert(im.c == 3); + int i, j, k; + float scale[] = {0.299, 0.587, 0.114}; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + float val = 0; + for(k = 0; k < 3; ++k){ + val += scale[k]*get_pixel(im, i, j, k); + } + im.data[0*im.h*im.w + im.w*j + i] = val; + im.data[1*im.h*im.w + im.w*j + i] = val; + im.data[2*im.h*im.w + im.w*j + i] = val; + } + } +} + image grayscale_image(image im) { assert(im.c == 3); int i, j, k; image gray = make_image(im.w, im.h, 1); - float scale[] = {0.587, 0.299, 0.114}; + float scale[] = {0.299, 0.587, 0.114}; for(k = 0; k < im.c; ++k){ for(j = 0; j < im.h; ++j){ for(i = 0; i < im.w; ++i){ @@ -1042,21 +1196,6 @@ void saturate_exposure_image(image im, float sat, float exposure) constrain_image(im); } -float bilinear_interpolate(image im, float x, float y, int c) -{ - int ix = (int) floorf(x); - int iy = (int) floorf(y); - - float dx = x - ix; - float dy = y - iy; - - float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) + - dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) + - (1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) + - dy * dx * get_pixel_extend(im, ix+1, iy+1, c); - return val; -} - image resize_image(image im, int w, int h) { image resized = make_image(w, h, im.c); @@ -1119,16 +1258,16 @@ void test_resize(char *filename) distort_image(c4, .1, .66666, 1.5); - show_image(im, "Original"); - show_image(gray, "Gray"); - show_image(c1, "C1"); - show_image(c2, "C2"); - show_image(c3, "C3"); - show_image(c4, "C4"); + show_image(im, "Original", 1); + show_image(gray, "Gray", 1); + show_image(c1, "C1", 1); + show_image(c2, "C2", 1); + show_image(c3, "C3", 1); + show_image(c4, "C4", 1); #ifdef OPENCV while(1){ - image aug = random_augment_image(im, 0, .75, 320, 448, 320); - show_image(aug, "aug"); + image aug = random_augment_image(im, 0, .75, 320, 448, 320, 320); + show_image(aug, "aug", 1); free_image(aug); @@ -1143,10 +1282,9 @@ void test_resize(char *filename) float dhue = rand_uniform(-hue, hue); distort_image(c, dhue, dsat, dexp); - show_image(c, "rand"); + show_image(c, "rand", 1); printf("%f %f %f\n", dhue, dsat, dexp); free_image(c); - cvWaitKey(0); } #endif } @@ -1206,33 +1344,6 @@ image get_image_layer(image m, int l) } return out; } - -float get_pixel(image m, int x, int y, int c) -{ - assert(x < m.w && y < m.h && c < m.c); - return m.data[c*m.h*m.w + y*m.w + x]; -} -float get_pixel_extend(image m, int x, int y, int c) -{ - if(x < 0) x = 0; - if(x >= m.w) x = m.w-1; - if(y < 0) y = 0; - if(y >= m.h) y = m.h-1; - if(c < 0 || c >= m.c) return 0; - return get_pixel(m, x, y, c); -} -void set_pixel(image m, int x, int y, int c, float val) -{ - if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return; - assert(x < m.w && y < m.h && c < m.c); - m.data[c*m.h*m.w + y*m.w + x] = val; -} -void add_pixel(image m, int x, int y, int c, float val) -{ - assert(x < m.w && y < m.h && c < m.c); - m.data[c*m.h*m.w + y*m.w + x] += val; -} - void print_image(image m) { int i, j, k; @@ -1325,7 +1436,7 @@ void show_image_normalized(image im, const char *name) { image c = copy_image(im); normalize_image(c); - show_image(c, name); + show_image(c, name, 1); free_image(c); } @@ -1343,7 +1454,7 @@ void show_images(image *ims, int n, char *window) */ normalize_image(m); save_image(m, window); - show_image(m, window); + show_image(m, window, 1); free_image(m); } diff --git a/image.darknet/src/image.h b/image.darknet/src/image.h index 39c3962..3392bb9 100644 --- a/image.darknet/src/image.h +++ b/image.darknet/src/image.h @@ -7,81 +7,63 @@ #include #include #include "box.h" +#include "darknet.h" -typedef struct { - int h; - int w; - int c; - float *data; -} image; +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef OPENCV +void *open_video_stream(const char *f, int c, int w, int h, int fps); +image get_image_from_stream(void *p); +image load_image_cv(char *filename, int channels); +int show_image_cv(image im, const char* name, int ms); +#endif float get_color(int c, int x, int max); -void flip_image(image a); void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b); -void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b); void draw_bbox(image a, box bbox, int w, float r, float g, float b); -void draw_label(image a, int r, int c, image label, const float *rgb); void write_label(image a, int r, int c, image *characters, char *string, float *rgb); -void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **labels, int classes); image image_distance(image a, image b); void scale_image(image m, float s); -image crop_image(image im, int dx, int dy, int w, int h); +image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect); image random_crop_image(image im, int w, int h); -image random_augment_image(image im, float angle, float aspect, int low, int high, int size); -void random_distort_image(image im, float hue, float saturation, float exposure); -image resize_image(image im, int w, int h); -image resize_min(image im, int min); +image random_augment_image(image im, float angle, float aspect, int low, int high, int w, int h); +augment_args random_augment_args(image im, float angle, float aspect, int low, int high, int w, int h); +void letterbox_image_into(image im, int w, int h, image boxed); image resize_max(image im, int max); void translate_image(image m, float s); -void normalize_image(image p); -image rotate_image(image m, float rad); -void rotate_image_cw(image im, int times); void embed_image(image source, image dest, int dx, int dy); +void place_image(image im, int w, int h, int dx, int dy, image canvas); void saturate_image(image im, float sat); void exposure_image(image im, float sat); void distort_image(image im, float hue, float sat, float val); void saturate_exposure_image(image im, float sat, float exposure); +void rgb_to_hsv(image im); void hsv_to_rgb(image im); -void rgbgr_image(image im); -void constrain_image(image im); -void composite_3d(char *f1, char *f2, char *out, int delta); -int best_3d_shift_r(image a, image b, int min, int max); +void yuv_to_rgb(image im); +void rgb_to_yuv(image im); -image grayscale_image(image im); -image threshold_image(image im, float thresh); image collapse_image_layers(image source, int border); image collapse_images_horz(image *ims, int n); image collapse_images_vert(image *ims, int n); -void show_image(image p, const char *name); void show_image_normalized(image im, const char *name); -void save_image_png(image im, const char *name); -void save_image(image p, const char *name); void show_images(image *ims, int n, char *window); void show_image_layers(image p, char *name); void show_image_collapsed(image p, char *name); void print_image(image m); -image make_image(int w, int h, int c); -image make_random_image(int w, int h, int c); image make_empty_image(int w, int h, int c); -image float_to_image(int w, int h, int c, float *data); -image copy_image(image p); -image load_image(char *filename, int w, int h, int c); -image load_image_color(char *filename, int w, int h); -image **load_alphabet(); - -float get_pixel(image m, int x, int y, int c); -float get_pixel_extend(image m, int x, int y, int c); -void set_pixel(image m, int x, int y, int c, float val); -void add_pixel(image m, int x, int y, int c, float val); -float bilinear_interpolate(image im, float x, float y, int c); +void copy_image_into(image src, image dest); image get_image_layer(image m, int l); -void free_image(image m); -void test_resize(char *filename); +#ifdef __cplusplus +} +#endif + #endif diff --git a/image.darknet/src/image_opencv.cpp b/image.darknet/src/image_opencv.cpp new file mode 100644 index 0000000..7511280 --- /dev/null +++ b/image.darknet/src/image_opencv.cpp @@ -0,0 +1,135 @@ +#ifdef OPENCV + +#include "stdio.h" +#include "stdlib.h" +#include "opencv2/opencv.hpp" +#include "image.h" + +using namespace cv; + +extern "C" { + +IplImage *image_to_ipl(image im) +{ + int x,y,c; + IplImage *disp = cvCreateImage(cvSize(im.w,im.h), IPL_DEPTH_8U, im.c); + int step = disp->widthStep; + for(y = 0; y < im.h; ++y){ + for(x = 0; x < im.w; ++x){ + for(c= 0; c < im.c; ++c){ + float val = im.data[c*im.h*im.w + y*im.w + x]; + disp->imageData[y*step + x*im.c + c] = (unsigned char)(val*255); + } + } + } + return disp; +} + +image ipl_to_image(IplImage* src) +{ + int h = src->height; + int w = src->width; + int c = src->nChannels; + image im = make_image(w, h, c); + unsigned char *data = (unsigned char *)src->imageData; + int step = src->widthStep; + int i, j, k; + + for(i = 0; i < h; ++i){ + for(k= 0; k < c; ++k){ + for(j = 0; j < w; ++j){ + im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.; + } + } + } + return im; +} + +Mat image_to_mat(image im) +{ + image copy = copy_image(im); + constrain_image(copy); + if(im.c == 3) rgbgr_image(copy); + + IplImage *ipl = image_to_ipl(copy); + Mat m = cvarrToMat(ipl, true); + cvReleaseImage(&ipl); + free_image(copy); + return m; +} + +image mat_to_image(Mat m) +{ + IplImage ipl = m; + image im = ipl_to_image(&ipl); + rgbgr_image(im); + return im; +} + +void *open_video_stream(const char *f, int c, int w, int h, int fps) +{ + VideoCapture *cap; + if(f) cap = new VideoCapture(f); + else cap = new VideoCapture(c); + if(!cap->isOpened()) return 0; + if(w) cap->set(CV_CAP_PROP_FRAME_WIDTH, w); + if(h) cap->set(CV_CAP_PROP_FRAME_HEIGHT, w); + if(fps) cap->set(CV_CAP_PROP_FPS, w); + return (void *) cap; +} + +image get_image_from_stream(void *p) +{ + VideoCapture *cap = (VideoCapture *)p; + Mat m; + *cap >> m; + if(m.empty()) return make_empty_image(0,0,0); + return mat_to_image(m); +} + +image load_image_cv(char *filename, int channels) +{ + int flag = -1; + if (channels == 0) flag = -1; + else if (channels == 1) flag = 0; + else if (channels == 3) flag = 1; + else { + fprintf(stderr, "OpenCV can't force load with %d channels\n", channels); + } + Mat m; + m = imread(filename, flag); + if(!m.data){ + fprintf(stderr, "Cannot load image \"%s\"\n", filename); + char buff[256]; + sprintf(buff, "echo %s >> bad.list", filename); + system(buff); + return make_image(10,10,3); + //exit(0); + } + image im = mat_to_image(m); + return im; +} + +int show_image_cv(image im, const char* name, int ms) +{ + Mat m = image_to_mat(im); + imshow(name, m); + int c = waitKey(ms); + if (c != -1) c = c%256; + return c; +} + +void make_window(char *name, int w, int h, int fullscreen) +{ + namedWindow(name, WINDOW_NORMAL); + if (fullscreen) { + setWindowProperty(name, CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + } else { + resizeWindow(name, w, h); + if(strcmp(name, "Demo") == 0) moveWindow(name, 0, 0); + } +} + +} + +#endif diff --git a/image.darknet/src/iseg_layer.c b/image.darknet/src/iseg_layer.c new file mode 100644 index 0000000..2bf03a8 --- /dev/null +++ b/image.darknet/src/iseg_layer.c @@ -0,0 +1,225 @@ +#include "iseg_layer.h" +#include "activations.h" +#include "blas.h" +#include "box.h" +#include "cuda.h" +#include "utils.h" + +#include +#include +#include +#include + +layer make_iseg_layer(int batch, int w, int h, int classes, int ids) +{ + layer l = {0}; + l.type = ISEG; + + l.h = h; + l.w = w; + l.c = classes + ids; + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; + l.classes = classes; + l.batch = batch; + l.extra = ids; + l.cost = calloc(1, sizeof(float)); + l.outputs = h*w*l.c; + l.inputs = l.outputs; + l.truths = 90*(l.w*l.h+1); + l.delta = calloc(batch*l.outputs, sizeof(float)); + l.output = calloc(batch*l.outputs, sizeof(float)); + + l.counts = calloc(90, sizeof(int)); + l.sums = calloc(90, sizeof(float*)); + if(ids){ + int i; + for(i = 0; i < 90; ++i){ + l.sums[i] = calloc(ids, sizeof(float)); + } + } + + l.forward = forward_iseg_layer; + l.backward = backward_iseg_layer; +#ifdef GPU + l.forward_gpu = forward_iseg_layer_gpu; + l.backward_gpu = backward_iseg_layer_gpu; + l.output_gpu = cuda_make_array(l.output, batch*l.outputs); + l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); +#endif + + fprintf(stderr, "iseg\n"); + srand(0); + + return l; +} + +void resize_iseg_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + + l->outputs = h*w*l->c; + l->inputs = l->outputs; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); +#endif +} + +void forward_iseg_layer(const layer l, network net) +{ + + double time = what_time_is_it_now(); + int i,b,j,k; + int ids = l.extra; + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + +#ifndef GPU + for (b = 0; b < l.batch; ++b){ + int index = b*l.outputs; + activate_array(l.output + index, l.classes*l.w*l.h, LOGISTIC); + } +#endif + + for (b = 0; b < l.batch; ++b){ + // a priori, each pixel has no class + for(i = 0; i < l.classes; ++i){ + for(k = 0; k < l.w*l.h; ++k){ + int index = b*l.outputs + i*l.w*l.h + k; + l.delta[index] = 0 - l.output[index]; + } + } + + // a priori, embedding should be small magnitude + for(i = 0; i < ids; ++i){ + for(k = 0; k < l.w*l.h; ++k){ + int index = b*l.outputs + (i+l.classes)*l.w*l.h + k; + l.delta[index] = .1 * (0 - l.output[index]); + } + } + + + memset(l.counts, 0, 90*sizeof(int)); + for(i = 0; i < 90; ++i){ + fill_cpu(ids, 0, l.sums[i], 1); + + int c = net.truth[b*l.truths + i*(l.w*l.h+1)]; + if(c < 0) break; + // add up metric embeddings for each instance + for(k = 0; k < l.w*l.h; ++k){ + int index = b*l.outputs + c*l.w*l.h + k; + float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k]; + if(v){ + l.delta[index] = v - l.output[index]; + axpy_cpu(ids, 1, l.output + b*l.outputs + l.classes*l.w*l.h + k, l.w*l.h, l.sums[i], 1); + ++l.counts[i]; + } + } + } + + float *mse = calloc(90, sizeof(float)); + for(i = 0; i < 90; ++i){ + int c = net.truth[b*l.truths + i*(l.w*l.h+1)]; + if(c < 0) break; + for(k = 0; k < l.w*l.h; ++k){ + float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k]; + if(v){ + int z; + float sum = 0; + for(z = 0; z < ids; ++z){ + int index = b*l.outputs + (l.classes + z)*l.w*l.h + k; + sum += pow(l.sums[i][z]/l.counts[i] - l.output[index], 2); + } + mse[i] += sum; + } + } + mse[i] /= l.counts[i]; + } + + // Calculate average embedding + for(i = 0; i < 90; ++i){ + if(!l.counts[i]) continue; + scal_cpu(ids, 1.f/l.counts[i], l.sums[i], 1); + if(b == 0 && net.gpu_index == 0){ + printf("%4d, %6.3f, ", l.counts[i], mse[i]); + for(j = 0; j < ids; ++j){ + printf("%6.3f,", l.sums[i][j]); + } + printf("\n"); + } + } + free(mse); + + // Calculate embedding loss + for(i = 0; i < 90; ++i){ + if(!l.counts[i]) continue; + for(k = 0; k < l.w*l.h; ++k){ + float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k]; + if(v){ + for(j = 0; j < 90; ++j){ + if(!l.counts[j])continue; + int z; + for(z = 0; z < ids; ++z){ + int index = b*l.outputs + (l.classes + z)*l.w*l.h + k; + float diff = l.sums[j][z] - l.output[index]; + if (j == i) l.delta[index] += diff < 0? -.1 : .1; + else l.delta[index] += -(diff < 0? -.1 : .1); + } + } + } + } + } + + for(i = 0; i < ids; ++i){ + for(k = 0; k < l.w*l.h; ++k){ + int index = b*l.outputs + (i+l.classes)*l.w*l.h + k; + l.delta[index] *= .01; + } + } + } + + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + printf("took %lf sec\n", what_time_is_it_now() - time); +} + +void backward_iseg_layer(const layer l, network net) +{ + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void forward_iseg_layer_gpu(const layer l, network net) +{ + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + int b; + for (b = 0; b < l.batch; ++b){ + activate_array_gpu(l.output_gpu + b*l.outputs, l.classes*l.w*l.h, LOGISTIC); + //if(l.extra) activate_array_gpu(l.output_gpu + b*l.outputs + l.classes*l.w*l.h, l.extra*l.w*l.h, LOGISTIC); + } + + cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); + forward_iseg_layer(l, net); + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); +} + +void backward_iseg_layer_gpu(const layer l, network net) +{ + int b; + for (b = 0; b < l.batch; ++b){ + //if(l.extra) gradient_array_gpu(l.output_gpu + b*l.outputs + l.classes*l.w*l.h, l.extra*l.w*l.h, LOGISTIC, l.delta_gpu + b*l.outputs + l.classes*l.w*l.h); + } + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif + diff --git a/image.darknet/src/iseg_layer.h b/image.darknet/src/iseg_layer.h new file mode 100644 index 0000000..dd8e64e --- /dev/null +++ b/image.darknet/src/iseg_layer.h @@ -0,0 +1,19 @@ +#ifndef ISEG_LAYER_H +#define ISEG_LAYER_H + +#include "darknet.h" +#include "layer.h" +#include "network.h" + +layer make_iseg_layer(int batch, int w, int h, int classes, int ids); +void forward_iseg_layer(const layer l, network net); +void backward_iseg_layer(const layer l, network net); +void resize_iseg_layer(layer *l, int w, int h); +int iseg_num_detections(layer l, float thresh); + +#ifdef GPU +void forward_iseg_layer_gpu(const layer l, network net); +void backward_iseg_layer_gpu(layer l, network net); +#endif + +#endif diff --git a/image.darknet/src/l2norm_layer.c b/image.darknet/src/l2norm_layer.c new file mode 100644 index 0000000..d099479 --- /dev/null +++ b/image.darknet/src/l2norm_layer.c @@ -0,0 +1,63 @@ +#include "l2norm_layer.h" +#include "activations.h" +#include "blas.h" +#include "cuda.h" + +#include +#include +#include +#include +#include + +layer make_l2norm_layer(int batch, int inputs) +{ + fprintf(stderr, "l2norm %4d\n", inputs); + layer l = {0}; + l.type = L2NORM; + l.batch = batch; + l.inputs = inputs; + l.outputs = inputs; + l.output = calloc(inputs*batch, sizeof(float)); + l.scales = calloc(inputs*batch, sizeof(float)); + l.delta = calloc(inputs*batch, sizeof(float)); + + l.forward = forward_l2norm_layer; + l.backward = backward_l2norm_layer; + #ifdef GPU + l.forward_gpu = forward_l2norm_layer_gpu; + l.backward_gpu = backward_l2norm_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.scales_gpu = cuda_make_array(l.output, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + #endif + return l; +} + +void forward_l2norm_layer(const layer l, network net) +{ + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + l2normalize_cpu(l.output, l.scales, l.batch, l.out_c, l.out_w*l.out_h); +} + +void backward_l2norm_layer(const layer l, network net) +{ + //axpy_cpu(l.inputs*l.batch, 1, l.scales, 1, l.delta, 1); + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void forward_l2norm_layer_gpu(const layer l, network net) +{ + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + l2normalize_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_w*l.out_h); +} + +void backward_l2norm_layer_gpu(const layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.scales_gpu, 1, l.delta_gpu, 1); + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} + +#endif diff --git a/image.darknet/src/l2norm_layer.h b/image.darknet/src/l2norm_layer.h new file mode 100644 index 0000000..1ca6f71 --- /dev/null +++ b/image.darknet/src/l2norm_layer.h @@ -0,0 +1,15 @@ +#ifndef L2NORM_LAYER_H +#define L2NORM_LAYER_H +#include "layer.h" +#include "network.h" + +layer make_l2norm_layer(int batch, int inputs); +void forward_l2norm_layer(const layer l, network net); +void backward_l2norm_layer(const layer l, network net); + +#ifdef GPU +void forward_l2norm_layer_gpu(const layer l, network net); +void backward_l2norm_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/image.darknet/src/layer.c b/image.darknet/src/layer.c index 622cf26..c27b477 100644 --- a/image.darknet/src/layer.c +++ b/image.darknet/src/layer.c @@ -1,5 +1,6 @@ #include "layer.h" #include "cuda.h" + #include void free_layer(layer l) @@ -32,7 +33,6 @@ void free_layer(layer l) if(l.scale_updates) free(l.scale_updates); if(l.weights) free(l.weights); if(l.weight_updates) free(l.weight_updates); - if(l.col_image) free(l.col_image); if(l.delta) free(l.delta); if(l.output) free(l.output); if(l.squared) free(l.squared); @@ -80,7 +80,6 @@ void free_layer(layer l) if(l.rolling_variance_gpu) cuda_free(l.rolling_variance_gpu); if(l.variance_delta_gpu) cuda_free(l.variance_delta_gpu); if(l.mean_delta_gpu) cuda_free(l.mean_delta_gpu); - if(l.col_image_gpu) cuda_free(l.col_image_gpu); if(l.x_gpu) cuda_free(l.x_gpu); if(l.x_norm_gpu) cuda_free(l.x_norm_gpu); if(l.weights_gpu) cuda_free(l.weights_gpu); diff --git a/image.darknet/src/layer.h b/image.darknet/src/layer.h index 806542b..af6cd2a 100644 --- a/image.darknet/src/layer.h +++ b/image.darknet/src/layer.h @@ -1,271 +1 @@ -#ifndef BASE_LAYER_H -#define BASE_LAYER_H - -#include "activations.h" -#include "stddef.h" -#include "tree.h" - -struct network_state; - -struct layer; -typedef struct layer layer; - -typedef enum { - CONVOLUTIONAL, - DECONVOLUTIONAL, - CONNECTED, - MAXPOOL, - SOFTMAX, - DETECTION, - DROPOUT, - CROP, - ROUTE, - COST, - NORMALIZATION, - AVGPOOL, - LOCAL, - SHORTCUT, - ACTIVE, - RNN, - GRU, - CRNN, - BATCHNORM, - NETWORK, - XNOR, - REGION, - REORG, - BLANK -} LAYER_TYPE; - -typedef enum{ - SSE, MASKED, SMOOTH -} COST_TYPE; - -struct layer{ - LAYER_TYPE type; - ACTIVATION activation; - COST_TYPE cost_type; - void (*forward) (struct layer, struct network_state); - void (*backward) (struct layer, struct network_state); - void (*update) (struct layer, int, float, float, float); - void (*forward_gpu) (struct layer, struct network_state); - void (*backward_gpu) (struct layer, struct network_state); - void (*update_gpu) (struct layer, int, float, float, float); - int batch_normalize; - int shortcut; - int batch; - int forced; - int flipped; - int inputs; - int outputs; - int truths; - int h,w,c; - int out_h, out_w, out_c; - int n; - int max_boxes; - int groups; - int size; - int side; - int stride; - int reverse; - int pad; - int sqrt; - int flip; - int index; - int binary; - int xnor; - int steps; - int hidden; - float dot; - float angle; - float jitter; - float saturation; - float exposure; - float shift; - float ratio; - int softmax; - int classes; - int coords; - int background; - int rescore; - int objectness; - int does_cost; - int joint; - int noadjust; - int reorg; - int log; - - int adam; - float B1; - float B2; - float eps; - int t; - - float alpha; - float beta; - float kappa; - - float coord_scale; - float object_scale; - float noobject_scale; - float class_scale; - int bias_match; - int random; - float thresh; - int classfix; - int absolute; - - int dontload; - int dontloadscales; - - float temperature; - float probability; - float scale; - - char * cweights; - int * indexes; - int * input_layers; - int * input_sizes; - int * map; - float * rand; - float * cost; - float * state; - float * prev_state; - float * forgot_state; - float * forgot_delta; - float * state_delta; - - float * concat; - float * concat_delta; - - float * binary_weights; - - float * biases; - float * bias_updates; - - float * scales; - float * scale_updates; - - float * weights; - float * weight_updates; - - float * col_image; - float * delta; - float * output; - float * squared; - float * norms; - - float * spatial_mean; - float * mean; - float * variance; - - float * mean_delta; - float * variance_delta; - - float * rolling_mean; - float * rolling_variance; - - float * x; - float * x_norm; - - float * m; - float * v; - - float * z_cpu; - float * r_cpu; - float * h_cpu; - - float * binary_input; - - struct layer *input_layer; - struct layer *self_layer; - struct layer *output_layer; - - struct layer *input_gate_layer; - struct layer *state_gate_layer; - struct layer *input_save_layer; - struct layer *state_save_layer; - struct layer *input_state_layer; - struct layer *state_state_layer; - - struct layer *input_z_layer; - struct layer *state_z_layer; - - struct layer *input_r_layer; - struct layer *state_r_layer; - - struct layer *input_h_layer; - struct layer *state_h_layer; - - tree *softmax_tree; - - size_t workspace_size; - - #ifdef GPU - int *indexes_gpu; - - float *z_gpu; - float *r_gpu; - float *h_gpu; - - float *m_gpu; - float *v_gpu; - - float * prev_state_gpu; - float * forgot_state_gpu; - float * forgot_delta_gpu; - float * state_gpu; - float * state_delta_gpu; - float * gate_gpu; - float * gate_delta_gpu; - float * save_gpu; - float * save_delta_gpu; - float * concat_gpu; - float * concat_delta_gpu; - - float *binary_input_gpu; - float *binary_weights_gpu; - - float * mean_gpu; - float * variance_gpu; - - float * rolling_mean_gpu; - float * rolling_variance_gpu; - - float * variance_delta_gpu; - float * mean_delta_gpu; - - float * col_image_gpu; - - float * x_gpu; - float * x_norm_gpu; - float * weights_gpu; - float * weight_updates_gpu; - - float * biases_gpu; - float * bias_updates_gpu; - - float * scales_gpu; - float * scale_updates_gpu; - - float * output_gpu; - float * delta_gpu; - float * rand_gpu; - float * squared_gpu; - float * norms_gpu; - #ifdef CUDNN - cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; - cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; - cudnnFilterDescriptor_t weightDesc; - cudnnFilterDescriptor_t dweightDesc; - cudnnConvolutionDescriptor_t convDesc; - cudnnConvolutionFwdAlgo_t fw_algo; - cudnnConvolutionBwdDataAlgo_t bd_algo; - cudnnConvolutionBwdFilterAlgo_t bf_algo; - #endif - #endif -}; - -void free_layer(layer); - -#endif +#include "darknet.h" diff --git a/image.darknet/src/list.h b/image.darknet/src/list.h index fb818c2..6b445c7 100644 --- a/image.darknet/src/list.h +++ b/image.darknet/src/list.h @@ -1,26 +1,13 @@ #ifndef LIST_H #define LIST_H - -typedef struct node{ - void *val; - struct node *next; - struct node *prev; -} node; - -typedef struct list{ - int size; - node *front; - node *back; -} list; +#include "darknet.h" list *make_list(); int list_find(list *l, void *val); void list_insert(list *, void *); -void **list_to_array(list *l); -void free_list(list *l); void free_list_contents(list *l); #endif diff --git a/image.darknet/src/local_layer.c b/image.darknet/src/local_layer.c index 31f0ca6..74f6910 100644 --- a/image.darknet/src/local_layer.c +++ b/image.darknet/src/local_layer.c @@ -57,9 +57,10 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in float scale = sqrt(2./(size*size*c)); for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1,1); - l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float)); l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float)); + + l.workspace_size = out_h*out_w*size*size*c; l.forward = forward_local_layer; l.backward = backward_local_layer; @@ -76,7 +77,6 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in l.biases_gpu = cuda_make_array(l.biases, l.outputs); l.bias_updates_gpu = cuda_make_array(l.bias_updates, l.outputs); - l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c); l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n); l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); @@ -88,7 +88,7 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in return l; } -void forward_local_layer(const local_layer l, network_state state) +void forward_local_layer(const local_layer l, network net) { int out_h = local_out_height(l); int out_w = local_out_width(l); @@ -100,13 +100,13 @@ void forward_local_layer(const local_layer l, network_state state) } for(i = 0; i < l.batch; ++i){ - float *input = state.input + i*l.w*l.h*l.c; + float *input = net.input + i*l.w*l.h*l.c; im2col_cpu(input, l.c, l.h, l.w, - l.size, l.stride, l.pad, l.col_image); + l.size, l.stride, l.pad, net.workspace); float *output = l.output + i*l.outputs; for(j = 0; j < locations; ++j){ float *a = l.weights + j*l.size*l.size*l.c*l.n; - float *b = l.col_image + j; + float *b = net.workspace + j; float *c = output + j; int m = l.n; @@ -119,7 +119,7 @@ void forward_local_layer(const local_layer l, network_state state) activate_array(l.output, l.outputs*l.batch, l.activation); } -void backward_local_layer(local_layer l, network_state state) +void backward_local_layer(local_layer l, network net) { int i, j; int locations = l.out_w*l.out_h; @@ -131,13 +131,13 @@ void backward_local_layer(local_layer l, network_state state) } for(i = 0; i < l.batch; ++i){ - float *input = state.input + i*l.w*l.h*l.c; + float *input = net.input + i*l.w*l.h*l.c; im2col_cpu(input, l.c, l.h, l.w, - l.size, l.stride, l.pad, l.col_image); + l.size, l.stride, l.pad, net.workspace); for(j = 0; j < locations; ++j){ float *a = l.delta + i*l.outputs + j; - float *b = l.col_image + j; + float *b = net.workspace + j; float *c = l.weight_updates + j*l.size*l.size*l.c*l.n; int m = l.n; int n = l.size*l.size*l.c; @@ -146,11 +146,11 @@ void backward_local_layer(local_layer l, network_state state) gemm(0,1,m,n,k,1,a,locations,b,locations,1,c,n); } - if(state.delta){ + if(net.delta){ for(j = 0; j < locations; ++j){ float *a = l.weights + j*l.size*l.size*l.c*l.n; float *b = l.delta + i*l.outputs + j; - float *c = l.col_image + j; + float *c = net.workspace + j; int m = l.size*l.size*l.c; int n = 1; @@ -159,13 +159,18 @@ void backward_local_layer(local_layer l, network_state state) gemm(1,0,m,n,k,1,a,m,b,locations,0,c,locations); } - col2im_cpu(l.col_image, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w); + col2im_cpu(net.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, net.delta+i*l.c*l.h*l.w); } } } -void update_local_layer(local_layer l, int batch, float learning_rate, float momentum, float decay) +void update_local_layer(local_layer l, update_args a) { + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1); @@ -178,7 +183,7 @@ void update_local_layer(local_layer l, int batch, float learning_rate, float mom #ifdef GPU -void forward_local_layer_gpu(const local_layer l, network_state state) +void forward_local_layer_gpu(const local_layer l, network net) { int out_h = local_out_height(l); int out_w = local_out_width(l); @@ -186,83 +191,88 @@ void forward_local_layer_gpu(const local_layer l, network_state state) int locations = out_h * out_w; for(i = 0; i < l.batch; ++i){ - copy_ongpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1); + copy_gpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1); } for(i = 0; i < l.batch; ++i){ - float *input = state.input + i*l.w*l.h*l.c; - im2col_ongpu(input, l.c, l.h, l.w, - l.size, l.stride, l.pad, l.col_image_gpu); + float *input = net.input_gpu + i*l.w*l.h*l.c; + im2col_gpu(input, l.c, l.h, l.w, + l.size, l.stride, l.pad, net.workspace); float *output = l.output_gpu + i*l.outputs; for(j = 0; j < locations; ++j){ float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n; - float *b = l.col_image_gpu + j; + float *b = net.workspace + j; float *c = output + j; int m = l.n; int n = 1; int k = l.size*l.size*l.c; - gemm_ongpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations); + gemm_gpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations); } } - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); } -void backward_local_layer_gpu(local_layer l, network_state state) +void backward_local_layer_gpu(local_layer l, network net) { int i, j; int locations = l.out_w*l.out_h; - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); for(i = 0; i < l.batch; ++i){ - axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1); + axpy_gpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1); } for(i = 0; i < l.batch; ++i){ - float *input = state.input + i*l.w*l.h*l.c; - im2col_ongpu(input, l.c, l.h, l.w, - l.size, l.stride, l.pad, l.col_image_gpu); + float *input = net.input_gpu + i*l.w*l.h*l.c; + im2col_gpu(input, l.c, l.h, l.w, + l.size, l.stride, l.pad, net.workspace); for(j = 0; j < locations; ++j){ float *a = l.delta_gpu + i*l.outputs + j; - float *b = l.col_image_gpu + j; + float *b = net.workspace + j; float *c = l.weight_updates_gpu + j*l.size*l.size*l.c*l.n; int m = l.n; int n = l.size*l.size*l.c; int k = 1; - gemm_ongpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n); + gemm_gpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n); } - if(state.delta){ + if(net.delta_gpu){ for(j = 0; j < locations; ++j){ float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n; float *b = l.delta_gpu + i*l.outputs + j; - float *c = l.col_image_gpu + j; + float *c = net.workspace + j; int m = l.size*l.size*l.c; int n = 1; int k = l.n; - gemm_ongpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations); + gemm_gpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations); } - col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w); + col2im_gpu(net.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, net.delta_gpu+i*l.c*l.h*l.w); } } } -void update_local_layer_gpu(local_layer l, int batch, float learning_rate, float momentum, float decay) +void update_local_layer_gpu(local_layer l, update_args a) { + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; - axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); - scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1); + axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1); - axpy_ongpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); - axpy_ongpu(size, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); - scal_ongpu(size, momentum, l.weight_updates_gpu, 1); + axpy_gpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(size, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(size, momentum, l.weight_updates_gpu, 1); } void pull_local_layer(local_layer l) diff --git a/image.darknet/src/local_layer.h b/image.darknet/src/local_layer.h index 28915d8..776e572 100644 --- a/image.darknet/src/local_layer.h +++ b/image.darknet/src/local_layer.h @@ -10,9 +10,9 @@ typedef layer local_layer; #ifdef GPU -void forward_local_layer_gpu(local_layer layer, network_state state); -void backward_local_layer_gpu(local_layer layer, network_state state); -void update_local_layer_gpu(local_layer layer, int batch, float learning_rate, float momentum, float decay); +void forward_local_layer_gpu(local_layer layer, network net); +void backward_local_layer_gpu(local_layer layer, network net); +void update_local_layer_gpu(local_layer layer, update_args a); void push_local_layer(local_layer layer); void pull_local_layer(local_layer layer); @@ -20,9 +20,9 @@ void pull_local_layer(local_layer layer); local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation); -void forward_local_layer(const local_layer layer, network_state state); -void backward_local_layer(local_layer layer, network_state state); -void update_local_layer(local_layer layer, int batch, float learning_rate, float momentum, float decay); +void forward_local_layer(const local_layer layer, network net); +void backward_local_layer(local_layer layer, network net); +void update_local_layer(local_layer layer, update_args a); void bias_output(float *output, float *biases, int batch, int n, int size); void backward_bias(float *bias_updates, float *delta, int batch, int n, int size); diff --git a/image.darknet/src/logistic_layer.c b/image.darknet/src/logistic_layer.c new file mode 100644 index 0000000..b2b3d6b --- /dev/null +++ b/image.darknet/src/logistic_layer.c @@ -0,0 +1,71 @@ +#include "logistic_layer.h" +#include "activations.h" +#include "blas.h" +#include "cuda.h" + +#include +#include +#include +#include +#include + +layer make_logistic_layer(int batch, int inputs) +{ + fprintf(stderr, "logistic x entropy %4d\n", inputs); + layer l = {0}; + l.type = LOGXENT; + l.batch = batch; + l.inputs = inputs; + l.outputs = inputs; + l.loss = calloc(inputs*batch, sizeof(float)); + l.output = calloc(inputs*batch, sizeof(float)); + l.delta = calloc(inputs*batch, sizeof(float)); + l.cost = calloc(1, sizeof(float)); + + l.forward = forward_logistic_layer; + l.backward = backward_logistic_layer; + #ifdef GPU + l.forward_gpu = forward_logistic_layer_gpu; + l.backward_gpu = backward_logistic_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.loss_gpu = cuda_make_array(l.loss, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + #endif + return l; +} + +void forward_logistic_layer(const layer l, network net) +{ + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + activate_array(l.output, l.outputs*l.batch, LOGISTIC); + if(net.truth){ + logistic_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); + } +} + +void backward_logistic_layer(const layer l, network net) +{ + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void forward_logistic_layer_gpu(const layer l, network net) +{ + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, LOGISTIC); + if(net.truth){ + logistic_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth_gpu, l.delta_gpu, l.loss_gpu); + cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); + } +} + +void backward_logistic_layer_gpu(const layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} + +#endif diff --git a/image.darknet/src/logistic_layer.h b/image.darknet/src/logistic_layer.h new file mode 100644 index 0000000..9c25bee --- /dev/null +++ b/image.darknet/src/logistic_layer.h @@ -0,0 +1,15 @@ +#ifndef LOGISTIC_LAYER_H +#define LOGISTIC_LAYER_H +#include "layer.h" +#include "network.h" + +layer make_logistic_layer(int batch, int inputs); +void forward_logistic_layer(const layer l, network net); +void backward_logistic_layer(const layer l, network net); + +#ifdef GPU +void forward_logistic_layer_gpu(const layer l, network net); +void backward_logistic_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/image.darknet/src/lstm_layer.c b/image.darknet/src/lstm_layer.c new file mode 100644 index 0000000..fb07de2 --- /dev/null +++ b/image.darknet/src/lstm_layer.c @@ -0,0 +1,626 @@ +#include "lstm_layer.h" +#include "connected_layer.h" +#include "utils.h" +#include "cuda.h" +#include "blas.h" +#include "gemm.h" + +#include +#include +#include +#include + +static void increment_layer(layer *l, int steps) +{ + int num = l->outputs*l->batch*steps; + l->output += num; + l->delta += num; + l->x += num; + l->x_norm += num; + +#ifdef GPU + l->output_gpu += num; + l->delta_gpu += num; + l->x_gpu += num; + l->x_norm_gpu += num; +#endif +} + +layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam) +{ + fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs); + batch = batch / steps; + layer l = { 0 }; + l.batch = batch; + l.type = LSTM; + l.steps = steps; + l.inputs = inputs; + + l.uf = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uf) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uf->batch = batch; + + l.ui = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.ui) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.ui->batch = batch; + + l.ug = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.ug) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.ug->batch = batch; + + l.uo = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uo) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uo->batch = batch; + + l.wf = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wf) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wf->batch = batch; + + l.wi = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wi) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wi->batch = batch; + + l.wg = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wg) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wg->batch = batch; + + l.wo = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wo) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wo->batch = batch; + + l.batch_normalize = batch_normalize; + l.outputs = outputs; + + l.output = calloc(outputs*batch*steps, sizeof(float)); + l.state = calloc(outputs*batch, sizeof(float)); + + l.forward = forward_lstm_layer; + l.update = update_lstm_layer; + + l.prev_state_cpu = calloc(batch*outputs, sizeof(float)); + l.prev_cell_cpu = calloc(batch*outputs, sizeof(float)); + l.cell_cpu = calloc(batch*outputs*steps, sizeof(float)); + + l.f_cpu = calloc(batch*outputs, sizeof(float)); + l.i_cpu = calloc(batch*outputs, sizeof(float)); + l.g_cpu = calloc(batch*outputs, sizeof(float)); + l.o_cpu = calloc(batch*outputs, sizeof(float)); + l.c_cpu = calloc(batch*outputs, sizeof(float)); + l.h_cpu = calloc(batch*outputs, sizeof(float)); + l.temp_cpu = calloc(batch*outputs, sizeof(float)); + l.temp2_cpu = calloc(batch*outputs, sizeof(float)); + l.temp3_cpu = calloc(batch*outputs, sizeof(float)); + l.dc_cpu = calloc(batch*outputs, sizeof(float)); + l.dh_cpu = calloc(batch*outputs, sizeof(float)); + +#ifdef GPU + l.forward_gpu = forward_lstm_layer_gpu; + l.backward_gpu = backward_lstm_layer_gpu; + l.update_gpu = update_lstm_layer_gpu; + + l.output_gpu = cuda_make_array(0, batch*outputs*steps); + l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps); + + l.prev_state_gpu = cuda_make_array(0, batch*outputs); + l.prev_cell_gpu = cuda_make_array(0, batch*outputs); + l.cell_gpu = cuda_make_array(0, batch*outputs*steps); + + l.f_gpu = cuda_make_array(0, batch*outputs); + l.i_gpu = cuda_make_array(0, batch*outputs); + l.g_gpu = cuda_make_array(0, batch*outputs); + l.o_gpu = cuda_make_array(0, batch*outputs); + l.c_gpu = cuda_make_array(0, batch*outputs); + l.h_gpu = cuda_make_array(0, batch*outputs); + l.temp_gpu = cuda_make_array(0, batch*outputs); + l.temp2_gpu = cuda_make_array(0, batch*outputs); + l.temp3_gpu = cuda_make_array(0, batch*outputs); + l.dc_gpu = cuda_make_array(0, batch*outputs); + l.dh_gpu = cuda_make_array(0, batch*outputs); +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w); + cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w); + cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w); + cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w); + + cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w); + cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w); + cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w); + cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w); +#endif + +#endif + + return l; +} + +void update_lstm_layer(layer l, update_args a) +{ + update_connected_layer(*(l.wf), a); + update_connected_layer(*(l.wi), a); + update_connected_layer(*(l.wg), a); + update_connected_layer(*(l.wo), a); + update_connected_layer(*(l.uf), a); + update_connected_layer(*(l.ui), a); + update_connected_layer(*(l.ug), a); + update_connected_layer(*(l.uo), a); +} + +void forward_lstm_layer(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1); + + fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1); + if (state.train) { + fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input = l.h_cpu; + forward_connected_layer(wf, s); + forward_connected_layer(wi, s); + forward_connected_layer(wg, s); + forward_connected_layer(wo, s); + + s.input = state.input; + forward_connected_layer(uf, s); + forward_connected_layer(ui, s); + forward_connected_layer(ug, s); + forward_connected_layer(uo, s); + + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1); + activate_array(l.h_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1); + + state.input += l.inputs*l.batch; + l.output += l.outputs*l.batch; + l.cell_cpu += l.outputs*l.batch; + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_lstm_layer(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input += l.inputs*l.batch*(l.steps - 1); + if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1); + + l.output += l.outputs*l.batch*(l.steps - 1); + l.cell_cpu += l.outputs*l.batch*(l.steps - 1); + l.delta += l.outputs*l.batch*(l.steps - 1); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_cpu(l.outputs*l.batch, l.cell_cpu - l.outputs*l.batch, 1, l.prev_cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.cell_cpu, 1, l.c_cpu, 1); + if (i != 0) copy_cpu(l.outputs*l.batch, l.output - l.outputs*l.batch, 1, l.prev_state_cpu, 1); + copy_cpu(l.outputs*l.batch, l.output, 1, l.h_cpu, 1); + + l.dh_cpu = (i == 0) ? 0 : l.delta - l.outputs*l.batch; + + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + + copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1); + + gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu); + axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1); + gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wo.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wo, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uo.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(uo, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wg, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ug.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(ug, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wi.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wi, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ui.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(ui, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1); + gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wf.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wf, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uf.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(uf, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1); + + state.input -= l.inputs*l.batch; + if (state.delta) state.delta -= l.inputs*l.batch; + l.output -= l.outputs*l.batch; + l.cell_cpu -= l.outputs*l.batch; + l.delta -= l.outputs*l.batch; + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } +} + +#ifdef GPU +void update_lstm_layer_gpu(layer l, update_args a) +{ + update_connected_layer_gpu(*(l.wf), a); + update_connected_layer_gpu(*(l.wi), a); + update_connected_layer_gpu(*(l.wg), a); + update_connected_layer_gpu(*(l.wo), a); + update_connected_layer_gpu(*(l.uf), a); + update_connected_layer_gpu(*(l.ui), a); + update_connected_layer_gpu(*(l.ug), a); + update_connected_layer_gpu(*(l.uo), a); +} + +void forward_lstm_layer_gpu(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + fill_gpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1); + + fill_gpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1); + if (state.train) { + fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input_gpu = l.h_gpu; + forward_connected_layer_gpu(wf, s); + forward_connected_layer_gpu(wi, s); + forward_connected_layer_gpu(wg, s); + forward_connected_layer_gpu(wo, s); + + s.input_gpu = state.input_gpu; + forward_connected_layer_gpu(uf, s); + forward_connected_layer_gpu(ui, s); + forward_connected_layer_gpu(ug, s); + forward_connected_layer_gpu(uo, s); + + copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + + copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + + copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + + activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1); + activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH); + mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); + copy_gpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); + + state.input_gpu += l.inputs*l.batch; + l.output_gpu += l.outputs*l.batch; + l.cell_gpu += l.outputs*l.batch; + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_lstm_layer_gpu(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input_gpu += l.inputs*l.batch*(l.steps - 1); + if (state.delta_gpu) state.delta_gpu += l.inputs*l.batch*(l.steps - 1); + + l.output_gpu += l.outputs*l.batch*(l.steps - 1); + l.cell_gpu += l.outputs*l.batch*(l.steps - 1); + l.delta_gpu += l.outputs*l.batch*(l.steps - 1); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_gpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1); + copy_gpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1); + if (i != 0) copy_gpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1); + + l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; + + copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + + copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + + copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + + activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH); + + copy_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1); + mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1); + + gradient_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu); + axpy_gpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH); + mul_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wo, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(uo, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wg, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(ug, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wi, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(ui, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wf, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(uf, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1); + + state.input_gpu -= l.inputs*l.batch; + if (state.delta_gpu) state.delta_gpu -= l.inputs*l.batch; + l.output_gpu -= l.outputs*l.batch; + l.cell_gpu -= l.outputs*l.batch; + l.delta_gpu -= l.outputs*l.batch; + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } +} +#endif diff --git a/image.darknet/src/lstm_layer.h b/image.darknet/src/lstm_layer.h new file mode 100644 index 0000000..b9f07e6 --- /dev/null +++ b/image.darknet/src/lstm_layer.h @@ -0,0 +1,20 @@ +#ifndef LSTM_LAYER_H +#define LSTM_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" +#define USET + +layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam); + +void forward_lstm_layer(layer l, network net); +void update_lstm_layer(layer l, update_args a); + +#ifdef GPU +void forward_lstm_layer_gpu(layer l, network net); +void backward_lstm_layer_gpu(layer l, network net); +void update_lstm_layer_gpu(layer l, update_args a); + +#endif +#endif diff --git a/image.darknet/src/matrix.c b/image.darknet/src/matrix.c index ee14979..799916b 100644 --- a/image.darknet/src/matrix.c +++ b/image.darknet/src/matrix.c @@ -1,5 +1,6 @@ #include "matrix.h" #include "utils.h" +#include "blas.h" #include #include #include @@ -73,6 +74,20 @@ void matrix_add_matrix(matrix from, matrix to) } } +matrix copy_matrix(matrix m) +{ + matrix c = {0}; + c.rows = m.rows; + c.cols = m.cols; + c.vals = calloc(c.rows, sizeof(float *)); + int i; + for(i = 0; i < c.rows; ++i){ + c.vals[i] = calloc(c.cols, sizeof(float)); + copy_cpu(c.cols, m.vals[i], 1, c.vals[i], 1); + } + return c; +} + matrix make_matrix(int rows, int cols) { int i; diff --git a/image.darknet/src/matrix.h b/image.darknet/src/matrix.h index 641b596..879acd7 100644 --- a/image.darknet/src/matrix.h +++ b/image.darknet/src/matrix.h @@ -1,20 +1,11 @@ #ifndef MATRIX_H #define MATRIX_H -typedef struct matrix{ - int rows, cols; - float **vals; -} matrix; +#include "darknet.h" -matrix make_matrix(int rows, int cols); -void free_matrix(matrix m); +matrix copy_matrix(matrix m); void print_matrix(matrix m); -matrix csv_to_matrix(char *filename); -void matrix_to_csv(matrix m); matrix hold_out_matrix(matrix *m, int n); -float matrix_topk_accuracy(matrix truth, matrix guess, int k); -void matrix_add_matrix(matrix from, matrix to); -void scale_matrix(matrix m, float scale); matrix resize_matrix(matrix m, int size); float *pop_column(matrix *m, int c); diff --git a/image.darknet/src/maxpool_layer.c b/image.darknet/src/maxpool_layer.c index 031d116..fb05635 100644 --- a/image.darknet/src/maxpool_layer.c +++ b/image.darknet/src/maxpool_layer.c @@ -27,8 +27,8 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.w = w; l.c = c; l.pad = padding; - l.out_w = (w + 2*padding)/stride; - l.out_h = (h + 2*padding)/stride; + l.out_w = (w + padding - size)/stride + 1; + l.out_h = (h + padding - size)/stride + 1; l.out_c = c; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; @@ -43,7 +43,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s #ifdef GPU l.forward_gpu = forward_maxpool_layer_gpu; l.backward_gpu = backward_maxpool_layer_gpu; - l.indexes_gpu = cuda_make_int_array(output_size); + l.indexes_gpu = cuda_make_int_array(0, output_size); l.output_gpu = cuda_make_array(l.output, output_size); l.delta_gpu = cuda_make_array(l.delta, output_size); #endif @@ -57,8 +57,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->w = w; l->inputs = h*w*l->c; - l->out_w = (w + 2*l->pad)/l->stride; - l->out_h = (h + 2*l->pad)/l->stride; + l->out_w = (w + l->pad - l->size)/l->stride + 1; + l->out_h = (h + l->pad - l->size)/l->stride + 1; l->outputs = l->out_w * l->out_h * l->c; int output_size = l->outputs * l->batch; @@ -70,17 +70,17 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) cuda_free((float *)l->indexes_gpu); cuda_free(l->output_gpu); cuda_free(l->delta_gpu); - l->indexes_gpu = cuda_make_int_array(output_size); + l->indexes_gpu = cuda_make_int_array(0, output_size); l->output_gpu = cuda_make_array(l->output, output_size); l->delta_gpu = cuda_make_array(l->delta, output_size); #endif } -void forward_maxpool_layer(const maxpool_layer l, network_state state) +void forward_maxpool_layer(const maxpool_layer l, network net) { int b,i,j,k,m,n; - int w_offset = -l.pad; - int h_offset = -l.pad; + int w_offset = -l.pad/2; + int h_offset = -l.pad/2; int h = l.out_h; int w = l.out_w; @@ -100,7 +100,7 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c)); int valid = (cur_h >= 0 && cur_h < l.h && cur_w >= 0 && cur_w < l.w); - float val = (valid != 0) ? state.input[index] : -FLT_MAX; + float val = (valid != 0) ? net.input[index] : -FLT_MAX; max_i = (val > max) ? index : max_i; max = (val > max) ? val : max; } @@ -113,7 +113,7 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) } } -void backward_maxpool_layer(const maxpool_layer l, network_state state) +void backward_maxpool_layer(const maxpool_layer l, network net) { int i; int h = l.out_h; @@ -121,7 +121,7 @@ void backward_maxpool_layer(const maxpool_layer l, network_state state) int c = l.c; for(i = 0; i < h*w*c*l.batch; ++i){ int index = l.indexes[i]; - state.delta[index] += l.delta[i]; + net.delta[index] += l.delta[i]; } } diff --git a/image.darknet/src/maxpool_layer.h b/image.darknet/src/maxpool_layer.h index ce56dd8..ceb5190 100644 --- a/image.darknet/src/maxpool_layer.h +++ b/image.darknet/src/maxpool_layer.h @@ -11,12 +11,12 @@ typedef layer maxpool_layer; image get_maxpool_image(maxpool_layer l); maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding); void resize_maxpool_layer(maxpool_layer *l, int w, int h); -void forward_maxpool_layer(const maxpool_layer l, network_state state); -void backward_maxpool_layer(const maxpool_layer l, network_state state); +void forward_maxpool_layer(const maxpool_layer l, network net); +void backward_maxpool_layer(const maxpool_layer l, network net); #ifdef GPU -void forward_maxpool_layer_gpu(maxpool_layer l, network_state state); -void backward_maxpool_layer_gpu(maxpool_layer l, network_state state); +void forward_maxpool_layer_gpu(maxpool_layer l, network net); +void backward_maxpool_layer_gpu(maxpool_layer l, network net); #endif #endif diff --git a/image.darknet/src/maxpool_layer_kernels.cu b/image.darknet/src/maxpool_layer_kernels.cu index 6381cc1..869ef46 100644 --- a/image.darknet/src/maxpool_layer_kernels.cu +++ b/image.darknet/src/maxpool_layer_kernels.cu @@ -9,8 +9,8 @@ extern "C" { __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) { - int h = (in_h + 2*pad)/stride; - int w = (in_w + 2*pad)/stride; + int h = (in_h + pad - size)/stride + 1; + int w = (in_w + pad - size)/stride + 1; int c = in_c; int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -24,8 +24,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c id /= c; int b = id; - int w_offset = -pad; - int h_offset = -pad; + int w_offset = -pad/2; + int h_offset = -pad/2; int out_index = j + w*(i + h*(k + c*b)); float max = -INFINITY; @@ -49,8 +49,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *delta, float *prev_delta, int *indexes) { - int h = (in_h + 2*pad)/stride; - int w = (in_w + 2*pad)/stride; + int h = (in_h + pad - size)/stride + 1; + int w = (in_w + pad - size)/stride + 1; int c = in_c; int area = (size-1)/stride; @@ -66,8 +66,8 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ id /= in_c; int b = id; - int w_offset = -pad; - int h_offset = -pad; + int w_offset = -pad/2; + int h_offset = -pad/2; float d = 0; int l, m; @@ -84,7 +84,7 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ prev_delta[index] += d; } -extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state) +extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network net) { int h = layer.out_h; int w = layer.out_w; @@ -92,15 +92,15 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta size_t n = h*w*c*layer.batch; - forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); + forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, net.input_gpu, layer.output_gpu, layer.indexes_gpu); check_error(cudaPeekAtLastError()); } -extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state state) +extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network net) { size_t n = layer.h*layer.w*layer.c*layer.batch; - backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); + backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, net.delta_gpu, layer.indexes_gpu); check_error(cudaPeekAtLastError()); } diff --git a/image.darknet/src/network.c b/image.darknet/src/network.c index 0914e37..aaab799 100644 --- a/image.darknet/src/network.c +++ b/image.darknet/src/network.c @@ -17,6 +17,7 @@ #include "activation_layer.h" #include "detection_layer.h" #include "region_layer.h" +#include "yolo_layer.h" #include "normalization_layer.h" #include "batchnorm_layer.h" #include "maxpool_layer.h" @@ -26,55 +27,95 @@ #include "softmax_layer.h" #include "dropout_layer.h" #include "route_layer.h" +#include "upsample_layer.h" #include "shortcut_layer.h" +#include "parser.h" +#include "data.h" + +load_args get_base_args(network *net) +{ + load_args args = {0}; + args.w = net->w; + args.h = net->h; + args.size = net->w; + + args.min = net->min_crop; + args.max = net->max_crop; + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.center = net->center; + args.saturation = net->saturation; + args.hue = net->hue; + return args; +} + +network *load_network(char *cfg, char *weights, int clear) +{ + network *net = parse_network_cfg(cfg); + if(weights && weights[0] != 0){ + load_weights(net, weights); + } + if(clear) (*net->seen) = 0; + return net; +} -int get_current_batch(network net) +size_t get_current_batch(network *net) { - int batch_num = (*net.seen)/(net.batch*net.subdivisions); + size_t batch_num = (*net->seen)/(net->batch*net->subdivisions); return batch_num; } -void reset_momentum(network net) +void reset_network_state(network *net, int b) { - if (net.momentum == 0) return; - net.learning_rate = 0; - net.momentum = 0; - net.decay = 0; - #ifdef GPU - //if(net.gpu_index >= 0) update_network_gpu(net); - #endif + int i; + for (i = 0; i < net->n; ++i) { + #ifdef GPU + layer l = net->layers[i]; + if(l.state_gpu){ + fill_gpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1); + } + if(l.h_gpu){ + fill_gpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1); + } + #endif + } } -float get_current_rate(network net) +void reset_rnn(network *net) { - int batch_num = get_current_batch(net); + reset_network_state(net, 0); +} + +float get_current_rate(network *net) +{ + size_t batch_num = get_current_batch(net); int i; float rate; - switch (net.policy) { + if (batch_num < net->burn_in) return net->learning_rate * pow((float)batch_num / net->burn_in, net->power); + switch (net->policy) { case CONSTANT: - return net.learning_rate; + return net->learning_rate; case STEP: - return net.learning_rate * pow(net.scale, batch_num/net.step); + return net->learning_rate * pow(net->scale, batch_num/net->step); case STEPS: - rate = net.learning_rate; - for(i = 0; i < net.num_steps; ++i){ - if(net.steps[i] > batch_num) return rate; - rate *= net.scales[i]; - //if(net.steps[i] > batch_num - 1 && net.scales[i] > 1) reset_momentum(net); + rate = net->learning_rate; + for(i = 0; i < net->num_steps; ++i){ + if(net->steps[i] > batch_num) return rate; + rate *= net->scales[i]; } return rate; case EXP: - return net.learning_rate * pow(net.gamma, batch_num); + return net->learning_rate * pow(net->gamma, batch_num); case POLY: - if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power); - return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power); + return net->learning_rate * pow(1 - (float)batch_num / net->max_batches, net->power); case RANDOM: - return net.learning_rate * pow(rand_uniform(0,1), net.power); + return net->learning_rate * pow(rand_uniform(0,1), net->power); case SIG: - return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step)))); + return net->learning_rate * (1./(1.+exp(net->gamma*(batch_num - net->step)))); default: fprintf(stderr, "Policy is weird!\n"); - return net.learning_rate; + return net->learning_rate; } } @@ -95,6 +136,8 @@ char *get_layer_string(LAYER_TYPE a) return "rnn"; case GRU: return "gru"; + case LSTM: + return "lstm"; case CRNN: return "crnn"; case MAXPOOL: @@ -109,6 +152,8 @@ char *get_layer_string(LAYER_TYPE a) return "detection"; case REGION: return "region"; + case YOLO: + return "yolo"; case DROPOUT: return "dropout"; case CROP: @@ -129,59 +174,75 @@ char *get_layer_string(LAYER_TYPE a) return "none"; } -network make_network(int n) +network *make_network(int n) { - network net = {0}; - net.n = n; - net.layers = calloc(net.n, sizeof(layer)); - net.seen = calloc(1, sizeof(int)); - #ifdef GPU - net.input_gpu = calloc(1, sizeof(float *)); - net.truth_gpu = calloc(1, sizeof(float *)); - #endif + network *net = calloc(1, sizeof(network)); + net->n = n; + net->layers = calloc(net->n, sizeof(layer)); + net->seen = calloc(1, sizeof(size_t)); + net->t = calloc(1, sizeof(int)); + net->cost = calloc(1, sizeof(float)); return net; } -void forward_network(network net, network_state state) +void forward_network(network *netp) { - state.workspace = net.workspace; +#ifdef GPU + if(netp->gpu_index >= 0){ + forward_network_gpu(netp); + return; + } +#endif + network net = *netp; int i; for(i = 0; i < net.n; ++i){ - state.index = i; + net.index = i; layer l = net.layers[i]; if(l.delta){ - scal_cpu(l.outputs * l.batch, 0, l.delta, 1); + fill_cpu(l.outputs * l.batch, 0, l.delta, 1); + } + l.forward(l, net); + net.input = l.output; + if(l.truth) { + net.truth = l.output; } - l.forward(l, state); - state.input = l.output; } + calc_network_cost(netp); } -void update_network(network net) +void update_network(network *netp) { +#ifdef GPU + if(netp->gpu_index >= 0){ + update_network_gpu(netp); + return; + } +#endif + network net = *netp; int i; - int update_batch = net.batch*net.subdivisions; - float rate = get_current_rate(net); + update_args a = {0}; + a.batch = net.batch*net.subdivisions; + a.learning_rate = get_current_rate(netp); + a.momentum = net.momentum; + a.decay = net.decay; + a.adam = net.adam; + a.B1 = net.B1; + a.B2 = net.B2; + a.eps = net.eps; + ++*net.t; + a.t = *net.t; + for(i = 0; i < net.n; ++i){ layer l = net.layers[i]; if(l.update){ - l.update(l, update_batch, rate, net.momentum, net.decay); + l.update(l, a); } } } -float *get_network_output(network net) -{ -#ifdef GPU - if (gpu_index >= 0) return get_network_output_gpu(net); -#endif - int i; - for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break; - return net.layers[i].output; -} - -float get_network_cost(network net) +void calc_network_cost(network *netp) { + network net = *netp; int i; float sum = 0; int count = 0; @@ -191,120 +252,90 @@ float get_network_cost(network net) ++count; } } - return sum/count; + *net.cost = sum/count; } -int get_predicted_class_network(network net) +int get_predicted_class_network(network *net) { - float *out = get_network_output(net); - int k = get_network_output_size(net); - return max_index(out, k); + return max_index(net->output, net->outputs); } -void backward_network(network net, network_state state) +void backward_network(network *netp) { +#ifdef GPU + if(netp->gpu_index >= 0){ + backward_network_gpu(netp); + return; + } +#endif + network net = *netp; int i; - float *original_input = state.input; - float *original_delta = state.delta; - state.workspace = net.workspace; + network orig = net; for(i = net.n-1; i >= 0; --i){ - state.index = i; + layer l = net.layers[i]; + if(l.stopbackward) break; if(i == 0){ - state.input = original_input; - state.delta = original_delta; + net = orig; }else{ layer prev = net.layers[i-1]; - state.input = prev.output; - state.delta = prev.delta; + net.input = prev.output; + net.delta = prev.delta; } - layer l = net.layers[i]; - l.backward(l, state); + net.index = i; + l.backward(l, net); } } -float train_network_datum(network net, float *x, float *y) +float train_network_datum(network *net) { -#ifdef GPU - if(gpu_index >= 0) return train_network_datum_gpu(net, x, y); -#endif - network_state state; - *net.seen += net.batch; - state.index = 0; - state.net = net; - state.input = x; - state.delta = 0; - state.truth = y; - state.train = 1; - forward_network(net, state); - backward_network(net, state); - float error = get_network_cost(net); - if(((*net.seen)/net.batch)%net.subdivisions == 0) update_network(net); + *net->seen += net->batch; + net->train = 1; + forward_network(net); + backward_network(net); + float error = *net->cost; + if(((*net->seen)/net->batch)%net->subdivisions == 0) update_network(net); return error; } -float train_network_sgd(network net, data d, int n) +float train_network_sgd(network *net, data d, int n) { - int batch = net.batch; - float *X = calloc(batch*d.X.cols, sizeof(float)); - float *y = calloc(batch*d.y.cols, sizeof(float)); + int batch = net->batch; int i; float sum = 0; for(i = 0; i < n; ++i){ - get_random_batch(d, batch, X, y); - float err = train_network_datum(net, X, y); + get_random_batch(d, batch, net->input, net->truth); + float err = train_network_datum(net); sum += err; } - free(X); - free(y); return (float)sum/(n*batch); } -float train_network(network net, data d) +float train_network(network *net, data d) { - assert(d.X.rows % net.batch == 0); - int batch = net.batch; + assert(d.X.rows % net->batch == 0); + int batch = net->batch; int n = d.X.rows / batch; - float *X = calloc(batch*d.X.cols, sizeof(float)); - float *y = calloc(batch*d.y.cols, sizeof(float)); int i; float sum = 0; for(i = 0; i < n; ++i){ - get_next_batch(d, batch, i*batch, X, y); - float err = train_network_datum(net, X, y); + get_next_batch(d, batch, i*batch, net->input, net->truth); + float err = train_network_datum(net); sum += err; } - free(X); - free(y); return (float)sum/(n*batch); } - -float train_network_batch(network net, data d, int n) +void set_temp_network(network *net, float t) { - int i,j; - network_state state; - state.index = 0; - state.net = net; - state.train = 1; - state.delta = 0; - float sum = 0; - int batch = 2; - for(i = 0; i < n; ++i){ - for(j = 0; j < batch; ++j){ - int index = rand()%d.X.rows; - state.input = d.X.vals[index]; - state.truth = d.y.vals[index]; - forward_network(net, state); - backward_network(net, state); - sum += get_network_cost(net); - } - update_network(net); + int i; + for(i = 0; i < net->n; ++i){ + net->layers[i].temperature = t; } - return (float)sum/(n*batch); } + void set_batch_network(network *net, int b) { net->batch = b; @@ -315,6 +346,11 @@ void set_batch_network(network *net, int b) if(net->layers[i].type == CONVOLUTIONAL){ cudnn_convolutional_setup(net->layers + i); } + if(net->layers[i].type == DECONVOLUTIONAL){ + layer *l = net->layers + i; + cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, l->out_h, l->out_w); + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); + } #endif } } @@ -323,9 +359,7 @@ int resize_network(network *net, int w, int h) { #ifdef GPU cuda_set_device(net->gpu_index); - if(gpu_index >= 0){ - cuda_free(net->workspace); - } + cuda_free(net->workspace); #endif int i; //if(w == net->w && h == net->h) return 0; @@ -345,8 +379,14 @@ int resize_network(network *net, int w, int h) resize_maxpool_layer(&l, w, h); }else if(l.type == REGION){ resize_region_layer(&l, w, h); + }else if(l.type == YOLO){ + resize_yolo_layer(&l, w, h); }else if(l.type == ROUTE){ resize_route_layer(&l, net); + }else if(l.type == SHORTCUT){ + resize_shortcut_layer(&l, w, h); + }else if(l.type == UPSAMPLE){ + resize_upsample_layer(&l, w, h); }else if(l.type == REORG){ resize_reorg_layer(&l, w, h); }else if(l.type == AVGPOOL){ @@ -359,21 +399,32 @@ int resize_network(network *net, int w, int h) error("Cannot resize this type of layer"); } if(l.workspace_size > workspace_size) workspace_size = l.workspace_size; + if(l.workspace_size > 2000000000) assert(0); inputs = l.outputs; net->layers[i] = l; w = l.out_w; h = l.out_h; if(l.type == AVGPOOL) break; } + layer out = get_network_output_layer(net); + net->inputs = net->layers[0].inputs; + net->outputs = out.outputs; + net->truths = out.outputs; + if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths; + net->output = out.output; + free(net->input); + free(net->truth); + net->input = calloc(net->inputs*net->batch, sizeof(float)); + net->truth = calloc(net->truths*net->batch, sizeof(float)); #ifdef GPU if(gpu_index >= 0){ - if(net->input_gpu) { - cuda_free(*net->input_gpu); - *net->input_gpu = 0; - cuda_free(*net->truth_gpu); - *net->truth_gpu = 0; + cuda_free(net->input_gpu); + cuda_free(net->truth_gpu); + net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch); + net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch); + if(workspace_size){ + net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); } - net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); }else { free(net->workspace); net->workspace = calloc(1, workspace_size); @@ -386,34 +437,25 @@ int resize_network(network *net, int w, int h) return 0; } -int get_network_output_size(network net) +layer get_network_detection_layer(network *net) { int i; - for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break; - return net.layers[i].outputs; -} - -int get_network_input_size(network net) -{ - return net.layers[0].inputs; -} - -detection_layer get_network_detection_layer(network net) -{ - int i; - for(i = 0; i < net.n; ++i){ - if(net.layers[i].type == DETECTION){ - return net.layers[i]; + for(i = 0; i < net->n; ++i){ + if(net->layers[i].type == DETECTION){ + return net->layers[i]; } } fprintf(stderr, "Detection layer not found!!\n"); - detection_layer l = {0}; + layer l = {0}; return l; } -image get_network_image_layer(network net, int i) +image get_network_image_layer(network *net, int i) { - layer l = net.layers[i]; + layer l = net->layers[i]; +#ifdef GPU + //cuda_pull_array(l.output_gpu, l.output, l.outputs); +#endif if (l.out_w && l.out_h && l.out_c){ return float_to_image(l.out_w, l.out_h, l.out_c, l.output); } @@ -421,10 +463,10 @@ image get_network_image_layer(network net, int i) return def; } -image get_network_image(network net) +image get_network_image(network *net) { int i; - for(i = net.n-1; i >= 0; --i){ + for(i = net->n-1; i >= 0; --i){ image m = get_network_image_layer(net, i); if(m.h != 0) return m; } @@ -432,60 +474,134 @@ image get_network_image(network net) return def; } -void visualize_network(network net) +void visualize_network(network *net) { image *prev = 0; int i; char buff[256]; - for(i = 0; i < net.n; ++i){ + for(i = 0; i < net->n; ++i){ sprintf(buff, "Layer %d", i); - layer l = net.layers[i]; + layer l = net->layers[i]; if(l.type == CONVOLUTIONAL){ prev = visualize_convolutional_layer(l, buff, prev); } } } -void top_predictions(network net, int k, int *index) +void top_predictions(network *net, int k, int *index) { - int size = get_network_output_size(net); - float *out = get_network_output(net); - top_k(out, size, k, index); + top_k(net->output, net->outputs, k, index); } -float *network_predict(network net, float *input) +float *network_predict(network *net, float *input) { -#ifdef GPU - if(gpu_index >= 0) return network_predict_gpu(net, input); -#endif - - network_state state; - state.net = net; - state.index = 0; - state.input = input; - state.truth = 0; - state.train = 0; - state.delta = 0; - forward_network(net, state); - float *out = get_network_output(net); + network orig = *net; + net->input = input; + net->truth = 0; + net->train = 0; + net->delta = 0; + forward_network(net); + float *out = net->output; + *net = orig; return out; } -matrix network_predict_data_multi(network net, data test, int n) +int num_detections(network *net, float thresh) +{ + int i; + int s = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO){ + s += yolo_num_detections(l, thresh); + } + if(l.type == DETECTION || l.type == REGION){ + s += l.w*l.h*l.n; + } + } + return s; +} + +detection *make_network_boxes(network *net, float thresh, int *num) +{ + layer l = net->layers[net->n - 1]; + int i; + int nboxes = num_detections(net, thresh); + if(num) *num = nboxes; + detection *dets = calloc(nboxes, sizeof(detection)); + for(i = 0; i < nboxes; ++i){ + dets[i].prob = calloc(l.classes, sizeof(float)); + if(l.coords > 4){ + dets[i].mask = calloc(l.coords-4, sizeof(float)); + } + } + return dets; +} + +void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets) +{ + int j; + for(j = 0; j < net->n; ++j){ + layer l = net->layers[j]; + if(l.type == YOLO){ + int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets); + dets += count; + } + if(l.type == REGION){ + get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets); + dets += l.w*l.h*l.n; + } + if(l.type == DETECTION){ + get_detection_detections(l, w, h, thresh, dets); + dets += l.w*l.h*l.n; + } + } +} + +detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num) +{ + detection *dets = make_network_boxes(net, thresh, num); + fill_network_boxes(net, w, h, thresh, hier, map, relative, dets); + return dets; +} + +void free_detections(detection *dets, int n) +{ + int i; + for(i = 0; i < n; ++i){ + free(dets[i].prob); + if(dets[i].mask) free(dets[i].mask); + } + free(dets); +} + +float *network_predict_image(network *net, image im) +{ + image imr = letterbox_image(im, net->w, net->h); + set_batch_network(net, 1); + float *p = network_predict(net, imr.data); + free_image(imr); + return p; +} + +int network_width(network *net){return net->w;} +int network_height(network *net){return net->h;} + +matrix network_predict_data_multi(network *net, data test, int n) { int i,j,b,m; - int k = get_network_output_size(net); + int k = net->outputs; matrix pred = make_matrix(test.X.rows, k); - float *X = calloc(net.batch*test.X.rows, sizeof(float)); - for(i = 0; i < test.X.rows; i += net.batch){ - for(b = 0; b < net.batch; ++b){ + float *X = calloc(net->batch*test.X.rows, sizeof(float)); + for(i = 0; i < test.X.rows; i += net->batch){ + for(b = 0; b < net->batch; ++b){ if(i+b == test.X.rows) break; memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float)); } for(m = 0; m < n; ++m){ float *out = network_predict(net, X); - for(b = 0; b < net.batch; ++b){ + for(b = 0; b < net->batch; ++b){ if(i+b == test.X.rows) break; for(j = 0; j < k; ++j){ pred.vals[i+b][j] += out[j+b*k]/n; @@ -497,19 +613,19 @@ matrix network_predict_data_multi(network net, data test, int n) return pred; } -matrix network_predict_data(network net, data test) +matrix network_predict_data(network *net, data test) { int i,j,b; - int k = get_network_output_size(net); + int k = net->outputs; matrix pred = make_matrix(test.X.rows, k); - float *X = calloc(net.batch*test.X.cols, sizeof(float)); - for(i = 0; i < test.X.rows; i += net.batch){ - for(b = 0; b < net.batch; ++b){ + float *X = calloc(net->batch*test.X.cols, sizeof(float)); + for(i = 0; i < test.X.rows; i += net->batch){ + for(b = 0; b < net->batch; ++b){ if(i+b == test.X.rows) break; memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float)); } float *out = network_predict(net, X); - for(b = 0; b < net.batch; ++b){ + for(b = 0; b < net->batch; ++b){ if(i+b == test.X.rows) break; for(j = 0; j < k; ++j){ pred.vals[i+b][j] = out[j+b*k]; @@ -520,11 +636,11 @@ matrix network_predict_data(network net, data test) return pred; } -void print_network(network net) +void print_network(network *net) { int i,j; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; float *output = l.output; int n = l.outputs; float mean = mean_array(output, n); @@ -537,7 +653,7 @@ void print_network(network net) } } -void compare_networks(network n1, network n2, data test) +void compare_networks(network *n1, network *n2, data test) { matrix g1 = network_predict_data(n1, test); matrix g2 = network_predict_data(n2, test); @@ -562,7 +678,7 @@ void compare_networks(network n1, network n2, data test) printf("%f\n", num/den); } -float network_accuracy(network net, data d) +float network_accuracy(network *net, data d) { matrix guess = network_predict_data(net, d); float acc = matrix_topk_accuracy(d.y, guess,1); @@ -570,7 +686,7 @@ float network_accuracy(network net, data d) return acc; } -float *network_accuracies(network net, data d, int n) +float *network_accuracies(network *net, data d, int n) { static float acc[2]; matrix guess = network_predict_data(net, d); @@ -580,7 +696,16 @@ float *network_accuracies(network net, data d, int n) return acc; } -float network_accuracy_multi(network net, data d, int n) +layer get_network_output_layer(network *net) +{ + int i; + for(i = net->n - 1; i >= 0; --i){ + if(net->layers[i].type != COST) break; + } + return net->layers[i]; +} + +float network_accuracy_multi(network *net, data d, int n) { matrix guess = network_predict_data_multi(net, d, n); float acc = matrix_topk_accuracy(d.y, guess,1); @@ -588,17 +713,417 @@ float network_accuracy_multi(network net, data d, int n) return acc; } -void free_network(network net) +void free_network(network *net) { int i; - for(i = 0; i < net.n; ++i){ - free_layer(net.layers[i]); + for(i = 0; i < net->n; ++i){ + free_layer(net->layers[i]); } - free(net.layers); + free(net->layers); + if(net->input) free(net->input); + if(net->truth) free(net->truth); #ifdef GPU - if(*net.input_gpu) cuda_free(*net.input_gpu); - if(*net.truth_gpu) cuda_free(*net.truth_gpu); - if(net.input_gpu) free(net.input_gpu); - if(net.truth_gpu) free(net.truth_gpu); + if(net->input_gpu) cuda_free(net->input_gpu); + if(net->truth_gpu) cuda_free(net->truth_gpu); #endif + free(net); +} + +// Some day... +// ^ What the hell is this comment for? + + +layer network_output_layer(network *net) +{ + int i; + for(i = net->n - 1; i >= 0; --i){ + if(net->layers[i].type != COST) break; + } + return net->layers[i]; } + +int network_inputs(network *net) +{ + return net->layers[0].inputs; +} + +int network_outputs(network *net) +{ + return network_output_layer(net).outputs; +} + +float *network_output(network *net) +{ + return network_output_layer(net).output; +} + +#ifdef GPU + +void forward_network_gpu(network *netp) +{ + network net = *netp; + cuda_set_device(net.gpu_index); + cuda_push_array(net.input_gpu, net.input, net.inputs*net.batch); + if(net.truth){ + cuda_push_array(net.truth_gpu, net.truth, net.truths*net.batch); + } + + int i; + for(i = 0; i < net.n; ++i){ + net.index = i; + layer l = net.layers[i]; + if(l.delta_gpu){ + fill_gpu(l.outputs * l.batch, 0, l.delta_gpu, 1); + } + l.forward_gpu(l, net); + net.input_gpu = l.output_gpu; + net.input = l.output; + if(l.truth) { + net.truth_gpu = l.output_gpu; + net.truth = l.output; + } + } + pull_network_output(netp); + calc_network_cost(netp); +} + +void backward_network_gpu(network *netp) +{ + int i; + network net = *netp; + network orig = net; + cuda_set_device(net.gpu_index); + for(i = net.n-1; i >= 0; --i){ + layer l = net.layers[i]; + if(l.stopbackward) break; + if(i == 0){ + net = orig; + }else{ + layer prev = net.layers[i-1]; + net.input = prev.output; + net.delta = prev.delta; + net.input_gpu = prev.output_gpu; + net.delta_gpu = prev.delta_gpu; + } + net.index = i; + l.backward_gpu(l, net); + } +} + +void update_network_gpu(network *netp) +{ + network net = *netp; + cuda_set_device(net.gpu_index); + int i; + update_args a = {0}; + a.batch = net.batch*net.subdivisions; + a.learning_rate = get_current_rate(netp); + a.momentum = net.momentum; + a.decay = net.decay; + a.adam = net.adam; + a.B1 = net.B1; + a.B2 = net.B2; + a.eps = net.eps; + ++*net.t; + a.t = (*net.t); + + for(i = 0; i < net.n; ++i){ + layer l = net.layers[i]; + if(l.update_gpu){ + l.update_gpu(l, a); + } + } +} + +void harmless_update_network_gpu(network *netp) +{ + network net = *netp; + cuda_set_device(net.gpu_index); + int i; + for(i = 0; i < net.n; ++i){ + layer l = net.layers[i]; + if(l.weight_updates_gpu) fill_gpu(l.nweights, 0, l.weight_updates_gpu, 1); + if(l.bias_updates_gpu) fill_gpu(l.nbiases, 0, l.bias_updates_gpu, 1); + if(l.scale_updates_gpu) fill_gpu(l.nbiases, 0, l.scale_updates_gpu, 1); + } +} + +typedef struct { + network *net; + data d; + float *err; +} train_args; + +void *train_thread(void *ptr) +{ + train_args args = *(train_args*)ptr; + free(ptr); + cuda_set_device(args.net->gpu_index); + *args.err = train_network(args.net, args.d); + return 0; +} + +pthread_t train_network_in_thread(network *net, data d, float *err) +{ + pthread_t thread; + train_args *ptr = (train_args *)calloc(1, sizeof(train_args)); + ptr->net = net; + ptr->d = d; + ptr->err = err; + if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed"); + return thread; +} + +void merge_weights(layer l, layer base) +{ + if (l.type == CONVOLUTIONAL) { + axpy_cpu(l.n, 1, l.bias_updates, 1, base.biases, 1); + axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weights, 1); + if (l.scales) { + axpy_cpu(l.n, 1, l.scale_updates, 1, base.scales, 1); + } + } else if(l.type == CONNECTED) { + axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.biases, 1); + axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weights, 1); + } +} + +void scale_weights(layer l, float s) +{ + if (l.type == CONVOLUTIONAL) { + scal_cpu(l.n, s, l.biases, 1); + scal_cpu(l.nweights, s, l.weights, 1); + if (l.scales) { + scal_cpu(l.n, s, l.scales, 1); + } + } else if(l.type == CONNECTED) { + scal_cpu(l.outputs, s, l.biases, 1); + scal_cpu(l.outputs*l.inputs, s, l.weights, 1); + } +} + + +void pull_weights(layer l) +{ + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + cuda_pull_array(l.biases_gpu, l.bias_updates, l.n); + cuda_pull_array(l.weights_gpu, l.weight_updates, l.nweights); + if(l.scales) cuda_pull_array(l.scales_gpu, l.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_pull_array(l.biases_gpu, l.bias_updates, l.outputs); + cuda_pull_array(l.weights_gpu, l.weight_updates, l.outputs*l.inputs); + } +} + +void push_weights(layer l) +{ + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weights_gpu, l.weights, l.nweights); + if(l.scales) cuda_push_array(l.scales_gpu, l.scales, l.n); + } else if(l.type == CONNECTED){ + cuda_push_array(l.biases_gpu, l.biases, l.outputs); + cuda_push_array(l.weights_gpu, l.weights, l.outputs*l.inputs); + } +} + +void distribute_weights(layer l, layer base) +{ + if (l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL) { + cuda_push_array(l.biases_gpu, base.biases, l.n); + cuda_push_array(l.weights_gpu, base.weights, l.nweights); + if (base.scales) cuda_push_array(l.scales_gpu, base.scales, l.n); + } else if (l.type == CONNECTED) { + cuda_push_array(l.biases_gpu, base.biases, l.outputs); + cuda_push_array(l.weights_gpu, base.weights, l.outputs*l.inputs); + } +} + + +/* + + void pull_updates(layer l) + { + if(l.type == CONVOLUTIONAL){ + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); + } + } + + void push_updates(layer l) + { + if(l.type == CONVOLUTIONAL){ + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); + } + } + + void update_layer(layer l, network net) + { + int update_batch = net.batch*net.subdivisions; + float rate = get_current_rate(net); + l.t = get_current_batch(net); + if(l.update_gpu){ + l.update_gpu(l, update_batch, rate*l.learning_rate_scale, net.momentum, net.decay); + } + } + void merge_updates(layer l, layer base) + { + if (l.type == CONVOLUTIONAL) { + axpy_cpu(l.n, 1, l.bias_updates, 1, base.bias_updates, 1); + axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weight_updates, 1); + if (l.scale_updates) { + axpy_cpu(l.n, 1, l.scale_updates, 1, base.scale_updates, 1); + } + } else if(l.type == CONNECTED) { + axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.bias_updates, 1); + axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weight_updates, 1); + } + } + + void distribute_updates(layer l, layer base) + { + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.n); + cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.nweights); + if(base.scale_updates) cuda_push_array(l.scale_updates_gpu, base.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.outputs); + cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.outputs*l.inputs); + } + } + */ + +/* + void sync_layer(network *nets, int n, int j) + { + int i; + network net = nets[0]; + layer base = net.layers[j]; + scale_weights(base, 0); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i].gpu_index); + layer l = nets[i].layers[j]; + pull_weights(l); + merge_weights(l, base); + } + scale_weights(base, 1./n); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i].gpu_index); + layer l = nets[i].layers[j]; + distribute_weights(l, base); + } + } + */ + +void sync_layer(network **nets, int n, int j) +{ + int i; + network *net = nets[0]; + layer base = net->layers[j]; + scale_weights(base, 0); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i]->gpu_index); + layer l = nets[i]->layers[j]; + pull_weights(l); + merge_weights(l, base); + } + scale_weights(base, 1./n); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i]->gpu_index); + layer l = nets[i]->layers[j]; + distribute_weights(l, base); + } +} + +typedef struct{ + network **nets; + int n; + int j; +} sync_args; + +void *sync_layer_thread(void *ptr) +{ + sync_args args = *(sync_args*)ptr; + sync_layer(args.nets, args.n, args.j); + free(ptr); + return 0; +} + +pthread_t sync_layer_in_thread(network **nets, int n, int j) +{ + pthread_t thread; + sync_args *ptr = (sync_args *)calloc(1, sizeof(sync_args)); + ptr->nets = nets; + ptr->n = n; + ptr->j = j; + if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed"); + return thread; +} + +void sync_nets(network **nets, int n, int interval) +{ + int j; + int layers = nets[0]->n; + pthread_t *threads = (pthread_t *) calloc(layers, sizeof(pthread_t)); + + *(nets[0]->seen) += interval * (n-1) * nets[0]->batch * nets[0]->subdivisions; + for (j = 0; j < n; ++j){ + *(nets[j]->seen) = *(nets[0]->seen); + } + for (j = 0; j < layers; ++j) { + threads[j] = sync_layer_in_thread(nets, n, j); + } + for (j = 0; j < layers; ++j) { + pthread_join(threads[j], 0); + } + free(threads); +} + +float train_networks(network **nets, int n, data d, int interval) +{ + int i; + int batch = nets[0]->batch; + int subdivisions = nets[0]->subdivisions; + assert(batch * subdivisions * n == d.X.rows); + pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t)); + float *errors = (float *) calloc(n, sizeof(float)); + + float sum = 0; + for(i = 0; i < n; ++i){ + data p = get_data_part(d, i, n); + threads[i] = train_network_in_thread(nets[i], p, errors + i); + } + for(i = 0; i < n; ++i){ + pthread_join(threads[i], 0); + //printf("%f\n", errors[i]); + sum += errors[i]; + } + //cudaDeviceSynchronize(); + if (get_current_batch(nets[0]) % interval == 0) { + printf("Syncing... "); + fflush(stdout); + sync_nets(nets, n, interval); + printf("Done!\n"); + } + //cudaDeviceSynchronize(); + free(threads); + free(errors); + return (float)sum/(n); +} + +void pull_network_output(network *net) +{ + layer l = get_network_output_layer(net); + cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch); +} + +#endif diff --git a/image.darknet/src/network.h b/image.darknet/src/network.h index e48cbc2..1b0dfd1 100644 --- a/image.darknet/src/network.h +++ b/image.darknet/src/network.h @@ -1,129 +1,29 @@ // Oh boy, why am I about to do this.... #ifndef NETWORK_H #define NETWORK_H +#include "darknet.h" #include "image.h" #include "layer.h" #include "data.h" #include "tree.h" -typedef enum { - CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM -} learning_rate_policy; - -typedef struct network{ - float *workspace; - int n; - int batch; - int *seen; - float epoch; - int subdivisions; - float momentum; - float decay; - layer *layers; - int outputs; - float *output; - learning_rate_policy policy; - - float learning_rate; - float gamma; - float scale; - float power; - int time_steps; - int step; - int max_batches; - float *scales; - int *steps; - int num_steps; - int burn_in; - - int adam; - float B1; - float B2; - float eps; - - int inputs; - int h, w, c; - int max_crop; - int min_crop; - float angle; - float aspect; - float exposure; - float saturation; - float hue; - - int gpu_index; - tree *hierarchy; - - #ifdef GPU - float **input_gpu; - float **truth_gpu; - #endif -} network; - -typedef struct network_state { - float *truth; - float *input; - float *delta; - float *workspace; - int train; - int index; - network net; -} network_state; #ifdef GPU -float train_networks(network *nets, int n, data d, int interval); -void sync_nets(network *nets, int n, int interval); -float train_network_datum_gpu(network net, float *x, float *y); -float *network_predict_gpu(network net, float *input); -float * get_network_output_gpu_layer(network net, int i); -float * get_network_delta_gpu_layer(network net, int i); -float *get_network_output_gpu(network net); -void forward_network_gpu(network net, network_state state); -void backward_network_gpu(network net, network_state state); -void update_network_gpu(network net); +void pull_network_output(network *net); #endif -float get_current_rate(network net); -int get_current_batch(network net); -void free_network(network net); -void compare_networks(network n1, network n2, data d); +void compare_networks(network *n1, network *n2, data d); char *get_layer_string(LAYER_TYPE a); -network make_network(int n); -void forward_network(network net, network_state state); -void backward_network(network net, network_state state); -void update_network(network net); +network *make_network(int n); -float train_network(network net, data d); -float train_network_batch(network net, data d, int n); -float train_network_sgd(network net, data d, int n); -float train_network_datum(network net, float *x, float *y); -matrix network_predict_data(network net, data test); -float *network_predict(network net, float *input); -float network_accuracy(network net, data d); -float *network_accuracies(network net, data d, int n); -float network_accuracy_multi(network net, data d, int n); -void top_predictions(network net, int n, int *index); -float *get_network_output(network net); -float *get_network_output_layer(network net, int i); -float *get_network_delta_layer(network net, int i); -float *get_network_delta(network net); -int get_network_output_size_layer(network net, int i); -int get_network_output_size(network net); -image get_network_image(network net); -image get_network_image_layer(network net, int i); -int get_predicted_class_network(network net); -void print_network(network net); -void visualize_network(network net); +float network_accuracy_multi(network *net, data d, int n); +int get_predicted_class_network(network *net); +void print_network(network *net); int resize_network(network *net, int w, int h); -void set_batch_network(network *net, int b); -int get_network_input_size(network net); -float get_network_cost(network net); - -int get_network_nuisance(network net); -int get_network_background(network net); +void calc_network_cost(network *net); #endif diff --git a/image.darknet/src/network_kernels.cu b/image.darknet/src/network_kernels.cu deleted file mode 100644 index 313cd6d..0000000 --- a/image.darknet/src/network_kernels.cu +++ /dev/null @@ -1,408 +0,0 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" - -extern "C" { -#include -#include -#include - -#include "network.h" -#include "image.h" -#include "data.h" -#include "utils.h" -#include "parser.h" - -#include "crop_layer.h" -#include "connected_layer.h" -#include "rnn_layer.h" -#include "gru_layer.h" -#include "crnn_layer.h" -#include "detection_layer.h" -#include "region_layer.h" -#include "convolutional_layer.h" -#include "activation_layer.h" -#include "maxpool_layer.h" -#include "reorg_layer.h" -#include "avgpool_layer.h" -#include "normalization_layer.h" -#include "batchnorm_layer.h" -#include "cost_layer.h" -#include "local_layer.h" -#include "softmax_layer.h" -#include "dropout_layer.h" -#include "route_layer.h" -#include "shortcut_layer.h" -#include "blas.h" -} - -float * get_network_output_gpu_layer(network net, int i); -float * get_network_delta_gpu_layer(network net, int i); -float * get_network_output_gpu(network net); - -void forward_network_gpu(network net, network_state state) -{ - state.workspace = net.workspace; - int i; - for(i = 0; i < net.n; ++i){ - state.index = i; - layer l = net.layers[i]; - if(l.delta_gpu){ - fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); - } - l.forward_gpu(l, state); - state.input = l.output_gpu; - } -} - -void backward_network_gpu(network net, network_state state) -{ - state.workspace = net.workspace; - int i; - float * original_input = state.input; - float * original_delta = state.delta; - for(i = net.n-1; i >= 0; --i){ - state.index = i; - layer l = net.layers[i]; - if(i == 0){ - state.input = original_input; - state.delta = original_delta; - }else{ - layer prev = net.layers[i-1]; - state.input = prev.output_gpu; - state.delta = prev.delta_gpu; - } - l.backward_gpu(l, state); - } -} - -void update_network_gpu(network net) -{ - cuda_set_device(net.gpu_index); - int i; - int update_batch = net.batch*net.subdivisions; - float rate = get_current_rate(net); - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; - l.t = get_current_batch(net); - if(l.update_gpu){ - l.update_gpu(l, update_batch, rate, net.momentum, net.decay); - } - } -} - -void forward_backward_network_gpu(network net, float *x, float *y) -{ - network_state state; - state.index = 0; - state.net = net; - int x_size = get_network_input_size(net)*net.batch; - int y_size = get_network_output_size(net)*net.batch; - if(net.layers[net.n-1].truths) y_size = net.layers[net.n-1].truths*net.batch; - if(!*net.input_gpu){ - *net.input_gpu = cuda_make_array(x, x_size); - *net.truth_gpu = cuda_make_array(y, y_size); - }else{ - cuda_push_array(*net.input_gpu, x, x_size); - cuda_push_array(*net.truth_gpu, y, y_size); - } - state.input = *net.input_gpu; - state.delta = 0; - state.truth = *net.truth_gpu; - state.train = 1; - forward_network_gpu(net, state); - backward_network_gpu(net, state); -} - -float train_network_datum_gpu(network net, float *x, float *y) -{ - *net.seen += net.batch; - forward_backward_network_gpu(net, x, y); - float error = get_network_cost(net); - if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net); - - return error; -} - -typedef struct { - network net; - data d; - float *err; -} train_args; - -void *train_thread(void *ptr) -{ - train_args args = *(train_args*)ptr; - free(ptr); - cuda_set_device(args.net.gpu_index); - *args.err = train_network(args.net, args.d); - return 0; -} - -pthread_t train_network_in_thread(network net, data d, float *err) -{ - pthread_t thread; - train_args *ptr = (train_args *)calloc(1, sizeof(train_args)); - ptr->net = net; - ptr->d = d; - ptr->err = err; - if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed"); - return thread; -} - -void pull_updates(layer l) -{ - if(l.type == CONVOLUTIONAL){ - cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); - cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.n*l.size*l.size*l.c); - if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n); - } else if(l.type == CONNECTED){ - cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs); - cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); - } -} - -void push_updates(layer l) -{ - if(l.type == CONVOLUTIONAL){ - cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); - cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.n*l.size*l.size*l.c); - if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n); - } else if(l.type == CONNECTED){ - cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs); - cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); - } -} - -void update_layer(layer l, network net) -{ - int update_batch = net.batch*net.subdivisions; - float rate = get_current_rate(net); - l.t = get_current_batch(net); - if(l.update_gpu){ - l.update_gpu(l, update_batch, rate, net.momentum, net.decay); - } -} - -void merge_weights(layer l, layer base) -{ - if (l.type == CONVOLUTIONAL) { - axpy_cpu(l.n, 1, l.biases, 1, base.biases, 1); - axpy_cpu(l.n*l.size*l.size*l.c, 1, l.weights, 1, base.weights, 1); - if (l.scales) { - axpy_cpu(l.n, 1, l.scales, 1, base.scales, 1); - } - } else if(l.type == CONNECTED) { - axpy_cpu(l.outputs, 1, l.biases, 1, base.biases, 1); - axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, base.weights, 1); - } -} - -void scale_weights(layer l, float s) -{ - if (l.type == CONVOLUTIONAL) { - scal_cpu(l.n, s, l.biases, 1); - scal_cpu(l.n*l.size*l.size*l.c, s, l.weights, 1); - if (l.scales) { - scal_cpu(l.n, s, l.scales, 1); - } - } else if(l.type == CONNECTED) { - scal_cpu(l.outputs, s, l.biases, 1); - scal_cpu(l.outputs*l.inputs, s, l.weights, 1); - } -} - - -void pull_weights(layer l) -{ - if(l.type == CONVOLUTIONAL){ - cuda_pull_array(l.biases_gpu, l.biases, l.n); - cuda_pull_array(l.weights_gpu, l.weights, l.n*l.size*l.size*l.c); - if(l.scales) cuda_pull_array(l.scales_gpu, l.scales, l.n); - } else if(l.type == CONNECTED){ - cuda_pull_array(l.biases_gpu, l.biases, l.outputs); - cuda_pull_array(l.weights_gpu, l.weights, l.outputs*l.inputs); - } -} - -void push_weights(layer l) -{ - if(l.type == CONVOLUTIONAL){ - cuda_push_array(l.biases_gpu, l.biases, l.n); - cuda_push_array(l.weights_gpu, l.weights, l.n*l.size*l.size*l.c); - if(l.scales) cuda_push_array(l.scales_gpu, l.scales, l.n); - } else if(l.type == CONNECTED){ - cuda_push_array(l.biases_gpu, l.biases, l.outputs); - cuda_push_array(l.weights_gpu, l.weights, l.outputs*l.inputs); - } -} - -void distribute_weights(layer l, layer base) -{ - if(l.type == CONVOLUTIONAL){ - cuda_push_array(l.biases_gpu, base.biases, l.n); - cuda_push_array(l.weights_gpu, base.weights, l.n*l.size*l.size*l.c); - if(base.scales) cuda_push_array(l.scales_gpu, base.scales, l.n); - } else if(l.type == CONNECTED){ - cuda_push_array(l.biases_gpu, base.biases, l.outputs); - cuda_push_array(l.weights_gpu, base.weights, l.outputs*l.inputs); - } -} - - -void merge_updates(layer l, layer base) -{ - if (l.type == CONVOLUTIONAL) { - axpy_cpu(l.n, 1, l.bias_updates, 1, base.bias_updates, 1); - axpy_cpu(l.n*l.size*l.size*l.c, 1, l.weight_updates, 1, base.weight_updates, 1); - if (l.scale_updates) { - axpy_cpu(l.n, 1, l.scale_updates, 1, base.scale_updates, 1); - } - } else if(l.type == CONNECTED) { - axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.bias_updates, 1); - axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weight_updates, 1); - } -} - -void distribute_updates(layer l, layer base) -{ - if(l.type == CONVOLUTIONAL){ - cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.n); - cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.n*l.size*l.size*l.c); - if(base.scale_updates) cuda_push_array(l.scale_updates_gpu, base.scale_updates, l.n); - } else if(l.type == CONNECTED){ - cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.outputs); - cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.outputs*l.inputs); - } -} - -void sync_layer(network *nets, int n, int j) -{ - //printf("Syncing layer %d\n", j); - int i; - network net = nets[0]; - layer base = net.layers[j]; - cuda_set_device(net.gpu_index); - pull_weights(base); - for (i = 1; i < n; ++i) { - cuda_set_device(nets[i].gpu_index); - layer l = nets[i].layers[j]; - pull_weights(l); - merge_weights(l, base); - } - scale_weights(base, 1./n); - for (i = 0; i < n; ++i) { - cuda_set_device(nets[i].gpu_index); - layer l = nets[i].layers[j]; - distribute_weights(l, base); - } - //printf("Done syncing layer %d\n", j); -} - -typedef struct{ - network *nets; - int n; - int j; -} sync_args; - -void *sync_layer_thread(void *ptr) -{ - sync_args args = *(sync_args*)ptr; - sync_layer(args.nets, args.n, args.j); - free(ptr); - return 0; -} - -pthread_t sync_layer_in_thread(network *nets, int n, int j) -{ - pthread_t thread; - sync_args *ptr = (sync_args *)calloc(1, sizeof(sync_args)); - ptr->nets = nets; - ptr->n = n; - ptr->j = j; - if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed"); - return thread; -} - -void sync_nets(network *nets, int n, int interval) -{ - int j; - int layers = nets[0].n; - pthread_t *threads = (pthread_t *) calloc(layers, sizeof(pthread_t)); - - *nets[0].seen += interval * (n-1) * nets[0].batch * nets[0].subdivisions; - for (j = 0; j < n; ++j){ - *nets[j].seen = *nets[0].seen; - } - for (j = 0; j < layers; ++j) { - threads[j] = sync_layer_in_thread(nets, n, j); - } - for (j = 0; j < layers; ++j) { - pthread_join(threads[j], 0); - } - free(threads); -} - -float train_networks(network *nets, int n, data d, int interval) -{ - int i; - int batch = nets[0].batch; - int subdivisions = nets[0].subdivisions; - assert(batch * subdivisions * n == d.X.rows); - pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t)); - float *errors = (float *) calloc(n, sizeof(float)); - - float sum = 0; - for(i = 0; i < n; ++i){ - data p = get_data_part(d, i, n); - threads[i] = train_network_in_thread(nets[i], p, errors + i); - } - for(i = 0; i < n; ++i){ - pthread_join(threads[i], 0); - //printf("%f\n", errors[i]); - sum += errors[i]; - } - //cudaDeviceSynchronize(); - if (get_current_batch(nets[0]) % interval == 0) { - printf("Syncing... "); - fflush(stdout); - sync_nets(nets, n, interval); - printf("Done!\n"); - } - //cudaDeviceSynchronize(); - free(threads); - free(errors); - return (float)sum/(n); -} - -float *get_network_output_layer_gpu(network net, int i) -{ - layer l = net.layers[i]; - if(l.type != REGION) cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch); - return l.output; -} - -float *get_network_output_gpu(network net) -{ - int i; - for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break; - return get_network_output_layer_gpu(net, i); -} - -float *network_predict_gpu(network net, float *input) -{ - cuda_set_device(net.gpu_index); - int size = get_network_input_size(net) * net.batch; - network_state state; - state.index = 0; - state.net = net; - state.input = cuda_make_array(input, size); - state.truth = 0; - state.train = 0; - state.delta = 0; - forward_network_gpu(net, state); - float *out = get_network_output_gpu(net); - cuda_free(state.input); - return out; -} - diff --git a/image.darknet/src/nightmare.c b/image.darknet/src/nightmare.c deleted file mode 100644 index ec7166c..0000000 --- a/image.darknet/src/nightmare.c +++ /dev/null @@ -1,308 +0,0 @@ - -#include "network.h" -#include "parser.h" -#include "blas.h" -#include "utils.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -// ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2 - -float abs_mean(float *x, int n) -{ - int i; - float sum = 0; - for (i = 0; i < n; ++i){ - sum += fabs(x[i]); - } - return sum/n; -} - -void calculate_loss(float *output, float *delta, int n, float thresh) -{ - int i; - float mean = mean_array(output, n); - float var = variance_array(output, n); - for(i = 0; i < n; ++i){ - if(delta[i] > mean + thresh*sqrt(var)) delta[i] = output[i]; - else delta[i] = 0; - } -} - -void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm) -{ - //scale_image(orig, 2); - //translate_image(orig, -1); - net->n = max_layer + 1; - - int dx = rand()%16 - 8; - int dy = rand()%16 - 8; - int flip = rand()%2; - - image crop = crop_image(orig, dx, dy, orig.w, orig.h); - image im = resize_image(crop, (int)(orig.w * scale), (int)(orig.h * scale)); - if(flip) flip_image(im); - - resize_network(net, im.w, im.h); - layer last = net->layers[net->n-1]; - //net->layers[net->n - 1].activation = LINEAR; - - image delta = make_image(im.w, im.h, im.c); - - network_state state = {0}; - -#ifdef GPU - state.input = cuda_make_array(im.data, im.w*im.h*im.c); - state.delta = cuda_make_array(im.data, im.w*im.h*im.c); - - forward_network_gpu(*net, state); - copy_ongpu(last.outputs, last.output_gpu, 1, last.delta_gpu, 1); - - cuda_pull_array(last.delta_gpu, last.delta, last.outputs); - calculate_loss(last.delta, last.delta, last.outputs, thresh); - cuda_push_array(last.delta_gpu, last.delta, last.outputs); - - backward_network_gpu(*net, state); - - cuda_pull_array(state.delta, delta.data, im.w*im.h*im.c); - cuda_free(state.input); - cuda_free(state.delta); -#else - state.input = im.data; - state.delta = delta.data; - forward_network(*net, state); - copy_cpu(last.outputs, last.output, 1, last.delta, 1); - calculate_loss(last.output, last.delta, last.outputs, thresh); - backward_network(*net, state); -#endif - - if(flip) flip_image(delta); - //normalize_array(delta.data, delta.w*delta.h*delta.c); - image resized = resize_image(delta, orig.w, orig.h); - image out = crop_image(resized, -dx, -dy, orig.w, orig.h); - - /* - image g = grayscale_image(out); - free_image(out); - out = g; - */ - - //rate = rate / abs_mean(out.data, out.w*out.h*out.c); - - if(norm) normalize_array(out.data, out.w*out.h*out.c); - axpy_cpu(orig.w*orig.h*orig.c, rate, out.data, 1, orig.data, 1); - - /* - normalize_array(orig.data, orig.w*orig.h*orig.c); - scale_image(orig, sqrt(var)); - translate_image(orig, mean); - */ - - //translate_image(orig, 1); - //scale_image(orig, .5); - //normalize_image(orig); - - constrain_image(orig); - - free_image(crop); - free_image(im); - free_image(delta); - free_image(resized); - free_image(out); - -} - -void smooth(image recon, image update, float lambda, int num) -{ - int i, j, k; - int ii, jj; - for(k = 0; k < recon.c; ++k){ - for(j = 0; j < recon.h; ++j){ - for(i = 0; i < recon.w; ++i){ - int out_index = i + recon.w*(j + recon.h*k); - for(jj = j-num; jj <= j + num && jj < recon.h; ++jj){ - if (jj < 0) continue; - for(ii = i-num; ii <= i + num && ii < recon.w; ++ii){ - if (ii < 0) continue; - int in_index = ii + recon.w*(jj + recon.h*k); - update.data[out_index] += lambda * (recon.data[in_index] - recon.data[out_index]); - } - } - } - } - } -} - -void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters) -{ - int iter = 0; - for (iter = 0; iter < iters; ++iter) { - image delta = make_image(recon.w, recon.h, recon.c); - - network_state state = {0}; -#ifdef GPU - state.input = cuda_make_array(recon.data, recon.w*recon.h*recon.c); - state.delta = cuda_make_array(delta.data, delta.w*delta.h*delta.c); - state.truth = cuda_make_array(features, get_network_output_size(net)); - - forward_network_gpu(net, state); - backward_network_gpu(net, state); - - cuda_pull_array(state.delta, delta.data, delta.w*delta.h*delta.c); - - cuda_free(state.input); - cuda_free(state.delta); - cuda_free(state.truth); -#else - state.input = recon.data; - state.delta = delta.data; - state.truth = features; - - forward_network(net, state); - backward_network(net, state); -#endif - - axpy_cpu(recon.w*recon.h*recon.c, 1, delta.data, 1, update.data, 1); - smooth(recon, update, lambda, smooth_size); - - axpy_cpu(recon.w*recon.h*recon.c, rate, update.data, 1, recon.data, 1); - scal_cpu(recon.w*recon.h*recon.c, momentum, update.data, 1); - - //float mag = mag_array(recon.data, recon.w*recon.h*recon.c); - //scal_cpu(recon.w*recon.h*recon.c, 600/mag, recon.data, 1); - - constrain_image(recon); - free_image(delta); - } -} - - -void run_nightmare(int argc, char **argv) -{ - srand(0); - if(argc < 4){ - fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [layer] [options! (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[2]; - char *weights = argv[3]; - char *input = argv[4]; - int max_layer = atoi(argv[5]); - - int range = find_int_arg(argc, argv, "-range", 1); - int norm = find_int_arg(argc, argv, "-norm", 1); - int rounds = find_int_arg(argc, argv, "-rounds", 1); - int iters = find_int_arg(argc, argv, "-iters", 10); - int octaves = find_int_arg(argc, argv, "-octaves", 4); - float zoom = find_float_arg(argc, argv, "-zoom", 1.); - float rate = find_float_arg(argc, argv, "-rate", .04); - float thresh = find_float_arg(argc, argv, "-thresh", 1.); - float rotate = find_float_arg(argc, argv, "-rotate", 0); - float momentum = find_float_arg(argc, argv, "-momentum", .9); - float lambda = find_float_arg(argc, argv, "-lambda", .01); - char *prefix = find_char_arg(argc, argv, "-prefix", 0); - int reconstruct = find_arg(argc, argv, "-reconstruct"); - int smooth_size = find_int_arg(argc, argv, "-smooth", 1); - - network net = parse_network_cfg(cfg); - load_weights(&net, weights); - char *cfgbase = basecfg(cfg); - char *imbase = basecfg(input); - - set_batch_network(&net, 1); - image im = load_image_color(input, 0, 0); - if(0){ - float scale = 1; - if(im.w > 512 || im.h > 512){ - if(im.w > im.h) scale = 512.0/im.w; - else scale = 512.0/im.h; - } - image resized = resize_image(im, scale*im.w, scale*im.h); - free_image(im); - im = resized; - } - - float *features = 0; - image update; - if (reconstruct){ - resize_network(&net, im.w, im.h); - - int zz = 0; - network_predict(net, im.data); - image out_im = get_network_image(net); - image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz); - //flip_image(crop); - image f_im = resize_image(crop, out_im.w, out_im.h); - free_image(crop); - printf("%d features\n", out_im.w*out_im.h*out_im.c); - - - im = resize_image(im, im.w, im.h); - f_im = resize_image(f_im, f_im.w, f_im.h); - features = f_im.data; - - int i; - for(i = 0; i < 14*14*512; ++i){ - features[i] += rand_uniform(-.19, .19); - } - - free_image(im); - im = make_random_image(im.w, im.h, im.c); - update = make_image(im.w, im.h, im.c); - - } - - int e; - int n; - for(e = 0; e < rounds; ++e){ - fprintf(stderr, "Iteration: "); - fflush(stderr); - for(n = 0; n < iters; ++n){ - fprintf(stderr, "%d, ", n); - fflush(stderr); - if(reconstruct){ - reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1); - //if ((n+1)%30 == 0) rate *= .5; - show_image(im, "reconstruction"); -#ifdef OPENCV - cvWaitKey(10); -#endif - }else{ - int layer = max_layer + rand()%range - range/2; - int octave = rand()%octaves; - optimize_picture(&net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm); - } - } - fprintf(stderr, "done\n"); - if(0){ - image g = grayscale_image(im); - free_image(im); - im = g; - } - char buff[256]; - if (prefix){ - sprintf(buff, "%s/%s_%s_%d_%06d",prefix, imbase, cfgbase, max_layer, e); - }else{ - sprintf(buff, "%s_%s_%d_%06d",imbase, cfgbase, max_layer, e); - } - printf("%d %s\n", e, buff); - save_image(im, buff); - //show_image(im, buff); - //cvWaitKey(0); - - if(rotate){ - image rot = rotate_image(im, rotate); - free_image(im); - im = rot; - } - image crop = crop_image(im, im.w * (1. - zoom)/2., im.h * (1.-zoom)/2., im.w*zoom, im.h*zoom); - image resized = resize_image(crop, im.w, im.h); - free_image(im); - free_image(crop); - im = resized; - } -} - diff --git a/image.darknet/src/normalization_layer.c b/image.darknet/src/normalization_layer.c index 069a079..424714f 100644 --- a/image.darknet/src/normalization_layer.c +++ b/image.darknet/src/normalization_layer.c @@ -1,5 +1,6 @@ #include "normalization_layer.h" #include "blas.h" + #include layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa) @@ -62,7 +63,7 @@ void resize_normalization_layer(layer *layer, int w, int h) #endif } -void forward_normalization_layer(const layer layer, network_state state) +void forward_normalization_layer(const layer layer, network net) { int k,b; int w = layer.w; @@ -73,7 +74,7 @@ void forward_normalization_layer(const layer layer, network_state state) for(b = 0; b < layer.batch; ++b){ float *squared = layer.squared + w*h*c*b; float *norms = layer.norms + w*h*c*b; - float *input = state.input + w*h*c*b; + float *input = net.input + w*h*c*b; pow_cpu(w*h*c, 2, input, 1, squared, 1); const_cpu(w*h, layer.kappa, norms, 1); @@ -90,10 +91,10 @@ void forward_normalization_layer(const layer layer, network_state state) } } pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, layer.output, 1); - mul_cpu(w*h*c*layer.batch, state.input, 1, layer.output, 1); + mul_cpu(w*h*c*layer.batch, net.input, 1, layer.output, 1); } -void backward_normalization_layer(const layer layer, network_state state) +void backward_normalization_layer(const layer layer, network net) { // TODO This is approximate ;-) // Also this should add in to delta instead of overwritting. @@ -101,50 +102,50 @@ void backward_normalization_layer(const layer layer, network_state state) int w = layer.w; int h = layer.h; int c = layer.c; - pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, state.delta, 1); - mul_cpu(w*h*c*layer.batch, layer.delta, 1, state.delta, 1); + pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, net.delta, 1); + mul_cpu(w*h*c*layer.batch, layer.delta, 1, net.delta, 1); } #ifdef GPU -void forward_normalization_layer_gpu(const layer layer, network_state state) +void forward_normalization_layer_gpu(const layer layer, network net) { int k,b; int w = layer.w; int h = layer.h; int c = layer.c; - scal_ongpu(w*h*c*layer.batch, 0, layer.squared_gpu, 1); + scal_gpu(w*h*c*layer.batch, 0, layer.squared_gpu, 1); for(b = 0; b < layer.batch; ++b){ float *squared = layer.squared_gpu + w*h*c*b; float *norms = layer.norms_gpu + w*h*c*b; - float *input = state.input + w*h*c*b; - pow_ongpu(w*h*c, 2, input, 1, squared, 1); + float *input = net.input_gpu + w*h*c*b; + pow_gpu(w*h*c, 2, input, 1, squared, 1); - const_ongpu(w*h, layer.kappa, norms, 1); + const_gpu(w*h, layer.kappa, norms, 1); for(k = 0; k < layer.size/2; ++k){ - axpy_ongpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1); + axpy_gpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1); } for(k = 1; k < layer.c; ++k){ - copy_ongpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1); + copy_gpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1); int prev = k - ((layer.size-1)/2) - 1; int next = k + (layer.size/2); - if(prev >= 0) axpy_ongpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1); - if(next < layer.c) axpy_ongpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1); + if(prev >= 0) axpy_gpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1); + if(next < layer.c) axpy_gpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1); } } - pow_ongpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, layer.output_gpu, 1); - mul_ongpu(w*h*c*layer.batch, state.input, 1, layer.output_gpu, 1); + pow_gpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, layer.output_gpu, 1); + mul_gpu(w*h*c*layer.batch, net.input_gpu, 1, layer.output_gpu, 1); } -void backward_normalization_layer_gpu(const layer layer, network_state state) +void backward_normalization_layer_gpu(const layer layer, network net) { // TODO This is approximate ;-) int w = layer.w; int h = layer.h; int c = layer.c; - pow_ongpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, state.delta, 1); - mul_ongpu(w*h*c*layer.batch, layer.delta_gpu, 1, state.delta, 1); + pow_gpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, net.delta_gpu, 1); + mul_gpu(w*h*c*layer.batch, layer.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/src/normalization_layer.h b/image.darknet/src/normalization_layer.h index ab32776..665baa5 100644 --- a/image.darknet/src/normalization_layer.h +++ b/image.darknet/src/normalization_layer.h @@ -7,13 +7,13 @@ layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa); void resize_normalization_layer(layer *layer, int h, int w); -void forward_normalization_layer(const layer layer, network_state state); -void backward_normalization_layer(const layer layer, network_state state); +void forward_normalization_layer(const layer layer, network net); +void backward_normalization_layer(const layer layer, network net); void visualize_normalization_layer(layer layer, char *window); #ifdef GPU -void forward_normalization_layer_gpu(const layer layer, network_state state); -void backward_normalization_layer_gpu(const layer layer, network_state state); +void forward_normalization_layer_gpu(const layer layer, network net); +void backward_normalization_layer_gpu(const layer layer, network net); #endif #endif diff --git a/image.darknet/src/option_list.c b/image.darknet/src/option_list.c index f935af3..2f52781 100644 --- a/image.darknet/src/option_list.c +++ b/image.darknet/src/option_list.c @@ -32,6 +32,23 @@ list *read_data_cfg(char *filename) return options; } +metadata get_metadata(char *file) +{ + metadata m = {0}; + list *options = read_data_cfg(file); + + char *name_list = option_find_str(options, "names", 0); + if(!name_list) name_list = option_find_str(options, "labels", 0); + if(!name_list) { + fprintf(stderr, "No names or labels found\n"); + } else { + m.names = get_labels(name_list); + } + m.classes = option_find_int(options, "classes", 2); + free_list(options); + return m; +} + int read_option(char *s, list *options) { size_t i; diff --git a/image.darknet/src/option_list.h b/image.darknet/src/option_list.h index 054b3fd..844bd87 100644 --- a/image.darknet/src/option_list.h +++ b/image.darknet/src/option_list.h @@ -9,13 +9,9 @@ typedef struct{ } kvp; -list *read_data_cfg(char *filename); int read_option(char *s, list *options); void option_insert(list *l, char *key, char *val); char *option_find(list *l, char *key); -char *option_find_str(list *l, char *key, char *def); -int option_find_int(list *l, char *key, int def); -int option_find_int_quiet(list *l, char *key, int def); float option_find_float(list *l, char *key, float def); float option_find_float_quiet(list *l, char *key, float def); void option_unused(list *l); diff --git a/image.darknet/src/parser.c b/image.darknet/src/parser.c index 3f39a13..c8141c9 100644 --- a/image.darknet/src/parser.c +++ b/image.darknet/src/parser.c @@ -1,14 +1,17 @@ #include #include #include +#include #include "activation_layer.h" +#include "logistic_layer.h" +#include "l2norm_layer.h" #include "activations.h" -#include "assert.h" #include "avgpool_layer.h" #include "batchnorm_layer.h" #include "blas.h" #include "connected_layer.h" +#include "deconvolutional_layer.h" #include "convolutional_layer.h" #include "cost_layer.h" #include "crnn_layer.h" @@ -23,11 +26,15 @@ #include "option_list.h" #include "parser.h" #include "region_layer.h" +#include "yolo_layer.h" +#include "iseg_layer.h" #include "reorg_layer.h" #include "rnn_layer.h" #include "route_layer.h" +#include "upsample_layer.h" #include "shortcut_layer.h" #include "softmax_layer.h" +#include "lstm_layer.h" #include "utils.h" typedef struct{ @@ -45,14 +52,21 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[cost]")==0) return COST; if (strcmp(type, "[detection]")==0) return DETECTION; if (strcmp(type, "[region]")==0) return REGION; + if (strcmp(type, "[yolo]")==0) return YOLO; + if (strcmp(type, "[iseg]")==0) return ISEG; if (strcmp(type, "[local]")==0) return LOCAL; if (strcmp(type, "[conv]")==0 || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL; + if (strcmp(type, "[deconv]")==0 + || strcmp(type, "[deconvolutional]")==0) return DECONVOLUTIONAL; if (strcmp(type, "[activation]")==0) return ACTIVE; + if (strcmp(type, "[logistic]")==0) return LOGXENT; + if (strcmp(type, "[l2norm]")==0) return L2NORM; if (strcmp(type, "[net]")==0 || strcmp(type, "[network]")==0) return NETWORK; if (strcmp(type, "[crnn]")==0) return CRNN; if (strcmp(type, "[gru]")==0) return GRU; + if (strcmp(type, "[lstm]") == 0) return LSTM; if (strcmp(type, "[rnn]")==0) return RNN; if (strcmp(type, "[conn]")==0 || strcmp(type, "[connected]")==0) return CONNECTED; @@ -68,6 +82,7 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[soft]")==0 || strcmp(type, "[softmax]")==0) return SOFTMAX; if (strcmp(type, "[route]")==0) return ROUTE; + if (strcmp(type, "[upsample]")==0) return UPSAMPLE; return BLANK; } @@ -111,7 +126,7 @@ typedef struct size_params{ int c; int index; int time_steps; - network net; + network *net; } size_params; local_layer parse_local(list *options, size_params params) @@ -135,6 +150,32 @@ local_layer parse_local(list *options, size_params params) return layer; } +layer parse_deconvolutional(list *options, size_params params) +{ + int n = option_find_int(options, "filters",1); + int size = option_find_int(options, "size",1); + int stride = option_find_int(options, "stride",1); + + char *activation_s = option_find_str(options, "activation", "logistic"); + ACTIVATION activation = get_activation(activation_s); + + int batch,h,w,c; + h = params.h; + w = params.w; + c = params.c; + batch=params.batch; + if(!(h && w && c)) error("Layer before deconvolutional layer must output image."); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + int pad = option_find_int_quiet(options, "pad",0); + int padding = option_find_int_quiet(options, "padding",0); + if(pad) padding = size/2; + + layer l = make_deconvolutional_layer(batch,h,w,c,n,size,stride,padding, activation, batch_normalize, params.net->adam); + + return l; +} + + convolutional_layer parse_convolutional(list *options, size_params params) { int n = option_find_int(options, "filters",1); @@ -142,6 +183,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int stride = option_find_int(options, "stride",1); int pad = option_find_int_quiet(options, "pad",0); int padding = option_find_int_quiet(options, "padding",0); + int groups = option_find_int_quiet(options, "groups", 1); if(pad) padding = size/2; char *activation_s = option_find_str(options, "activation", "logistic"); @@ -157,14 +199,9 @@ convolutional_layer parse_convolutional(list *options, size_params params) int binary = option_find_int_quiet(options, "binary", 0); int xnor = option_find_int_quiet(options, "xnor", 0); - convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam); + convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,groups,size,stride,padding,activation, batch_normalize, binary, xnor, params.net->adam); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); - if(params.net.adam){ - layer.B1 = params.net.B1; - layer.B2 = params.net.B2; - layer.eps = params.net.eps; - } return layer; } @@ -187,13 +224,11 @@ layer parse_crnn(list *options, size_params params) layer parse_rnn(list *options, size_params params) { int output = option_find_int(options, "output",1); - int hidden = option_find_int(options, "hidden",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); - int logistic = option_find_int_quiet(options, "logistic", 0); - layer l = make_rnn_layer(params.batch, params.inputs, hidden, output, params.time_steps, activation, batch_normalize, logistic); + layer l = make_rnn_layer(params.batch, params.inputs, output, params.time_steps, activation, batch_normalize, params.net->adam); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -205,31 +240,114 @@ layer parse_gru(list *options, size_params params) int output = option_find_int(options, "output",1); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); - layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize); + layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam); + l.tanh = option_find_int_quiet(options, "tanh", 0); + + return l; +} + +layer parse_lstm(list *options, size_params params) +{ + int output = option_find_int(options, "output", 1); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + + layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam); return l; } -connected_layer parse_connected(list *options, size_params params) +layer parse_connected(list *options, size_params params) { int output = option_find_int(options, "output",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); - connected_layer layer = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize); - - return layer; + layer l = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize, params.net->adam); + return l; } -softmax_layer parse_softmax(list *options, size_params params) +layer parse_softmax(list *options, size_params params) { int groups = option_find_int_quiet(options, "groups",1); - softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups); - layer.temperature = option_find_float_quiet(options, "temperature", 1); + layer l = make_softmax_layer(params.batch, params.inputs, groups); + l.temperature = option_find_float_quiet(options, "temperature", 1); char *tree_file = option_find_str(options, "tree", 0); - if (tree_file) layer.softmax_tree = read_tree(tree_file); - return layer; + if (tree_file) l.softmax_tree = read_tree(tree_file); + l.w = params.w; + l.h = params.h; + l.c = params.c; + l.spatial = option_find_float_quiet(options, "spatial", 0); + l.noloss = option_find_int_quiet(options, "noloss", 0); + return l; +} + +int *parse_yolo_mask(char *a, int *num) +{ + int *mask = 0; + if(a){ + int len = strlen(a); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (a[i] == ',') ++n; + } + mask = calloc(n, sizeof(int)); + for(i = 0; i < n; ++i){ + int val = atoi(a); + mask[i] = val; + a = strchr(a, ',')+1; + } + *num = n; + } + return mask; +} + +layer parse_yolo(list *options, size_params params) +{ + int classes = option_find_int(options, "classes", 20); + int total = option_find_int(options, "num", 1); + int num = total; + + char *a = option_find_str(options, "mask", 0); + int *mask = parse_yolo_mask(a, &num); + layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes); + assert(l.outputs == params.inputs); + + l.max_boxes = option_find_int_quiet(options, "max",90); + l.jitter = option_find_float(options, "jitter", .2); + + l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); + l.truth_thresh = option_find_float(options, "truth_thresh", 1); + l.random = option_find_int_quiet(options, "random", 0); + + char *map_file = option_find_str(options, "map", 0); + if (map_file) l.map = read_map(map_file); + + a = option_find_str(options, "anchors", 0); + if(a){ + int len = strlen(a); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (a[i] == ',') ++n; + } + for(i = 0; i < n; ++i){ + float bias = atof(a); + l.biases[i] = bias; + a = strchr(a, ',')+1; + } + } + return l; +} + +layer parse_iseg(list *options, size_params params) +{ + int classes = option_find_int(options, "classes", 20); + int ids = option_find_int(options, "ids", 32); + layer l = make_iseg_layer(params.batch, params.w, params.h, classes, ids); + assert(l.outputs == params.inputs); + return l; } layer parse_region(list *options, size_params params) @@ -245,6 +363,7 @@ layer parse_region(list *options, size_params params) l.sqrt = option_find_int_quiet(options, "sqrt", 0); l.softmax = option_find_int(options, "softmax", 0); + l.background = option_find_int_quiet(options, "background", 0); l.max_boxes = option_find_int_quiet(options, "max",30); l.jitter = option_find_float(options, "jitter", .2); l.rescore = option_find_int_quiet(options, "rescore",0); @@ -257,6 +376,7 @@ layer parse_region(list *options, size_params params) l.coord_scale = option_find_float(options, "coord_scale", 1); l.object_scale = option_find_float(options, "object_scale", 1); l.noobject_scale = option_find_float(options, "noobject_scale", 1); + l.mask_scale = option_find_float(options, "mask_scale", 1); l.class_scale = option_find_float(options, "class_scale", 1); l.bias_match = option_find_int_quiet(options, "bias_match",0); @@ -281,6 +401,7 @@ layer parse_region(list *options, size_params params) } return l; } + detection_layer parse_detection(list *options, size_params params) { int coords = option_find_int(options, "coords", 1); @@ -293,7 +414,7 @@ detection_layer parse_detection(list *options, size_params params) layer.softmax = option_find_int(options, "softmax", 0); layer.sqrt = option_find_int(options, "sqrt", 0); - layer.max_boxes = option_find_int_quiet(options, "max",30); + layer.max_boxes = option_find_int_quiet(options, "max",90); layer.coord_scale = option_find_float(options, "coord_scale", 1); layer.forced = option_find_int(options, "forced", 0); layer.object_scale = option_find_float(options, "object_scale", 1); @@ -312,6 +433,8 @@ cost_layer parse_cost(list *options, size_params params) float scale = option_find_float_quiet(options, "scale",1); cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale); layer.ratio = option_find_float_quiet(options, "ratio",0); + layer.noobject_scale = option_find_float_quiet(options, "noobj", 1); + layer.thresh = option_find_float_quiet(options, "thresh",0); return layer; } @@ -343,6 +466,8 @@ layer parse_reorg(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int reverse = option_find_int_quiet(options, "reverse",0); + int flatten = option_find_int_quiet(options, "flatten",0); + int extra = option_find_int_quiet(options, "extra",0); int batch,h,w,c; h = params.h; @@ -351,7 +476,7 @@ layer parse_reorg(list *options, size_params params) batch=params.batch; if(!(h && w && c)) error("Layer before reorg layer must output image."); - layer layer = make_reorg_layer(batch,w,h,c,stride,reverse); + layer layer = make_reorg_layer(batch,w,h,c,stride,reverse, flatten, extra); return layer; } @@ -359,7 +484,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int size = option_find_int(options, "size",stride); - int padding = option_find_int_quiet(options, "padding", (size-1)/2); + int padding = option_find_int_quiet(options, "padding", size-1); int batch,h,w,c; h = params.h; @@ -411,24 +536,45 @@ layer parse_batchnorm(list *options, size_params params) return l; } -layer parse_shortcut(list *options, size_params params, network net) +layer parse_shortcut(list *options, size_params params, network *net) { - char *l = option_find(options, "from"); + char *l = option_find(options, "from"); int index = atoi(l); if(index < 0) index = params.index + index; int batch = params.batch; - layer from = net.layers[index]; + layer from = net->layers[index]; layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); s.activation = activation; + s.alpha = option_find_float_quiet(options, "alpha", 1); + s.beta = option_find_float_quiet(options, "beta", 1); return s; } +layer parse_l2norm(list *options, size_params params) +{ + layer l = make_l2norm_layer(params.batch, params.inputs); + l.h = l.out_h = params.h; + l.w = l.out_w = params.w; + l.c = l.out_c = params.c; + return l; +} + + +layer parse_logistic(list *options, size_params params) +{ + layer l = make_logistic_layer(params.batch, params.inputs); + l.h = l.out_h = params.h; + l.w = l.out_w = params.w; + l.c = l.out_c = params.c; + return l; +} + layer parse_activation(list *options, size_params params) { char *activation_s = option_find_str(options, "activation", "linear"); @@ -436,19 +582,25 @@ layer parse_activation(list *options, size_params params) layer l = make_activation_layer(params.batch, params.inputs, activation); - l.out_h = params.h; - l.out_w = params.w; - l.out_c = params.c; - l.h = params.h; - l.w = params.w; - l.c = params.c; + l.h = l.out_h = params.h; + l.w = l.out_w = params.w; + l.c = l.out_c = params.c; + + return l; +} + +layer parse_upsample(list *options, size_params params, network *net) +{ + int stride = option_find_int(options, "stride",2); + layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride); + l.scale = option_find_float_quiet(options, "scale", 1); return l; } -route_layer parse_route(list *options, size_params params, network net) +route_layer parse_route(list *options, size_params params, network *net) { - char *l = option_find(options, "layers"); + char *l = option_find(options, "layers"); int len = strlen(l); if(!l) error("Route Layer must specify input layers"); int n = 1; @@ -464,19 +616,19 @@ route_layer parse_route(list *options, size_params params, network net) l = strchr(l, ',')+1; if(index < 0) index = params.index + index; layers[i] = index; - sizes[i] = net.layers[index].outputs; + sizes[i] = net->layers[index].outputs; } int batch = params.batch; route_layer layer = make_route_layer(batch, n, layers, sizes); - convolutional_layer first = net.layers[layers[0]]; + convolutional_layer first = net->layers[layers[0]]; layer.out_w = first.out_w; layer.out_h = first.out_h; layer.out_c = first.out_c; for(i = 1; i < n; ++i){ int index = layers[i]; - convolutional_layer next = net.layers[index]; + convolutional_layer next = net->layers[index]; if(next.out_w == first.out_w && next.out_h == first.out_h){ layer.out_c += next.out_c; }else{ @@ -508,15 +660,17 @@ void parse_net_options(list *options, network *net) net->decay = option_find_float(options, "decay", .0001); int subdivs = option_find_int(options, "subdivisions",1); net->time_steps = option_find_int_quiet(options, "time_steps",1); + net->notruth = option_find_int_quiet(options, "notruth",0); net->batch /= subdivs; net->batch *= net->time_steps; net->subdivisions = subdivs; + net->random = option_find_int_quiet(options, "random", 0); net->adam = option_find_int_quiet(options, "adam", 0); if(net->adam){ net->B1 = option_find_float(options, "B1", .9); net->B2 = option_find_float(options, "B2", .999); - net->eps = option_find_float(options, "eps", .000001); + net->eps = option_find_float(options, "eps", .0000001); } net->h = option_find_int_quiet(options, "height",0); @@ -525,6 +679,10 @@ void parse_net_options(list *options, network *net) net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c); net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2); net->min_crop = option_find_int_quiet(options, "min_crop",net->w); + net->max_ratio = option_find_float_quiet(options, "max_ratio", (float) net->max_crop / net->w); + net->min_ratio = option_find_float_quiet(options, "min_ratio", (float) net->min_crop / net->w); + net->center = option_find_int_quiet(options, "center",0); + net->clip = option_find_float_quiet(options, "clip", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); @@ -537,12 +695,13 @@ void parse_net_options(list *options, network *net) char *policy_s = option_find_str(options, "policy", "constant"); net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); + net->power = option_find_float_quiet(options, "power", 4); if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); net->scale = option_find_float(options, "scale", 1); } else if (net->policy == STEPS){ - char *l = option_find(options, "steps"); - char *p = option_find(options, "scales"); + char *l = option_find(options, "steps"); + char *p = option_find(options, "scales"); if(!l || !p) error("STEPS policy must have steps and scales in cfg file"); int len = strlen(l); @@ -570,7 +729,6 @@ void parse_net_options(list *options, network *net) net->gamma = option_find_float(options, "gamma", 1); net->step = option_find_int(options, "step", 1); } else if (net->policy == POLY || net->policy == RANDOM){ - net->power = option_find_float(options, "power", 1); } net->max_batches = option_find_int(options, "max_batches", 0); } @@ -581,26 +739,26 @@ int is_network(section *s) || strcmp(s->type, "[network]")==0); } -network parse_network_cfg(char *filename) +network *parse_network_cfg(char *filename) { list *sections = read_cfg(filename); node *n = sections->front; if(!n) error("Config file has no sections"); - network net = make_network(sections->size - 1); - net.gpu_index = gpu_index; + network *net = make_network(sections->size - 1); + net->gpu_index = gpu_index; size_params params; section *s = (section *)n->val; list *options = s->options; if(!is_network(s)) error("First section must be [net] or [network]"); - parse_net_options(options, &net); - - params.h = net.h; - params.w = net.w; - params.c = net.c; - params.inputs = net.inputs; - params.batch = net.batch; - params.time_steps = net.time_steps; + parse_net_options(options, net); + + params.h = net->h; + params.w = net->w; + params.c = net->c; + params.inputs = net->inputs; + params.batch = net->batch; + params.time_steps = net->time_steps; params.net = net; size_t workspace_size = 0; @@ -617,14 +775,22 @@ network parse_network_cfg(char *filename) LAYER_TYPE lt = string_to_layer_type(s->type); if(lt == CONVOLUTIONAL){ l = parse_convolutional(options, params); + }else if(lt == DECONVOLUTIONAL){ + l = parse_deconvolutional(options, params); }else if(lt == LOCAL){ l = parse_local(options, params); }else if(lt == ACTIVE){ l = parse_activation(options, params); + }else if(lt == LOGXENT){ + l = parse_logistic(options, params); + }else if(lt == L2NORM){ + l = parse_l2norm(options, params); }else if(lt == RNN){ l = parse_rnn(options, params); }else if(lt == GRU){ l = parse_gru(options, params); + }else if (lt == LSTM) { + l = parse_lstm(options, params); }else if(lt == CRNN){ l = parse_crnn(options, params); }else if(lt == CONNECTED){ @@ -635,11 +801,15 @@ network parse_network_cfg(char *filename) l = parse_cost(options, params); }else if(lt == REGION){ l = parse_region(options, params); + }else if(lt == YOLO){ + l = parse_yolo(options, params); + }else if(lt == ISEG){ + l = parse_iseg(options, params); }else if(lt == DETECTION){ l = parse_detection(options, params); }else if(lt == SOFTMAX){ l = parse_softmax(options, params); - net.hierarchy = l.softmax_tree; + net->hierarchy = l.softmax_tree; }else if(lt == NORMALIZATION){ l = parse_normalization(options, params); }else if(lt == BATCHNORM){ @@ -652,23 +822,33 @@ network parse_network_cfg(char *filename) l = parse_avgpool(options, params); }else if(lt == ROUTE){ l = parse_route(options, params, net); + }else if(lt == UPSAMPLE){ + l = parse_upsample(options, params, net); }else if(lt == SHORTCUT){ l = parse_shortcut(options, params, net); }else if(lt == DROPOUT){ l = parse_dropout(options, params); - l.output = net.layers[count-1].output; - l.delta = net.layers[count-1].delta; + l.output = net->layers[count-1].output; + l.delta = net->layers[count-1].delta; #ifdef GPU - l.output_gpu = net.layers[count-1].output_gpu; - l.delta_gpu = net.layers[count-1].delta_gpu; + l.output_gpu = net->layers[count-1].output_gpu; + l.delta_gpu = net->layers[count-1].delta_gpu; #endif }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } + l.clip = net->clip; + l.truth = option_find_int_quiet(options, "truth", 0); + l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); + l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); + l.dontsave = option_find_int_quiet(options, "dontsave", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); + l.numload = option_find_int_quiet(options, "numload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); + l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); + l.smooth = option_find_float_quiet(options, "smooth", 0); option_unused(options); - net.layers[count] = l; + net->layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; free_section(s); n = n->next; @@ -679,20 +859,30 @@ network parse_network_cfg(char *filename) params.c = l.out_c; params.inputs = l.outputs; } - } + } free_list(sections); - net.outputs = get_network_output_size(net); - net.output = get_network_output(net); + layer out = get_network_output_layer(net); + net->outputs = out.outputs; + net->truths = out.outputs; + if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths; + net->output = out.output; + net->input = calloc(net->inputs*net->batch, sizeof(float)); + net->truth = calloc(net->truths*net->batch, sizeof(float)); +#ifdef GPU + net->output_gpu = out.output_gpu; + net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch); + net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch); +#endif if(workspace_size){ //printf("%ld\n", workspace_size); #ifdef GPU if(gpu_index >= 0){ - net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); + net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); }else { - net.workspace = calloc(1, workspace_size); + net->workspace = calloc(1, workspace_size); } #else - net.workspace = calloc(1, workspace_size); + net->workspace = calloc(1, workspace_size); #endif } return net; @@ -704,7 +894,7 @@ list *read_cfg(char *filename) if(file == 0) file_error(filename); char *line; int nu = 0; - list *sections = make_list(); + list *options = make_list(); section *current = 0; while((line=fgetl(file)) != 0){ ++ nu; @@ -712,7 +902,7 @@ list *read_cfg(char *filename) switch(line[0]){ case '[': current = malloc(sizeof(section)); - list_insert(sections, current); + list_insert(options, current); current->options = make_list(); current->type = line; break; @@ -730,7 +920,7 @@ list *read_cfg(char *filename) } } fclose(file); - return sections; + return options; } void save_convolutional_weights_binary(layer l, FILE *fp) @@ -776,7 +966,7 @@ void save_convolutional_weights(layer l, FILE *fp) pull_convolutional_layer(l); } #endif - int num = l.n*l.c*l.size*l.size; + int num = l.nweights; fwrite(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize){ fwrite(l.scales, sizeof(float), l.n, fp); @@ -784,10 +974,6 @@ void save_convolutional_weights(layer l, FILE *fp) fwrite(l.rolling_variance, sizeof(float), l.n, fp); } fwrite(l.weights, sizeof(float), num, fp); - if(l.adam){ - fwrite(l.m, sizeof(float), num, fp); - fwrite(l.v, sizeof(float), num, fp); - } } void save_batchnorm_weights(layer l, FILE *fp) @@ -818,11 +1004,11 @@ void save_connected_weights(layer l, FILE *fp) } } -void save_weights_upto(network net, char *filename, int cutoff) +void save_weights_upto(network *net, char *filename, int cutoff) { #ifdef GPU - if(net.gpu_index >= 0){ - cuda_set_device(net.gpu_index); + if(net->gpu_index >= 0){ + cuda_set_device(net->gpu_index); } #endif fprintf(stderr, "Saving weights to %s\n", filename); @@ -830,17 +1016,18 @@ void save_weights_upto(network net, char *filename, int cutoff) if(!fp) file_error(filename); int major = 0; - int minor = 1; + int minor = 2; int revision = 0; fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); - fwrite(net.seen, sizeof(int), 1, fp); + fwrite(net->seen, sizeof(size_t), 1, fp); int i; - for(i = 0; i < net.n && i < cutoff; ++i){ - layer l = net.layers[i]; - if(l.type == CONVOLUTIONAL){ + for(i = 0; i < net->n && i < cutoff; ++i){ + layer l = net->layers[i]; + if (l.dontsave) continue; + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ save_convolutional_weights(l, fp); } if(l.type == CONNECTED){ save_connected_weights(l, fp); @@ -850,14 +1037,29 @@ void save_weights_upto(network net, char *filename, int cutoff) save_connected_weights(*(l.input_layer), fp); save_connected_weights(*(l.self_layer), fp); save_connected_weights(*(l.output_layer), fp); - } if(l.type == GRU){ - save_connected_weights(*(l.input_z_layer), fp); - save_connected_weights(*(l.input_r_layer), fp); - save_connected_weights(*(l.input_h_layer), fp); - save_connected_weights(*(l.state_z_layer), fp); - save_connected_weights(*(l.state_r_layer), fp); - save_connected_weights(*(l.state_h_layer), fp); - } if(l.type == CRNN){ + } if (l.type == LSTM) { + save_connected_weights(*(l.wi), fp); + save_connected_weights(*(l.wf), fp); + save_connected_weights(*(l.wo), fp); + save_connected_weights(*(l.wg), fp); + save_connected_weights(*(l.ui), fp); + save_connected_weights(*(l.uf), fp); + save_connected_weights(*(l.uo), fp); + save_connected_weights(*(l.ug), fp); + } if (l.type == GRU) { + if(1){ + save_connected_weights(*(l.wz), fp); + save_connected_weights(*(l.wr), fp); + save_connected_weights(*(l.wh), fp); + save_connected_weights(*(l.uz), fp); + save_connected_weights(*(l.ur), fp); + save_connected_weights(*(l.uh), fp); + }else{ + save_connected_weights(*(l.reset_layer), fp); + save_connected_weights(*(l.update_layer), fp); + save_connected_weights(*(l.state_layer), fp); + } + } if(l.type == CRNN){ save_convolutional_weights(*(l.input_layer), fp); save_convolutional_weights(*(l.self_layer), fp); save_convolutional_weights(*(l.output_layer), fp); @@ -875,9 +1077,9 @@ void save_weights_upto(network net, char *filename, int cutoff) } fclose(fp); } -void save_weights(network net, char *filename) +void save_weights(network *net, char *filename) { - save_weights_upto(net, filename, net.n); + save_weights_upto(net, filename, net->n); } void transpose_matrix(float *a, int rows, int cols) @@ -965,7 +1167,8 @@ void load_convolutional_weights(layer l, FILE *fp) //load_convolutional_weights_binary(l, fp); //return; } - int num = l.n*l.c*l.size*l.size; + if(l.numload) l.n = l.numload; + int num = l.c/l.groups*l.n*l.size*l.size; fread(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize && (!l.dontloadscales)){ fread(l.scales, sizeof(float), l.n, fp); @@ -986,12 +1189,19 @@ void load_convolutional_weights(layer l, FILE *fp) fill_cpu(l.n, 0, l.rolling_mean, 1); fill_cpu(l.n, 0, l.rolling_variance, 1); } + if(0){ + int i; + for(i = 0; i < l.n; ++i){ + printf("%g, ", l.rolling_mean[i]); + } + printf("\n"); + for(i = 0; i < l.n; ++i){ + printf("%g, ", l.rolling_variance[i]); + } + printf("\n"); + } } fread(l.weights, sizeof(float), num, fp); - if(l.adam){ - fread(l.m, sizeof(float), num, fp); - fread(l.v, sizeof(float), num, fp); - } //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1); if (l.flipped) { transpose_matrix(l.weights, l.c*l.size*l.size, l.n); @@ -1005,7 +1215,7 @@ void load_convolutional_weights(layer l, FILE *fp) } -void load_weights_upto(network *net, char *filename, int cutoff) +void load_weights_upto(network *net, char *filename, int start, int cutoff) { #ifdef GPU if(net->gpu_index >= 0){ @@ -1023,14 +1233,20 @@ void load_weights_upto(network *net, char *filename, int cutoff) fread(&major, sizeof(int), 1, fp); fread(&minor, sizeof(int), 1, fp); fread(&revision, sizeof(int), 1, fp); - fread(net->seen, sizeof(int), 1, fp); + if ((major*10 + minor) >= 2 && major < 1000 && minor < 1000){ + fread(net->seen, sizeof(size_t), 1, fp); + } else { + int iseen = 0; + fread(&iseen, sizeof(int), 1, fp); + *net->seen = iseen; + } int transpose = (major > 1000) || (minor > 1000); int i; - for(i = 0; i < net->n && i < cutoff; ++i){ + for(i = start; i < net->n && i < cutoff; ++i){ layer l = net->layers[i]; if (l.dontload) continue; - if(l.type == CONVOLUTIONAL){ + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ load_convolutional_weights(l, fp); } if(l.type == CONNECTED){ @@ -1049,13 +1265,29 @@ void load_weights_upto(network *net, char *filename, int cutoff) load_connected_weights(*(l.self_layer), fp, transpose); load_connected_weights(*(l.output_layer), fp, transpose); } - if(l.type == GRU){ - load_connected_weights(*(l.input_z_layer), fp, transpose); - load_connected_weights(*(l.input_r_layer), fp, transpose); - load_connected_weights(*(l.input_h_layer), fp, transpose); - load_connected_weights(*(l.state_z_layer), fp, transpose); - load_connected_weights(*(l.state_r_layer), fp, transpose); - load_connected_weights(*(l.state_h_layer), fp, transpose); + if (l.type == LSTM) { + load_connected_weights(*(l.wi), fp, transpose); + load_connected_weights(*(l.wf), fp, transpose); + load_connected_weights(*(l.wo), fp, transpose); + load_connected_weights(*(l.wg), fp, transpose); + load_connected_weights(*(l.ui), fp, transpose); + load_connected_weights(*(l.uf), fp, transpose); + load_connected_weights(*(l.uo), fp, transpose); + load_connected_weights(*(l.ug), fp, transpose); + } + if (l.type == GRU) { + if(1){ + load_connected_weights(*(l.wz), fp, transpose); + load_connected_weights(*(l.wr), fp, transpose); + load_connected_weights(*(l.wh), fp, transpose); + load_connected_weights(*(l.uz), fp, transpose); + load_connected_weights(*(l.ur), fp, transpose); + load_connected_weights(*(l.uh), fp, transpose); + }else{ + load_connected_weights(*(l.reset_layer), fp, transpose); + load_connected_weights(*(l.update_layer), fp, transpose); + load_connected_weights(*(l.state_layer), fp, transpose); + } } if(l.type == LOCAL){ int locations = l.out_w*l.out_h; @@ -1075,6 +1307,6 @@ void load_weights_upto(network *net, char *filename, int cutoff) void load_weights(network *net, char *filename) { - load_weights_upto(net, filename, net->n); + load_weights_upto(net, filename, 0, net->n); } diff --git a/image.darknet/src/parser.h b/image.darknet/src/parser.h index 6cff4fb..81aef2c 100644 --- a/image.darknet/src/parser.h +++ b/image.darknet/src/parser.h @@ -1,13 +1,9 @@ #ifndef PARSER_H #define PARSER_H +#include "darknet.h" #include "network.h" -network parse_network_cfg(char *filename); void save_network(network net, char *filename); -void save_weights(network net, char *filename); -void save_weights_upto(network net, char *filename, int cutoff); void save_weights_double(network net, char *filename); -void load_weights(network *net, char *filename); -void load_weights_upto(network *net, char *filename, int cutoff); #endif diff --git a/image.darknet/src/region_layer.c b/image.darknet/src/region_layer.c index f5522c3..179f5e3 100644 --- a/image.darknet/src/region_layer.c +++ b/image.darknet/src/region_layer.c @@ -4,6 +4,7 @@ #include "box.h" #include "cuda.h" #include "utils.h" + #include #include #include @@ -18,6 +19,10 @@ layer make_region_layer(int batch, int w, int h, int n, int classes, int coords) l.batch = batch; l.h = h; l.w = w; + l.c = n*(classes + coords + 1); + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; l.classes = classes; l.coords = coords; l.cost = calloc(1, sizeof(float)); @@ -25,7 +30,7 @@ layer make_region_layer(int batch, int w, int h, int n, int classes, int coords) l.bias_updates = calloc(n*2, sizeof(float)); l.outputs = h*w*n*(classes + coords + 1); l.inputs = l.outputs; - l.truths = 30*(5); + l.truths = 30*(l.coords + 1); l.delta = calloc(batch*l.outputs, sizeof(float)); l.output = calloc(batch*l.outputs, sizeof(float)); int i; @@ -68,19 +73,19 @@ void resize_region_layer(layer *l, int w, int h) #endif } -box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h) +box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h, int stride) { box b; - b.x = (i + logistic_activate(x[index + 0])) / w; - b.y = (j + logistic_activate(x[index + 1])) / h; - b.w = exp(x[index + 2]) * biases[2*n] / w; - b.h = exp(x[index + 3]) * biases[2*n+1] / h; + b.x = (i + x[index + 0*stride]) / w; + b.y = (j + x[index + 1*stride]) / h; + b.w = exp(x[index + 2*stride]) * biases[2*n] / w; + b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; return b; } -float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale) +float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale, int stride) { - box pred = get_region_box(x, biases, n, index, i, j, w, h); + box pred = get_region_box(x, biases, n, index, i, j, w, h, stride); float iou = box_iou(pred, truth); float tx = (truth.x*w - i); @@ -88,34 +93,47 @@ float delta_region_box(box truth, float *x, float *biases, int n, int index, int float tw = log(truth.w*w / biases[2*n]); float th = log(truth.h*h / biases[2*n + 1]); - delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0])); - delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1])); - delta[index + 2] = scale * (tw - x[index + 2]); - delta[index + 3] = scale * (th - x[index + 3]); + delta[index + 0*stride] = scale * (tx - x[index + 0*stride]); + delta[index + 1*stride] = scale * (ty - x[index + 1*stride]); + delta[index + 2*stride] = scale * (tw - x[index + 2*stride]); + delta[index + 3*stride] = scale * (th - x[index + 3*stride]); return iou; } -void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, float *avg_cat) +void delta_region_mask(float *truth, float *x, int n, int index, float *delta, int stride, int scale) +{ + int i; + for(i = 0; i < n; ++i){ + delta[index + i*stride] = scale*(truth[i] - x[index + i*stride]); + } +} + + +void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, int stride, float *avg_cat, int tag) { int i, n; if(hier){ float pred = 1; while(class >= 0){ - pred *= output[index + class]; + pred *= output[index + stride*class]; int g = hier->group[class]; int offset = hier->group_offset[g]; for(i = 0; i < hier->group_size[g]; ++i){ - delta[index + offset + i] = scale * (0 - output[index + offset + i]); + delta[index + stride*(offset + i)] = scale * (0 - output[index + stride*(offset + i)]); } - delta[index + class] = scale * (1 - output[index + class]); + delta[index + stride*class] = scale * (1 - output[index + stride*class]); class = hier->parent[class]; } *avg_cat += pred; } else { + if (delta[index] && tag){ + delta[index + stride*class] = scale * (1 - output[index + stride*class]); + return; + } for(n = 0; n < classes; ++n){ - delta[index + n] = scale * (((n == class)?1 : 0) - output[index + n]); - if(n == class) *avg_cat += output[index + n]; + delta[index + stride*n] = scale * (((n == class)?1 : 0) - output[index + stride*n]); + if(n == class) *avg_cat += output[index + stride*n]; } } } @@ -130,42 +148,45 @@ float tisnan(float x) return (x != x); } -void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output); -void forward_region_layer(const layer l, network_state state) +int entry_index(layer l, int batch, int location, int entry) +{ + int n = location / (l.w*l.h); + int loc = location % (l.w*l.h); + return batch*l.outputs + n*l.w*l.h*(l.coords+l.classes+1) + entry*l.w*l.h + loc; +} + +void forward_region_layer(const layer l, network net) { int i,j,b,t,n; - int size = l.coords + l.classes + 1; - memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + #ifndef GPU - flatten(l.output, l.w*l.h, size*l.n, l.batch, 1); -#endif for (b = 0; b < l.batch; ++b){ - for(i = 0; i < l.h*l.w*l.n; ++i){ - int index = size*i + b*l.outputs; - l.output[index + 4] = logistic_activate(l.output[index + 4]); + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, l.coords); + if(!l.background) activate_array(l.output + index, l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, l.coords + 1); + if(!l.softmax && !l.softmax_tree) activate_array(l.output + index, l.classes*l.w*l.h, LOGISTIC); } } - - -#ifndef GPU if (l.softmax_tree){ - for (b = 0; b < l.batch; ++b){ - for(i = 0; i < l.h*l.w*l.n; ++i){ - int index = size*i + b*l.outputs; - softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5); - } + int i; + int count = l.coords + 1; + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_cpu(net.input + count, group_size, l.batch, l.inputs, l.n*l.w*l.h, 1, l.n*l.w*l.h, l.temperature, l.output + count); + count += group_size; } } else if (l.softmax){ - for (b = 0; b < l.batch; ++b){ - for(i = 0; i < l.h*l.w*l.n; ++i){ - int index = size*i + b*l.outputs; - softmax(l.output + index + 5, l.classes, 1, l.output + index + 5); - } - } + int index = entry_index(l, 0, 0, l.coords + !l.background); + softmax_cpu(net.input + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output + index); } #endif - if(!state.train) return; + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if(!net.train) return; float avg_iou = 0; float recall = 0; float avg_cat = 0; @@ -178,26 +199,29 @@ void forward_region_layer(const layer l, network_state state) if(l.softmax_tree){ int onlyclass = 0; for(t = 0; t < 30; ++t){ - box truth = float_to_box(state.truth + t*5 + b*l.truths); + box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1); if(!truth.x) break; - int class = state.truth[t*5 + b*l.truths + 4]; + int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords]; float maxp = 0; int maxi = 0; if(truth.x > 100000 && truth.y > 100000){ for(n = 0; n < l.n*l.w*l.h; ++n){ - int index = size*n + b*l.outputs + 5; - float scale = l.output[index-1]; - l.delta[index - 1] = l.noobject_scale * ((0 - l.output[index - 1]) * logistic_gradient(l.output[index - 1])); - float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class); + int class_index = entry_index(l, b, n, l.coords + 1); + int obj_index = entry_index(l, b, n, l.coords); + float scale = l.output[obj_index]; + l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]); + float p = scale*get_hierarchy_probability(l.output + class_index, l.softmax_tree, class, l.w*l.h); if(p > maxp){ maxp = p; maxi = n; } } - int index = size*maxi + b*l.outputs + 5; - delta_region_class(l.output, l.delta, index, class, l.classes, l.softmax_tree, l.class_scale, &avg_cat); - if(l.output[index - 1] < .3) l.delta[index - 1] = l.object_scale * ((.3 - l.output[index - 1]) * logistic_gradient(l.output[index - 1])); - else l.delta[index - 1] = 0; + int class_index = entry_index(l, b, maxi, l.coords + 1); + int obj_index = entry_index(l, b, maxi, l.coords); + delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax); + if(l.output[obj_index] < .3) l.delta[obj_index] = l.object_scale * (.3 - l.output[obj_index]); + else l.delta[obj_index] = 0; + l.delta[obj_index] = 0; ++class_count; onlyclass = 1; break; @@ -208,190 +232,276 @@ void forward_region_layer(const layer l, network_state state) for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { - int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs; - box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h); + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h); float best_iou = 0; for(t = 0; t < 30; ++t){ - box truth = float_to_box(state.truth + t*5 + b*l.truths); + box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1); if(!truth.x) break; float iou = box_iou(pred, truth); if (iou > best_iou) { best_iou = iou; } } - avg_anyobj += l.output[index + 4]; - l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4])); + int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, l.coords); + avg_anyobj += l.output[obj_index]; + l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]); + if(l.background) l.delta[obj_index] = l.noobject_scale * (1 - l.output[obj_index]); if (best_iou > l.thresh) { - l.delta[index + 4] = 0; + l.delta[obj_index] = 0; } - if(*(state.net.seen) < 12800){ + if(*(net.seen) < 12800){ box truth = {0}; truth.x = (i + .5)/l.w; truth.y = (j + .5)/l.h; truth.w = l.biases[2*n]/l.w; truth.h = l.biases[2*n+1]/l.h; - delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01); + delta_region_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, l.delta, .01, l.w*l.h); } } } } for(t = 0; t < 30; ++t){ - box truth = float_to_box(state.truth + t*5 + b*l.truths); + box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1); if(!truth.x) break; float best_iou = 0; - int best_index = 0; int best_n = 0; i = (truth.x * l.w); j = (truth.y * l.h); - //printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h); box truth_shift = truth; truth_shift.x = 0; truth_shift.y = 0; - //printf("index %d %d\n",i, j); for(n = 0; n < l.n; ++n){ - int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs; - box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h); + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h); if(l.bias_match){ pred.w = l.biases[2*n]/l.w; pred.h = l.biases[2*n+1]/l.h; } - //printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h); pred.x = 0; pred.y = 0; float iou = box_iou(pred, truth_shift); if (iou > best_iou){ - best_index = index; best_iou = iou; best_n = n; } } - //printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h); - float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale); + int box_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 0); + float iou = delta_region_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, l.delta, l.coord_scale * (2 - truth.w*truth.h), l.w*l.h); + if(l.coords > 4){ + int mask_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 4); + delta_region_mask(net.truth + t*(l.coords + 1) + b*l.truths + 5, l.output, l.coords - 4, mask_index, l.delta, l.w*l.h, l.mask_scale); + } if(iou > .5) recall += 1; avg_iou += iou; - //l.delta[best_index + 4] = iou - l.output[best_index + 4]; - avg_obj += l.output[best_index + 4]; - l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]); + int obj_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords); + avg_obj += l.output[obj_index]; + l.delta[obj_index] = l.object_scale * (1 - l.output[obj_index]); if (l.rescore) { - l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]); + l.delta[obj_index] = l.object_scale * (iou - l.output[obj_index]); + } + if(l.background){ + l.delta[obj_index] = l.object_scale * (0 - l.output[obj_index]); } - - int class = state.truth[t*5 + b*l.truths + 4]; + int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords]; if (l.map) class = l.map[class]; - delta_region_class(l.output, l.delta, best_index + 5, class, l.classes, l.softmax_tree, l.class_scale, &avg_cat); + int class_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords + 1); + delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax); ++count; ++class_count; } } - //printf("\n"); -#ifndef GPU - flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0); -#endif *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count); } -void backward_region_layer(const layer l, network_state state) +void backward_region_layer(const layer l, network net) { - axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); + /* + int b; + int size = l.coords + l.classes + 1; + for (b = 0; b < l.batch*l.n; ++b){ + int index = (b*size + 4)*l.w*l.h; + gradient_array(l.output + index, l.w*l.h, LOGISTIC, l.delta + index); + } + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); + */ +} + +void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +{ + int i; + int new_w=0; + int new_h=0; + if (((float)netw/w) < ((float)neth/h)) { + new_w = netw; + new_h = (h * netw)/w; + } else { + new_h = neth; + new_w = (w * neth)/h; + } + for (i = 0; i < n; ++i){ + box b = dets[i].bbox; + b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); + b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); + b.w *= (float)netw/new_w; + b.h *= (float)neth/new_h; + if(!relative){ + b.x *= w; + b.w *= w; + b.y *= h; + b.h *= h; + } + dets[i].bbox = b; + } } -void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map, float tree_thresh) +void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets) { - int i,j,n; + int i,j,n,z; float *predictions = l.output; + if (l.batch == 2) { + float *flip = l.output + l.outputs; + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w/2; ++i) { + for (n = 0; n < l.n; ++n) { + for(z = 0; z < l.classes + l.coords + 1; ++z){ + int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; + int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); + float swap = flip[i1]; + flip[i1] = flip[i2]; + flip[i2] = swap; + if(z == 0){ + flip[i1] = -flip[i1]; + flip[i2] = -flip[i2]; + } + } + } + } + } + for(i = 0; i < l.outputs; ++i){ + l.output[i] = (l.output[i] + flip[i])/2.; + } + } for (i = 0; i < l.w*l.h; ++i){ int row = i / l.w; int col = i % l.w; for(n = 0; n < l.n; ++n){ - int index = i*l.n + n; - int p_index = index * (l.classes + 5) + 4; - float scale = predictions[p_index]; - int box_index = index * (l.classes + 5); - boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h); - boxes[index].x *= w; - boxes[index].y *= h; - boxes[index].w *= w; - boxes[index].h *= h; - - int class_index = index * (l.classes + 5) + 5; + int index = n*l.w*l.h + i; + for(j = 0; j < l.classes; ++j){ + dets[index].prob[j] = 0; + } + int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); + int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); + int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4); + float scale = l.background ? 1 : predictions[obj_index]; + dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h, l.w*l.h); + dets[index].objectness = scale > thresh ? scale : 0; + if(dets[index].mask){ + for(j = 0; j < l.coords - 4; ++j){ + dets[index].mask[j] = l.output[mask_index + j*l.w*l.h]; + } + } + + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background); if(l.softmax_tree){ - hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0); + hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h); if(map){ for(j = 0; j < 200; ++j){ - float prob = scale*predictions[class_index+map[j]]; - probs[index][j] = (prob > thresh) ? prob : 0; + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]); + float prob = scale*predictions[class_index]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; } } else { - int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh); - probs[index][j] = (scale > thresh) ? scale : 0; - probs[index][l.classes] = scale; + int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h); + dets[index].prob[j] = (scale > thresh) ? scale : 0; } } else { - for(j = 0; j < l.classes; ++j){ - float prob = scale*predictions[class_index+j]; - probs[index][j] = (prob > thresh) ? prob : 0; + if(dets[index].objectness){ + for(j = 0; j < l.classes; ++j){ + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j); + float prob = scale*predictions[class_index]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; + } } } - if(only_objectness){ - probs[index][0] = scale; - } } } + correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative); } #ifdef GPU -void forward_region_layer_gpu(const layer l, network_state state) +void forward_region_layer_gpu(const layer l, network net) { - /* - if(!state.train){ - copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); - return; - } - */ - flatten_ongpu(state.input, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 1, l.output_gpu); - if(l.softmax_tree){ - int i; - int count = 5; - for (i = 0; i < l.softmax_tree->groups; ++i) { - int group_size = l.softmax_tree->group_size[i]; - softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count); - count += group_size; + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + int b, n; + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + if(l.coords > 4){ + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC); + } + index = entry_index(l, b, n*l.w*l.h, l.coords); + if(!l.background) activate_array_gpu(l.output_gpu + index, l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, l.coords + 1); + if(!l.softmax && !l.softmax_tree) activate_array_gpu(l.output_gpu + index, l.classes*l.w*l.h, LOGISTIC); } - }else if (l.softmax){ - softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5); } - - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); - float *truth_cpu = 0; - if(state.truth){ - int num_truth = l.batch*l.truths; - truth_cpu = calloc(num_truth, sizeof(float)); - cuda_pull_array(state.truth, truth_cpu, num_truth); + if (l.softmax_tree){ + int index = entry_index(l, 0, 0, l.coords + 1); + softmax_tree(net.input_gpu + index, l.w*l.h, l.batch*l.n, l.inputs/l.n, 1, l.output_gpu + index, *l.softmax_tree); + } else if (l.softmax) { + int index = entry_index(l, 0, 0, l.coords + !l.background); + softmax_gpu(net.input_gpu + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu + index); } - cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); - network_state cpu_state = state; - cpu_state.train = state.train; - cpu_state.truth = truth_cpu; - cpu_state.input = in_cpu; - forward_region_layer(l, cpu_state); + if(!net.train || l.onlyforward){ + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + return; + } + + cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); + forward_region_layer(l, net); //cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); - free(cpu_state.input); - if(!state.train) return; + if(!net.train) return; cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); - if(cpu_state.truth) free(cpu_state.truth); } -void backward_region_layer_gpu(layer l, network_state state) +void backward_region_layer_gpu(const layer l, network net) { - flatten_ongpu(l.delta_gpu, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 0, state.delta); + int b, n; + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + gradient_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC, l.delta_gpu + index); + if(l.coords > 4){ + index = entry_index(l, b, n*l.w*l.h, 4); + gradient_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC, l.delta_gpu + index); + } + index = entry_index(l, b, n*l.w*l.h, l.coords); + if(!l.background) gradient_array_gpu(l.output_gpu + index, l.w*l.h, LOGISTIC, l.delta_gpu + index); + } + } + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); } #endif +void zero_objectness(layer l) +{ + int i, n; + for (i = 0; i < l.w*l.h; ++i){ + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); + l.output[obj_index] = 0; + } + } +} + diff --git a/image.darknet/src/region_layer.h b/image.darknet/src/region_layer.h index 9a3b7cd..9f12fd1 100644 --- a/image.darknet/src/region_layer.h +++ b/image.darknet/src/region_layer.h @@ -1,18 +1,18 @@ #ifndef REGION_LAYER_H #define REGION_LAYER_H +#include "darknet.h" #include "layer.h" #include "network.h" -layer make_region_layer(int batch, int h, int w, int n, int classes, int coords); -void forward_region_layer(const layer l, network_state state); -void backward_region_layer(const layer l, network_state state); -void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map, float tree_thresh); +layer make_region_layer(int batch, int w, int h, int n, int classes, int coords); +void forward_region_layer(const layer l, network net); +void backward_region_layer(const layer l, network net); void resize_region_layer(layer *l, int w, int h); #ifdef GPU -void forward_region_layer_gpu(const layer l, network_state state); -void backward_region_layer_gpu(layer l, network_state state); +void forward_region_layer_gpu(const layer l, network net); +void backward_region_layer_gpu(layer l, network net); #endif #endif diff --git a/image.darknet/src/reorg_layer.c b/image.darknet/src/reorg_layer.c index 2abca8f..31d6b84 100644 --- a/image.darknet/src/reorg_layer.c +++ b/image.darknet/src/reorg_layer.c @@ -1,18 +1,21 @@ #include "reorg_layer.h" #include "cuda.h" #include "blas.h" + #include -layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse) +layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse, int flatten, int extra) { layer l = {0}; l.type = REORG; l.batch = batch; l.stride = stride; + l.extra = extra; l.h = h; l.w = w; l.c = c; + l.flatten = flatten; if(reverse){ l.out_w = w*stride; l.out_h = h*stride; @@ -23,10 +26,20 @@ layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse) l.out_c = c*(stride*stride); } l.reverse = reverse; - fprintf(stderr, "reorg /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; - int output_size = l.out_h * l.out_w * l.out_c * batch; + if(l.extra){ + l.out_w = l.out_h = l.out_c = 0; + l.outputs = l.inputs + l.extra; + } + + if(extra){ + fprintf(stderr, "reorg %4d -> %4d\n", l.inputs, l.outputs); + } else { + fprintf(stderr, "reorg /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + } + int output_size = l.outputs * batch; l.output = calloc(output_size, sizeof(float)); l.delta = calloc(output_size, sizeof(float)); @@ -75,40 +88,86 @@ void resize_reorg_layer(layer *l, int w, int h) #endif } -void forward_reorg_layer(const layer l, network_state state) +void forward_reorg_layer(const layer l, network net) { - if(l.reverse){ - reorg_cpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output); - }else { - reorg_cpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output); + int i; + if(l.flatten){ + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + if(l.reverse){ + flatten(l.output, l.w*l.h, l.c, l.batch, 0); + }else{ + flatten(l.output, l.w*l.h, l.c, l.batch, 1); + } + } else if (l.extra) { + for(i = 0; i < l.batch; ++i){ + copy_cpu(l.inputs, net.input + i*l.inputs, 1, l.output + i*l.outputs, 1); + } + } else if (l.reverse){ + reorg_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output); + } else { + reorg_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output); } } -void backward_reorg_layer(const layer l, network_state state) +void backward_reorg_layer(const layer l, network net) { - if(l.reverse){ - reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 0, state.delta); + int i; + if(l.flatten){ + memcpy(net.delta, l.delta, l.outputs*l.batch*sizeof(float)); + if(l.reverse){ + flatten(net.delta, l.w*l.h, l.c, l.batch, 1); + }else{ + flatten(net.delta, l.w*l.h, l.c, l.batch, 0); + } + } else if(l.reverse){ + reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 0, net.delta); + } else if (l.extra) { + for(i = 0; i < l.batch; ++i){ + copy_cpu(l.inputs, l.delta + i*l.outputs, 1, net.delta + i*l.inputs, 1); + } }else{ - reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 1, state.delta); + reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 1, net.delta); } } #ifdef GPU -void forward_reorg_layer_gpu(layer l, network_state state) +void forward_reorg_layer_gpu(layer l, network net) { - if(l.reverse){ - reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output_gpu); + int i; + if(l.flatten){ + if(l.reverse){ + flatten_gpu(net.input_gpu, l.w*l.h, l.c, l.batch, 0, l.output_gpu); + }else{ + flatten_gpu(net.input_gpu, l.w*l.h, l.c, l.batch, 1, l.output_gpu); + } + } else if (l.extra) { + for(i = 0; i < l.batch; ++i){ + copy_gpu(l.inputs, net.input_gpu + i*l.inputs, 1, l.output_gpu + i*l.outputs, 1); + } + } else if (l.reverse) { + reorg_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, l.output_gpu); }else { - reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output_gpu); + reorg_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, l.output_gpu); } } -void backward_reorg_layer_gpu(layer l, network_state state) +void backward_reorg_layer_gpu(layer l, network net) { - if(l.reverse){ - reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, state.delta); - }else{ - reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, state.delta); + if(l.flatten){ + if(l.reverse){ + flatten_gpu(l.delta_gpu, l.w*l.h, l.c, l.batch, 1, net.delta_gpu); + }else{ + flatten_gpu(l.delta_gpu, l.w*l.h, l.c, l.batch, 0, net.delta_gpu); + } + } else if (l.extra) { + int i; + for(i = 0; i < l.batch; ++i){ + copy_gpu(l.inputs, l.delta_gpu + i*l.outputs, 1, net.delta_gpu + i*l.inputs, 1); + } + } else if(l.reverse){ + reorg_gpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, net.delta_gpu); + } else { + reorg_gpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, net.delta_gpu); } } #endif diff --git a/image.darknet/src/reorg_layer.h b/image.darknet/src/reorg_layer.h index 21c22cd..e6513a5 100644 --- a/image.darknet/src/reorg_layer.h +++ b/image.darknet/src/reorg_layer.h @@ -6,14 +6,14 @@ #include "layer.h" #include "network.h" -layer make_reorg_layer(int batch, int h, int w, int c, int stride, int reverse); +layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse, int flatten, int extra); void resize_reorg_layer(layer *l, int w, int h); -void forward_reorg_layer(const layer l, network_state state); -void backward_reorg_layer(const layer l, network_state state); +void forward_reorg_layer(const layer l, network net); +void backward_reorg_layer(const layer l, network net); #ifdef GPU -void forward_reorg_layer_gpu(layer l, network_state state); -void backward_reorg_layer_gpu(layer l, network_state state); +void forward_reorg_layer_gpu(layer l, network net); +void backward_reorg_layer_gpu(layer l, network net); #endif #endif diff --git a/image.darknet/src/rnn.c b/image.darknet/src/rnn.c deleted file mode 100644 index eca6f55..0000000 --- a/image.darknet/src/rnn.c +++ /dev/null @@ -1,492 +0,0 @@ -#include "network.h" -#include "cost_layer.h" -#include "utils.h" -#include "blas.h" -#include "parser.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -typedef struct { - float *x; - float *y; -} float_pair; - -int *read_tokenized_data(char *filename, size_t *read) -{ - size_t size = 512; - size_t count = 0; - FILE *fp = fopen(filename, "r"); - int *d = calloc(size, sizeof(int)); - int n, one; - one = fscanf(fp, "%d", &n); - while(one == 1){ - ++count; - if(count > size){ - size = size*2; - d = realloc(d, size*sizeof(int)); - } - d[count-1] = n; - one = fscanf(fp, "%d", &n); - } - fclose(fp); - d = realloc(d, count*sizeof(int)); - *read = count; - return d; -} - -char **read_tokens(char *filename, size_t *read) -{ - size_t size = 512; - size_t count = 0; - FILE *fp = fopen(filename, "r"); - char **d = calloc(size, sizeof(char *)); - char *line; - while((line=fgetl(fp)) != 0){ - ++count; - if(count > size){ - size = size*2; - d = realloc(d, size*sizeof(char *)); - } - d[count-1] = line; - } - fclose(fp); - d = realloc(d, count*sizeof(char *)); - *read = count; - return d; -} - -float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps) -{ - float *x = calloc(batch * steps * characters, sizeof(float)); - float *y = calloc(batch * steps * characters, sizeof(float)); - int i,j; - for(i = 0; i < batch; ++i){ - for(j = 0; j < steps; ++j){ - int curr = tokens[(offsets[i])%len]; - int next = tokens[(offsets[i] + 1)%len]; - - x[(j*batch + i)*characters + curr] = 1; - y[(j*batch + i)*characters + next] = 1; - - offsets[i] = (offsets[i] + 1) % len; - - if(curr >= characters || curr < 0 || next >= characters || next < 0){ - error("Bad char"); - } - } - } - float_pair p; - p.x = x; - p.y = y; - return p; -} - -float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps) -{ - float *x = calloc(batch * steps * characters, sizeof(float)); - float *y = calloc(batch * steps * characters, sizeof(float)); - int i,j; - for(i = 0; i < batch; ++i){ - for(j = 0; j < steps; ++j){ - unsigned char curr = text[(offsets[i])%len]; - unsigned char next = text[(offsets[i] + 1)%len]; - - x[(j*batch + i)*characters + curr] = 1; - y[(j*batch + i)*characters + next] = 1; - - offsets[i] = (offsets[i] + 1) % len; - - if(curr > 255 || curr <= 0 || next > 255 || next <= 0){ - /*text[(index+j+2)%len] = 0; - printf("%ld %d %d %d %d\n", index, j, len, (int)text[index+j], (int)text[index+j+1]); - printf("%s", text+index); - */ - error("Bad char"); - } - } - } - float_pair p; - p.x = x; - p.y = y; - return p; -} - -void reset_rnn_state(network net, int b) -{ - int i; - for (i = 0; i < net.n; ++i) { - #ifdef GPU - layer l = net.layers[i]; - if(l.state_gpu){ - fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1); - } - #endif - } -} - -void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int tokenized) -{ - srand(time(0)); - unsigned char *text = 0; - int *tokens = 0; - size_t size; - if(tokenized){ - tokens = read_tokenized_data(filename, &size); - } else { - FILE *fp = fopen(filename, "rb"); - - fseek(fp, 0, SEEK_END); - size = ftell(fp); - fseek(fp, 0, SEEK_SET); - - text = calloc(size+1, sizeof(char)); - fread(text, 1, size, fp); - fclose(fp); - } - - char *backup_directory = "/home/pjreddie/backup/"; - char *base = basecfg(cfgfile); - fprintf(stderr, "%s\n", base); - float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - - int inputs = get_network_input_size(net); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int batch = net.batch; - int steps = net.time_steps; - if(clear) *net.seen = 0; - int i = (*net.seen)/net.batch; - - int streams = batch/steps; - size_t *offsets = calloc(streams, sizeof(size_t)); - int j; - for(j = 0; j < streams; ++j){ - offsets[j] = rand_size_t()%size; - } - - clock_t time; - while(get_current_batch(net) < net.max_batches){ - i += 1; - time=clock(); - float_pair p; - if(tokenized){ - p = get_rnn_token_data(tokens, offsets, inputs, size, streams, steps); - }else{ - p = get_rnn_data(text, offsets, inputs, size, streams, steps); - } - - float loss = train_network_datum(net, p.x, p.y) / (batch); - free(p.x); - free(p.y); - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - int chars = get_current_batch(net)*batch; - fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds, %f epochs\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), (float) chars/size); - - for(j = 0; j < streams; ++j){ - //printf("%d\n", j); - if(rand()%10 == 0){ - //fprintf(stderr, "Reset\n"); - offsets[j] = rand_size_t()%size; - reset_rnn_state(net, j); - } - } - - if(i%1000==0){ - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - if(i%10==0){ - char buff[256]; - sprintf(buff, "%s/%s.backup", backup_directory, base); - save_weights(net, buff); - } - } - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - -void print_symbol(int n, char **tokens){ - if(tokens){ - printf("%s ", tokens[n]); - } else { - printf("%c", n); - } -} - -void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed, char *token_file) -{ - char **tokens = 0; - if(token_file){ - size_t n; - tokens = read_tokens(token_file, &n); - } - - srand(rseed); - char *base = basecfg(cfgfile); - fprintf(stderr, "%s\n", base); - - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int inputs = get_network_input_size(net); - - int i, j; - for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; - int c = 0; - int len = strlen(seed); - float *input = calloc(inputs, sizeof(float)); - - /* - fill_cpu(inputs, 0, input, 1); - for(i = 0; i < 10; ++i){ - network_predict(net, input); - } - fill_cpu(inputs, 0, input, 1); - */ - - for(i = 0; i < len-1; ++i){ - c = seed[i]; - input[c] = 1; - network_predict(net, input); - input[c] = 0; - print_symbol(c, tokens); - } - if(len) c = seed[len-1]; - print_symbol(c, tokens); - for(i = 0; i < num; ++i){ - input[c] = 1; - float *out = network_predict(net, input); - input[c] = 0; - for(j = 32; j < 127; ++j){ - //printf("%d %c %f\n",j, j, out[j]); - } - for(j = 0; j < inputs; ++j){ - if (out[j] < .0001) out[j] = 0; - } - c = sample_array(out, inputs); - print_symbol(c, tokens); - } - printf("\n"); -} - -void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file) -{ - char **tokens = 0; - if(token_file){ - size_t n; - tokens = read_tokens(token_file, &n); - } - - srand(rseed); - char *base = basecfg(cfgfile); - fprintf(stderr, "%s\n", base); - - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int inputs = get_network_input_size(net); - - int i, j; - for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; - int c = 0; - float *input = calloc(inputs, sizeof(float)); - float *out = 0; - - while((c = getc(stdin)) != EOF){ - input[c] = 1; - out = network_predict(net, input); - input[c] = 0; - } - for(i = 0; i < num; ++i){ - for(j = 0; j < inputs; ++j){ - if (out[j] < .0001) out[j] = 0; - } - int next = sample_array(out, inputs); - if(c == '.' && next == '\n') break; - c = next; - print_symbol(c, tokens); - - input[c] = 1; - out = network_predict(net, input); - input[c] = 0; - } - printf("\n"); -} - -void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed) -{ - char *base = basecfg(cfgfile); - fprintf(stderr, "%s\n", base); - - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int inputs = get_network_input_size(net); - - int count = 0; - int words = 1; - int c; - int len = strlen(seed); - float *input = calloc(inputs, sizeof(float)); - int i; - for(i = 0; i < len; ++i){ - c = seed[i]; - input[(int)c] = 1; - network_predict(net, input); - input[(int)c] = 0; - } - float sum = 0; - c = getc(stdin); - float log2 = log(2); - int in = 0; - while(c != EOF){ - int next = getc(stdin); - if(next == EOF) break; - if(next < 0 || next >= 255) error("Out of range character"); - - input[c] = 1; - float *out = network_predict(net, input); - input[c] = 0; - - if(c == '.' && next == '\n') in = 0; - if(!in) { - if(c == '>' && next == '>'){ - in = 1; - ++words; - } - c = next; - continue; - } - ++count; - sum += log(out[next])/log2; - c = next; - printf("%d %d Perplexity: %4.4f Word Perplexity: %4.4f\n", count, words, pow(2, -sum/count), pow(2, -sum/words)); - } -} - -void valid_char_rnn(char *cfgfile, char *weightfile, char *seed) -{ - char *base = basecfg(cfgfile); - fprintf(stderr, "%s\n", base); - - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int inputs = get_network_input_size(net); - - int count = 0; - int words = 1; - int c; - int len = strlen(seed); - float *input = calloc(inputs, sizeof(float)); - int i; - for(i = 0; i < len; ++i){ - c = seed[i]; - input[(int)c] = 1; - network_predict(net, input); - input[(int)c] = 0; - } - float sum = 0; - c = getc(stdin); - float log2 = log(2); - while(c != EOF){ - int next = getc(stdin); - if(next == EOF) break; - if(next < 0 || next >= 255) error("Out of range character"); - ++count; - if(next == ' ' || next == '\n' || next == '\t') ++words; - input[c] = 1; - float *out = network_predict(net, input); - input[c] = 0; - sum += log(out[next])/log2; - c = next; - printf("%d Perplexity: %4.4f Word Perplexity: %4.4f\n", count, pow(2, -sum/count), pow(2, -sum/words)); - } -} - -void vec_char_rnn(char *cfgfile, char *weightfile, char *seed) -{ - char *base = basecfg(cfgfile); - fprintf(stderr, "%s\n", base); - - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int inputs = get_network_input_size(net); - - int c; - int seed_len = strlen(seed); - float *input = calloc(inputs, sizeof(float)); - int i; - char *line; - while((line=fgetl(stdin)) != 0){ - reset_rnn_state(net, 0); - for(i = 0; i < seed_len; ++i){ - c = seed[i]; - input[(int)c] = 1; - network_predict(net, input); - input[(int)c] = 0; - } - strip(line); - int str_len = strlen(line); - for(i = 0; i < str_len; ++i){ - c = line[i]; - input[(int)c] = 1; - network_predict(net, input); - input[(int)c] = 0; - } - c = ' '; - input[(int)c] = 1; - network_predict(net, input); - input[(int)c] = 0; - - layer l = net.layers[0]; - #ifdef GPU - cuda_pull_array(l.output_gpu, l.output, l.outputs); - #endif - printf("%s", line); - for(i = 0; i < l.outputs; ++i){ - printf(",%g", l.output[i]); - } - printf("\n"); - } -} - -void run_char_rnn(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - char *filename = find_char_arg(argc, argv, "-file", "data/shakespeare.txt"); - char *seed = find_char_arg(argc, argv, "-seed", "\n\n"); - int len = find_int_arg(argc, argv, "-len", 1000); - float temp = find_float_arg(argc, argv, "-temp", .7); - int rseed = find_int_arg(argc, argv, "-srand", time(0)); - int clear = find_arg(argc, argv, "-clear"); - int tokenized = find_arg(argc, argv, "-tokenized"); - char *tokens = find_char_arg(argc, argv, "-tokens", 0); - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename, clear, tokenized); - else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights, seed); - else if(0==strcmp(argv[2], "validtactic")) valid_tactic_rnn(cfg, weights, seed); - else if(0==strcmp(argv[2], "vec")) vec_char_rnn(cfg, weights, seed); - else if(0==strcmp(argv[2], "generate")) test_char_rnn(cfg, weights, len, seed, temp, rseed, tokens); - else if(0==strcmp(argv[2], "generatetactic")) test_tactic_rnn(cfg, weights, len, temp, rseed, tokens); -} diff --git a/image.darknet/src/rnn_layer.c b/image.darknet/src/rnn_layer.c index 83fda13..8c9b457 100644 --- a/image.darknet/src/rnn_layer.c +++ b/image.darknet/src/rnn_layer.c @@ -26,7 +26,7 @@ static void increment_layer(layer *l, int steps) #endif } -layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, ACTIVATION activation, int batch_normalize, int log) +layer make_rnn_layer(int batch, int inputs, int outputs, int steps, ACTIVATION activation, int batch_normalize, int adam) { fprintf(stderr, "RNN Layer: %d inputs, %d outputs\n", inputs, outputs); batch = batch / steps; @@ -34,24 +34,24 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, l.batch = batch; l.type = RNN; l.steps = steps; - l.hidden = hidden; l.inputs = inputs; - l.state = calloc(batch*hidden*(steps+1), sizeof(float)); + l.state = calloc(batch*outputs, sizeof(float)); + l.prev_state = calloc(batch*outputs, sizeof(float)); l.input_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_layer) = make_connected_layer(batch*steps, inputs, hidden, activation, batch_normalize); + *(l.input_layer) = make_connected_layer(batch*steps, inputs, outputs, activation, batch_normalize, adam); l.input_layer->batch = batch; l.self_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.self_layer) = make_connected_layer(batch*steps, hidden, hidden, (log==2)?LOGGY:(log==1?LOGISTIC:activation), batch_normalize); + *(l.self_layer) = make_connected_layer(batch*steps, outputs, outputs, activation, batch_normalize, adam); l.self_layer->batch = batch; l.output_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.output_layer) = make_connected_layer(batch*steps, hidden, outputs, activation, batch_normalize); + *(l.output_layer) = make_connected_layer(batch*steps, outputs, outputs, activation, batch_normalize, adam); l.output_layer->batch = batch; l.outputs = outputs; @@ -65,66 +65,72 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, l.forward_gpu = forward_rnn_layer_gpu; l.backward_gpu = backward_rnn_layer_gpu; l.update_gpu = update_rnn_layer_gpu; - l.state_gpu = cuda_make_array(l.state, batch*hidden*(steps+1)); + l.state_gpu = cuda_make_array(0, batch*outputs); + l.prev_state_gpu = cuda_make_array(0, batch*outputs); l.output_gpu = l.output_layer->output_gpu; l.delta_gpu = l.output_layer->delta_gpu; +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.input_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_layer->out_c, l.input_layer->out_h, l.input_layer->out_w); + cudnnSetTensor4dDescriptor(l.self_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.self_layer->out_c, l.self_layer->out_h, l.self_layer->out_w); + cudnnSetTensor4dDescriptor(l.output_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.output_layer->out_c, l.output_layer->out_h, l.output_layer->out_w); +#endif #endif return l; } -void update_rnn_layer(layer l, int batch, float learning_rate, float momentum, float decay) +void update_rnn_layer(layer l, update_args a) { - update_connected_layer(*(l.input_layer), batch, learning_rate, momentum, decay); - update_connected_layer(*(l.self_layer), batch, learning_rate, momentum, decay); - update_connected_layer(*(l.output_layer), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.input_layer), a); + update_connected_layer(*(l.self_layer), a); + update_connected_layer(*(l.output_layer), a); } -void forward_rnn_layer(layer l, network_state state) +void forward_rnn_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1); - fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1); - fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1); - if(state.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, self_layer.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, input_layer.delta, 1); + if(net.train) fill_cpu(l.outputs * l.batch, 0, l.state, 1); for (i = 0; i < l.steps; ++i) { - s.input = state.input; + s.input = net.input; forward_connected_layer(input_layer, s); s.input = l.state; forward_connected_layer(self_layer, s); float *old_state = l.state; - if(state.train) l.state += l.hidden*l.batch; + if(net.train) l.state += l.outputs*l.batch; if(l.shortcut){ - copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1); + copy_cpu(l.outputs * l.batch, old_state, 1, l.state, 1); }else{ - fill_cpu(l.hidden * l.batch, 0, l.state, 1); + fill_cpu(l.outputs * l.batch, 0, l.state, 1); } - axpy_cpu(l.hidden * l.batch, 1, input_layer.output, 1, l.state, 1); - axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, input_layer.output, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, self_layer.output, 1, l.state, 1); s.input = l.state; forward_connected_layer(output_layer, s); - state.input += l.inputs*l.batch; + net.input += l.inputs*l.batch; increment_layer(&input_layer, 1); increment_layer(&self_layer, 1); increment_layer(&output_layer, 1); } } -void backward_rnn_layer(layer l, network_state state) +void backward_rnn_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -134,34 +140,34 @@ void backward_rnn_layer(layer l, network_state state) increment_layer(&self_layer, l.steps-1); increment_layer(&output_layer, l.steps-1); - l.state += l.hidden*l.batch*l.steps; + l.state += l.outputs*l.batch*l.steps; for (i = l.steps-1; i >= 0; --i) { - copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1); - axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1); + copy_cpu(l.outputs * l.batch, input_layer.output, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, self_layer.output, 1, l.state, 1); s.input = l.state; s.delta = self_layer.delta; backward_connected_layer(output_layer, s); - l.state -= l.hidden*l.batch; + l.state -= l.outputs*l.batch; /* if(i > 0){ - copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1); - axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1); + copy_cpu(l.outputs * l.batch, input_layer.output - l.outputs*l.batch, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, self_layer.output - l.outputs*l.batch, 1, l.state, 1); }else{ - fill_cpu(l.hidden * l.batch, 0, l.state, 1); + fill_cpu(l.outputs * l.batch, 0, l.state, 1); } */ s.input = l.state; - s.delta = self_layer.delta - l.hidden*l.batch; + s.delta = self_layer.delta - l.outputs*l.batch; if (i == 0) s.delta = 0; backward_connected_layer(self_layer, s); - copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1); - if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1); - s.input = state.input + i*l.inputs*l.batch; - if(state.delta) s.delta = state.delta + i*l.inputs*l.batch; + copy_cpu(l.outputs*l.batch, self_layer.delta, 1, input_layer.delta, 1); + if (i > 0 && l.shortcut) axpy_cpu(l.outputs*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.outputs*l.batch, 1); + s.input = net.input + i*l.inputs*l.batch; + if(net.delta) s.delta = net.delta + i*l.inputs*l.batch; else s.delta = 0; backward_connected_layer(input_layer, s); @@ -187,58 +193,56 @@ void push_rnn_layer(layer l) push_connected_layer(*(l.output_layer)); } -void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_rnn_layer_gpu(layer l, update_args a) { - update_connected_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.input_layer), a); + update_connected_layer_gpu(*(l.self_layer), a); + update_connected_layer_gpu(*(l.output_layer), a); } -void forward_rnn_layer_gpu(layer l, network_state state) +void forward_rnn_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = {0}; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); - fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1); - if(state.train) fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, self_layer.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, input_layer.delta_gpu, 1); + + if(net.train) { + fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1); + } for (i = 0; i < l.steps; ++i) { - s.input = state.input; + s.input_gpu = net.input_gpu; forward_connected_layer_gpu(input_layer, s); - s.input = l.state_gpu; + s.input_gpu = l.state_gpu; forward_connected_layer_gpu(self_layer, s); - float *old_state = l.state_gpu; - if(state.train) l.state_gpu += l.hidden*l.batch; - if(l.shortcut){ - copy_ongpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1); - }else{ - fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); - } - axpy_ongpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); - axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); - s.input = l.state_gpu; + s.input_gpu = l.state_gpu; forward_connected_layer_gpu(output_layer, s); - state.input += l.inputs*l.batch; + net.input_gpu += l.inputs*l.batch; increment_layer(&input_layer, 1); increment_layer(&self_layer, 1); increment_layer(&output_layer, 1); } } -void backward_rnn_layer_gpu(layer l, network_state state) +void backward_rnn_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = {0}; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -246,32 +250,43 @@ void backward_rnn_layer_gpu(layer l, network_state state) increment_layer(&input_layer, l.steps - 1); increment_layer(&self_layer, l.steps - 1); increment_layer(&output_layer, l.steps - 1); - l.state_gpu += l.hidden*l.batch*l.steps; + float *last_input = input_layer.output_gpu; + float *last_self = self_layer.output_gpu; for (i = l.steps-1; i >= 0; --i) { + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); - s.input = l.state_gpu; - s.delta = self_layer.delta_gpu; + s.input_gpu = l.state_gpu; + s.delta_gpu = self_layer.delta_gpu; backward_connected_layer_gpu(output_layer, s); - l.state_gpu -= l.hidden*l.batch; + if(i != 0) { + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1); + }else { + copy_gpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.state_gpu, 1); + } - copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); + copy_gpu(l.outputs*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); - s.input = l.state_gpu; - s.delta = self_layer.delta_gpu - l.hidden*l.batch; - if (i == 0) s.delta = 0; + s.input_gpu = l.state_gpu; + s.delta_gpu = (i > 0) ? self_layer.delta_gpu - l.outputs*l.batch : 0; + if (i == 0) s.delta_gpu = 0; backward_connected_layer_gpu(self_layer, s); - //copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); - if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1); - s.input = state.input + i*l.inputs*l.batch; - if(state.delta) s.delta = state.delta + i*l.inputs*l.batch; - else s.delta = 0; + s.input_gpu = net.input_gpu + i*l.inputs*l.batch; + if(net.delta_gpu) s.delta_gpu = net.delta_gpu + i*l.inputs*l.batch; + else s.delta_gpu = 0; backward_connected_layer_gpu(input_layer, s); increment_layer(&input_layer, -1); increment_layer(&self_layer, -1); increment_layer(&output_layer, -1); } + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, last_input, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, last_self, 1, l.state_gpu, 1); } #endif diff --git a/image.darknet/src/rnn_layer.h b/image.darknet/src/rnn_layer.h index bb9478b..270a63f 100644 --- a/image.darknet/src/rnn_layer.h +++ b/image.darknet/src/rnn_layer.h @@ -7,16 +7,16 @@ #include "network.h" #define USET -layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, ACTIVATION activation, int batch_normalize, int log); +layer make_rnn_layer(int batch, int inputs, int outputs, int steps, ACTIVATION activation, int batch_normalize, int adam); -void forward_rnn_layer(layer l, network_state state); -void backward_rnn_layer(layer l, network_state state); -void update_rnn_layer(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_rnn_layer(layer l, network net); +void backward_rnn_layer(layer l, network net); +void update_rnn_layer(layer l, update_args a); #ifdef GPU -void forward_rnn_layer_gpu(layer l, network_state state); -void backward_rnn_layer_gpu(layer l, network_state state); -void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_rnn_layer_gpu(layer l, network net); +void backward_rnn_layer_gpu(layer l, network net); +void update_rnn_layer_gpu(layer l, update_args a); void push_rnn_layer(layer l); void pull_rnn_layer(layer l); #endif diff --git a/image.darknet/src/rnn_vid.c b/image.darknet/src/rnn_vid.c deleted file mode 100644 index 36912d6..0000000 --- a/image.darknet/src/rnn_vid.c +++ /dev/null @@ -1,211 +0,0 @@ -#include "network.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "blas.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -image get_image_from_stream(CvCapture *cap); -image ipl_to_image(IplImage* src); - -void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters); - - -typedef struct { - float *x; - float *y; -} float_pair; - -float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int steps) -{ - int b; - assert(net.batch == steps + 1); - image out_im = get_network_image(net); - int output_size = out_im.w*out_im.h*out_im.c; - printf("%d %d %d\n", out_im.w, out_im.h, out_im.c); - float *feats = calloc(net.batch*batch*output_size, sizeof(float)); - for(b = 0; b < batch; ++b){ - int input_size = net.w*net.h*net.c; - float *input = calloc(input_size*net.batch, sizeof(float)); - char *filename = files[rand()%n]; - CvCapture *cap = cvCaptureFromFile(filename); - int frames = cvGetCaptureProperty(cap, CV_CAP_PROP_FRAME_COUNT); - int index = rand() % (frames - steps - 2); - if (frames < (steps + 4)){ - --b; - free(input); - continue; - } - - printf("frames: %d, index: %d\n", frames, index); - cvSetCaptureProperty(cap, CV_CAP_PROP_POS_FRAMES, index); - - int i; - for(i = 0; i < net.batch; ++i){ - IplImage* src = cvQueryFrame(cap); - image im = ipl_to_image(src); - rgbgr_image(im); - image re = resize_image(im, net.w, net.h); - //show_image(re, "loaded"); - //cvWaitKey(10); - memcpy(input + i*input_size, re.data, input_size*sizeof(float)); - free_image(im); - free_image(re); - } - float *output = network_predict(net, input); - - free(input); - - for(i = 0; i < net.batch; ++i){ - memcpy(feats + (b + i*batch)*output_size, output + i*output_size, output_size*sizeof(float)); - } - - cvReleaseCapture(&cap); - } - - //printf("%d %d %d\n", out_im.w, out_im.h, out_im.c); - float_pair p = {0}; - p.x = feats; - p.y = feats + output_size*batch; //+ out_im.w*out_im.h*out_im.c; - - return p; -} - - -void train_vid_rnn(char *cfgfile, char *weightfile) -{ - char *train_videos = "data/vid/train.txt"; - char *backup_directory = "/home/pjreddie/backup/"; - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; - - list *plist = get_paths(train_videos); - int N = plist->size; - char **paths = (char **)list_to_array(plist); - clock_t time; - int steps = net.time_steps; - int batch = net.batch / net.time_steps; - - network extractor = parse_network_cfg("cfg/extractor.cfg"); - load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv"); - - while(get_current_batch(net) < net.max_batches){ - i += 1; - time=clock(); - float_pair p = get_rnn_vid_data(extractor, paths, N, batch, steps); - - float loss = train_network_datum(net, p.x, p.y) / (net.batch); - - - free(p.x); - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time)); - if(i%100==0){ - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - if(i%10==0){ - char buff[256]; - sprintf(buff, "%s/%s.backup", backup_directory, base); - save_weights(net, buff); - } - } - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - - -image save_reconstruction(network net, image *init, float *feat, char *name, int i) -{ - image recon; - if (init) { - recon = copy_image(*init); - } else { - recon = make_random_image(net.w, net.h, 3); - } - - image update = make_image(net.w, net.h, 3); - reconstruct_picture(net, feat, recon, update, .01, .9, .1, 2, 50); - char buff[256]; - sprintf(buff, "%s%d", name, i); - save_image(recon, buff); - free_image(update); - return recon; -} - -void generate_vid_rnn(char *cfgfile, char *weightfile) -{ - network extractor = parse_network_cfg("cfg/extractor.recon.cfg"); - load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv"); - - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&extractor, 1); - set_batch_network(&net, 1); - - int i; - CvCapture *cap = cvCaptureFromFile("/extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4"); - float *feat; - float *next; - image last; - for(i = 0; i < 25; ++i){ - image im = get_image_from_stream(cap); - image re = resize_image(im, extractor.w, extractor.h); - feat = network_predict(extractor, re.data); - if(i > 0){ - printf("%f %f\n", mean_array(feat, 14*14*512), variance_array(feat, 14*14*512)); - printf("%f %f\n", mean_array(next, 14*14*512), variance_array(next, 14*14*512)); - printf("%f\n", mse_array(feat, 14*14*512)); - axpy_cpu(14*14*512, -1, feat, 1, next, 1); - printf("%f\n", mse_array(next, 14*14*512)); - } - next = network_predict(net, feat); - - free_image(im); - - free_image(save_reconstruction(extractor, 0, feat, "feat", i)); - free_image(save_reconstruction(extractor, 0, next, "next", i)); - if (i==24) last = copy_image(re); - free_image(re); - } - for(i = 0; i < 30; ++i){ - next = network_predict(net, next); - image new = save_reconstruction(extractor, &last, next, "new", i); - free_image(last); - last = new; - } -} - -void run_vid_rnn(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - //char *filename = (argc > 5) ? argv[5]: 0; - if(0==strcmp(argv[2], "train")) train_vid_rnn(cfg, weights); - else if(0==strcmp(argv[2], "generate")) generate_vid_rnn(cfg, weights); -} -#else -void run_vid_rnn(int argc, char **argv){} -#endif - diff --git a/image.darknet/src/route_layer.c b/image.darknet/src/route_layer.c index dce7118..a8970a4 100644 --- a/image.darknet/src/route_layer.c +++ b/image.darknet/src/route_layer.c @@ -1,6 +1,7 @@ #include "route_layer.h" #include "cuda.h" #include "blas.h" + #include route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes) @@ -70,13 +71,13 @@ void resize_route_layer(route_layer *l, network *net) } -void forward_route_layer(const route_layer l, network_state state) +void forward_route_layer(const route_layer l, network net) { int i, j; int offset = 0; for(i = 0; i < l.n; ++i){ int index = l.input_layers[i]; - float *input = state.net.layers[index].output; + float *input = net.layers[index].output; int input_size = l.input_sizes[i]; for(j = 0; j < l.batch; ++j){ copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1); @@ -85,13 +86,13 @@ void forward_route_layer(const route_layer l, network_state state) } } -void backward_route_layer(const route_layer l, network_state state) +void backward_route_layer(const route_layer l, network net) { int i, j; int offset = 0; for(i = 0; i < l.n; ++i){ int index = l.input_layers[i]; - float *delta = state.net.layers[index].delta; + float *delta = net.layers[index].delta; int input_size = l.input_sizes[i]; for(j = 0; j < l.batch; ++j){ axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1); @@ -101,31 +102,31 @@ void backward_route_layer(const route_layer l, network_state state) } #ifdef GPU -void forward_route_layer_gpu(const route_layer l, network_state state) +void forward_route_layer_gpu(const route_layer l, network net) { int i, j; int offset = 0; for(i = 0; i < l.n; ++i){ int index = l.input_layers[i]; - float *input = state.net.layers[index].output_gpu; + float *input = net.layers[index].output_gpu; int input_size = l.input_sizes[i]; for(j = 0; j < l.batch; ++j){ - copy_ongpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1); + copy_gpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1); } offset += input_size; } } -void backward_route_layer_gpu(const route_layer l, network_state state) +void backward_route_layer_gpu(const route_layer l, network net) { int i, j; int offset = 0; for(i = 0; i < l.n; ++i){ int index = l.input_layers[i]; - float *delta = state.net.layers[index].delta_gpu; + float *delta = net.layers[index].delta_gpu; int input_size = l.input_sizes[i]; for(j = 0; j < l.batch; ++j){ - axpy_ongpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1); + axpy_gpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1); } offset += input_size; } diff --git a/image.darknet/src/route_layer.h b/image.darknet/src/route_layer.h index 45467d9..1d40330 100644 --- a/image.darknet/src/route_layer.h +++ b/image.darknet/src/route_layer.h @@ -6,13 +6,13 @@ typedef layer route_layer; route_layer make_route_layer(int batch, int n, int *input_layers, int *input_size); -void forward_route_layer(const route_layer l, network_state state); -void backward_route_layer(const route_layer l, network_state state); +void forward_route_layer(const route_layer l, network net); +void backward_route_layer(const route_layer l, network net); void resize_route_layer(route_layer *l, network *net); #ifdef GPU -void forward_route_layer_gpu(const route_layer l, network_state state); -void backward_route_layer_gpu(const route_layer l, network_state state); +void forward_route_layer_gpu(const route_layer l, network net); +void backward_route_layer_gpu(const route_layer l, network net); #endif #endif diff --git a/image.darknet/src/shortcut_layer.c b/image.darknet/src/shortcut_layer.c index 8bca50f..49d17f5 100644 --- a/image.darknet/src/shortcut_layer.c +++ b/image.darknet/src/shortcut_layer.c @@ -1,12 +1,14 @@ #include "shortcut_layer.h" #include "cuda.h" #include "blas.h" +#include "activations.h" + #include #include layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2) { - fprintf(stderr,"Shortcut Layer: %d\n", index); + fprintf(stderr, "res %3d %4d x%4d x%4d -> %4d x%4d x%4d\n",index, w2,h2,c2, w,h,c); layer l = {0}; l.type = SHORTCUT; l.batch = batch; @@ -36,32 +38,53 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int return l; } -void forward_shortcut_layer(const layer l, network_state state) +void resize_shortcut_layer(layer *l, int w, int h) +{ + assert(l->w == l->out_w); + assert(l->h == l->out_h); + l->w = l->out_w = w; + l->h = l->out_h = h; + l->outputs = w*h*l->out_c; + l->inputs = l->outputs; + l->delta = realloc(l->delta, l->outputs*l->batch*sizeof(float)); + l->output = realloc(l->output, l->outputs*l->batch*sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); +#endif + +} + + +void forward_shortcut_layer(const layer l, network net) { - copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); - shortcut_cpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.output); + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + shortcut_cpu(l.batch, l.w, l.h, l.c, net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.alpha, l.beta, l.output); activate_array(l.output, l.outputs*l.batch, l.activation); } -void backward_shortcut_layer(const layer l, network_state state) +void backward_shortcut_layer(const layer l, network net) { gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); - axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1); - shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); + axpy_cpu(l.outputs*l.batch, l.alpha, l.delta, 1, net.delta, 1); + shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, 1, l.beta, net.layers[l.index].delta); } #ifdef GPU -void forward_shortcut_layer_gpu(const layer l, network_state state) +void forward_shortcut_layer_gpu(const layer l, network net) { - copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - shortcut_gpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + shortcut_gpu(l.batch, l.w, l.h, l.c, net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.alpha, l.beta, l.output_gpu); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); } -void backward_shortcut_layer_gpu(const layer l, network_state state) +void backward_shortcut_layer_gpu(const layer l, network net) { - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - axpy_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1, state.delta, 1); - shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, state.net.layers[l.index].delta_gpu); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + axpy_gpu(l.outputs*l.batch, l.alpha, l.delta_gpu, 1, net.delta_gpu, 1); + shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, 1, l.beta, net.layers[l.index].delta_gpu); } #endif diff --git a/image.darknet/src/shortcut_layer.h b/image.darknet/src/shortcut_layer.h index c09a809..5f684fc 100644 --- a/image.darknet/src/shortcut_layer.h +++ b/image.darknet/src/shortcut_layer.h @@ -5,12 +5,13 @@ #include "network.h" layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2); -void forward_shortcut_layer(const layer l, network_state state); -void backward_shortcut_layer(const layer l, network_state state); +void forward_shortcut_layer(const layer l, network net); +void backward_shortcut_layer(const layer l, network net); +void resize_shortcut_layer(layer *l, int w, int h); #ifdef GPU -void forward_shortcut_layer_gpu(const layer l, network_state state); -void backward_shortcut_layer_gpu(const layer l, network_state state); +void forward_shortcut_layer_gpu(const layer l, network net); +void backward_shortcut_layer_gpu(const layer l, network net); #endif #endif diff --git a/image.darknet/src/softmax_layer.c b/image.darknet/src/softmax_layer.c index 5d15314..9cbc6be 100644 --- a/image.darknet/src/softmax_layer.c +++ b/image.darknet/src/softmax_layer.c @@ -1,6 +1,7 @@ #include "softmax_layer.h" #include "blas.h" #include "cuda.h" + #include #include #include @@ -17,8 +18,10 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups) l.groups = groups; l.inputs = inputs; l.outputs = inputs; + l.loss = calloc(inputs*batch, sizeof(float)); l.output = calloc(inputs*batch, sizeof(float)); l.delta = calloc(inputs*batch, sizeof(float)); + l.cost = calloc(1, sizeof(float)); l.forward = forward_softmax_layer; l.backward = backward_softmax_layer; @@ -27,45 +30,35 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups) l.backward_gpu = backward_softmax_layer_gpu; l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.loss_gpu = cuda_make_array(l.loss, inputs*batch); l.delta_gpu = cuda_make_array(l.delta, inputs*batch); #endif return l; } -void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output) +void forward_softmax_layer(const softmax_layer l, network net) { - int b; - for(b = 0; b < batch; ++b){ + if(l.softmax_tree){ int i; int count = 0; - for(i = 0; i < hierarchy->groups; ++i){ - int group_size = hierarchy->group_size[i]; - softmax(input+b*inputs + count, group_size, temp, output+b*inputs + count); + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_cpu(net.input + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output + count); count += group_size; } + } else { + softmax_cpu(net.input, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output); } -} -void forward_softmax_layer(const softmax_layer l, network_state state) -{ - int b; - int inputs = l.inputs / l.groups; - int batch = l.batch * l.groups; - if(l.softmax_tree){ - softmax_tree(state.input, batch, inputs, l.temperature, l.softmax_tree, l.output); - } else { - for(b = 0; b < batch; ++b){ - softmax(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs); - } + if(net.truth && !l.noloss){ + softmax_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); } } -void backward_softmax_layer(const softmax_layer l, network_state state) +void backward_softmax_layer(const softmax_layer l, network net) { - int i; - for(i = 0; i < l.inputs*l.batch; ++i){ - state.delta[i] += l.delta[i]; - } + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); } #ifdef GPU @@ -75,26 +68,40 @@ void pull_softmax_layer_output(const softmax_layer layer) cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch); } -void forward_softmax_layer_gpu(const softmax_layer l, network_state state) +void forward_softmax_layer_gpu(const softmax_layer l, network net) { - int inputs = l.inputs / l.groups; - int batch = l.batch * l.groups; if(l.softmax_tree){ + softmax_tree(net.input_gpu, 1, l.batch, l.inputs, l.temperature, l.output_gpu, *l.softmax_tree); + /* int i; int count = 0; for (i = 0; i < l.softmax_tree->groups; ++i) { int group_size = l.softmax_tree->group_size[i]; - softmax_gpu(state.input+count, group_size, inputs, batch, l.temperature, l.output_gpu + count); + softmax_gpu(net.input_gpu + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output_gpu + count); count += group_size; } + */ } else { - softmax_gpu(state.input, inputs, inputs, batch, l.temperature, l.output_gpu); + if(l.spatial){ + softmax_gpu(net.input_gpu, l.c, l.batch*l.c, l.inputs/l.c, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu); + }else{ + softmax_gpu(net.input_gpu, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output_gpu); + } + } + if(net.truth && !l.noloss){ + softmax_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth_gpu, l.delta_gpu, l.loss_gpu); + if(l.softmax_tree){ + mask_gpu(l.batch*l.inputs, l.delta_gpu, SECRET_NUM, net.truth_gpu, 0); + mask_gpu(l.batch*l.inputs, l.loss_gpu, SECRET_NUM, net.truth_gpu, 0); + } + cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); } } -void backward_softmax_layer_gpu(const softmax_layer layer, network_state state) +void backward_softmax_layer_gpu(const softmax_layer layer, network net) { - axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1); + axpy_gpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/src/softmax_layer.h b/image.darknet/src/softmax_layer.h index 821a8dd..2e3ffe0 100644 --- a/image.darknet/src/softmax_layer.h +++ b/image.darknet/src/softmax_layer.h @@ -7,13 +7,13 @@ typedef layer softmax_layer; void softmax_array(float *input, int n, float temp, float *output); softmax_layer make_softmax_layer(int batch, int inputs, int groups); -void forward_softmax_layer(const softmax_layer l, network_state state); -void backward_softmax_layer(const softmax_layer l, network_state state); +void forward_softmax_layer(const softmax_layer l, network net); +void backward_softmax_layer(const softmax_layer l, network net); #ifdef GPU void pull_softmax_layer_output(const softmax_layer l); -void forward_softmax_layer_gpu(const softmax_layer l, network_state state); -void backward_softmax_layer_gpu(const softmax_layer l, network_state state); +void forward_softmax_layer_gpu(const softmax_layer l, network net); +void backward_softmax_layer_gpu(const softmax_layer l, network net); #endif #endif diff --git a/image.darknet/src/stb_image.h b/image.darknet/src/stb_image.h index d0fa9c2..d9c21bc 100644 --- a/image.darknet/src/stb_image.h +++ b/image.darknet/src/stb_image.h @@ -1,5 +1,5 @@ -/* stb_image - v2.06 - public domain image loader - http://nothings.org/stb_image.h - no warranty implied; use at your own risk +/* stb_image - v2.19 - public domain image loader - http://nothings.org/stb + no warranty implied; use at your own risk Do this: #define STB_IMAGE_IMPLEMENTATION @@ -21,17 +21,20 @@ avoid problematic images and only need the trivial interface JPEG baseline & progressive (12 bpc/arithmetic not supported, same as stock IJG lib) - PNG 1/2/4/8-bit-per-channel (16 bpc not supported) + PNG 1/2/4/8/16-bit-per-channel TGA (not sure what subset, if a subset) BMP non-1bpp, non-RLE - PSD (composited view only, no extra channels) + PSD (composited view only, no extra channels, 8/16 bit-per-channel) GIF (*comp always reports as 4-channel) HDR (radiance rgbE format) PIC (Softimage PIC) PNM (PPM and PGM binary only) + Animated GIF still needs a proper API, but here's one way to do it: + http://gist.github.com/urraka/685d9a6340b26b830d49 + - decode from memory or through FILE (define STBI_NO_STDIO to remove code) - decode from arbitrary I/O callbacks - SIMD acceleration on x86/x64 (SSE2) and ARM (NEON) @@ -39,176 +42,65 @@ Full documentation under "DOCUMENTATION" below. - Revision 2.00 release notes: - - - Progressive JPEG is now supported. - - - PPM and PGM binary formats are now supported, thanks to Ken Miller. - - - x86 platforms now make use of SSE2 SIMD instructions for - JPEG decoding, and ARM platforms can use NEON SIMD if requested. - This work was done by Fabian "ryg" Giesen. SSE2 is used by - default, but NEON must be enabled explicitly; see docs. - - With other JPEG optimizations included in this version, we see - 2x speedup on a JPEG on an x86 machine, and a 1.5x speedup - on a JPEG on an ARM machine, relative to previous versions of this - library. The same results will not obtain for all JPGs and for all - x86/ARM machines. (Note that progressive JPEGs are significantly - slower to decode than regular JPEGs.) This doesn't mean that this - is the fastest JPEG decoder in the land; rather, it brings it - closer to parity with standard libraries. If you want the fastest - decode, look elsewhere. (See "Philosophy" section of docs below.) - - See final bullet items below for more info on SIMD. - - - Added STBI_MALLOC, STBI_REALLOC, and STBI_FREE macros for replacing - the memory allocator. Unlike other STBI libraries, these macros don't - support a context parameter, so if you need to pass a context in to - the allocator, you'll have to store it in a global or a thread-local - variable. - - - Split existing STBI_NO_HDR flag into two flags, STBI_NO_HDR and - STBI_NO_LINEAR. - STBI_NO_HDR: suppress implementation of .hdr reader format - STBI_NO_LINEAR: suppress high-dynamic-range light-linear float API - - - You can suppress implementation of any of the decoders to reduce - your code footprint by #defining one or more of the following - symbols before creating the implementation. - - STBI_NO_JPEG - STBI_NO_PNG - STBI_NO_BMP - STBI_NO_PSD - STBI_NO_TGA - STBI_NO_GIF - STBI_NO_HDR - STBI_NO_PIC - STBI_NO_PNM (.ppm and .pgm) - - - You can request *only* certain decoders and suppress all other ones - (this will be more forward-compatible, as addition of new decoders - doesn't require you to disable them explicitly): - - STBI_ONLY_JPEG - STBI_ONLY_PNG - STBI_ONLY_BMP - STBI_ONLY_PSD - STBI_ONLY_TGA - STBI_ONLY_GIF - STBI_ONLY_HDR - STBI_ONLY_PIC - STBI_ONLY_PNM (.ppm and .pgm) - - Note that you can define multiples of these, and you will get all - of them ("only x" and "only y" is interpreted to mean "only x&y"). - - - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still - want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB - - - Compilation of all SIMD code can be suppressed with - #define STBI_NO_SIMD - It should not be necessary to disable SIMD unless you have issues - compiling (e.g. using an x86 compiler which doesn't support SSE - intrinsics or that doesn't support the method used to detect - SSE2 support at run-time), and even those can be reported as - bugs so I can refine the built-in compile-time checking to be - smarter. - - - The old STBI_SIMD system which allowed installing a user-defined - IDCT etc. has been removed. If you need this, don't upgrade. My - assumption is that almost nobody was doing this, and those who - were will find the built-in SIMD more satisfactory anyway. - - - RGB values computed for JPEG images are slightly different from - previous versions of stb_image. (This is due to using less - integer precision in SIMD.) The C code has been adjusted so - that the same RGB values will be computed regardless of whether - SIMD support is available, so your app should always produce - consistent results. But these results are slightly different from - previous versions. (Specifically, about 3% of available YCbCr values - will compute different RGB results from pre-1.49 versions by +-1; - most of the deviating values are one smaller in the G channel.) - - - If you must produce consistent results with previous versions of - stb_image, #define STBI_JPEG_OLD and you will get the same results - you used to; however, you will not get the SIMD speedups for - the YCbCr-to-RGB conversion step (although you should still see - significant JPEG speedup from the other changes). - - Please note that STBI_JPEG_OLD is a temporary feature; it will be - removed in future versions of the library. It is only intended for - near-term back-compatibility use. - - - Latest revision history: - 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value - 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning - 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit - 2.03 (2015-04-12) additional corruption checking - stbi_set_flip_vertically_on_load - fix NEON support; fix mingw support - 2.02 (2015-01-19) fix incorrect assert, fix warning - 2.01 (2015-01-17) fix various warnings - 2.00b (2014-12-25) fix STBI_MALLOC in progressive JPEG - 2.00 (2014-12-25) optimize JPEG, including x86 SSE2 & ARM NEON SIMD - progressive JPEG - PGM/PPM support - STBI_MALLOC,STBI_REALLOC,STBI_FREE - STBI_NO_*, STBI_ONLY_* - GIF bugfix - 1.48 (2014-12-14) fix incorrectly-named assert() - 1.47 (2014-12-14) 1/2/4-bit PNG support (both grayscale and paletted) - optimize PNG - fix bug in interlaced PNG with user-specified channel count +LICENSE + + See end of file for license information. + +RECENT REVISION HISTORY: + + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings + 2.16 (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes + 2.15 (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-12-04) experimental 16-bit API, only for PNG so far; fixes + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) 16-bit PNGS; enable SSE2 in non-gcc x64 + RGB-format JPEG; remove white matting in PSD; + allocate large structures on the stack; + correct channel count for PNG & BMP + 2.10 (2016-01-22) avoid warning introduced in 2.09 + 2.09 (2016-01-16) 16-bit TGA; comments in PNM files; STBI_REALLOC_SIZED See end of file for full revision history. ============================ Contributors ========================= - Image formats Bug fixes & warning fixes - Sean Barrett (jpeg, png, bmp) Marc LeBlanc - Nicolas Schulz (hdr, psd) Christpher Lloyd - Jonathan Dummer (tga) Dave Moore - Jean-Marc Lienher (gif) Won Chun - Tom Seddon (pic) the Horde3D community - Thatcher Ulrich (psd) Janez Zemva - Ken Miller (pgm, ppm) Jonathan Blow - Laurent Gomila - Aruelien Pocheville - Extensions, features Ryamond Barbiero - Jetro Lauha (stbi_info) David Woo - Martin "SpartanJ" Golini (stbi_info) Martin Golini - James "moose2000" Brown (iPhone PNG) Roy Eltham - Ben "Disch" Wenger (io callbacks) Luke Graham - Omar Cornut (1/2/4-bit PNG) Thomas Ruf - Nicolas Guillemot (vertical flip) John Bartholomew - Ken Hamada - Optimizations & bugfixes Cort Stratton - Fabian "ryg" Giesen Blazej Dariusz Roszkowski - Arseny Kapoulkine Thibault Reuille - Paul Du Bois - Guillaume George - If your name should be here but Jerry Jansson - isn't, let Sean know. Hayaki Saito - Johan Duparc - Ronny Chevalier - Michal Cichon - Tero Hanninen - Sergio Gonzalez - Cass Everitt - Engin Manap - Martins Mozeiko - Joseph Thomson - Phil Jordan - -License: - This software is in the public domain. Where that dedication is not - recognized, you are granted a perpetual, irrevocable license to copy - and modify this file however you want. - + Image formats Extensions, features + Sean Barrett (jpeg, png, bmp) Jetro Lauha (stbi_info) + Nicolas Schulz (hdr, psd) Martin "SpartanJ" Golini (stbi_info) + Jonathan Dummer (tga) James "moose2000" Brown (iPhone PNG) + Jean-Marc Lienher (gif) Ben "Disch" Wenger (io callbacks) + Tom Seddon (pic) Omar Cornut (1/2/4-bit PNG) + Thatcher Ulrich (psd) Nicolas Guillemot (vertical flip) + Ken Miller (pgm, ppm) Richard Mitton (16-bit PSD) + github:urraka (animated gif) Junggon Kim (PNM comments) + Christopher Forseth (animated gif) Daniel Gibson (16-bit TGA) + socks-the-fox (16-bit PNG) + Jeremy Sawicki (handle all ImageNet JPGs) + Optimizations & bugfixes Mikhail Morozov (1-bit BMP) + Fabian "ryg" Giesen Anael Seghezzi (is-16-bit query) + Arseny Kapoulkine + John-Mark Allen + + Bug & warning fixes + Marc LeBlanc David Woo Guillaume George Martins Mozeiko + Christpher Lloyd Jerry Jansson Joseph Thomson Phil Jordan + Dave Moore Roy Eltham Hayaki Saito Nathan Reed + Won Chun Luke Graham Johan Duparc Nick Verigakis + the Horde3D community Thomas Ruf Ronny Chevalier github:rlyeh + Janez Zemva John Bartholomew Michal Cichon github:romigrou + Jonathan Blow Ken Hamada Tero Hanninen github:svdijk + Laurent Gomila Cort Stratton Sergio Gonzalez github:snagar + Aruelien Pocheville Thibault Reuille Cass Everitt github:Zelex + Ryamond Barbiero Paul Du Bois Engin Manap github:grim210 + Aldo Culquicondor Philipp Wiesemann Dale Weiler github:sammyhw + Oriol Ferrer Mesia Josh Tobin Matthew Gregan github:phprus + Julian Raschke Gregory Mullen Baldur Karlsson github:poppolopoppo + Christian Floisand Kevin Schmidt github:darealshinji + Blazej Dariusz Roszkowski github:Michaelangel007 */ #ifndef STBI_INCLUDE_STB_IMAGE_H @@ -217,10 +109,8 @@ // DOCUMENTATION // // Limitations: -// - no 16-bit-per-channel PNG // - no 12-bit-per-channel JPEG // - no JPEGs with arithmetic coding -// - no 1-bit BMP // - GIF always returns *comp=4 // // Basic usage (see HDR discussion below for HDR usage): @@ -233,10 +123,10 @@ // stbi_image_free(data) // // Standard parameters: -// int *x -- outputs image width in pixels -// int *y -- outputs image height in pixels -// int *comp -- outputs # of image components in image file -// int req_comp -- if non-zero, # of image components requested in result +// int *x -- outputs image width in pixels +// int *y -- outputs image height in pixels +// int *channels_in_file -- outputs # of image components in image file +// int desired_channels -- if non-zero, # of image components requested in result // // The return value from an image loader is an 'unsigned char *' which points // to the pixel data, or NULL on an allocation failure or if the image is @@ -244,11 +134,12 @@ // with each pixel consisting of N interleaved 8-bit components; the first // pixel pointed to is top-left-most in the image. There is no padding between // image scanlines or between pixels, regardless of format. The number of -// components N is 'req_comp' if req_comp is non-zero, or *comp otherwise. -// If req_comp is non-zero, *comp has the number of components that _would_ -// have been output otherwise. E.g. if you set req_comp to 4, you will always -// get RGBA output, but you can check *comp to see if it's trivially opaque -// because e.g. there were only 3 channels in the source image. +// components N is 'desired_channels' if desired_channels is non-zero, or +// *channels_in_file otherwise. If desired_channels is non-zero, +// *channels_in_file has the number of components that _would_ have been +// output otherwise. E.g. if you set desired_channels to 4, you will always +// get RGBA output, but you can check *channels_in_file to see if it's trivially +// opaque because e.g. there were only 3 channels in the source image. // // An output image with N components has the following components interleaved // in this order in each pixel: @@ -260,10 +151,10 @@ // 4 red, green, blue, alpha // // If image loading fails for any reason, the return value will be NULL, -// and *x, *y, *comp will be unchanged. The function stbi_failure_reason() -// can be queried for an extremely brief, end-user unfriendly explanation -// of why the load failed. Define STBI_NO_FAILURE_STRINGS to avoid -// compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly +// and *x, *y, *channels_in_file will be unchanged. The function +// stbi_failure_reason() can be queried for an extremely brief, end-user +// unfriendly explanation of why the load failed. Define STBI_NO_FAILURE_STRINGS +// to avoid compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly // more user-friendly ones. // // Paletted PNG, BMP, GIF, and PIC images are automatically depalettized. @@ -282,13 +173,13 @@ // and for best performance I may provide less-easy-to-use APIs that give higher // performance, in addition to the easy to use ones. Nevertheless, it's important // to keep in mind that from the standpoint of you, a client of this library, -// all you care about is #1 and #3, and stb libraries do not emphasize #3 above all. +// all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all. // // Some secondary priorities arise directly from the first two, some of which // make more explicit reasons why performance can't be emphasized. // // - Portable ("ease of use") -// - Small footprint ("easy to maintain") +// - Small source code footprint ("easy to maintain") // - No dependencies ("ease of use") // // =========================================================================== @@ -320,13 +211,6 @@ // (at least this is true for iOS and Android). Therefore, the NEON support is // toggled by a build flag: define STBI_NEON to get NEON loops. // -// The output of the JPEG decoder is slightly different from versions where -// SIMD support was introduced (that is, for versions before 1.49). The -// difference is only +-1 in the 8-bit RGB channels, and only on a small -// fraction of pixels. You can force the pre-1.49 behavior by defining -// STBI_JPEG_OLD, but this will disable some of the SIMD decoding path -// and hence cost some performance. -// // If for some reason you do not want to use any of SIMD code, or if // you have issues compiling it, you can disable it entirely by // defining STBI_NO_SIMD. @@ -382,6 +266,41 @@ // says there's premultiplied data (currently only happens in iPhone images, // and only if iPhone convert-to-rgb processing is on). // +// =========================================================================== +// +// ADDITIONAL CONFIGURATION +// +// - You can suppress implementation of any of the decoders to reduce +// your code footprint by #defining one or more of the following +// symbols before creating the implementation. +// +// STBI_NO_JPEG +// STBI_NO_PNG +// STBI_NO_BMP +// STBI_NO_PSD +// STBI_NO_TGA +// STBI_NO_GIF +// STBI_NO_HDR +// STBI_NO_PIC +// STBI_NO_PNM (.ppm and .pgm) +// +// - You can request *only* certain decoders and suppress all other ones +// (this will be more forward-compatible, as addition of new decoders +// doesn't require you to disable them explicitly): +// +// STBI_ONLY_JPEG +// STBI_ONLY_PNG +// STBI_ONLY_BMP +// STBI_ONLY_PSD +// STBI_ONLY_TGA +// STBI_ONLY_GIF +// STBI_ONLY_HDR +// STBI_ONLY_PIC +// STBI_ONLY_PNM (.ppm and .pgm) +// +// - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still +// want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB +// #ifndef STBI_NO_STDIO @@ -392,7 +311,7 @@ enum { - STBI_default = 0, // only used for req_comp + STBI_default = 0, // only used for desired_channels STBI_grey = 1, STBI_grey_alpha = 2, @@ -401,6 +320,7 @@ enum }; typedef unsigned char stbi_uc; +typedef unsigned short stbi_us; #ifdef __cplusplus extern "C" { @@ -428,34 +348,60 @@ typedef struct int (*eof) (void *user); // returns nonzero if we are at end of file/data } stbi_io_callbacks; -STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *comp, int req_comp); -STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *comp, int req_comp); -STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *comp, int req_comp); +//////////////////////////////////// +// +// 8-bits-per-channel interface +// + +STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *channels_in_file, int desired_channels); +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +#endif + #ifndef STBI_NO_STDIO -STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *comp, int req_comp); +STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); // for stbi_load_from_file, file pointer is left pointing immediately after image #endif +//////////////////////////////////// +// +// 16-bits-per-channel interface +// + +STBIDEF stbi_us *stbi_load_16_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO +STBIDEF stbi_us *stbi_load_16 (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_from_file_16(FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +#endif + +//////////////////////////////////// +// +// float-per-channel interface +// #ifndef STBI_NO_LINEAR - STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *comp, int req_comp); - STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp); - STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp); + STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); #ifndef STBI_NO_STDIO - STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *comp, int req_comp); + STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); #endif #endif #ifndef STBI_NO_HDR STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); STBIDEF void stbi_hdr_to_ldr_scale(float scale); -#endif +#endif // STBI_NO_HDR #ifndef STBI_NO_LINEAR STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); STBIDEF void stbi_ldr_to_hdr_scale(float scale); -#endif // STBI_NO_HDR +#endif // STBI_NO_LINEAR // stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user); @@ -476,11 +422,14 @@ STBIDEF void stbi_image_free (void *retval_from_stbi_load); // get image dimensions & components without fully decoding STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp); STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len); +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *clbk, void *user); #ifndef STBI_NO_STDIO -STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); -STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); - +STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit (char const *filename); +STBIDEF int stbi_is_16_bit_from_file(FILE *f); #endif @@ -561,9 +510,10 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const ch #include // ptrdiff_t on osx #include #include +#include #if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) -#include // ldexp +#include // ldexp, pow #endif #ifndef STBI_NO_STDIO @@ -619,18 +569,22 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (32 - (y)))) #endif -#if defined(STBI_MALLOC) && defined(STBI_FREE) && defined(STBI_REALLOC) +#if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED)) // ok -#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) +#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) && !defined(STBI_REALLOC_SIZED) // ok #else -#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC." +#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC (or STBI_REALLOC_SIZED)." #endif #ifndef STBI_MALLOC -#define STBI_MALLOC(sz) malloc(sz) -#define STBI_REALLOC(p,sz) realloc(p,sz) -#define STBI_FREE(p) free(p) +#define STBI_MALLOC(sz) malloc(sz) +#define STBI_REALLOC(p,newsz) realloc(p,newsz) +#define STBI_FREE(p) free(p) +#endif + +#ifndef STBI_REALLOC_SIZED +#define STBI_REALLOC_SIZED(p,oldsz,newsz) STBI_REALLOC(p,newsz) #endif // x86/x64 detection @@ -640,12 +594,14 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #define STBI__X86_TARGET #endif -#if defined(__GNUC__) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) -// NOTE: not clear do we actually need this for the 64-bit path? +#if defined(__GNUC__) && defined(STBI__X86_TARGET) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) // gcc doesn't support sse2 intrinsics unless you compile with -msse2, -// (but compiling with -msse2 allows the compiler to use SSE2 everywhere; -// this is just broken and gcc are jerks for not fixing it properly -// http://www.virtualdub.org/blog/pivot/entry.php?id=363 ) +// which in turn means it gets to use SSE2 everywhere. This is unfortunate, +// but previous attempts to provide the SSE2 functions with runtime +// detection caused numerous issues. The way architecture extensions are +// exposed in GCC/Clang is, sadly, not really suited for one-file libs. +// New behavior: if compiled with -msse2, we use SSE2 without any +// detection; if not, we don't use it at all. #define STBI_NO_SIMD #endif @@ -664,7 +620,7 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #define STBI_NO_SIMD #endif -#if !defined(STBI_NO_SIMD) && defined(STBI__X86_TARGET) +#if !defined(STBI_NO_SIMD) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) #define STBI_SSE2 #include @@ -693,7 +649,7 @@ static int stbi__cpuid3(void) #define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name -static int stbi__sse2_available() +static int stbi__sse2_available(void) { int info3 = stbi__cpuid3(); return ((info3 >> 26) & 1) != 0; @@ -701,16 +657,12 @@ static int stbi__sse2_available() #else // assume GCC-style if not VC++ #define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) -static int stbi__sse2_available() +static int stbi__sse2_available(void) { -#if defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__) >= 408 // GCC 4.8 or later - // GCC 4.8+ has a nice way to do this - return __builtin_cpu_supports("sse2"); -#else - // portable way to do this, preferably without using GCC inline ASM? - // just bail for now. - return 0; -#endif + // If we're even attempting to compile this on GCC/Clang, that means + // -msse2 is on, which means the compiler is allowed to use SSE2 + // instructions at will, and so are we. + return 1; } #endif #endif @@ -749,7 +701,7 @@ typedef struct stbi_uc buffer_start[128]; stbi_uc *img_buffer, *img_buffer_end; - stbi_uc *img_buffer_original; + stbi_uc *img_buffer_original, *img_buffer_original_end; } stbi__context; @@ -761,7 +713,7 @@ static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len) s->io.read = NULL; s->read_from_callbacks = 0; s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer; - s->img_buffer_end = (stbi_uc *) buffer+len; + s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *) buffer+len; } // initialize a callback-based context @@ -773,6 +725,7 @@ static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void * s->read_from_callbacks = 1; s->img_buffer_original = s->buffer_start; stbi__refill_buffer(s); + s->img_buffer_original_end = s->img_buffer_end; } #ifndef STBI_NO_STDIO @@ -814,59 +767,76 @@ static void stbi__rewind(stbi__context *s) // but we just rewind to the beginning of the initial buffer, because // we only use it after doing 'test', which only ever looks at at most 92 bytes s->img_buffer = s->img_buffer_original; + s->img_buffer_end = s->img_buffer_original_end; } +enum +{ + STBI_ORDER_RGB, + STBI_ORDER_BGR +}; + +typedef struct +{ + int bits_per_channel; + int num_channels; + int channel_order; +} stbi__result_info; + #ifndef STBI_NO_JPEG static int stbi__jpeg_test(stbi__context *s); -static stbi_uc *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PNG static int stbi__png_test(stbi__context *s); -static stbi_uc *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__png_is16(stbi__context *s); #endif #ifndef STBI_NO_BMP static int stbi__bmp_test(stbi__context *s); -static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_TGA static int stbi__tga_test(stbi__context *s); -static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PSD static int stbi__psd_test(stbi__context *s); -static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc); static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__psd_is16(stbi__context *s); #endif #ifndef STBI_NO_HDR static int stbi__hdr_test(stbi__context *s); -static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PIC static int stbi__pic_test(stbi__context *s); -static stbi_uc *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_GIF static int stbi__gif_test(stbi__context *s); -static stbi_uc *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp); static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PNM static int stbi__pnm_test(stbi__context *s); -static stbi_uc *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); #endif @@ -889,6 +859,81 @@ static void *stbi__malloc(size_t size) return STBI_MALLOC(size); } +// stb_image uses ints pervasively, including for offset calculations. +// therefore the largest decoded image size we can support with the +// current code, even on 64-bit targets, is INT_MAX. this is not a +// significant limitation for the intended use case. +// +// we do, however, need to make sure our size calculations don't +// overflow. hence a few helper functions for size calculations that +// multiply integers together, making sure that they're non-negative +// and no overflow occurs. + +// return 1 if the sum is valid, 0 on overflow. +// negative terms are considered invalid. +static int stbi__addsizes_valid(int a, int b) +{ + if (b < 0) return 0; + // now 0 <= b <= INT_MAX, hence also + // 0 <= INT_MAX - b <= INTMAX. + // And "a + b <= INT_MAX" (which might overflow) is the + // same as a <= INT_MAX - b (no overflow) + return a <= INT_MAX - b; +} + +// returns 1 if the product is valid, 0 on overflow. +// negative factors are considered invalid. +static int stbi__mul2sizes_valid(int a, int b) +{ + if (a < 0 || b < 0) return 0; + if (b == 0) return 1; // mul-by-0 is always safe + // portable way to check for no overflows in a*b + return a <= INT_MAX/b; +} + +// returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow +static int stbi__mad2sizes_valid(int a, int b, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a*b, add); +} + +// returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow +static int stbi__mad3sizes_valid(int a, int b, int c, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__addsizes_valid(a*b*c, add); +} + +// returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__mul2sizes_valid(a*b*c, d) && stbi__addsizes_valid(a*b*c*d, add); +} +#endif + +// mallocs with size overflow checking +static void *stbi__malloc_mad2(int a, int b, int add) +{ + if (!stbi__mad2sizes_valid(a, b, add)) return NULL; + return stbi__malloc(a*b + add); +} + +static void *stbi__malloc_mad3(int a, int b, int c, int add) +{ + if (!stbi__mad3sizes_valid(a, b, c, add)) return NULL; + return stbi__malloc(a*b*c + add); +} + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) +{ + if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; + return stbi__malloc(a*b*c*d + add); +} +#endif + // stbi__err - error // stbi__errpf - error returning pointer to float // stbi__errpuc - error returning pointer to unsigned char @@ -901,8 +946,8 @@ static void *stbi__malloc(size_t size) #define stbi__err(x,y) stbi__err(x) #endif -#define stbi__errpf(x,y) ((float *) (stbi__err(x,y)?NULL:NULL)) -#define stbi__errpuc(x,y) ((unsigned char *) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpf(x,y) ((float *)(size_t) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpuc(x,y) ((unsigned char *)(size_t) (stbi__err(x,y)?NULL:NULL)) STBIDEF void stbi_image_free(void *retval_from_stbi_load) { @@ -924,33 +969,38 @@ STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) stbi__vertically_flip_on_load = flag_true_if_should_flip; } -static unsigned char *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) { + memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields + ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed + ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order + ri->num_channels = 0; + #ifndef STBI_NO_JPEG - if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp); + if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_PNG - if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp); + if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_BMP - if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp); + if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_GIF - if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp); + if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_PSD - if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp); + if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp, ri, bpc); #endif #ifndef STBI_NO_PIC - if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp); + if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_PNM - if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp); + if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_HDR if (stbi__hdr_test(s)) { - float *hdr = stbi__hdr_load(s, x,y,comp,req_comp); + float *hdr = stbi__hdr_load(s, x,y,comp,req_comp, ri); return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); } #endif @@ -958,58 +1008,138 @@ static unsigned char *stbi__load_main(stbi__context *s, int *x, int *y, int *com #ifndef STBI_NO_TGA // test tga last because it's a crappy test! if (stbi__tga_test(s)) - return stbi__tga_load(s,x,y,comp,req_comp); + return stbi__tga_load(s,x,y,comp,req_comp, ri); #endif return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); } -static unsigned char *stbi__load_flip(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static stbi_uc *stbi__convert_16_to_8(stbi__uint16 *orig, int w, int h, int channels) { - unsigned char *result = stbi__load_main(s, x, y, comp, req_comp); + int i; + int img_len = w * h * channels; + stbi_uc *reduced; - if (stbi__vertically_flip_on_load && result != NULL) { - int w = *x, h = *y; - int depth = req_comp ? req_comp : *comp; - int row,col,z; - stbi_uc temp; - - // @OPTIMIZE: use a bigger temp buffer and memcpy multiple pixels at once - for (row = 0; row < (h>>1); row++) { - for (col = 0; col < w; col++) { - for (z = 0; z < depth; z++) { - temp = result[(row * w + col) * depth + z]; - result[(row * w + col) * depth + z] = result[((h - row - 1) * w + col) * depth + z]; - result[((h - row - 1) * w + col) * depth + z] = temp; - } - } + reduced = (stbi_uc *) stbi__malloc(img_len); + if (reduced == NULL) return stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling + + STBI_FREE(orig); + return reduced; +} + +static stbi__uint16 *stbi__convert_8_to_16(stbi_uc *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi__uint16 *enlarged; + + enlarged = (stbi__uint16 *) stbi__malloc(img_len*2); + if (enlarged == NULL) return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff + + STBI_FREE(orig); + return enlarged; +} + +static void stbi__vertical_flip(void *image, int w, int h, int bytes_per_pixel) +{ + int row; + size_t bytes_per_row = (size_t)w * bytes_per_pixel; + stbi_uc temp[2048]; + stbi_uc *bytes = (stbi_uc *)image; + + for (row = 0; row < (h>>1); row++) { + stbi_uc *row0 = bytes + row*bytes_per_row; + stbi_uc *row1 = bytes + (h - row - 1)*bytes_per_row; + // swap row0 with row1 + size_t bytes_left = bytes_per_row; + while (bytes_left) { + size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp); + memcpy(temp, row0, bytes_copy); + memcpy(row0, row1, bytes_copy); + memcpy(row1, temp, bytes_copy); + row0 += bytes_copy; + row1 += bytes_copy; + bytes_left -= bytes_copy; } } +} - return result; +static void stbi__vertical_flip_slices(void *image, int w, int h, int z, int bytes_per_pixel) +{ + int slice; + int slice_size = w * h * bytes_per_pixel; + + stbi_uc *bytes = (stbi_uc *)image; + for (slice = 0; slice < z; ++slice) { + stbi__vertical_flip(bytes, w, h, bytes_per_pixel); + bytes += slice_size; + } +} + +static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 8) { + STBI_ASSERT(ri.bits_per_channel == 16); + result = stbi__convert_16_to_8((stbi__uint16 *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 8; + } + + // @TODO: move stbi__convert_format to here + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc)); + } + + return (unsigned char *) result; } +static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 16) { + STBI_ASSERT(ri.bits_per_channel == 8); + result = stbi__convert_8_to_16((stbi_uc *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 16; + } + + // @TODO: move stbi__convert_format16 to here + // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16)); + } + + return (stbi__uint16 *) result; +} + +#if !defined(STBI_NO_HDR) || !defined(STBI_NO_LINEAR) static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp) { if (stbi__vertically_flip_on_load && result != NULL) { - int w = *x, h = *y; - int depth = req_comp ? req_comp : *comp; - int row,col,z; - float temp; - - // @OPTIMIZE: use a bigger temp buffer and memcpy multiple pixels at once - for (row = 0; row < (h>>1); row++) { - for (col = 0; col < w; col++) { - for (z = 0; z < depth; z++) { - temp = result[(row * w + col) * depth + z]; - result[(row * w + col) * depth + z] = result[((h - row - 1) * w + col) * depth + z]; - result[((h - row - 1) * w + col) * depth + z] = temp; - } - } - } + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(float)); } } - +#endif #ifndef STBI_NO_STDIO @@ -1041,28 +1171,83 @@ STBIDEF stbi_uc *stbi_load_from_file(FILE *f, int *x, int *y, int *comp, int req unsigned char *result; stbi__context s; stbi__start_file(&s,f); - result = stbi__load_flip(&s,x,y,comp,req_comp); + result = stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); if (result) { // need to 'unget' all the characters in the IO buffer fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); } return result; } + +STBIDEF stbi__uint16 *stbi_load_from_file_16(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__uint16 *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_16bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi_us *stbi_load_16(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + stbi__uint16 *result; + if (!f) return (stbi_us *) stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file_16(f,x,y,comp,req_comp); + fclose(f); + return result; +} + + #endif //!STBI_NO_STDIO +STBIDEF stbi_us *stbi_load_16_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + STBIDEF stbi_uc *stbi_load_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) { stbi__context s; stbi__start_mem(&s,buffer,len); - return stbi__load_flip(&s,x,y,comp,req_comp); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); } STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) { stbi__context s; stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); - return stbi__load_flip(&s,x,y,comp,req_comp); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_mem(&s,buffer,len); + + result = (unsigned char*) stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); + if (stbi__vertically_flip_on_load) { + stbi__vertical_flip_slices( result, *x, *y, *z, *comp ); + } + + return result; } +#endif #ifndef STBI_NO_LINEAR static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) @@ -1070,13 +1255,14 @@ static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int unsigned char *data; #ifndef STBI_NO_HDR if (stbi__hdr_test(s)) { - float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp); + stbi__result_info ri; + float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp, &ri); if (hdr_data) stbi__float_postprocess(hdr_data,x,y,comp,req_comp); return hdr_data; } #endif - data = stbi__load_flip(s, x, y, comp, req_comp); + data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp); if (data) return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); @@ -1146,13 +1332,18 @@ STBIDEF int stbi_is_hdr (char const *filename) return result; } -STBIDEF int stbi_is_hdr_from_file(FILE *f) +STBIDEF int stbi_is_hdr_from_file(FILE *f) { #ifndef STBI_NO_HDR + long pos = ftell(f); + int res; stbi__context s; stbi__start_file(&s,f); - return stbi__hdr_test(&s); + res = stbi__hdr_test(&s); + fseek(f, pos, SEEK_SET); + return res; #else + STBI_NOTUSED(f); return 0; #endif } @@ -1165,18 +1356,21 @@ STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); return stbi__hdr_test(&s); #else + STBI_NOTUSED(clbk); + STBI_NOTUSED(user); return 0; #endif } -static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; +#ifndef STBI_NO_LINEAR static float stbi__l2h_gamma=2.2f, stbi__l2h_scale=1.0f; -#ifndef STBI_NO_LINEAR STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } #endif +static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; + STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1/gamma; } STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1/scale; } @@ -1285,17 +1479,23 @@ static stbi__uint32 stbi__get32be(stbi__context *s) return (z << 16) + stbi__get16be(s); } +#if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) +// nothing +#else static int stbi__get16le(stbi__context *s) { int z = stbi__get8(s); return z + (stbi__get8(s) << 8); } +#endif +#ifndef STBI_NO_BMP static stbi__uint32 stbi__get32le(stbi__context *s) { stbi__uint32 z = stbi__get16le(s); return z + (stbi__get16le(s) << 16); } +#endif #define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings @@ -1324,7 +1524,7 @@ static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int r if (req_comp == img_n) return data; STBI_ASSERT(req_comp >= 1 && req_comp <= 4); - good = (unsigned char *) stbi__malloc(req_comp * x * y); + good = (unsigned char *) stbi__malloc_mad3(req_comp, x, y, 0); if (good == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); @@ -1334,26 +1534,75 @@ static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int r unsigned char *src = data + j * x * img_n ; unsigned char *dest = good + j * x * req_comp; - #define COMBO(a,b) ((a)*8+(b)) - #define CASE(a,b) case COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0], dest[1]=255; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=255; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=255; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = 255; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +static stbi__uint16 stbi__compute_y_16(int r, int g, int b) +{ + return (stbi__uint16) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + stbi__uint16 *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (stbi__uint16 *) stbi__malloc(req_comp * x * y * 2); + if (good == NULL) { + STBI_FREE(data); + return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + stbi__uint16 *src = data + j * x * img_n ; + stbi__uint16 *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) // convert source image with img_n components to one with req_comp components; // avoid switch per pixel, so use switch per scanline and massive macros - switch (COMBO(img_n, req_comp)) { - CASE(1,2) dest[0]=src[0], dest[1]=255; break; - CASE(1,3) dest[0]=dest[1]=dest[2]=src[0]; break; - CASE(1,4) dest[0]=dest[1]=dest[2]=src[0], dest[3]=255; break; - CASE(2,1) dest[0]=src[0]; break; - CASE(2,3) dest[0]=dest[1]=dest[2]=src[0]; break; - CASE(2,4) dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; break; - CASE(3,4) dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=255; break; - CASE(3,1) dest[0]=stbi__compute_y(src[0],src[1],src[2]); break; - CASE(3,2) dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = 255; break; - CASE(4,1) dest[0]=stbi__compute_y(src[0],src[1],src[2]); break; - CASE(4,2) dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = src[3]; break; - CASE(4,3) dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; break; + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0], dest[1]=0xffff; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=0xffff; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=0xffff; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = 0xffff; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; } break; default: STBI_ASSERT(0); } - #undef CASE + #undef STBI__CASE } STBI_FREE(data); @@ -1364,7 +1613,9 @@ static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int r static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) { int i,k,n; - float *output = (float *) stbi__malloc(x * y * comp * sizeof(float)); + float *output; + if (!data) return NULL; + output = (float *) stbi__malloc_mad4(x, y, comp, sizeof(float), 0); if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); } // compute number of non-alpha components if (comp & 1) n = comp; else n = comp-1; @@ -1384,7 +1635,9 @@ static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp) { int i,k,n; - stbi_uc *output = (stbi_uc *) stbi__malloc(x * y * comp); + stbi_uc *output; + if (!data) return NULL; + output = (stbi_uc *) stbi__malloc_mad3(x, y, comp, 0); if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); } // compute number of non-alpha components if (comp & 1) n = comp; else n = comp-1; @@ -1449,7 +1702,7 @@ typedef struct stbi__context *s; stbi__huffman huff_dc[4]; stbi__huffman huff_ac[4]; - stbi_uc dequant[4][64]; + stbi__uint16 dequant[4][64]; stbi__int16 fast_ac[4][1 << FAST_BITS]; // sizes for components, interleaved MCUs @@ -1485,6 +1738,9 @@ typedef struct int succ_high; int succ_low; int eob_run; + int jfif; + int app14_color_transform; // Adobe APP14 tag + int rgb; int scan_n, order[4]; int restart_interval, todo; @@ -1497,7 +1753,8 @@ typedef struct static int stbi__build_huffman(stbi__huffman *h, int *count) { - int i,j,k=0,code; + int i,j,k=0; + unsigned int code; // build size list for each symbol (from JPEG spec) for (i=0; i < 16; ++i) for (j=0; j < count[i]; ++j) @@ -1513,7 +1770,7 @@ static int stbi__build_huffman(stbi__huffman *h, int *count) if (h->size[k] == j) { while (h->size[k] == j) h->code[k++] = (stbi__uint16) (code++); - if (code-1 >= (1 << j)) return stbi__err("bad code lengths","Corrupt JPEG"); + if (code-1 >= (1u << j)) return stbi__err("bad code lengths","Corrupt JPEG"); } // compute largest code + 1 for this size, preshifted as needed later h->maxcode[j] = code << (16-j); @@ -1554,10 +1811,10 @@ static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) // magnitude code followed by receive_extend code int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); int m = 1 << (magbits - 1); - if (k < m) k += (-1 << magbits) + 1; + if (k < m) k += (~0U << magbits) + 1; // if the result is small enough, we can fit it in fast_ac table if (k >= -128 && k <= 127) - fast_ac[i] = (stbi__int16) ((k << 8) + (run << 4) + (len + magbits)); + fast_ac[i] = (stbi__int16) ((k * 256) + (run * 16) + (len + magbits)); } } } @@ -1566,9 +1823,10 @@ static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) static void stbi__grow_buffer_unsafe(stbi__jpeg *j) { do { - int b = j->nomore ? 0 : stbi__get8(j->s); + unsigned int b = j->nomore ? 0 : stbi__get8(j->s); if (b == 0xff) { int c = stbi__get8(j->s); + while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes if (c != 0) { j->marker = (unsigned char) c; j->nomore = 1; @@ -1581,7 +1839,7 @@ static void stbi__grow_buffer_unsafe(stbi__jpeg *j) } // (1 << n) - 1 -static stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; +static const stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; // decode a jpeg huffman value from the bitstream stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) @@ -1634,7 +1892,7 @@ stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) } // bias[n] = (-1<s); if (x != 0xff) return STBI__MARKER_none; while (x == 0xff) - x = stbi__get8(j->s); + x = stbi__get8(j->s); // consume repeated 0xff fill bytes return x; } @@ -2417,7 +2675,7 @@ static void stbi__jpeg_reset(stbi__jpeg *j) j->code_bits = 0; j->code_buffer = 0; j->nomore = 0; - j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = 0; + j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0; j->marker = STBI__MARKER_none; j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; j->eob_run = 0; @@ -2549,7 +2807,7 @@ static int stbi__parse_entropy_coded_data(stbi__jpeg *z) } } -static void stbi__jpeg_dequantize(short *data, stbi_uc *dequant) +static void stbi__jpeg_dequantize(short *data, stbi__uint16 *dequant) { int i; for (i=0; i < 64; ++i) @@ -2591,13 +2849,14 @@ static int stbi__process_marker(stbi__jpeg *z, int m) L = stbi__get16be(z->s)-2; while (L > 0) { int q = stbi__get8(z->s); - int p = q >> 4; + int p = q >> 4, sixteen = (p != 0); int t = q & 15,i; - if (p != 0) return stbi__err("bad DQT type","Corrupt JPEG"); + if (p != 0 && p != 1) return stbi__err("bad DQT type","Corrupt JPEG"); if (t > 3) return stbi__err("bad DQT table","Corrupt JPEG"); + for (i=0; i < 64; ++i) - z->dequant[t][stbi__jpeg_dezigzag[i]] = stbi__get8(z->s); - L -= 65; + z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s)); + L -= (sixteen ? 129 : 65); } return L==0; @@ -2630,12 +2889,50 @@ static int stbi__process_marker(stbi__jpeg *z, int m) } return L==0; } + // check for comment block or APP blocks if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { - stbi__skip(z->s, stbi__get16be(z->s)-2); + L = stbi__get16be(z->s); + if (L < 2) { + if (m == 0xFE) + return stbi__err("bad COM len","Corrupt JPEG"); + else + return stbi__err("bad APP len","Corrupt JPEG"); + } + L -= 2; + + if (m == 0xE0 && L >= 5) { // JFIF APP0 segment + static const unsigned char tag[5] = {'J','F','I','F','\0'}; + int ok = 1; + int i; + for (i=0; i < 5; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 5; + if (ok) + z->jfif = 1; + } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment + static const unsigned char tag[6] = {'A','d','o','b','e','\0'}; + int ok = 1; + int i; + for (i=0; i < 6; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 6; + if (ok) { + stbi__get8(z->s); // version + stbi__get16be(z->s); // flags0 + stbi__get16be(z->s); // flags1 + z->app14_color_transform = stbi__get8(z->s); // color transform + L -= 6; + } + } + + stbi__skip(z->s, L); return 1; } - return 0; + + return stbi__err("unknown marker","Corrupt JPEG"); } // after we see SOS @@ -2678,6 +2975,28 @@ static int stbi__process_scan_header(stbi__jpeg *z) return 1; } +static int stbi__free_jpeg_components(stbi__jpeg *z, int ncomp, int why) +{ + int i; + for (i=0; i < ncomp; ++i) { + if (z->img_comp[i].raw_data) { + STBI_FREE(z->img_comp[i].raw_data); + z->img_comp[i].raw_data = NULL; + z->img_comp[i].data = NULL; + } + if (z->img_comp[i].raw_coeff) { + STBI_FREE(z->img_comp[i].raw_coeff); + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].coeff = 0; + } + if (z->img_comp[i].linebuf) { + STBI_FREE(z->img_comp[i].linebuf); + z->img_comp[i].linebuf = NULL; + } + } + return why; +} + static int stbi__process_frame_header(stbi__jpeg *z, int scan) { stbi__context *s = z->s; @@ -2687,7 +3006,7 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires c = stbi__get8(s); - if (c != 3 && c != 1) return stbi__err("bad component count","Corrupt JPEG"); // JFIF requires + if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count","Corrupt JPEG"); s->img_n = c; for (i=0; i < c; ++i) { z->img_comp[i].data = NULL; @@ -2696,11 +3015,12 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) if (Lf != 8+3*s->img_n) return stbi__err("bad SOF len","Corrupt JPEG"); + z->rgb = 0; for (i=0; i < s->img_n; ++i) { + static const unsigned char rgb[3] = { 'R', 'G', 'B' }; z->img_comp[i].id = stbi__get8(s); - if (z->img_comp[i].id != i+1) // JFIF requires - if (z->img_comp[i].id != i) // some version of jpegtran outputs non-JFIF-compliant files! - return stbi__err("bad component ID","Corrupt JPEG"); + if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) + ++z->rgb; q = stbi__get8(s); z->img_comp[i].h = (q >> 4); if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H","Corrupt JPEG"); z->img_comp[i].v = q & 15; if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V","Corrupt JPEG"); @@ -2709,7 +3029,7 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) if (scan != STBI__SCAN_load) return 1; - if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); + if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) return stbi__err("too large", "Image too large to decode"); for (i=0; i < s->img_n; ++i) { if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h; @@ -2721,6 +3041,7 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) z->img_v_max = v_max; z->img_mcu_w = h_max * 8; z->img_mcu_h = v_max * 8; + // these sizes can't be more than 17 bits z->img_mcu_x = (s->img_x + z->img_mcu_w-1) / z->img_mcu_w; z->img_mcu_y = (s->img_y + z->img_mcu_h-1) / z->img_mcu_h; @@ -2732,28 +3053,27 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) // the bogus oversized data from using interleaved MCUs and their // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't // discard the extra data until colorspace conversion + // + // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier) + // so these muls can't overflow with 32-bit ints (which we require) z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; - z->img_comp[i].raw_data = stbi__malloc(z->img_comp[i].w2 * z->img_comp[i].h2+15); - - if (z->img_comp[i].raw_data == NULL) { - for(--i; i >= 0; --i) { - STBI_FREE(z->img_comp[i].raw_data); - z->img_comp[i].data = NULL; - } - return stbi__err("outofmem", "Out of memory"); - } + z->img_comp[i].coeff = 0; + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].linebuf = NULL; + z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15); + if (z->img_comp[i].raw_data == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); // align blocks for idct using mmx/sse z->img_comp[i].data = (stbi_uc*) (((size_t) z->img_comp[i].raw_data + 15) & ~15); - z->img_comp[i].linebuf = NULL; if (z->progressive) { - z->img_comp[i].coeff_w = (z->img_comp[i].w2 + 7) >> 3; - z->img_comp[i].coeff_h = (z->img_comp[i].h2 + 7) >> 3; - z->img_comp[i].raw_coeff = STBI_MALLOC(z->img_comp[i].coeff_w * z->img_comp[i].coeff_h * 64 * sizeof(short) + 15); + // w2, h2 are multiples of 8 (see above) + z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8; + z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8; + z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15); + if (z->img_comp[i].raw_coeff == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); z->img_comp[i].coeff = (short*) (((size_t) z->img_comp[i].raw_coeff + 15) & ~15); - } else { - z->img_comp[i].coeff = 0; - z->img_comp[i].raw_coeff = 0; } } @@ -2772,6 +3092,8 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) { int m; + z->jfif = 0; + z->app14_color_transform = -1; // valid values are 0,1,2 z->marker = STBI__MARKER_none; // initialize cached marker to empty m = stbi__get_marker(z); if (!stbi__SOI(m)) return stbi__err("no SOI","Corrupt JPEG"); @@ -2813,12 +3135,15 @@ static int stbi__decode_jpeg_image(stbi__jpeg *j) if (x == 255) { j->marker = stbi__get8(j->s); break; - } else if (x != 0) { - return stbi__err("junk before marker", "Corrupt JPEG"); } } // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 } + } else if (stbi__DNL(m)) { + int Ld = stbi__get16be(j->s); + stbi__uint32 NL = stbi__get16be(j->s); + if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); + if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); } else { if (!stbi__process_marker(j, m)) return 0; } @@ -3037,38 +3362,9 @@ static stbi_uc *stbi__resample_row_generic(stbi_uc *out, stbi_uc *in_near, stbi_ return out; } -#ifdef STBI_JPEG_OLD -// this is the same YCbCr-to-RGB calculation that stb_image has used -// historically before the algorithm changes in 1.49 -#define float2fixed(x) ((int) ((x) * 65536 + 0.5)) -static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) -{ - int i; - for (i=0; i < count; ++i) { - int y_fixed = (y[i] << 16) + 32768; // rounding - int r,g,b; - int cr = pcr[i] - 128; - int cb = pcb[i] - 128; - r = y_fixed + cr*float2fixed(1.40200f); - g = y_fixed - cr*float2fixed(0.71414f) - cb*float2fixed(0.34414f); - b = y_fixed + cb*float2fixed(1.77200f); - r >>= 16; - g >>= 16; - b >>= 16; - if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } - if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } - if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } - out[0] = (stbi_uc)r; - out[1] = (stbi_uc)g; - out[2] = (stbi_uc)b; - out[3] = 255; - out += step; - } -} -#else // this is a reduced-precision calculation of YCbCr-to-RGB introduced // to make sure the code produces the same results in both SIMD and scalar -#define float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) +#define stbi__float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) { int i; @@ -3077,9 +3373,9 @@ static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc int r,g,b; int cr = pcr[i] - 128; int cb = pcb[i] - 128; - r = y_fixed + cr* float2fixed(1.40200f); - g = y_fixed + (cr*-float2fixed(0.71414f)) + ((cb*-float2fixed(0.34414f)) & 0xffff0000); - b = y_fixed + cb* float2fixed(1.77200f); + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + (cr*-stbi__float2fixed(0.71414f)) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); r >>= 20; g >>= 20; b >>= 20; @@ -3093,7 +3389,6 @@ static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc out += step; } } -#endif #if defined(STBI_SSE2) || defined(STBI_NEON) static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc const *pcb, stbi_uc const *pcr, int count, int step) @@ -3212,9 +3507,9 @@ static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc cons int r,g,b; int cr = pcr[i] - 128; int cb = pcb[i] - 128; - r = y_fixed + cr* float2fixed(1.40200f); - g = y_fixed + cr*-float2fixed(0.71414f) + ((cb*-float2fixed(0.34414f)) & 0xffff0000); - b = y_fixed + cb* float2fixed(1.77200f); + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + cr*-stbi__float2fixed(0.71414f) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); r >>= 20; g >>= 20; b >>= 20; @@ -3240,18 +3535,14 @@ static void stbi__setup_jpeg(stbi__jpeg *j) #ifdef STBI_SSE2 if (stbi__sse2_available()) { j->idct_block_kernel = stbi__idct_simd; - #ifndef STBI_JPEG_OLD j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; - #endif j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; } #endif #ifdef STBI_NEON j->idct_block_kernel = stbi__idct_simd; - #ifndef STBI_JPEG_OLD j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; - #endif j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; #endif } @@ -3259,23 +3550,7 @@ static void stbi__setup_jpeg(stbi__jpeg *j) // clean up the temporary component buffers static void stbi__cleanup_jpeg(stbi__jpeg *j) { - int i; - for (i=0; i < j->s->img_n; ++i) { - if (j->img_comp[i].raw_data) { - STBI_FREE(j->img_comp[i].raw_data); - j->img_comp[i].raw_data = NULL; - j->img_comp[i].data = NULL; - } - if (j->img_comp[i].raw_coeff) { - STBI_FREE(j->img_comp[i].raw_coeff); - j->img_comp[i].raw_coeff = 0; - j->img_comp[i].coeff = 0; - } - if (j->img_comp[i].linebuf) { - STBI_FREE(j->img_comp[i].linebuf); - j->img_comp[i].linebuf = NULL; - } - } + stbi__free_jpeg_components(j, j->s->img_n, 0); } typedef struct @@ -3288,9 +3563,16 @@ typedef struct int ypos; // which pre-expansion row we're on } stbi__resample; +// fast 0..255 * 0..255 => 0..255 rounded multiplication +static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) +{ + unsigned int t = x*y + 128; + return (stbi_uc) ((t + (t >>8)) >> 8); +} + static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp, int req_comp) { - int n, decode_n; + int n, decode_n, is_rgb; z->s->img_n = 0; // make stbi__cleanup_jpeg safe // validate req_comp @@ -3300,9 +3582,11 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; } // determine actual number of components to generate - n = req_comp ? req_comp : z->s->img_n; + n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1; + + is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif)); - if (z->s->img_n == 3 && n < 3) + if (z->s->img_n == 3 && n < 3 && !is_rgb) decode_n = 1; else decode_n = z->s->img_n; @@ -3339,7 +3623,7 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp } // can't error after this so, this is safe - output = (stbi_uc *) stbi__malloc(n * z->s->img_x * z->s->img_y + 1); + output = (stbi_uc *) stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1); if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } // now go ahead and resample @@ -3362,7 +3646,39 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp if (n >= 3) { stbi_uc *y = coutput[0]; if (z->s->img_n == 3) { - z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + if (is_rgb) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = y[i]; + out[1] = coutput[1][i]; + out[2] = coutput[2][i]; + out[3] = 255; + out += n; + } + } else { + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else if (z->s->img_n == 4) { + if (z->app14_color_transform == 0) { // CMYK + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(coutput[0][i], m); + out[1] = stbi__blinn_8x8(coutput[1][i], m); + out[2] = stbi__blinn_8x8(coutput[2][i], m); + out[3] = 255; + out += n; + } + } else if (z->app14_color_transform == 2) { // YCCK + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(255 - out[0], m); + out[1] = stbi__blinn_8x8(255 - out[1], m); + out[2] = stbi__blinn_8x8(255 - out[2], m); + out += n; + } + } else { // YCbCr + alpha? Ignore the fourth channel for now + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } } else for (i=0; i < z->s->img_x; ++i) { out[0] = out[1] = out[2] = y[i]; @@ -3370,37 +3686,70 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp out += n; } } else { - stbi_uc *y = coutput[0]; - if (n == 1) - for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; - else - for (i=0; i < z->s->img_x; ++i) *out++ = y[i], *out++ = 255; + if (is_rgb) { + if (n == 1) + for (i=0; i < z->s->img_x; ++i) + *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + else { + for (i=0; i < z->s->img_x; ++i, out += 2) { + out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + out[1] = 255; + } + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 0) { + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + stbi_uc r = stbi__blinn_8x8(coutput[0][i], m); + stbi_uc g = stbi__blinn_8x8(coutput[1][i], m); + stbi_uc b = stbi__blinn_8x8(coutput[2][i], m); + out[0] = stbi__compute_y(r, g, b); + out[1] = 255; + out += n; + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 2) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]); + out[1] = 255; + out += n; + } + } else { + stbi_uc *y = coutput[0]; + if (n == 1) + for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; + else + for (i=0; i < z->s->img_x; ++i) *out++ = y[i], *out++ = 255; + } } } stbi__cleanup_jpeg(z); *out_x = z->s->img_x; *out_y = z->s->img_y; - if (comp) *comp = z->s->img_n; // report original components, not output + if (comp) *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output return output; } } -static unsigned char *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { - stbi__jpeg j; - j.s = s; - stbi__setup_jpeg(&j); - return load_jpeg_image(&j, x,y,comp,req_comp); + unsigned char* result; + stbi__jpeg* j = (stbi__jpeg*) stbi__malloc(sizeof(stbi__jpeg)); + STBI_NOTUSED(ri); + j->s = s; + stbi__setup_jpeg(j); + result = load_jpeg_image(j, x,y,comp,req_comp); + STBI_FREE(j); + return result; } static int stbi__jpeg_test(stbi__context *s) { int r; - stbi__jpeg j; - j.s = s; - stbi__setup_jpeg(&j); - r = stbi__decode_jpeg_header(&j, STBI__SCAN_type); + stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + j->s = s; + stbi__setup_jpeg(j); + r = stbi__decode_jpeg_header(j, STBI__SCAN_type); stbi__rewind(s); + STBI_FREE(j); return r; } @@ -3412,15 +3761,18 @@ static int stbi__jpeg_info_raw(stbi__jpeg *j, int *x, int *y, int *comp) } if (x) *x = j->s->img_x; if (y) *y = j->s->img_y; - if (comp) *comp = j->s->img_n; + if (comp) *comp = j->s->img_n >= 3 ? 3 : 1; return 1; } static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) { - stbi__jpeg j; - j.s = s; - return stbi__jpeg_info_raw(&j, x, y, comp); + int result; + stbi__jpeg* j = (stbi__jpeg*) (stbi__malloc(sizeof(stbi__jpeg))); + j->s = s; + result = stbi__jpeg_info_raw(j, x, y, comp); + STBI_FREE(j); + return result; } #endif @@ -3466,7 +3818,7 @@ stbi_inline static int stbi__bit_reverse(int v, int bits) return stbi__bitreverse16(v) >> (16-bits); } -static int stbi__zbuild_huffman(stbi__zhuffman *z, stbi_uc *sizelist, int num) +static int stbi__zbuild_huffman(stbi__zhuffman *z, const stbi_uc *sizelist, int num) { int i,k=0; int code, next_code[16], sizes[17]; @@ -3501,10 +3853,10 @@ static int stbi__zbuild_huffman(stbi__zhuffman *z, stbi_uc *sizelist, int num) z->size [c] = (stbi_uc ) s; z->value[c] = (stbi__uint16) i; if (s <= STBI__ZFAST_BITS) { - int k = stbi__bit_reverse(next_code[s],s); - while (k < (1 << STBI__ZFAST_BITS)) { - z->fast[k] = fastv; - k += (1 << s); + int j = stbi__bit_reverse(next_code[s],s); + while (j < (1 << STBI__ZFAST_BITS)) { + z->fast[j] = fastv; + j += (1 << s); } } ++next_code[s]; @@ -3543,7 +3895,7 @@ static void stbi__fill_bits(stbi__zbuf *z) { do { STBI_ASSERT(z->code_buffer < (1U << z->num_bits)); - z->code_buffer |= stbi__zget8(z) << z->num_bits; + z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits; z->num_bits += 8; } while (z->num_bits <= 24); } @@ -3593,14 +3945,15 @@ stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room for n bytes { char *q; - int cur, limit; + int cur, limit, old_limit; z->zout = zout; if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG"); cur = (int) (z->zout - z->zout_start); - limit = (int) (z->zout_end - z->zout_start); + limit = old_limit = (int) (z->zout_end - z->zout_start); while (cur + n > limit) limit *= 2; - q = (char *) STBI_REALLOC(z->zout_start, limit); + q = (char *) STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); + STBI_NOTUSED(old_limit); if (q == NULL) return stbi__err("outofmem", "Out of memory"); z->zout_start = q; z->zout = q + cur; @@ -3608,18 +3961,18 @@ static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room return 1; } -static int stbi__zlength_base[31] = { +static const int stbi__zlength_base[31] = { 3,4,5,6,7,8,9,10,11,13, 15,17,19,23,27,31,35,43,51,59, 67,83,99,115,131,163,195,227,258,0,0 }; -static int stbi__zlength_extra[31]= +static const int stbi__zlength_extra[31]= { 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; -static int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, +static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, 257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; -static int stbi__zdist_extra[32] = +static const int stbi__zdist_extra[32] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; static int stbi__parse_huffman_block(stbi__zbuf *a) @@ -3666,7 +4019,7 @@ static int stbi__parse_huffman_block(stbi__zbuf *a) static int stbi__compute_huffman_codes(stbi__zbuf *a) { - static stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; + static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; stbi__zhuffman z_codelength; stbi_uc lencodes[286+32+137];//padding for maximum single op stbi_uc codelength_sizes[19]; @@ -3675,6 +4028,7 @@ static int stbi__compute_huffman_codes(stbi__zbuf *a) int hlit = stbi__zreceive(a,5) + 257; int hdist = stbi__zreceive(a,5) + 1; int hclen = stbi__zreceive(a,4) + 4; + int ntot = hlit + hdist; memset(codelength_sizes, 0, sizeof(codelength_sizes)); for (i=0; i < hclen; ++i) { @@ -3684,33 +4038,35 @@ static int stbi__compute_huffman_codes(stbi__zbuf *a) if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0; n = 0; - while (n < hlit + hdist) { + while (n < ntot) { int c = stbi__zhuffman_decode(a, &z_codelength); if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG"); if (c < 16) lencodes[n++] = (stbi_uc) c; - else if (c == 16) { - c = stbi__zreceive(a,2)+3; - memset(lencodes+n, lencodes[n-1], c); - n += c; - } else if (c == 17) { - c = stbi__zreceive(a,3)+3; - memset(lencodes+n, 0, c); - n += c; - } else { - STBI_ASSERT(c == 18); - c = stbi__zreceive(a,7)+11; - memset(lencodes+n, 0, c); + else { + stbi_uc fill = 0; + if (c == 16) { + c = stbi__zreceive(a,2)+3; + if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG"); + fill = lencodes[n-1]; + } else if (c == 17) + c = stbi__zreceive(a,3)+3; + else { + STBI_ASSERT(c == 18); + c = stbi__zreceive(a,7)+11; + } + if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG"); + memset(lencodes+n, fill, c); n += c; } } - if (n != hlit+hdist) return stbi__err("bad codelengths","Corrupt PNG"); + if (n != ntot) return stbi__err("bad codelengths","Corrupt PNG"); if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0; if (!stbi__zbuild_huffman(&a->z_distance, lencodes+hlit, hdist)) return 0; return 1; } -static int stbi__parse_uncomperssed_block(stbi__zbuf *a) +static int stbi__parse_uncompressed_block(stbi__zbuf *a) { stbi_uc header[4]; int len,nlen,k; @@ -3752,9 +4108,24 @@ static int stbi__parse_zlib_header(stbi__zbuf *a) return 1; } -// @TODO: should statically initialize these for optimal thread safety -static stbi_uc stbi__zdefault_length[288], stbi__zdefault_distance[32]; -static void stbi__init_zdefaults(void) +static const stbi_uc stbi__zdefault_length[288] = +{ + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8 +}; +static const stbi_uc stbi__zdefault_distance[32] = +{ + 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 +}; +/* +Init algorithm: { int i; // use <= to match clearly with spec for (i=0; i <= 143; ++i) stbi__zdefault_length[i] = 8; @@ -3764,6 +4135,7 @@ static void stbi__init_zdefaults(void) for (i=0; i <= 31; ++i) stbi__zdefault_distance[i] = 5; } +*/ static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) { @@ -3776,13 +4148,12 @@ static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) final = stbi__zreceive(a,1); type = stbi__zreceive(a,2); if (type == 0) { - if (!stbi__parse_uncomperssed_block(a)) return 0; + if (!stbi__parse_uncompressed_block(a)) return 0; } else if (type == 3) { return 0; } else { if (type == 1) { // use fixed code lengths - if (!stbi__zdefault_distance[31]) stbi__init_zdefaults(); if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , 288)) return 0; if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; } else { @@ -3907,7 +4278,7 @@ static stbi__pngchunk stbi__get_chunk_header(stbi__context *s) static int stbi__check_png_header(stbi__context *s) { - static stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; + static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; int i; for (i=0; i < 8; ++i) if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG"); @@ -3918,6 +4289,7 @@ typedef struct { stbi__context *s; stbi_uc *idata, *expanded, *out; + int depth; } stbi__png; @@ -3952,35 +4324,40 @@ static int stbi__paeth(int a, int b, int c) return c; } -static stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; +static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; // create the png data from post-deflated data static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) { + int bytes = (depth == 16? 2 : 1); stbi__context *s = a->s; - stbi__uint32 i,j,stride = x*out_n; + stbi__uint32 i,j,stride = x*out_n*bytes; stbi__uint32 img_len, img_width_bytes; int k; int img_n = s->img_n; // copy it into a local for later + int output_bytes = out_n*bytes; + int filter_bytes = img_n*bytes; + int width = x; + STBI_ASSERT(out_n == s->img_n || out_n == s->img_n+1); - a->out = (stbi_uc *) stbi__malloc(x * y * out_n); // extra bytes to write off the end into + a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into if (!a->out) return stbi__err("outofmem", "Out of memory"); + if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); img_width_bytes = (((img_n * x * depth) + 7) >> 3); img_len = (img_width_bytes + 1) * y; - if (s->img_x == x && s->img_y == y) { - if (raw_len != img_len) return stbi__err("not enough pixels","Corrupt PNG"); - } else { // interlaced: - if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); - } + + // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, + // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), + // so just check for raw_len < img_len always. + if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); for (j=0; j < y; ++j) { stbi_uc *cur = a->out + stride*j; - stbi_uc *prior = cur - stride; + stbi_uc *prior; int filter = *raw++; - int filter_bytes = img_n; - int width = x; + if (filter > 4) return stbi__err("invalid filter","Corrupt PNG"); @@ -3990,6 +4367,7 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r filter_bytes = 1; width = img_width_bytes; } + prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above // if first row, use special filter that doesn't sample previous row if (j == 0) filter = first_row_filter[filter]; @@ -4013,6 +4391,14 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r raw += img_n; cur += out_n; prior += out_n; + } else if (depth == 16) { + if (img_n != out_n) { + cur[filter_bytes] = 255; // first pixel top byte + cur[filter_bytes+1] = 255; // first pixel bottom byte + } + raw += filter_bytes; + cur += output_bytes; + prior += output_bytes; } else { raw += 1; cur += 1; @@ -4021,38 +4407,47 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r // this is a little gross, so that we don't switch per-pixel or per-component if (depth < 8 || img_n == out_n) { - int nk = (width - 1)*img_n; - #define CASE(f) \ + int nk = (width - 1)*filter_bytes; + #define STBI__CASE(f) \ case f: \ for (k=0; k < nk; ++k) switch (filter) { // "none" filter turns into a memcpy here; make that explicit. case STBI__F_none: memcpy(cur, raw, nk); break; - CASE(STBI__F_sub) cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); break; - CASE(STBI__F_up) cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; - CASE(STBI__F_avg) cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); break; - CASE(STBI__F_paeth) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); break; - CASE(STBI__F_avg_first) cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); break; - CASE(STBI__F_paeth_first) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); } break; } - #undef CASE + #undef STBI__CASE raw += nk; } else { STBI_ASSERT(img_n+1 == out_n); - #define CASE(f) \ + #define STBI__CASE(f) \ case f: \ - for (i=x-1; i >= 1; --i, cur[img_n]=255,raw+=img_n,cur+=out_n,prior+=out_n) \ - for (k=0; k < img_n; ++k) + for (i=x-1; i >= 1; --i, cur[filter_bytes]=255,raw+=filter_bytes,cur+=output_bytes,prior+=output_bytes) \ + for (k=0; k < filter_bytes; ++k) switch (filter) { - CASE(STBI__F_none) cur[k] = raw[k]; break; - CASE(STBI__F_sub) cur[k] = STBI__BYTECAST(raw[k] + cur[k-out_n]); break; - CASE(STBI__F_up) cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; - CASE(STBI__F_avg) cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-out_n])>>1)); break; - CASE(STBI__F_paeth) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-out_n],prior[k],prior[k-out_n])); break; - CASE(STBI__F_avg_first) cur[k] = STBI__BYTECAST(raw[k] + (cur[k-out_n] >> 1)); break; - CASE(STBI__F_paeth_first) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-out_n],0,0)); break; + STBI__CASE(STBI__F_none) { cur[k] = raw[k]; } break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k- output_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k- output_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],prior[k],prior[k- output_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k- output_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],0,0)); } break; + } + #undef STBI__CASE + + // the loop above sets the high byte of the pixels' alpha, but for + // 16 bit png files we also need the low byte set. we'll do that here. + if (depth == 16) { + cur = a->out + stride*j; // start at the beginning of the row again + for (i=0; i < x; ++i,cur+=output_bytes) { + cur[filter_bytes+1] = 255; + } } - #undef CASE } } @@ -4109,25 +4504,36 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01); } if (img_n != out_n) { + int q; // insert alpha = 255 - stbi_uc *cur = a->out + stride*j; - int i; + cur = a->out + stride*j; if (img_n == 1) { - for (i=x-1; i >= 0; --i) { - cur[i*2+1] = 255; - cur[i*2+0] = cur[i]; + for (q=x-1; q >= 0; --q) { + cur[q*2+1] = 255; + cur[q*2+0] = cur[q]; } } else { STBI_ASSERT(img_n == 3); - for (i=x-1; i >= 0; --i) { - cur[i*4+3] = 255; - cur[i*4+2] = cur[i*3+2]; - cur[i*4+1] = cur[i*3+1]; - cur[i*4+0] = cur[i*3+0]; + for (q=x-1; q >= 0; --q) { + cur[q*4+3] = 255; + cur[q*4+2] = cur[q*3+2]; + cur[q*4+1] = cur[q*3+1]; + cur[q*4+0] = cur[q*3+0]; } } } } + } else if (depth == 16) { + // force the image data from big-endian to platform-native. + // this is done in a separate pass due to the decoding relying + // on the data being untouched, but could probably be done + // per-line during decode if care is taken. + stbi_uc *cur = a->out; + stbi__uint16 *cur16 = (stbi__uint16*)cur; + + for(i=0; i < x*y*out_n; ++i,cur16++,cur+=2) { + *cur16 = (cur[0] << 8) | cur[1]; + } } return 1; @@ -4135,13 +4541,15 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced) { + int bytes = (depth == 16 ? 2 : 1); + int out_bytes = out_n * bytes; stbi_uc *final; int p; if (!interlaced) return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); // de-interlacing - final = (stbi_uc *) stbi__malloc(a->s->img_x * a->s->img_y * out_n); + final = (stbi_uc *) stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); for (p=0; p < 7; ++p) { int xorig[] = { 0,4,0,2,0,1,0 }; int yorig[] = { 0,0,4,0,2,0,1 }; @@ -4161,8 +4569,8 @@ static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint3 for (i=0; i < x; ++i) { int out_y = j*yspc[p]+yorig[p]; int out_x = i*xspc[p]+xorig[p]; - memcpy(final + out_y*a->s->img_x*out_n + out_x*out_n, - a->out + (j*x+i)*out_n, out_n); + memcpy(final + out_y*a->s->img_x*out_bytes + out_x*out_bytes, + a->out + (j*x+i)*out_bytes, out_bytes); } } STBI_FREE(a->out); @@ -4200,12 +4608,37 @@ static int stbi__compute_transparency(stbi__png *z, stbi_uc tc[3], int out_n) return 1; } +static int stbi__compute_transparency16(stbi__png *z, stbi__uint16 tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi__uint16 *p = (stbi__uint16*) z->out; + + // compute color-based transparency, assuming we've + // already got 65535 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i = 0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 65535); + p += 2; + } + } else { + for (i = 0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int pal_img_n) { stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; stbi_uc *p, *temp_out, *orig = a->out; - p = (stbi_uc *) stbi__malloc(pixel_count * pal_img_n); + p = (stbi_uc *) stbi__malloc_mad2(pixel_count, pal_img_n, 0); if (p == NULL) return stbi__err("outofmem", "Out of memory"); // between here and free(out) below, exitting would leak @@ -4271,9 +4704,10 @@ static void stbi__de_iphone(stbi__png *z) stbi_uc a = p[3]; stbi_uc t = p[0]; if (a) { - p[0] = p[2] * 255 / a; - p[1] = p[1] * 255 / a; - p[2] = t * 255 / a; + stbi_uc half = a / 2; + p[0] = (p[2] * 255 + half) / a; + p[1] = (p[1] * 255 + half) / a; + p[2] = ( t * 255 + half) / a; } else { p[0] = p[2]; p[2] = t; @@ -4292,14 +4726,15 @@ static void stbi__de_iphone(stbi__png *z) } } -#define STBI__PNG_TYPE(a,b,c,d) (((a) << 24) + ((b) << 16) + ((c) << 8) + (d)) +#define STBI__PNG_TYPE(a,b,c,d) (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d)) static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) { stbi_uc palette[1024], pal_img_n=0; stbi_uc has_trans=0, tc[3]; + stbi__uint16 tc16[3]; stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0; - int first=1,k,interlace=0, color=0, depth=0, is_iphone=0; + int first=1,k,interlace=0, color=0, is_iphone=0; stbi__context *s = z->s; z->expanded = NULL; @@ -4324,8 +4759,9 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG"); s->img_x = stbi__get32be(s); if (s->img_x > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); s->img_y = stbi__get32be(s); if (s->img_y > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); - depth = stbi__get8(s); if (depth != 1 && depth != 2 && depth != 4 && depth != 8) return stbi__err("1/2/4/8-bit only","PNG not supported: 1/2/4/8-bit only"); + z->depth = stbi__get8(s); if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) return stbi__err("1/2/4/8/16-bit only","PNG not supported: 1/2/4/8/16-bit only"); color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3 && z->depth == 16) return stbi__err("bad ctype","Corrupt PNG"); if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype","Corrupt PNG"); comp = stbi__get8(s); if (comp) return stbi__err("bad comp method","Corrupt PNG"); filter= stbi__get8(s); if (filter) return stbi__err("bad filter method","Corrupt PNG"); @@ -4373,8 +4809,11 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG"); if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG"); has_trans = 1; - for (k=0; k < s->img_n; ++k) - tc[k] = (stbi_uc) (stbi__get16be(s) & 255) * stbi__depth_scale_table[depth]; // non 8-bit images will be larger + if (z->depth == 16) { + for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is + } else { + for (k = 0; k < s->img_n; ++k) tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger + } } break; } @@ -4385,11 +4824,13 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (scan == STBI__SCAN_header) { s->img_n = pal_img_n; return 1; } if ((int)(ioff + c.length) < (int)ioff) return 0; if (ioff + c.length > idata_limit) { + stbi__uint32 idata_limit_old = idata_limit; stbi_uc *p; if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096; while (ioff + c.length > idata_limit) idata_limit *= 2; - p = (stbi_uc *) STBI_REALLOC(z->idata, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); + STBI_NOTUSED(idata_limit_old); + p = (stbi_uc *) STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); z->idata = p; } if (!stbi__getn(s, z->idata+ioff,c.length)) return stbi__err("outofdata","Corrupt PNG"); @@ -4403,7 +4844,7 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (scan != STBI__SCAN_load) return 1; if (z->idata == NULL) return stbi__err("no IDAT","Corrupt PNG"); // initial guess for decoded data size to avoid unnecessary reallocs - bpl = (s->img_x * depth + 7) / 8; // bytes per line, per component + bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */; z->expanded = (stbi_uc *) stbi_zlib_decode_malloc_guesssize_headerflag((char *) z->idata, ioff, raw_len, (int *) &raw_len, !is_iphone); if (z->expanded == NULL) return 0; // zlib should set error @@ -4412,9 +4853,14 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) s->img_out_n = s->img_n+1; else s->img_out_n = s->img_n; - if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, depth, color, interlace)) return 0; - if (has_trans) - if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) return 0; + if (has_trans) { + if (z->depth == 16) { + if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) return 0; + } else { + if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + } + } if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2) stbi__de_iphone(z); if (pal_img_n) { @@ -4424,6 +4870,9 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (req_comp >= 3) s->img_out_n = req_comp; if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) return 0; + } else if (has_trans) { + // non-paletted image with tRNS -> source image has (constant) alpha + ++s->img_n; } STBI_FREE(z->expanded); z->expanded = NULL; return 1; @@ -4451,21 +4900,28 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) } } -static unsigned char *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp) +static void *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp, stbi__result_info *ri) { - unsigned char *result=NULL; + void *result=NULL; if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { + if (p->depth < 8) + ri->bits_per_channel = 8; + else + ri->bits_per_channel = p->depth; result = p->out; p->out = NULL; if (req_comp && req_comp != p->s->img_out_n) { - result = stbi__convert_format(result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + if (ri->bits_per_channel == 8) + result = stbi__convert_format((unsigned char *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + else + result = stbi__convert_format16((stbi__uint16 *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); p->s->img_out_n = req_comp; if (result == NULL) return result; } *x = p->s->img_x; *y = p->s->img_y; - if (n) *n = p->s->img_out_n; + if (n) *n = p->s->img_n; } STBI_FREE(p->out); p->out = NULL; STBI_FREE(p->expanded); p->expanded = NULL; @@ -4474,11 +4930,11 @@ static unsigned char *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req return result; } -static unsigned char *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { stbi__png p; p.s = s; - return stbi__do_png(&p, x,y,comp,req_comp); + return stbi__do_png(&p, x,y,comp,req_comp, ri); } static int stbi__png_test(stbi__context *s) @@ -4507,6 +4963,19 @@ static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp) p.s = s; return stbi__png_info_raw(&p, x, y, comp); } + +static int stbi__png_is16(stbi__context *s) +{ + stbi__png p; + p.s = s; + if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) + return 0; + if (p.depth != 16) { + stbi__rewind(p.s); + return 0; + } + return 1; +} #endif // Microsoft/Windows BMP image @@ -4558,36 +5027,46 @@ static int stbi__bitcount(unsigned int a) return a & 0xff; } +// extract an arbitrarily-aligned N-bit value (N=bits) +// from v, and then make it 8-bits long and fractionally +// extend it to full full range. static int stbi__shiftsigned(int v, int shift, int bits) { - int result; - int z=0; - - if (shift < 0) v <<= -shift; - else v >>= shift; - result = v; - - z = bits; - while (z < 8) { - result += v >> z; - z += bits; - } - return result; + static unsigned int mul_table[9] = { + 0, + 0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/, + 0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/, + }; + static unsigned int shift_table[9] = { + 0, 0,0,1,0,2,4,6,0, + }; + if (shift < 0) + v <<= -shift; + else + v >>= shift; + STBI_ASSERT(v >= 0 && v < 256); + v >>= (8-bits); + STBI_ASSERT(bits >= 0 && bits <= 8); + return (int) ((unsigned) v * mul_table[bits]) >> shift_table[bits]; } -static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +typedef struct { - stbi_uc *out; - unsigned int mr=0,mg=0,mb=0,ma=0, fake_a=0; - stbi_uc pal[256][4]; - int psize=0,i,j,compress=0,width; - int bpp, flip_vertically, pad, target, offset, hsz; + int bpp, offset, hsz; + unsigned int mr,mg,mb,ma, all_a; +} stbi__bmp_data; + +static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) +{ + int hsz; if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP"); stbi__get32le(s); // discard filesize stbi__get16le(s); // discard reserved stbi__get16le(s); // discard reserved - offset = stbi__get32le(s); - hsz = stbi__get32le(s); + info->offset = stbi__get32le(s); + info->hsz = hsz = stbi__get32le(s); + info->mr = info->mg = info->mb = info->ma = 0; + if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); if (hsz == 12) { s->img_x = stbi__get16le(s); @@ -4597,15 +5076,9 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int s->img_y = stbi__get32le(s); } if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); - bpp = stbi__get16le(s); - if (bpp == 1) return stbi__errpuc("monochrome", "BMP type not supported: 1-bit"); - flip_vertically = ((int) s->img_y) > 0; - s->img_y = abs((int) s->img_y); - if (hsz == 12) { - if (bpp < 24) - psize = (offset - 14 - 24) / 3; - } else { - compress = stbi__get32le(s); + info->bpp = stbi__get16le(s); + if (hsz != 12) { + int compress = stbi__get32le(s); if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); stbi__get32le(s); // discard sizeof stbi__get32le(s); // discard hres @@ -4619,27 +5092,25 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int stbi__get32le(s); stbi__get32le(s); } - if (bpp == 16 || bpp == 32) { - mr = mg = mb = 0; + if (info->bpp == 16 || info->bpp == 32) { if (compress == 0) { - if (bpp == 32) { - mr = 0xffu << 16; - mg = 0xffu << 8; - mb = 0xffu << 0; - ma = 0xffu << 24; - fake_a = 1; // @TODO: check for cases like alpha value is all 0 and switch it to 255 - STBI_NOTUSED(fake_a); + if (info->bpp == 32) { + info->mr = 0xffu << 16; + info->mg = 0xffu << 8; + info->mb = 0xffu << 0; + info->ma = 0xffu << 24; + info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 } else { - mr = 31u << 10; - mg = 31u << 5; - mb = 31u << 0; + info->mr = 31u << 10; + info->mg = 31u << 5; + info->mb = 31u << 0; } } else if (compress == 3) { - mr = stbi__get32le(s); - mg = stbi__get32le(s); - mb = stbi__get32le(s); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); // not documented, but generated by photoshop and handled by mspaint - if (mr == mg && mg == mb) { + if (info->mr == info->mg && info->mg == info->mb) { // ?!?!? return stbi__errpuc("bad BMP", "bad BMP"); } @@ -4647,11 +5118,13 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int return stbi__errpuc("bad BMP", "bad BMP"); } } else { - STBI_ASSERT(hsz == 108 || hsz == 124); - mr = stbi__get32le(s); - mg = stbi__get32le(s); - mb = stbi__get32le(s); - ma = stbi__get32le(s); + int i; + if (hsz != 108 && hsz != 124) + return stbi__errpuc("bad BMP", "bad BMP"); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->ma = stbi__get32le(s); stbi__get32le(s); // discard color space for (i=0; i < 12; ++i) stbi__get32le(s); // discard color space parameters @@ -4662,63 +5135,119 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int stbi__get32le(s); // discard reserved } } - if (bpp < 16) - psize = (offset - 14 - hsz) >> 2; } + return (void *) 1; +} + + +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + unsigned int mr=0,mg=0,mb=0,ma=0, all_a; + stbi_uc pal[256][4]; + int psize=0,i,j,width; + int flip_vertically, pad, target; + stbi__bmp_data info; + STBI_NOTUSED(ri); + + info.all_a = 255; + if (stbi__bmp_parse_header(s, &info) == NULL) + return NULL; // error code already set + + flip_vertically = ((int) s->img_y) > 0; + s->img_y = abs((int) s->img_y); + + mr = info.mr; + mg = info.mg; + mb = info.mb; + ma = info.ma; + all_a = info.all_a; + + if (info.hsz == 12) { + if (info.bpp < 24) + psize = (info.offset - 14 - 24) / 3; + } else { + if (info.bpp < 16) + psize = (info.offset - 14 - info.hsz) >> 2; + } + s->img_n = ma ? 4 : 3; if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 target = req_comp; else target = s->img_n; // if they want monochrome, we'll post-convert - out = (stbi_uc *) stbi__malloc(target * s->img_x * s->img_y); + + // sanity-check size + if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "Corrupt BMP"); + + out = (stbi_uc *) stbi__malloc_mad3(target, s->img_x, s->img_y, 0); if (!out) return stbi__errpuc("outofmem", "Out of memory"); - if (bpp < 16) { + if (info.bpp < 16) { int z=0; if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); } for (i=0; i < psize; ++i) { pal[i][2] = stbi__get8(s); pal[i][1] = stbi__get8(s); pal[i][0] = stbi__get8(s); - if (hsz != 12) stbi__get8(s); + if (info.hsz != 12) stbi__get8(s); pal[i][3] = 255; } - stbi__skip(s, offset - 14 - hsz - psize * (hsz == 12 ? 3 : 4)); - if (bpp == 4) width = (s->img_x + 1) >> 1; - else if (bpp == 8) width = s->img_x; + stbi__skip(s, info.offset - 14 - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); + if (info.bpp == 1) width = (s->img_x + 7) >> 3; + else if (info.bpp == 4) width = (s->img_x + 1) >> 1; + else if (info.bpp == 8) width = s->img_x; else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } pad = (-width)&3; - for (j=0; j < (int) s->img_y; ++j) { - for (i=0; i < (int) s->img_x; i += 2) { - int v=stbi__get8(s),v2=0; - if (bpp == 4) { - v2 = v & 15; - v >>= 4; + if (info.bpp == 1) { + for (j=0; j < (int) s->img_y; ++j) { + int bit_offset = 7, v = stbi__get8(s); + for (i=0; i < (int) s->img_x; ++i) { + int color = (v>>bit_offset)&0x1; + out[z++] = pal[color][0]; + out[z++] = pal[color][1]; + out[z++] = pal[color][2]; + if((--bit_offset) < 0) { + bit_offset = 7; + v = stbi__get8(s); + } } - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) out[z++] = 255; - if (i+1 == (int) s->img_x) break; - v = (bpp == 8) ? stbi__get8(s) : v2; - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) out[z++] = 255; + stbi__skip(s, pad); + } + } else { + for (j=0; j < (int) s->img_y; ++j) { + for (i=0; i < (int) s->img_x; i += 2) { + int v=stbi__get8(s),v2=0; + if (info.bpp == 4) { + v2 = v & 15; + v >>= 4; + } + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + v = (info.bpp == 8) ? stbi__get8(s) : v2; + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + } + stbi__skip(s, pad); } - stbi__skip(s, pad); } } else { int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0; int z = 0; int easy=0; - stbi__skip(s, offset - 14 - hsz); - if (bpp == 24) width = 3 * s->img_x; - else if (bpp == 16) width = 2*s->img_x; + stbi__skip(s, info.offset - 14 - info.hsz); + if (info.bpp == 24) width = 3 * s->img_x; + else if (info.bpp == 16) width = 2*s->img_x; else /* bpp = 32 and pad = 0 */ width=0; pad = (-width) & 3; - if (bpp == 24) { + if (info.bpp == 24) { easy = 1; - } else if (bpp == 32) { + } else if (info.bpp == 32) { if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) easy = 2; } @@ -4739,22 +5268,31 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int out[z+0] = stbi__get8(s); z += 3; a = (easy == 2 ? stbi__get8(s) : 255); + all_a |= a; if (target == 4) out[z++] = a; } } else { + int bpp = info.bpp; for (i=0; i < (int) s->img_x; ++i) { stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s)); - int a; + unsigned int a; out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); + all_a |= a; if (target == 4) out[z++] = STBI__BYTECAST(a); } } stbi__skip(s, pad); } } + + // if alpha channel is all 0s, replace with all 255s + if (target == 4 && all_a == 0) + for (i=4*s->img_x*s->img_y-1; i >= 0; i -= 4) + out[i] = 255; + if (flip_vertically) { stbi_uc t; for (j=0; j < (int) s->img_y>>1; ++j) { @@ -4781,20 +5319,55 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int // Targa Truevision - TGA // by Jonathan Dummer #ifndef STBI_NO_TGA +// returns STBI_rgb or whatever, 0 on error +static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16) +{ + // only RGB or RGBA (incl. 16bit) or grey allowed + if (is_rgb16) *is_rgb16 = 0; + switch(bits_per_pixel) { + case 8: return STBI_grey; + case 16: if(is_grey) return STBI_grey_alpha; + // fallthrough + case 15: if(is_rgb16) *is_rgb16 = 1; + return STBI_rgb; + case 24: // fallthrough + case 32: return bits_per_pixel/8; + default: return 0; + } +} + static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) { - int tga_w, tga_h, tga_comp; - int sz; + int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp; + int sz, tga_colormap_type; stbi__get8(s); // discard Offset - sz = stbi__get8(s); // color type - if( sz > 1 ) { + tga_colormap_type = stbi__get8(s); // colormap type + if( tga_colormap_type > 1 ) { stbi__rewind(s); return 0; // only RGB or indexed allowed } - sz = stbi__get8(s); // image type - // only RGB or grey allowed, +/- RLE - if ((sz != 1) && (sz != 2) && (sz != 3) && (sz != 9) && (sz != 10) && (sz != 11)) return 0; - stbi__skip(s,9); + tga_image_type = stbi__get8(s); // image type + if ( tga_colormap_type == 1 ) { // colormapped (paletted) image + if (tga_image_type != 1 && tga_image_type != 9) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip image x and y origin + tga_colormap_bpp = sz; + } else { // "normal" image w/o colormap - only RGB or grey allowed, +/- RLE + if ( (tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11) ) { + stbi__rewind(s); + return 0; // only RGB or grey allowed, +/- RLE + } + stbi__skip(s,9); // skip colormap specification and image x/y origin + tga_colormap_bpp = 0; + } tga_w = stbi__get16le(s); if( tga_w < 1 ) { stbi__rewind(s); @@ -4805,45 +5378,81 @@ static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) stbi__rewind(s); return 0; // test height } - sz = stbi__get8(s); // bits per pixel - // only RGB or RGBA or grey allowed - if ((sz != 8) && (sz != 16) && (sz != 24) && (sz != 32)) { - stbi__rewind(s); - return 0; + tga_bits_per_pixel = stbi__get8(s); // bits per pixel + stbi__get8(s); // ignore alpha bits + if (tga_colormap_bpp != 0) { + if((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) { + // when using a colormap, tga_bits_per_pixel is the size of the indexes + // I don't think anything but 8 or 16bit indexes makes sense + stbi__rewind(s); + return 0; + } + tga_comp = stbi__tga_get_comp(tga_colormap_bpp, 0, NULL); + } else { + tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL); + } + if(!tga_comp) { + stbi__rewind(s); + return 0; } - tga_comp = sz; if (x) *x = tga_w; if (y) *y = tga_h; - if (comp) *comp = tga_comp / 8; + if (comp) *comp = tga_comp; return 1; // seems to have passed everything } static int stbi__tga_test(stbi__context *s) { - int res; - int sz; + int res = 0; + int sz, tga_color_type; stbi__get8(s); // discard Offset - sz = stbi__get8(s); // color type - if ( sz > 1 ) return 0; // only RGB or indexed allowed + tga_color_type = stbi__get8(s); // color type + if ( tga_color_type > 1 ) goto errorEnd; // only RGB or indexed allowed sz = stbi__get8(s); // image type - if ( (sz != 1) && (sz != 2) && (sz != 3) && (sz != 9) && (sz != 10) && (sz != 11) ) return 0; // only RGB or grey allowed, +/- RLE - stbi__get16be(s); // discard palette start - stbi__get16be(s); // discard palette length - stbi__get8(s); // discard bits per palette color entry - stbi__get16be(s); // discard x origin - stbi__get16be(s); // discard y origin - if ( stbi__get16be(s) < 1 ) return 0; // test width - if ( stbi__get16be(s) < 1 ) return 0; // test height + if ( tga_color_type == 1 ) { // colormapped (paletted) image + if (sz != 1 && sz != 9) goto errorEnd; // colortype 1 demands image type 1 or 9 + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + stbi__skip(s,4); // skip image x and y origin + } else { // "normal" image w/o colormap + if ( (sz != 2) && (sz != 3) && (sz != 10) && (sz != 11) ) goto errorEnd; // only RGB or grey allowed, +/- RLE + stbi__skip(s,9); // skip colormap specification and image x/y origin + } + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test width + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test height sz = stbi__get8(s); // bits per pixel - if ( (sz != 8) && (sz != 16) && (sz != 24) && (sz != 32) ) - res = 0; - else - res = 1; + if ( (tga_color_type == 1) && (sz != 8) && (sz != 16) ) goto errorEnd; // for colormapped images, bpp is size of an index + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + + res = 1; // if we got this far, everything's good and we can return 1 instead of 0 + +errorEnd: stbi__rewind(s); return res; } -static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +// read 16bit value and convert to 24bit RGB +static void stbi__tga_read_rgb16(stbi__context *s, stbi_uc* out) +{ + stbi__uint16 px = (stbi__uint16)stbi__get16le(s); + stbi__uint16 fiveBitMask = 31; + // we have 3 channels with 5bits each + int r = (px >> 10) & fiveBitMask; + int g = (px >> 5) & fiveBitMask; + int b = px & fiveBitMask; + // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later + out[0] = (stbi_uc)((r * 255)/31); + out[1] = (stbi_uc)((g * 255)/31); + out[2] = (stbi_uc)((b * 255)/31); + + // some people claim that the most significant bit might be used for alpha + // (possibly if an alpha-bit is set in the "image descriptor byte") + // but that only made 16bit test images completely translucent.. + // so let's treat all 15 and 16bit TGAs as RGB with no alpha. +} + +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { // read in the TGA header stuff int tga_offset = stbi__get8(s); @@ -4858,16 +5467,18 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int int tga_width = stbi__get16le(s); int tga_height = stbi__get16le(s); int tga_bits_per_pixel = stbi__get8(s); - int tga_comp = tga_bits_per_pixel / 8; + int tga_comp, tga_rgb16=0; int tga_inverted = stbi__get8(s); + // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?) // image data unsigned char *tga_data; unsigned char *tga_palette = NULL; int i, j; - unsigned char raw_data[4]; + unsigned char raw_data[4] = {0}; int RLE_count = 0; int RLE_repeating = 0; int read_next_pixel = 1; + STBI_NOTUSED(ri); // do a tiny bit of precessing if ( tga_image_type >= 8 ) @@ -4875,41 +5486,33 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int tga_image_type -= 8; tga_is_RLE = 1; } - /* int tga_alpha_bits = tga_inverted & 15; */ tga_inverted = 1 - ((tga_inverted >> 5) & 1); - // error check - if ( //(tga_indexed) || - (tga_width < 1) || (tga_height < 1) || - (tga_image_type < 1) || (tga_image_type > 3) || - ((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16) && - (tga_bits_per_pixel != 24) && (tga_bits_per_pixel != 32)) - ) - { - return NULL; // we don't report this as a bad TGA because we don't even know if it's TGA - } - // If I'm paletted, then I'll use the number of bits from the palette - if ( tga_indexed ) - { - tga_comp = tga_palette_bits / 8; - } + if ( tga_indexed ) tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16); + else tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16); + + if(!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency + return stbi__errpuc("bad format", "Can't find out TGA pixelformat"); // tga info *x = tga_width; *y = tga_height; if (comp) *comp = tga_comp; - tga_data = (unsigned char*)stbi__malloc( (size_t)tga_width * tga_height * tga_comp ); + if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0)) + return stbi__errpuc("too large", "Corrupt TGA"); + + tga_data = (unsigned char*)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0); if (!tga_data) return stbi__errpuc("outofmem", "Out of memory"); // skip to the data's starting position (offset usually = 0) stbi__skip(s, tga_offset ); - if ( !tga_indexed && !tga_is_RLE) { + if ( !tga_indexed && !tga_is_RLE && !tga_rgb16 ) { for (i=0; i < tga_height; ++i) { - int y = tga_inverted ? tga_height -i - 1 : i; - stbi_uc *tga_row = tga_data + y*tga_width*tga_comp; + int row = tga_inverted ? tga_height -i - 1 : i; + stbi_uc *tga_row = tga_data + row*tga_width*tga_comp; stbi__getn(s, tga_row, tga_width * tga_comp); } } else { @@ -4919,15 +5522,22 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int // any data to skip? (offset usually = 0) stbi__skip(s, tga_palette_start ); // load the palette - tga_palette = (unsigned char*)stbi__malloc( tga_palette_len * tga_palette_bits / 8 ); + tga_palette = (unsigned char*)stbi__malloc_mad2(tga_palette_len, tga_comp, 0); if (!tga_palette) { STBI_FREE(tga_data); return stbi__errpuc("outofmem", "Out of memory"); } - if (!stbi__getn(s, tga_palette, tga_palette_len * tga_palette_bits / 8 )) { - STBI_FREE(tga_data); - STBI_FREE(tga_palette); - return stbi__errpuc("bad palette", "Corrupt TGA"); + if (tga_rgb16) { + stbi_uc *pal_entry = tga_palette; + STBI_ASSERT(tga_comp == STBI_rgb); + for (i=0; i < tga_palette_len; ++i) { + stbi__tga_read_rgb16(s, pal_entry); + pal_entry += tga_comp; + } + } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) { + STBI_FREE(tga_data); + STBI_FREE(tga_palette); + return stbi__errpuc("bad palette", "Corrupt TGA"); } } // load the data @@ -4957,23 +5567,22 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int // load however much data we did have if ( tga_indexed ) { - // read in 1 byte, then perform the lookup - int pal_idx = stbi__get8(s); - if ( pal_idx >= tga_palette_len ) - { - // invalid index + // read in index, then perform the lookup + int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s); + if ( pal_idx >= tga_palette_len ) { + // invalid index pal_idx = 0; } - pal_idx *= tga_bits_per_pixel / 8; - for (j = 0; j*8 < tga_bits_per_pixel; ++j) - { + pal_idx *= tga_comp; + for (j = 0; j < tga_comp; ++j) { raw_data[j] = tga_palette[pal_idx+j]; } - } else - { + } else if(tga_rgb16) { + STBI_ASSERT(tga_comp == STBI_rgb); + stbi__tga_read_rgb16(s, raw_data); + } else { // read in the data raw - for (j = 0; j*8 < tga_bits_per_pixel; ++j) - { + for (j = 0; j < tga_comp; ++j) { raw_data[j] = stbi__get8(s); } } @@ -5012,8 +5621,8 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int } } - // swap RGB - if (tga_comp >= 3) + // swap RGB - if the source data was RGB16, it already is in the right order + if (tga_comp >= 3 && !tga_rgb16) { unsigned char* tga_pixel = tga_data; for (i=0; i < tga_width * tga_height; ++i) @@ -5049,13 +5658,53 @@ static int stbi__psd_test(stbi__context *s) return r; } -static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static int stbi__psd_decode_rle(stbi__context *s, stbi_uc *p, int pixelCount) { - int pixelCount; + int count, nleft, len; + + count = 0; + while ((nleft = pixelCount - count) > 0) { + len = stbi__get8(s); + if (len == 128) { + // No-op. + } else if (len < 128) { + // Copy next len+1 bytes literally. + len++; + if (len > nleft) return 0; // corrupt data + count += len; + while (len) { + *p = stbi__get8(s); + p += 4; + len--; + } + } else if (len > 128) { + stbi_uc val; + // Next -len+1 bytes in the dest are replicated from next source byte. + // (Interpret len as a negative 8-bit int.) + len = 257 - len; + if (len > nleft) return 0; // corrupt data + val = stbi__get8(s); + count += len; + while (len) { + *p = val; + p += 4; + len--; + } + } + } + + return 1; +} + +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + int pixelCount; int channelCount, compression; - int channel, i, count, len; + int channel, i; + int bitdepth; int w,h; stbi_uc *out; + STBI_NOTUSED(ri); // Check identifier if (stbi__get32be(s) != 0x38425053) // "8BPS" @@ -5078,8 +5727,9 @@ static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int w = stbi__get32be(s); // Make sure the depth is 8 bits. - if (stbi__get16be(s) != 8) - return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 bit"); + bitdepth = stbi__get16be(s); + if (bitdepth != 8 && bitdepth != 16) + return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit"); // Make sure the color mode is RGB. // Valid options are: @@ -5111,8 +5761,18 @@ static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int if (compression > 1) return stbi__errpuc("bad compression", "PSD has an unknown compression format"); + // Check size + if (!stbi__mad3sizes_valid(4, w, h, 0)) + return stbi__errpuc("too large", "Corrupt PSD"); + // Create the destination image. - out = (stbi_uc *) stbi__malloc(4 * w*h); + + if (!compression && bitdepth == 16 && bpc == 16) { + out = (stbi_uc *) stbi__malloc_mad3(8, w, h, 0); + ri->bits_per_channel = 16; + } else + out = (stbi_uc *) stbi__malloc(4 * w*h); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); pixelCount = w*h; @@ -5144,61 +5804,86 @@ static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int *p = (channel == 3 ? 255 : 0); } else { // Read the RLE data. - count = 0; - while (count < pixelCount) { - len = stbi__get8(s); - if (len == 128) { - // No-op. - } else if (len < 128) { - // Copy next len+1 bytes literally. - len++; - count += len; - while (len) { - *p = stbi__get8(s); - p += 4; - len--; - } - } else if (len > 128) { - stbi_uc val; - // Next -len+1 bytes in the dest are replicated from next source byte. - // (Interpret len as a negative 8-bit int.) - len ^= 0x0FF; - len += 2; - val = stbi__get8(s); - count += len; - while (len) { - *p = val; - p += 4; - len--; - } - } + if (!stbi__psd_decode_rle(s, p, pixelCount)) { + STBI_FREE(out); + return stbi__errpuc("corrupt", "bad RLE data"); } } } } else { // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) - // where each channel consists of an 8-bit value for each pixel in the image. + // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image. // Read the data by channel. for (channel = 0; channel < 4; channel++) { - stbi_uc *p; - - p = out + channel; - if (channel > channelCount) { + if (channel >= channelCount) { // Fill this channel with default data. - for (i = 0; i < pixelCount; i++, p += 4) - *p = channel == 3 ? 255 : 0; + if (bitdepth == 16 && bpc == 16) { + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + stbi__uint16 val = channel == 3 ? 65535 : 0; + for (i = 0; i < pixelCount; i++, q += 4) + *q = val; + } else { + stbi_uc *p = out+channel; + stbi_uc val = channel == 3 ? 255 : 0; + for (i = 0; i < pixelCount; i++, p += 4) + *p = val; + } } else { - // Read the data. - for (i = 0; i < pixelCount; i++, p += 4) - *p = stbi__get8(s); + if (ri->bits_per_channel == 16) { // output bpc + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + for (i = 0; i < pixelCount; i++, q += 4) + *q = (stbi__uint16) stbi__get16be(s); + } else { + stbi_uc *p = out+channel; + if (bitdepth == 16) { // input bpc + for (i = 0; i < pixelCount; i++, p += 4) + *p = (stbi_uc) (stbi__get16be(s) >> 8); + } else { + for (i = 0; i < pixelCount; i++, p += 4) + *p = stbi__get8(s); + } + } + } + } + } + + // remove weird white matte from PSD + if (channelCount >= 4) { + if (ri->bits_per_channel == 16) { + for (i=0; i < w*h; ++i) { + stbi__uint16 *pixel = (stbi__uint16 *) out + 4*i; + if (pixel[3] != 0 && pixel[3] != 65535) { + float a = pixel[3] / 65535.0f; + float ra = 1.0f / a; + float inv_a = 65535.0f * (1 - ra); + pixel[0] = (stbi__uint16) (pixel[0]*ra + inv_a); + pixel[1] = (stbi__uint16) (pixel[1]*ra + inv_a); + pixel[2] = (stbi__uint16) (pixel[2]*ra + inv_a); + } + } + } else { + for (i=0; i < w*h; ++i) { + unsigned char *pixel = out + 4*i; + if (pixel[3] != 0 && pixel[3] != 255) { + float a = pixel[3] / 255.0f; + float ra = 1.0f / a; + float inv_a = 255.0f * (1 - ra); + pixel[0] = (unsigned char) (pixel[0]*ra + inv_a); + pixel[1] = (unsigned char) (pixel[1]*ra + inv_a); + pixel[2] = (unsigned char) (pixel[2]*ra + inv_a); + } } } } + // convert to desired output format if (req_comp && req_comp != 4) { - out = stbi__convert_format(out, 4, req_comp, w, h); + if (ri->bits_per_channel == 16) + out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, 4, req_comp, w, h); + else + out = stbi__convert_format(out, 4, req_comp, w, h); if (out == NULL) return out; // stbi__convert_format frees input on failure } @@ -5350,7 +6035,6 @@ static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *c if (count >= 128) { // Repeated stbi_uc value[4]; - int i; if (count==128) count = stbi__get16be(s); @@ -5383,10 +6067,13 @@ static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *c return result; } -static stbi_uc *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp) +static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp, stbi__result_info *ri) { stbi_uc *result; - int i, x,y; + int i, x,y, internal_comp; + STBI_NOTUSED(ri); + + if (!comp) comp = &internal_comp; for (i=0; i<92; ++i) stbi__get8(s); @@ -5394,14 +6081,14 @@ static stbi_uc *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int re x = stbi__get16be(s); y = stbi__get16be(s); if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pic header)"); - if ((1 << 28) / x < y) return stbi__errpuc("too large", "Image too large to decode"); + if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode"); stbi__get32be(s); //skip `ratio' stbi__get16be(s); //skip `fields' stbi__get16be(s); //skip `pad' // intermediate buffer is RGBA - result = (stbi_uc *) stbi__malloc(x*y*4); + result = (stbi_uc *) stbi__malloc_mad3(x, y, 4, 0); memset(result, 0xff, x*y*4); if (!stbi__pic_load_core(s,x,y,comp, result)) { @@ -5439,10 +6126,12 @@ typedef struct { int w,h; stbi_uc *out; // output buffer (always 4 components) + stbi_uc *background; // The current "background" as far as a gif is concerned + stbi_uc *history; int flags, bgindex, ratio, transparent, eflags; stbi_uc pal[256][4]; stbi_uc lpal[256][4]; - stbi__gif_lzw codes[4096]; + stbi__gif_lzw codes[8192]; stbi_uc *color_table; int parse, step; int lflags; @@ -5450,6 +6139,7 @@ typedef struct int max_x, max_y; int cur_x, cur_y; int line_size; + int delay; } stbi__gif; static int stbi__gif_test_raw(stbi__context *s) @@ -5510,19 +6200,22 @@ static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_in static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) { - stbi__gif g; - if (!stbi__gif_header(s, &g, comp, 1)) { + stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif)); + if (!stbi__gif_header(s, g, comp, 1)) { + STBI_FREE(g); stbi__rewind( s ); return 0; } - if (x) *x = g.w; - if (y) *y = g.h; + if (x) *x = g->w; + if (y) *y = g->h; + STBI_FREE(g); return 1; } static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) { stbi_uc *p, *c; + int idx; // recurse to decode the prefixes, since the linked-list is backwards, // and working backwards through an interleaved image would be nasty @@ -5531,10 +6224,12 @@ static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) if (g->cur_y >= g->max_y) return; - p = &g->out[g->cur_x + g->cur_y]; - c = &g->color_table[g->codes[code].suffix * 4]; + idx = g->cur_x + g->cur_y; + p = &g->out[idx]; + g->history[idx / 4] = 1; - if (c[3] >= 128) { + c = &g->color_table[g->codes[code].suffix * 4]; + if (c[3] > 128) { // don't render transparent pixels; p[0] = c[2]; p[1] = c[1]; p[2] = c[0]; @@ -5557,7 +6252,7 @@ static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) { stbi_uc lzw_cs; - stbi__int32 len, code; + stbi__int32 len, init_code; stbi__uint32 first; stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; stbi__gif_lzw *p; @@ -5570,10 +6265,10 @@ static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) codemask = (1 << codesize) - 1; bits = 0; valid_bits = 0; - for (code = 0; code < clear; code++) { - g->codes[code].prefix = -1; - g->codes[code].first = (stbi_uc) code; - g->codes[code].suffix = (stbi_uc) code; + for (init_code = 0; init_code < clear; init_code++) { + g->codes[init_code].prefix = -1; + g->codes[init_code].first = (stbi_uc) init_code; + g->codes[init_code].suffix = (stbi_uc) init_code; } // support no starting clear code @@ -5608,11 +6303,16 @@ static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) stbi__skip(s,len); return g->out; } else if (code <= avail) { - if (first) return stbi__errpuc("no clear code", "Corrupt GIF"); + if (first) { + return stbi__errpuc("no clear code", "Corrupt GIF"); + } if (oldcode >= 0) { p = &g->codes[avail++]; - if (avail > 4096) return stbi__errpuc("too many codes", "Corrupt GIF"); + if (avail > 8192) { + return stbi__errpuc("too many codes", "Corrupt GIF"); + } + p->prefix = (stbi__int16) oldcode; p->first = g->codes[oldcode].first; p->suffix = (code == avail) ? p->first : g->codes[code].first; @@ -5634,43 +6334,70 @@ static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) } } -static void stbi__fill_gif_background(stbi__gif *g) -{ - int i; - stbi_uc *c = g->pal[g->bgindex]; - // @OPTIMIZE: write a dword at a time - for (i = 0; i < g->w * g->h * 4; i += 4) { - stbi_uc *p = &g->out[i]; - p[0] = c[2]; - p[1] = c[1]; - p[2] = c[0]; - p[3] = c[3]; - } -} - // this function is designed to support animated gifs, although stb_image doesn't support it -static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp) +// two back is the image from two frames ago, used for a very specific disposal format +static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp, stbi_uc *two_back) { - int i; - stbi_uc *old_out = 0; + int dispose; + int first_frame; + int pi; + int pcount; + // on first frame, any non-written pixels get the background colour (non-transparent) + first_frame = 0; if (g->out == 0) { if (!stbi__gif_header(s, g, comp,0)) return 0; // stbi__g_failure_reason set by stbi__gif_header g->out = (stbi_uc *) stbi__malloc(4 * g->w * g->h); + g->background = (stbi_uc *) stbi__malloc(4 * g->w * g->h); + g->history = (stbi_uc *) stbi__malloc(g->w * g->h); if (g->out == 0) return stbi__errpuc("outofmem", "Out of memory"); - stbi__fill_gif_background(g); + + // image is treated as "tranparent" at the start - ie, nothing overwrites the current background; + // background colour is only used for pixels that are not rendered first frame, after that "background" + // color refers to teh color that was there the previous frame. + memset( g->out, 0x00, 4 * g->w * g->h ); + memset( g->background, 0x00, 4 * g->w * g->h ); // state of the background (starts transparent) + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + first_frame = 1; } else { - // animated-gif-only path - if (((g->eflags & 0x1C) >> 2) == 3) { - old_out = g->out; - g->out = (stbi_uc *) stbi__malloc(4 * g->w * g->h); - if (g->out == 0) return stbi__errpuc("outofmem", "Out of memory"); - memcpy(g->out, old_out, g->w*g->h*4); + // second frame - how do we dispoase of the previous one? + dispose = (g->eflags & 0x1C) >> 2; + pcount = g->w * g->h; + + if ((dispose == 3) && (two_back == 0)) { + dispose = 2; // if I don't have an image to revert back to, default to the old background } + + if (dispose == 3) { // use previous graphic + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &two_back[pi * 4], 4 ); + } + } + } else if (dispose == 2) { + // restore what was changed last frame to background before that frame; + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &g->background[pi * 4], 4 ); + } + } + } else { + // This is a non-disposal case eithe way, so just + // leave the pixels as is, and they will become the new background + // 1: do not dispose + // 0: not specified. + } + + // background is what out is after the undoing of the previou frame; + memcpy( g->background, g->out, 4 * g->w * g->h ); } + // clear my history; + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + for (;;) { - switch (stbi__get8(s)) { + int tag = stbi__get8(s); + switch (tag) { case 0x2C: /* Image Descriptor */ { stbi__int32 x, y, w, h; @@ -5705,38 +6432,60 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); g->color_table = (stbi_uc *) g->lpal; } else if (g->flags & 0x80) { - for (i=0; i < 256; ++i) // @OPTIMIZE: stbi__jpeg_reset only the previous transparent - g->pal[i][3] = 255; - if (g->transparent >= 0 && (g->eflags & 0x01)) - g->pal[g->transparent][3] = 0; g->color_table = (stbi_uc *) g->pal; } else - return stbi__errpuc("missing color table", "Corrupt GIF"); - + return stbi__errpuc("missing color table", "Corrupt GIF"); + o = stbi__process_gif_raster(s, g); if (o == NULL) return NULL; - if (req_comp && req_comp != 4) - o = stbi__convert_format(o, 4, req_comp, g->w, g->h); + // if this was the first frame, + pcount = g->w * g->h; + if (first_frame && (g->bgindex > 0)) { + // if first frame, any pixel not drawn to gets the background color + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi] == 0) { + g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; + memcpy( &g->out[pi * 4], &g->pal[g->bgindex], 4 ); + } + } + } + return o; } case 0x21: // Comment Extension. { int len; - if (stbi__get8(s) == 0xF9) { // Graphic Control Extension. + int ext = stbi__get8(s); + if (ext == 0xF9) { // Graphic Control Extension. len = stbi__get8(s); if (len == 4) { g->eflags = stbi__get8(s); - stbi__get16le(s); // delay - g->transparent = stbi__get8(s); + g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. + + // unset old transparent + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 255; + } + if (g->eflags & 0x01) { + g->transparent = stbi__get8(s); + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 0; + } + } else { + // don't need transparent + stbi__skip(s, 1); + g->transparent = -1; + } } else { stbi__skip(s, len); break; } - } - while ((len = stbi__get8(s)) != 0) + } + while ((len = stbi__get8(s)) != 0) { stbi__skip(s, len); + } break; } @@ -5749,19 +6498,90 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i } } -static stbi_uc *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + if (stbi__gif_test(s)) { + int layers = 0; + stbi_uc *u = 0; + stbi_uc *out = 0; + stbi_uc *two_back = 0; + stbi__gif g; + int stride; + memset(&g, 0, sizeof(g)); + if (delays) { + *delays = 0; + } + + do { + u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + + if (u) { + *x = g.w; + *y = g.h; + ++layers; + stride = g.w * g.h * 4; + + if (out) { + out = (stbi_uc*) STBI_REALLOC( out, layers * stride ); + if (delays) { + *delays = (int*) STBI_REALLOC( *delays, sizeof(int) * layers ); + } + } else { + out = (stbi_uc*)stbi__malloc( layers * stride ); + if (delays) { + *delays = (int*) stbi__malloc( layers * sizeof(int) ); + } + } + memcpy( out + ((layers - 1) * stride), u, stride ); + if (layers >= 2) { + two_back = out - 2 * stride; + } + + if (delays) { + (*delays)[layers - 1U] = g.delay; + } + } + } while (u != 0); + + // free temp buffer; + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); + + // do the final conversion after loading everything; + if (req_comp && req_comp != 4) + out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + + *z = layers; + return out; + } else { + return stbi__errpuc("not GIF", "Image was not as a gif type."); + } +} + +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { stbi_uc *u = 0; stbi__gif g; memset(&g, 0, sizeof(g)); - u = stbi__gif_load_next(s, &g, comp, req_comp); + u = stbi__gif_load_next(s, &g, comp, req_comp, 0); if (u == (stbi_uc *) s) u = 0; // end of animated gif marker if (u) { *x = g.w; *y = g.h; + + // moved conversion to after successful load so that the same + // can be done for multiple frames. + if (req_comp && req_comp != 4) + u = stbi__convert_format(u, 4, req_comp, g.w, g.h); } + // free buffers needed for multiple frame loading; + STBI_FREE(g.history); + STBI_FREE(g.background); + return u; } @@ -5775,20 +6595,24 @@ static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp) // Radiance RGBE HDR loader // originally by Nicolas Schulz #ifndef STBI_NO_HDR -static int stbi__hdr_test_core(stbi__context *s) +static int stbi__hdr_test_core(stbi__context *s, const char *signature) { - const char *signature = "#?RADIANCE\n"; int i; for (i=0; signature[i]; ++i) if (stbi__get8(s) != signature[i]) - return 0; + return 0; + stbi__rewind(s); return 1; } static int stbi__hdr_test(stbi__context* s) { - int r = stbi__hdr_test_core(s); + int r = stbi__hdr_test_core(s, "#?RADIANCE\n"); stbi__rewind(s); + if(!r) { + r = stbi__hdr_test_core(s, "#?RGBE\n"); + stbi__rewind(s); + } return r; } @@ -5842,7 +6666,7 @@ static void stbi__hdr_convert(float *output, stbi_uc *input, int req_comp) } } -static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { char buffer[STBI__HDR_BUFLEN]; char *token; @@ -5853,10 +6677,12 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re int len; unsigned char count, value; int i, j, k, c1,c2, z; - + const char *headerToken; + STBI_NOTUSED(ri); // Check identifier - if (strcmp(stbi__hdr_gettoken(s,buffer), "#?RADIANCE") != 0) + headerToken = stbi__hdr_gettoken(s,buffer); + if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0) return stbi__errpf("not HDR", "Corrupt HDR image"); // Parse header @@ -5885,8 +6711,13 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re if (comp) *comp = 3; if (req_comp == 0) req_comp = 3; + if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0)) + return stbi__errpf("too large", "HDR image is too large"); + // Read data - hdr_data = (float *) stbi__malloc(height * width * req_comp * sizeof(float)); + hdr_data = (float *) stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0); + if (!hdr_data) + return stbi__errpf("outofmem", "Out of memory"); // Load image data // image data is stored as some number of sca @@ -5925,20 +6756,29 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re len <<= 8; len |= stbi__get8(s); if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); } - if (scanline == NULL) scanline = (stbi_uc *) stbi__malloc(width * 4); + if (scanline == NULL) { + scanline = (stbi_uc *) stbi__malloc_mad2(width, 4, 0); + if (!scanline) { + STBI_FREE(hdr_data); + return stbi__errpf("outofmem", "Out of memory"); + } + } for (k = 0; k < 4; ++k) { + int nleft; i = 0; - while (i < width) { + while ((nleft = width - i) > 0) { count = stbi__get8(s); if (count > 128) { // Run value = stbi__get8(s); count -= 128; + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } for (z = 0; z < count; ++z) scanline[i++ * 4 + k] = value; } else { // Dump + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } for (z = 0; z < count; ++z) scanline[i++ * 4 + k] = stbi__get8(s); } @@ -5947,7 +6787,8 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re for (i=0; i < width; ++i) stbi__hdr_convert(hdr_data+(j*width + i)*req_comp, scanline + i*4, req_comp); } - STBI_FREE(scanline); + if (scanline) + STBI_FREE(scanline); } return hdr_data; @@ -5958,8 +6799,13 @@ static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) char buffer[STBI__HDR_BUFLEN]; char *token; int valid = 0; + int dummy; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; - if (strcmp(stbi__hdr_gettoken(s,buffer), "#?RADIANCE") != 0) { + if (stbi__hdr_test(s) == 0) { stbi__rewind( s ); return 0; } @@ -5996,29 +6842,17 @@ static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) #ifndef STBI_NO_BMP static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) { - int hsz; - if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') { - stbi__rewind( s ); - return 0; - } - stbi__skip(s,12); - hsz = stbi__get32le(s); - if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) { - stbi__rewind( s ); - return 0; - } - if (hsz == 12) { - *x = stbi__get16le(s); - *y = stbi__get16le(s); - } else { - *x = stbi__get32le(s); - *y = stbi__get32le(s); - } - if (stbi__get16le(s) != 1) { - stbi__rewind( s ); - return 0; - } - *comp = stbi__get16le(s) / 8; + void *p; + stbi__bmp_data info; + + info.all_a = 255; + p = stbi__bmp_parse_header(s, &info); + stbi__rewind( s ); + if (p == NULL) + return 0; + if (x) *x = s->img_x; + if (y) *y = s->img_y; + if (comp) *comp = info.ma ? 4 : 3; return 1; } #endif @@ -6026,7 +6860,10 @@ static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) #ifndef STBI_NO_PSD static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) { - int channelCount; + int channelCount, dummy, depth; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; if (stbi__get32be(s) != 0x38425053) { stbi__rewind( s ); return 0; @@ -6043,7 +6880,8 @@ static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) } *y = stbi__get32be(s); *x = stbi__get32be(s); - if (stbi__get16be(s) != 8) { + depth = stbi__get16be(s); + if (depth != 8 && depth != 16) { stbi__rewind( s ); return 0; } @@ -6054,22 +6892,61 @@ static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) *comp = 4; return 1; } + +static int stbi__psd_is16(stbi__context *s) +{ + int channelCount, depth; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + (void) stbi__get32be(s); + (void) stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 16) { + stbi__rewind( s ); + return 0; + } + return 1; +} #endif #ifndef STBI_NO_PIC static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) { - int act_comp=0,num_packets=0,chained; + int act_comp=0,num_packets=0,chained,dummy; stbi__pic_packet packets[10]; - stbi__skip(s, 92); + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) { + stbi__rewind(s); + return 0; + } + + stbi__skip(s, 88); *x = stbi__get16be(s); *y = stbi__get16be(s); - if (stbi__at_eof(s)) return 0; + if (stbi__at_eof(s)) { + stbi__rewind( s); + return 0; + } if ( (*x) != 0 && (1 << 28) / (*x) < (*y)) { - stbi__rewind( s ); - return 0; + stbi__rewind( s ); + return 0; } stbi__skip(s, 8); @@ -6129,16 +7006,22 @@ static int stbi__pnm_test(stbi__context *s) return 1; } -static stbi_uc *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { stbi_uc *out; + STBI_NOTUSED(ri); + if (!stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n)) return 0; + *x = s->img_x; *y = s->img_y; - *comp = s->img_n; + if (comp) *comp = s->img_n; + + if (!stbi__mad3sizes_valid(s->img_n, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "PNM too large"); - out = (stbi_uc *) stbi__malloc(s->img_n * s->img_x * s->img_y); + out = (stbi_uc *) stbi__malloc_mad3(s->img_n, s->img_x, s->img_y, 0); if (!out) return stbi__errpuc("outofmem", "Out of memory"); stbi__getn(s, out, s->img_n * s->img_x * s->img_y); @@ -6156,8 +7039,16 @@ static int stbi__pnm_isspace(char c) static void stbi__pnm_skip_whitespace(stbi__context *s, char *c) { - while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) - *c = (char) stbi__get8(s); + for (;;) { + while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) + *c = (char) stbi__get8(s); + + if (stbi__at_eof(s) || *c != '#') + break; + + while (!stbi__at_eof(s) && *c != '\n' && *c != '\r' ) + *c = (char) stbi__get8(s); + } } static int stbi__pnm_isdigit(char c) @@ -6179,16 +7070,20 @@ static int stbi__pnm_getinteger(stbi__context *s, char *c) static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp) { - int maxv; + int maxv, dummy; char c, p, t; - stbi__rewind( s ); + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + stbi__rewind(s); // Get identifier p = (char) stbi__get8(s); t = (char) stbi__get8(s); if (p != 'P' || (t != '5' && t != '6')) { - stbi__rewind( s ); + stbi__rewind(s); return 0; } @@ -6254,6 +7149,19 @@ static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp) return stbi__err("unknown image type", "Image not of any known type, or corrupt"); } +static int stbi__is_16_main(stbi__context *s) +{ + #ifndef STBI_NO_PNG + if (stbi__png_is16(s)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_is16(s)) return 1; + #endif + + return 0; +} + #ifndef STBI_NO_STDIO STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) { @@ -6275,6 +7183,27 @@ STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp) fseek(f,pos,SEEK_SET); return r; } + +STBIDEF int stbi_is_16_bit(char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_is_16_bit_from_file(f); + fclose(f); + return result; +} + +STBIDEF int stbi_is_16_bit_from_file(FILE *f) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__is_16_main(&s); + fseek(f,pos,SEEK_SET); + return r; +} #endif // !STBI_NO_STDIO STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp) @@ -6291,10 +7220,63 @@ STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int return stbi__info_main(&s,x,y,comp); } +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__is_16_main(&s); +} + +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *c, void *user) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__is_16_main(&s); +} + #endif // STB_IMAGE_IMPLEMENTATION /* revision history: + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug + 1-bit BMP + *_is_16_bit api + avoid warnings + 2.16 (2017-07-23) all functions have 16-bit variants; + STBI_NO_STDIO works again; + compilation fixes; + fix rounding in unpremultiply; + optimize vertical flip; + disable raw_len validation; + documentation fixes + 2.15 (2017-03-18) fix png-1,2,4 bug; now all Imagenet JPGs decode; + warning fixes; disable run-time SSE detection on gcc; + uniform handling of optional "return" values; + thread-safe initialization of zlib tables + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-11-29) add 16-bit API, only supported for PNG right now + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) allocate large structures on the stack + remove white matting for transparent PSD + fix reported channel count for PNG & BMP + re-enable SSE2 in non-gcc 64-bit + support RGB-formatted JPEG + read 16-bit PNGs (only as 8-bit) + 2.10 (2016-01-22) avoid warning introduced in 2.09 by STBI_REALLOC_SIZED + 2.09 (2016-01-16) allow comments in PNM files + 16-bit-per-pixel TGA (not bit-per-component) + info() for TGA could break due to .hdr handling + info() for BMP to shares code instead of sloppy parse + can use STBI_REALLOC_SIZED if allocator doesn't support realloc + code cleanup + 2.08 (2015-09-13) fix to 2.07 cleanup, reading RGB PSD as RGBA + 2.07 (2015-09-13) fix compiler warnings + partial animated GIF support + limited 16-bpc PSD support + #ifdef unused functions + bug with < 92 byte PIC,PNM,HDR,TGA 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit @@ -6435,3 +7417,46 @@ STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int 0.50 (2006-11-19) first released version */ + + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/image.darknet/src/stb_image_write.h b/image.darknet/src/stb_image_write.h index f5250b3..c05e958 100644 --- a/image.darknet/src/stb_image_write.h +++ b/image.darknet/src/stb_image_write.h @@ -1,7 +1,6 @@ -/* stb_image_write - v0.98 - public domain - http://nothings.org/stb/stb_image_write.h - writes out PNG/BMP/TGA images to C stdio - Sean Barrett 2010 - no warranty implied; use at your own risk - +/* stb_image_write - v1.09 - public domain - http://nothings.org/stb/stb_image_write.h + writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015 + no warranty implied; use at your own risk Before #including, @@ -11,31 +10,67 @@ Will probably not work correctly with strict-aliasing optimizations. + If using a modern Microsoft Compiler, non-safe versions of CRT calls may cause + compilation warnings or even errors. To avoid this, also before #including, + + #define STBI_MSC_SECURE_CRT + ABOUT: This header file is a library for writing images to C stdio. It could be adapted to write to memory or a general streaming interface; let me know. The PNG output is not optimal; it is 20-50% larger than the file - written by a decent optimizing implementation. This library is designed - for source code compactness and simplicitly, not optimal image file size - or run-time performance. + written by a decent optimizing implementation; though providing a custom + zlib compress function (see STBIW_ZLIB_COMPRESS) can mitigate that. + This library is designed for source code compactness and simplicity, + not optimal image file size or run-time performance. BUILDING: You can #define STBIW_ASSERT(x) before the #include to avoid using assert.h. You can #define STBIW_MALLOC(), STBIW_REALLOC(), and STBIW_FREE() to replace malloc,realloc,free. - You can define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_ZLIB_COMPRESS to use a custom zlib-style compress function + for PNG compression (instead of the builtin one), it must have the following signature: + unsigned char * my_compress(unsigned char *data, int data_len, int *out_len, int quality); + The returned data will be freed with STBIW_FREE() (free() by default), + so it must be heap allocated with STBIW_MALLOC() (malloc() by default), USAGE: - There are four functions, one for each image file format: + There are five functions, one for each image file format: int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); - int stbi_write_hdr(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_jpg(char const *filename, int w, int h, int comp, const void *data, int quality); + int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); + + void stbi_flip_vertically_on_write(int flag); // flag is non-zero to flip data vertically + + There are also five equivalent functions that use an arbitrary write function. You are + expected to open/close your file-equivalent before and after calling these: + + int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); + int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + + where the callback is: + void stbi_write_func(void *context, void *data, int size); + + You can configure it with these global variables: + int stbi_write_tga_with_rle; // defaults to true; set to 0 to disable RLE + int stbi_write_png_compression_level; // defaults to 8; set to higher for more compression + int stbi_write_force_png_filter; // defaults to -1; set to 0..5 to force a filter mode + + + You can define STBI_WRITE_NO_STDIO to disable the file variant of these + functions, so the library will not use stdio.h at all. However, this will + also disable HDR writing, because it requires stdio for formatted output. Each function returns 0 on failure and non-0 on success. @@ -59,63 +94,138 @@ writer, both because it is in BGR order and because it may have padding at the end of the line.) + PNG allows you to set the deflate compression level by setting the global + variable 'stbi_write_png_compression_level' (it defaults to 8). + HDR expects linear float data. Since the format is always 32-bit rgb(e) data, alpha (if provided) is discarded, and for monochrome data it is replicated across all three channels. + TGA supports RLE or non-RLE compressed data. To use non-RLE-compressed + data, set the global variable 'stbi_write_tga_with_rle' to 0. + + JPEG does ignore alpha channels in input data; quality is between 1 and 100. + Higher quality looks better but results in a bigger image. + JPEG baseline (no JPEG progressive). + CREDITS: - PNG/BMP/TGA - Sean Barrett - HDR - Baldur Karlsson - TGA monochrome: - Jean-Sebastien Guay - misc enhancements: - Tim Kelsey + + Sean Barrett - PNG/BMP/TGA + Baldur Karlsson - HDR + Jean-Sebastien Guay - TGA monochrome + Tim Kelsey - misc enhancements + Alan Hickman - TGA RLE + Emmanuel Julien - initial file IO callback implementation + Jon Olick - original jo_jpeg.cpp code + Daniel Gibson - integrate JPEG, allow external zlib + Aarni Koskela - allow choosing PNG filter + bugfixes: github:Chribba + Guillaume Chereau + github:jry2 + github:romigrou + Sergio Gonzalez + Jonas Karlsson + Filip Wasil + Thatcher Ulrich + github:poppolopoppo + Patrick Boettcher + github:xeekworx + Cap Petschulat + Simon Rodriguez + Ivan Tikhonov + github:ignotion + Adam Schackart + +LICENSE + + See end of file for license information. + */ #ifndef INCLUDE_STB_IMAGE_WRITE_H #define INCLUDE_STB_IMAGE_WRITE_H +// if STB_IMAGE_WRITE_STATIC causes problems, try defining STBIWDEF to 'inline' or 'static inline' +#ifndef STBIWDEF +#ifdef STB_IMAGE_WRITE_STATIC +#define STBIWDEF static +#else #ifdef __cplusplus -extern "C" { +#define STBIWDEF extern "C" +#else +#define STBIWDEF extern +#endif +#endif #endif -extern int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); -extern int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); -extern int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); -extern int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); +#ifndef STB_IMAGE_WRITE_STATIC // C++ forbids static forward declarations +extern int stbi_write_tga_with_rle; +extern int stbi_write_png_compression_level; +extern int stbi_write_force_png_filter; +#endif -#ifdef __cplusplus -} +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); #endif +typedef void stbi_write_func(void *context, void *data, int size); + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + +STBIWDEF void stbi_flip_vertically_on_write(int flip_boolean); + #endif//INCLUDE_STB_IMAGE_WRITE_H #ifdef STB_IMAGE_WRITE_IMPLEMENTATION +#ifdef _WIN32 + #ifndef _CRT_SECURE_NO_WARNINGS + #define _CRT_SECURE_NO_WARNINGS + #endif + #ifndef _CRT_NONSTDC_NO_DEPRECATE + #define _CRT_NONSTDC_NO_DEPRECATE + #endif +#endif + +#ifndef STBI_WRITE_NO_STDIO +#include +#endif // STBI_WRITE_NO_STDIO + #include #include -#include #include #include -#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && defined(STBIW_REALLOC) +#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && (defined(STBIW_REALLOC) || defined(STBIW_REALLOC_SIZED)) // ok -#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) +#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) && !defined(STBIW_REALLOC_SIZED) // ok #else -#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC." +#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC (or STBIW_REALLOC_SIZED)." #endif #ifndef STBIW_MALLOC -#define STBIW_MALLOC(sz) malloc(sz) -#define STBIW_REALLOC(p,sz) realloc(p,sz) -#define STBIW_FREE(p) free(p) +#define STBIW_MALLOC(sz) malloc(sz) +#define STBIW_REALLOC(p,newsz) realloc(p,newsz) +#define STBIW_FREE(p) free(p) +#endif + +#ifndef STBIW_REALLOC_SIZED +#define STBIW_REALLOC_SIZED(p,oldsz,newsz) STBIW_REALLOC(p,newsz) #endif + + #ifndef STBIW_MEMMOVE #define STBIW_MEMMOVE(a,b,sz) memmove(a,b,sz) #endif @@ -126,22 +236,90 @@ extern int stbi_write_hdr(char const *filename, int w, int h, int comp, const fl #define STBIW_ASSERT(x) assert(x) #endif +#define STBIW_UCHAR(x) (unsigned char) ((x) & 0xff) + +#ifdef STB_IMAGE_WRITE_STATIC +static int stbi__flip_vertically_on_write=0; +static int stbi_write_png_compression_level = 8; +static int stbi_write_tga_with_rle = 1; +static int stbi_write_force_png_filter = -1; +#else +int stbi_write_png_compression_level = 8; +int stbi__flip_vertically_on_write=0; +int stbi_write_tga_with_rle = 1; +int stbi_write_force_png_filter = -1; +#endif + +STBIWDEF void stbi_flip_vertically_on_write(int flag) +{ + stbi__flip_vertically_on_write = flag; +} + +typedef struct +{ + stbi_write_func *func; + void *context; +} stbi__write_context; + +// initialize a callback-based context +static void stbi__start_write_callbacks(stbi__write_context *s, stbi_write_func *c, void *context) +{ + s->func = c; + s->context = context; +} + +#ifndef STBI_WRITE_NO_STDIO + +static void stbi__stdio_write(void *context, void *data, int size) +{ + fwrite(data,1,size,(FILE*) context); +} + +static int stbi__start_write_file(stbi__write_context *s, const char *filename) +{ + FILE *f; +#ifdef STBI_MSC_SECURE_CRT + if (fopen_s(&f, filename, "wb")) + f = NULL; +#else + f = fopen(filename, "wb"); +#endif + stbi__start_write_callbacks(s, stbi__stdio_write, (void *) f); + return f != NULL; +} + +static void stbi__end_write_file(stbi__write_context *s) +{ + fclose((FILE *)s->context); +} + +#endif // !STBI_WRITE_NO_STDIO + typedef unsigned int stbiw_uint32; typedef int stb_image_write_test[sizeof(stbiw_uint32)==4 ? 1 : -1]; -static void writefv(FILE *f, const char *fmt, va_list v) +static void stbiw__writefv(stbi__write_context *s, const char *fmt, va_list v) { while (*fmt) { switch (*fmt++) { case ' ': break; - case '1': { unsigned char x = (unsigned char) va_arg(v, int); fputc(x,f); break; } - case '2': { int x = va_arg(v,int); unsigned char b[2]; - b[0] = (unsigned char) x; b[1] = (unsigned char) (x>>8); - fwrite(b,2,1,f); break; } - case '4': { stbiw_uint32 x = va_arg(v,int); unsigned char b[4]; - b[0]=(unsigned char)x; b[1]=(unsigned char)(x>>8); - b[2]=(unsigned char)(x>>16); b[3]=(unsigned char)(x>>24); - fwrite(b,4,1,f); break; } + case '1': { unsigned char x = STBIW_UCHAR(va_arg(v, int)); + s->func(s->context,&x,1); + break; } + case '2': { int x = va_arg(v,int); + unsigned char b[2]; + b[0] = STBIW_UCHAR(x); + b[1] = STBIW_UCHAR(x>>8); + s->func(s->context,b,2); + break; } + case '4': { stbiw_uint32 x = va_arg(v,int); + unsigned char b[4]; + b[0]=STBIW_UCHAR(x); + b[1]=STBIW_UCHAR(x>>8); + b[2]=STBIW_UCHAR(x>>16); + b[3]=STBIW_UCHAR(x>>24); + s->func(s->context,b,4); + break; } default: STBIW_ASSERT(0); return; @@ -149,22 +327,70 @@ static void writefv(FILE *f, const char *fmt, va_list v) } } -static void write3(FILE *f, unsigned char a, unsigned char b, unsigned char c) +static void stbiw__writef(stbi__write_context *s, const char *fmt, ...) +{ + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); +} + +static void stbiw__putc(stbi__write_context *s, unsigned char c) +{ + s->func(s->context, &c, 1); +} + +static void stbiw__write3(stbi__write_context *s, unsigned char a, unsigned char b, unsigned char c) { unsigned char arr[3]; arr[0] = a, arr[1] = b, arr[2] = c; - fwrite(arr, 3, 1, f); + s->func(s->context, arr, 3); } -static void write_pixels(FILE *f, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) +static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, int write_alpha, int expand_mono, unsigned char *d) { unsigned char bg[3] = { 255, 0, 255}, px[3]; + int k; + + if (write_alpha < 0) + s->func(s->context, &d[comp - 1], 1); + + switch (comp) { + case 2: // 2 pixels = mono + alpha, alpha is written separately, so same as 1-channel case + case 1: + if (expand_mono) + stbiw__write3(s, d[0], d[0], d[0]); // monochrome bmp + else + s->func(s->context, d, 1); // monochrome TGA + break; + case 4: + if (!write_alpha) { + // composite against pink background + for (k = 0; k < 3; ++k) + px[k] = bg[k] + ((d[k] - bg[k]) * d[3]) / 255; + stbiw__write3(s, px[1 - rgb_dir], px[1], px[1 + rgb_dir]); + break; + } + /* FALLTHROUGH */ + case 3: + stbiw__write3(s, d[1 - rgb_dir], d[1], d[1 + rgb_dir]); + break; + } + if (write_alpha > 0) + s->func(s->context, &d[comp - 1], 1); +} + +static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) +{ stbiw_uint32 zero = 0; - int i,j,k, j_end; + int i,j, j_end; if (y <= 0) return; + if (stbi__flip_vertically_on_write) + vdir *= -1; + if (vdir < 0) j_end = -1, j = y-1; else @@ -173,73 +399,157 @@ static void write_pixels(FILE *f, int rgb_dir, int vdir, int x, int y, int comp, for (; j != j_end; j += vdir) { for (i=0; i < x; ++i) { unsigned char *d = (unsigned char *) data + (j*x+i)*comp; - if (write_alpha < 0) - fwrite(&d[comp-1], 1, 1, f); - switch (comp) { - case 1: fwrite(d, 1, 1, f); - break; - case 2: if (expand_mono) - write3(f, d[0],d[0],d[0]); // monochrome bmp - else - fwrite(d, 1, 1, f); // monochrome TGA - break; - case 4: - if (!write_alpha) { - // composite against pink background - for (k=0; k < 3; ++k) - px[k] = bg[k] + ((d[k] - bg[k]) * d[3])/255; - write3(f, px[1-rgb_dir],px[1],px[1+rgb_dir]); - break; - } - /* FALLTHROUGH */ - case 3: - write3(f, d[1-rgb_dir],d[1],d[1+rgb_dir]); - break; - } - if (write_alpha > 0) - fwrite(&d[comp-1], 1, 1, f); + stbiw__write_pixel(s, rgb_dir, comp, write_alpha, expand_mono, d); } - fwrite(&zero,scanline_pad,1,f); + s->func(s->context, &zero, scanline_pad); } } -static int outfile(char const *filename, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...) +static int stbiw__outfile(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...) { - FILE *f; - if (y < 0 || x < 0) return 0; - f = fopen(filename, "wb"); - if (f) { + if (y < 0 || x < 0) { + return 0; + } else { va_list v; va_start(v, fmt); - writefv(f, fmt, v); + stbiw__writefv(s, fmt, v); va_end(v); - write_pixels(f,rgb_dir,vdir,x,y,comp,data,alpha,pad,expand_mono); - fclose(f); + stbiw__write_pixels(s,rgb_dir,vdir,x,y,comp,data,alpha,pad, expand_mono); + return 1; } - return f != NULL; } -int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) +static int stbi_write_bmp_core(stbi__write_context *s, int x, int y, int comp, const void *data) { int pad = (-x*3) & 3; - return outfile(filename,-1,-1,x,y,comp,1,(void *) data,0,pad, + return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *) data,0,pad, "11 4 22 4" "4 44 22 444444", 'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40, // file header 40, x,y, 1,24, 0,0,0,0,0,0); // bitmap header } -int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_bmp_core(&s, x, y, comp, data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_bmp_core(&s, x, y, comp, data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif //!STBI_WRITE_NO_STDIO + +static int stbi_write_tga_core(stbi__write_context *s, int x, int y, int comp, void *data) { int has_alpha = (comp == 2 || comp == 4); int colorbytes = has_alpha ? comp-1 : comp; int format = colorbytes < 2 ? 3 : 2; // 3 color channels (RGB/RGBA) = 2, 1 color channel (Y/YA) = 3 - return outfile(filename, -1,-1, x, y, comp, 0, (void *) data, has_alpha, 0, - "111 221 2222 11", 0,0,format, 0,0,0, 0,0,x,y, (colorbytes+has_alpha)*8, has_alpha*8); + + if (y < 0 || x < 0) + return 0; + + if (!stbi_write_tga_with_rle) { + return stbiw__outfile(s, -1, -1, x, y, comp, 0, (void *) data, has_alpha, 0, + "111 221 2222 11", 0, 0, format, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8); + } else { + int i,j,k; + int jend, jdir; + + stbiw__writef(s, "111 221 2222 11", 0,0,format+8, 0,0,0, 0,0,x,y, (colorbytes + has_alpha) * 8, has_alpha * 8); + + if (stbi__flip_vertically_on_write) { + j = 0; + jend = y; + jdir = 1; + } else { + j = y-1; + jend = -1; + jdir = -1; + } + for (; j != jend; j += jdir) { + unsigned char *row = (unsigned char *) data + j * x * comp; + int len; + + for (i = 0; i < x; i += len) { + unsigned char *begin = row + i * comp; + int diff = 1; + len = 1; + + if (i < x - 1) { + ++len; + diff = memcmp(begin, row + (i + 1) * comp, comp); + if (diff) { + const unsigned char *prev = begin; + for (k = i + 2; k < x && len < 128; ++k) { + if (memcmp(prev, row + k * comp, comp)) { + prev += comp; + ++len; + } else { + --len; + break; + } + } + } else { + for (k = i + 2; k < x && len < 128; ++k) { + if (!memcmp(begin, row + k * comp, comp)) { + ++len; + } else { + break; + } + } + } + } + + if (diff) { + unsigned char header = STBIW_UCHAR(len - 1); + s->func(s->context, &header, 1); + for (k = 0; k < len; ++k) { + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin + k * comp); + } + } else { + unsigned char header = STBIW_UCHAR(len - 129); + s->func(s->context, &header, 1); + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin); + } + } + } + } + return 1; +} + +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_tga_core(&s, x, y, comp, (void *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_tga_core(&s, x, y, comp, (void *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; } +#endif // ************************************************************************************************* // Radiance RGBE HDR writer // by Baldur Karlsson + #define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) @@ -247,7 +557,7 @@ void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) int exponent; float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2])); - if (maxcomp < 1e-32) { + if (maxcomp < 1e-32f) { rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0; } else { float normalize = (float) frexp(maxcomp, &exponent) * 256.0f/maxcomp; @@ -259,27 +569,27 @@ void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) } } -void stbiw__write_run_data(FILE *f, int length, unsigned char databyte) +void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char databyte) { - unsigned char lengthbyte = (unsigned char) (length+128); + unsigned char lengthbyte = STBIW_UCHAR(length+128); STBIW_ASSERT(length+128 <= 255); - fwrite(&lengthbyte, 1, 1, f); - fwrite(&databyte, 1, 1, f); + s->func(s->context, &lengthbyte, 1); + s->func(s->context, &databyte, 1); } -void stbiw__write_dump_data(FILE *f, int length, unsigned char *data) +void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *data) { - unsigned char lengthbyte = (unsigned char )(length & 0xff); + unsigned char lengthbyte = STBIW_UCHAR(length); STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code - fwrite(&lengthbyte, 1, 1, f); - fwrite(data, length, 1, f); + s->func(s->context, &lengthbyte, 1); + s->func(s->context, data, length); } -void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scratch, const float *scanline) +void stbiw__write_hdr_scanline(stbi__write_context *s, int width, int ncomp, unsigned char *scratch, float *scanline) { unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; unsigned char rgbe[4]; - float linear[3] = {0}; + float linear[3]; int x; scanlineheader[2] = (width&0xff00)>>8; @@ -288,31 +598,31 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra /* skip RLE for images too small or large */ if (width < 8 || width >= 32768) { for (x=0; x < width; x++) { - switch (comp) { + switch (ncomp) { case 4: /* fallthrough */ - case 3: linear[2] = scanline[x*comp + 2]; - linear[1] = scanline[x*comp + 1]; - linear[0] = scanline[x*comp + 0]; + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; break; - case 2: /* fallthrough */ - case 1: linear[0] = linear[1] = linear[2] = scanline[x*comp + 0]; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; break; } stbiw__linear_to_rgbe(rgbe, linear); - fwrite(rgbe, 4, 1, f); + s->func(s->context, rgbe, 4); } } else { int c,r; /* encode into scratch buffer */ for (x=0; x < width; x++) { - switch(comp) { + switch(ncomp) { case 4: /* fallthrough */ - case 3: linear[2] = scanline[x*comp + 2]; - linear[1] = scanline[x*comp + 1]; - linear[0] = scanline[x*comp + 0]; + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; break; - case 2: /* fallthrough */ - case 1: linear[0] = linear[1] = linear[2] = scanline[x*comp + 0]; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; break; } stbiw__linear_to_rgbe(rgbe, linear); @@ -322,7 +632,7 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra scratch[x + width*3] = rgbe[3]; } - fwrite(scanlineheader, 4, 1, f); + s->func(s->context, scanlineheader, 4); /* RLE each component separately */ for (c=0; c < 4; c++) { @@ -343,7 +653,7 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra while (x < r) { int len = r-x; if (len > 128) len = 128; - stbiw__write_dump_data(f, len, &comp[x]); + stbiw__write_dump_data(s, len, &comp[x]); x += len; } // if there's a run, output it @@ -355,7 +665,7 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra while (x < r) { int len = r-x; if (len > 127) len = 127; - stbiw__write_run_data(f, len, comp[x]); + stbiw__write_run_data(s, len, comp[x]); x += len; } } @@ -364,28 +674,59 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra } } -int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) +static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, float *data) { - int i; - FILE *f; - if (y <= 0 || x <= 0 || data == NULL) return 0; - f = fopen(filename, "wb"); - if (f) { - /* Each component is stored separately. Allocate scratch space for full output scanline. */ + if (y <= 0 || x <= 0 || data == NULL) + return 0; + else { + // Each component is stored separately. Allocate scratch space for full output scanline. unsigned char *scratch = (unsigned char *) STBIW_MALLOC(x*4); - fprintf(f, "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n" ); - fprintf(f, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n" , y, x); + int i, len; + char buffer[128]; + char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n"; + s->func(s->context, header, sizeof(header)-1); + +#ifdef STBI_MSC_SECURE_CRT + len = sprintf_s(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#else + len = sprintf(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#endif + s->func(s->context, buffer, len); + for(i=0; i < y; i++) - stbiw__write_hdr_scanline(f, x, comp, scratch, data + comp*i*x); + stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp*x*(stbi__flip_vertically_on_write ? y-1-i : i)*x); STBIW_FREE(scratch); - fclose(f); + return 1; } - return f != NULL; } -///////////////////////////////////////////////////////// -// PNG +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_hdr_core(&s, x, y, comp, (float *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_hdr_core(&s, x, y, comp, (float *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif // STBI_WRITE_NO_STDIO + +////////////////////////////////////////////////////////////////////////////// +// +// PNG writer +// + +#ifndef STBIW_ZLIB_COMPRESS // stretchy buffer; stbiw__sbpush() == vector<>::push_back() -- stbiw__sbcount() == vector<>::size() #define stbiw__sbraw(a) ((int *) (a) - 2) #define stbiw__sbm(a) stbiw__sbraw(a)[0] @@ -402,7 +743,7 @@ int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *da static void *stbiw__sbgrowf(void **arr, int increment, int itemsize) { int m = *arr ? 2*stbiw__sbm(*arr)+increment : increment+1; - void *p = STBIW_REALLOC(*arr ? stbiw__sbraw(*arr) : 0, itemsize * m + sizeof(int)*2); + void *p = STBIW_REALLOC_SIZED(*arr ? stbiw__sbraw(*arr) : 0, *arr ? (stbiw__sbm(*arr)*itemsize + sizeof(int)*2) : 0, itemsize * m + sizeof(int)*2); STBIW_ASSERT(p); if (p) { if (!*arr) ((int *) p)[1] = 0; @@ -415,7 +756,7 @@ static void *stbiw__sbgrowf(void **arr, int increment, int itemsize) static unsigned char *stbiw__zlib_flushf(unsigned char *data, unsigned int *bitbuffer, int *bitcount) { while (*bitcount >= 8) { - stbiw__sbpush(data, (unsigned char) *bitbuffer); + stbiw__sbpush(data, STBIW_UCHAR(*bitbuffer)); *bitbuffer >>= 8; *bitcount -= 8; } @@ -466,8 +807,14 @@ static unsigned int stbiw__zhash(unsigned char *data) #define stbiw__ZHASH 16384 +#endif // STBIW_ZLIB_COMPRESS + unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality) { +#ifdef STBIW_ZLIB_COMPRESS + // user provided a zlib compress implementation, use that + return STBIW_ZLIB_COMPRESS(data, data_len, out_len, quality); +#else // use builtin static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 }; static unsigned char lengtheb[]= { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0 }; static unsigned short distc[] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 }; @@ -475,7 +822,9 @@ unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_l unsigned int bitbuf=0; int i,j, bitcount=0; unsigned char *out = NULL; - unsigned char **hash_table[stbiw__ZHASH]; // 64KB on the stack! + unsigned char ***hash_table = (unsigned char***) STBIW_MALLOC(stbiw__ZHASH * sizeof(char**)); + if (hash_table == NULL) + return NULL; if (quality < 5) quality = 5; stbiw__sbpush(out, 0x78); // DEFLATE 32K window @@ -547,43 +896,77 @@ unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_l for (i=0; i < stbiw__ZHASH; ++i) (void) stbiw__sbfree(hash_table[i]); + STBIW_FREE(hash_table); { // compute adler32 on input - unsigned int i=0, s1=1, s2=0, blocklen = data_len % 5552; - int j=0; + unsigned int s1=1, s2=0; + int blocklen = (int) (data_len % 5552); + j=0; while (j < data_len) { for (i=0; i < blocklen; ++i) s1 += data[j+i], s2 += s1; s1 %= 65521, s2 %= 65521; j += blocklen; blocklen = 5552; } - stbiw__sbpush(out, (unsigned char) (s2 >> 8)); - stbiw__sbpush(out, (unsigned char) s2); - stbiw__sbpush(out, (unsigned char) (s1 >> 8)); - stbiw__sbpush(out, (unsigned char) s1); + stbiw__sbpush(out, STBIW_UCHAR(s2 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s2)); + stbiw__sbpush(out, STBIW_UCHAR(s1 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s1)); } *out_len = stbiw__sbn(out); // make returned pointer freeable STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len); return (unsigned char *) stbiw__sbraw(out); +#endif // STBIW_ZLIB_COMPRESS } -unsigned int stbiw__crc32(unsigned char *buffer, int len) +static unsigned int stbiw__crc32(unsigned char *buffer, int len) { - static unsigned int crc_table[256]; + static unsigned int crc_table[256] = + { + 0x00000000, 0x77073096, 0xEE0E612C, 0x990951BA, 0x076DC419, 0x706AF48F, 0xE963A535, 0x9E6495A3, + 0x0eDB8832, 0x79DCB8A4, 0xE0D5E91E, 0x97D2D988, 0x09B64C2B, 0x7EB17CBD, 0xE7B82D07, 0x90BF1D91, + 0x1DB71064, 0x6AB020F2, 0xF3B97148, 0x84BE41DE, 0x1ADAD47D, 0x6DDDE4EB, 0xF4D4B551, 0x83D385C7, + 0x136C9856, 0x646BA8C0, 0xFD62F97A, 0x8A65C9EC, 0x14015C4F, 0x63066CD9, 0xFA0F3D63, 0x8D080DF5, + 0x3B6E20C8, 0x4C69105E, 0xD56041E4, 0xA2677172, 0x3C03E4D1, 0x4B04D447, 0xD20D85FD, 0xA50AB56B, + 0x35B5A8FA, 0x42B2986C, 0xDBBBC9D6, 0xACBCF940, 0x32D86CE3, 0x45DF5C75, 0xDCD60DCF, 0xABD13D59, + 0x26D930AC, 0x51DE003A, 0xC8D75180, 0xBFD06116, 0x21B4F4B5, 0x56B3C423, 0xCFBA9599, 0xB8BDA50F, + 0x2802B89E, 0x5F058808, 0xC60CD9B2, 0xB10BE924, 0x2F6F7C87, 0x58684C11, 0xC1611DAB, 0xB6662D3D, + 0x76DC4190, 0x01DB7106, 0x98D220BC, 0xEFD5102A, 0x71B18589, 0x06B6B51F, 0x9FBFE4A5, 0xE8B8D433, + 0x7807C9A2, 0x0F00F934, 0x9609A88E, 0xE10E9818, 0x7F6A0DBB, 0x086D3D2D, 0x91646C97, 0xE6635C01, + 0x6B6B51F4, 0x1C6C6162, 0x856530D8, 0xF262004E, 0x6C0695ED, 0x1B01A57B, 0x8208F4C1, 0xF50FC457, + 0x65B0D9C6, 0x12B7E950, 0x8BBEB8EA, 0xFCB9887C, 0x62DD1DDF, 0x15DA2D49, 0x8CD37CF3, 0xFBD44C65, + 0x4DB26158, 0x3AB551CE, 0xA3BC0074, 0xD4BB30E2, 0x4ADFA541, 0x3DD895D7, 0xA4D1C46D, 0xD3D6F4FB, + 0x4369E96A, 0x346ED9FC, 0xAD678846, 0xDA60B8D0, 0x44042D73, 0x33031DE5, 0xAA0A4C5F, 0xDD0D7CC9, + 0x5005713C, 0x270241AA, 0xBE0B1010, 0xC90C2086, 0x5768B525, 0x206F85B3, 0xB966D409, 0xCE61E49F, + 0x5EDEF90E, 0x29D9C998, 0xB0D09822, 0xC7D7A8B4, 0x59B33D17, 0x2EB40D81, 0xB7BD5C3B, 0xC0BA6CAD, + 0xEDB88320, 0x9ABFB3B6, 0x03B6E20C, 0x74B1D29A, 0xEAD54739, 0x9DD277AF, 0x04DB2615, 0x73DC1683, + 0xE3630B12, 0x94643B84, 0x0D6D6A3E, 0x7A6A5AA8, 0xE40ECF0B, 0x9309FF9D, 0x0A00AE27, 0x7D079EB1, + 0xF00F9344, 0x8708A3D2, 0x1E01F268, 0x6906C2FE, 0xF762575D, 0x806567CB, 0x196C3671, 0x6E6B06E7, + 0xFED41B76, 0x89D32BE0, 0x10DA7A5A, 0x67DD4ACC, 0xF9B9DF6F, 0x8EBEEFF9, 0x17B7BE43, 0x60B08ED5, + 0xD6D6A3E8, 0xA1D1937E, 0x38D8C2C4, 0x4FDFF252, 0xD1BB67F1, 0xA6BC5767, 0x3FB506DD, 0x48B2364B, + 0xD80D2BDA, 0xAF0A1B4C, 0x36034AF6, 0x41047A60, 0xDF60EFC3, 0xA867DF55, 0x316E8EEF, 0x4669BE79, + 0xCB61B38C, 0xBC66831A, 0x256FD2A0, 0x5268E236, 0xCC0C7795, 0xBB0B4703, 0x220216B9, 0x5505262F, + 0xC5BA3BBE, 0xB2BD0B28, 0x2BB45A92, 0x5CB36A04, 0xC2D7FFA7, 0xB5D0CF31, 0x2CD99E8B, 0x5BDEAE1D, + 0x9B64C2B0, 0xEC63F226, 0x756AA39C, 0x026D930A, 0x9C0906A9, 0xEB0E363F, 0x72076785, 0x05005713, + 0x95BF4A82, 0xE2B87A14, 0x7BB12BAE, 0x0CB61B38, 0x92D28E9B, 0xE5D5BE0D, 0x7CDCEFB7, 0x0BDBDF21, + 0x86D3D2D4, 0xF1D4E242, 0x68DDB3F8, 0x1FDA836E, 0x81BE16CD, 0xF6B9265B, 0x6FB077E1, 0x18B74777, + 0x88085AE6, 0xFF0F6A70, 0x66063BCA, 0x11010B5C, 0x8F659EFF, 0xF862AE69, 0x616BFFD3, 0x166CCF45, + 0xA00AE278, 0xD70DD2EE, 0x4E048354, 0x3903B3C2, 0xA7672661, 0xD06016F7, 0x4969474D, 0x3E6E77DB, + 0xAED16A4A, 0xD9D65ADC, 0x40DF0B66, 0x37D83BF0, 0xA9BCAE53, 0xDEBB9EC5, 0x47B2CF7F, 0x30B5FFE9, + 0xBDBDF21C, 0xCABAC28A, 0x53B39330, 0x24B4A3A6, 0xBAD03605, 0xCDD70693, 0x54DE5729, 0x23D967BF, + 0xB3667A2E, 0xC4614AB8, 0x5D681B02, 0x2A6F2B94, 0xB40BBE37, 0xC30C8EA1, 0x5A05DF1B, 0x2D02EF8D + }; + unsigned int crc = ~0u; - int i,j; - if (crc_table[1] == 0) - for(i=0; i < 256; i++) - for (crc_table[i]=i, j=0; j < 8; ++j) - crc_table[i] = (crc_table[i] >> 1) ^ (crc_table[i] & 1 ? 0xedb88320 : 0); + int i; for (i=0; i < len; ++i) crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)]; return ~crc; } -#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=(unsigned char)(a),(o)[1]=(unsigned char)(b),(o)[2]=(unsigned char)(c),(o)[3]=(unsigned char)(d),(o)+=4) +#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=STBIW_UCHAR(a),(o)[1]=STBIW_UCHAR(b),(o)[2]=STBIW_UCHAR(c),(o)[3]=STBIW_UCHAR(d),(o)+=4) #define stbiw__wp32(data,v) stbiw__wpng4(data, (v)>>24,(v)>>16,(v)>>8,(v)); #define stbiw__wptag(data,s) stbiw__wpng4(data, s[0],s[1],s[2],s[3]) @@ -596,66 +979,94 @@ static void stbiw__wpcrc(unsigned char **data, int len) static unsigned char stbiw__paeth(int a, int b, int c) { int p = a + b - c, pa = abs(p-a), pb = abs(p-b), pc = abs(p-c); - if (pa <= pb && pa <= pc) return (unsigned char) a; - if (pb <= pc) return (unsigned char) b; - return (unsigned char) c; + if (pa <= pb && pa <= pc) return STBIW_UCHAR(a); + if (pb <= pc) return STBIW_UCHAR(b); + return STBIW_UCHAR(c); +} + +// @OPTIMIZE: provide an option that always forces left-predict or paeth predict +static void stbiw__encode_png_line(unsigned char *pixels, int stride_bytes, int width, int height, int y, int n, int filter_type, signed char *line_buffer) +{ + static int mapping[] = { 0,1,2,3,4 }; + static int firstmap[] = { 0,1,0,5,6 }; + int *mymap = (y != 0) ? mapping : firstmap; + int i; + int type = mymap[filter_type]; + unsigned char *z = pixels + stride_bytes * (stbi__flip_vertically_on_write ? height-1-y : y); + int signed_stride = stbi__flip_vertically_on_write ? -stride_bytes : stride_bytes; + for (i = 0; i < n; ++i) { + switch (type) { + case 0: line_buffer[i] = z[i]; break; + case 1: line_buffer[i] = z[i]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - (z[i-signed_stride]>>1); break; + case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-signed_stride],0)); break; + case 5: line_buffer[i] = z[i]; break; + case 6: line_buffer[i] = z[i]; break; + } + } + for (i=n; i < width*n; ++i) { + switch (type) { + case 0: line_buffer[i] = z[i]; break; + case 1: line_buffer[i] = z[i] - z[i-n]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - ((z[i-n] + z[i-signed_stride])>>1); break; + case 4: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-signed_stride], z[i-signed_stride-n]); break; + case 5: line_buffer[i] = z[i] - (z[i-n]>>1); break; + case 6: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; + } + } } unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len) { + int force_filter = stbi_write_force_png_filter; int ctype[5] = { -1, 0, 4, 2, 6 }; unsigned char sig[8] = { 137,80,78,71,13,10,26,10 }; unsigned char *out,*o, *filt, *zlib; signed char *line_buffer; - int i,j,k,p,zlen; + int j,zlen; if (stride_bytes == 0) stride_bytes = x * n; + if (force_filter >= 5) { + force_filter = -1; + } + filt = (unsigned char *) STBIW_MALLOC((x*n+1) * y); if (!filt) return 0; line_buffer = (signed char *) STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; } for (j=0; j < y; ++j) { - static int mapping[] = { 0,1,2,3,4 }; - static int firstmap[] = { 0,1,0,5,6 }; - int *mymap = j ? mapping : firstmap; - int best = 0, bestval = 0x7fffffff; - for (p=0; p < 2; ++p) { - for (k= p?best:0; k < 5; ++k) { - int type = mymap[k],est=0; - unsigned char *z = pixels + stride_bytes*j; - for (i=0; i < n; ++i) - switch (type) { - case 0: line_buffer[i] = z[i]; break; - case 1: line_buffer[i] = z[i]; break; - case 2: line_buffer[i] = z[i] - z[i-stride_bytes]; break; - case 3: line_buffer[i] = z[i] - (z[i-stride_bytes]>>1); break; - case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-stride_bytes],0)); break; - case 5: line_buffer[i] = z[i]; break; - case 6: line_buffer[i] = z[i]; break; - } - for (i=n; i < x*n; ++i) { - switch (type) { - case 0: line_buffer[i] = z[i]; break; - case 1: line_buffer[i] = z[i] - z[i-n]; break; - case 2: line_buffer[i] = z[i] - z[i-stride_bytes]; break; - case 3: line_buffer[i] = z[i] - ((z[i-n] + z[i-stride_bytes])>>1); break; - case 4: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-stride_bytes], z[i-stride_bytes-n]); break; - case 5: line_buffer[i] = z[i] - (z[i-n]>>1); break; - case 6: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; - } - } - if (p) break; - for (i=0; i < x*n; ++i) + int filter_type; + if (force_filter > -1) { + filter_type = force_filter; + stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, force_filter, line_buffer); + } else { // Estimate the best filter by running through all of them: + int best_filter = 0, best_filter_val = 0x7fffffff, est, i; + for (filter_type = 0; filter_type < 5; filter_type++) { + stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, filter_type, line_buffer); + + // Estimate the entropy of the line using this filter; the less, the better. + est = 0; + for (i = 0; i < x*n; ++i) { est += abs((signed char) line_buffer[i]); - if (est < bestval) { bestval = est; best = k; } + } + if (est < best_filter_val) { + best_filter_val = est; + best_filter = filter_type; + } + } + if (filter_type != best_filter) { // If the last iteration already got us the best filter, don't redo it + stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, best_filter, line_buffer); + filter_type = best_filter; } } - // when we get here, best contains the filter type, and line_buffer contains the data - filt[j*(x*n+1)] = (unsigned char) best; + // when we get here, filter_type contains the filter type, and line_buffer contains the data + filt[j*(x*n+1)] = (unsigned char) filter_type; STBIW_MEMMOVE(filt+j*(x*n+1)+1, line_buffer, x*n); } STBIW_FREE(line_buffer); - zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, 8); // increase 8 to get smaller but use more memory + zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, stbi_write_png_compression_level); STBIW_FREE(filt); if (!zlib) return 0; @@ -671,7 +1082,7 @@ unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, in stbiw__wp32(o, x); stbiw__wp32(o, y); *o++ = 8; - *o++ = (unsigned char) ctype[n]; + *o++ = STBIW_UCHAR(ctype[n]); *o++ = 0; *o++ = 0; *o++ = 0; @@ -693,22 +1104,407 @@ unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, in return out; } -int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes) +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes) { FILE *f; int len; unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len); - if (!png) return 0; + if (png == NULL) return 0; +#ifdef STBI_MSC_SECURE_CRT + if (fopen_s(&f, filename, "wb")) + f = NULL; +#else f = fopen(filename, "wb"); +#endif if (!f) { STBIW_FREE(png); return 0; } fwrite(png, 1, len, f); fclose(f); STBIW_FREE(png); return 1; } +#endif + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int stride_bytes) +{ + int len; + unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + func(context, png, len); + STBIW_FREE(png); + return 1; +} + + +/* *************************************************************************** + * + * JPEG writer + * + * This is based on Jon Olick's jo_jpeg.cpp: + * public domain Simple, Minimalistic JPEG writer - http://www.jonolick.com/code.html + */ + +static const unsigned char stbiw__jpg_ZigZag[] = { 0,1,5,6,14,15,27,28,2,4,7,13,16,26,29,42,3,8,12,17,25,30,41,43,9,11,18, + 24,31,40,44,53,10,19,23,32,39,45,52,54,20,22,33,38,46,51,55,60,21,34,37,47,50,56,59,61,35,36,48,49,57,58,62,63 }; + +static void stbiw__jpg_writeBits(stbi__write_context *s, int *bitBufP, int *bitCntP, const unsigned short *bs) { + int bitBuf = *bitBufP, bitCnt = *bitCntP; + bitCnt += bs[1]; + bitBuf |= bs[0] << (24 - bitCnt); + while(bitCnt >= 8) { + unsigned char c = (bitBuf >> 16) & 255; + stbiw__putc(s, c); + if(c == 255) { + stbiw__putc(s, 0); + } + bitBuf <<= 8; + bitCnt -= 8; + } + *bitBufP = bitBuf; + *bitCntP = bitCnt; +} + +static void stbiw__jpg_DCT(float *d0p, float *d1p, float *d2p, float *d3p, float *d4p, float *d5p, float *d6p, float *d7p) { + float d0 = *d0p, d1 = *d1p, d2 = *d2p, d3 = *d3p, d4 = *d4p, d5 = *d5p, d6 = *d6p, d7 = *d7p; + float z1, z2, z3, z4, z5, z11, z13; + + float tmp0 = d0 + d7; + float tmp7 = d0 - d7; + float tmp1 = d1 + d6; + float tmp6 = d1 - d6; + float tmp2 = d2 + d5; + float tmp5 = d2 - d5; + float tmp3 = d3 + d4; + float tmp4 = d3 - d4; + + // Even part + float tmp10 = tmp0 + tmp3; // phase 2 + float tmp13 = tmp0 - tmp3; + float tmp11 = tmp1 + tmp2; + float tmp12 = tmp1 - tmp2; + + d0 = tmp10 + tmp11; // phase 3 + d4 = tmp10 - tmp11; + + z1 = (tmp12 + tmp13) * 0.707106781f; // c4 + d2 = tmp13 + z1; // phase 5 + d6 = tmp13 - z1; + + // Odd part + tmp10 = tmp4 + tmp5; // phase 2 + tmp11 = tmp5 + tmp6; + tmp12 = tmp6 + tmp7; + + // The rotator is modified from fig 4-8 to avoid extra negations. + z5 = (tmp10 - tmp12) * 0.382683433f; // c6 + z2 = tmp10 * 0.541196100f + z5; // c2-c6 + z4 = tmp12 * 1.306562965f + z5; // c2+c6 + z3 = tmp11 * 0.707106781f; // c4 + + z11 = tmp7 + z3; // phase 5 + z13 = tmp7 - z3; + + *d5p = z13 + z2; // phase 6 + *d3p = z13 - z2; + *d1p = z11 + z4; + *d7p = z11 - z4; + + *d0p = d0; *d2p = d2; *d4p = d4; *d6p = d6; +} + +static void stbiw__jpg_calcBits(int val, unsigned short bits[2]) { + int tmp1 = val < 0 ? -val : val; + val = val < 0 ? val-1 : val; + bits[1] = 1; + while(tmp1 >>= 1) { + ++bits[1]; + } + bits[0] = val & ((1<0)&&(DU[end0pos]==0); --end0pos) { + } + // end0pos = first element in reverse order !=0 + if(end0pos == 0) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + return DU[0]; + } + for(i = 1; i <= end0pos; ++i) { + int startpos = i; + int nrzeroes; + unsigned short bits[2]; + for (; DU[i]==0 && i<=end0pos; ++i) { + } + nrzeroes = i-startpos; + if ( nrzeroes >= 16 ) { + int lng = nrzeroes>>4; + int nrmarker; + for (nrmarker=1; nrmarker <= lng; ++nrmarker) + stbiw__jpg_writeBits(s, bitBuf, bitCnt, M16zeroes); + nrzeroes &= 15; + } + stbiw__jpg_calcBits(DU[i], bits); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTAC[(nrzeroes<<4)+bits[1]]); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits); + } + if(end0pos != 63) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + } + return DU[0]; +} + +static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality) { + // Constants that don't pollute global namespace + static const unsigned char std_dc_luminance_nrcodes[] = {0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0}; + static const unsigned char std_dc_luminance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_luminance_nrcodes[] = {0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d}; + static const unsigned char std_ac_luminance_values[] = { + 0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08, + 0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28, + 0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59, + 0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89, + 0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6, + 0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2, + 0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + static const unsigned char std_dc_chrominance_nrcodes[] = {0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0}; + static const unsigned char std_dc_chrominance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_chrominance_nrcodes[] = {0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77}; + static const unsigned char std_ac_chrominance_values[] = { + 0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91, + 0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26, + 0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58, + 0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87, + 0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4, + 0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda, + 0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + // Huffman tables + static const unsigned short YDC_HT[256][2] = { {0,2},{2,3},{3,3},{4,3},{5,3},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9}}; + static const unsigned short UVDC_HT[256][2] = { {0,2},{1,2},{2,2},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9},{1022,10},{2046,11}}; + static const unsigned short YAC_HT[256][2] = { + {10,4},{0,2},{1,2},{4,3},{11,4},{26,5},{120,7},{248,8},{1014,10},{65410,16},{65411,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {12,4},{27,5},{121,7},{502,9},{2038,11},{65412,16},{65413,16},{65414,16},{65415,16},{65416,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {28,5},{249,8},{1015,10},{4084,12},{65417,16},{65418,16},{65419,16},{65420,16},{65421,16},{65422,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{503,9},{4085,12},{65423,16},{65424,16},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1016,10},{65430,16},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2039,11},{65438,16},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {123,7},{4086,12},{65446,16},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {250,8},{4087,12},{65454,16},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{32704,15},{65462,16},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65470,16},{65471,16},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65479,16},{65480,16},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1017,10},{65488,16},{65489,16},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{65497,16},{65498,16},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2040,11},{65506,16},{65507,16},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {65515,16},{65516,16},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65525,16},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const unsigned short UVAC_HT[256][2] = { + {0,2},{1,2},{4,3},{10,4},{24,5},{25,5},{56,6},{120,7},{500,9},{1014,10},{4084,12},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {11,4},{57,6},{246,8},{501,9},{2038,11},{4085,12},{65416,16},{65417,16},{65418,16},{65419,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {26,5},{247,8},{1015,10},{4086,12},{32706,15},{65420,16},{65421,16},{65422,16},{65423,16},{65424,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {27,5},{248,8},{1016,10},{4087,12},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{65430,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{502,9},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{65438,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1017,10},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{65446,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {121,7},{2039,11},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{65454,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2040,11},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{65462,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {249,8},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{65470,16},{65471,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {503,9},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{65479,16},{65480,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{65488,16},{65489,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{65497,16},{65498,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{65506,16},{65507,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{65515,16},{65516,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {16352,14},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{65525,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{32707,15},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const int YQT[] = {16,11,10,16,24,40,51,61,12,12,14,19,26,58,60,55,14,13,16,24,40,57,69,56,14,17,22,29,51,87,80,62,18,22, + 37,56,68,109,103,77,24,35,55,64,81,104,113,92,49,64,78,87,103,121,120,101,72,92,95,98,112,100,103,99}; + static const int UVQT[] = {17,18,24,47,99,99,99,99,18,21,26,66,99,99,99,99,24,26,56,99,99,99,99,99,47,66,99,99,99,99,99,99, + 99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99}; + static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, + 1.0f * 2.828427125f, 0.785694958f * 2.828427125f, 0.541196100f * 2.828427125f, 0.275899379f * 2.828427125f }; + + int row, col, i, k; + float fdtbl_Y[64], fdtbl_UV[64]; + unsigned char YTable[64], UVTable[64]; + + if(!data || !width || !height || comp > 4 || comp < 1) { + return 0; + } + + quality = quality ? quality : 90; + quality = quality < 1 ? 1 : quality > 100 ? 100 : quality; + quality = quality < 50 ? 5000 / quality : 200 - quality * 2; + + for(i = 0; i < 64; ++i) { + int uvti, yti = (YQT[i]*quality+50)/100; + YTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (yti < 1 ? 1 : yti > 255 ? 255 : yti); + uvti = (UVQT[i]*quality+50)/100; + UVTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (uvti < 1 ? 1 : uvti > 255 ? 255 : uvti); + } + + for(row = 0, k = 0; row < 8; ++row) { + for(col = 0; col < 8; ++col, ++k) { + fdtbl_Y[k] = 1 / (YTable [stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + fdtbl_UV[k] = 1 / (UVTable[stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + } + } + + // Write Headers + { + static const unsigned char head0[] = { 0xFF,0xD8,0xFF,0xE0,0,0x10,'J','F','I','F',0,1,1,0,0,1,0,1,0,0,0xFF,0xDB,0,0x84,0 }; + static const unsigned char head2[] = { 0xFF,0xDA,0,0xC,3,1,0,2,0x11,3,0x11,0,0x3F,0 }; + const unsigned char head1[] = { 0xFF,0xC0,0,0x11,8,(unsigned char)(height>>8),STBIW_UCHAR(height),(unsigned char)(width>>8),STBIW_UCHAR(width), + 3,1,0x11,0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 }; + s->func(s->context, (void*)head0, sizeof(head0)); + s->func(s->context, (void*)YTable, sizeof(YTable)); + stbiw__putc(s, 1); + s->func(s->context, UVTable, sizeof(UVTable)); + s->func(s->context, (void*)head1, sizeof(head1)); + s->func(s->context, (void*)(std_dc_luminance_nrcodes+1), sizeof(std_dc_luminance_nrcodes)-1); + s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values)); + stbiw__putc(s, 0x10); // HTYACinfo + s->func(s->context, (void*)(std_ac_luminance_nrcodes+1), sizeof(std_ac_luminance_nrcodes)-1); + s->func(s->context, (void*)std_ac_luminance_values, sizeof(std_ac_luminance_values)); + stbiw__putc(s, 1); // HTUDCinfo + s->func(s->context, (void*)(std_dc_chrominance_nrcodes+1), sizeof(std_dc_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_dc_chrominance_values, sizeof(std_dc_chrominance_values)); + stbiw__putc(s, 0x11); // HTUACinfo + s->func(s->context, (void*)(std_ac_chrominance_nrcodes+1), sizeof(std_ac_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_ac_chrominance_values, sizeof(std_ac_chrominance_values)); + s->func(s->context, (void*)head2, sizeof(head2)); + } + + // Encode 8x8 macroblocks + { + static const unsigned short fillBits[] = {0x7F, 7}; + const unsigned char *imageData = (const unsigned char *)data; + int DCY=0, DCU=0, DCV=0; + int bitBuf=0, bitCnt=0; + // comp == 2 is grey+alpha (alpha is ignored) + int ofsG = comp > 2 ? 1 : 0, ofsB = comp > 2 ? 2 : 0; + int x, y, pos; + for(y = 0; y < height; y += 8) { + for(x = 0; x < width; x += 8) { + float YDU[64], UDU[64], VDU[64]; + for(row = y, pos = 0; row < y+8; ++row) { + for(col = x; col < x+8; ++col, ++pos) { + int p = (stbi__flip_vertically_on_write ? height-1-row : row)*width*comp + col*comp; + float r, g, b; + if(row >= height) { + p -= width*comp*(row+1 - height); + } + if(col >= width) { + p -= comp*(col+1 - width); + } + + r = imageData[p+0]; + g = imageData[p+ofsG]; + b = imageData[p+ofsB]; + YDU[pos]=+0.29900f*r+0.58700f*g+0.11400f*b-128; + UDU[pos]=-0.16874f*r-0.33126f*g+0.50000f*b; + VDU[pos]=+0.50000f*r-0.41869f*g-0.08131f*b; + } + } + + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, YDU, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, UDU, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, VDU, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + } + } + + // Do the bit alignment of the EOI marker + stbiw__jpg_writeBits(s, &bitBuf, &bitCnt, fillBits); + } + + // EOI + stbiw__putc(s, 0xFF); + stbiw__putc(s, 0xD9); + + return 1; +} + +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality); +} + + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_jpg_core(&s, x, y, comp, data, quality); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + #endif // STB_IMAGE_WRITE_IMPLEMENTATION /* Revision history + 1.09 (2018-02-11) + fix typo in zlib quality API, improve STB_I_W_STATIC in C++ + 1.08 (2018-01-29) + add stbi__flip_vertically_on_write, external zlib, zlib quality, choose PNG filter + 1.07 (2017-07-24) + doc fix + 1.06 (2017-07-23) + writing JPEG (using Jon Olick's code) + 1.05 ??? + 1.04 (2017-03-03) + monochrome BMP expansion + 1.03 ??? + 1.02 (2016-04-02) + avoid allocating large structures on the stack + 1.01 (2016-01-16) + STBIW_REALLOC_SIZED: support allocators with no realloc support + avoid race-condition in crc initialization + minor compile issues + 1.00 (2015-09-14) + installable file IO function + 0.99 (2015-09-13) + warning fixes; TGA rle support 0.98 (2015-04-08) added STBIW_MALLOC, STBIW_ASSERT etc 0.97 (2015-01-18) @@ -728,3 +1524,45 @@ int stbi_write_png(char const *filename, int x, int y, int comp, const void *dat first public release 0.90 first internal release */ + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/image.darknet/src/super.c b/image.darknet/src/super.c deleted file mode 100644 index 63e9860..0000000 --- a/image.darknet/src/super.c +++ /dev/null @@ -1,131 +0,0 @@ -#include "network.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -void train_super(char *cfgfile, char *weightfile) -{ - char *train_images = "/data/imagenet/imagenet1k.train.list"; - char *backup_directory = "/home/pjreddie/backup/"; - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; - data train, buffer; - - - list *plist = get_paths(train_images); - //int N = plist->size; - char **paths = (char **)list_to_array(plist); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.scale = 4; - args.paths = paths; - args.n = imgs; - args.m = plist->size; - args.d = &buffer; - args.type = SUPER_DATA; - - pthread_t load_thread = load_data_in_thread(args); - clock_t time; - //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ - i += 1; - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data_in_thread(args); - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - - time=clock(); - float loss = train_network(net, train); - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); - if(i%1000==0){ - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - if(i%100==0){ - char buff[256]; - sprintf(buff, "%s/%s.backup", backup_directory, base); - save_weights(net, buff); - } - free_data(train); - } - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - -void test_super(char *cfgfile, char *weightfile, char *filename) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - - clock_t time; - char buff[256]; - char *input = buff; - while(1){ - if(filename){ - strncpy(input, filename, 256); - }else{ - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input, 0, 0); - resize_network(&net, im.w, im.h); - printf("%d %d\n", im.w, im.h); - - float *X = im.data; - time=clock(); - network_predict(net, X); - image out = get_network_image(net); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - save_image(out, "out"); - - free_image(im); - if (filename) break; - } -} - - -void run_super(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5] : 0; - if(0==strcmp(argv[2], "train")) train_super(cfg, weights); - else if(0==strcmp(argv[2], "test")) test_super(cfg, weights, filename); - /* - else if(0==strcmp(argv[2], "valid")) validate_super(cfg, weights); - */ -} diff --git a/image.darknet/src/swag.c b/image.darknet/src/swag.c deleted file mode 100644 index 2cb3093..0000000 --- a/image.darknet/src/swag.c +++ /dev/null @@ -1,91 +0,0 @@ -#include "network.h" -#include "detection_layer.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "box.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -void train_swag(char *cfgfile, char *weightfile) -{ - char *train_images = "data/voc.0712.trainval"; - char *backup_directory = "/home/pjreddie/backup/"; - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; - data train, buffer; - - layer l = net.layers[net.n - 1]; - - int side = l.side; - int classes = l.classes; - float jitter = l.jitter; - - list *plist = get_paths(train_images); - //int N = plist->size; - char **paths = (char **)list_to_array(plist); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.n = imgs; - args.m = plist->size; - args.classes = classes; - args.jitter = jitter; - args.num_boxes = side; - args.d = &buffer; - args.type = REGION_DATA; - - pthread_t load_thread = load_data_in_thread(args); - clock_t time; - //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ - i += 1; - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data_in_thread(args); - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - - time=clock(); - float loss = train_network(net, train); - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); - if(i%1000==0 || i == 600){ - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - free_data(train); - } - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - -void run_swag(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - if(0==strcmp(argv[2], "train")) train_swag(cfg, weights); -} diff --git a/image.darknet/src/tag.c b/image.darknet/src/tag.c deleted file mode 100644 index 1e43e7d..0000000 --- a/image.darknet/src/tag.c +++ /dev/null @@ -1,153 +0,0 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -void train_tag(char *cfgfile, char *weightfile, int clear) -{ - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - char *backup_directory = "/home/pjreddie/backup/"; - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - if(clear) *net.seen = 0; - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = 1024; - list *plist = get_paths("/home/pjreddie/tag/train.list"); - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - int N = plist->size; - clock_t time; - pthread_t load_thread; - data train; - data buffer; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - - args.min = net.w; - args.max = net.max_crop; - args.size = net.w; - - args.paths = paths; - args.classes = net.outputs; - args.n = imgs; - args.m = N; - args.d = &buffer; - args.type = TAG_DATA; - - args.angle = net.angle; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; - - fprintf(stderr, "%d classes\n", net.outputs); - - load_thread = load_data_in_thread(args); - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - - load_thread = load_data_in_thread(args); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - float loss = train_network(net, train); - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - free_data(train); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); - } - if(get_current_batch(net)%100 == 0){ - char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); - save_weights(net, buff); - } - } - char buff[256]; - sprintf(buff, "%s/%s.weights", backup_directory, base); - save_weights(net, buff); - - pthread_join(load_thread, 0); - free_data(buffer); - free_network(net); - free_ptrs((void**)paths, plist->size); - free_list(plist); - free(base); -} - -void test_tag(char *cfgfile, char *weightfile, char *filename) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - int i = 0; - char **names = get_labels("data/tags.txt"); - clock_t time; - int indexes[10]; - char buff[256]; - char *input = buff; - int size = net.w; - while(1){ - if(filename){ - strncpy(input, filename, 256); - }else{ - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input, 0, 0); - image r = resize_min(im, size); - resize_network(&net, r.w, r.h); - printf("%d %d\n", r.w, r.h); - - float *X = r.data; - time=clock(); - float *predictions = network_predict(net, X); - top_predictions(net, 10, indexes); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - for(i = 0; i < 10; ++i){ - int index = indexes[i]; - printf("%.1f%%: %s\n", predictions[index]*100, names[index]); - } - if(r.data != im.data) free_image(r); - free_image(im); - if (filename) break; - } -} - - -void run_tag(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - int clear = find_arg(argc, argv, "-clear"); - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5] : 0; - if(0==strcmp(argv[2], "train")) train_tag(cfg, weights, clear); - else if(0==strcmp(argv[2], "test")) test_tag(cfg, weights, filename); -} - diff --git a/image.darknet/src/tree.c b/image.darknet/src/tree.c index dd44515..67b6d43 100644 --- a/image.darknet/src/tree.c +++ b/image.darknet/src/tree.c @@ -24,33 +24,33 @@ void change_leaves(tree *t, char *leaf_list) fprintf(stderr, "Found %d leaves.\n", found); } -float get_hierarchy_probability(float *x, tree *hier, int c) +float get_hierarchy_probability(float *x, tree *hier, int c, int stride) { float p = 1; while(c >= 0){ - p = p * x[c]; + p = p * x[c*stride]; c = hier->parent[c]; } return p; } -void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves) +void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride) { int j; for(j = 0; j < n; ++j){ int parent = hier->parent[j]; if(parent >= 0){ - predictions[j] *= predictions[parent]; + predictions[j*stride] *= predictions[parent*stride]; } } if(only_leaves){ for(j = 0; j < n; ++j){ - if(!hier->leaf[j]) predictions[j] = 0; + if(!hier->leaf[j]) predictions[j*stride] = 0; } } } -int hierarchy_top_prediction(float *predictions, tree *hier, float thresh) +int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride) { float p = 1; int group = 0; @@ -61,7 +61,7 @@ int hierarchy_top_prediction(float *predictions, tree *hier, float thresh) for(i = 0; i < hier->group_size[group]; ++i){ int index = i + hier->group_offset[group]; - float val = predictions[i + hier->group_offset[group]]; + float val = predictions[(i + hier->group_offset[group])*stride]; if(val > max){ max_i = index; max = val; @@ -71,6 +71,8 @@ int hierarchy_top_prediction(float *predictions, tree *hier, float thresh) p = p*max; group = hier->child[max_i]; if(hier->child[max_i] < 0) return max_i; + } else if (group == 0){ + return max_i; } else { return hier->parent[hier->group_offset[group]]; } diff --git a/image.darknet/src/tree.h b/image.darknet/src/tree.h index dbd4c39..3802b8e 100644 --- a/image.darknet/src/tree.h +++ b/image.darknet/src/tree.h @@ -1,23 +1,8 @@ #ifndef TREE_H #define TREE_H +#include "darknet.h" -typedef struct{ - int *leaf; - int n; - int *parent; - int *child; - int *group; - char **name; - - int groups; - int *group_size; - int *group_offset; -} tree; - -tree *read_tree(char *filename); -void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves); -void change_leaves(tree *t, char *leaf_list); -int hierarchy_top_prediction(float *predictions, tree *hier, float thresh); -float get_hierarchy_probability(float *x, tree *hier, int c); +int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride); +float get_hierarchy_probability(float *x, tree *hier, int c, int stride); #endif diff --git a/image.darknet/src/upsample_layer.c b/image.darknet/src/upsample_layer.c new file mode 100644 index 0000000..605f21f --- /dev/null +++ b/image.darknet/src/upsample_layer.c @@ -0,0 +1,106 @@ +#include "upsample_layer.h" +#include "cuda.h" +#include "blas.h" + +#include + +layer make_upsample_layer(int batch, int w, int h, int c, int stride) +{ + layer l = {0}; + l.type = UPSAMPLE; + l.batch = batch; + l.w = w; + l.h = h; + l.c = c; + l.out_w = w*stride; + l.out_h = h*stride; + l.out_c = c; + if(stride < 0){ + stride = -stride; + l.reverse=1; + l.out_w = w/stride; + l.out_h = h/stride; + } + l.stride = stride; + l.outputs = l.out_w*l.out_h*l.out_c; + l.inputs = l.w*l.h*l.c; + l.delta = calloc(l.outputs*batch, sizeof(float)); + l.output = calloc(l.outputs*batch, sizeof(float));; + + l.forward = forward_upsample_layer; + l.backward = backward_upsample_layer; + #ifdef GPU + l.forward_gpu = forward_upsample_layer_gpu; + l.backward_gpu = backward_upsample_layer_gpu; + + l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); + l.output_gpu = cuda_make_array(l.output, l.outputs*batch); + #endif + if(l.reverse) fprintf(stderr, "downsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + else fprintf(stderr, "upsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + return l; +} + +void resize_upsample_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + l->out_w = w*l->stride; + l->out_h = h*l->stride; + if(l->reverse){ + l->out_w = w/l->stride; + l->out_h = h/l->stride; + } + l->outputs = l->out_w*l->out_h*l->out_c; + l->inputs = l->h*l->w*l->c; + l->delta = realloc(l->delta, l->outputs*l->batch*sizeof(float)); + l->output = realloc(l->output, l->outputs*l->batch*sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); +#endif + +} + +void forward_upsample_layer(const layer l, network net) +{ + fill_cpu(l.outputs*l.batch, 0, l.output, 1); + if(l.reverse){ + upsample_cpu(l.output, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input); + }else{ + upsample_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output); + } +} + +void backward_upsample_layer(const layer l, network net) +{ + if(l.reverse){ + upsample_cpu(l.delta, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, net.delta); + }else{ + upsample_cpu(net.delta, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta); + } +} + +#ifdef GPU +void forward_upsample_layer_gpu(const layer l, network net) +{ + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); + if(l.reverse){ + upsample_gpu(l.output_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input_gpu); + }else{ + upsample_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output_gpu); + } +} + +void backward_upsample_layer_gpu(const layer l, network net) +{ + if(l.reverse){ + upsample_gpu(l.delta_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, net.delta_gpu); + }else{ + upsample_gpu(net.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta_gpu); + } +} +#endif diff --git a/image.darknet/src/upsample_layer.h b/image.darknet/src/upsample_layer.h new file mode 100644 index 0000000..86790d1 --- /dev/null +++ b/image.darknet/src/upsample_layer.h @@ -0,0 +1,15 @@ +#ifndef UPSAMPLE_LAYER_H +#define UPSAMPLE_LAYER_H +#include "darknet.h" + +layer make_upsample_layer(int batch, int w, int h, int c, int stride); +void forward_upsample_layer(const layer l, network net); +void backward_upsample_layer(const layer l, network net); +void resize_upsample_layer(layer *l, int w, int h); + +#ifdef GPU +void forward_upsample_layer_gpu(const layer l, network net); +void backward_upsample_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/image.darknet/src/utils.c b/image.darknet/src/utils.c index b5181d7..626b467 100644 --- a/image.darknet/src/utils.c +++ b/image.darknet/src/utils.c @@ -6,9 +6,56 @@ #include #include #include +#include +#include #include "utils.h" + +/* +// old timing. is it better? who knows!! +double get_wall_time() +{ + struct timeval time; + if (gettimeofday(&time,NULL)){ + return 0; + } + return (double)time.tv_sec + (double)time.tv_usec * .000001; +} +*/ + +double what_time_is_it_now() +{ + struct timeval time; + if (gettimeofday(&time,NULL)){ + return 0; + } + return (double)time.tv_sec + (double)time.tv_usec * .000001; +} + +int *read_intlist(char *gpu_list, int *ngpus, int d) +{ + int *gpus = 0; + if(gpu_list){ + int len = strlen(gpu_list); + *ngpus = 1; + int i; + for(i = 0; i < len; ++i){ + if (gpu_list[i] == ',') ++*ngpus; + } + gpus = calloc(*ngpus, sizeof(int)); + for(i = 0; i < *ngpus; ++i){ + gpus[i] = atoi(gpu_list); + gpu_list = strchr(gpu_list, ',')+1; + } + } else { + gpus = calloc(1, sizeof(float)); + *gpus = d; + *ngpus = 1; + } + return gpus; +} + int *read_map(char *filename) { int n = 0; @@ -47,6 +94,22 @@ void shuffle(void *arr, size_t n, size_t size) } } +int *random_index_order(int min, int max) +{ + int *inds = calloc(max-min, sizeof(int)); + int i; + for(i = min; i < max; ++i){ + inds[i] = i; + } + for(i = min; i < max-1; ++i){ + int swap = inds[i]; + int index = i + rand()%(max-i); + inds[i] = inds[index]; + inds[index] = swap; + } + return inds; +} + void del_arg(int argc, char **argv, int index) { int i; @@ -194,6 +257,21 @@ void error(const char *s) exit(-1); } +unsigned char *read_file(char *filename) +{ + FILE *fp = fopen(filename, "rb"); + size_t size; + + fseek(fp, 0, SEEK_END); + size = ftell(fp); + fseek(fp, 0, SEEK_SET); + + unsigned char *text = calloc(size+1, sizeof(char)); + fread(text, 1, size, fp); + fclose(fp); + return text; +} + void malloc_error() { fprintf(stderr, "Malloc error\n"); @@ -524,6 +602,20 @@ int sample_array(float *a, int n) return n-1; } +int max_int_index(int *a, int n) +{ + if(n <= 0) return -1; + int i, max_i = 0; + int max = a[0]; + for(i = 1; i < n; ++i){ + if(a[i] > max){ + max = a[i]; + max_i = i; + } + } + return max_i; +} + int max_index(float *a, int n) { if(n <= 0) return -1; @@ -538,6 +630,15 @@ int max_index(float *a, int n) return max_i; } +int int_index(int *a, int val, int n) +{ + int i; + for(i = 0; i < n; ++i){ + if(a[i] == val) return i; + } + return -1; +} + int rand_int(int min, int max) { if (max < min){ @@ -585,13 +686,13 @@ float rand_normal() size_t rand_size_t() { return ((size_t)(rand()&0xff) << 56) | - ((size_t)(rand()&0xff) << 48) | - ((size_t)(rand()&0xff) << 40) | - ((size_t)(rand()&0xff) << 32) | - ((size_t)(rand()&0xff) << 24) | - ((size_t)(rand()&0xff) << 16) | - ((size_t)(rand()&0xff) << 8) | - ((size_t)(rand()&0xff) << 0); + ((size_t)(rand()&0xff) << 48) | + ((size_t)(rand()&0xff) << 40) | + ((size_t)(rand()&0xff) << 32) | + ((size_t)(rand()&0xff) << 24) | + ((size_t)(rand()&0xff) << 16) | + ((size_t)(rand()&0xff) << 8) | + ((size_t)(rand()&0xff) << 0); } float rand_uniform(float min, float max) diff --git a/image.darknet/src/utils.h b/image.darknet/src/utils.h index bbc6765..ef24da7 100644 --- a/image.darknet/src/utils.h +++ b/image.darknet/src/utils.h @@ -2,16 +2,22 @@ #define UTILS_H #include #include +#include "darknet.h" #include "list.h" -#define SECRET_NUM -1234 -#define TWO_PI 6.2831853071795864769252866 +#define TIME(a) \ + do { \ + double start = what_time_is_it_now(); \ + a; \ + printf("%s took: %f seconds\n", #a, what_time_is_it_now() - start); \ + } while (0) -int *read_map(char *filename); +#define TWO_PI 6.2831853071795864769252866f + +double what_time_is_it_now(); void shuffle(void *arr, size_t n, size_t size); void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections); void free_ptrs(void **ptrs, int n); -char *basecfg(char *cfgfile); int alphanum_to_int(char c); char int_to_alphanum(int i); int read_int(int fd); @@ -21,44 +27,27 @@ void write_all(int fd, char *buffer, size_t bytes); int read_all_fail(int fd, char *buffer, size_t bytes); int write_all_fail(int fd, char *buffer, size_t bytes); void find_replace(char *str, char *orig, char *rep, char *output); -void error(const char *s); void malloc_error(); void file_error(char *s); void strip(char *s); void strip_char(char *s, char bad); -void top_k(float *a, int n, int k, int *index); list *split_str(char *s, char delim); char *fgetl(FILE *fp); list *parse_csv_line(char *line); char *copy_string(char *s); int count_fields(char *line); float *parse_fields(char *line, int n); -void normalize_array(float *a, int n); -void scale_array(float *a, int n, float s); void translate_array(float *a, int n, float s); -int max_index(float *a, int n); float constrain(float min, float max, float a); int constrain_int(int a, int min, int max); -float mse_array(float *a, int n); -float rand_normal(); -size_t rand_size_t(); -float rand_uniform(float min, float max); float rand_scale(float s); int rand_int(int min, int max); -float sum_array(float *a, int n); -float mean_array(float *a, int n); void mean_arrays(float **a, int n, int els, float *avg); -float variance_array(float *a, int n); -float mag_array(float *a, int n); float dist_array(float *a, float *b, int n, int sub); float **one_hot_encode(float *a, int n, int k); float sec(clock_t clocks); -int find_int_arg(int argc, char **argv, char *arg, int def); -float find_float_arg(int argc, char **argv, char *arg, float def); -int find_arg(int argc, char* argv[], char *arg); -char *find_char_arg(int argc, char **argv, char *arg, char *def); -int sample_array(float *a, int n); void print_statistics(float *a, int n); +int int_index(int *a, int val, int n); #endif diff --git a/image.darknet/src/voxel.c b/image.darknet/src/voxel.c deleted file mode 100644 index 1b53880..0000000 --- a/image.darknet/src/voxel.c +++ /dev/null @@ -1,169 +0,0 @@ -#include "network.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -image get_image_from_stream(CvCapture *cap); -#endif - -void extract_voxel(char *lfile, char *rfile, char *prefix) -{ -#ifdef OPENCV - int w = 1920; - int h = 1080; - int shift = 0; - int count = 0; - CvCapture *lcap = cvCaptureFromFile(lfile); - CvCapture *rcap = cvCaptureFromFile(rfile); - while(1){ - image l = get_image_from_stream(lcap); - image r = get_image_from_stream(rcap); - if(!l.w || !r.w) break; - if(count%100 == 0) { - shift = best_3d_shift_r(l, r, -l.h/100, l.h/100); - printf("%d\n", shift); - } - image ls = crop_image(l, (l.w - w)/2, (l.h - h)/2, w, h); - image rs = crop_image(r, 105 + (r.w - w)/2, (r.h - h)/2 + shift, w, h); - char buff[256]; - sprintf(buff, "%s_%05d_l", prefix, count); - save_image(ls, buff); - sprintf(buff, "%s_%05d_r", prefix, count); - save_image(rs, buff); - free_image(l); - free_image(r); - free_image(ls); - free_image(rs); - ++count; - } - -#else - printf("need OpenCV for extraction\n"); -#endif -} - -void train_voxel(char *cfgfile, char *weightfile) -{ - char *train_images = "/data/imagenet/imagenet1k.train.list"; - char *backup_directory = "/home/pjreddie/backup/"; - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; - data train, buffer; - - - list *plist = get_paths(train_images); - //int N = plist->size; - char **paths = (char **)list_to_array(plist); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.scale = 4; - args.paths = paths; - args.n = imgs; - args.m = plist->size; - args.d = &buffer; - args.type = SUPER_DATA; - - pthread_t load_thread = load_data_in_thread(args); - clock_t time; - //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ - i += 1; - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data_in_thread(args); - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - - time=clock(); - float loss = train_network(net, train); - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); - if(i%1000==0){ - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - if(i%100==0){ - char buff[256]; - sprintf(buff, "%s/%s.backup", backup_directory, base); - save_weights(net, buff); - } - free_data(train); - } - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - -void test_voxel(char *cfgfile, char *weightfile, char *filename) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - - clock_t time; - char buff[256]; - char *input = buff; - while(1){ - if(filename){ - strncpy(input, filename, 256); - }else{ - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input, 0, 0); - resize_network(&net, im.w, im.h); - printf("%d %d\n", im.w, im.h); - - float *X = im.data; - time=clock(); - network_predict(net, X); - image out = get_network_image(net); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - save_image(out, "out"); - - free_image(im); - if (filename) break; - } -} - - -void run_voxel(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5] : 0; - if(0==strcmp(argv[2], "train")) train_voxel(cfg, weights); - else if(0==strcmp(argv[2], "test")) test_voxel(cfg, weights, filename); - else if(0==strcmp(argv[2], "extract")) extract_voxel(argv[3], argv[4], argv[5]); - /* - else if(0==strcmp(argv[2], "valid")) validate_voxel(cfg, weights); - */ -} diff --git a/image.darknet/src/writing.c b/image.darknet/src/writing.c deleted file mode 100644 index 0a76d48..0000000 --- a/image.darknet/src/writing.c +++ /dev/null @@ -1,150 +0,0 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -void train_writing(char *cfgfile, char *weightfile) -{ - char *backup_directory = "/home/pjreddie/backup/"; - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - list *plist = get_paths("figures.list"); - char **paths = (char **)list_to_array(plist); - clock_t time; - int N = plist->size; - printf("N: %d\n", N); - image out = get_network_image(net); - - data train, buffer; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.out_w = out.w; - args.out_h = out.h; - args.paths = paths; - args.n = imgs; - args.m = N; - args.d = &buffer; - args.type = WRITING_DATA; - - pthread_t load_thread = load_data_in_thread(args); - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data_in_thread(args); - printf("Loaded %lf seconds\n",sec(clock()-time)); - - time=clock(); - float loss = train_network(net, train); - - /* - image pred = float_to_image(64, 64, 1, out); - print_image(pred); - */ - - /* - image im = float_to_image(256, 256, 3, train.X.vals[0]); - image lab = float_to_image(64, 64, 1, train.y.vals[0]); - image pred = float_to_image(64, 64, 1, out); - show_image(im, "image"); - show_image(lab, "label"); - print_image(lab); - show_image(pred, "pred"); - cvWaitKey(0); - */ - - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - free_data(train); - if(get_current_batch(net)%100 == 0){ - char buff[256]; - sprintf(buff, "%s/%s_batch_%d.weights", backup_directory, base, get_current_batch(net)); - save_weights(net, buff); - } - if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); - } - } -} - -void test_writing(char *cfgfile, char *weightfile, char *filename) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - clock_t time; - char buff[256]; - char *input = buff; - while(1){ - if(filename){ - strncpy(input, filename, 256); - }else{ - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - - image im = load_image_color(input, 0, 0); - resize_network(&net, im.w, im.h); - printf("%d %d %d\n", im.h, im.w, im.c); - float *X = im.data; - time=clock(); - network_predict(net, X); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - image pred = get_network_image(net); - - image upsampled = resize_image(pred, im.w, im.h); - image thresh = threshold_image(upsampled, .5); - pred = thresh; - - show_image(pred, "prediction"); - show_image(im, "orig"); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif - - free_image(upsampled); - free_image(thresh); - free_image(im); - if (filename) break; - } -} - -void run_writing(int argc, char **argv) -{ - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5] : 0; - if(0==strcmp(argv[2], "train")) train_writing(cfg, weights); - else if(0==strcmp(argv[2], "test")) test_writing(cfg, weights, filename); -} - diff --git a/image.darknet/src/yolo.c b/image.darknet/src/yolo.c deleted file mode 100644 index ee5f73b..0000000 --- a/image.darknet/src/yolo.c +++ /dev/null @@ -1,355 +0,0 @@ -#include "network.h" -#include "detection_layer.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "box.h" -#include "demo.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; - -void train_yolo(char *cfgfile, char *weightfile) -{ - char *train_images = "/data/voc/train.txt"; - char *backup_directory = "/home/pjreddie/backup/"; - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; - data train, buffer; - - - layer l = net.layers[net.n - 1]; - - int side = l.side; - int classes = l.classes; - float jitter = l.jitter; - - list *plist = get_paths(train_images); - //int N = plist->size; - char **paths = (char **)list_to_array(plist); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.n = imgs; - args.m = plist->size; - args.classes = classes; - args.jitter = jitter; - args.num_boxes = side; - args.d = &buffer; - args.type = REGION_DATA; - - args.angle = net.angle; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; - - pthread_t load_thread = load_data_in_thread(args); - clock_t time; - //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ - i += 1; - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data_in_thread(args); - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - - time=clock(); - float loss = train_network(net, train); - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); - if(i%1000==0 || (i < 1000 && i%100 == 0)){ - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - free_data(train); - } - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - -void print_yolo_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h) -{ - int i, j; - for(i = 0; i < total; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - for(j = 0; j < classes; ++j){ - if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j], - xmin, ymin, xmax, ymax); - } - } -} - -void validate_yolo(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - char *base = "results/comp4_det_test_"; - //list *plist = get_paths("data/voc.2007.test"); - list *plist = get_paths("/home/pjreddie/data/voc/2007_test.txt"); - //list *plist = get_paths("data/voc.2012.test"); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - - int j; - FILE **fps = calloc(classes, sizeof(FILE *)); - for(j = 0; j < classes; ++j){ - char buff[1024]; - snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); - fps[j] = fopen(buff, "w"); - } - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - int t; - - float thresh = .001; - int nms = 1; - float iou_thresh = .5; - - int nthreads = 8; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.type = IMAGE_DATA; - - for(t = 0; t < nthreads; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - time_t start = time(0); - for(i = nthreads; i < m+nthreads; i += nthreads){ - fprintf(stderr, "%d\n", i); - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - pthread_join(thr[t], 0); - val[t] = buf[t]; - val_resized[t] = buf_resized[t]; - } - for(t = 0; t < nthreads && i+t < m; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - char *path = paths[i+t-nthreads]; - char *id = basecfg(path); - float *X = val_resized[t].data; - network_predict(net, X); - int w = val[t].w; - int h = val[t].h; - get_detection_boxes(l, w, h, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, classes, iou_thresh); - print_yolo_detections(fps, id, boxes, probs, l.side*l.side*l.n, classes, w, h); - free(id); - free_image(val[t]); - free_image(val_resized[t]); - } - } - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); -} - -void validate_yolo_recall(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - char *base = "results/comp4_det_test_"; - list *plist = get_paths("data/voc.2007.test"); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - int side = l.side; - - int j, k; - FILE **fps = calloc(classes, sizeof(FILE *)); - for(j = 0; j < classes; ++j){ - char buff[1024]; - snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); - fps[j] = fopen(buff, "w"); - } - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - - float thresh = .001; - float iou_thresh = .5; - float nms = 0; - - int total = 0; - int correct = 0; - int proposals = 0; - float avg_iou = 0; - - for(i = 0; i < m; ++i){ - char *path = paths[i]; - image orig = load_image_color(path, 0, 0); - image sized = resize_image(orig, net.w, net.h); - char *id = basecfg(path); - network_predict(net, sized.data); - get_detection_boxes(l, orig.w, orig.h, thresh, probs, boxes, 1); - if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms); - - char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - - int num_labels = 0; - box_label *truth = read_boxes(labelpath, &num_labels); - for(k = 0; k < side*side*l.n; ++k){ - if(probs[k][0] > thresh){ - ++proposals; - } - } - for (j = 0; j < num_labels; ++j) { - ++total; - box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h}; - float best_iou = 0; - for(k = 0; k < side*side*l.n; ++k){ - float iou = box_iou(boxes[k], t); - if(probs[k][0] > thresh && iou > best_iou){ - best_iou = iou; - } - } - avg_iou += best_iou; - if(best_iou > iou_thresh){ - ++correct; - } - } - - fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); - free(id); - free_image(orig); - free_image(sized); - } -} - -void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) -{ - image **alphabet = load_alphabet(); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - detection_layer l = net.layers[net.n-1]; - set_batch_network(&net, 1); - srand(2222222); - clock_t time; - char buff[256]; - char *input = buff; - int j; - float nms=.4; - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); - while(1){ - if(filename){ - strncpy(input, filename, 256); - } else { - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input,0,0); - image sized = resize_image(im, net.w, net.h); - float *X = sized.data; - time=clock(); - network_predict(net, X); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms); - //draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20); - draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20); - save_image(im, "predictions"); - show_image(im, "predictions"); - - free_image(im); - free_image(sized); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif - if (filename) break; - } -} - -void run_yolo(int argc, char **argv) -{ - char *prefix = find_char_arg(argc, argv, "-prefix", 0); - float thresh = find_float_arg(argc, argv, "-thresh", .2); - int cam_index = find_int_arg(argc, argv, "-c", 0); - int frame_skip = find_int_arg(argc, argv, "-s", 0); - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5]: 0; - if(0==strcmp(argv[2], "test")) test_yolo(cfg, weights, filename, thresh); - else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights); - else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); - else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); - else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, voc_names, 20, frame_skip, prefix, .5); -} diff --git a/image.darknet/src/yolo_layer.c b/image.darknet/src/yolo_layer.c new file mode 100644 index 0000000..c338036 --- /dev/null +++ b/image.darknet/src/yolo_layer.c @@ -0,0 +1,374 @@ +#include "yolo_layer.h" +#include "activations.h" +#include "blas.h" +#include "box.h" +#include "cuda.h" +#include "utils.h" + +#include +#include +#include +#include + +layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes) +{ + int i; + layer l = {0}; + l.type = YOLO; + + l.n = n; + l.total = total; + l.batch = batch; + l.h = h; + l.w = w; + l.c = n*(classes + 4 + 1); + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; + l.classes = classes; + l.cost = calloc(1, sizeof(float)); + l.biases = calloc(total*2, sizeof(float)); + if(mask) l.mask = mask; + else{ + l.mask = calloc(n, sizeof(int)); + for(i = 0; i < n; ++i){ + l.mask[i] = i; + } + } + l.bias_updates = calloc(n*2, sizeof(float)); + l.outputs = h*w*n*(classes + 4 + 1); + l.inputs = l.outputs; + l.truths = 90*(4 + 1); + l.delta = calloc(batch*l.outputs, sizeof(float)); + l.output = calloc(batch*l.outputs, sizeof(float)); + for(i = 0; i < total*2; ++i){ + l.biases[i] = .5; + } + + l.forward = forward_yolo_layer; + l.backward = backward_yolo_layer; +#ifdef GPU + l.forward_gpu = forward_yolo_layer_gpu; + l.backward_gpu = backward_yolo_layer_gpu; + l.output_gpu = cuda_make_array(l.output, batch*l.outputs); + l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); +#endif + + fprintf(stderr, "yolo\n"); + srand(0); + + return l; +} + +void resize_yolo_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + + l->outputs = h*w*l->n*(l->classes + 4 + 1); + l->inputs = l->outputs; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); +#endif +} + +box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) +{ + box b; + b.x = (i + x[index + 0*stride]) / lw; + b.y = (j + x[index + 1*stride]) / lh; + b.w = exp(x[index + 2*stride]) * biases[2*n] / w; + b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; + return b; +} + +float delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride) +{ + box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); + float iou = box_iou(pred, truth); + + float tx = (truth.x*lw - i); + float ty = (truth.y*lh - j); + float tw = log(truth.w*w / biases[2*n]); + float th = log(truth.h*h / biases[2*n + 1]); + + delta[index + 0*stride] = scale * (tx - x[index + 0*stride]); + delta[index + 1*stride] = scale * (ty - x[index + 1*stride]); + delta[index + 2*stride] = scale * (tw - x[index + 2*stride]); + delta[index + 3*stride] = scale * (th - x[index + 3*stride]); + return iou; +} + + +void delta_yolo_class(float *output, float *delta, int index, int class, int classes, int stride, float *avg_cat) +{ + int n; + if (delta[index]){ + delta[index + stride*class] = 1 - output[index + stride*class]; + if(avg_cat) *avg_cat += output[index + stride*class]; + return; + } + for(n = 0; n < classes; ++n){ + delta[index + stride*n] = ((n == class)?1 : 0) - output[index + stride*n]; + if(n == class && avg_cat) *avg_cat += output[index + stride*n]; + } +} + +static int entry_index(layer l, int batch, int location, int entry) +{ + int n = location / (l.w*l.h); + int loc = location % (l.w*l.h); + return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc; +} + +void forward_yolo_layer(const layer l, network net) +{ + int i,j,b,t,n; + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + +#ifndef GPU + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC); + } + } +#endif + + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if(!net.train) return; + float avg_iou = 0; + float recall = 0; + float recall75 = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; + int class_count = 0; + *(l.cost) = 0; + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.w*l.h); + float best_iou = 0; + int best_t = 0; + for(t = 0; t < l.max_boxes; ++t){ + box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1); + if(!truth.x) break; + float iou = box_iou(pred, truth); + if (iou > best_iou) { + best_iou = iou; + best_t = t; + } + } + int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); + avg_anyobj += l.output[obj_index]; + l.delta[obj_index] = 0 - l.output[obj_index]; + if (best_iou > l.ignore_thresh) { + l.delta[obj_index] = 0; + } + if (best_iou > l.truth_thresh) { + l.delta[obj_index] = 1 - l.output[obj_index]; + + int class = net.truth[best_t*(4 + 1) + b*l.truths + 4]; + if (l.map) class = l.map[class]; + int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0); + box truth = float_to_box(net.truth + best_t*(4 + 1) + b*l.truths, 1); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + } + } + } + } + for(t = 0; t < l.max_boxes; ++t){ + box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1); + + if(!truth.x) break; + float best_iou = 0; + int best_n = 0; + i = (truth.x * l.w); + j = (truth.y * l.h); + box truth_shift = truth; + truth_shift.x = truth_shift.y = 0; + for(n = 0; n < l.total; ++n){ + box pred = {0}; + pred.w = l.biases[2*n]/net.w; + pred.h = l.biases[2*n+1]/net.h; + float iou = box_iou(pred, truth_shift); + if (iou > best_iou){ + best_iou = iou; + best_n = n; + } + } + + int mask_n = int_index(l.mask, best_n, l.n); + if(mask_n >= 0){ + int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + float iou = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + + int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); + avg_obj += l.output[obj_index]; + l.delta[obj_index] = 1 - l.output[obj_index]; + + int class = net.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class = l.map[class]; + int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat); + + ++count; + ++class_count; + if(iou > .5) recall += 1; + if(iou > .75) recall75 += 1; + avg_iou += iou; + } + } + } + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", net.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count); +} + +void backward_yolo_layer(const layer l, network net) +{ + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); +} + +void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +{ + int i; + int new_w=0; + int new_h=0; + if (((float)netw/w) < ((float)neth/h)) { + new_w = netw; + new_h = (h * netw)/w; + } else { + new_h = neth; + new_w = (w * neth)/h; + } + for (i = 0; i < n; ++i){ + box b = dets[i].bbox; + b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); + b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); + b.w *= (float)netw/new_w; + b.h *= (float)neth/new_h; + if(!relative){ + b.x *= w; + b.w *= w; + b.y *= h; + b.h *= h; + } + dets[i].bbox = b; + } +} + +int yolo_num_detections(layer l, float thresh) +{ + int i, n; + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4); + if(l.output[obj_index] > thresh){ + ++count; + } + } + } + return count; +} + +void avg_flipped_yolo(layer l) +{ + int i,j,n,z; + float *flip = l.output + l.outputs; + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w/2; ++i) { + for (n = 0; n < l.n; ++n) { + for(z = 0; z < l.classes + 4 + 1; ++z){ + int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; + int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); + float swap = flip[i1]; + flip[i1] = flip[i2]; + flip[i2] = swap; + if(z == 0){ + flip[i1] = -flip[i1]; + flip[i2] = -flip[i2]; + } + } + } + } + } + for(i = 0; i < l.outputs; ++i){ + l.output[i] = (l.output[i] + flip[i])/2.; + } +} + +int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets) +{ + int i,j,n; + float *predictions = l.output; + if (l.batch == 2) avg_flipped_yolo(l); + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + int row = i / l.w; + int col = i % l.w; + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4); + float objectness = predictions[obj_index]; + if(objectness <= thresh) continue; + int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); + dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].objectness = objectness; + dets[count].classes = l.classes; + for(j = 0; j < l.classes; ++j){ + int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j); + float prob = objectness*predictions[class_index]; + dets[count].prob[j] = (prob > thresh) ? prob : 0; + } + ++count; + } + } + correct_yolo_boxes(dets, count, w, h, netw, neth, relative); + return count; +} + +#ifdef GPU + +void forward_yolo_layer_gpu(const layer l, network net) +{ + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + int b, n; + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array_gpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); + } + } + if(!net.train || l.onlyforward){ + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + return; + } + + cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); + forward_yolo_layer(l, net); + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); +} + +void backward_yolo_layer_gpu(const layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif + diff --git a/image.darknet/src/yolo_layer.h b/image.darknet/src/yolo_layer.h new file mode 100644 index 0000000..d2a0243 --- /dev/null +++ b/image.darknet/src/yolo_layer.h @@ -0,0 +1,19 @@ +#ifndef YOLO_LAYER_H +#define YOLO_LAYER_H + +#include "darknet.h" +#include "layer.h" +#include "network.h" + +layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes); +void forward_yolo_layer(const layer l, network net); +void backward_yolo_layer(const layer l, network net); +void resize_yolo_layer(layer *l, int w, int h); +int yolo_num_detections(layer l, float thresh); + +#ifdef GPU +void forward_yolo_layer_gpu(const layer l, network net); +void backward_yolo_layer_gpu(layer l, network net); +#endif + +#endif From 372151cc0e101906896bb2b10fd81417e2e7434a Mon Sep 17 00:00:00 2001 From: Jeroen Staab Date: Mon, 24 Dec 2018 16:15:20 +0100 Subject: [PATCH 3/8] Updated inst/include/darknet/ with YOLO repository. An dynamic link into latest version of yolo would be cool.. --- .../inst/include/darknet/LICENSE.fuck | 13 + .../inst/include/darknet/LICENSE.gen | 91 + .../inst/include/darknet/LICENSE.gpl | 674 ++++ .../inst/include/darknet/LICENSE.meta | 8 + .../inst/include/darknet/LICENSE.mit | 22 + image.darknet/inst/include/darknet/LICENSE.v1 | 13 + image.darknet/inst/include/darknet/Makefile | 51 +- .../inst/include/darknet/Makefile.bak | 82 + image.darknet/inst/include/darknet/README.md | 2 +- .../inst/include/darknet/cfg/alexnet.cfg | 9 +- .../inst/include/darknet/cfg/cifar.cfg | 27 +- .../inst/include/darknet/cfg/cifar.test.cfg | 2 - .../inst/include/darknet/cfg/coco.data | 4 +- .../inst/include/darknet/cfg/darknet.cfg | 33 +- .../inst/include/darknet/cfg/darknet19.cfg | 29 +- .../include/darknet/cfg/darknet19_448.cfg | 3 - .../inst/include/darknet/cfg/darknet53.cfg | 566 ++++ .../cfg/{msr_50.cfg => darknet53_448.cfg} | 371 +-- .../cfg/{yolo-voc.cfg => darknet9000.cfg} | 95 +- .../inst/include/darknet/cfg/densenet201.cfg | 1951 ++++++++++++ .../inst/include/darknet/cfg/extraction.cfg | 11 +- .../include/darknet/cfg/extraction22k.cfg | 3 - image.darknet/inst/include/darknet/cfg/go.cfg | 132 + .../inst/include/darknet/cfg/go.test.cfg | 37 +- .../inst/include/darknet/cfg/gru.cfg | 25 +- .../include/darknet/cfg/imagenet22k.dataset | 1 + .../darknet/cfg/imagenet9k.hierarchy.dataset | 9 + .../inst/include/darknet/cfg/openimages.data | 8 + .../inst/include/darknet/cfg/resnet101.cfg | 990 ++++++ .../cfg/{msr_152.cfg => resnet152.cfg} | 302 +- .../cfg/{yolov1/yolo2.cfg => resnet18.cfg} | 201 +- .../darknet/cfg/{msr_34.cfg => resnet34.cfg} | 158 +- .../inst/include/darknet/cfg/resnet50.cfg | 510 +++ .../include/darknet/cfg/resnext101-32x4d.cfg | 1053 +++++++ .../include/darknet/cfg/resnext152-32x4d.cfg | 1562 ++++++++++ .../inst/include/darknet/cfg/resnext50.cfg | 523 ++++ .../inst/include/darknet/cfg/rnn.cfg | 2 - .../inst/include/darknet/cfg/rnn.train.cfg | 2 - .../inst/include/darknet/cfg/strided.cfg | 3 - .../inst/include/darknet/cfg/tiny.cfg | 6 +- .../inst/include/darknet/cfg/vgg-16.cfg | 12 +- .../inst/include/darknet/cfg/yolo9000.cfg | 19 +- .../{yolov1/tiny-yolo.cfg => yolov1-tiny.cfg} | 8 +- .../cfg/{yolov1/yolo.cfg => yolov1.cfg} | 4 + .../include/darknet/cfg/yolov1/tiny-coco.cfg | 125 - .../include/darknet/cfg/yolov1/xyolo.test.cfg | 143 - .../include/darknet/cfg/yolov1/yolo-coco.cfg | 255 -- .../include/darknet/cfg/yolov1/yolo-small.cfg | 239 -- .../include/darknet/cfg/yolov1/yolo.train.cfg | 257 -- ...{tiny-yolo-voc.cfg => yolov2-tiny-voc.cfg} | 10 +- .../cfg/{tiny-yolo.cfg => yolov2-tiny.cfg} | 21 +- .../darknet/cfg/{yolo.cfg => yolov2-voc.cfg} | 34 +- .../include/darknet/cfg/yolov3-openimages.cfg | 789 +++++ .../inst/include/darknet/cfg/yolov3-spp.cfg | 822 +++++ .../inst/include/darknet/cfg/yolov3-tiny.cfg | 182 ++ .../inst/include/darknet/cfg/yolov3-voc.cfg | 785 +++++ .../inst/include/darknet/data/kite.jpg | Bin 0 -> 1415684 bytes .../include/darknet/data/openimages.names | 601 ++++ .../include/darknet/{src => examples}/art.c | 32 +- .../inst/include/darknet/examples/attention.c | 459 +++ .../darknet/{src => examples}/captcha.c | 55 +- .../include/darknet/{src => examples}/cifar.c | 72 +- .../darknet/{src => examples}/classifier.c | 555 ++-- .../include/darknet/{src => examples}/coco.c | 151 +- .../darknet/{src => examples}/darknet.c | 301 +- .../darknet/examples/detector-scipy-opencv.py | 56 + .../inst/include/darknet/examples/detector.c | 850 +++++ .../inst/include/darknet/examples/detector.py | 27 + .../include/darknet/{src => examples}/dice.c | 6 +- .../inst/include/darknet/examples/go.c | 1370 +++++++++ .../darknet/examples/instance-segmenter.c | 267 ++ .../inst/include/darknet/examples/lsd.c | 1378 +++++++++ .../darknet/{src => examples}/nightmare.c | 230 +- .../inst/include/darknet/examples/regressor.c | 240 ++ .../include/darknet/{src => examples}/rnn.c | 204 +- .../darknet/{src => 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115 +- .../inst/include/darknet/src/utils.h | 33 +- .../inst/include/darknet/src/yolo_layer.c | 374 +++ .../inst/include/darknet/src/yolo_layer.h | 19 + 193 files changed, 28684 insertions(+), 8626 deletions(-) create mode 100644 image.darknet/inst/include/darknet/LICENSE.fuck create mode 100644 image.darknet/inst/include/darknet/LICENSE.gen create mode 100644 image.darknet/inst/include/darknet/LICENSE.gpl create mode 100644 image.darknet/inst/include/darknet/LICENSE.meta create mode 100644 image.darknet/inst/include/darknet/LICENSE.mit create mode 100644 image.darknet/inst/include/darknet/LICENSE.v1 create mode 100644 image.darknet/inst/include/darknet/Makefile.bak create mode 100644 image.darknet/inst/include/darknet/cfg/darknet53.cfg rename image.darknet/inst/include/darknet/cfg/{msr_50.cfg => darknet53_448.cfg} (85%) rename image.darknet/inst/include/darknet/cfg/{yolo-voc.cfg => darknet9000.cfg} (70%) create mode 100644 image.darknet/inst/include/darknet/cfg/densenet201.cfg create 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=> yolov1.cfg} (98%) delete mode 100644 image.darknet/inst/include/darknet/cfg/yolov1/tiny-coco.cfg delete mode 100644 image.darknet/inst/include/darknet/cfg/yolov1/xyolo.test.cfg delete mode 100644 image.darknet/inst/include/darknet/cfg/yolov1/yolo-coco.cfg delete mode 100644 image.darknet/inst/include/darknet/cfg/yolov1/yolo-small.cfg delete mode 100644 image.darknet/inst/include/darknet/cfg/yolov1/yolo.train.cfg rename image.darknet/inst/include/darknet/cfg/{tiny-yolo-voc.cfg => yolov2-tiny-voc.cfg} (93%) rename image.darknet/inst/include/darknet/cfg/{tiny-yolo.cfg => yolov2-tiny.cfg} (82%) rename image.darknet/inst/include/darknet/cfg/{yolo.cfg => yolov2-voc.cfg} (87%) create mode 100644 image.darknet/inst/include/darknet/cfg/yolov3-openimages.cfg create mode 100644 image.darknet/inst/include/darknet/cfg/yolov3-spp.cfg create mode 100644 image.darknet/inst/include/darknet/cfg/yolov3-tiny.cfg create mode 100644 image.darknet/inst/include/darknet/cfg/yolov3-voc.cfg create mode 100644 image.darknet/inst/include/darknet/data/kite.jpg create mode 100644 image.darknet/inst/include/darknet/data/openimages.names rename image.darknet/inst/include/darknet/{src => examples}/art.c (64%) create mode 100644 image.darknet/inst/include/darknet/examples/attention.c rename image.darknet/inst/include/darknet/{src => examples}/captcha.c (88%) rename image.darknet/inst/include/darknet/{src => examples}/cifar.c (77%) rename image.darknet/inst/include/darknet/{src => examples}/classifier.c (72%) rename image.darknet/inst/include/darknet/{src => examples}/coco.c (72%) rename image.darknet/inst/include/darknet/{src => examples}/darknet.c (62%) create mode 100644 image.darknet/inst/include/darknet/examples/detector-scipy-opencv.py create mode 100644 image.darknet/inst/include/darknet/examples/detector.c create mode 100644 image.darknet/inst/include/darknet/examples/detector.py rename image.darknet/inst/include/darknet/{src => examples}/dice.c (95%) create mode 100644 image.darknet/inst/include/darknet/examples/go.c create mode 100644 image.darknet/inst/include/darknet/examples/instance-segmenter.c create mode 100644 image.darknet/inst/include/darknet/examples/lsd.c rename image.darknet/inst/include/darknet/{src => examples}/nightmare.c (58%) create mode 100644 image.darknet/inst/include/darknet/examples/regressor.c rename image.darknet/inst/include/darknet/{src => examples}/rnn.c (72%) rename image.darknet/inst/include/darknet/{src => examples}/rnn_vid.c (96%) create mode 100644 image.darknet/inst/include/darknet/examples/segmenter.c rename image.darknet/inst/include/darknet/{src => examples}/super.c (78%) rename image.darknet/inst/include/darknet/{src => examples}/swag.c (92%) rename image.darknet/inst/include/darknet/{src => examples}/tag.c (72%) rename image.darknet/inst/include/darknet/{src => examples}/voxel.c (96%) rename image.darknet/inst/include/darknet/{src => examples}/writing.c (90%) rename image.darknet/inst/include/darknet/{src => examples}/yolo.c (68%) create mode 100644 image.darknet/inst/include/darknet/include/darknet.h create mode 100644 image.darknet/inst/include/darknet/python/darknet.py create mode 100644 image.darknet/inst/include/darknet/python/proverbot.py create mode 100644 image.darknet/inst/include/darknet/scripts/get_coco_dataset.sh delete mode 100644 image.darknet/inst/include/darknet/src/detector.c delete mode 100644 image.darknet/inst/include/darknet/src/go.c create mode 100644 image.darknet/inst/include/darknet/src/image_opencv.cpp create mode 100644 image.darknet/inst/include/darknet/src/iseg_layer.c create mode 100644 image.darknet/inst/include/darknet/src/iseg_layer.h create mode 100644 image.darknet/inst/include/darknet/src/l2norm_layer.c create mode 100644 image.darknet/inst/include/darknet/src/l2norm_layer.h create mode 100644 image.darknet/inst/include/darknet/src/logistic_layer.c create mode 100644 image.darknet/inst/include/darknet/src/logistic_layer.h create mode 100644 image.darknet/inst/include/darknet/src/lstm_layer.c create mode 100644 image.darknet/inst/include/darknet/src/lstm_layer.h delete mode 100644 image.darknet/inst/include/darknet/src/network_kernels.cu create mode 100644 image.darknet/inst/include/darknet/src/upsample_layer.c create mode 100644 image.darknet/inst/include/darknet/src/upsample_layer.h create mode 100644 image.darknet/inst/include/darknet/src/yolo_layer.c create mode 100644 image.darknet/inst/include/darknet/src/yolo_layer.h diff --git a/image.darknet/inst/include/darknet/LICENSE.fuck b/image.darknet/inst/include/darknet/LICENSE.fuck new file mode 100644 index 0000000..8b1a9d8 --- /dev/null +++ b/image.darknet/inst/include/darknet/LICENSE.fuck @@ -0,0 +1,13 @@ + DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE + Version 2, December 2004 + +Copyright (C) 2004 Sam Hocevar + +Everyone is permitted to copy and distribute verbatim or modified +copies of this license document, and changing it is allowed as long +as the name is 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + {one line to give the program's name and a brief idea of what it does.} + Copyright (C) {year} {name of author} + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + {project} Copyright (C) {year} {fullname} + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/image.darknet/inst/include/darknet/LICENSE.meta b/image.darknet/inst/include/darknet/LICENSE.meta new file mode 100644 index 0000000..6728bd2 --- /dev/null +++ b/image.darknet/inst/include/darknet/LICENSE.meta @@ -0,0 +1,8 @@ + META-LICENSE + Version 1, June 21 2017 + +Any and all licenses may be applied to the software either individually +or in concert. Any issues, ambiguities, paradoxes, or metaphysical quandries +arising from this combination should be discussed with a local faith leader, +hermit, or guru. The Oxford comma shall be used. + diff --git a/image.darknet/inst/include/darknet/LICENSE.mit b/image.darknet/inst/include/darknet/LICENSE.mit new file mode 100644 index 0000000..5bd806c --- /dev/null +++ b/image.darknet/inst/include/darknet/LICENSE.mit @@ -0,0 +1,22 @@ +MIT License + +Copyright (c) 2017 Joseph Redmon + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + diff --git a/image.darknet/inst/include/darknet/LICENSE.v1 b/image.darknet/inst/include/darknet/LICENSE.v1 new file mode 100644 index 0000000..5b8709a --- /dev/null +++ b/image.darknet/inst/include/darknet/LICENSE.v1 @@ -0,0 +1,13 @@ + YOLO LICENSE + Version 1, July 10 2015 + +THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER +SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN +TROUBLE HERE ARE SOME OTHER BUZZWORDS COMMONLY IN THESE THINGS WARRANTIES +LIABILITY CONTRACT TORT LIABLE CLAIMS RESTRICTION MERCHANTABILITY SUBJECT TO +THE FOLLOWING CONDITIONS: + +1. #yolo +2. #swag +3. #blazeit + diff --git a/image.darknet/inst/include/darknet/Makefile b/image.darknet/inst/include/darknet/Makefile index 3d3d5e4..63e15e6 100644 --- a/image.darknet/inst/include/darknet/Makefile +++ b/image.darknet/inst/include/darknet/Makefile @@ -1,27 +1,37 @@ GPU=0 CUDNN=0 OPENCV=0 +OPENMP=0 DEBUG=0 -ARCH= -gencode arch=compute_20,code=[sm_20,sm_21] \ - -gencode arch=compute_30,code=sm_30 \ +ARCH= -gencode arch=compute_30,code=sm_30 \ -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=[sm_50,compute_50] \ -gencode arch=compute_52,code=[sm_52,compute_52] +# -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated? # This is what I use, uncomment if you know your arch and want to specify -# ARCH= -gencode arch=compute_52,code=compute_52 +# ARCH= -gencode arch=compute_52,code=compute_52 -VPATH=./src/ +VPATH=./src/:./examples +SLIB=libdarknet.so +ALIB=libdarknet.a EXEC=darknet OBJDIR=./obj/ CC=gcc +CPP=g++ NVCC=nvcc +AR=ar +ARFLAGS=rcs OPTS=-Ofast LDFLAGS= -lm -pthread -COMMON= -CFLAGS=-Wall -Wfatal-errors +COMMON= -Iinclude/ -Isrc/ +CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC + +ifeq ($(OPENMP), 1) +CFLAGS+= -fopenmp +endif ifeq ($(DEBUG), 1) OPTS=-O0 -g @@ -32,7 +42,7 @@ CFLAGS+=$(OPTS) ifeq ($(OPENCV), 1) COMMON+= -DOPENCV CFLAGS+= -DOPENCV -LDFLAGS+= `pkg-config --libs opencv` +LDFLAGS+= `pkg-config --libs opencv` -lstdc++ COMMON+= `pkg-config --cflags opencv` endif @@ -48,19 +58,32 @@ CFLAGS+= -DCUDNN LDFLAGS+= -lcudnn endif -OBJ=gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o tree.o +OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o lstm_layer.o l2norm_layer.o yolo_layer.o iseg_layer.o image_opencv.o +EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o instance-segmenter.o darknet.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ -OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o +OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o avgpool_layer_kernels.o endif +EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA)) OBJS = $(addprefix $(OBJDIR), $(OBJ)) -DEPS = $(wildcard src/*.h) Makefile +DEPS = $(wildcard src/*.h) Makefile include/darknet.h + +all: obj backup results $(SLIB) $(ALIB) $(EXEC) +#all: obj results $(SLIB) $(ALIB) $(EXEC) + + +$(EXEC): $(EXECOBJ) $(ALIB) + $(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB) + +$(ALIB): $(OBJS) + $(AR) $(ARFLAGS) $@ $^ -all: obj backup results $(EXEC) +$(SLIB): $(OBJS) + $(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS) -$(EXEC): $(OBJS) - $(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) +$(OBJDIR)%.o: %.cpp $(DEPS) + $(CPP) $(COMMON) $(CFLAGS) -c $< -o $@ $(OBJDIR)%.o: %.c $(DEPS) $(CC) $(COMMON) $(CFLAGS) -c $< -o $@ @@ -78,5 +101,5 @@ results: .PHONY: clean clean: - rm -rf $(OBJS) $(EXEC) + rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/* diff --git a/image.darknet/inst/include/darknet/Makefile.bak b/image.darknet/inst/include/darknet/Makefile.bak new file mode 100644 index 0000000..3d3d5e4 --- /dev/null +++ b/image.darknet/inst/include/darknet/Makefile.bak @@ -0,0 +1,82 @@ +GPU=0 +CUDNN=0 +OPENCV=0 +DEBUG=0 + +ARCH= -gencode arch=compute_20,code=[sm_20,sm_21] \ + -gencode arch=compute_30,code=sm_30 \ + -gencode arch=compute_35,code=sm_35 \ + -gencode arch=compute_50,code=[sm_50,compute_50] \ + -gencode arch=compute_52,code=[sm_52,compute_52] + +# This is what I use, uncomment if you know your arch and want to specify +# ARCH= -gencode arch=compute_52,code=compute_52 + +VPATH=./src/ +EXEC=darknet +OBJDIR=./obj/ + +CC=gcc +NVCC=nvcc +OPTS=-Ofast +LDFLAGS= -lm -pthread +COMMON= +CFLAGS=-Wall -Wfatal-errors + +ifeq ($(DEBUG), 1) +OPTS=-O0 -g +endif + +CFLAGS+=$(OPTS) + +ifeq ($(OPENCV), 1) +COMMON+= -DOPENCV +CFLAGS+= -DOPENCV +LDFLAGS+= `pkg-config --libs opencv` +COMMON+= `pkg-config --cflags opencv` +endif + +ifeq ($(GPU), 1) +COMMON+= -DGPU -I/usr/local/cuda/include/ +CFLAGS+= -DGPU +LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand +endif + +ifeq ($(CUDNN), 1) +COMMON+= -DCUDNN +CFLAGS+= -DCUDNN +LDFLAGS+= -lcudnn +endif + +OBJ=gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o tree.o +ifeq ($(GPU), 1) +LDFLAGS+= -lstdc++ +OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o +endif + +OBJS = $(addprefix $(OBJDIR), $(OBJ)) +DEPS = $(wildcard src/*.h) Makefile + +all: obj backup results $(EXEC) + +$(EXEC): $(OBJS) + $(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) + +$(OBJDIR)%.o: %.c $(DEPS) + $(CC) $(COMMON) $(CFLAGS) -c $< -o $@ + +$(OBJDIR)%.o: %.cu $(DEPS) + $(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@ + +obj: + mkdir -p obj +backup: + mkdir -p backup +results: + mkdir -p results + +.PHONY: clean + +clean: + rm -rf $(OBJS) $(EXEC) + diff --git a/image.darknet/inst/include/darknet/README.md b/image.darknet/inst/include/darknet/README.md index d255dab..09fdeee 100644 --- a/image.darknet/inst/include/darknet/README.md +++ b/image.darknet/inst/include/darknet/README.md @@ -1,6 +1,6 @@ ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) -#Darknet# +# Darknet # Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. For more information see the [Darknet project website](http://pjreddie.com/darknet). diff --git a/image.darknet/inst/include/darknet/cfg/alexnet.cfg b/image.darknet/inst/include/darknet/cfg/alexnet.cfg index 7e5a9b2..e2ed4bb 100644 --- a/image.darknet/inst/include/darknet/cfg/alexnet.cfg +++ b/image.darknet/inst/include/darknet/cfg/alexnet.cfg @@ -1,5 +1,9 @@ [net] -batch=128 +# Training +# batch=128 +# subdivisions=1 +# Testing +batch=1 subdivisions=1 height=227 width=227 @@ -90,6 +94,3 @@ activation=linear [softmax] groups=1 -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/cifar.cfg b/image.darknet/inst/include/darknet/cfg/cifar.cfg index f2c801a..b2f69f5 100644 --- a/image.darknet/inst/include/darknet/cfg/cifar.cfg +++ b/image.darknet/inst/include/darknet/cfg/cifar.cfg @@ -1,25 +1,23 @@ [net] batch=128 subdivisions=1 -height=32 -width=32 +height=28 +width=28 channels=3 -momentum=0.9 -decay=0.0005 +max_crop=32 +min_crop=32 + +hue=.1 +saturation=.75 +exposure=.75 learning_rate=0.4 policy=poly power=4 -max_batches = 50000 +max_batches = 5000 +momentum=0.9 +decay=0.0005 -[crop] -crop_width=28 -crop_height=28 -flip=1 -angle=0 -saturation = 1 -exposure = 1 -noadjust=1 [convolutional] batch_normalize=1 @@ -121,6 +119,3 @@ activation=leaky [softmax] groups=1 - -[cost] - diff --git a/image.darknet/inst/include/darknet/cfg/cifar.test.cfg b/image.darknet/inst/include/darknet/cfg/cifar.test.cfg index d3afcdd..18b6c54 100644 --- a/image.darknet/inst/include/darknet/cfg/cifar.test.cfg +++ b/image.darknet/inst/include/darknet/cfg/cifar.test.cfg @@ -115,5 +115,3 @@ activation=leaky groups=1 temperature=3 -[cost] - diff --git a/image.darknet/inst/include/darknet/cfg/coco.data b/image.darknet/inst/include/darknet/cfg/coco.data index 610151d..3003841 100644 --- a/image.darknet/inst/include/darknet/cfg/coco.data +++ b/image.darknet/inst/include/darknet/cfg/coco.data @@ -1,7 +1,7 @@ classes= 80 train = /home/pjreddie/data/coco/trainvalno5k.txt -#valid = coco_testdev -valid = data/coco_val_5k.list +valid = coco_testdev +#valid = data/coco_val_5k.list names = data/coco.names backup = /home/pjreddie/backup/ eval=coco diff --git a/image.darknet/inst/include/darknet/cfg/darknet.cfg b/image.darknet/inst/include/darknet/cfg/darknet.cfg index 60b939a..375107f 100644 --- a/image.darknet/inst/include/darknet/cfg/darknet.cfg +++ b/image.darknet/inst/include/darknet/cfg/darknet.cfg @@ -1,17 +1,30 @@ [net] -batch=128 +# Training +# batch=128 +# subdivisions=1 +# Testing +batch=1 subdivisions=1 -height=224 -width=224 +height=256 +width=256 +min_crop=128 +max_crop=448 channels=3 momentum=0.9 decay=0.0005 -max_crop=320 +burn_in=1000 learning_rate=0.1 policy=poly power=4 -max_batches=1600000 +max_batches=800000 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + [convolutional] batch_normalize=1 @@ -84,7 +97,6 @@ activation=leaky [maxpool] size=2 stride=2 -padding=1 [convolutional] batch_normalize=1 @@ -94,18 +106,15 @@ stride=1 pad=1 activation=leaky +[avgpool] + [convolutional] filters=1000 size=1 stride=1 pad=1 -activation=leaky - -[avgpool] +activation=linear [softmax] groups=1 -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/darknet19.cfg b/image.darknet/inst/include/darknet/cfg/darknet19.cfg index bf73fb7..28ac966 100644 --- a/image.darknet/inst/include/darknet/cfg/darknet19.cfg +++ b/image.darknet/inst/include/darknet/cfg/darknet19.cfg @@ -1,17 +1,31 @@ [net] -batch=128 -subdivisions=1 -height=224 -width=224 +# Training +#batch=128 +#subdivisions=2 + +# Testing + batch=1 + subdivisions=1 + +height=256 +width=256 +min_crop=128 +max_crop=448 channels=3 momentum=0.9 decay=0.0005 -max_crop=448 +burn_in=1000 learning_rate=0.1 policy=poly power=4 -max_batches=1600000 +max_batches=800000 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 [convolutional] batch_normalize=1 @@ -189,6 +203,3 @@ activation=linear [softmax] groups=1 -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/darknet19_448.cfg b/image.darknet/inst/include/darknet/cfg/darknet19_448.cfg index 133c688..c6df730 100644 --- a/image.darknet/inst/include/darknet/cfg/darknet19_448.cfg +++ b/image.darknet/inst/include/darknet/cfg/darknet19_448.cfg @@ -195,6 +195,3 @@ activation=linear [softmax] groups=1 -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/darknet53.cfg b/image.darknet/inst/include/darknet/cfg/darknet53.cfg new file mode 100644 index 0000000..7b6d576 --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/darknet53.cfg @@ -0,0 +1,566 @@ +[net] +# Training +# batch=128 +# subdivisions=4 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + diff --git a/image.darknet/inst/include/darknet/cfg/msr_50.cfg b/image.darknet/inst/include/darknet/cfg/darknet53_448.cfg similarity index 85% rename from image.darknet/inst/include/darknet/cfg/msr_50.cfg rename to image.darknet/inst/include/darknet/cfg/darknet53_448.cfg index 2edd21c..dedab1b 100644 --- a/image.darknet/inst/include/darknet/cfg/msr_50.cfg +++ b/image.darknet/inst/include/darknet/cfg/darknet53_448.cfg @@ -1,48 +1,47 @@ [net] -batch=128 -subdivisions=8 -height=256 -width=256 +# Training - start training with darknet53.weights +# batch=128 +# subdivisions=8 + +# Testing +batch=1 +subdivisions=1 + +height=448 +width=448 channels=3 -momentum=0.9 -decay=0.0001 +min_crop=448 +max_crop=512 -learning_rate=0.05 +learning_rate=0.001 policy=poly power=4 -max_batches=500000 - +max_batches=100000 +momentum=0.9 +decay=0.0005 -[crop] -crop_height=224 -crop_width=224 -flip=1 -saturation=1 -exposure=1 -angle=0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky -##### Conv 1 ##### +# Downsample [convolutional] batch_normalize=1 filters=64 -size=7 +size=3 stride=2 pad=1 activation=leaky -[maxpool] -size=3 -stride=2 - - -##### Conv 2_x ##### - - [convolutional] batch_normalize=1 -filters=64 +filters=32 size=1 stride=1 pad=1 @@ -56,27 +55,18 @@ stride=1 pad=1 activation=leaky -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 +[shortcut] +from=-3 activation=linear -[route] -layers=-4 +# Downsample [convolutional] batch_normalize=1 -size=1 -stride=1 +filters=128 +size=3 +stride=2 pad=1 -activation=linear -filters=256 - -[shortcut] -from = -3 activation=leaky [convolutional] @@ -89,54 +79,65 @@ activation=leaky [convolutional] batch_normalize=1 -filters=64 +filters=128 size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + [convolutional] batch_normalize=1 -filters=256 +filters=64 size=1 stride=1 pad=1 -activation=linear - -[shortcut] -from = -4 activation=leaky [convolutional] batch_normalize=1 -filters=64 -size=1 +filters=128 +size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + +# Downsample + [convolutional] batch_normalize=1 -filters=64 +filters=256 size=3 -stride=1 +stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 -filters=256 +filters=128 size=1 stride=1 pad=1 -activation=linear - -[shortcut] -from = -4 activation=leaky +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky -##### Conv 3_x ##### +[shortcut] +from=-3 +activation=linear [convolutional] batch_normalize=1 @@ -148,34 +149,35 @@ activation=leaky [convolutional] batch_normalize=1 -filters=128 +filters=256 size=3 -stride=2 +stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + [convolutional] batch_normalize=1 -filters=512 +filters=128 size=1 stride=1 pad=1 -activation=linear - -[route] -layers=-4 +activation=leaky [convolutional] batch_normalize=1 -size=1 -stride=2 +filters=256 +size=3 +stride=1 pad=1 -activation=linear -filters=512 +activation=leaky [shortcut] -from = -3 -activation=leaky +from=-3 +activation=linear [convolutional] batch_normalize=1 @@ -187,51 +189,56 @@ activation=leaky [convolutional] batch_normalize=1 -filters=128 +filters=256 size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + + [convolutional] batch_normalize=1 -filters=512 +filters=128 size=1 stride=1 pad=1 -activation=linear - -[shortcut] -from = -4 activation=leaky [convolutional] batch_normalize=1 -filters=128 -size=1 +filters=256 +size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + [convolutional] batch_normalize=1 filters=128 -size=3 +size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 -filters=512 -size=1 +filters=256 +size=3 stride=1 pad=1 -activation=linear +activation=leaky [shortcut] -from = -4 -activation=leaky +from=-3 +activation=linear [convolutional] batch_normalize=1 @@ -243,38 +250,41 @@ activation=leaky [convolutional] batch_normalize=1 -filters=128 +filters=256 size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + [convolutional] batch_normalize=1 -filters=512 +filters=128 size=1 stride=1 pad=1 -activation=linear - -[shortcut] -from = -4 activation=leaky - -##### Conv 4_x ##### - [convolutional] batch_normalize=1 filters=256 -size=1 +size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + +# Downsample + [convolutional] batch_normalize=1 -filters=256 +filters=512 size=3 stride=2 pad=1 @@ -282,26 +292,23 @@ activation=leaky [convolutional] batch_normalize=1 -filters=1024 +filters=256 size=1 stride=1 pad=1 -activation=linear - -[route] -layers=-4 +activation=leaky [convolutional] batch_normalize=1 -size=1 -stride=2 +filters=512 +size=3 +stride=1 pad=1 -activation=linear -filters=1024 +activation=leaky [shortcut] -from = -3 -activation=leaky +from=-3 +activation=linear [convolutional] @@ -314,23 +321,16 @@ activation=leaky [convolutional] batch_normalize=1 -filters=256 +filters=512 size=3 stride=1 pad=1 activation=leaky -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 +[shortcut] +from=-3 activation=linear -[shortcut] -from = -4 -activation=leaky [convolutional] batch_normalize=1 @@ -342,23 +342,16 @@ activation=leaky [convolutional] batch_normalize=1 -filters=256 +filters=512 size=3 stride=1 pad=1 activation=leaky -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 +[shortcut] +from=-3 activation=linear -[shortcut] -from = -4 -activation=leaky [convolutional] batch_normalize=1 @@ -370,51 +363,57 @@ activation=leaky [convolutional] batch_normalize=1 -filters=256 +filters=512 size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + [convolutional] batch_normalize=1 -filters=1024 +filters=256 size=1 stride=1 pad=1 -activation=linear - -[shortcut] -from = -4 activation=leaky [convolutional] batch_normalize=1 -filters=256 -size=1 +filters=512 +size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + + [convolutional] batch_normalize=1 filters=256 -size=3 +size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 -filters=1024 -size=1 +filters=512 +size=3 stride=1 pad=1 -activation=linear +activation=leaky [shortcut] -from = -4 -activation=leaky +from=-3 +activation=linear + [convolutional] batch_normalize=1 @@ -426,38 +425,41 @@ activation=leaky [convolutional] batch_normalize=1 -filters=256 +filters=512 size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + [convolutional] batch_normalize=1 -filters=1024 +filters=256 size=1 stride=1 pad=1 -activation=linear - -[shortcut] -from = -4 activation=leaky - -##### Conv 5_x ##### - [convolutional] batch_normalize=1 filters=512 -size=1 +size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + +# Downsample + [convolutional] batch_normalize=1 -filters=512 +filters=1024 size=3 stride=2 pad=1 @@ -465,28 +467,23 @@ activation=leaky [convolutional] batch_normalize=1 -filters=2048 +filters=512 size=1 stride=1 pad=1 -activation=linear - - -[route] -layers=-4 +activation=leaky [convolutional] batch_normalize=1 -size=1 -stride=2 +filters=1024 +size=3 +stride=1 pad=1 -activation=linear -filters=2048 - -[shortcut] -from = -3 activation=leaky +[shortcut] +from=-3 +activation=linear [convolutional] batch_normalize=1 @@ -498,61 +495,65 @@ activation=leaky [convolutional] batch_normalize=1 -filters=512 +filters=1024 size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + [convolutional] batch_normalize=1 -filters=2048 +filters=512 size=1 stride=1 pad=1 -activation=linear - -[shortcut] -from = -4 activation=leaky [convolutional] batch_normalize=1 -filters=512 -size=1 +filters=1024 +size=3 stride=1 pad=1 activation=leaky +[shortcut] +from=-3 +activation=linear + [convolutional] batch_normalize=1 filters=512 -size=3 +size=1 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 -filters=2048 -size=1 +filters=1024 +size=3 stride=1 pad=1 -activation=linear +activation=leaky [shortcut] -from = -4 -activation=leaky +from=-3 +activation=linear [avgpool] -[connected] -output=1000 -activation=leaky +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear [softmax] groups=1 -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/yolo-voc.cfg b/image.darknet/inst/include/darknet/cfg/darknet9000.cfg similarity index 70% rename from image.darknet/inst/include/darknet/cfg/yolo-voc.cfg rename to image.darknet/inst/include/darknet/cfg/darknet9000.cfg index ceb3f2a..9dd2dfb 100644 --- a/image.darknet/inst/include/darknet/cfg/yolo-voc.cfg +++ b/image.darknet/inst/include/darknet/cfg/darknet9000.cfg @@ -1,21 +1,27 @@ [net] -batch=64 -subdivisions=8 -height=416 -width=416 +# Training +# batch=128 +# subdivisions=4 +# Testing +batch = 1 +subdivisions = 1 +height=448 +width=448 +max_crop=512 channels=3 momentum=0.9 decay=0.0005 -angle=0 -saturation = 1.5 -exposure = 1.5 -hue=.1 -learning_rate=0.0001 -max_batches = 45000 -policy=steps -steps=100,25000,35000 -scales=10,.1,.1 +learning_rate=0.001 +policy=poly +power=4 +max_batches=100000 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 [convolutional] batch_normalize=1 @@ -181,64 +187,19 @@ stride=1 pad=1 activation=leaky - -####### - [convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 +filters=9418 +size=1 stride=1 pad=1 -filters=1024 -activation=leaky +activation=linear -[route] -layers=-9 +[avgpool] -[reorg] -stride=2 +[softmax] +groups=1 +tree=data/9k.tree -[route] -layers=-1,-3 - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=125 -activation=linear +[cost] +type=masked -[region] -anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 -bias_match=1 -classes=20 -coords=4 -num=5 -softmax=1 -jitter=.2 -rescore=1 - -object_scale=5 -noobject_scale=1 -class_scale=1 -coord_scale=1 - -absolute=1 -thresh = .6 -random=0 diff --git a/image.darknet/inst/include/darknet/cfg/densenet201.cfg b/image.darknet/inst/include/darknet/cfg/densenet201.cfg new file mode 100644 index 0000000..65b4aec --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/densenet201.cfg @@ -0,0 +1,1951 @@ +[net] +# Training +# batch=128 +# subdivisions=4 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +max_crop=448 +channels=3 +momentum=0.9 +decay=0.0005 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=1600000 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1,-3 + + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[avgpool] + +[softmax] +groups=1 + diff --git a/image.darknet/inst/include/darknet/cfg/extraction.cfg b/image.darknet/inst/include/darknet/cfg/extraction.cfg index 94e1067..66cb15f 100644 --- a/image.darknet/inst/include/darknet/cfg/extraction.cfg +++ b/image.darknet/inst/include/darknet/cfg/extraction.cfg @@ -1,6 +1,12 @@ [net] -batch=128 +# Training +# batch=128 +# subdivisions=4 + +# Testing +batch=1 subdivisions=1 + height=224 width=224 max_crop=320 @@ -201,6 +207,3 @@ activation=leaky [softmax] groups=1 -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/extraction22k.cfg b/image.darknet/inst/include/darknet/cfg/extraction22k.cfg index 4cec6da..b5f5409 100644 --- a/image.darknet/inst/include/darknet/cfg/extraction22k.cfg +++ b/image.darknet/inst/include/darknet/cfg/extraction22k.cfg @@ -204,6 +204,3 @@ activation=leaky [softmax] groups=1 -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/go.cfg b/image.darknet/inst/include/darknet/cfg/go.cfg new file mode 100644 index 0000000..c730092 --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/go.cfg @@ -0,0 +1,132 @@ +[net] +batch=512 +subdivisions=1 +height=19 +width=19 +channels=1 +momentum=0.9 +decay=0.0005 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=10000000 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=256 +size=3 +stride=1 +pad=1 +activation=relu +batch_normalize=1 + +[convolutional] +filters=1 +size=1 +stride=1 +pad=1 +activation=linear + +[reorg] +extra=1 +stride=1 + +[softmax] + diff --git a/image.darknet/inst/include/darknet/cfg/go.test.cfg b/image.darknet/inst/include/darknet/cfg/go.test.cfg index 6b92d33..1e4e438 100644 --- a/image.darknet/inst/include/darknet/cfg/go.test.cfg +++ b/image.darknet/inst/include/darknet/cfg/go.test.cfg @@ -7,13 +7,13 @@ channels=1 momentum=0.9 decay=0.0005 -learning_rate=0.1 +learning_rate=0.01 policy=poly power=4 -max_batches=400000 +max_batches=100000 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -21,7 +21,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -29,7 +29,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -37,7 +37,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -45,7 +45,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -53,7 +53,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -61,7 +61,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -69,7 +69,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -77,7 +77,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -85,7 +85,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -93,7 +93,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -101,7 +101,7 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 @@ -109,14 +109,13 @@ activation=relu batch_normalize=1 [convolutional] -filters=192 +filters=256 size=3 stride=1 pad=1 activation=relu batch_normalize=1 - [convolutional] filters=1 size=1 @@ -124,8 +123,10 @@ stride=1 pad=1 activation=linear +[reorg] +extra=1 +stride=1 + [softmax] -[cost] -type=sse diff --git a/image.darknet/inst/include/darknet/cfg/gru.cfg b/image.darknet/inst/include/darknet/cfg/gru.cfg index f9a0699..6064221 100644 --- a/image.darknet/inst/include/darknet/cfg/gru.cfg +++ b/image.darknet/inst/include/darknet/cfg/gru.cfg @@ -1,27 +1,25 @@ [net] -subdivisions=1 inputs=256 -batch = 1 momentum=0.9 -decay=0.001 +decay=0.0 +subdivisions=1 +batch = 1 time_steps=1 -learning_rate=0.5 +learning_rate=.002 +adam=1 -policy=poly +policy=constant power=4 -max_batches=2000 +max_batches=1000000 [gru] -batch_normalize=1 -output = 1024 +output = 256 [gru] -batch_normalize=1 -output = 1024 +output = 256 [gru] -batch_normalize=1 -output = 1024 +output = 256 [connected] output=256 @@ -29,6 +27,3 @@ activation=linear [softmax] -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/imagenet22k.dataset b/image.darknet/inst/include/darknet/cfg/imagenet22k.dataset index 920785d..e25ef00 100644 --- a/image.darknet/inst/include/darknet/cfg/imagenet22k.dataset +++ b/image.darknet/inst/include/darknet/cfg/imagenet22k.dataset @@ -1,6 +1,7 @@ classes=21842 train = /data/imagenet/imagenet22k.train.list valid = /data/imagenet/imagenet22k.valid.list +#valid = /data/imagenet/imagenet1k.valid.list backup = /home/pjreddie/backup/ labels = data/imagenet.labels.list names = data/imagenet.shortnames.list diff --git a/image.darknet/inst/include/darknet/cfg/imagenet9k.hierarchy.dataset b/image.darknet/inst/include/darknet/cfg/imagenet9k.hierarchy.dataset new file mode 100644 index 0000000..41fb71b --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/imagenet9k.hierarchy.dataset @@ -0,0 +1,9 @@ +classes=9418 +train = data/9k.train.list +valid = /data/imagenet/imagenet1k.valid.list +leaves = data/imagenet1k.labels +backup = /home/pjreddie/backup/ +labels = data/9k.labels +names = data/9k.names +top=5 + diff --git a/image.darknet/inst/include/darknet/cfg/openimages.data b/image.darknet/inst/include/darknet/cfg/openimages.data new file mode 100644 index 0000000..fa80e5a --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/openimages.data @@ -0,0 +1,8 @@ +classes= 601 +train = /home/pjreddie/data/openimsv4/openimages.train.list +#valid = coco_testdev +valid = data/coco_val_5k.list +names = data/openimages.names +backup = /home/pjreddie/backup/ +eval=coco + diff --git a/image.darknet/inst/include/darknet/cfg/resnet101.cfg b/image.darknet/inst/include/darknet/cfg/resnet101.cfg new file mode 100644 index 0000000..de45882 --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/resnet101.cfg @@ -0,0 +1,990 @@ +[net] +# Training +# batch=128 +# subdivisions=2 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +# Conv 4 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +#Conv 5 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + + + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[avgpool] + +[softmax] +groups=1 + +[cost] +type=sse + diff --git a/image.darknet/inst/include/darknet/cfg/msr_152.cfg b/image.darknet/inst/include/darknet/cfg/resnet152.cfg similarity index 92% rename from image.darknet/inst/include/darknet/cfg/msr_152.cfg rename to image.darknet/inst/include/darknet/cfg/resnet152.cfg index b19c999..e8e3297 100644 --- a/image.darknet/inst/include/darknet/cfg/msr_152.cfg +++ b/image.darknet/inst/include/darknet/cfg/resnet152.cfg @@ -1,26 +1,30 @@ [net] -batch=128 -subdivisions=8 +# Training +# batch=128 +# subdivisions=8 + +# Testing +batch=1 +subdivisions=1 + height=256 width=256 +max_crop=448 channels=3 momentum=0.9 -decay=0.0001 +decay=0.0005 +burn_in=1000 learning_rate=0.1 policy=poly power=4 -max_batches=500000 - -[crop] -crop_height=224 -crop_width=224 -flip=1 -saturation=1 -exposure=1 -angle=0 +max_batches=1600000 -##### Conv 1 ##### +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 [convolutional] batch_normalize=1 @@ -31,13 +35,9 @@ pad=1 activation=leaky [maxpool] -size=3 +size=2 stride=2 - -##### Conv 2_x ##### - - [convolutional] batch_normalize=1 filters=64 @@ -62,19 +62,8 @@ stride=1 pad=1 activation=linear -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=1 -pad=1 -activation=linear -filters=256 - [shortcut] -from = -3 +from=-4 activation=leaky [convolutional] @@ -102,8 +91,7 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky [convolutional] @@ -131,13 +119,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - -##### Conv 3_x ##### - [convolutional] batch_normalize=1 filters=128 @@ -162,23 +146,10 @@ stride=1 pad=1 activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=512 - [shortcut] -from = -3 +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=128 @@ -204,11 +175,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=128 @@ -234,11 +203,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=128 @@ -264,11 +231,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=128 @@ -294,11 +259,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=128 @@ -324,11 +287,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=128 @@ -354,11 +315,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=128 @@ -384,14 +343,11 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - -##### Conv 4_x ##### - +# Conv 4 [convolutional] batch_normalize=1 filters=256 @@ -416,23 +372,10 @@ stride=1 pad=1 activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=1024 - [shortcut] -from = -3 +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -458,11 +401,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -488,11 +429,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -518,11 +457,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -548,11 +485,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -578,11 +513,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -608,11 +541,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -638,11 +569,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -668,11 +597,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -698,11 +625,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -728,11 +653,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -758,11 +681,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -788,11 +709,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -818,11 +737,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -848,11 +765,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -878,11 +793,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -908,11 +821,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -938,11 +849,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -968,11 +877,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -998,11 +905,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1028,11 +933,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1058,11 +961,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1088,11 +989,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1118,11 +1017,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1148,11 +1045,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1178,11 +1073,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1208,11 +1101,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1238,11 +1129,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1268,11 +1157,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1298,11 +1185,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1328,11 +1213,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1358,11 +1241,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1388,11 +1269,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1418,11 +1297,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1448,11 +1325,9 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=256 @@ -1478,13 +1353,10 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky - -##### Conv 5_x ##### - +#Conv 5 [convolutional] batch_normalize=1 filters=512 @@ -1509,23 +1381,10 @@ stride=1 pad=1 activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=2048 - [shortcut] -from = -3 +from=-4 activation=leaky - [convolutional] batch_normalize=1 filters=512 @@ -1551,8 +1410,7 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky [convolutional] @@ -1580,19 +1438,23 @@ pad=1 activation=linear [shortcut] -from = -4 - +from=-4 activation=leaky -[avgpool] -[connected] -output=1000 -activation=leaky + + + + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[avgpool] [softmax] groups=1 -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/yolov1/yolo2.cfg b/image.darknet/inst/include/darknet/cfg/resnet18.cfg similarity index 58% rename from image.darknet/inst/include/darknet/cfg/yolov1/yolo2.cfg rename to image.darknet/inst/include/darknet/cfg/resnet18.cfg index b46a0d6..275f4bd 100644 --- a/image.darknet/inst/include/darknet/cfg/yolov1/yolo2.cfg +++ b/image.darknet/inst/include/darknet/cfg/resnet18.cfg @@ -1,17 +1,32 @@ [net] +# Training +# batch=128 +# subdivisions=1 + +# Testing batch=1 subdivisions=1 -height=448 -width=448 + +height=256 +width=256 channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 momentum=0.9 decay=0.0005 -learning_rate=0.0005 -policy=steps -steps=200,400,600,20000,30000 -scales=2.5,2,2,.1,.1 -max_batches = 40000 +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + [convolutional] batch_normalize=1 @@ -25,29 +40,32 @@ activation=leaky size=2 stride=2 + +# Residual Block [convolutional] batch_normalize=1 -filters=192 +filters=64 size=3 stride=1 pad=1 activation=leaky -[maxpool] -size=2 -stride=2 - [convolutional] batch_normalize=1 -filters=128 -size=1 +filters=64 +size=3 stride=1 pad=1 +activation=linear + +[shortcut] activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 -filters=256 +filters=64 size=3 stride=1 pad=1 @@ -55,51 +73,41 @@ activation=leaky [convolutional] batch_normalize=1 -filters=256 -size=1 +filters=64 +size=3 stride=1 pad=1 +activation=linear + +[shortcut] activation=leaky +from=-3 +# Strided Residual Block [convolutional] batch_normalize=1 -filters=512 +filters=128 size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 stride=2 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 -filters=512 +filters=128 size=3 stride=1 pad=1 -activation=leaky +activation=linear -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 +[shortcut] activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 -filters=512 +filters=128 size=3 stride=1 pad=1 @@ -107,145 +115,114 @@ activation=leaky [convolutional] batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 +filters=128 size=3 stride=1 pad=1 +activation=linear + +[shortcut] activation=leaky +from=-3 + +# Strided Residual Block [convolutional] batch_normalize=1 filters=256 -size=1 -stride=1 +size=3 +stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 -filters=512 +filters=256 size=3 stride=1 pad=1 +activation=linear + +[shortcut] activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 -filters=512 -size=1 +filters=256 +size=3 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 -filters=1024 +filters=256 size=3 stride=1 pad=1 +activation=linear + +[shortcut] activation=leaky +from=-3 -[maxpool] -size=2 -stride=2 +# Strided Residual Block [convolutional] batch_normalize=1 filters=512 -size=1 -stride=1 +size=3 +stride=2 pad=1 activation=leaky [convolutional] batch_normalize=1 -filters=1024 +filters=512 size=3 stride=1 pad=1 +activation=linear + +[shortcut] activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=512 -size=1 +size=3 stride=1 pad=1 activation=leaky [convolutional] batch_normalize=1 -filters=1024 +filters=512 size=3 stride=1 pad=1 +activation=linear + +[shortcut] activation=leaky +from=-3 -####### -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky -[convolutional] -batch_normalize=1 -size=3 -stride=2 -pad=1 -filters=1024 -activation=leaky -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky +[avgpool] [convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[local] -size=3 +filters=1000 +size=1 stride=1 pad=1 -filters=256 -activation=leaky - -[connected] -output= 1715 activation=linear -[detection] -classes=20 -coords=4 -rescore=1 -side=7 -num=3 -softmax=0 -sqrt=1 -jitter=.2 - -object_scale=1 -noobject_scale=.5 -class_scale=1 -coord_scale=5 +[softmax] +groups=1 diff --git a/image.darknet/inst/include/darknet/cfg/msr_34.cfg b/image.darknet/inst/include/darknet/cfg/resnet34.cfg similarity index 77% rename from image.darknet/inst/include/darknet/cfg/msr_34.cfg rename to image.darknet/inst/include/darknet/cfg/resnet34.cfg index 5ae23cf..9f68f09 100644 --- a/image.darknet/inst/include/darknet/cfg/msr_34.cfg +++ b/image.darknet/inst/include/darknet/cfg/resnet34.cfg @@ -1,24 +1,32 @@ [net] -batch=128 +# Training +# batch=128 +# subdivisions=2 + +# Testing +batch=1 subdivisions=1 + height=256 width=256 channels=3 -momentum=0.9 -decay=0.0005 +min_crop=128 +max_crop=448 +burn_in=1000 learning_rate=0.1 policy=poly power=4 -max_batches=500000 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 -[crop] -crop_height=224 -crop_width=224 -flip=1 -saturation=1 -exposure=1 -angle=0 [convolutional] batch_normalize=1 @@ -29,9 +37,10 @@ pad=1 activation=leaky [maxpool] -size=3 +size=2 stride=2 +# Residual Block [convolutional] batch_normalize=1 filters=64 @@ -46,11 +55,13 @@ filters=64 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=64 @@ -65,11 +76,13 @@ filters=64 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=64 @@ -84,14 +97,13 @@ filters=64 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 - - - +activation=leaky +from=-3 +# Strided Residual Block [convolutional] batch_normalize=1 filters=128 @@ -106,11 +118,13 @@ filters=128 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=128 @@ -125,11 +139,13 @@ filters=128 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=128 @@ -144,11 +160,13 @@ filters=128 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=128 @@ -163,16 +181,13 @@ filters=128 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 - - - - - +activation=leaky +from=-3 +# Strided Residual Block [convolutional] batch_normalize=1 filters=256 @@ -187,11 +202,13 @@ filters=256 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=256 @@ -206,11 +223,13 @@ filters=256 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=256 @@ -225,11 +244,13 @@ filters=256 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=256 @@ -244,11 +265,13 @@ filters=256 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=256 @@ -263,11 +286,13 @@ filters=256 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=256 @@ -282,19 +307,13 @@ filters=256 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 - - - - - - - - +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=512 @@ -309,11 +328,13 @@ filters=512 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=512 @@ -328,11 +349,13 @@ filters=512 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 +# Residual Block [convolutional] batch_normalize=1 filters=512 @@ -347,20 +370,23 @@ filters=512 size=3 stride=1 pad=1 -activation=leaky +activation=linear [shortcut] -from = -3 +activation=leaky +from=-3 + + [avgpool] -[connected] -output=1000 -activation=leaky +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear [softmax] groups=1 -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/resnet50.cfg b/image.darknet/inst/include/darknet/cfg/resnet50.cfg new file mode 100644 index 0000000..d0d7c51 --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/resnet50.cfg @@ -0,0 +1,510 @@ +[net] +# Training +# batch=128 +# subdivisions=4 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +# Conv 4 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +#Conv 5 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + + diff --git a/image.darknet/inst/include/darknet/cfg/resnext101-32x4d.cfg b/image.darknet/inst/include/darknet/cfg/resnext101-32x4d.cfg new file mode 100644 index 0000000..8538ccc --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/resnext101-32x4d.cfg @@ -0,0 +1,1053 @@ +[net] +# Training +# batch=128 +# subdivisions=8 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + diff --git a/image.darknet/inst/include/darknet/cfg/resnext152-32x4d.cfg b/image.darknet/inst/include/darknet/cfg/resnext152-32x4d.cfg new file mode 100644 index 0000000..48279fd --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/resnext152-32x4d.cfg @@ -0,0 +1,1562 @@ +[net] +# Training +# batch=128 +# subdivisions=16 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + diff --git a/image.darknet/inst/include/darknet/cfg/resnext50.cfg b/image.darknet/inst/include/darknet/cfg/resnext50.cfg new file mode 100644 index 0000000..12aebdf --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/resnext50.cfg @@ -0,0 +1,523 @@ +[net] +# Training +# batch=128 +# subdivisions=4 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +# Conv 4 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +#Conv 5 +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + + diff --git a/image.darknet/inst/include/darknet/cfg/rnn.cfg b/image.darknet/inst/include/darknet/cfg/rnn.cfg index 68c032d..61b202f 100644 --- a/image.darknet/inst/include/darknet/cfg/rnn.cfg +++ b/image.darknet/inst/include/darknet/cfg/rnn.cfg @@ -35,6 +35,4 @@ activation=leaky [softmax] -[cost] -type=sse diff --git a/image.darknet/inst/include/darknet/cfg/rnn.train.cfg b/image.darknet/inst/include/darknet/cfg/rnn.train.cfg index 9139757..b974899 100644 --- a/image.darknet/inst/include/darknet/cfg/rnn.train.cfg +++ b/image.darknet/inst/include/darknet/cfg/rnn.train.cfg @@ -35,6 +35,4 @@ activation=leaky [softmax] -[cost] -type=sse diff --git a/image.darknet/inst/include/darknet/cfg/strided.cfg b/image.darknet/inst/include/darknet/cfg/strided.cfg index a52700b..2f74508 100644 --- a/image.darknet/inst/include/darknet/cfg/strided.cfg +++ b/image.darknet/inst/include/darknet/cfg/strided.cfg @@ -180,6 +180,3 @@ activation=ramp [softmax] -[cost] -type=sse - diff --git a/image.darknet/inst/include/darknet/cfg/tiny.cfg b/image.darknet/inst/include/darknet/cfg/tiny.cfg index 99c2603..f97327c 100644 --- a/image.darknet/inst/include/darknet/cfg/tiny.cfg +++ b/image.darknet/inst/include/darknet/cfg/tiny.cfg @@ -1,6 +1,10 @@ [net] +# Train batch=128 subdivisions=1 +# Test +# batch=1 +# subdivisions=1 height=224 width=224 channels=3 @@ -167,6 +171,4 @@ activation=linear [softmax] groups=1 -[cost] -type=sse diff --git a/image.darknet/inst/include/darknet/cfg/vgg-16.cfg b/image.darknet/inst/include/darknet/cfg/vgg-16.cfg index 2b6f702..c73b17b 100644 --- a/image.darknet/inst/include/darknet/cfg/vgg-16.cfg +++ b/image.darknet/inst/include/darknet/cfg/vgg-16.cfg @@ -1,6 +1,12 @@ [net] -batch=128 -subdivisions=4 +# Training +# batch=128 +# subdivisions=4 + +# Testing +batch=1 +subdivisions=1 + height=256 width=256 channels=3 @@ -148,6 +154,4 @@ activation=linear [softmax] groups=1 -[cost] -type=sse diff --git a/image.darknet/inst/include/darknet/cfg/yolo9000.cfg b/image.darknet/inst/include/darknet/cfg/yolo9000.cfg index 981491d..e745f78 100644 --- a/image.darknet/inst/include/darknet/cfg/yolo9000.cfg +++ b/image.darknet/inst/include/darknet/cfg/yolo9000.cfg @@ -1,17 +1,24 @@ [net] +# Testing batch=1 subdivisions=1 -height=416 -width=416 +# Training +# batch=64 +# subdivisions=8 +batch=1 +subdivisions=1 +height=544 +width=544 channels=3 momentum=0.9 decay=0.0005 -learning_rate=0.00001 -max_batches = 242200 +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 policy=steps -steps=500,200000,240000 -scales=10,.1,.1 +steps=400000,450000 +scales=.1,.1 hue=.1 saturation=.75 diff --git a/image.darknet/inst/include/darknet/cfg/yolov1/tiny-yolo.cfg b/image.darknet/inst/include/darknet/cfg/yolov1-tiny.cfg similarity index 94% rename from image.darknet/inst/include/darknet/cfg/yolov1/tiny-yolo.cfg rename to image.darknet/inst/include/darknet/cfg/yolov1-tiny.cfg index ac4b346..a5e7b49 100644 --- a/image.darknet/inst/include/darknet/cfg/yolov1/tiny-yolo.cfg +++ b/image.darknet/inst/include/darknet/cfg/yolov1-tiny.cfg @@ -1,6 +1,10 @@ [net] -batch=64 -subdivisions=2 +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=8 height=448 width=448 channels=3 diff --git a/image.darknet/inst/include/darknet/cfg/yolov1/yolo.cfg b/image.darknet/inst/include/darknet/cfg/yolov1.cfg similarity index 98% rename from image.darknet/inst/include/darknet/cfg/yolov1/yolo.cfg rename to image.darknet/inst/include/darknet/cfg/yolov1.cfg index c4f415c..06cf6e6 100644 --- a/image.darknet/inst/include/darknet/cfg/yolov1/yolo.cfg +++ b/image.darknet/inst/include/darknet/cfg/yolov1.cfg @@ -1,6 +1,10 @@ [net] +# Testing batch=1 subdivisions=1 +# Training +# batch=64 +# subdivisions=8 height=448 width=448 channels=3 diff --git a/image.darknet/inst/include/darknet/cfg/yolov1/tiny-coco.cfg b/image.darknet/inst/include/darknet/cfg/yolov1/tiny-coco.cfg deleted file mode 100644 index e58c73a..0000000 --- a/image.darknet/inst/include/darknet/cfg/yolov1/tiny-coco.cfg +++ /dev/null @@ -1,125 +0,0 @@ -[net] -batch=64 -subdivisions=2 -height=448 -width=448 -channels=3 -momentum=0.9 -decay=0.0005 - -hue = .1 -saturation=.75 -exposure=.75 - -learning_rate=0.0005 -policy=steps -steps=200,400,600,800,100000,150000 -scales=2.5,2,2,2,.1,.1 -max_batches = 200000 - -[convolutional] -batch_normalize=1 -filters=16 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=256 -activation=leaky - -[connected] -output= 4655 -activation=linear - -[detection] -classes=80 -coords=4 -rescore=1 -side=7 -num=3 -softmax=0 -sqrt=1 -jitter=.2 - -object_scale=1 -noobject_scale=.5 -class_scale=1 -coord_scale=5 diff --git a/image.darknet/inst/include/darknet/cfg/yolov1/xyolo.test.cfg b/image.darknet/inst/include/darknet/cfg/yolov1/xyolo.test.cfg deleted file mode 100644 index 5f3e6f4..0000000 --- a/image.darknet/inst/include/darknet/cfg/yolov1/xyolo.test.cfg +++ /dev/null @@ -1,143 +0,0 @@ -[net] -batch=1 -subdivisions=1 -height=448 -width=448 -channels=3 -momentum=0.9 -decay=0.0005 - -learning_rate=0.0001 -policy=steps -steps=20,40,60,80,20000,30000 -scales=5,5,2,2,.1,.1 -max_batches = 40000 - -[convolutional] -batch_normalize=1 -filters=16 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[batchnorm] - -[convolutional] -xnor = 1 -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[batchnorm] - -[convolutional] -xnor = 1 -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[batchnorm] - -[convolutional] -xnor = 1 -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[batchnorm] - -[convolutional] -xnor = 1 -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[batchnorm] - -[convolutional] -xnor = 1 -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[batchnorm] - -[convolutional] -xnor = 1 -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[batchnorm] - -[convolutional] -xnor = 1 -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[connected] -output= 1470 -activation=linear - -[detection] -classes=20 -coords=4 -rescore=1 -side=7 -num=2 -softmax=0 -sqrt=1 -jitter=.2 - -object_scale=1 -noobject_scale=.5 -class_scale=1 -coord_scale=5 - diff --git a/image.darknet/inst/include/darknet/cfg/yolov1/yolo-coco.cfg b/image.darknet/inst/include/darknet/cfg/yolov1/yolo-coco.cfg deleted file mode 100644 index ed3f2d6..0000000 --- a/image.darknet/inst/include/darknet/cfg/yolov1/yolo-coco.cfg +++ /dev/null @@ -1,255 +0,0 @@ -[net] -batch=64 -subdivisions=4 -height=448 -width=448 -channels=3 -momentum=0.9 -decay=0.0005 - -hue = .1 -saturation=.75 -exposure=.75 - -learning_rate=0.0005 -policy=steps -steps=200,400,600,800,100000,150000 -scales=2.5,2,2,2,.1,.1 -max_batches = 200000 - -[convolutional] -batch_normalize=1 -filters=64 -size=7 -stride=2 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=192 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -####### - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=2 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[local] -size=3 -stride=1 -pad=1 -filters=256 -activation=leaky - -[connected] -output= 4655 -activation=linear - -[detection] -classes=80 -coords=4 -rescore=1 -side=7 -num=3 -softmax=0 -sqrt=1 -jitter=.2 - -object_scale=1 -noobject_scale=.5 -class_scale=1 -coord_scale=5 - diff --git a/image.darknet/inst/include/darknet/cfg/yolov1/yolo-small.cfg b/image.darknet/inst/include/darknet/cfg/yolov1/yolo-small.cfg deleted file mode 100644 index 2a84485..0000000 --- a/image.darknet/inst/include/darknet/cfg/yolov1/yolo-small.cfg +++ /dev/null @@ -1,239 +0,0 @@ -[net] -batch=64 -subdivisions=64 -height=448 -width=448 -channels=3 -momentum=0.9 -decay=0.0005 - -learning_rate=0.001 -policy=steps -steps=200,400,600,20000,30000 -scales=2.5,2,2,.1,.1 -max_batches = 40000 - -[crop] -crop_width=448 -crop_height=448 -flip=0 -angle=0 -saturation = 1.5 -exposure = 1.5 - -[convolutional] -filters=64 -size=7 -stride=2 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -filters=192 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -####### - -[convolutional] -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -size=3 -stride=2 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[connected] -output=512 -activation=leaky - -[connected] -output=4096 -activation=leaky - -[dropout] -probability=.5 - -[connected] -output= 1470 -activation=linear - -[detection] -classes=20 -coords=4 -rescore=1 -side=7 -num=2 -softmax=0 -sqrt=1 -jitter=.2 - -object_scale=1 -noobject_scale=.5 -class_scale=1 -coord_scale=5 - diff --git a/image.darknet/inst/include/darknet/cfg/yolov1/yolo.train.cfg b/image.darknet/inst/include/darknet/cfg/yolov1/yolo.train.cfg deleted file mode 100644 index 01aeb5e..0000000 --- a/image.darknet/inst/include/darknet/cfg/yolov1/yolo.train.cfg +++ /dev/null @@ -1,257 +0,0 @@ -[net] -batch=64 -subdivisions=4 -height=448 -width=448 -channels=3 -momentum=0.9 -decay=0.0005 -saturation=1.5 -exposure=1.5 -hue=.1 - -learning_rate=0.0005 -policy=steps -steps=200,400,600,20000,30000 -scales=2.5,2,2,.1,.1 -max_batches = 40000 - -[convolutional] -batch_normalize=1 -filters=64 -size=7 -stride=2 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=192 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -####### - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=2 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[local] -size=3 -stride=1 -pad=1 -filters=256 -activation=leaky - -[dropout] -probability=.5 - -[connected] -output= 1715 -activation=linear - -[detection] -classes=20 -coords=4 -rescore=1 -side=7 -num=3 -softmax=0 -sqrt=1 -jitter=.2 - -object_scale=1 -noobject_scale=.5 -class_scale=1 -coord_scale=5 - diff --git a/image.darknet/inst/include/darknet/cfg/tiny-yolo-voc.cfg b/image.darknet/inst/include/darknet/cfg/yolov2-tiny-voc.cfg similarity index 93% rename from image.darknet/inst/include/darknet/cfg/tiny-yolo-voc.cfg rename to image.darknet/inst/include/darknet/cfg/yolov2-tiny-voc.cfg index 1f33c35..c4c127c 100644 --- a/image.darknet/inst/include/darknet/cfg/tiny-yolo-voc.cfg +++ b/image.darknet/inst/include/darknet/cfg/yolov2-tiny-voc.cfg @@ -1,6 +1,10 @@ [net] -batch=64 -subdivisions=8 +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=2 width=416 height=416 channels=3 @@ -12,7 +16,7 @@ exposure = 1.5 hue=.1 learning_rate=0.001 -max_batches = 40100 +max_batches = 40200 policy=steps steps=-1,100,20000,30000 scales=.1,10,.1,.1 diff --git a/image.darknet/inst/include/darknet/cfg/tiny-yolo.cfg b/image.darknet/inst/include/darknet/cfg/yolov2-tiny.cfg similarity index 82% rename from image.darknet/inst/include/darknet/cfg/tiny-yolo.cfg rename to image.darknet/inst/include/darknet/cfg/yolov2-tiny.cfg index 5580098..81d0ac4 100644 --- a/image.darknet/inst/include/darknet/cfg/tiny-yolo.cfg +++ b/image.darknet/inst/include/darknet/cfg/yolov2-tiny.cfg @@ -1,6 +1,10 @@ [net] -batch=64 -subdivisions=8 +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=2 width=416 height=416 channels=3 @@ -12,10 +16,11 @@ exposure = 1.5 hue=.1 learning_rate=0.001 -max_batches = 120000 +burn_in=1000 +max_batches = 500200 policy=steps -steps=-1,100,80000,100000 -scales=.1,10,.1,.1 +steps=400000,450000 +scales=.1,.1 [convolutional] batch_normalize=1 @@ -104,7 +109,7 @@ batch_normalize=1 size=3 stride=1 pad=1 -filters=1024 +filters=512 activation=leaky [convolutional] @@ -115,14 +120,14 @@ filters=425 activation=linear [region] -anchors = 0.738768,0.874946, 2.42204,2.65704, 4.30971,7.04493, 10.246,4.59428, 12.6868,11.8741 +anchors = 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828 bias_match=1 classes=80 coords=4 num=5 softmax=1 jitter=.2 -rescore=1 +rescore=0 object_scale=5 noobject_scale=1 diff --git a/image.darknet/inst/include/darknet/cfg/yolo.cfg b/image.darknet/inst/include/darknet/cfg/yolov2-voc.cfg similarity index 87% rename from image.darknet/inst/include/darknet/cfg/yolo.cfg rename to image.darknet/inst/include/darknet/cfg/yolov2-voc.cfg index fda339a..dbf2de2 100644 --- a/image.darknet/inst/include/darknet/cfg/yolo.cfg +++ b/image.darknet/inst/include/darknet/cfg/yolov2-voc.cfg @@ -1,8 +1,12 @@ [net] +# Testing batch=1 subdivisions=1 -width=416 +# Training +# batch=64 +# subdivisions=8 height=416 +width=416 channels=3 momentum=0.9 decay=0.0005 @@ -12,10 +16,11 @@ exposure = 1.5 hue=.1 learning_rate=0.001 -max_batches = 120000 +burn_in=1000 +max_batches = 80200 policy=steps -steps=-1,100,80000,100000 -scales=.1,10,.1,.1 +steps=40000,60000 +scales=.1,.1 [convolutional] batch_normalize=1 @@ -203,11 +208,19 @@ activation=leaky [route] layers=-9 +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=64 +activation=leaky + [reorg] stride=2 [route] -layers=-1,-3 +layers=-1,-4 [convolutional] batch_normalize=1 @@ -221,17 +234,18 @@ activation=leaky size=1 stride=1 pad=1 -filters=425 +filters=125 activation=linear + [region] -anchors = 0.738768,0.874946, 2.42204,2.65704, 4.30971,7.04493, 10.246,4.59428, 12.6868,11.8741 +anchors = 1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071 bias_match=1 -classes=80 +classes=20 coords=4 num=5 softmax=1 -jitter=.2 +jitter=.3 rescore=1 object_scale=5 @@ -241,4 +255,4 @@ coord_scale=1 absolute=1 thresh = .6 -random=0 +random=1 diff --git a/image.darknet/inst/include/darknet/cfg/yolov3-openimages.cfg b/image.darknet/inst/include/darknet/cfg/yolov3-openimages.cfg new file mode 100644 index 0000000..65d241a --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/yolov3-openimages.cfg @@ -0,0 +1,789 @@ +[net] +# Testing + batch=1 + subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=5000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=1818 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=601 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=1818 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=601 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=1818 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=601 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + diff --git a/image.darknet/inst/include/darknet/cfg/yolov3-spp.cfg b/image.darknet/inst/include/darknet/cfg/yolov3-spp.cfg new file mode 100644 index 0000000..4ad2a05 --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/yolov3-spp.cfg @@ -0,0 +1,822 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 + +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + diff --git a/image.darknet/inst/include/darknet/cfg/yolov3-tiny.cfg b/image.darknet/inst/include/darknet/cfg/yolov3-tiny.cfg new file mode 100644 index 0000000..cfca3cf --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/yolov3-tiny.cfg @@ -0,0 +1,182 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=2 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 diff --git a/image.darknet/inst/include/darknet/cfg/yolov3-voc.cfg b/image.darknet/inst/include/darknet/cfg/yolov3-voc.cfg new file mode 100644 index 0000000..3f3e8df --- /dev/null +++ b/image.darknet/inst/include/darknet/cfg/yolov3-voc.cfg @@ -0,0 +1,785 @@ +[net] +# Testing + batch=1 + subdivisions=1 +# Training +# batch=64 +# subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 50200 +policy=steps +steps=40000,45000 +scales=.1,.1 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + diff --git a/image.darknet/inst/include/darknet/data/kite.jpg b/image.darknet/inst/include/darknet/data/kite.jpg new file mode 100644 index 0000000000000000000000000000000000000000..9eb325ac5fc375cb2513380087dd713be9be19d8 GIT binary patch literal 1415684 zcmeFZc|4Ts`#AoLu_u*M<4u{OgzRO>o@~jIER}U)Vq#_(`!=?8>LiJzll2s-gc1gW zA!Q9GVq%6NTb7}$jb;2EOR97Fe1D(M=lgsA^?T3jnCG7RzOU=Pw)?uT>w50a%7>L+ zVEZXOLp=Zj0U$>pM*v`@4;riMkGcc^#>RjI005i-4}=$B0b4u4zf{P!wKhB0&WCJY 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z#?qFr;@xXyvRdnXPL67huC;1OYSdd+jjY!#l6`yn-*aEXvn|z%+LOZHZ}4IQi? ztgubAZDv+*k{4!?H{iL*+CEm(30iYhoVi=GUkd2@Uz*pxy8Papyw&Q|RpV-tS|_{L zWo>QV`~23Nx8~LEEgYzd*5WB7wnm2L6D)lp8`Baa#~}fCIyf?IUo$xQzJ{GRH$qM+ zzG+6z_OiQP@58>kYxb>UI;p9)r8vK4^-`7kuDV}d&&u14)2!gsLAsaBh3+;9Qrc-@ zjf+jUaX{^s86`_@0kCin4W2Tb6O)WoecRePE8TWczK!V9U3#5VTWgk0%GcIfzsp@e zsb2Balf&A5&7|{4Zk8m9^}fRe%iCE$*!<>=WQq?h*htps)Bg8#rKHRJ_^$^CYSm=w zB`#UcD(cos_Sx$E?-%!SoV6D$0dh zZO@)~5v~-1hodVgDP1*ernypH=S8)zr&jOEuYZ-VbGrj}oMh!B*0+=GzWP7>3-uwi z(k%5D?QSGihTuyi9(A_Jn*u}o;)NO`aCd*MF>V+_yGkW_xO#9{w>x{*ioM;fD_>2X z?{(?DdX!@4RlkCVTqH9NvTRHtsR!SwX40A?|;@fMJc<#wA*PdG}_)?PWIbfW@cQ( zE*^NylSCNXvGS&oa%FM6FlLcmSRl$stel^a=jHPACogF^DM?B$4NC2`cJJ{&xpe8v zdrKc=_ za=^DNYvyg*P4-vizT0oF<+iUL*rlzN-X=VW>o%O7@ zb0moqKb;wN1UQXYlApRVOTJ}KnmJNpWp84&DA$!0Rk - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -image get_image_from_stream(CvCapture *cap); -#endif +#include "darknet.h" +#include void demo_art(char *cfgfile, char *weightfile, int cam_index) { #ifdef OPENCV - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); srand(2222222); - CvCapture * cap; - cap = cvCaptureFromCAM(cam_index); + void * cap = open_video_stream(0, cam_index, 0,0,0); char *window = "ArtJudgementBot9000!!!"; if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow(window, CV_WINDOW_NORMAL); - cvResizeWindow(window, 512, 512); int i; int idx[] = {37, 401, 434}; int n = sizeof(idx)/sizeof(idx[0]); while(1){ image in = get_image_from_stream(cap); - image in_s = resize_image(in, net.w, net.h); - show_image(in, window); + image in_s = resize_image(in, net->w, net->h); float *p = network_predict(net, in_s.data); @@ -58,10 +41,9 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index) } printf("]\n"); + show_image(in, window, 1); free_image(in_s); free_image(in); - - cvWaitKey(1); } #endif } diff --git a/image.darknet/inst/include/darknet/examples/attention.c b/image.darknet/inst/include/darknet/examples/attention.c new file mode 100644 index 0000000..cd1e579 --- /dev/null +++ b/image.darknet/inst/include/darknet/examples/attention.c @@ -0,0 +1,459 @@ +#include "darknet.h" + +#include +#include + +void extend_data_truth(data *d, int n, float val) +{ + int i, j; + for(i = 0; i < d->y.rows; ++i){ + d->y.vals[i] = realloc(d->y.vals[i], (d->y.cols+n)*sizeof(float)); + for(j = 0; j < n; ++j){ + d->y.vals[i][d->y.cols + j] = val; + } + } + d->y.cols += n; +} + +matrix network_loss_data(network *net, data test) +{ + int i,b; + int k = 1; + matrix pred = make_matrix(test.X.rows, k); + float *X = calloc(net->batch*test.X.cols, sizeof(float)); + float *y = calloc(net->batch*test.y.cols, sizeof(float)); + for(i = 0; i < test.X.rows; i += net->batch){ + for(b = 0; b < net->batch; ++b){ + if(i+b == test.X.rows) break; + memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float)); + memcpy(y+b*test.y.cols, test.y.vals[i+b], test.y.cols*sizeof(float)); + } + + network orig = *net; + net->input = X; + net->truth = y; + net->train = 0; + net->delta = 0; + forward_network(net); + *net = orig; + + float *delta = net->layers[net->n-1].output; + for(b = 0; b < net->batch; ++b){ + if(i+b == test.X.rows) break; + int t = max_index(y + b*test.y.cols, 1000); + float err = sum_array(delta + b*net->outputs, net->outputs); + pred.vals[i+b][0] = -err; + //pred.vals[i+b][0] = 1-delta[b*net->outputs + t]; + } + } + free(X); + free(y); + return pred; +} + +void train_attention(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear) +{ + int i, j; + + float avg_cls_loss = -1; + float avg_att_loss = -1; + char *base = basecfg(cfgfile); + printf("%s\n", base); + printf("%d\n", ngpus); + network **nets = calloc(ngpus, sizeof(network*)); + + srand(time(0)); + int seed = rand(); + for(i = 0; i < ngpus; ++i){ + srand(seed); +#ifdef GPU + cuda_set_device(gpus[i]); +#endif + nets[i] = load_network(cfgfile, weightfile, clear); + nets[i]->learning_rate *= ngpus; + } + srand(time(0)); + network *net = nets[0]; + + int imgs = net->batch * net->subdivisions * ngpus; + + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + list *options = read_data_cfg(datacfg); + + char *backup_directory = option_find_str(options, "backup", "/backup/"); + char *label_list = option_find_str(options, "labels", "data/labels.list"); + char *train_list = option_find_str(options, "train", "data/train.list"); + int classes = option_find_int(options, "classes", 2); + + char **labels = get_labels(label_list); + list *plist = get_paths(train_list); + char **paths = (char **)list_to_array(plist); + printf("%d\n", plist->size); + int N = plist->size; + double time; + + int divs=3; + int size=2; + + load_args args = {0}; + args.w = divs*net->w/size; + args.h = divs*net->h/size; + args.size = divs*net->w/size; + args.threads = 32; + args.hierarchy = net->hierarchy; + + args.min = net->min_ratio*args.w; + args.max = net->max_ratio*args.w; + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.saturation = net->saturation; + args.hue = net->hue; + + args.paths = paths; + args.classes = classes; + args.n = imgs; + args.m = N; + args.labels = labels; + args.type = CLASSIFICATION_DATA; + + data train; + data buffer; + pthread_t load_thread; + args.d = &buffer; + load_thread = load_data(args); + + int epoch = (*net->seen)/N; + while(get_current_batch(net) < net->max_batches || net->max_batches == 0){ + time = what_time_is_it_now(); + + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data(args); + data resized = resize_data(train, net->w, net->h); + extend_data_truth(&resized, divs*divs, 0); + data *tiles = tile_data(train, divs, size); + + printf("Loaded: %lf seconds\n", what_time_is_it_now()-time); + time = what_time_is_it_now(); + + float aloss = 0; + float closs = 0; + int z; + for (i = 0; i < divs*divs/ngpus; ++i) { +#pragma omp parallel for + for(j = 0; j < ngpus; ++j){ + int index = i*ngpus + j; + extend_data_truth(tiles+index, divs*divs, SECRET_NUM); + matrix deltas = network_loss_data(nets[j], tiles[index]); + for(z = 0; z < resized.y.rows; ++z){ + resized.y.vals[z][train.y.cols + index] = deltas.vals[z][0]; + } + free_matrix(deltas); + } + } + int *inds = calloc(resized.y.rows, sizeof(int)); + for(z = 0; z < resized.y.rows; ++z){ + int index = max_index(resized.y.vals[z] + train.y.cols, divs*divs); + inds[z] = index; + for(i = 0; i < divs*divs; ++i){ + resized.y.vals[z][train.y.cols + i] = (i == index)? 1 : 0; + } + } + data best = select_data(tiles, inds); + free(inds); + #ifdef GPU + if (ngpus == 1) { + closs = train_network(net, best); + } else { + closs = train_networks(nets, ngpus, best, 4); + } + #endif + for (i = 0; i < divs*divs; ++i) { + printf("%.2f ", resized.y.vals[0][train.y.cols + i]); + if((i+1)%divs == 0) printf("\n"); + free_data(tiles[i]); + } + free_data(best); + printf("\n"); + image im = float_to_image(64,64,3,resized.X.vals[0]); + //show_image(im, "orig"); + //cvWaitKey(100); + /* + image im1 = float_to_image(64,64,3,tiles[i].X.vals[0]); + image im2 = float_to_image(64,64,3,resized.X.vals[0]); + show_image(im1, "tile"); + show_image(im2, "res"); + */ +#ifdef GPU + if (ngpus == 1) { + aloss = train_network(net, resized); + } else { + aloss = train_networks(nets, ngpus, resized, 4); + } +#endif + for(i = 0; i < divs*divs; ++i){ + printf("%f ", nets[0]->output[1000 + i]); + if ((i+1) % divs == 0) printf("\n"); + } + printf("\n"); + + free_data(resized); + free_data(train); + if(avg_cls_loss == -1) avg_cls_loss = closs; + if(avg_att_loss == -1) avg_att_loss = aloss; + avg_cls_loss = avg_cls_loss*.9 + closs*.1; + avg_att_loss = avg_att_loss*.9 + aloss*.1; + + printf("%ld, %.3f: Att: %f, %f avg, Class: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, aloss, avg_att_loss, closs, avg_cls_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen); + if(*net->seen/N > epoch){ + epoch = *net->seen/N; + char buff[256]; + sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); + save_weights(net, buff); + } + if(get_current_batch(net)%1000 == 0){ + char buff[256]; + sprintf(buff, "%s/%s.backup",backup_directory,base); + save_weights(net, buff); + } + } + char buff[256]; + sprintf(buff, "%s/%s.weights", backup_directory, base); + save_weights(net, buff); + pthread_join(load_thread, 0); + + free_network(net); + free_ptrs((void**)labels, classes); + free_ptrs((void**)paths, plist->size); + free_list(plist); + free(base); +} + +void validate_attention_single(char *datacfg, char *filename, char *weightfile) +{ + int i, j; + network *net = load_network(filename, weightfile, 0); + set_batch_network(net, 1); + srand(time(0)); + + list *options = read_data_cfg(datacfg); + + char *label_list = option_find_str(options, "labels", "data/labels.list"); + char *leaf_list = option_find_str(options, "leaves", 0); + if(leaf_list) change_leaves(net->hierarchy, leaf_list); + char *valid_list = option_find_str(options, "valid", "data/train.list"); + int classes = option_find_int(options, "classes", 2); + int topk = option_find_int(options, "top", 1); + + char **labels = get_labels(label_list); + list *plist = get_paths(valid_list); + + char **paths = (char **)list_to_array(plist); + int m = plist->size; + free_list(plist); + + float avg_acc = 0; + float avg_topk = 0; + int *indexes = calloc(topk, sizeof(int)); + int divs = 4; + int size = 2; + int extra = 0; + float *avgs = calloc(classes, sizeof(float)); + int *inds = calloc(divs*divs, sizeof(int)); + + for(i = 0; i < m; ++i){ + int class = -1; + char *path = paths[i]; + for(j = 0; j < classes; ++j){ + if(strstr(path, labels[j])){ + class = j; + break; + } + } + image im = load_image_color(paths[i], 0, 0); + image resized = resize_min(im, net->w*divs/size); + image crop = crop_image(resized, (resized.w - net->w*divs/size)/2, (resized.h - net->h*divs/size)/2, net->w*divs/size, net->h*divs/size); + image rcrop = resize_image(crop, net->w, net->h); + //show_image(im, "orig"); + //show_image(crop, "cropped"); + //cvWaitKey(0); + float *pred = network_predict(net, rcrop.data); + //pred[classes + 56] = 0; + for(j = 0; j < divs*divs; ++j){ + printf("%.2f ", pred[classes + j]); + if((j+1)%divs == 0) printf("\n"); + } + printf("\n"); + copy_cpu(classes, pred, 1, avgs, 1); + top_k(pred + classes, divs*divs, divs*divs, inds); + show_image(crop, "crop"); + for(j = 0; j < extra; ++j){ + int index = inds[j]; + int row = index / divs; + int col = index % divs; + int y = row * crop.h / divs - (net->h - crop.h/divs)/2; + int x = col * crop.w / divs - (net->w - crop.w/divs)/2; + printf("%d %d %d %d\n", row, col, y, x); + image tile = crop_image(crop, x, y, net->w, net->h); + float *pred = network_predict(net, tile.data); + axpy_cpu(classes, 1., pred, 1, avgs, 1); + show_image(tile, "tile"); + //cvWaitKey(10); + } + if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1); + + if(rcrop.data != resized.data) free_image(rcrop); + if(resized.data != im.data) free_image(resized); + free_image(im); + free_image(crop); + top_k(pred, classes, topk, indexes); + + if(indexes[0] == class) avg_acc += 1; + for(j = 0; j < topk; ++j){ + if(indexes[j] == class) avg_topk += 1; + } + + printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); + } +} + +void validate_attention_multi(char *datacfg, char *filename, char *weightfile) +{ + int i, j; + network *net = load_network(filename, weightfile, 0); + set_batch_network(net, 1); + srand(time(0)); + + list *options = read_data_cfg(datacfg); + + char *label_list = option_find_str(options, "labels", "data/labels.list"); + char *valid_list = option_find_str(options, "valid", "data/train.list"); + int classes = option_find_int(options, "classes", 2); + int topk = option_find_int(options, "top", 1); + + char **labels = get_labels(label_list); + list *plist = get_paths(valid_list); + int scales[] = {224, 288, 320, 352, 384}; + int nscales = sizeof(scales)/sizeof(scales[0]); + + char **paths = (char **)list_to_array(plist); + int m = plist->size; + free_list(plist); + + float avg_acc = 0; + float avg_topk = 0; + int *indexes = calloc(topk, sizeof(int)); + + for(i = 0; i < m; ++i){ + int class = -1; + char *path = paths[i]; + for(j = 0; j < classes; ++j){ + if(strstr(path, labels[j])){ + class = j; + break; + } + } + float *pred = calloc(classes, sizeof(float)); + image im = load_image_color(paths[i], 0, 0); + for(j = 0; j < nscales; ++j){ + image r = resize_min(im, scales[j]); + resize_network(net, r.w, r.h); + float *p = network_predict(net, r.data); + if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1 , 1); + axpy_cpu(classes, 1, p, 1, pred, 1); + flip_image(r); + p = network_predict(net, r.data); + axpy_cpu(classes, 1, p, 1, pred, 1); + if(r.data != im.data) free_image(r); + } + free_image(im); + top_k(pred, classes, topk, indexes); + free(pred); + if(indexes[0] == class) avg_acc += 1; + for(j = 0; j < topk; ++j){ + if(indexes[j] == class) avg_topk += 1; + } + + printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); + } +} + +void predict_attention(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top) +{ + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + srand(2222222); + + list *options = read_data_cfg(datacfg); + + char *name_list = option_find_str(options, "names", 0); + if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list"); + if(top == 0) top = option_find_int(options, "top", 1); + + int i = 0; + char **names = get_labels(name_list); + clock_t time; + int *indexes = calloc(top, sizeof(int)); + char buff[256]; + char *input = buff; + while(1){ + if(filename){ + strncpy(input, filename, 256); + }else{ + printf("Enter Image Path: "); + fflush(stdout); + input = fgets(input, 256, stdin); + if(!input) return; + strtok(input, "\n"); + } + image im = load_image_color(input, 0, 0); + image r = letterbox_image(im, net->w, net->h); + //resize_network(&net, r.w, r.h); + //printf("%d %d\n", r.w, r.h); + + float *X = r.data; + time=clock(); + float *predictions = network_predict(net, X); + if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1); + top_k(predictions, net->outputs, top, indexes); + fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + for(i = 0; i < top; ++i){ + int index = indexes[i]; + //if(net->hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net->hierarchy->parent[index] >= 0) ? names[net->hierarchy->parent[index]] : "Root"); + //else printf("%s: %f\n",names[index], predictions[index]); + printf("%5.2f%%: %s\n", predictions[index]*100, names[index]); + } + if(r.data != im.data) free_image(r); + free_image(im); + if (filename) break; + } +} + + +void run_attention(int argc, char **argv) +{ + if(argc < 4){ + fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); + return; + } + + char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); + int ngpus; + int *gpus = read_intlist(gpu_list, &ngpus, gpu_index); + + + int top = find_int_arg(argc, argv, "-t", 0); + int clear = find_arg(argc, argv, "-clear"); + char *data = argv[3]; + char *cfg = argv[4]; + char *weights = (argc > 5) ? argv[5] : 0; + char *filename = (argc > 6) ? argv[6]: 0; + char *layer_s = (argc > 7) ? argv[7]: 0; + if(0==strcmp(argv[2], "predict")) predict_attention(data, cfg, weights, filename, top); + else if(0==strcmp(argv[2], "train")) train_attention(data, cfg, weights, gpus, ngpus, clear); + else if(0==strcmp(argv[2], "valid")) validate_attention_single(data, cfg, weights); + else if(0==strcmp(argv[2], "validmulti")) validate_attention_multi(data, cfg, weights); +} + + diff --git a/image.darknet/inst/include/darknet/src/captcha.c b/image.darknet/inst/include/darknet/examples/captcha.c similarity index 88% rename from image.darknet/inst/include/darknet/src/captcha.c rename to image.darknet/inst/include/darknet/examples/captcha.c index 3d449b2..41d6d07 100644 --- a/image.darknet/inst/include/darknet/src/captcha.c +++ b/image.darknet/inst/include/darknet/examples/captcha.c @@ -1,6 +1,4 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" +#include "darknet.h" void fix_data_captcha(data d, int mask) { @@ -32,13 +30,10 @@ void train_captcha(char *cfgfile, char *weightfile) float avg_loss = -1; char *base = basecfg(cfgfile); printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + network *net = load_network(cfgfile, weightfile, 0); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); int imgs = 1024; - int i = *net.seen/imgs; + int i = *net->seen/imgs; int solved = 1; list *plist; char **labels = get_labels("/data/captcha/reimgs.labels.list"); @@ -55,8 +50,8 @@ void train_captcha(char *cfgfile, char *weightfile) data buffer; load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.paths = paths; args.classes = 26; args.n = imgs; @@ -85,7 +80,7 @@ void train_captcha(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%d: %f, %f avg, %lf seconds, %ld images\n", i, loss, avg_loss, sec(clock()-time), *net->seen); free_data(train); if(i%100==0){ char buff[256]; @@ -97,11 +92,8 @@ void train_captcha(char *cfgfile, char *weightfile) void test_captcha(char *cfgfile, char *weightfile, char *filename) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); srand(2222222); int i = 0; char **names = get_labels("/data/captcha/reimgs.labels.list"); @@ -118,7 +110,7 @@ void test_captcha(char *cfgfile, char *weightfile, char *filename) if(!input) return; strtok(input, "\n"); } - image im = load_image_color(input, net.w, net.h); + image im = load_image_color(input, net->w, net->h); float *X = im.data; float *predictions = network_predict(net, X); top_predictions(net, 26, indexes); @@ -138,21 +130,18 @@ void test_captcha(char *cfgfile, char *weightfile, char *filename) void valid_captcha(char *cfgfile, char *weightfile, char *filename) { char **labels = get_labels("/data/captcha/reimgs.labels.list"); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, 0); list *plist = get_paths("/data/captcha/reimgs.fg.list"); char **paths = (char **)list_to_array(plist); int N = plist->size; - int outputs = net.outputs; + int outputs = net->outputs; - set_batch_network(&net, 1); + set_batch_network(net, 1); srand(2222222); int i, j; for(i = 0; i < N; ++i){ if (i%100 == 0) fprintf(stderr, "%d\n", i); - image im = load_image_color(paths[i], net.w, net.h); + image im = load_image_color(paths[i], net->w, net->h); float *X = im.data; float *predictions = network_predict(net, X); //printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); @@ -187,9 +176,9 @@ void valid_captcha(char *cfgfile, char *weightfile, char *filename) if(weightfile){ load_weights(&net, weightfile); } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); int imgs = 1024; - int i = net.seen/imgs; + int i = net->seen/imgs; list *plist = get_paths("/data/captcha/train.auto5"); char **paths = (char **)list_to_array(plist); printf("%d\n", plist->size); @@ -203,10 +192,10 @@ void valid_captcha(char *cfgfile, char *weightfile, char *filename) printf("Loaded: %lf seconds\n", sec(clock()-time)); time=clock(); float loss = train_network(net, train); - net.seen += imgs; + net->seen += imgs; if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen); + printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net->seen); free_data(train); if(i%10==0){ char buff[256]; @@ -253,9 +242,9 @@ network net = parse_network_cfg(cfgfile); if(weightfile){ load_weights(&net, weightfile); } -printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); +printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); int imgs = 1024; -int i = net.seen/imgs; +int i = net->seen/imgs; list *plist = get_paths("/data/captcha/encode.list"); char **paths = (char **)list_to_array(plist); printf("%d\n", plist->size); @@ -268,10 +257,10 @@ while(1){ printf("Loaded: %lf seconds\n", sec(clock()-time)); time=clock(); float loss = train_network(net, train); - net.seen += imgs; + net->seen += imgs; if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen); + printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net->seen); free_matrix(train.X); if(i%100==0){ char buff[256]; diff --git a/image.darknet/inst/include/darknet/src/cifar.c b/image.darknet/inst/include/darknet/examples/cifar.c similarity index 77% rename from image.darknet/inst/include/darknet/src/cifar.c rename to image.darknet/inst/include/darknet/examples/cifar.c index d0ac459..a5f5f24 100644 --- a/image.darknet/inst/include/darknet/src/cifar.c +++ b/image.darknet/inst/include/darknet/examples/cifar.c @@ -1,12 +1,4 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" -#include "option_list.h" -#include "blas.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif +#include "darknet.h" void train_cifar(char *cfgfile, char *weightfile) { @@ -14,28 +6,25 @@ void train_cifar(char *cfgfile, char *weightfile) float avg_loss = -1; char *base = basecfg(cfgfile); printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + network *net = load_network(cfgfile, weightfile, 0); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); char *backup_directory = "/home/pjreddie/backup/"; int classes = 10; int N = 50000; char **labels = get_labels("data/cifar/labels.txt"); - int epoch = (*net.seen)/N; + int epoch = (*net->seen)/N; data train = load_all_cifar10(); - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ + while(get_current_batch(net) < net->max_batches || net->max_batches == 0){ clock_t time=clock(); float loss = train_network_sgd(net, train, 1); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; + printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen); + if(*net->seen/N > epoch){ + epoch = *net->seen/N; char buff[256]; sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); save_weights(net, buff); @@ -62,18 +51,15 @@ void train_cifar_distill(char *cfgfile, char *weightfile) float avg_loss = -1; char *base = basecfg(cfgfile); printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + network *net = load_network(cfgfile, weightfile, 0); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); char *backup_directory = "/home/pjreddie/backup/"; int classes = 10; int N = 50000; char **labels = get_labels("data/cifar/labels.txt"); - int epoch = (*net.seen)/N; + int epoch = (*net->seen)/N; data train = load_all_cifar10(); matrix soft = csv_to_matrix("results/ensemble.csv"); @@ -83,15 +69,15 @@ void train_cifar_distill(char *cfgfile, char *weightfile) scale_matrix(train.y, 1. - weight); matrix_add_matrix(soft, train.y); - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ + while(get_current_batch(net) < net->max_batches || net->max_batches == 0){ clock_t time=clock(); float loss = train_network_sgd(net, train, 1); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; + printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen); + if(*net->seen/N > epoch){ + epoch = *net->seen/N; char buff[256]; sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); save_weights(net, buff); @@ -114,11 +100,8 @@ void train_cifar_distill(char *cfgfile, char *weightfile) void test_cifar_multi(char *filename, char *weightfile) { - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(filename, weightfile, 0); + set_batch_network(net, 1); srand(time(0)); float avg_acc = 0; @@ -146,10 +129,7 @@ void test_cifar_multi(char *filename, char *weightfile) void test_cifar(char *filename, char *weightfile) { - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(filename, weightfile, 0); srand(time(0)); clock_t time; @@ -177,23 +157,20 @@ char *labels[] = {"airplane","automobile","bird","cat","deer","dog","frog","hors int class = max_index(train.y.vals[i], 10); char buff[256]; sprintf(buff, "data/cifar/train/%d_%s",i,labels[class]); - save_image_png(im, buff); + save_image_options(im, buff, PNG, 0); } for(i = 0; i < test.X.rows; ++i){ image im = float_to_image(32, 32, 3, test.X.vals[i]); int class = max_index(test.y.vals[i], 10); char buff[256]; sprintf(buff, "data/cifar/test/%d_%s",i,labels[class]); - save_image_png(im, buff); + save_image_options(im, buff, PNG, 0); } } void test_cifar_csv(char *filename, char *weightfile) { - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(filename, weightfile, 0); srand(time(0)); data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin"); @@ -215,12 +192,9 @@ void test_cifar_csv(char *filename, char *weightfile) free_data(test); } -void test_cifar_csvtrain(char *filename, char *weightfile) +void test_cifar_csvtrain(char *cfg, char *weights) { - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(cfg, weights, 0); srand(time(0)); data test = load_all_cifar10(); diff --git a/image.darknet/inst/include/darknet/src/classifier.c b/image.darknet/inst/include/darknet/examples/classifier.c similarity index 72% rename from image.darknet/inst/include/darknet/src/classifier.c rename to image.darknet/inst/include/darknet/examples/classifier.c index 586530a..df91a08 100644 --- a/image.darknet/inst/include/darknet/src/classifier.c +++ b/image.darknet/inst/include/darknet/examples/classifier.c @@ -1,17 +1,7 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" -#include "option_list.h" -#include "blas.h" -#include "assert.h" -#include "classifier.h" -#include "cuda.h" -#include +#include "darknet.h" -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -image get_image_from_stream(CvCapture *cap); -#endif +#include +#include float *get_regression_values(char **labels, int n) { @@ -33,7 +23,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, char *base = basecfg(cfgfile); printf("%s\n", base); printf("%d\n", ngpus); - network *nets = calloc(ngpus, sizeof(network)); + network **nets = calloc(ngpus, sizeof(network*)); srand(time(0)); int seed = rand(); @@ -42,54 +32,61 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, #ifdef GPU cuda_set_device(gpus[i]); #endif - nets[i] = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&nets[i], weightfile); - } - if(clear) *nets[i].seen = 0; - nets[i].learning_rate *= ngpus; + nets[i] = load_network(cfgfile, weightfile, clear); + nets[i]->learning_rate *= ngpus; } srand(time(0)); - network net = nets[0]; + network *net = nets[0]; - int imgs = net.batch * net.subdivisions * ngpus; + int imgs = net->batch * net->subdivisions * ngpus; - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); list *options = read_data_cfg(datacfg); char *backup_directory = option_find_str(options, "backup", "/backup/"); + int tag = option_find_int_quiet(options, "tag", 0); char *label_list = option_find_str(options, "labels", "data/labels.list"); char *train_list = option_find_str(options, "train", "data/train.list"); + char *tree = option_find_str(options, "tree", 0); + if (tree) net->hierarchy = read_tree(tree); int classes = option_find_int(options, "classes", 2); - char **labels = get_labels(label_list); + char **labels = 0; + if(!tag){ + labels = get_labels(label_list); + } list *plist = get_paths(train_list); char **paths = (char **)list_to_array(plist); printf("%d\n", plist->size); int N = plist->size; - clock_t time; + double time; load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.threads = 32; - args.hierarchy = net.hierarchy; - - args.min = net.min_crop; - args.max = net.max_crop; - args.angle = net.angle; - args.aspect = net.aspect; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; - args.size = net.w; + args.hierarchy = net->hierarchy; + + args.min = net->min_ratio*net->w; + args.max = net->max_ratio*net->w; + printf("%d %d\n", args.min, args.max); + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.saturation = net->saturation; + args.hue = net->hue; + args.size = net->w; args.paths = paths; args.classes = classes; args.n = imgs; args.m = N; args.labels = labels; - args.type = CLASSIFICATION_DATA; + if (tag){ + args.type = TAG_DATA; + } else { + args.type = CLASSIFICATION_DATA; + } data train; data buffer; @@ -97,16 +94,40 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.d = &buffer; load_thread = load_data(args); - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - time=clock(); + int count = 0; + int epoch = (*net->seen)/N; + while(get_current_batch(net) < net->max_batches || net->max_batches == 0){ + if(net->random && count++%40 == 0){ + printf("Resizing\n"); + int dim = (rand() % 11 + 4) * 32; + //if (get_current_batch(net)+200 > net->max_batches) dim = 608; + //int dim = (rand() % 4 + 16) * 32; + printf("%d\n", dim); + args.w = dim; + args.h = dim; + args.size = dim; + args.min = net->min_ratio*dim; + args.max = net->max_ratio*dim; + printf("%d %d\n", args.min, args.max); + + pthread_join(load_thread, 0); + train = buffer; + free_data(train); + load_thread = load_data(args); + + for(i = 0; i < ngpus; ++i){ + resize_network(nets[i], dim, dim); + } + net = nets[0]; + } + time = what_time_is_it_now(); pthread_join(load_thread, 0); train = buffer; load_thread = load_data(args); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); + printf("Loaded: %lf seconds\n", what_time_is_it_now()-time); + time = what_time_is_it_now(); float loss = 0; #ifdef GPU @@ -120,15 +141,15 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, #endif if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen); free_data(train); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; + if(*net->seen/N > epoch){ + epoch = *net->seen/N; char buff[256]; sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); save_weights(net, buff); } - if(get_current_batch(net)%100 == 0){ + if(get_current_batch(net)%1000 == 0){ char buff[256]; sprintf(buff, "%s/%s.backup",backup_directory,base); save_weights(net, buff); @@ -137,132 +158,19 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, char buff[256]; sprintf(buff, "%s/%s.weights", backup_directory, base); save_weights(net, buff); + pthread_join(load_thread, 0); free_network(net); - free_ptrs((void**)labels, classes); + if(labels) free_ptrs((void**)labels, classes); free_ptrs((void**)paths, plist->size); free_list(plist); free(base); } - -/* - void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear) - { - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - if(clear) *net.seen = 0; - - int imgs = net.batch * net.subdivisions; - - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - list *options = read_data_cfg(datacfg); - - char *backup_directory = option_find_str(options, "backup", "/backup/"); - char *label_list = option_find_str(options, "labels", "data/labels.list"); - char *train_list = option_find_str(options, "train", "data/train.list"); - int classes = option_find_int(options, "classes", 2); - - char **labels = get_labels(label_list); - list *plist = get_paths(train_list); - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - int N = plist->size; - clock_t time; - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.threads = 8; - - args.min = net.min_crop; - args.max = net.max_crop; - args.angle = net.angle; - args.aspect = net.aspect; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; - args.size = net.w; - args.hierarchy = net.hierarchy; - - args.paths = paths; - args.classes = classes; - args.n = imgs; - args.m = N; - args.labels = labels; - args.type = CLASSIFICATION_DATA; - - data train; - data buffer; - pthread_t load_thread; - args.d = &buffer; - load_thread = load_data(args); - - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - time=clock(); - - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data(args); - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - -#ifdef OPENCV -if(0){ -int u; -for(u = 0; u < imgs; ++u){ - image im = float_to_image(net.w, net.h, 3, train.X.vals[u]); - show_image(im, "loaded"); - cvWaitKey(0); -} -} -#endif - -float loss = train_network(net, train); -free_data(train); - -if(avg_loss == -1) avg_loss = loss; -avg_loss = avg_loss*.9 + loss*.1; -printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); -if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); -} -if(get_current_batch(net)%100 == 0){ - char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); - save_weights(net, buff); -} -} -char buff[256]; -sprintf(buff, "%s/%s.weights", backup_directory, base); -save_weights(net, buff); - -free_network(net); -free_ptrs((void**)labels, classes); -free_ptrs((void**)paths, plist->size); -free_list(plist); -free(base); -} -*/ - void validate_classifier_crop(char *datacfg, char *filename, char *weightfile) { int i = 0; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(filename, weightfile, 0); srand(time(0)); list *options = read_data_cfg(datacfg); @@ -288,8 +196,8 @@ void validate_classifier_crop(char *datacfg, char *filename, char *weightfile) data val, buffer; load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.paths = paths; args.classes = classes; @@ -326,11 +234,8 @@ void validate_classifier_crop(char *datacfg, char *filename, char *weightfile) void validate_classifier_10(char *datacfg, char *filename, char *weightfile) { int i, j; - network net = parse_network_cfg(filename); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(filename, weightfile, 0); + set_batch_network(net, 1); srand(time(0)); list *options = read_data_cfg(datacfg); @@ -360,8 +265,8 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile) break; } } - int w = net.w; - int h = net.h; + int w = net->w; + int h = net->h; int shift = 32; image im = load_image_color(paths[i], w+shift, h+shift); image images[10]; @@ -379,7 +284,7 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile) float *pred = calloc(classes, sizeof(float)); for(j = 0; j < 10; ++j){ float *p = network_predict(net, images[j].data); - if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1); + if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1, 1); axpy_cpu(classes, 1, p, 1, pred, 1); free_image(images[j]); } @@ -398,11 +303,8 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile) void validate_classifier_full(char *datacfg, char *filename, char *weightfile) { int i, j; - network net = parse_network_cfg(filename); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(filename, weightfile, 0); + set_batch_network(net, 1); srand(time(0)); list *options = read_data_cfg(datacfg); @@ -423,7 +325,7 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile) float avg_topk = 0; int *indexes = calloc(topk, sizeof(int)); - int size = net.w; + int size = net->w; for(i = 0; i < m; ++i){ int class = -1; char *path = paths[i]; @@ -435,12 +337,12 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile) } image im = load_image_color(paths[i], 0, 0); image resized = resize_min(im, size); - resize_network(&net, resized.w, resized.h); + resize_network(net, resized.w, resized.h); //show_image(im, "orig"); //show_image(crop, "cropped"); //cvWaitKey(0); float *pred = network_predict(net, resized.data); - if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1); + if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1); free_image(im); free_image(resized); @@ -459,18 +361,15 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile) void validate_classifier_single(char *datacfg, char *filename, char *weightfile) { int i, j; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(filename, weightfile, 0); + set_batch_network(net, 1); srand(time(0)); list *options = read_data_cfg(datacfg); char *label_list = option_find_str(options, "labels", "data/labels.list"); char *leaf_list = option_find_str(options, "leaves", 0); - if(leaf_list) change_leaves(net.hierarchy, leaf_list); + if(leaf_list) change_leaves(net->hierarchy, leaf_list); char *valid_list = option_find_str(options, "valid", "data/train.list"); int classes = option_find_int(options, "classes", 2); int topk = option_find_int(options, "top", 1); @@ -496,15 +395,14 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile) } } image im = load_image_color(paths[i], 0, 0); - image resized = resize_min(im, net.w); - image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h); + image crop = center_crop_image(im, net->w, net->h); + //grayscale_image_3c(crop); //show_image(im, "orig"); //show_image(crop, "cropped"); //cvWaitKey(0); float *pred = network_predict(net, crop.data); - if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1); + if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1); - if(resized.data != im.data) free_image(resized); free_image(im); free_image(crop); top_k(pred, classes, topk, indexes); @@ -514,18 +412,16 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile) if(indexes[j] == class) avg_topk += 1; } + printf("%s, %d, %f, %f, \n", paths[i], class, pred[0], pred[1]); printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); } } -void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) +void validate_classifier_multi(char *datacfg, char *cfg, char *weights) { int i, j; - network net = parse_network_cfg(filename); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); srand(time(0)); list *options = read_data_cfg(datacfg); @@ -537,7 +433,8 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) char **labels = get_labels(label_list); list *plist = get_paths(valid_list); - int scales[] = {224, 288, 320, 352, 384}; + //int scales[] = {224, 288, 320, 352, 384}; + int scales[] = {224, 256, 288, 320}; int nscales = sizeof(scales)/sizeof(scales[0]); char **paths = (char **)list_to_array(plist); @@ -560,10 +457,10 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) float *pred = calloc(classes, sizeof(float)); image im = load_image_color(paths[i], 0, 0); for(j = 0; j < nscales; ++j){ - image r = resize_min(im, scales[j]); - resize_network(&net, r.w, r.h); + image r = resize_max(im, scales[j]); + resize_network(net, r.w, r.h); float *p = network_predict(net, r.data); - if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1); + if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1 , 1); axpy_cpu(classes, 1, p, 1, pred, 1); flip_image(r); p = network_predict(net, r.data); @@ -584,11 +481,8 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); srand(2222222); list *options = read_data_cfg(datacfg); @@ -629,7 +523,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena time=clock(); float *predictions = network_predict(net, X); - layer l = net.layers[layer_num]; + layer l = net->layers[layer_num]; for(i = 0; i < l.c; ++i){ if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]); } @@ -665,11 +559,8 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); srand(2222222); list *options = read_data_cfg(datacfg); @@ -684,7 +575,6 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi int *indexes = calloc(top, sizeof(int)); char buff[256]; char *input = buff; - int size = net.w; while(1){ if(filename){ strncpy(input, filename, 256); @@ -696,20 +586,23 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi strtok(input, "\n"); } image im = load_image_color(input, 0, 0); - image r = resize_min(im, size); - resize_network(&net, r.w, r.h); - printf("%d %d\n", r.w, r.h); + image r = letterbox_image(im, net->w, net->h); + //image r = resize_min(im, 320); + //printf("%d %d\n", r.w, r.h); + //resize_network(net, r.w, r.h); + //printf("%d %d\n", r.w, r.h); float *X = r.data; time=clock(); float *predictions = network_predict(net, X); - if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0); - top_k(predictions, net.outputs, top, indexes); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1); + top_k(predictions, net->outputs, top, indexes); + fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time)); for(i = 0; i < top; ++i){ int index = indexes[i]; - if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root"); - else printf("%s: %f\n",names[index], predictions[index]); + //if(net->hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net->hierarchy->parent[index] >= 0) ? names[net->hierarchy->parent[index]] : "Root"); + //else printf("%s: %f\n",names[index], predictions[index]); + printf("%5.2f%%: %s\n", predictions[index]*100, names[index]); } if(r.data != im.data) free_image(r); free_image(im); @@ -721,11 +614,8 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi void label_classifier(char *datacfg, char *filename, char *weightfile) { int i; - network net = parse_network_cfg(filename); - set_batch_network(&net, 1); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(filename, weightfile, 0); + set_batch_network(net, 1); srand(time(0)); list *options = read_data_cfg(datacfg); @@ -743,8 +633,8 @@ void label_classifier(char *datacfg, char *filename, char *weightfile) for(i = 0; i < m; ++i){ image im = load_image_color(paths[i], 0, 0); - image resized = resize_min(im, net.w); - image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h); + image resized = resize_min(im, net->w); + image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h); float *pred = network_predict(net, crop.data); if(resized.data != im.data) free_image(resized); @@ -756,14 +646,50 @@ void label_classifier(char *datacfg, char *filename, char *weightfile) } } +void csv_classifier(char *datacfg, char *cfgfile, char *weightfile) +{ + int i,j; + network *net = load_network(cfgfile, weightfile, 0); + srand(time(0)); + + list *options = read_data_cfg(datacfg); + + char *test_list = option_find_str(options, "test", "data/test.list"); + int top = option_find_int(options, "top", 1); + + list *plist = get_paths(test_list); + + char **paths = (char **)list_to_array(plist); + int m = plist->size; + free_list(plist); + int *indexes = calloc(top, sizeof(int)); + + for(i = 0; i < m; ++i){ + double time = what_time_is_it_now(); + char *path = paths[i]; + image im = load_image_color(path, 0, 0); + image r = letterbox_image(im, net->w, net->h); + float *predictions = network_predict(net, r.data); + if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1); + top_k(predictions, net->outputs, top, indexes); + + printf("%s", path); + for(j = 0; j < top; ++j){ + printf("\t%d", indexes[j]); + } + printf("\n"); + + free_image(im); + free_image(r); + + fprintf(stderr, "%lf seconds, %d images, %d total\n", what_time_is_it_now() - time, i+1, m); + } +} void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer) { int curr = 0; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, 0); srand(time(0)); list *options = read_data_cfg(datacfg); @@ -782,18 +708,18 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_ data val, buffer; load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.paths = paths; args.classes = classes; - args.n = net.batch; + args.n = net->batch; args.m = 0; args.labels = 0; args.d = &buffer; args.type = OLD_CLASSIFICATION_DATA; pthread_t load_thread = load_data_in_thread(args); - for(curr = net.batch; curr < m; curr += net.batch){ + for(curr = net->batch; curr < m; curr += net->batch){ time=clock(); pthread_join(load_thread, 0); @@ -801,7 +727,7 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_ if(curr < m){ args.paths = paths + curr; - if (curr + net.batch > m) args.n = m - curr; + if (curr + net->batch > m) args.n = m - curr; load_thread = load_data_in_thread(args); } fprintf(stderr, "Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time)); @@ -811,11 +737,11 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_ int i, j; if (target_layer >= 0){ - //layer l = net.layers[target_layer]; + //layer l = net->layers[target_layer]; } for(i = 0; i < pred.rows; ++i){ - printf("%s", paths[curr-net.batch+i]); + printf("%s", paths[curr-net->batch+i]); for(j = 0; j < pred.cols; ++j){ printf("\t%g", pred.vals[i][j]); } @@ -829,6 +755,44 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_ } } +void file_output_classifier(char *datacfg, char *filename, char *weightfile, char *listfile) +{ + int i,j; + network *net = load_network(filename, weightfile, 0); + set_batch_network(net, 1); + srand(time(0)); + + list *options = read_data_cfg(datacfg); + + //char *label_list = option_find_str(options, "names", "data/labels.list"); + int classes = option_find_int(options, "classes", 2); + + list *plist = get_paths(listfile); + + char **paths = (char **)list_to_array(plist); + int m = plist->size; + free_list(plist); + + for(i = 0; i < m; ++i){ + image im = load_image_color(paths[i], 0, 0); + image resized = resize_min(im, net->w); + image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h); + + float *pred = network_predict(net, crop.data); + if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 0, 1); + + if(resized.data != im.data) free_image(resized); + free_image(im); + free_image(crop); + + printf("%s", paths[i]); + for(j = 0; j < classes; ++j){ + printf("\t%g", pred[j]); + } + printf("\n"); + } +} + void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) { @@ -837,21 +801,12 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i float roll = .2; printf("Classifier Demo\n"); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); list *options = read_data_cfg(datacfg); srand(2222222); - CvCapture * cap; - - if(filename){ - cap = cvCaptureFromFile(filename); - }else{ - cap = cvCaptureFromCAM(cam_index); - } + void * cap = open_video_stream(filename, cam_index, 0,0,0); int top = option_find_int(options, "top", 1); @@ -875,7 +830,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i image in = get_image_from_stream(cap); if(!in.data) break; - image in_s = resize_image(in, net.w, net.h); + image in_s = resize_image(in, net->w, net->h); image out = in; int x1 = out.w / 20; @@ -948,8 +903,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i } if(1){ - show_image(out, "Threat"); - cvWaitKey(10); + show_image(out, "Threat", 10); } free_image(in_s); free_image(in); @@ -969,21 +923,12 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697}; printf("Classifier Demo\n"); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); list *options = read_data_cfg(datacfg); srand(2222222); - CvCapture * cap; - - if(filename){ - cap = cvCaptureFromFile(filename); - }else{ - cap = cvCaptureFromCAM(cam_index); - } + void * cap = open_video_stream(filename, cam_index, 0,0,0); int top = option_find_int(options, "top", 1); @@ -993,8 +938,6 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde int *indexes = calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL); - cvResizeWindow("Threat Detection", 512, 512); float fps = 0; int i; @@ -1003,8 +946,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde gettimeofday(&tval_before, NULL); image in = get_image_from_stream(cap); - image in_s = resize_image(in, net.w, net.h); - show_image(in, "Threat Detection"); + image in_s = resize_image(in, net->w, net->h); float *predictions = network_predict(net, in_s.data); top_predictions(net, top, indexes); @@ -1029,11 +971,10 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde } } + show_image(in, "Threat Detection", 10); free_image(in_s); free_image(in); - cvWaitKey(10); - gettimeofday(&tval_after, NULL); timersub(&tval_after, &tval_before, &tval_result); float curr = 1000000.f/((long int)tval_result.tv_usec); @@ -1045,33 +986,28 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) { #ifdef OPENCV + char *base = basecfg(cfgfile); + image **alphabet = load_alphabet(); printf("Classifier Demo\n"); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); list *options = read_data_cfg(datacfg); srand(2222222); - CvCapture * cap; - if(filename){ - cap = cvCaptureFromFile(filename); - }else{ - cap = cvCaptureFromCAM(cam_index); - } + int w = 1280; + int h = 720; + void * cap = open_video_stream(filename, cam_index, w, h, 0); int top = option_find_int(options, "top", 1); - char *name_list = option_find_str(options, "names", 0); + char *label_list = option_find_str(options, "labels", 0); + char *name_list = option_find_str(options, "names", label_list); char **names = get_labels(name_list); int *indexes = calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow("Classifier", CV_WINDOW_NORMAL); - cvResizeWindow("Classifier", 512, 512); float fps = 0; int i; @@ -1080,27 +1016,38 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind gettimeofday(&tval_before, NULL); image in = get_image_from_stream(cap); - image in_s = resize_image(in, net.w, net.h); - show_image(in, "Classifier"); + //image in_s = resize_image(in, net->w, net->h); + image in_s = letterbox_image(in, net->w, net->h); float *predictions = network_predict(net, in_s.data); - if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1); + if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1); top_predictions(net, top, indexes); printf("\033[2J"); printf("\033[1;1H"); printf("\nFPS:%.0f\n",fps); + int lh = in.h*.03; + int toph = 3*lh; + + float rgb[3] = {1,1,1}; for(i = 0; i < top; ++i){ + printf("%d\n", toph); int index = indexes[i]; printf("%.1f%%: %s\n", predictions[index]*100, names[index]); + + char buff[1024]; + sprintf(buff, "%3.1f%%: %s\n", predictions[index]*100, names[index]); + image label = get_label(alphabet, buff, lh); + draw_label(in, toph, lh, label, rgb); + toph += 2*lh; + free_image(label); } + show_image(in, base, 10); free_image(in_s); free_image(in); - cvWaitKey(10); - gettimeofday(&tval_after, NULL); timersub(&tval_after, &tval_before, &tval_result); float curr = 1000000.f/((long int)tval_result.tv_usec); @@ -1118,27 +1065,9 @@ void run_classifier(int argc, char **argv) } char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); - int *gpus = 0; - int gpu = 0; - int ngpus = 0; - if(gpu_list){ - printf("%s\n", gpu_list); - int len = strlen(gpu_list); - ngpus = 1; - int i; - for(i = 0; i < len; ++i){ - if (gpu_list[i] == ',') ++ngpus; - } - gpus = calloc(ngpus, sizeof(int)); - for(i = 0; i < ngpus; ++i){ - gpus[i] = atoi(gpu_list); - gpu_list = strchr(gpu_list, ',')+1; - } - } else { - gpu = gpu_index; - gpus = &gpu; - ngpus = 1; - } + int ngpus; + int *gpus = read_intlist(gpu_list, &ngpus, gpu_index); + int cam_index = find_int_arg(argc, argv, "-c", 0); int top = find_int_arg(argc, argv, "-t", 0); @@ -1150,12 +1079,14 @@ void run_classifier(int argc, char **argv) char *layer_s = (argc > 7) ? argv[7]: 0; int layer = layer_s ? atoi(layer_s) : -1; if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); + else if(0==strcmp(argv[2], "fout")) file_output_classifier(data, cfg, weights, filename); else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear); else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer); + else if(0==strcmp(argv[2], "csv")) csv_classifier(data, cfg, weights); else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights); else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights); else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights); diff --git a/image.darknet/inst/include/darknet/src/coco.c b/image.darknet/inst/include/darknet/examples/coco.c similarity index 72% rename from image.darknet/inst/include/darknet/src/coco.c rename to image.darknet/inst/include/darknet/examples/coco.c index 8f3c968..6a50b89 100644 --- a/image.darknet/inst/include/darknet/src/coco.c +++ b/image.darknet/inst/include/darknet/examples/coco.c @@ -1,16 +1,6 @@ -#include - -#include "network.h" -#include "detection_layer.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "box.h" -#include "demo.h" +#include "darknet.h" -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif +#include char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"}; @@ -27,17 +17,14 @@ void train_coco(char *cfgfile, char *weightfile) char *base = basecfg(cfgfile); printf("%s\n", base); float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; + network *net = load_network(cfgfile, weightfile, 0); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + int imgs = net->batch*net->subdivisions; + int i = *net->seen/imgs; data train, buffer; - layer l = net.layers[net.n - 1]; + layer l = net->layers[net->n - 1]; int side = l.side; int classes = l.classes; @@ -48,8 +35,8 @@ void train_coco(char *cfgfile, char *weightfile) char **paths = (char **)list_to_array(plist); load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.paths = paths; args.n = imgs; args.m = plist->size; @@ -59,15 +46,15 @@ void train_coco(char *cfgfile, char *weightfile) args.d = &buffer; args.type = REGION_DATA; - args.angle = net.angle; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; + args.angle = net->angle; + args.exposure = net->exposure; + args.saturation = net->saturation; + args.hue = net->hue; pthread_t load_thread = load_data_in_thread(args); clock_t time; //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ + while(get_current_batch(net) < net->max_batches){ i += 1; time=clock(); pthread_join(load_thread, 0); @@ -77,7 +64,7 @@ void train_coco(char *cfgfile, char *weightfile) printf("Loaded: %lf seconds\n", sec(clock()-time)); /* - image im = float_to_image(net.w, net.h, 3, train.X.vals[113]); + image im = float_to_image(net->w, net->h, 3, train.X.vals[113]); image copy = copy_image(im); draw_coco(copy, train.y.vals[113], 7, "truth"); cvWaitKey(0); @@ -107,14 +94,14 @@ void train_coco(char *cfgfile, char *weightfile) save_weights(net, buff); } -void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxes, int classes, int w, int h) +static void print_cocos(FILE *fp, int image_id, detection *dets, int num_boxes, int classes, int w, int h) { int i, j; for(i = 0; i < num_boxes; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; + float xmin = dets[i].bbox.x - dets[i].bbox.w/2.; + float xmax = dets[i].bbox.x + dets[i].bbox.w/2.; + float ymin = dets[i].bbox.y - dets[i].bbox.h/2.; + float ymax = dets[i].bbox.y + dets[i].bbox.h/2.; if (xmin < 0) xmin = 0; if (ymin < 0) ymin = 0; @@ -127,7 +114,7 @@ void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxe float bh = ymax - ymin; for(j = 0; j < classes; ++j){ - if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]); + if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]); } } } @@ -138,14 +125,11 @@ int get_coco_image_id(char *filename) return atoi(p+1); } -void validate_coco(char *cfgfile, char *weightfile) +void validate_coco(char *cfg, char *weights) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); srand(time(0)); char *base = "results/"; @@ -154,20 +138,14 @@ void validate_coco(char *cfgfile, char *weightfile) //list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); char **paths = (char **)list_to_array(plist); - layer l = net.layers[net.n-1]; + layer l = net->layers[net->n-1]; int classes = l.classes; - int side = l.side; - int j; char buff[1024]; snprintf(buff, 1024, "%s/coco_results.json", base); FILE *fp = fopen(buff, "w"); fprintf(fp, "[\n"); - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - int m = plist->size; int i=0; int t; @@ -184,8 +162,8 @@ void validate_coco(char *cfgfile, char *weightfile) pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.type = IMAGE_DATA; for(t = 0; t < nthreads; ++t){ @@ -215,9 +193,11 @@ void validate_coco(char *cfgfile, char *weightfile) network_predict(net, X); int w = val[t].w; int h = val[t].h; - get_detection_boxes(l, w, h, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh); - print_cocos(fp, image_id, boxes, probs, side*side*l.n, classes, w, h); + int nboxes = 0; + detection *dets = get_network_boxes(net, w, h, thresh, 0, 0, 0, &nboxes); + if (nms) do_nms_sort(dets, l.side*l.side*l.n, classes, iou_thresh); + print_cocos(fp, image_id, dets, l.side*l.side*l.n, classes, w, h); + free_detections(dets, nboxes); free_image(val[t]); free_image(val_resized[t]); } @@ -231,19 +211,16 @@ void validate_coco(char *cfgfile, char *weightfile) void validate_coco_recall(char *cfgfile, char *weightfile) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); srand(time(0)); char *base = "results/comp4_det_test_"; list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); char **paths = (char **)list_to_array(plist); - layer l = net.layers[net.n-1]; + layer l = net->layers[net->n-1]; int classes = l.classes; int side = l.side; @@ -254,9 +231,6 @@ void validate_coco_recall(char *cfgfile, char *weightfile) snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]); fps[j] = fopen(buff, "w"); } - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); int m = plist->size; int i=0; @@ -264,7 +238,6 @@ void validate_coco_recall(char *cfgfile, char *weightfile) float thresh = .001; int nms = 0; float iou_thresh = .5; - float nms_thresh = .5; int total = 0; int correct = 0; @@ -274,11 +247,13 @@ void validate_coco_recall(char *cfgfile, char *weightfile) for(i = 0; i < m; ++i){ char *path = paths[i]; image orig = load_image_color(path, 0, 0); - image sized = resize_image(orig, net.w, net.h); + image sized = resize_image(orig, net->w, net->h); char *id = basecfg(path); network_predict(net, sized.data); - get_detection_boxes(l, 1, 1, thresh, probs, boxes, 1); - if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); + + int nboxes = 0; + detection *dets = get_network_boxes(net, orig.w, orig.h, thresh, 0, 0, 1, &nboxes); + if (nms) do_nms_obj(dets, side*side*l.n, 1, nms); char labelpath[4096]; find_replace(path, "images", "labels", labelpath); @@ -289,7 +264,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); for(k = 0; k < side*side*l.n; ++k){ - if(probs[k][0] > thresh){ + if(dets[k].objectness > thresh){ ++proposals; } } @@ -298,8 +273,8 @@ void validate_coco_recall(char *cfgfile, char *weightfile) box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h}; float best_iou = 0; for(k = 0; k < side*side*l.n; ++k){ - float iou = box_iou(boxes[k], t); - if(probs[k][0] > thresh && iou > best_iou){ + float iou = box_iou(dets[k].bbox, t); + if(dets[k].objectness > thresh && iou > best_iou){ best_iou = iou; } } @@ -308,7 +283,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) ++correct; } } - + free_detections(dets, nboxes); fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); free(id); free_image(orig); @@ -319,21 +294,14 @@ void validate_coco_recall(char *cfgfile, char *weightfile) void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) { image **alphabet = load_alphabet(); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - detection_layer l = net.layers[net.n-1]; - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + layer l = net->layers[net->n-1]; + set_batch_network(net, 1); srand(2222222); float nms = .4; clock_t time; char buff[256]; char *input = buff; - int j; - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); while(1){ if(filename){ strncpy(input, filename, 256); @@ -345,22 +313,22 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) strtok(input, "\n"); } image im = load_image_color(input,0,0); - image sized = resize_image(im, net.w, net.h); + image sized = resize_image(im, net->w, net->h); float *X = sized.data; time=clock(); network_predict(net, X); printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms); - draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, alphabet, 80); + + int nboxes = 0; + detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes); + if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms); + + draw_detections(im, dets, l.side*l.side*l.n, thresh, coco_classes, alphabet, 80); save_image(im, "prediction"); - show_image(im, "predictions"); + show_image(im, "predictions", 0); + free_detections(dets, nboxes); free_image(im); free_image(sized); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif if (filename) break; } } @@ -380,9 +348,10 @@ void run_coco(int argc, char **argv) char *cfg = argv[3]; char *weights = (argc > 4) ? argv[4] : 0; char *filename = (argc > 5) ? argv[5]: 0; + int avg = find_int_arg(argc, argv, "-avg", 1); if(0==strcmp(argv[2], "test")) test_coco(cfg, weights, filename, thresh); else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights); else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); - else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, coco_classes, 80, frame_skip, prefix, .5); + else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, coco_classes, 80, frame_skip, prefix, avg, .5, 0,0,0,0); } diff --git a/image.darknet/inst/include/darknet/src/darknet.c b/image.darknet/inst/include/darknet/examples/darknet.c similarity index 62% rename from image.darknet/inst/include/darknet/src/darknet.c rename to image.darknet/inst/include/darknet/examples/darknet.c index 6e56072..d538359 100644 --- a/image.darknet/inst/include/darknet/src/darknet.c +++ b/image.darknet/inst/include/darknet/examples/darknet.c @@ -1,56 +1,46 @@ +#include "darknet.h" + #include #include #include -#include "parser.h" -#include "utils.h" -#include "cuda.h" -#include "blas.h" -#include "connected_layer.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top); -extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh); -extern void run_voxel(int argc, char **argv); +extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen); extern void run_yolo(int argc, char **argv); extern void run_detector(int argc, char **argv); extern void run_coco(int argc, char **argv); -extern void run_writing(int argc, char **argv); -extern void run_captcha(int argc, char **argv); extern void run_nightmare(int argc, char **argv); -extern void run_dice(int argc, char **argv); -extern void run_compare(int argc, char **argv); extern void run_classifier(int argc, char **argv); +extern void run_regressor(int argc, char **argv); +extern void run_segmenter(int argc, char **argv); +extern void run_isegmenter(int argc, char **argv); extern void run_char_rnn(int argc, char **argv); -extern void run_vid_rnn(int argc, char **argv); extern void run_tag(int argc, char **argv); extern void run_cifar(int argc, char **argv); extern void run_go(int argc, char **argv); extern void run_art(int argc, char **argv); extern void run_super(int argc, char **argv); +extern void run_lsd(int argc, char **argv); void average(int argc, char *argv[]) { char *cfgfile = argv[2]; char *outfile = argv[3]; gpu_index = -1; - network net = parse_network_cfg(cfgfile); - network sum = parse_network_cfg(cfgfile); + network *net = parse_network_cfg(cfgfile); + network *sum = parse_network_cfg(cfgfile); char *weightfile = argv[4]; - load_weights(&sum, weightfile); + load_weights(sum, weightfile); int i, j; int n = argc - 5; for(i = 0; i < n; ++i){ weightfile = argv[i+5]; - load_weights(&net, weightfile); - for(j = 0; j < net.n; ++j){ - layer l = net.layers[j]; - layer out = sum.layers[j]; + load_weights(net, weightfile); + for(j = 0; j < net->n; ++j){ + layer l = net->layers[j]; + layer out = sum->layers[j]; if(l.type == CONVOLUTIONAL){ int num = l.n*l.c*l.size*l.size; axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1); @@ -68,8 +58,8 @@ void average(int argc, char *argv[]) } } n = n+1; - for(j = 0; j < net.n; ++j){ - layer l = sum.layers[j]; + for(j = 0; j < net->n; ++j){ + layer l = sum->layers[j]; if(l.type == CONVOLUTIONAL){ int num = l.n*l.c*l.size*l.size; scal_cpu(l.n, 1./n, l.biases, 1); @@ -88,19 +78,57 @@ void average(int argc, char *argv[]) save_weights(sum, outfile); } +long numops(network *net) +{ + int i; + long ops = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == CONVOLUTIONAL){ + ops += 2l * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w; + } else if(l.type == CONNECTED){ + ops += 2l * l.inputs * l.outputs; + } else if (l.type == RNN){ + ops += 2l * l.input_layer->inputs * l.input_layer->outputs; + ops += 2l * l.self_layer->inputs * l.self_layer->outputs; + ops += 2l * l.output_layer->inputs * l.output_layer->outputs; + } else if (l.type == GRU){ + ops += 2l * l.uz->inputs * l.uz->outputs; + ops += 2l * l.uh->inputs * l.uh->outputs; + ops += 2l * l.ur->inputs * l.ur->outputs; + ops += 2l * l.wz->inputs * l.wz->outputs; + ops += 2l * l.wh->inputs * l.wh->outputs; + ops += 2l * l.wr->inputs * l.wr->outputs; + } else if (l.type == LSTM){ + ops += 2l * l.uf->inputs * l.uf->outputs; + ops += 2l * l.ui->inputs * l.ui->outputs; + ops += 2l * l.ug->inputs * l.ug->outputs; + ops += 2l * l.uo->inputs * l.uo->outputs; + ops += 2l * l.wf->inputs * l.wf->outputs; + ops += 2l * l.wi->inputs * l.wi->outputs; + ops += 2l * l.wg->inputs * l.wg->outputs; + ops += 2l * l.wo->inputs * l.wo->outputs; + } + } + return ops; +} + void speed(char *cfgfile, int tics) { if (tics == 0) tics = 1000; - network net = parse_network_cfg(cfgfile); - set_batch_network(&net, 1); + network *net = parse_network_cfg(cfgfile); + set_batch_network(net, 1); int i; - time_t start = time(0); - image im = make_image(net.w, net.h, net.c); + double time=what_time_is_it_now(); + image im = make_image(net->w, net->h, net->c*net->batch); for(i = 0; i < tics; ++i){ network_predict(net, im.data); } - double t = difftime(time(0), start); + double t = what_time_is_it_now() - time; + long ops = numops(net); printf("\n%d evals, %f Seconds\n", tics, t); + printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.); + printf("FLOPS: %.2f Bn\n", (float)ops/1000000000.*tics/t); printf("Speed: %f sec/eval\n", t/tics); printf("Speed: %f Hz\n", tics/t); } @@ -108,17 +136,8 @@ void speed(char *cfgfile, int tics) void operations(char *cfgfile) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); - int i; - long ops = 0; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; - if(l.type == CONVOLUTIONAL){ - ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w; - } else if(l.type == CONNECTED){ - ops += 2l * l.inputs * l.outputs; - } - } + network *net = parse_network_cfg(cfgfile); + long ops = numops(net); printf("Floating Point Operations: %ld\n", ops); printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.); } @@ -126,52 +145,75 @@ void operations(char *cfgfile) void oneoff(char *cfgfile, char *weightfile, char *outfile) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); - int oldn = net.layers[net.n - 2].n; - int c = net.layers[net.n - 2].c; - scal_cpu(oldn*c, .1, net.layers[net.n - 2].weights, 1); - scal_cpu(oldn, 0, net.layers[net.n - 2].biases, 1); - net.layers[net.n - 2].n = 9418; - net.layers[net.n - 2].biases += 5; - net.layers[net.n - 2].weights += 5*c; + network *net = parse_network_cfg(cfgfile); + int oldn = net->layers[net->n - 2].n; + int c = net->layers[net->n - 2].c; + scal_cpu(oldn*c, .1, net->layers[net->n - 2].weights, 1); + scal_cpu(oldn, 0, net->layers[net->n - 2].biases, 1); + net->layers[net->n - 2].n = 11921; + net->layers[net->n - 2].biases += 5; + net->layers[net->n - 2].weights += 5*c; if(weightfile){ - load_weights(&net, weightfile); + load_weights(net, weightfile); } - net.layers[net.n - 2].biases -= 5; - net.layers[net.n - 2].weights -= 5*c; - net.layers[net.n - 2].n = oldn; + net->layers[net->n - 2].biases -= 5; + net->layers[net->n - 2].weights -= 5*c; + net->layers[net->n - 2].n = oldn; printf("%d\n", oldn); - layer l = net.layers[net.n - 2]; + layer l = net->layers[net->n - 2]; copy_cpu(l.n/3, l.biases, 1, l.biases + l.n/3, 1); copy_cpu(l.n/3, l.biases, 1, l.biases + 2*l.n/3, 1); copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + l.n/3*l.c, 1); copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + 2*l.n/3*l.c, 1); - *net.seen = 0; + *net->seen = 0; save_weights(net, outfile); } -void partial(char *cfgfile, char *weightfile, char *outfile, int max) +void oneoff2(char *cfgfile, char *weightfile, char *outfile, int l) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); + network *net = parse_network_cfg(cfgfile); if(weightfile){ - load_weights_upto(&net, weightfile, max); + load_weights_upto(net, weightfile, 0, net->n); + load_weights_upto(net, weightfile, l, net->n); } - *net.seen = 0; + *net->seen = 0; + save_weights_upto(net, outfile, net->n); +} + +void partial(char *cfgfile, char *weightfile, char *outfile, int max) +{ + gpu_index = -1; + network *net = load_network(cfgfile, weightfile, 1); save_weights_upto(net, outfile, max); } -#include "convolutional_layer.h" -void rescale_net(char *cfgfile, char *weightfile, char *outfile) +void print_weights(char *cfgfile, char *weightfile, int n) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); + network *net = load_network(cfgfile, weightfile, 1); + layer l = net->layers[n]; + int i, j; + //printf("["); + for(i = 0; i < l.n; ++i){ + //printf("["); + for(j = 0; j < l.size*l.size*l.c; ++j){ + //if(j > 0) printf(","); + printf("%g ", l.weights[i*l.size*l.size*l.c + j]); + } + printf("\n"); + //printf("]%s\n", (i == l.n-1)?"":","); } + //printf("]"); +} + +void rescale_net(char *cfgfile, char *weightfile, char *outfile) +{ + gpu_index = -1; + network *net = load_network(cfgfile, weightfile, 0); int i; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; if(l.type == CONVOLUTIONAL){ rescale_weights(l, 2, -.5); break; @@ -183,13 +225,10 @@ void rescale_net(char *cfgfile, char *weightfile, char *outfile) void rgbgr_net(char *cfgfile, char *weightfile, char *outfile) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, 0); int i; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; if(l.type == CONVOLUTIONAL){ rgbgr_weights(l); break; @@ -201,13 +240,10 @@ void rgbgr_net(char *cfgfile, char *weightfile, char *outfile) void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if (weightfile) { - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, 0); int i; - for (i = 0; i < net.n; ++i) { - layer l = net.layers[i]; + for (i = 0; i < net->n; ++i) { + layer l = net->layers[i]; if (l.type == CONVOLUTIONAL && l.batch_normalize) { denormalize_convolutional_layer(l); } @@ -242,18 +278,15 @@ layer normalize_layer(layer l, int n) void normalize_net(char *cfgfile, char *weightfile, char *outfile) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, 0); int i; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; if(l.type == CONVOLUTIONAL && !l.batch_normalize){ - net.layers[i] = normalize_layer(l, l.n); + net->layers[i] = normalize_layer(l, l.n); } if (l.type == CONNECTED && !l.batch_normalize) { - net.layers[i] = normalize_layer(l, l.outputs); + net->layers[i] = normalize_layer(l, l.outputs); } if (l.type == GRU && l.batch_normalize) { *l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs); @@ -262,7 +295,7 @@ void normalize_net(char *cfgfile, char *weightfile, char *outfile) *l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs); *l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs); *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs); - net.layers[i].batch_normalize=1; + net->layers[i].batch_normalize=1; } } save_weights(net, outfile); @@ -271,13 +304,10 @@ void normalize_net(char *cfgfile, char *weightfile, char *outfile) void statistics_net(char *cfgfile, char *weightfile) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if (weightfile) { - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, 0); int i; - for (i = 0; i < net.n; ++i) { - layer l = net.layers[i]; + for (i = 0; i < net->n; ++i) { + layer l = net->layers[i]; if (l.type == CONNECTED && l.batch_normalize) { printf("Connected Layer %d\n", i); statistics_connected_layer(l); @@ -304,20 +334,17 @@ void statistics_net(char *cfgfile, char *weightfile) void denormalize_net(char *cfgfile, char *weightfile, char *outfile) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); - if (weightfile) { - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, 0); int i; - for (i = 0; i < net.n; ++i) { - layer l = net.layers[i]; - if (l.type == CONVOLUTIONAL && l.batch_normalize) { + for (i = 0; i < net->n; ++i) { + layer l = net->layers[i]; + if ((l.type == DECONVOLUTIONAL || l.type == CONVOLUTIONAL) && l.batch_normalize) { denormalize_convolutional_layer(l); - net.layers[i].batch_normalize=0; + net->layers[i].batch_normalize=0; } if (l.type == CONNECTED && l.batch_normalize) { denormalize_connected_layer(l); - net.layers[i].batch_normalize=0; + net->layers[i].batch_normalize=0; } if (l.type == GRU && l.batch_normalize) { denormalize_connected_layer(*l.input_z_layer); @@ -332,22 +359,42 @@ void denormalize_net(char *cfgfile, char *weightfile, char *outfile) l.state_z_layer->batch_normalize = 0; l.state_r_layer->batch_normalize = 0; l.state_h_layer->batch_normalize = 0; - net.layers[i].batch_normalize=0; + net->layers[i].batch_normalize=0; } } save_weights(net, outfile); } -void visualize(char *cfgfile, char *weightfile) +void mkimg(char *cfgfile, char *weightfile, int h, int w, int num, char *prefix) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); + network *net = load_network(cfgfile, weightfile, 0); + image *ims = get_weights(net->layers[0]); + int n = net->layers[0].n; + int z; + for(z = 0; z < num; ++z){ + image im = make_image(h, w, 3); + fill_image(im, .5); + int i; + for(i = 0; i < 100; ++i){ + image r = copy_image(ims[rand()%n]); + rotate_image_cw(r, rand()%4); + random_distort_image(r, 1, 1.5, 1.5); + int dx = rand()%(w-r.w); + int dy = rand()%(h-r.h); + ghost_image(r, im, dx, dy); + free_image(r); + } + char buff[256]; + sprintf(buff, "%s/gen_%d", prefix, z); + save_image(im, buff); + free_image(im); } +} + +void visualize(char *cfgfile, char *weightfile) +{ + network *net = load_network(cfgfile, weightfile, 0); visualize_network(net); -#ifdef OPENCV - cvWaitKey(0); -#endif } int main(int argc, char **argv) @@ -376,46 +423,44 @@ int main(int argc, char **argv) average(argc, argv); } else if (0 == strcmp(argv[1], "yolo")){ run_yolo(argc, argv); - } else if (0 == strcmp(argv[1], "voxel")){ - run_voxel(argc, argv); } else if (0 == strcmp(argv[1], "super")){ run_super(argc, argv); + } else if (0 == strcmp(argv[1], "lsd")){ + run_lsd(argc, argv); } else if (0 == strcmp(argv[1], "detector")){ run_detector(argc, argv); } else if (0 == strcmp(argv[1], "detect")){ - float thresh = find_float_arg(argc, argv, "-thresh", .24); + float thresh = find_float_arg(argc, argv, "-thresh", .5); char *filename = (argc > 4) ? argv[4]: 0; - test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5); + char *outfile = find_char_arg(argc, argv, "-out", 0); + int fullscreen = find_arg(argc, argv, "-fullscreen"); + test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5, outfile, fullscreen); } else if (0 == strcmp(argv[1], "cifar")){ run_cifar(argc, argv); } else if (0 == strcmp(argv[1], "go")){ run_go(argc, argv); } else if (0 == strcmp(argv[1], "rnn")){ run_char_rnn(argc, argv); - } else if (0 == strcmp(argv[1], "vid")){ - run_vid_rnn(argc, argv); } else if (0 == strcmp(argv[1], "coco")){ run_coco(argc, argv); } else if (0 == strcmp(argv[1], "classify")){ predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5); } else if (0 == strcmp(argv[1], "classifier")){ run_classifier(argc, argv); + } else if (0 == strcmp(argv[1], "regressor")){ + run_regressor(argc, argv); + } else if (0 == strcmp(argv[1], "isegmenter")){ + run_isegmenter(argc, argv); + } else if (0 == strcmp(argv[1], "segmenter")){ + run_segmenter(argc, argv); } else if (0 == strcmp(argv[1], "art")){ run_art(argc, argv); } else if (0 == strcmp(argv[1], "tag")){ run_tag(argc, argv); - } else if (0 == strcmp(argv[1], "compare")){ - run_compare(argc, argv); - } else if (0 == strcmp(argv[1], "dice")){ - run_dice(argc, argv); - } else if (0 == strcmp(argv[1], "writing")){ - run_writing(argc, argv); } else if (0 == strcmp(argv[1], "3d")){ composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0); } else if (0 == strcmp(argv[1], "test")){ test_resize(argv[2]); - } else if (0 == strcmp(argv[1], "captcha")){ - run_captcha(argc, argv); } else if (0 == strcmp(argv[1], "nightmare")){ run_nightmare(argc, argv); } else if (0 == strcmp(argv[1], "rgbgr")){ @@ -436,12 +481,18 @@ int main(int argc, char **argv) speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0); } else if (0 == strcmp(argv[1], "oneoff")){ oneoff(argv[2], argv[3], argv[4]); + } else if (0 == strcmp(argv[1], "oneoff2")){ + oneoff2(argv[2], argv[3], argv[4], atoi(argv[5])); + } else if (0 == strcmp(argv[1], "print")){ + print_weights(argv[2], argv[3], atoi(argv[4])); } else if (0 == strcmp(argv[1], "partial")){ partial(argv[2], argv[3], argv[4], atoi(argv[5])); } else if (0 == strcmp(argv[1], "average")){ average(argc, argv); } else if (0 == strcmp(argv[1], "visualize")){ visualize(argv[2], (argc > 3) ? argv[3] : 0); + } else if (0 == strcmp(argv[1], "mkimg")){ + mkimg(argv[2], argv[3], atoi(argv[4]), atoi(argv[5]), atoi(argv[6]), argv[7]); } else if (0 == strcmp(argv[1], "imtest")){ test_resize(argv[2]); } else { diff --git a/image.darknet/inst/include/darknet/examples/detector-scipy-opencv.py b/image.darknet/inst/include/darknet/examples/detector-scipy-opencv.py new file mode 100644 index 0000000..3bfc591 --- /dev/null +++ b/image.darknet/inst/include/darknet/examples/detector-scipy-opencv.py @@ -0,0 +1,56 @@ +# Stupid python path shit. +# Instead just add darknet.py to somewhere in your python path +# OK actually that might not be a great idea, idk, work in progress +# Use at your own risk. or don't, i don't care + +from scipy.misc import imread +import cv2 + +def array_to_image(arr): + arr = arr.transpose(2,0,1) + c = arr.shape[0] + h = arr.shape[1] + w = arr.shape[2] + arr = (arr/255.0).flatten() + data = dn.c_array(dn.c_float, arr) + im = dn.IMAGE(w,h,c,data) + return im + +def detect2(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45): + boxes = dn.make_boxes(net) + probs = dn.make_probs(net) + num = dn.num_boxes(net) + dn.network_detect(net, image, thresh, hier_thresh, nms, boxes, probs) + res = [] + for j in range(num): + for i in range(meta.classes): + if probs[j][i] > 0: + res.append((meta.names[i], probs[j][i], (boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h))) + res = sorted(res, key=lambda x: -x[1]) + dn.free_ptrs(dn.cast(probs, dn.POINTER(dn.c_void_p)), num) + return res + +import sys, os +sys.path.append(os.path.join(os.getcwd(),'python/')) + +import darknet as dn + +# Darknet +net = dn.load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0) +meta = dn.load_meta("cfg/coco.data") +r = dn.detect(net, meta, "data/dog.jpg") +print r + +# scipy +arr= imread('data/dog.jpg') +im = array_to_image(arr) +r = detect2(net, meta, im) +print r + +# OpenCV +arr = cv2.imread('data/dog.jpg') +im = array_to_image(arr) +dn.rgbgr_image(im) +r = detect2(net, meta, im) +print r + diff --git a/image.darknet/inst/include/darknet/examples/detector.c b/image.darknet/inst/include/darknet/examples/detector.c new file mode 100644 index 0000000..318f7fb --- /dev/null +++ b/image.darknet/inst/include/darknet/examples/detector.c @@ -0,0 +1,850 @@ +#include "darknet.h" + +static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; + + +void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear) +{ + list *options = read_data_cfg(datacfg); + char *train_images = option_find_str(options, "train", "data/train.list"); + char *backup_directory = option_find_str(options, "backup", "/backup/"); + + srand(time(0)); + char *base = basecfg(cfgfile); + printf("%s\n", base); + float avg_loss = -1; + network **nets = calloc(ngpus, sizeof(network)); + + srand(time(0)); + int seed = rand(); + int i; + for(i = 0; i < ngpus; ++i){ + srand(seed); +#ifdef GPU + cuda_set_device(gpus[i]); +#endif + nets[i] = load_network(cfgfile, weightfile, clear); + nets[i]->learning_rate *= ngpus; + } + srand(time(0)); + network *net = nets[0]; + + int imgs = net->batch * net->subdivisions * ngpus; + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + data train, buffer; + + layer l = net->layers[net->n - 1]; + + int classes = l.classes; + float jitter = l.jitter; + + list *plist = get_paths(train_images); + //int N = plist->size; + char **paths = (char **)list_to_array(plist); + + load_args args = get_base_args(net); + args.coords = l.coords; + args.paths = paths; + args.n = imgs; + args.m = plist->size; + args.classes = classes; + args.jitter = jitter; + args.num_boxes = l.max_boxes; + args.d = &buffer; + args.type = DETECTION_DATA; + //args.type = INSTANCE_DATA; + args.threads = 64; + + pthread_t load_thread = load_data(args); + double time; + int count = 0; + //while(i*imgs < N*120){ + while(get_current_batch(net) < net->max_batches){ + if(l.random && count++%10 == 0){ + printf("Resizing\n"); + int dim = (rand() % 10 + 10) * 32; + if (get_current_batch(net)+200 > net->max_batches) dim = 608; + //int dim = (rand() % 4 + 16) * 32; + printf("%d\n", dim); + args.w = dim; + args.h = dim; + + pthread_join(load_thread, 0); + train = buffer; + free_data(train); + load_thread = load_data(args); + + #pragma omp parallel for + for(i = 0; i < ngpus; ++i){ + resize_network(nets[i], dim, dim); + } + net = nets[0]; + } + time=what_time_is_it_now(); + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data(args); + + /* + int k; + for(k = 0; k < l.max_boxes; ++k){ + box b = float_to_box(train.y.vals[10] + 1 + k*5); + if(!b.x) break; + printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h); + } + */ + /* + int zz; + for(zz = 0; zz < train.X.cols; ++zz){ + image im = float_to_image(net->w, net->h, 3, train.X.vals[zz]); + int k; + for(k = 0; k < l.max_boxes; ++k){ + box b = float_to_box(train.y.vals[zz] + k*5, 1); + printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); + draw_bbox(im, b, 1, 1,0,0); + } + show_image(im, "truth11"); + cvWaitKey(0); + save_image(im, "truth11"); + } + */ + + printf("Loaded: %lf seconds\n", what_time_is_it_now()-time); + + time=what_time_is_it_now(); + float loss = 0; +#ifdef GPU + if(ngpus == 1){ + loss = train_network(net, train); + } else { + loss = train_networks(nets, ngpus, train, 4); + } +#else + loss = train_network(net, train); +#endif + if (avg_loss < 0) avg_loss = loss; + avg_loss = avg_loss*.9 + loss*.1; + + i = get_current_batch(net); + printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, i*imgs); + if(i%100==0){ +#ifdef GPU + if(ngpus != 1) sync_nets(nets, ngpus, 0); +#endif + char buff[256]; + sprintf(buff, "%s/%s.backup", backup_directory, base); + save_weights(net, buff); + } + if(i%10000==0 || (i < 1000 && i%100 == 0)){ +#ifdef GPU + if(ngpus != 1) sync_nets(nets, ngpus, 0); +#endif + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + save_weights(net, buff); + } + free_data(train); + } +#ifdef GPU + if(ngpus != 1) sync_nets(nets, ngpus, 0); +#endif + char buff[256]; + sprintf(buff, "%s/%s_final.weights", backup_directory, base); + save_weights(net, buff); +} + + +static int get_coco_image_id(char *filename) +{ + char *p = strrchr(filename, '/'); + char *c = strrchr(filename, '_'); + if(c) p = c; + return atoi(p+1); +} + +static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h) +{ + int i, j; + int image_id = get_coco_image_id(image_path); + for(i = 0; i < num_boxes; ++i){ + float xmin = dets[i].bbox.x - dets[i].bbox.w/2.; + float xmax = dets[i].bbox.x + dets[i].bbox.w/2.; + float ymin = dets[i].bbox.y - dets[i].bbox.h/2.; + float ymax = dets[i].bbox.y + dets[i].bbox.h/2.; + + if (xmin < 0) xmin = 0; + if (ymin < 0) ymin = 0; + if (xmax > w) xmax = w; + if (ymax > h) ymax = h; + + float bx = xmin; + float by = ymin; + float bw = xmax - xmin; + float bh = ymax - ymin; + + for(j = 0; j < classes; ++j){ + if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]); + } + } +} + +void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h) +{ + int i, j; + for(i = 0; i < total; ++i){ + float xmin = dets[i].bbox.x - dets[i].bbox.w/2. + 1; + float xmax = dets[i].bbox.x + dets[i].bbox.w/2. + 1; + float ymin = dets[i].bbox.y - dets[i].bbox.h/2. + 1; + float ymax = dets[i].bbox.y + dets[i].bbox.h/2. + 1; + + if (xmin < 1) xmin = 1; + if (ymin < 1) ymin = 1; + if (xmax > w) xmax = w; + if (ymax > h) ymax = h; + + for(j = 0; j < classes; ++j){ + if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j], + xmin, ymin, xmax, ymax); + } + } +} + +void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h) +{ + int i, j; + for(i = 0; i < total; ++i){ + float xmin = dets[i].bbox.x - dets[i].bbox.w/2.; + float xmax = dets[i].bbox.x + dets[i].bbox.w/2.; + float ymin = dets[i].bbox.y - dets[i].bbox.h/2.; + float ymax = dets[i].bbox.y + dets[i].bbox.h/2.; + + if (xmin < 0) xmin = 0; + if (ymin < 0) ymin = 0; + if (xmax > w) xmax = w; + if (ymax > h) ymax = h; + + for(j = 0; j < classes; ++j){ + int class = j; + if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, dets[i].prob[class], + xmin, ymin, xmax, ymax); + } + } +} + +void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char *outfile) +{ + int j; + list *options = read_data_cfg(datacfg); + char *valid_images = option_find_str(options, "valid", "data/train.list"); + char *name_list = option_find_str(options, "names", "data/names.list"); + char *prefix = option_find_str(options, "results", "results"); + char **names = get_labels(name_list); + char *mapf = option_find_str(options, "map", 0); + int *map = 0; + if (mapf) map = read_map(mapf); + + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 2); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + srand(time(0)); + + list *plist = get_paths(valid_images); + char **paths = (char **)list_to_array(plist); + + layer l = net->layers[net->n-1]; + int classes = l.classes; + + char buff[1024]; + char *type = option_find_str(options, "eval", "voc"); + FILE *fp = 0; + FILE **fps = 0; + int coco = 0; + int imagenet = 0; + if(0==strcmp(type, "coco")){ + if(!outfile) outfile = "coco_results"; + snprintf(buff, 1024, "%s/%s.json", prefix, outfile); + fp = fopen(buff, "w"); + fprintf(fp, "[\n"); + coco = 1; + } else if(0==strcmp(type, "imagenet")){ + if(!outfile) outfile = "imagenet-detection"; + snprintf(buff, 1024, "%s/%s.txt", prefix, outfile); + fp = fopen(buff, "w"); + imagenet = 1; + classes = 200; + } else { + if(!outfile) outfile = "comp4_det_test_"; + fps = calloc(classes, sizeof(FILE *)); + for(j = 0; j < classes; ++j){ + snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]); + fps[j] = fopen(buff, "w"); + } + } + + int m = plist->size; + int i=0; + int t; + + float thresh = .005; + float nms = .45; + + int nthreads = 4; + image *val = calloc(nthreads, sizeof(image)); + image *val_resized = calloc(nthreads, sizeof(image)); + image *buf = calloc(nthreads, sizeof(image)); + image *buf_resized = calloc(nthreads, sizeof(image)); + pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); + + image input = make_image(net->w, net->h, net->c*2); + + load_args args = {0}; + args.w = net->w; + args.h = net->h; + //args.type = IMAGE_DATA; + args.type = LETTERBOX_DATA; + + for(t = 0; t < nthreads; ++t){ + args.path = paths[i+t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + double start = what_time_is_it_now(); + for(i = nthreads; i < m+nthreads; i += nthreads){ + fprintf(stderr, "%d\n", i); + for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ + pthread_join(thr[t], 0); + val[t] = buf[t]; + val_resized[t] = buf_resized[t]; + } + for(t = 0; t < nthreads && i+t < m; ++t){ + args.path = paths[i+t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ + char *path = paths[i+t-nthreads]; + char *id = basecfg(path); + copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data, 1); + flip_image(val_resized[t]); + copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data + net->w*net->h*net->c, 1); + + network_predict(net, input.data); + int w = val[t].w; + int h = val[t].h; + int num = 0; + detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &num); + if (nms) do_nms_sort(dets, num, classes, nms); + if (coco){ + print_cocos(fp, path, dets, num, classes, w, h); + } else if (imagenet){ + print_imagenet_detections(fp, i+t-nthreads+1, dets, num, classes, w, h); + } else { + print_detector_detections(fps, id, dets, num, classes, w, h); + } + free_detections(dets, num); + free(id); + free_image(val[t]); + free_image(val_resized[t]); + } + } + for(j = 0; j < classes; ++j){ + if(fps) fclose(fps[j]); + } + if(coco){ + fseek(fp, -2, SEEK_CUR); + fprintf(fp, "\n]\n"); + fclose(fp); + } + fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start); +} + + +void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile) +{ + int j; + list *options = read_data_cfg(datacfg); + char *valid_images = option_find_str(options, "valid", "data/train.list"); + char *name_list = option_find_str(options, "names", "data/names.list"); + char *prefix = option_find_str(options, "results", "results"); + char **names = get_labels(name_list); + char *mapf = option_find_str(options, "map", 0); + int *map = 0; + if (mapf) map = read_map(mapf); + + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + srand(time(0)); + + list *plist = get_paths(valid_images); + char **paths = (char **)list_to_array(plist); + + layer l = net->layers[net->n-1]; + int classes = l.classes; + + char buff[1024]; + char *type = option_find_str(options, "eval", "voc"); + FILE *fp = 0; + FILE **fps = 0; + int coco = 0; + int imagenet = 0; + if(0==strcmp(type, "coco")){ + if(!outfile) outfile = "coco_results"; + snprintf(buff, 1024, "%s/%s.json", prefix, outfile); + fp = fopen(buff, "w"); + fprintf(fp, "[\n"); + coco = 1; + } else if(0==strcmp(type, "imagenet")){ + if(!outfile) outfile = "imagenet-detection"; + snprintf(buff, 1024, "%s/%s.txt", prefix, outfile); + fp = fopen(buff, "w"); + imagenet = 1; + classes = 200; + } else { + if(!outfile) outfile = "comp4_det_test_"; + fps = calloc(classes, sizeof(FILE *)); + for(j = 0; j < classes; ++j){ + snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]); + fps[j] = fopen(buff, "w"); + } + } + + + int m = plist->size; + int i=0; + int t; + + float thresh = .005; + float nms = .45; + + int nthreads = 4; + image *val = calloc(nthreads, sizeof(image)); + image *val_resized = calloc(nthreads, sizeof(image)); + image *buf = calloc(nthreads, sizeof(image)); + image *buf_resized = calloc(nthreads, sizeof(image)); + pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); + + load_args args = {0}; + args.w = net->w; + args.h = net->h; + //args.type = IMAGE_DATA; + args.type = LETTERBOX_DATA; + + for(t = 0; t < nthreads; ++t){ + args.path = paths[i+t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + double start = what_time_is_it_now(); + for(i = nthreads; i < m+nthreads; i += nthreads){ + fprintf(stderr, "%d\n", i); + for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ + pthread_join(thr[t], 0); + val[t] = buf[t]; + val_resized[t] = buf_resized[t]; + } + for(t = 0; t < nthreads && i+t < m; ++t){ + args.path = paths[i+t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ + char *path = paths[i+t-nthreads]; + char *id = basecfg(path); + float *X = val_resized[t].data; + network_predict(net, X); + int w = val[t].w; + int h = val[t].h; + int nboxes = 0; + detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &nboxes); + if (nms) do_nms_sort(dets, nboxes, classes, nms); + if (coco){ + print_cocos(fp, path, dets, nboxes, classes, w, h); + } else if (imagenet){ + print_imagenet_detections(fp, i+t-nthreads+1, dets, nboxes, classes, w, h); + } else { + print_detector_detections(fps, id, dets, nboxes, classes, w, h); + } + free_detections(dets, nboxes); + free(id); + free_image(val[t]); + free_image(val_resized[t]); + } + } + for(j = 0; j < classes; ++j){ + if(fps) fclose(fps[j]); + } + if(coco){ + fseek(fp, -2, SEEK_CUR); + fprintf(fp, "\n]\n"); + fclose(fp); + } + fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start); +} + +void validate_detector_recall(char *cfgfile, char *weightfile) +{ + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + srand(time(0)); + + list *plist = get_paths("data/coco_val_5k.list"); + char **paths = (char **)list_to_array(plist); + + layer l = net->layers[net->n-1]; + + int j, k; + + int m = plist->size; + int i=0; + + float thresh = .001; + float iou_thresh = .5; + float nms = .4; + + int total = 0; + int correct = 0; + int proposals = 0; + float avg_iou = 0; + + for(i = 0; i < m; ++i){ + char *path = paths[i]; + image orig = load_image_color(path, 0, 0); + image sized = resize_image(orig, net->w, net->h); + char *id = basecfg(path); + network_predict(net, sized.data); + int nboxes = 0; + detection *dets = get_network_boxes(net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes); + if (nms) do_nms_obj(dets, nboxes, 1, nms); + + char labelpath[4096]; + find_replace(path, "images", "labels", labelpath); + find_replace(labelpath, "JPEGImages", "labels", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + + int num_labels = 0; + box_label *truth = read_boxes(labelpath, &num_labels); + for(k = 0; k < nboxes; ++k){ + if(dets[k].objectness > thresh){ + ++proposals; + } + } + for (j = 0; j < num_labels; ++j) { + ++total; + box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h}; + float best_iou = 0; + for(k = 0; k < l.w*l.h*l.n; ++k){ + float iou = box_iou(dets[k].bbox, t); + if(dets[k].objectness > thresh && iou > best_iou){ + best_iou = iou; + } + } + avg_iou += best_iou; + if(best_iou > iou_thresh){ + ++correct; + } + } + + fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); + free(id); + free_image(orig); + free_image(sized); + } +} + + +void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen) +{ + list *options = read_data_cfg(datacfg); + char *name_list = option_find_str(options, "names", "data/names.list"); + char **names = get_labels(name_list); + + image **alphabet = load_alphabet(); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + srand(2222222); + double time; + char buff[256]; + char *input = buff; + float nms=.45; + while(1){ + if(filename){ + strncpy(input, filename, 256); + } else { + printf("Enter Image Path: "); + fflush(stdout); + input = fgets(input, 256, stdin); + if(!input) return; + strtok(input, "\n"); + } + image im = load_image_color(input,0,0); + image sized = letterbox_image(im, net->w, net->h); + //image sized = resize_image(im, net->w, net->h); + //image sized2 = resize_max(im, net->w); + //image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h); + //resize_network(net, sized.w, sized.h); + layer l = net->layers[net->n-1]; + + + float *X = sized.data; + time=what_time_is_it_now(); + network_predict(net, X); + printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time); + int nboxes = 0; + detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes); + //printf("%d\n", nboxes); + //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes); + free_detections(dets, nboxes); + if(outfile){ + save_image(im, outfile); + } + else{ + save_image(im, "predictions"); +#ifdef OPENCV + make_window("predictions", 512, 512, 0); + show_image(im, "predictions", 0); +#endif + } + + free_image(im); + free_image(sized); + if (filename) break; + } +} + +/* +void censor_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip) +{ +#ifdef OPENCV + char *base = basecfg(cfgfile); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + + srand(2222222); + CvCapture * cap; + + int w = 1280; + int h = 720; + + if(filename){ + cap = cvCaptureFromFile(filename); + }else{ + cap = cvCaptureFromCAM(cam_index); + } + + if(w){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w); + } + if(h){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h); + } + + if(!cap) error("Couldn't connect to webcam.\n"); + cvNamedWindow(base, CV_WINDOW_NORMAL); + cvResizeWindow(base, 512, 512); + float fps = 0; + int i; + float nms = .45; + + while(1){ + image in = get_image_from_stream(cap); + //image in_s = resize_image(in, net->w, net->h); + image in_s = letterbox_image(in, net->w, net->h); + layer l = net->layers[net->n-1]; + + float *X = in_s.data; + network_predict(net, X); + int nboxes = 0; + detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 0, &nboxes); + //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + + for(i = 0; i < nboxes; ++i){ + if(dets[i].prob[class] > thresh){ + box b = dets[i].bbox; + int left = b.x-b.w/2.; + int top = b.y-b.h/2.; + censor_image(in, left, top, b.w, b.h); + } + } + show_image(in, base); + cvWaitKey(10); + free_detections(dets, nboxes); + + + free_image(in_s); + free_image(in); + + + float curr = 0; + fps = .9*fps + .1*curr; + for(i = 0; i < skip; ++i){ + image in = get_image_from_stream(cap); + free_image(in); + } + } + #endif +} + +void extract_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip) +{ +#ifdef OPENCV + char *base = basecfg(cfgfile); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + + srand(2222222); + CvCapture * cap; + + int w = 1280; + int h = 720; + + if(filename){ + cap = cvCaptureFromFile(filename); + }else{ + cap = cvCaptureFromCAM(cam_index); + } + + if(w){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w); + } + if(h){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h); + } + + if(!cap) error("Couldn't connect to webcam.\n"); + cvNamedWindow(base, CV_WINDOW_NORMAL); + cvResizeWindow(base, 512, 512); + float fps = 0; + int i; + int count = 0; + float nms = .45; + + while(1){ + image in = get_image_from_stream(cap); + //image in_s = resize_image(in, net->w, net->h); + image in_s = letterbox_image(in, net->w, net->h); + layer l = net->layers[net->n-1]; + + show_image(in, base); + + int nboxes = 0; + float *X = in_s.data; + network_predict(net, X); + detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 1, &nboxes); + //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + + for(i = 0; i < nboxes; ++i){ + if(dets[i].prob[class] > thresh){ + box b = dets[i].bbox; + int size = b.w*in.w > b.h*in.h ? b.w*in.w : b.h*in.h; + int dx = b.x*in.w-size/2.; + int dy = b.y*in.h-size/2.; + image bim = crop_image(in, dx, dy, size, size); + char buff[2048]; + sprintf(buff, "results/extract/%07d", count); + ++count; + save_image(bim, buff); + free_image(bim); + } + } + free_detections(dets, nboxes); + + + free_image(in_s); + free_image(in); + + + float curr = 0; + fps = .9*fps + .1*curr; + for(i = 0; i < skip; ++i){ + image in = get_image_from_stream(cap); + free_image(in); + } + } + #endif +} +*/ + +/* +void network_detect(network *net, image im, float thresh, float hier_thresh, float nms, detection *dets) +{ + network_predict_image(net, im); + layer l = net->layers[net->n-1]; + int nboxes = num_boxes(net); + fill_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 0, dets); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); +} +*/ + +void run_detector(int argc, char **argv) +{ + char *prefix = find_char_arg(argc, argv, "-prefix", 0); + float thresh = find_float_arg(argc, argv, "-thresh", .5); + float hier_thresh = find_float_arg(argc, argv, "-hier", .5); + int cam_index = find_int_arg(argc, argv, "-c", 0); + int frame_skip = find_int_arg(argc, argv, "-s", 0); + int avg = find_int_arg(argc, argv, "-avg", 3); + if(argc < 4){ + fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); + return; + } + char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); + char *outfile = find_char_arg(argc, argv, "-out", 0); + int *gpus = 0; + int gpu = 0; + int ngpus = 0; + if(gpu_list){ + printf("%s\n", gpu_list); + int len = strlen(gpu_list); + ngpus = 1; + int i; + for(i = 0; i < len; ++i){ + if (gpu_list[i] == ',') ++ngpus; + } + gpus = calloc(ngpus, sizeof(int)); + for(i = 0; i < ngpus; ++i){ + gpus[i] = atoi(gpu_list); + gpu_list = strchr(gpu_list, ',')+1; + } + } else { + gpu = gpu_index; + gpus = &gpu; + ngpus = 1; + } + + int clear = find_arg(argc, argv, "-clear"); + int fullscreen = find_arg(argc, argv, "-fullscreen"); + int width = find_int_arg(argc, argv, "-w", 0); + int height = find_int_arg(argc, argv, "-h", 0); + int fps = find_int_arg(argc, argv, "-fps", 0); + //int class = find_int_arg(argc, argv, "-class", 0); + + char *datacfg = argv[3]; + char *cfg = argv[4]; + char *weights = (argc > 5) ? argv[5] : 0; + char *filename = (argc > 6) ? argv[6]: 0; + if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, outfile, fullscreen); + else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear); + else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); + else if(0==strcmp(argv[2], "valid2")) validate_detector_flip(datacfg, cfg, weights, outfile); + else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights); + else if(0==strcmp(argv[2], "demo")) { + list *options = read_data_cfg(datacfg); + int classes = option_find_int(options, "classes", 20); + char *name_list = option_find_str(options, "names", "data/names.list"); + char **names = get_labels(name_list); + demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, avg, hier_thresh, width, height, fps, fullscreen); + } + //else if(0==strcmp(argv[2], "extract")) extract_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip); + //else if(0==strcmp(argv[2], "censor")) censor_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip); +} diff --git a/image.darknet/inst/include/darknet/examples/detector.py b/image.darknet/inst/include/darknet/examples/detector.py new file mode 100644 index 0000000..40bb365 --- /dev/null +++ b/image.darknet/inst/include/darknet/examples/detector.py @@ -0,0 +1,27 @@ +# Stupid python path shit. +# Instead just add darknet.py to somewhere in your python path +# OK actually that might not be a great idea, idk, work in progress +# Use at your own risk. or don't, i don't care + +import sys, os +sys.path.append(os.path.join(os.getcwd(),'python/')) + +import darknet as dn +import pdb + +dn.set_gpu(0) +net = dn.load_net("cfg/yolo-thor.cfg", "/home/pjreddie/backup/yolo-thor_final.weights", 0) +meta = dn.load_meta("cfg/thor.data") +r = dn.detect(net, meta, "data/bedroom.jpg") +print r + +# And then down here you could detect a lot more images like: +r = dn.detect(net, meta, "data/eagle.jpg") +print r +r = dn.detect(net, meta, "data/giraffe.jpg") +print r +r = dn.detect(net, meta, "data/horses.jpg") +print r +r = dn.detect(net, meta, "data/person.jpg") +print r + diff --git a/image.darknet/inst/include/darknet/src/dice.c b/image.darknet/inst/include/darknet/examples/dice.c similarity index 95% rename from image.darknet/inst/include/darknet/src/dice.c rename to image.darknet/inst/include/darknet/examples/dice.c index 2286459..f56d76c 100644 --- a/image.darknet/inst/include/darknet/src/dice.c +++ b/image.darknet/inst/include/darknet/examples/dice.c @@ -1,6 +1,4 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" +#include "darknet.h" char *dice_labels[] = {"face1","face2","face3","face4","face5","face6"}; @@ -33,7 +31,7 @@ void train_dice(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%d: %f, %f avg, %lf seconds, %ld images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if((i % 100) == 0) net.learning_rate *= .1; if(i%100==0){ diff --git a/image.darknet/inst/include/darknet/examples/go.c b/image.darknet/inst/include/darknet/examples/go.c new file mode 100644 index 0000000..688579d --- /dev/null +++ b/image.darknet/inst/include/darknet/examples/go.c @@ -0,0 +1,1370 @@ +#include "darknet.h" + +#include +#include +#include + +int inverted = 1; +int noi = 1; +static const int nind = 10; +int legal_go(float *b, float *ko, int p, int r, int c); +int check_ko(float *x, float *ko); + +typedef struct { + char **data; + int n; +} moves; + +char *fgetgo(FILE *fp) +{ + if(feof(fp)) return 0; + size_t size = 96; + char *line = malloc(size*sizeof(char)); + if(size != fread(line, sizeof(char), size, fp)){ + free(line); + return 0; + } + + return line; +} + +moves load_go_moves(char *filename) +{ + moves m; + m.n = 128; + m.data = calloc(128, sizeof(char*)); + FILE *fp = fopen(filename, "rb"); + int count = 0; + char *line = 0; + while ((line = fgetgo(fp))) { + if (count >= m.n) { + m.n *= 2; + m.data = realloc(m.data, m.n*sizeof(char*)); + } + m.data[count] = line; + ++count; + } + printf("%d\n", count); + m.n = count; + m.data = realloc(m.data, count*sizeof(char*)); + return m; +} + +void string_to_board(char *s, float *board) +{ + int i, j; + memset(board, 0, 2*19*19*sizeof(float)); + int count = 0; + for(i = 0; i < 91; ++i){ + char c = s[i]; + for(j = 0; j < 4; ++j){ + int me = (c >> (2*j)) & 1; + int you = (c >> (2*j + 1)) & 1; + if (me) board[count] = 1; + else if (you) board[count + 19*19] = 1; + ++count; + if(count >= 19*19) break; + } + } +} + +void board_to_string(char *s, float *board) +{ + int i, j; + memset(s, 0, (19*19/4+1)*sizeof(char)); + int count = 0; + for(i = 0; i < 91; ++i){ + for(j = 0; j < 4; ++j){ + int me = (board[count] == 1); + int you = (board[count + 19*19] == 1); + if (me) s[i] = s[i] | (1<<(2*j)); + if (you) s[i] = s[i] | (1<<(2*j + 1)); + ++count; + if(count >= 19*19) break; + } + } +} + +static int occupied(float *b, int i) +{ + if (b[i]) return 1; + if (b[i+19*19]) return -1; + return 0; +} + +data random_go_moves(moves m, int n) +{ + data d = {0}; + d.X = make_matrix(n, 19*19*3); + d.y = make_matrix(n, 19*19+2); + int i, j; + for(i = 0; i < n; ++i){ + float *board = d.X.vals[i]; + float *label = d.y.vals[i]; + char *b = m.data[rand()%m.n]; + int player = b[0] - '0'; + int result = b[1] - '0'; + int row = b[2]; + int col = b[3]; + string_to_board(b+4, board); + if(player > 0) for(j = 0; j < 19*19; ++j) board[19*19*2 + j] = 1; + label[19*19+1] = (player==result); + if(row >= 19 || col >= 19){ + label[19*19] = 1; + } else { + label[col + 19*row] = 1; + if(occupied(board, col + 19*row)) printf("hey\n"); + } + + int flip = rand()%2; + int rotate = rand()%4; + image in = float_to_image(19, 19, 3, board); + image out = float_to_image(19, 19, 1, label); + if(flip){ + flip_image(in); + flip_image(out); + } + rotate_image_cw(in, rotate); + rotate_image_cw(out, rotate); + } + return d; +} + + +void train_go(char *cfgfile, char *weightfile, char *filename, int *gpus, int ngpus, int clear) +{ + int i; + float avg_loss = -1; + char *base = basecfg(cfgfile); + printf("%s\n", base); + printf("%d\n", ngpus); + network **nets = calloc(ngpus, sizeof(network*)); + + srand(time(0)); + int seed = rand(); + for(i = 0; i < ngpus; ++i){ + srand(seed); +#ifdef GPU + cuda_set_device(gpus[i]); +#endif + nets[i] = load_network(cfgfile, weightfile, clear); + nets[i]->learning_rate *= ngpus; + } + network *net = nets[0]; + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + + char *backup_directory = "/home/pjreddie/backup/"; + + char buff[256]; + moves m = load_go_moves(filename); + //moves m = load_go_moves("games.txt"); + + int N = m.n; + printf("Moves: %d\n", N); + int epoch = (*net->seen)/N; + while(get_current_batch(net) < net->max_batches || net->max_batches == 0){ + double time=what_time_is_it_now(); + + data train = random_go_moves(m, net->batch*net->subdivisions*ngpus); + printf("Loaded: %lf seconds\n", what_time_is_it_now() - time); + time=what_time_is_it_now(); + + float loss = 0; +#ifdef GPU + if(ngpus == 1){ + loss = train_network(net, train); + } else { + loss = train_networks(nets, ngpus, train, 10); + } +#else + loss = train_network(net, train); +#endif + free_data(train); + + if(avg_loss == -1) avg_loss = loss; + avg_loss = avg_loss*.95 + loss*.05; + printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen); + if(*net->seen/N > epoch){ + epoch = *net->seen/N; + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory,base, epoch); + save_weights(net, buff); + + } + if(get_current_batch(net)%1000 == 0){ + char buff[256]; + sprintf(buff, "%s/%s.backup",backup_directory,base); + save_weights(net, buff); + } + if(get_current_batch(net)%10000 == 0){ + char buff[256]; + sprintf(buff, "%s/%s_%ld.backup",backup_directory,base,get_current_batch(net)); + save_weights(net, buff); + } + } + sprintf(buff, "%s/%s.weights", backup_directory, base); + save_weights(net, buff); + + free_network(net); + free(base); +} + +static void propagate_liberty(float *board, int *lib, int *visited, int row, int col, int side) +{ + if (row < 0 || row > 18 || col < 0 || col > 18) return; + int index = row*19 + col; + if (occupied(board,index) != side) return; + if (visited[index]) return; + visited[index] = 1; + lib[index] += 1; + propagate_liberty(board, lib, visited, row+1, col, side); + propagate_liberty(board, lib, visited, row-1, col, side); + propagate_liberty(board, lib, visited, row, col+1, side); + propagate_liberty(board, lib, visited, row, col-1, side); +} + + +static int *calculate_liberties(float *board) +{ + int *lib = calloc(19*19, sizeof(int)); + int visited[19*19]; + int i, j; + for(j = 0; j < 19; ++j){ + for(i = 0; i < 19; ++i){ + memset(visited, 0, 19*19*sizeof(int)); + int index = j*19 + i; + if(!occupied(board,index)){ + if ((i > 0) && occupied(board,index - 1)) propagate_liberty(board, lib, visited, j, i-1, occupied(board,index-1)); + if ((i < 18) && occupied(board,index + 1)) propagate_liberty(board, lib, visited, j, i+1, occupied(board,index+1)); + if ((j > 0) && occupied(board,index - 19)) propagate_liberty(board, lib, visited, j-1, i, occupied(board,index-19)); + if ((j < 18) && occupied(board,index + 19)) propagate_liberty(board, lib, visited, j+1, i, occupied(board,index+19)); + } + } + } + return lib; +} + +void print_board(FILE *stream, float *board, int player, int *indexes) +{ + int i,j,n; + fprintf(stream, " "); + for(i = 0; i < 19; ++i){ + fprintf(stream, "%c ", 'A' + i + 1*(i > 7 && noi)); + } + fprintf(stream, "\n"); + for(j = 0; j < 19; ++j){ + fprintf(stream, "%2d", (inverted) ? 19-j : j+1); + for(i = 0; i < 19; ++i){ + int index = j*19 + i; + if(indexes){ + int found = 0; + for(n = 0; n < nind; ++n){ + if(index == indexes[n]){ + found = 1; + /* + if(n == 0) fprintf(stream, "\uff11"); + else if(n == 1) fprintf(stream, "\uff12"); + else if(n == 2) fprintf(stream, "\uff13"); + else if(n == 3) fprintf(stream, "\uff14"); + else if(n == 4) fprintf(stream, "\uff15"); + */ + fprintf(stream, " %d", n+1); + } + } + if(found) continue; + } + //if(board[index]*-swap > 0) fprintf(stream, "\u25C9 "); + //else if(board[index]*-swap < 0) fprintf(stream, "\u25EF "); + if (occupied(board, index) == player) fprintf(stream, " X"); + else if (occupied(board, index) ==-player) fprintf(stream, " O"); + else fprintf(stream, " ."); + } + fprintf(stream, "\n"); + } +} + +void flip_board(float *board) +{ + int i; + for(i = 0; i < 19*19; ++i){ + float swap = board[i]; + board[i] = board[i+19*19]; + board[i+19*19] = swap; + board[i+19*19*2] = 1-board[i+19*19*2]; + } +} + +float predict_move2(network *net, float *board, float *move, int multi) +{ + float *output = network_predict(net, board); + copy_cpu(19*19+1, output, 1, move, 1); + float result = output[19*19 + 1]; + int i; + if(multi){ + image bim = float_to_image(19, 19, 3, board); + for(i = 1; i < 8; ++i){ + rotate_image_cw(bim, i); + if(i >= 4) flip_image(bim); + + float *output = network_predict(net, board); + image oim = float_to_image(19, 19, 1, output); + result += output[19*19 + 1]; + + if(i >= 4) flip_image(oim); + rotate_image_cw(oim, -i); + + axpy_cpu(19*19+1, 1, output, 1, move, 1); + + if(i >= 4) flip_image(bim); + rotate_image_cw(bim, -i); + } + result = result/8; + scal_cpu(19*19+1, 1./8., move, 1); + } + for(i = 0; i < 19*19; ++i){ + if(board[i] || board[i+19*19]) move[i] = 0; + } + return result; +} + +static void remove_connected(float *b, int *lib, int p, int r, int c) +{ + if (r < 0 || r >= 19 || c < 0 || c >= 19) return; + if (occupied(b, r*19 + c) != p) return; + if (lib[r*19 + c] != 1) return; + b[r*19 + c] = 0; + b[19*19 + r*19 + c] = 0; + remove_connected(b, lib, p, r+1, c); + remove_connected(b, lib, p, r-1, c); + remove_connected(b, lib, p, r, c+1); + remove_connected(b, lib, p, r, c-1); +} + + +void move_go(float *b, int p, int r, int c) +{ + int *l = calculate_liberties(b); + if(p > 0) b[r*19 + c] = 1; + else b[19*19 + r*19 + c] = 1; + remove_connected(b, l, -p, r+1, c); + remove_connected(b, l, -p, r-1, c); + remove_connected(b, l, -p, r, c+1); + remove_connected(b, l, -p, r, c-1); + free(l); +} + +int compare_board(float *a, float *b) +{ + if(memcmp(a, b, 19*19*3*sizeof(float)) == 0) return 1; + return 0; +} + +typedef struct mcts_tree{ + float *board; + struct mcts_tree **children; + float *prior; + int *visit_count; + float *value; + float *mean; + float *prob; + int total_count; + float result; + int done; + int pass; +} mcts_tree; + +void free_mcts(mcts_tree *root) +{ + if(!root) return; + int i; + free(root->board); + for(i = 0; i < 19*19+1; ++i){ + if(root->children[i]) free_mcts(root->children[i]); + } + free(root->children); + free(root->prior); + free(root->visit_count); + free(root->value); + free(root->mean); + free(root->prob); + free(root); +} + +float *network_predict_rotations(network *net, float *next) +{ + int n = net->batch; + float *in = calloc(19*19*3*n, sizeof(float)); + image im = float_to_image(19, 19, 3, next); + int i,j; + int *inds = random_index_order(0, 8); + for(j = 0; j < n; ++j){ + i = inds[j]; + rotate_image_cw(im, i); + if(i >= 4) flip_image(im); + memcpy(in + 19*19*3*j, im.data, 19*19*3*sizeof(float)); + if(i >= 4) flip_image(im); + rotate_image_cw(im, -i); + } + float *pred = network_predict(net, in); + for(j = 0; j < n; ++j){ + i = inds[j]; + image im = float_to_image(19, 19, 1, pred + j*(19*19 + 2)); + if(i >= 4) flip_image(im); + rotate_image_cw(im, -i); + if(j > 0){ + axpy_cpu(19*19+2, 1, im.data, 1, pred, 1); + } + } + free(in); + free(inds); + scal_cpu(19*19+2, 1./n, pred, 1); + return pred; +} + +mcts_tree *expand(float *next, float *ko, network *net) +{ + mcts_tree *root = calloc(1, sizeof(mcts_tree)); + root->board = next; + root->children = calloc(19*19+1, sizeof(mcts_tree*)); + root->prior = calloc(19*19 + 1, sizeof(float)); + root->prob = calloc(19*19 + 1, sizeof(float)); + root->mean = calloc(19*19 + 1, sizeof(float)); + root->value = calloc(19*19 + 1, sizeof(float)); + root->visit_count = calloc(19*19 + 1, sizeof(int)); + root->total_count = 1; + int i; + float *pred = network_predict_rotations(net, next); + copy_cpu(19*19+1, pred, 1, root->prior, 1); + float val = 2*pred[19*19 + 1] - 1; + root->result = val; + for(i = 0; i < 19*19+1; ++i) { + root->visit_count[i] = 0; + root->value[i] = 0; + root->mean[i] = val; + if(i < 19*19 && occupied(next, i)){ + root->value[i] = -1; + root->mean[i] = -1; + root->prior[i] = 0; + } + } + //print_board(stderr, next, flip?-1:1, 0); + return root; +} + +float *copy_board(float *board) +{ + float *next = calloc(19*19*3, sizeof(float)); + copy_cpu(19*19*3, board, 1, next, 1); + return next; +} + +float select_mcts(mcts_tree *root, network *net, float *prev, float cpuct) +{ + if(root->done) return -root->result; + int i; + float max = -1000; + int max_i = 0; + for(i = 0; i < 19*19+1; ++i){ + root->prob[i] = root->mean[i] + cpuct*root->prior[i] * sqrt(root->total_count) / (1. + root->visit_count[i]); + if(root->prob[i] > max){ + max = root->prob[i]; + max_i = i; + } + } + float val; + i = max_i; + root->visit_count[i]++; + root->total_count++; + if (root->children[i]) { + val = select_mcts(root->children[i], net, root->board, cpuct); + } else { + if(max_i < 19*19 && !legal_go(root->board, prev, 1, max_i/19, max_i%19)) { + root->mean[i] = -1; + root->value[i] = -1; + root->prior[i] = 0; + --root->total_count; + return select_mcts(root, net, prev, cpuct); + //printf("Detected ko\n"); + //getchar(); + } else { + float *next = copy_board(root->board); + if (max_i < 19*19) { + move_go(next, 1, max_i / 19, max_i % 19); + } + flip_board(next); + root->children[i] = expand(next, root->board, net); + val = -root->children[i]->result; + if(max_i == 19*19){ + root->children[i]->pass = 1; + if (root->pass){ + root->children[i]->done = 1; + } + } + } + } + root->value[i] += val; + root->mean[i] = root->value[i]/root->visit_count[i]; + return -val; +} + +mcts_tree *run_mcts(mcts_tree *tree, network *net, float *board, float *ko, int player, int n, float cpuct, float secs) +{ + int i; + double t = what_time_is_it_now(); + if(player < 0) flip_board(board); + if(!tree) tree = expand(copy_board(board), ko, net); + assert(compare_board(tree->board, board)); + for(i = 0; i < n; ++i){ + if (secs > 0 && (what_time_is_it_now() - t) > secs) break; + int max_i = max_int_index(tree->visit_count, 19*19+1); + if (tree->visit_count[max_i] >= n) break; + select_mcts(tree, net, ko, cpuct); + } + if(player < 0) flip_board(board); + //fprintf(stderr, "%f Seconds\n", what_time_is_it_now() - t); + return tree; +} + +mcts_tree *move_mcts(mcts_tree *tree, int index) +{ + if(index < 0 || index > 19*19 || !tree || !tree->children[index]) { + free_mcts(tree); + tree = 0; + } else { + mcts_tree *swap = tree; + tree = tree->children[index]; + swap->children[index] = 0; + free_mcts(swap); + } + return tree; +} + +typedef struct { + float value; + float mcts; + int row; + int col; +} move; + +move pick_move(mcts_tree *tree, float temp, int player) +{ + int i; + float probs[19*19+1] = {0}; + move m = {0}; + double sum = 0; + /* + for(i = 0; i < 19*19+1; ++i){ + probs[i] = tree->visit_count[i]; + } + */ + //softmax(probs, 19*19+1, temp, 1, probs); + for(i = 0; i < 19*19+1; ++i){ + sum += pow(tree->visit_count[i], 1./temp); + } + for(i = 0; i < 19*19+1; ++i){ + probs[i] = pow(tree->visit_count[i], 1./temp) / sum; + } + + int index = sample_array(probs, 19*19+1); + m.row = index / 19; + m.col = index % 19; + m.value = (tree->result+1.)/2.; + m.mcts = (tree->mean[index]+1.)/2.; + + int indexes[nind]; + top_k(probs, 19*19+1, nind, indexes); + print_board(stderr, tree->board, player, indexes); + + fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", index/19, index%19, tree->result, tree->prior[index], probs[index], tree->mean[index], (tree->children[index])?tree->children[index]->result:0, tree->visit_count[index]); + int ind = max_index(probs, 19*19+1); + fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", ind/19, ind%19, tree->result, tree->prior[ind], probs[ind], tree->mean[ind], (tree->children[ind])?tree->children[ind]->result:0, tree->visit_count[ind]); + ind = max_index(tree->prior, 19*19+1); + fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", ind/19, ind%19, tree->result, tree->prior[ind], probs[ind], tree->mean[ind], (tree->children[ind])?tree->children[ind]->result:0, tree->visit_count[ind]); + return m; +} + +/* + float predict_move(network *net, float *board, float *move, int multi, float *ko, float temp) + { + + int i; + + int max_v = 0; + int max_i = 0; + for(i = 0; i < 19*19+1; ++i){ + if(root->visit_count[i] > max_v){ + max_v = root->visit_count[i]; + max_i = i; + } + } + fprintf(stderr, "%f Seconds\n", what_time_is_it_now() - t); + int ind = max_index(root->mean, 19*19+1); + fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", max_i/19, max_i%19, root->result, root->prior[max_i], root->prob[max_i], root->mean[max_i], (root->children[max_i])?root->children[max_i]->result:0, root->visit_count[max_i]); + fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", ind/19, ind%19, root->result, root->prior[ind], root->prob[ind], root->mean[ind], (root->children[ind])?root->children[ind]->result:0, root->visit_count[ind]); + ind = max_index(root->prior, 19*19+1); + fprintf(stderr, "%d %d, Result: %f, Prior: %f, Prob: %f, Mean Value: %f, Child Result: %f, Visited: %d\n", ind/19, ind%19, root->result, root->prior[ind], root->prob[ind], root->mean[ind], (root->children[ind])?root->children[ind]->result:0, root->visit_count[ind]); + if(root->result < -.9 && root->mean[max_i] < -.9) return -1000.f; + + float val = root->result; + free_mcts(root); + return val; + } + */ + +static int makes_safe_go(float *b, int *lib, int p, int r, int c){ + if (r < 0 || r >= 19 || c < 0 || c >= 19) return 0; + if (occupied(b,r*19 + c) == -p){ + if (lib[r*19 + c] > 1) return 0; + else return 1; + } + if (!occupied(b,r*19 + c)) return 1; + if (lib[r*19 + c] > 1) return 1; + return 0; +} + +int suicide_go(float *b, int p, int r, int c) +{ + int *l = calculate_liberties(b); + int safe = 0; + safe = safe || makes_safe_go(b, l, p, r+1, c); + safe = safe || makes_safe_go(b, l, p, r-1, c); + safe = safe || makes_safe_go(b, l, p, r, c+1); + safe = safe || makes_safe_go(b, l, p, r, c-1); + free(l); + return !safe; +} + +int check_ko(float *x, float *ko) +{ + if(!ko) return 0; + float curr[19*19*3]; + copy_cpu(19*19*3, x, 1, curr, 1); + if(curr[19*19*2] != ko[19*19*2]) flip_board(curr); + if(compare_board(curr, ko)) return 1; + return 0; +} + +int legal_go(float *b, float *ko, int p, int r, int c) +{ + if (occupied(b, r*19+c)) return 0; + float curr[19*19*3]; + copy_cpu(19*19*3, b, 1, curr, 1); + move_go(curr, p, r, c); + if(check_ko(curr, ko)) return 0; + if(suicide_go(b, p, r, c)) return 0; + return 1; +} + +/* + move generate_move(mcts_tree *root, network *net, int player, float *board, int multi, float temp, float *ko, int print) + { + move m = {0}; +//root = run_mcts(tree, network *net, float *board, float *ko, int n, float cpuct) +int i, j; +int empty = 1; +for(i = 0; i < 19*19; ++i){ +if (occupied(board, i)) { +empty = 0; +break; +} +} +if(empty) { +m.value = .5; +m.mcts = .5; +m.row = 3; +m.col = 15; +return m; +} + +float move[362]; +if (player < 0) flip_board(board); +float result = predict_move(net, board, move, multi, ko, temp); +if (player < 0) flip_board(board); +if(result == -1000.f) return -2; + +for(i = 0; i < 19; ++i){ +for(j = 0; j < 19; ++j){ +if (!legal_go(board, ko, player, i, j)) move[i*19 + j] = 0; +} +} + +int indexes[nind]; +top_k(move, 19*19+1, nind, indexes); + + +int max = max_index(move, 19*19+1); +int row = max / 19; +int col = max % 19; +int index = sample_array(move, 19*19+1); + +if(print){ +top_k(move, 19*19+1, nind, indexes); +for(i = 0; i < nind; ++i){ +if (!move[indexes[i]]) indexes[i] = -1; +} +print_board(stderr, board, 1, indexes); +fprintf(stderr, "%s To Move\n", player > 0 ? "X" : "O"); +fprintf(stderr, "%.2f%% Win Chance\n", (result+1)/2*100); +for(i = 0; i < nind; ++i){ +int index = indexes[i]; +int row = index / 19; +int col = index % 19; +if(row == 19){ +fprintf(stderr, "%d: Pass, %.2f%%\n", i+1, move[index]*100); +} else { +fprintf(stderr, "%d: %c %d, %.2f%%\n", i+1, col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100); +} +} +} +if (row == 19) return -1; + +if (suicide_go(board, player, row, col)){ +return -1; +} + +if (suicide_go(board, player, index/19, index%19)){ +index = max; +} +if (index == 19*19) return -1; +return index; +} +*/ + +void valid_go(char *cfgfile, char *weightfile, int multi, char *filename) +{ + srand(time(0)); + char *base = basecfg(cfgfile); + printf("%s\n", base); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + + float *board = calloc(19*19*3, sizeof(float)); + float *move = calloc(19*19+2, sizeof(float)); + // moves m = load_go_moves("/home/pjreddie/backup/go.test"); + moves m = load_go_moves(filename); + + int N = m.n; + int i,j; + int correct = 0; + for (i = 0; i 0) for(j = 0; j < 19*19; ++j) board[19*19*2 + j] = 1; + predict_move2(net, board, move, multi); + int index = max_index(move, 19*19+1); + if(index == truth) ++correct; + printf("%d Accuracy %f\n", i, (float) correct/(i+1)); + } +} + +int print_game(float *board, FILE *fp) +{ + int i, j; + int count = 3; + fprintf(fp, "komi 6.5\n"); + fprintf(fp, "boardsize 19\n"); + fprintf(fp, "clear_board\n"); + for(j = 0; j < 19; ++j){ + for(i = 0; i < 19; ++i){ + if(occupied(board,j*19 + i) == 1) fprintf(fp, "play black %c%d\n", 'A'+i+(i>=8), 19-j); + if(occupied(board,j*19 + i) == -1) fprintf(fp, "play white %c%d\n", 'A'+i+(i>=8), 19-j); + if(occupied(board,j*19 + i)) ++count; + } + } + return count; +} + + +int stdin_ready() +{ + fd_set readfds; + FD_ZERO(&readfds); + + struct timeval timeout; + timeout.tv_sec = 0; + timeout.tv_usec = 0; + FD_SET(STDIN_FILENO, &readfds); + + if (select(1, &readfds, NULL, NULL, &timeout)){ + return 1; + } + return 0; +} + +mcts_tree *ponder(mcts_tree *tree, network *net, float *b, float *ko, int player, float cpuct) +{ + double t = what_time_is_it_now(); + int count = 0; + if (tree) count = tree->total_count; + while(!stdin_ready()){ + if (what_time_is_it_now() - t > 120) break; + tree = run_mcts(tree, net, b, ko, player, 100000, cpuct, .1); + } + fprintf(stderr, "Pondered %d moves...\n", tree->total_count - count); + return tree; +} + +void engine_go(char *filename, char *weightfile, int mcts_iters, float secs, float temp, float cpuct, int anon, int resign) +{ + mcts_tree *root = 0; + network *net = load_network(filename, weightfile, 0); + set_batch_network(net, 1); + srand(time(0)); + float *board = calloc(19*19*3, sizeof(float)); + flip_board(board); + float *one = calloc(19*19*3, sizeof(float)); + float *two = calloc(19*19*3, sizeof(float)); + int ponder_player = 0; + int passed = 0; + int move_num = 0; + int main_time = 0; + int byo_yomi_time = 0; + int byo_yomi_stones = 0; + int black_time_left = 0; + int black_stones_left = 0; + int white_time_left = 0; + int white_stones_left = 0; + float orig_time = secs; + int old_ponder = 0; + while(1){ + if(ponder_player){ + root = ponder(root, net, board, two, ponder_player, cpuct); + } + old_ponder = ponder_player; + ponder_player = 0; + char buff[256]; + int id = 0; + int has_id = (scanf("%d", &id) == 1); + scanf("%s", buff); + if (feof(stdin)) break; + fprintf(stderr, "%s\n", buff); + char ids[256]; + sprintf(ids, "%d", id); + //fprintf(stderr, "%s\n", buff); + if (!has_id) ids[0] = 0; + if (!strcmp(buff, "protocol_version")){ + printf("=%s 2\n\n", ids); + } else if (!strcmp(buff, "name")){ + if(anon){ + printf("=%s The Fool!\n\n", ids); + }else{ + printf("=%s DarkGo\n\n", ids); + } + } else if (!strcmp(buff, "time_settings")){ + ponder_player = old_ponder; + scanf("%d %d %d", &main_time, &byo_yomi_time, &byo_yomi_stones); + printf("=%s \n\n", ids); + } else if (!strcmp(buff, "time_left")){ + ponder_player = old_ponder; + char color[256]; + int time = 0, stones = 0; + scanf("%s %d %d", color, &time, &stones); + if (color[0] == 'b' || color[0] == 'B'){ + black_time_left = time; + black_stones_left = stones; + } else { + white_time_left = time; + white_stones_left = stones; + } + printf("=%s \n\n", ids); + } else if (!strcmp(buff, "version")){ + if(anon){ + printf("=%s :-DDDD\n\n", ids); + }else { + printf("=%s 1.0. Want more DarkGo? You can find me on OGS, unlimited games, no waiting! https://online-go.com/user/view/434218\n\n", ids); + } + } else if (!strcmp(buff, "known_command")){ + char comm[256]; + scanf("%s", comm); + int known = (!strcmp(comm, "protocol_version") || + !strcmp(comm, "name") || + !strcmp(comm, "version") || + !strcmp(comm, "known_command") || + !strcmp(comm, "list_commands") || + !strcmp(comm, "quit") || + !strcmp(comm, "boardsize") || + !strcmp(comm, "clear_board") || + !strcmp(comm, "komi") || + !strcmp(comm, "final_status_list") || + !strcmp(comm, "play") || + !strcmp(comm, "genmove_white") || + !strcmp(comm, "genmove_black") || + !strcmp(comm, "fixed_handicap") || + !strcmp(comm, "genmove")); + if(known) printf("=%s true\n\n", ids); + else printf("=%s false\n\n", ids); + } else if (!strcmp(buff, "list_commands")){ + printf("=%s protocol_version\nshowboard\nname\nversion\nknown_command\nlist_commands\nquit\nboardsize\nclear_board\nkomi\nplay\ngenmove_black\ngenmove_white\ngenmove\nfinal_status_list\nfixed_handicap\n\n", ids); + } else if (!strcmp(buff, "quit")){ + break; + } else if (!strcmp(buff, "boardsize")){ + int boardsize = 0; + scanf("%d", &boardsize); + //fprintf(stderr, "%d\n", boardsize); + if(boardsize != 19){ + printf("?%s unacceptable size\n\n", ids); + } else { + root = move_mcts(root, -1); + memset(board, 0, 3*19*19*sizeof(float)); + flip_board(board); + move_num = 0; + printf("=%s \n\n", ids); + } + } else if (!strcmp(buff, "fixed_handicap")){ + int handicap = 0; + scanf("%d", &handicap); + int indexes[] = {72, 288, 300, 60, 180, 174, 186, 66, 294}; + int i; + for(i = 0; i < handicap; ++i){ + board[indexes[i]] = 1; + ++move_num; + } + root = move_mcts(root, -1); + } else if (!strcmp(buff, "clear_board")){ + passed = 0; + memset(board, 0, 3*19*19*sizeof(float)); + flip_board(board); + move_num = 0; + root = move_mcts(root, -1); + printf("=%s \n\n", ids); + } else if (!strcmp(buff, "komi")){ + float komi = 0; + scanf("%f", &komi); + printf("=%s \n\n", ids); + } else if (!strcmp(buff, "showboard")){ + printf("=%s \n", ids); + print_board(stdout, board, 1, 0); + printf("\n"); + } else if (!strcmp(buff, "play") || !strcmp(buff, "black") || !strcmp(buff, "white")){ + ++move_num; + char color[256]; + if(!strcmp(buff, "play")) + { + scanf("%s ", color); + } else { + scanf(" "); + color[0] = buff[0]; + } + char c; + int r; + int count = scanf("%c%d", &c, &r); + int player = (color[0] == 'b' || color[0] == 'B') ? 1 : -1; + if((c == 'p' || c == 'P') && count < 2) { + passed = 1; + printf("=%s \n\n", ids); + char *line = fgetl(stdin); + free(line); + fflush(stdout); + fflush(stderr); + root = move_mcts(root, 19*19); + continue; + } else { + passed = 0; + } + if(c >= 'A' && c <= 'Z') c = c - 'A'; + if(c >= 'a' && c <= 'z') c = c - 'a'; + if(c >= 8) --c; + r = 19 - r; + fprintf(stderr, "move: %d %d\n", r, c); + + float *swap = two; + two = one; + one = swap; + move_go(board, player, r, c); + copy_cpu(19*19*3, board, 1, one, 1); + if(root) fprintf(stderr, "Prior: %f\n", root->prior[r*19 + c]); + if(root) fprintf(stderr, "Mean: %f\n", root->mean[r*19 + c]); + if(root) fprintf(stderr, "Result: %f\n", root->result); + root = move_mcts(root, r*19 + c); + if(root) fprintf(stderr, "Visited: %d\n", root->total_count); + else fprintf(stderr, "NOT VISITED\n"); + + printf("=%s \n\n", ids); + //print_board(stderr, board, 1, 0); + } else if (!strcmp(buff, "genmove") || !strcmp(buff, "genmove_black") || !strcmp(buff, "genmove_white")){ + ++move_num; + int player = 0; + if(!strcmp(buff, "genmove")){ + char color[256]; + scanf("%s", color); + player = (color[0] == 'b' || color[0] == 'B') ? 1 : -1; + } else if (!strcmp(buff, "genmove_black")){ + player = 1; + } else { + player = -1; + } + if(player > 0){ + if(black_time_left <= 30) secs = 2.5; + else secs = orig_time; + } else { + if(white_time_left <= 30) secs = 2.5; + else secs = orig_time; + } + ponder_player = -player; + + //tree = generate_move(net, player, board, multi, .1, two, 1); + double t = what_time_is_it_now(); + root = run_mcts(root, net, board, two, player, mcts_iters, cpuct, secs); + fprintf(stderr, "%f Seconds\n", what_time_is_it_now() - t); + move m = pick_move(root, temp, player); + root = move_mcts(root, m.row*19 + m.col); + + + if(move_num > resign && m.value < .1 && m.mcts < .1){ + printf("=%s resign\n\n", ids); + } else if(m.row == 19){ + printf("=%s pass\n\n", ids); + passed = 0; + } else { + int row = m.row; + int col = m.col; + + float *swap = two; + two = one; + one = swap; + + move_go(board, player, row, col); + copy_cpu(19*19*3, board, 1, one, 1); + row = 19 - row; + if (col >= 8) ++col; + printf("=%s %c%d\n\n", ids, 'A' + col, row); + } + + } else if (!strcmp(buff, "p")){ + //print_board(board, 1, 0); + } else if (!strcmp(buff, "final_status_list")){ + char type[256]; + scanf("%s", type); + fprintf(stderr, "final_status\n"); + char *line = fgetl(stdin); + free(line); + if(type[0] == 'd' || type[0] == 'D'){ + int i; + FILE *f = fopen("game.txt", "w"); + int count = print_game(board, f); + fprintf(f, "%s final_status_list dead\n", ids); + fclose(f); + FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); + for(i = 0; i < count; ++i){ + free(fgetl(p)); + free(fgetl(p)); + } + char *l = 0; + while((l = fgetl(p))){ + printf("%s\n", l); + free(l); + } + } else { + printf("?%s unknown command\n\n", ids); + } + } else if (!strcmp(buff, "kgs-genmove_cleanup")){ + char type[256]; + scanf("%s", type); + fprintf(stderr, "kgs-genmove_cleanup\n"); + char *line = fgetl(stdin); + free(line); + int i; + FILE *f = fopen("game.txt", "w"); + int count = print_game(board, f); + fprintf(f, "%s kgs-genmove_cleanup %s\n", ids, type); + fclose(f); + FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); + for(i = 0; i < count; ++i){ + free(fgetl(p)); + free(fgetl(p)); + } + char *l = 0; + while((l = fgetl(p))){ + printf("%s\n", l); + free(l); + } + } else { + char *line = fgetl(stdin); + free(line); + printf("?%s unknown command\n\n", ids); + } + fflush(stdout); + fflush(stderr); + } + printf("%d %d %d\n",passed, black_stones_left, white_stones_left); +} + +void test_go(char *cfg, char *weights, int multi) +{ + int i; + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); + srand(time(0)); + float *board = calloc(19*19*3, sizeof(float)); + flip_board(board); + float *move = calloc(19*19+1, sizeof(float)); + int color = 1; + while(1){ + float result = predict_move2(net, board, move, multi); + printf("%.2f%% Win Chance\n", (result+1)/2*100); + + int indexes[nind]; + int row, col; + top_k(move, 19*19+1, nind, indexes); + print_board(stderr, board, color, indexes); + for(i = 0; i < nind; ++i){ + int index = indexes[i]; + row = index / 19; + col = index % 19; + if(row == 19){ + printf("%d: Pass, %.2f%%\n", i+1, move[index]*100); + } else { + printf("%d: %c %d, %.2f%%\n", i+1, col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100); + } + } + //if(color == 1) printf("\u25EF Enter move: "); + //else printf("\u25C9 Enter move: "); + if(color == 1) printf("X Enter move: "); + else printf("O Enter move: "); + + char c; + char *line = fgetl(stdin); + int picked = 1; + int dnum = sscanf(line, "%d", &picked); + int cnum = sscanf(line, "%c", &c); + if (strlen(line) == 0 || dnum) { + --picked; + if (picked < nind){ + int index = indexes[picked]; + row = index / 19; + col = index % 19; + if(row < 19){ + move_go(board, 1, row, col); + } + } + } else if (cnum){ + if (c <= 'T' && c >= 'A'){ + int num = sscanf(line, "%c %d", &c, &row); + row = (inverted)?19 - row : row-1; + col = c - 'A'; + if (col > 7 && noi) col -= 1; + if (num == 2) move_go(board, 1, row, col); + } else if (c == 'p') { + // Pass + } else if(c=='b' || c == 'w'){ + char g; + int num = sscanf(line, "%c %c %d", &g, &c, &row); + row = (inverted)?19 - row : row-1; + col = c - 'A'; + if (col > 7 && noi) col -= 1; + if (num == 3) { + int mc = (g == 'b') ? 1 : -1; + if (mc == color) { + board[row*19 + col] = 1; + } else { + board[19*19 + row*19 + col] = 1; + } + } + } else if(c == 'c'){ + char g; + int num = sscanf(line, "%c %c %d", &g, &c, &row); + row = (inverted)?19 - row : row-1; + col = c - 'A'; + if (col > 7 && noi) col -= 1; + if (num == 3) { + board[row*19 + col] = 0; + board[19*19 + row*19 + col] = 0; + } + } + } + free(line); + flip_board(board); + color = -color; + } +} + +float score_game(float *board) +{ + int i; + FILE *f = fopen("game.txt", "w"); + int count = print_game(board, f); + fprintf(f, "final_score\n"); + fclose(f); + FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); + for(i = 0; i < count; ++i){ + free(fgetl(p)); + free(fgetl(p)); + } + char *l = 0; + float score = 0; + char player = 0; + while((l = fgetl(p))){ + fprintf(stderr, "%s \t", l); + int n = sscanf(l, "= %c+%f", &player, &score); + free(l); + if (n == 2) break; + } + if(player == 'W') score = -score; + pclose(p); + return score; +} + +void self_go(char *filename, char *weightfile, char *f2, char *w2, int multi) +{ + mcts_tree *tree1 = 0; + mcts_tree *tree2 = 0; + network *net = load_network(filename, weightfile, 0); + //set_batch_network(net, 1); + + network *net2; + if (f2) { + net2 = parse_network_cfg(f2); + if(w2){ + load_weights(net2, w2); + } + } else { + net2 = calloc(1, sizeof(network)); + *net2 = *net; + } + srand(time(0)); + char boards[600][93]; + int count = 0; + //set_batch_network(net, 1); + //set_batch_network(net2, 1); + float *board = calloc(19*19*3, sizeof(float)); + flip_board(board); + float *one = calloc(19*19*3, sizeof(float)); + float *two = calloc(19*19*3, sizeof(float)); + int done = 0; + int player = 1; + int p1 = 0; + int p2 = 0; + int total = 0; + float temp = .1; + int mcts_iters = 500; + float cpuct = 5; + while(1){ + if (done){ + tree1 = move_mcts(tree1, -1); + tree2 = move_mcts(tree2, -1); + float score = score_game(board); + if((score > 0) == (total%2==0)) ++p1; + else ++p2; + ++total; + fprintf(stderr, "Total: %d, Player 1: %f, Player 2: %f\n", total, (float)p1/total, (float)p2/total); + sleep(1); + /* + int i = (score > 0)? 0 : 1; + int j; + for(; i < count; i += 2){ + for(j = 0; j < 93; ++j){ + printf("%c", boards[i][j]); + } + printf("\n"); + } + */ + memset(board, 0, 3*19*19*sizeof(float)); + flip_board(board); + player = 1; + done = 0; + count = 0; + fflush(stdout); + fflush(stderr); + } + //print_board(stderr, board, 1, 0); + //sleep(1); + + if ((total%2==0) == (player==1)){ + //mcts_iters = 4500; + cpuct = 5; + } else { + //mcts_iters = 500; + cpuct = 1; + } + network *use = ((total%2==0) == (player==1)) ? net : net2; + mcts_tree *t = ((total%2==0) == (player==1)) ? tree1 : tree2; + t = run_mcts(t, use, board, two, player, mcts_iters, cpuct, 0); + move m = pick_move(t, temp, player); + if(((total%2==0) == (player==1))) tree1 = t; + else tree2 = t; + + tree1 = move_mcts(tree1, m.row*19 + m.col); + tree2 = move_mcts(tree2, m.row*19 + m.col); + + if(m.row == 19){ + done = 1; + continue; + } + int row = m.row; + int col = m.col; + + float *swap = two; + two = one; + one = swap; + + if(player < 0) flip_board(board); + boards[count][0] = row; + boards[count][1] = col; + board_to_string(boards[count] + 2, board); + if(player < 0) flip_board(board); + ++count; + + move_go(board, player, row, col); + copy_cpu(19*19*3, board, 1, one, 1); + + player = -player; + } +} + +void run_go(int argc, char **argv) +{ + //boards_go(); + if(argc < 4){ + fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); + return; + } + + char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); + int *gpus = 0; + int gpu = 0; + int ngpus = 0; + if(gpu_list){ + printf("%s\n", gpu_list); + int len = strlen(gpu_list); + ngpus = 1; + int i; + for(i = 0; i < len; ++i){ + if (gpu_list[i] == ',') ++ngpus; + } + gpus = calloc(ngpus, sizeof(int)); + for(i = 0; i < ngpus; ++i){ + gpus[i] = atoi(gpu_list); + gpu_list = strchr(gpu_list, ',')+1; + } + } else { + gpu = gpu_index; + gpus = &gpu; + ngpus = 1; + } + int clear = find_arg(argc, argv, "-clear"); + + char *cfg = argv[3]; + char *weights = (argc > 4) ? argv[4] : 0; + char *c2 = (argc > 5) ? argv[5] : 0; + char *w2 = (argc > 6) ? argv[6] : 0; + int multi = find_arg(argc, argv, "-multi"); + int anon = find_arg(argc, argv, "-anon"); + int iters = find_int_arg(argc, argv, "-iters", 500); + int resign = find_int_arg(argc, argv, "-resign", 175); + float cpuct = find_float_arg(argc, argv, "-cpuct", 5); + float temp = find_float_arg(argc, argv, "-temp", .1); + float time = find_float_arg(argc, argv, "-time", 0); + if(0==strcmp(argv[2], "train")) train_go(cfg, weights, c2, gpus, ngpus, clear); + else if(0==strcmp(argv[2], "valid")) valid_go(cfg, weights, multi, c2); + else if(0==strcmp(argv[2], "self")) self_go(cfg, weights, c2, w2, multi); + else if(0==strcmp(argv[2], "test")) test_go(cfg, weights, multi); + else if(0==strcmp(argv[2], "engine")) engine_go(cfg, weights, iters, time, temp, cpuct, anon, resign); +} + + diff --git a/image.darknet/inst/include/darknet/examples/instance-segmenter.c b/image.darknet/inst/include/darknet/examples/instance-segmenter.c new file mode 100644 index 0000000..664e714 --- /dev/null +++ b/image.darknet/inst/include/darknet/examples/instance-segmenter.c @@ -0,0 +1,267 @@ +#include "darknet.h" +#include +#include + +void normalize_image2(image p); +void train_isegmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int display) +{ + int i; + + float avg_loss = -1; + char *base = basecfg(cfgfile); + printf("%s\n", base); + printf("%d\n", ngpus); + network **nets = calloc(ngpus, sizeof(network*)); + + srand(time(0)); + int seed = rand(); + for(i = 0; i < ngpus; ++i){ + srand(seed); +#ifdef GPU + cuda_set_device(gpus[i]); +#endif + nets[i] = load_network(cfgfile, weightfile, clear); + nets[i]->learning_rate *= ngpus; + } + srand(time(0)); + network *net = nets[0]; + image pred = get_network_image(net); + + image embed = pred; + embed.c = 3; + embed.data += embed.w*embed.h*80; + + int div = net->w/pred.w; + assert(pred.w * div == net->w); + assert(pred.h * div == net->h); + + int imgs = net->batch * net->subdivisions * ngpus; + + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + list *options = read_data_cfg(datacfg); + + char *backup_directory = option_find_str(options, "backup", "/backup/"); + char *train_list = option_find_str(options, "train", "data/train.list"); + + list *plist = get_paths(train_list); + char **paths = (char **)list_to_array(plist); + printf("%d\n", plist->size); + int N = plist->size; + + load_args args = {0}; + args.w = net->w; + args.h = net->h; + args.threads = 32; + args.scale = div; + args.num_boxes = 90; + + args.min = net->min_crop; + args.max = net->max_crop; + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.saturation = net->saturation; + args.hue = net->hue; + args.size = net->w; + args.classes = 80; + + args.paths = paths; + args.n = imgs; + args.m = N; + args.type = ISEG_DATA; + + data train; + data buffer; + pthread_t load_thread; + args.d = &buffer; + load_thread = load_data(args); + + int epoch = (*net->seen)/N; + while(get_current_batch(net) < net->max_batches || net->max_batches == 0){ + double time = what_time_is_it_now(); + + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data(args); + + printf("Loaded: %lf seconds\n", what_time_is_it_now()-time); + time = what_time_is_it_now(); + + float loss = 0; +#ifdef GPU + if(ngpus == 1){ + loss = train_network(net, train); + } else { + loss = train_networks(nets, ngpus, train, 4); + } +#else + loss = train_network(net, train); +#endif + if(display){ + image tr = float_to_image(net->w/div, net->h/div, 80, train.y.vals[net->batch*(net->subdivisions-1)]); + image im = float_to_image(net->w, net->h, net->c, train.X.vals[net->batch*(net->subdivisions-1)]); + pred.c = 80; + image mask = mask_to_rgb(tr); + image prmask = mask_to_rgb(pred); + image ecopy = copy_image(embed); + normalize_image2(ecopy); + show_image(ecopy, "embed", 1); + free_image(ecopy); + + show_image(im, "input", 1); + show_image(prmask, "pred", 1); + show_image(mask, "truth", 100); + free_image(mask); + free_image(prmask); + } + if(avg_loss == -1) avg_loss = loss; + avg_loss = avg_loss*.9 + loss*.1; + printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen); + free_data(train); + if(*net->seen/N > epoch){ + epoch = *net->seen/N; + char buff[256]; + sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); + save_weights(net, buff); + } + if(get_current_batch(net)%100 == 0){ + char buff[256]; + sprintf(buff, "%s/%s.backup",backup_directory,base); + save_weights(net, buff); + } + } + char buff[256]; + sprintf(buff, "%s/%s.weights", backup_directory, base); + save_weights(net, buff); + + free_network(net); + free_ptrs((void**)paths, plist->size); + free_list(plist); + free(base); +} + +void predict_isegmenter(char *datafile, char *cfg, char *weights, char *filename) +{ + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); + srand(2222222); + + clock_t time; + char buff[256]; + char *input = buff; + while(1){ + if(filename){ + strncpy(input, filename, 256); + }else{ + printf("Enter Image Path: "); + fflush(stdout); + input = fgets(input, 256, stdin); + if(!input) return; + strtok(input, "\n"); + } + image im = load_image_color(input, 0, 0); + image sized = letterbox_image(im, net->w, net->h); + + float *X = sized.data; + time=clock(); + float *predictions = network_predict(net, X); + image pred = get_network_image(net); + image prmask = mask_to_rgb(pred); + printf("Predicted: %f\n", predictions[0]); + printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + show_image(sized, "orig", 1); + show_image(prmask, "pred", 0); + free_image(im); + free_image(sized); + free_image(prmask); + if (filename) break; + } +} + + +void demo_isegmenter(char *datacfg, char *cfg, char *weights, int cam_index, const char *filename) +{ +#ifdef OPENCV + printf("Classifier Demo\n"); + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); + + srand(2222222); + void * cap = open_video_stream(filename, cam_index, 0,0,0); + + if(!cap) error("Couldn't connect to webcam.\n"); + float fps = 0; + + while(1){ + struct timeval tval_before, tval_after, tval_result; + gettimeofday(&tval_before, NULL); + + image in = get_image_from_stream(cap); + image in_s = letterbox_image(in, net->w, net->h); + + network_predict(net, in_s.data); + + printf("\033[2J"); + printf("\033[1;1H"); + printf("\nFPS:%.0f\n",fps); + + image pred = get_network_image(net); + image prmask = mask_to_rgb(pred); + show_image(prmask, "Segmenter", 10); + + free_image(in_s); + free_image(in); + free_image(prmask); + + gettimeofday(&tval_after, NULL); + timersub(&tval_after, &tval_before, &tval_result); + float curr = 1000000.f/((long int)tval_result.tv_usec); + fps = .9*fps + .1*curr; + } +#endif +} + + +void run_isegmenter(int argc, char **argv) +{ + if(argc < 4){ + fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); + return; + } + + char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); + int *gpus = 0; + int gpu = 0; + int ngpus = 0; + if(gpu_list){ + printf("%s\n", gpu_list); + int len = strlen(gpu_list); + ngpus = 1; + int i; + for(i = 0; i < len; ++i){ + if (gpu_list[i] == ',') ++ngpus; + } + gpus = calloc(ngpus, sizeof(int)); + for(i = 0; i < ngpus; ++i){ + gpus[i] = atoi(gpu_list); + gpu_list = strchr(gpu_list, ',')+1; + } + } else { + gpu = gpu_index; + gpus = &gpu; + ngpus = 1; + } + + int cam_index = find_int_arg(argc, argv, "-c", 0); + int clear = find_arg(argc, argv, "-clear"); + int display = find_arg(argc, argv, "-display"); + char *data = argv[3]; + char *cfg = argv[4]; + char *weights = (argc > 5) ? argv[5] : 0; + char *filename = (argc > 6) ? argv[6]: 0; + if(0==strcmp(argv[2], "test")) predict_isegmenter(data, cfg, weights, filename); + else if(0==strcmp(argv[2], "train")) train_isegmenter(data, cfg, weights, gpus, ngpus, clear, display); + else if(0==strcmp(argv[2], "demo")) demo_isegmenter(data, cfg, weights, cam_index, filename); +} + + diff --git a/image.darknet/inst/include/darknet/examples/lsd.c b/image.darknet/inst/include/darknet/examples/lsd.c new file mode 100644 index 0000000..4ab944c --- /dev/null +++ b/image.darknet/inst/include/darknet/examples/lsd.c @@ -0,0 +1,1378 @@ +#include +#include "darknet.h" + +/* +void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg, char *aweight, int clear) +{ +#ifdef GPU + //char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt"; + char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list"; + //char *style_images = "/home/pjreddie/data/coco/trainvalno5k.txt"; + char *style_images = "/home/pjreddie/zelda.txt"; + char *backup_directory = "/home/pjreddie/backup/"; + srand(time(0)); + network fnet = load_network(fcfg, fweight, clear); + network gnet = load_network(gcfg, gweight, clear); + network anet = load_network(acfg, aweight, clear); + char *gbase = basecfg(gcfg); + char *abase = basecfg(acfg); + + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay); + int imgs = gnet->batch*gnet->subdivisions; + int i = *gnet->seen/imgs; + data train, tbuffer; + data style, sbuffer; + + + list *slist = get_paths(style_images); + char **spaths = (char **)list_to_array(slist); + + list *tlist = get_paths(train_images); + char **tpaths = (char **)list_to_array(tlist); + + load_args targs= get_base_args(gnet); + targs.paths = tpaths; + targs.n = imgs; + targs.m = tlist->size; + targs.d = &tbuffer; + targs.type = CLASSIFICATION_DATA; + targs.classes = 1; + char *ls[1] = {"zelda"}; + targs.labels = ls; + + load_args sargs = get_base_args(gnet); + sargs.paths = spaths; + sargs.n = imgs; + sargs.m = slist->size; + sargs.d = &sbuffer; + sargs.type = CLASSIFICATION_DATA; + sargs.classes = 1; + sargs.labels = ls; + + pthread_t tload_thread = load_data_in_thread(targs); + pthread_t sload_thread = load_data_in_thread(sargs); + clock_t time; + + float aloss_avg = -1; + float floss_avg = -1; + + fnet->train=1; + int x_size = fnet->inputs*fnet->batch; + int y_size = fnet->truths*fnet->batch; + float *X = calloc(x_size, sizeof(float)); + float *y = calloc(y_size, sizeof(float)); + + + int ax_size = anet->inputs*anet->batch; + int ay_size = anet->truths*anet->batch; + fill_gpu(ay_size, .9, anet->truth_gpu, 1); + anet->delta_gpu = cuda_make_array(0, ax_size); + anet->train = 1; + + int gx_size = gnet->inputs*gnet->batch; + int gy_size = gnet->truths*gnet->batch; + gstate.input = cuda_make_array(0, gx_size); + gstate.truth = 0; + gstate.delta = 0; + gstate.train = 1; + + while (get_current_batch(gnet) < gnet->max_batches) { + i += 1; + time=clock(); + pthread_join(tload_thread, 0); + pthread_join(sload_thread, 0); + train = tbuffer; + style = sbuffer; + tload_thread = load_data_in_thread(targs); + sload_thread = load_data_in_thread(sargs); + + printf("Loaded: %lf seconds\n", sec(clock()-time)); + + data generated = copy_data(train); + time=clock(); + + int j, k; + float floss = 0; + for(j = 0; j < fnet->subdivisions; ++j){ + layer imlayer = gnet->layers[gnet->n - 1]; + get_next_batch(train, fnet->batch, j*fnet->batch, X, y); + + cuda_push_array(fstate.input, X, x_size); + cuda_push_array(gstate.input, X, gx_size); + *gnet->seen += gnet->batch; + + forward_network_gpu(fnet, fstate); + float *feats = fnet->layers[fnet->n - 2].output_gpu; + copy_gpu(y_size, feats, 1, fstate.truth, 1); + + forward_network_gpu(gnet, gstate); + float *gen = gnet->layers[gnet->n-1].output_gpu; + copy_gpu(x_size, gen, 1, fstate.input, 1); + + fill_gpu(x_size, 0, fstate.delta, 1); + forward_network_gpu(fnet, fstate); + backward_network_gpu(fnet, fstate); + //HERE + + astate.input = gen; + fill_gpu(ax_size, 0, astate.delta, 1); + forward_network_gpu(anet, astate); + backward_network_gpu(anet, astate); + + float *delta = imlayer.delta_gpu; + fill_gpu(x_size, 0, delta, 1); + scal_gpu(x_size, 100, astate.delta, 1); + scal_gpu(x_size, .001, fstate.delta, 1); + axpy_gpu(x_size, 1, fstate.delta, 1, delta, 1); + axpy_gpu(x_size, 1, astate.delta, 1, delta, 1); + + //fill_gpu(x_size, 0, delta, 1); + //cuda_push_array(delta, X, x_size); + //axpy_gpu(x_size, -1, imlayer.output_gpu, 1, delta, 1); + //printf("pix error: %f\n", cuda_mag_array(delta, x_size)); + printf("fea error: %f\n", cuda_mag_array(fstate.delta, x_size)); + printf("adv error: %f\n", cuda_mag_array(astate.delta, x_size)); + //axpy_gpu(x_size, 1, astate.delta, 1, delta, 1); + + backward_network_gpu(gnet, gstate); + + floss += get_network_cost(fnet) /(fnet->subdivisions*fnet->batch); + + cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch); + for(k = 0; k < gnet->batch; ++k){ + int index = j*gnet->batch + k; + copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1); + generated.y.vals[index][0] = .1; + style.y.vals[index][0] = .9; + } + } + +*/ +/* + image sim = float_to_image(anet->w, anet->h, anet->c, style.X.vals[j]); + show_image(sim, "style"); + cvWaitKey(0); + */ + /* + + harmless_update_network_gpu(anet); + + data merge = concat_data(style, generated); + randomize_data(merge); + float aloss = train_network(anet, merge); + + update_network_gpu(gnet); + + free_data(merge); + free_data(train); + free_data(generated); + free_data(style); + if (aloss_avg < 0) aloss_avg = aloss; + if (floss_avg < 0) floss_avg = floss; + aloss_avg = aloss_avg*.9 + aloss*.1; + floss_avg = floss_avg*.9 + floss*.1; + + printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, floss, aloss, floss_avg, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs); + if(i%1000==0){ + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory, gbase, i); + save_weights(gnet, buff); + sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i); + save_weights(anet, buff); + } + if(i%100==0){ + char buff[256]; + sprintf(buff, "%s/%s.backup", backup_directory, gbase); + save_weights(gnet, buff); + sprintf(buff, "%s/%s.backup", backup_directory, abase); + save_weights(anet, buff); + } + } +#endif +} +*/ + +/* +void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear) +{ +#ifdef GPU + //char *train_images = "/home/pjreddie/data/coco/train1.txt"; + //char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt"; + char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list"; + char *backup_directory = "/home/pjreddie/backup/"; + srand(time(0)); + char *base = basecfg(cfg); + char *abase = basecfg(acfg); + printf("%s\n", base); + network net = load_network(cfg, weight, clear); + network anet = load_network(acfg, aweight, clear); + + int i, j, k; + layer imlayer = {0}; + for (i = 0; i < net->n; ++i) { + if (net->layers[i].out_c == 3) { + imlayer = net->layers[i]; + break; + } + } + + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + int imgs = net->batch*net->subdivisions; + i = *net->seen/imgs; + data train, buffer; + + + list *plist = get_paths(train_images); + //int N = plist->size; + char **paths = (char **)list_to_array(plist); + + load_args args = {0}; + args.w = net->w; + args.h = net->h; + args.paths = paths; + args.n = imgs; + args.m = plist->size; + args.d = &buffer; + + args.min = net->min_crop; + args.max = net->max_crop; + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.saturation = net->saturation; + args.hue = net->hue; + args.size = net->w; + args.type = CLASSIFICATION_DATA; + args.classes = 1; + char *ls[1] = {"coco"}; + args.labels = ls; + + pthread_t load_thread = load_data_in_thread(args); + clock_t time; + + network_state gstate = {0}; + gstate.index = 0; + gstate.net = net; + int x_size = get_network_input_size(net)*net->batch; + int y_size = x_size; + gstate.input = cuda_make_array(0, x_size); + gstate.truth = cuda_make_array(0, y_size); + gstate.delta = 0; + gstate.train = 1; + float *pixs = calloc(x_size, sizeof(float)); + float *graypixs = calloc(x_size, sizeof(float)); + float *y = calloc(y_size, sizeof(float)); + + network_state astate = {0}; + astate.index = 0; + astate.net = anet; + int ay_size = get_network_output_size(anet)*anet->batch; + astate.input = 0; + astate.truth = 0; + astate.delta = 0; + astate.train = 1; + + float *imerror = cuda_make_array(0, imlayer.outputs); + float *ones_gpu = cuda_make_array(0, ay_size); + fill_gpu(ay_size, .9, ones_gpu, 1); + + float aloss_avg = -1; + float gloss_avg = -1; + + //data generated = copy_data(train); + + while (get_current_batch(net) < net->max_batches) { + i += 1; + time=clock(); + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data_in_thread(args); + + printf("Loaded: %lf seconds\n", sec(clock()-time)); + + data gray = copy_data(train); + for(j = 0; j < imgs; ++j){ + image gim = float_to_image(net->w, net->h, net->c, gray.X.vals[j]); + grayscale_image_3c(gim); + train.y.vals[j][0] = .9; + + image yim = float_to_image(net->w, net->h, net->c, train.X.vals[j]); + //rgb_to_yuv(yim); + } + time=clock(); + float gloss = 0; + + for(j = 0; j < net->subdivisions; ++j){ + get_next_batch(train, net->batch, j*net->batch, pixs, y); + get_next_batch(gray, net->batch, j*net->batch, graypixs, y); + cuda_push_array(gstate.input, graypixs, x_size); + cuda_push_array(gstate.truth, pixs, y_size); + */ + /* + image origi = float_to_image(net->w, net->h, 3, pixs); + image grayi = float_to_image(net->w, net->h, 3, graypixs); + show_image(grayi, "gray"); + show_image(origi, "orig"); + cvWaitKey(0); + */ + /* + *net->seen += net->batch; + forward_network_gpu(net, gstate); + + fill_gpu(imlayer.outputs, 0, imerror, 1); + astate.input = imlayer.output_gpu; + astate.delta = imerror; + astate.truth = ones_gpu; + forward_network_gpu(anet, astate); + backward_network_gpu(anet, astate); + + scal_gpu(imlayer.outputs, .1, net->layers[net->n-1].delta_gpu, 1); + + backward_network_gpu(net, gstate); + + scal_gpu(imlayer.outputs, 1000, imerror, 1); + + printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs)); + printf("features %f\n", cuda_mag_array(net->layers[net->n-1].delta_gpu, imlayer.outputs)); + + axpy_gpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1); + + gloss += get_network_cost(net) /(net->subdivisions*net->batch); + + cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch); + for(k = 0; k < net->batch; ++k){ + int index = j*net->batch + k; + copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1); + gray.y.vals[index][0] = .1; + } + } + harmless_update_network_gpu(anet); + + data merge = concat_data(train, gray); + randomize_data(merge); + float aloss = train_network(anet, merge); + + update_network_gpu(net); + update_network_gpu(anet); + free_data(merge); + free_data(train); + free_data(gray); + if (aloss_avg < 0) aloss_avg = aloss; + aloss_avg = aloss_avg*.9 + aloss*.1; + gloss_avg = gloss_avg*.9 + gloss*.1; + + printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs); + if(i%1000==0){ + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + save_weights(net, buff); + sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i); + save_weights(anet, buff); + } + if(i%100==0){ + char buff[256]; + sprintf(buff, "%s/%s.backup", backup_directory, base); + save_weights(net, buff); + sprintf(buff, "%s/%s.backup", backup_directory, abase); + save_weights(anet, buff); + } + } + char buff[256]; + sprintf(buff, "%s/%s_final.weights", backup_directory, base); + save_weights(net, buff); +#endif +} +*/ + +void slerp(float *start, float *end, float s, int n, float *out) +{ + float omega = acos(dot_cpu(n, start, 1, end, 1)); + float so = sin(omega); + fill_cpu(n, 0, out, 1); + axpy_cpu(n, sin((1-s)*omega)/so, start, 1, out, 1); + axpy_cpu(n, sin(s*omega)/so, end, 1, out, 1); + + float mag = mag_array(out, n); + scale_array(out, n, 1./mag); +} + +image random_unit_vector_image(int w, int h, int c) +{ + image im = make_image(w, h, c); + int i; + for(i = 0; i < im.w*im.h*im.c; ++i){ + im.data[i] = rand_normal(); + } + float mag = mag_array(im.data, im.w*im.h*im.c); + scale_array(im.data, im.w*im.h*im.c, 1./mag); + return im; +} + +void inter_dcgan(char *cfgfile, char *weightfile) +{ + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + srand(2222222); + + clock_t time; + char buff[256]; + char *input = buff; + int i, imlayer = 0; + + for (i = 0; i < net->n; ++i) { + if (net->layers[i].out_c == 3) { + imlayer = i; + printf("%d\n", i); + break; + } + } + image start = random_unit_vector_image(net->w, net->h, net->c); + image end = random_unit_vector_image(net->w, net->h, net->c); + image im = make_image(net->w, net->h, net->c); + image orig = copy_image(start); + + int c = 0; + int count = 0; + int max_count = 15; + while(1){ + ++c; + + if(count == max_count){ + count = 0; + free_image(start); + start = end; + end = random_unit_vector_image(net->w, net->h, net->c); + if(c > 300){ + end = orig; + } + if(c>300 + max_count) return; + } + ++count; + + slerp(start.data, end.data, (float)count / max_count, im.w*im.h*im.c, im.data); + + float *X = im.data; + time=clock(); + network_predict(net, X); + image out = get_network_image_layer(net, imlayer); + //yuv_to_rgb(out); + normalize_image(out); + printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + //char buff[256]; + sprintf(buff, "out%05d", c); + save_image(out, "out"); + save_image(out, buff); + show_image(out, "out", 0); + } +} + +void test_dcgan(char *cfgfile, char *weightfile) +{ + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + srand(2222222); + + clock_t time; + char buff[256]; + char *input = buff; + int imlayer = 0; + + imlayer = net->n-1; + + while(1){ + image im = make_image(net->w, net->h, net->c); + int i; + for(i = 0; i < im.w*im.h*im.c; ++i){ + im.data[i] = rand_normal(); + } + //float mag = mag_array(im.data, im.w*im.h*im.c); + //scale_array(im.data, im.w*im.h*im.c, 1./mag); + + float *X = im.data; + time=clock(); + network_predict(net, X); + image out = get_network_image_layer(net, imlayer); + //yuv_to_rgb(out); + normalize_image(out); + printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + save_image(out, "out"); + show_image(out, "out", 0); + + free_image(im); + } +} + +void set_network_alpha_beta(network *net, float alpha, float beta) +{ + int i; + for(i = 0; i < net->n; ++i){ + if(net->layers[i].type == SHORTCUT){ + net->layers[i].alpha = alpha; + net->layers[i].beta = beta; + } + } +} + +void train_prog(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display, char *train_images, int maxbatch) +{ +#ifdef GPU + char *backup_directory = "/home/pjreddie/backup/"; + srand(time(0)); + char *base = basecfg(cfg); + char *abase = basecfg(acfg); + printf("%s\n", base); + network *gnet = load_network(cfg, weight, clear); + network *anet = load_network(acfg, aweight, clear); + + int i, j, k; + layer imlayer = gnet->layers[gnet->n-1]; + + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay); + int imgs = gnet->batch*gnet->subdivisions; + i = *gnet->seen/imgs; + data train, buffer; + + + list *plist = get_paths(train_images); + char **paths = (char **)list_to_array(plist); + + load_args args= get_base_args(anet); + args.paths = paths; + args.n = imgs; + args.m = plist->size; + args.d = &buffer; + args.type = CLASSIFICATION_DATA; + args.threads=16; + args.classes = 1; + char *ls[2] = {"imagenet", "zzzzzzzz"}; + args.labels = ls; + + pthread_t load_thread = load_data_in_thread(args); + clock_t time; + + gnet->train = 1; + anet->train = 1; + + int x_size = gnet->inputs*gnet->batch; + int y_size = gnet->truths*gnet->batch; + float *imerror = cuda_make_array(0, y_size); + + float aloss_avg = -1; + + if (maxbatch == 0) maxbatch = gnet->max_batches; + while (get_current_batch(gnet) < maxbatch) { + { + int cb = get_current_batch(gnet); + float alpha = (float) cb / (maxbatch/2); + if(alpha > 1) alpha = 1; + float beta = 1 - alpha; + printf("%f %f\n", alpha, beta); + set_network_alpha_beta(gnet, alpha, beta); + set_network_alpha_beta(anet, beta, alpha); + } + + i += 1; + time=clock(); + pthread_join(load_thread, 0); + train = buffer; + + load_thread = load_data_in_thread(args); + + printf("Loaded: %lf seconds\n", sec(clock()-time)); + + data gen = copy_data(train); + for (j = 0; j < imgs; ++j) { + train.y.vals[j][0] = 1; + gen.y.vals[j][0] = 0; + } + time=clock(); + + for (j = 0; j < gnet->subdivisions; ++j) { + get_next_batch(train, gnet->batch, j*gnet->batch, gnet->truth, 0); + int z; + for(z = 0; z < x_size; ++z){ + gnet->input[z] = rand_normal(); + } + /* + for(z = 0; z < gnet->batch; ++z){ + float mag = mag_array(gnet->input + z*gnet->inputs, gnet->inputs); + scale_array(gnet->input + z*gnet->inputs, gnet->inputs, 1./mag); + } + */ + *gnet->seen += gnet->batch; + forward_network(gnet); + + fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1); + fill_cpu(anet->truths*anet->batch, 1, anet->truth, 1); + copy_cpu(anet->inputs*anet->batch, imlayer.output, 1, anet->input, 1); + anet->delta_gpu = imerror; + forward_network(anet); + backward_network(anet); + + //float genaloss = *anet->cost / anet->batch; + + scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1); + scal_gpu(imlayer.outputs*imlayer.batch, 0, gnet->layers[gnet->n-1].delta_gpu, 1); + + axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, gnet->layers[gnet->n-1].delta_gpu, 1); + + backward_network(gnet); + + for(k = 0; k < gnet->batch; ++k){ + int index = j*gnet->batch + k; + copy_cpu(gnet->outputs, gnet->output + k*gnet->outputs, 1, gen.X.vals[index], 1); + } + } + harmless_update_network_gpu(anet); + + data merge = concat_data(train, gen); + float aloss = train_network(anet, merge); + +#ifdef OPENCV + if(display){ + image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]); + image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]); + show_image(im, "gen", 1); + show_image(im2, "train", 1); + save_image(im, "gen"); + save_image(im2, "train"); + } +#endif + + update_network_gpu(gnet); + + free_data(merge); + free_data(train); + free_data(gen); + if (aloss_avg < 0) aloss_avg = aloss; + aloss_avg = aloss_avg*.9 + aloss*.1; + + printf("%d: adv: %f | adv_avg: %f, %f rate, %lf seconds, %d images\n", i, aloss, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs); + if(i%10000==0){ + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + save_weights(gnet, buff); + sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i); + save_weights(anet, buff); + } + if(i%1000==0){ + char buff[256]; + sprintf(buff, "%s/%s.backup", backup_directory, base); + save_weights(gnet, buff); + sprintf(buff, "%s/%s.backup", backup_directory, abase); + save_weights(anet, buff); + } + } + char buff[256]; + sprintf(buff, "%s/%s_final.weights", backup_directory, base); + save_weights(gnet, buff); +#endif +} + +void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display, char *train_images, int maxbatch) +{ +#ifdef GPU + char *backup_directory = "/home/pjreddie/backup/"; + srand(time(0)); + char *base = basecfg(cfg); + char *abase = basecfg(acfg); + printf("%s\n", base); + network *gnet = load_network(cfg, weight, clear); + network *anet = load_network(acfg, aweight, clear); + //float orig_rate = anet->learning_rate; + + int i, j, k; + layer imlayer = {0}; + for (i = 0; i < gnet->n; ++i) { + if (gnet->layers[i].out_c == 3) { + imlayer = gnet->layers[i]; + break; + } + } + + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay); + int imgs = gnet->batch*gnet->subdivisions; + i = *gnet->seen/imgs; + data train, buffer; + + + list *plist = get_paths(train_images); + //int N = plist->size; + char **paths = (char **)list_to_array(plist); + + load_args args= get_base_args(anet); + args.paths = paths; + args.n = imgs; + args.m = plist->size; + args.d = &buffer; + args.type = CLASSIFICATION_DATA; + args.threads=16; + args.classes = 1; + char *ls[2] = {"imagenet", "zzzzzzzz"}; + args.labels = ls; + + pthread_t load_thread = load_data_in_thread(args); + clock_t time; + + gnet->train = 1; + anet->train = 1; + + int x_size = gnet->inputs*gnet->batch; + int y_size = gnet->truths*gnet->batch; + float *imerror = cuda_make_array(0, y_size); + + //int ay_size = anet->truths*anet->batch; + + float aloss_avg = -1; + + //data generated = copy_data(train); + + if (maxbatch == 0) maxbatch = gnet->max_batches; + while (get_current_batch(gnet) < maxbatch) { + i += 1; + time=clock(); + pthread_join(load_thread, 0); + train = buffer; + + //translate_data_rows(train, -.5); + //scale_data_rows(train, 2); + + load_thread = load_data_in_thread(args); + + printf("Loaded: %lf seconds\n", sec(clock()-time)); + + data gen = copy_data(train); + for (j = 0; j < imgs; ++j) { + train.y.vals[j][0] = 1; + gen.y.vals[j][0] = 0; + } + time=clock(); + + for(j = 0; j < gnet->subdivisions; ++j){ + get_next_batch(train, gnet->batch, j*gnet->batch, gnet->truth, 0); + int z; + for(z = 0; z < x_size; ++z){ + gnet->input[z] = rand_normal(); + } + for(z = 0; z < gnet->batch; ++z){ + float mag = mag_array(gnet->input + z*gnet->inputs, gnet->inputs); + scale_array(gnet->input + z*gnet->inputs, gnet->inputs, 1./mag); + } + /* + for(z = 0; z < 100; ++z){ + printf("%f, ", gnet->input[z]); + } + printf("\n"); + printf("input: %f %f\n", mean_array(gnet->input, x_size), variance_array(gnet->input, x_size)); + */ + + //cuda_push_array(gnet->input_gpu, gnet->input, x_size); + //cuda_push_array(gnet->truth_gpu, gnet->truth, y_size); + *gnet->seen += gnet->batch; + forward_network(gnet); + + fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1); + fill_cpu(anet->truths*anet->batch, 1, anet->truth, 1); + copy_cpu(anet->inputs*anet->batch, imlayer.output, 1, anet->input, 1); + anet->delta_gpu = imerror; + forward_network(anet); + backward_network(anet); + + //float genaloss = *anet->cost / anet->batch; + //printf("%f\n", genaloss); + + scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1); + scal_gpu(imlayer.outputs*imlayer.batch, 0, gnet->layers[gnet->n-1].delta_gpu, 1); + + //printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch)); + //printf("features %f\n", cuda_mag_array(gnet->layers[gnet->n-1].delta_gpu, imlayer.outputs*imlayer.batch)); + + axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, gnet->layers[gnet->n-1].delta_gpu, 1); + + backward_network(gnet); + + /* + for(k = 0; k < gnet->n; ++k){ + layer l = gnet->layers[k]; + cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch); + printf("%d: %f %f\n", k, mean_array(l.output, l.outputs*l.batch), variance_array(l.output, l.outputs*l.batch)); + } + */ + + for(k = 0; k < gnet->batch; ++k){ + int index = j*gnet->batch + k; + copy_cpu(gnet->outputs, gnet->output + k*gnet->outputs, 1, gen.X.vals[index], 1); + } + } + harmless_update_network_gpu(anet); + + data merge = concat_data(train, gen); + //randomize_data(merge); + float aloss = train_network(anet, merge); + + //translate_image(im, 1); + //scale_image(im, .5); + //translate_image(im2, 1); + //scale_image(im2, .5); +#ifdef OPENCV + if(display){ + image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]); + image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]); + show_image(im, "gen", 1); + show_image(im2, "train", 1); + save_image(im, "gen"); + save_image(im2, "train"); + } +#endif + + /* + if(aloss < .1){ + anet->learning_rate = 0; + } else if (aloss > .3){ + anet->learning_rate = orig_rate; + } + */ + + update_network_gpu(gnet); + + free_data(merge); + free_data(train); + free_data(gen); + if (aloss_avg < 0) aloss_avg = aloss; + aloss_avg = aloss_avg*.9 + aloss*.1; + + printf("%d: adv: %f | adv_avg: %f, %f rate, %lf seconds, %d images\n", i, aloss, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs); + if(i%10000==0){ + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + save_weights(gnet, buff); + sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i); + save_weights(anet, buff); + } + if(i%1000==0){ + char buff[256]; + sprintf(buff, "%s/%s.backup", backup_directory, base); + save_weights(gnet, buff); + sprintf(buff, "%s/%s.backup", backup_directory, abase); + save_weights(anet, buff); + } + } + char buff[256]; + sprintf(buff, "%s/%s_final.weights", backup_directory, base); + save_weights(gnet, buff); +#endif +} + +void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display) +{ +#ifdef GPU + //char *train_images = "/home/pjreddie/data/coco/train1.txt"; + //char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt"; + char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list"; + char *backup_directory = "/home/pjreddie/backup/"; + srand(time(0)); + char *base = basecfg(cfg); + char *abase = basecfg(acfg); + printf("%s\n", base); + network *net = load_network(cfg, weight, clear); + network *anet = load_network(acfg, aweight, clear); + + int i, j, k; + layer imlayer = {0}; + for (i = 0; i < net->n; ++i) { + if (net->layers[i].out_c == 3) { + imlayer = net->layers[i]; + break; + } + } + + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + int imgs = net->batch*net->subdivisions; + i = *net->seen/imgs; + data train, buffer; + + + list *plist = get_paths(train_images); + //int N = plist->size; + char **paths = (char **)list_to_array(plist); + + load_args args= get_base_args(net); + args.paths = paths; + args.n = imgs; + args.m = plist->size; + args.d = &buffer; + + args.type = CLASSIFICATION_DATA; + args.classes = 1; + char *ls[2] = {"imagenet"}; + args.labels = ls; + + pthread_t load_thread = load_data_in_thread(args); + clock_t time; + + int x_size = net->inputs*net->batch; + //int y_size = x_size; + net->delta = 0; + net->train = 1; + float *pixs = calloc(x_size, sizeof(float)); + float *graypixs = calloc(x_size, sizeof(float)); + //float *y = calloc(y_size, sizeof(float)); + + //int ay_size = anet->outputs*anet->batch; + anet->delta = 0; + anet->train = 1; + + float *imerror = cuda_make_array(0, imlayer.outputs*imlayer.batch); + + float aloss_avg = -1; + float gloss_avg = -1; + + //data generated = copy_data(train); + + while (get_current_batch(net) < net->max_batches) { + i += 1; + time=clock(); + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data_in_thread(args); + + printf("Loaded: %lf seconds\n", sec(clock()-time)); + + data gray = copy_data(train); + for(j = 0; j < imgs; ++j){ + image gim = float_to_image(net->w, net->h, net->c, gray.X.vals[j]); + grayscale_image_3c(gim); + train.y.vals[j][0] = .95; + gray.y.vals[j][0] = .05; + } + time=clock(); + float gloss = 0; + + for(j = 0; j < net->subdivisions; ++j){ + get_next_batch(train, net->batch, j*net->batch, pixs, 0); + get_next_batch(gray, net->batch, j*net->batch, graypixs, 0); + cuda_push_array(net->input_gpu, graypixs, net->inputs*net->batch); + cuda_push_array(net->truth_gpu, pixs, net->truths*net->batch); + /* + image origi = float_to_image(net->w, net->h, 3, pixs); + image grayi = float_to_image(net->w, net->h, 3, graypixs); + show_image(grayi, "gray"); + show_image(origi, "orig"); + cvWaitKey(0); + */ + *net->seen += net->batch; + forward_network_gpu(net); + + fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1); + copy_gpu(anet->inputs*anet->batch, imlayer.output_gpu, 1, anet->input_gpu, 1); + fill_gpu(anet->inputs*anet->batch, .95, anet->truth_gpu, 1); + anet->delta_gpu = imerror; + forward_network_gpu(anet); + backward_network_gpu(anet); + + scal_gpu(imlayer.outputs*imlayer.batch, 1./100., net->layers[net->n-1].delta_gpu, 1); + + scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1); + + printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch)); + printf("features %f\n", cuda_mag_array(net->layers[net->n-1].delta_gpu, imlayer.outputs*imlayer.batch)); + + axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, net->layers[net->n-1].delta_gpu, 1); + + backward_network_gpu(net); + + + gloss += *net->cost /(net->subdivisions*net->batch); + + for(k = 0; k < net->batch; ++k){ + int index = j*net->batch + k; + copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1); + } + } + harmless_update_network_gpu(anet); + + data merge = concat_data(train, gray); + //randomize_data(merge); + float aloss = train_network(anet, merge); + + update_network_gpu(net); + +#ifdef OPENCV + if(display){ + image im = float_to_image(anet->w, anet->h, anet->c, gray.X.vals[0]); + image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]); + show_image(im, "gen", 1); + show_image(im2, "train", 1); + } +#endif + free_data(merge); + free_data(train); + free_data(gray); + if (aloss_avg < 0) aloss_avg = aloss; + aloss_avg = aloss_avg*.9 + aloss*.1; + gloss_avg = gloss_avg*.9 + gloss*.1; + + printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs); + if(i%1000==0){ + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + save_weights(net, buff); + sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i); + save_weights(anet, buff); + } + if(i%100==0){ + char buff[256]; + sprintf(buff, "%s/%s.backup", backup_directory, base); + save_weights(net, buff); + sprintf(buff, "%s/%s.backup", backup_directory, abase); + save_weights(anet, buff); + } + } + char buff[256]; + sprintf(buff, "%s/%s_final.weights", backup_directory, base); + save_weights(net, buff); +#endif +} + +/* + void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfile, int clear) + { +#ifdef GPU +char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt"; +char *backup_directory = "/home/pjreddie/backup/"; +srand(time(0)); +char *base = basecfg(cfgfile); +printf("%s\n", base); +network net = parse_network_cfg(cfgfile); +if(weightfile){ +load_weights(&net, weightfile); +} +if(clear) *net->seen = 0; + +char *abase = basecfg(acfgfile); +network anet = parse_network_cfg(acfgfile); +if(aweightfile){ +load_weights(&anet, aweightfile); +} +if(clear) *anet->seen = 0; + +int i, j, k; +layer imlayer = {0}; +for (i = 0; i < net->n; ++i) { +if (net->layers[i].out_c == 3) { +imlayer = net->layers[i]; +break; +} +} + +printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); +int imgs = net->batch*net->subdivisions; +i = *net->seen/imgs; +data train, buffer; + + +list *plist = get_paths(train_images); +//int N = plist->size; +char **paths = (char **)list_to_array(plist); + +load_args args = {0}; +args.w = net->w; +args.h = net->h; +args.paths = paths; +args.n = imgs; +args.m = plist->size; +args.d = &buffer; + +args.min = net->min_crop; +args.max = net->max_crop; +args.angle = net->angle; +args.aspect = net->aspect; +args.exposure = net->exposure; +args.saturation = net->saturation; +args.hue = net->hue; +args.size = net->w; +args.type = CLASSIFICATION_DATA; +args.classes = 1; +char *ls[1] = {"coco"}; +args.labels = ls; + +pthread_t load_thread = load_data_in_thread(args); +clock_t time; + +network_state gstate = {0}; +gstate.index = 0; +gstate.net = net; +int x_size = get_network_input_size(net)*net->batch; +int y_size = 1*net->batch; +gstate.input = cuda_make_array(0, x_size); +gstate.truth = 0; +gstate.delta = 0; +gstate.train = 1; +float *X = calloc(x_size, sizeof(float)); +float *y = calloc(y_size, sizeof(float)); + +network_state astate = {0}; +astate.index = 0; +astate.net = anet; +int ay_size = get_network_output_size(anet)*anet->batch; +astate.input = 0; +astate.truth = 0; +astate.delta = 0; +astate.train = 1; + +float *imerror = cuda_make_array(0, imlayer.outputs); +float *ones_gpu = cuda_make_array(0, ay_size); +fill_gpu(ay_size, 1, ones_gpu, 1); + +float aloss_avg = -1; +float gloss_avg = -1; + +//data generated = copy_data(train); + +while (get_current_batch(net) < net->max_batches) { + i += 1; + time=clock(); + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data_in_thread(args); + + printf("Loaded: %lf seconds\n", sec(clock()-time)); + + data generated = copy_data(train); + time=clock(); + float gloss = 0; + + for(j = 0; j < net->subdivisions; ++j){ + get_next_batch(train, net->batch, j*net->batch, X, y); + cuda_push_array(gstate.input, X, x_size); + *net->seen += net->batch; + forward_network_gpu(net, gstate); + + fill_gpu(imlayer.outputs, 0, imerror, 1); + astate.input = imlayer.output_gpu; + astate.delta = imerror; + astate.truth = ones_gpu; + forward_network_gpu(anet, astate); + backward_network_gpu(anet, astate); + + scal_gpu(imlayer.outputs, 1, imerror, 1); + axpy_gpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1); + + backward_network_gpu(net, gstate); + + printf("features %f\n", cuda_mag_array(imlayer.delta_gpu, imlayer.outputs)); + printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs)); + + gloss += get_network_cost(net) /(net->subdivisions*net->batch); + + cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch); + for(k = 0; k < net->batch; ++k){ + int index = j*net->batch + k; + copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1); + generated.y.vals[index][0] = 0; + } + } + harmless_update_network_gpu(anet); + + data merge = concat_data(train, generated); + randomize_data(merge); + float aloss = train_network(anet, merge); + + update_network_gpu(net); + update_network_gpu(anet); + free_data(merge); + free_data(train); + free_data(generated); + if (aloss_avg < 0) aloss_avg = aloss; + aloss_avg = aloss_avg*.9 + aloss*.1; + gloss_avg = gloss_avg*.9 + gloss*.1; + + printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs); + if(i%1000==0){ + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + save_weights(net, buff); + sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i); + save_weights(anet, buff); + } + if(i%100==0){ + char buff[256]; + sprintf(buff, "%s/%s.backup", backup_directory, base); + save_weights(net, buff); + sprintf(buff, "%s/%s.backup", backup_directory, abase); + save_weights(anet, buff); + } +} +char buff[256]; +sprintf(buff, "%s/%s_final.weights", backup_directory, base); +save_weights(net, buff); +#endif +} +*/ + +/* + void train_lsd(char *cfgfile, char *weightfile, int clear) + { + char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt"; + char *backup_directory = "/home/pjreddie/backup/"; + srand(time(0)); + char *base = basecfg(cfgfile); + printf("%s\n", base); + float avg_loss = -1; + network net = parse_network_cfg(cfgfile); + if(weightfile){ + load_weights(&net, weightfile); + } + if(clear) *net->seen = 0; + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + int imgs = net->batch*net->subdivisions; + int i = *net->seen/imgs; + data train, buffer; + + + list *plist = get_paths(train_images); +//int N = plist->size; +char **paths = (char **)list_to_array(plist); + +load_args args = {0}; +args.w = net->w; +args.h = net->h; +args.paths = paths; +args.n = imgs; +args.m = plist->size; +args.d = &buffer; + +args.min = net->min_crop; +args.max = net->max_crop; +args.angle = net->angle; +args.aspect = net->aspect; +args.exposure = net->exposure; +args.saturation = net->saturation; +args.hue = net->hue; +args.size = net->w; +args.type = CLASSIFICATION_DATA; +args.classes = 1; +char *ls[1] = {"coco"}; +args.labels = ls; + +pthread_t load_thread = load_data_in_thread(args); +clock_t time; +//while(i*imgs < N*120){ +while(get_current_batch(net) < net->max_batches){ +i += 1; +time=clock(); +pthread_join(load_thread, 0); +train = buffer; +load_thread = load_data_in_thread(args); + +printf("Loaded: %lf seconds\n", sec(clock()-time)); + +time=clock(); +float loss = train_network(net, train); +if (avg_loss < 0) avg_loss = loss; +avg_loss = avg_loss*.9 + loss*.1; + +printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); +if(i%1000==0){ +char buff[256]; +sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); +save_weights(net, buff); +} +if(i%100==0){ +char buff[256]; +sprintf(buff, "%s/%s.backup", backup_directory, base); +save_weights(net, buff); +} +free_data(train); +} +char buff[256]; +sprintf(buff, "%s/%s_final.weights", backup_directory, base); +save_weights(net, buff); +} +*/ + +void test_lsd(char *cfg, char *weights, char *filename, int gray) +{ + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); + srand(2222222); + + clock_t time; + char buff[256]; + char *input = buff; + int i, imlayer = 0; + + for (i = 0; i < net->n; ++i) { + if (net->layers[i].out_c == 3) { + imlayer = i; + printf("%d\n", i); + break; + } + } + + while(1){ + if(filename){ + strncpy(input, filename, 256); + }else{ + printf("Enter Image Path: "); + fflush(stdout); + input = fgets(input, 256, stdin); + if(!input) return; + strtok(input, "\n"); + } + image im = load_image_color(input, 0, 0); + image resized = resize_min(im, net->w); + image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h); + if(gray) grayscale_image_3c(crop); + + float *X = crop.data; + time=clock(); + network_predict(net, X); + image out = get_network_image_layer(net, imlayer); + //yuv_to_rgb(out); + constrain_image(out); + printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + save_image(out, "out"); + show_image(out, "out", 1); + show_image(crop, "crop", 0); + + free_image(im); + free_image(resized); + free_image(crop); + if (filename) break; + } +} + + +void run_lsd(int argc, char **argv) +{ + if(argc < 4){ + fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); + return; + } + + int clear = find_arg(argc, argv, "-clear"); + int display = find_arg(argc, argv, "-display"); + int batches = find_int_arg(argc, argv, "-b", 0); + char *file = find_char_arg(argc, argv, "-file", "/home/pjreddie/data/imagenet/imagenet1k.train.list"); + + char *cfg = argv[3]; + char *weights = (argc > 4) ? argv[4] : 0; + char *filename = (argc > 5) ? argv[5] : 0; + char *acfg = argv[5]; + char *aweights = (argc > 6) ? argv[6] : 0; + //if(0==strcmp(argv[2], "train")) train_lsd(cfg, weights, clear); + //else if(0==strcmp(argv[2], "train2")) train_lsd2(cfg, weights, acfg, aweights, clear); + //else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear); + //else if(0==strcmp(argv[2], "train3")) train_lsd3(argv[3], argv[4], argv[5], argv[6], argv[7], argv[8], clear); + if(0==strcmp(argv[2], "traingan")) train_dcgan(cfg, weights, acfg, aweights, clear, display, file, batches); + else if(0==strcmp(argv[2], "trainprog")) train_prog(cfg, weights, acfg, aweights, clear, display, file, batches); + else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear, display); + else if(0==strcmp(argv[2], "gan")) test_dcgan(cfg, weights); + else if(0==strcmp(argv[2], "inter")) inter_dcgan(cfg, weights); + else if(0==strcmp(argv[2], "test")) test_lsd(cfg, weights, filename, 0); + else if(0==strcmp(argv[2], "color")) test_lsd(cfg, weights, filename, 1); + /* + else if(0==strcmp(argv[2], "valid")) validate_lsd(cfg, weights); + */ +} diff --git a/image.darknet/inst/include/darknet/src/nightmare.c b/image.darknet/inst/include/darknet/examples/nightmare.c similarity index 58% rename from image.darknet/inst/include/darknet/src/nightmare.c rename to image.darknet/inst/include/darknet/examples/nightmare.c index ec7166c..2978eb6 100644 --- a/image.darknet/inst/include/darknet/src/nightmare.c +++ b/image.darknet/inst/include/darknet/examples/nightmare.c @@ -1,12 +1,6 @@ +#include "darknet.h" -#include "network.h" -#include "parser.h" -#include "blas.h" -#include "utils.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif +#include // ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2 @@ -51,31 +45,30 @@ void optimize_picture(network *net, image orig, int max_layer, float scale, floa image delta = make_image(im.w, im.h, im.c); - network_state state = {0}; - #ifdef GPU - state.input = cuda_make_array(im.data, im.w*im.h*im.c); - state.delta = cuda_make_array(im.data, im.w*im.h*im.c); + net->delta_gpu = cuda_make_array(delta.data, im.w*im.h*im.c); + copy_cpu(net->inputs, im.data, 1, net->input, 1); - forward_network_gpu(*net, state); - copy_ongpu(last.outputs, last.output_gpu, 1, last.delta_gpu, 1); + forward_network_gpu(net); + copy_gpu(last.outputs, last.output_gpu, 1, last.delta_gpu, 1); cuda_pull_array(last.delta_gpu, last.delta, last.outputs); calculate_loss(last.delta, last.delta, last.outputs, thresh); cuda_push_array(last.delta_gpu, last.delta, last.outputs); - backward_network_gpu(*net, state); + backward_network_gpu(net); - cuda_pull_array(state.delta, delta.data, im.w*im.h*im.c); - cuda_free(state.input); - cuda_free(state.delta); + cuda_pull_array(net->delta_gpu, delta.data, im.w*im.h*im.c); + cuda_free(net->delta_gpu); + net->delta_gpu = 0; #else - state.input = im.data; - state.delta = delta.data; - forward_network(*net, state); + printf("\nnet: %d %d %d im: %d %d %d\n", net->w, net->h, net->inputs, im.w, im.h, im.c); + copy_cpu(net->inputs, im.data, 1, net->input, 1); + net->delta = delta.data; + forward_network(net); copy_cpu(last.outputs, last.output, 1, last.delta, 1); calculate_loss(last.output, last.delta, last.outputs, thresh); - backward_network(*net, state); + backward_network(net); #endif if(flip) flip_image(delta); @@ -90,6 +83,10 @@ void optimize_picture(network *net, image orig, int max_layer, float scale, floa */ //rate = rate / abs_mean(out.data, out.w*out.h*out.c); + image gray = make_image(out.w, out.h, out.c); + fill_image(gray, .5); + axpy_cpu(orig.w*orig.h*orig.c, -1, orig.data, 1, gray.data, 1); + axpy_cpu(orig.w*orig.h*orig.c, .1, gray.data, 1, out.data, 1); if(norm) normalize_array(out.data, out.w*out.h*out.c); axpy_cpu(orig.w*orig.h*orig.c, rate, out.data, 1, orig.data, 1); @@ -135,42 +132,44 @@ void smooth(image recon, image update, float lambda, int num) } } -void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters) +void reconstruct_picture(network *net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters) { int iter = 0; for (iter = 0; iter < iters; ++iter) { image delta = make_image(recon.w, recon.h, recon.c); - network_state state = {0}; #ifdef GPU - state.input = cuda_make_array(recon.data, recon.w*recon.h*recon.c); - state.delta = cuda_make_array(delta.data, delta.w*delta.h*delta.c); - state.truth = cuda_make_array(features, get_network_output_size(net)); + layer l = get_network_output_layer(net); + cuda_push_array(net->input_gpu, recon.data, recon.w*recon.h*recon.c); + //cuda_push_array(net->truth_gpu, features, net->truths); + net->delta_gpu = cuda_make_array(delta.data, delta.w*delta.h*delta.c); - forward_network_gpu(net, state); - backward_network_gpu(net, state); + forward_network_gpu(net); + cuda_push_array(l.delta_gpu, features, l.outputs); + axpy_gpu(l.outputs, -1, l.output_gpu, 1, l.delta_gpu, 1); + backward_network_gpu(net); - cuda_pull_array(state.delta, delta.data, delta.w*delta.h*delta.c); + cuda_pull_array(net->delta_gpu, delta.data, delta.w*delta.h*delta.c); - cuda_free(state.input); - cuda_free(state.delta); - cuda_free(state.truth); + cuda_free(net->delta_gpu); #else - state.input = recon.data; - state.delta = delta.data; - state.truth = features; + net->input = recon.data; + net->delta = delta.data; + net->truth = features; - forward_network(net, state); - backward_network(net, state); + forward_network(net); + backward_network(net); #endif + //normalize_array(delta.data, delta.w*delta.h*delta.c); axpy_cpu(recon.w*recon.h*recon.c, 1, delta.data, 1, update.data, 1); - smooth(recon, update, lambda, smooth_size); + //smooth(recon, update, lambda, smooth_size); axpy_cpu(recon.w*recon.h*recon.c, rate, update.data, 1, recon.data, 1); scal_cpu(recon.w*recon.h*recon.c, momentum, update.data, 1); - //float mag = mag_array(recon.data, recon.w*recon.h*recon.c); + float mag = mag_array(delta.data, recon.w*recon.h*recon.c); + printf("mag: %f\n", mag); //scal_cpu(recon.w*recon.h*recon.c, 600/mag, recon.data, 1); constrain_image(recon); @@ -178,6 +177,113 @@ void reconstruct_picture(network net, float *features, image recon, image update } } +/* +void run_lsd(int argc, char **argv) +{ + srand(0); + if(argc < 3){ + fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [options! (optional)]\n", argv[0], argv[1]); + return; + } + + char *cfg = argv[2]; + char *weights = argv[3]; + char *input = argv[4]; + + int norm = find_int_arg(argc, argv, "-norm", 1); + int rounds = find_int_arg(argc, argv, "-rounds", 1); + int iters = find_int_arg(argc, argv, "-iters", 10); + float rate = find_float_arg(argc, argv, "-rate", .04); + float momentum = find_float_arg(argc, argv, "-momentum", .9); + float lambda = find_float_arg(argc, argv, "-lambda", .01); + char *prefix = find_char_arg(argc, argv, "-prefix", 0); + int reconstruct = find_arg(argc, argv, "-reconstruct"); + int smooth_size = find_int_arg(argc, argv, "-smooth", 1); + + network net = parse_network_cfg(cfg); + load_weights(&net, weights); + char *cfgbase = basecfg(cfg); + char *imbase = basecfg(input); + + set_batch_network(&net, 1); + image im = load_image_color(input, 0, 0); + + float *features = 0; + image update; + if (reconstruct){ + im = letterbox_image(im, net->w, net->h); + + int zz = 0; + network_predict(net, im.data); + image out_im = get_network_image(net); + image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz); + //flip_image(crop); + image f_im = resize_image(crop, out_im.w, out_im.h); + free_image(crop); + printf("%d features\n", out_im.w*out_im.h*out_im.c); + + + im = resize_image(im, im.w, im.h); + f_im = resize_image(f_im, f_im.w, f_im.h); + features = f_im.data; + + int i; + for(i = 0; i < 14*14*512; ++i){ + features[i] += rand_uniform(-.19, .19); + } + + free_image(im); + im = make_random_image(im.w, im.h, im.c); + update = make_image(im.w, im.h, im.c); + + } + + int e; + int n; + for(e = 0; e < rounds; ++e){ + fprintf(stderr, "Iteration: "); + fflush(stderr); + for(n = 0; n < iters; ++n){ + fprintf(stderr, "%d, ", n); + fflush(stderr); + if(reconstruct){ + reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1); + //if ((n+1)%30 == 0) rate *= .5; + show_image(im, "reconstruction"); +#ifdef OPENCV + cvWaitKey(10); +#endif + }else{ + int layer = max_layer + rand()%range - range/2; + int octave = rand()%octaves; + optimize_picture(&net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm); + } + } + fprintf(stderr, "done\n"); + char buff[256]; + if (prefix){ + sprintf(buff, "%s/%s_%s_%d_%06d",prefix, imbase, cfgbase, max_layer, e); + }else{ + sprintf(buff, "%s_%s_%d_%06d",imbase, cfgbase, max_layer, e); + } + printf("%d %s\n", e, buff); + save_image(im, buff); + //show_image(im, buff); + //cvWaitKey(0); + + if(rotate){ + image rot = rotate_image(im, rotate); + free_image(im); + im = rot; + } + image crop = crop_image(im, im.w * (1. - zoom)/2., im.h * (1.-zoom)/2., im.w*zoom, im.h*zoom); + image resized = resize_image(crop, im.w, im.h); + free_image(im); + free_image(crop); + im = resized; + } +} +*/ void run_nightmare(int argc, char **argv) { @@ -207,12 +313,11 @@ void run_nightmare(int argc, char **argv) int reconstruct = find_arg(argc, argv, "-reconstruct"); int smooth_size = find_int_arg(argc, argv, "-smooth", 1); - network net = parse_network_cfg(cfg); - load_weights(&net, weights); + network *net = load_network(cfg, weights, 0); char *cfgbase = basecfg(cfg); char *imbase = basecfg(input); - set_batch_network(&net, 1); + set_batch_network(net, 1); image im = load_image_color(input, 0, 0); if(0){ float scale = 1; @@ -224,35 +329,40 @@ void run_nightmare(int argc, char **argv) free_image(im); im = resized; } + //im = letterbox_image(im, net->w, net->h); float *features = 0; image update; if (reconstruct){ - resize_network(&net, im.w, im.h); + net->n = max_layer; + im = letterbox_image(im, net->w, net->h); + //resize_network(&net, im.w, im.h); - int zz = 0; network_predict(net, im.data); - image out_im = get_network_image(net); - image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz); + if(net->layers[net->n-1].type == REGION){ + printf("region!\n"); + zero_objectness(net->layers[net->n-1]); + } + image out_im = copy_image(get_network_image(net)); + /* + image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz); //flip_image(crop); image f_im = resize_image(crop, out_im.w, out_im.h); free_image(crop); + */ printf("%d features\n", out_im.w*out_im.h*out_im.c); + features = out_im.data; - im = resize_image(im, im.w, im.h); - f_im = resize_image(f_im, f_im.w, f_im.h); - features = f_im.data; - + /* int i; - for(i = 0; i < 14*14*512; ++i){ - features[i] += rand_uniform(-.19, .19); + for(i = 0; i < 14*14*512; ++i){ + //features[i] += rand_uniform(-.19, .19); } - free_image(im); im = make_random_image(im.w, im.h, im.c); + */ update = make_image(im.w, im.h, im.c); - } int e; @@ -266,14 +376,11 @@ void run_nightmare(int argc, char **argv) if(reconstruct){ reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1); //if ((n+1)%30 == 0) rate *= .5; - show_image(im, "reconstruction"); -#ifdef OPENCV - cvWaitKey(10); -#endif + show_image(im, "reconstruction", 10); }else{ int layer = max_layer + rand()%range - range/2; int octave = rand()%octaves; - optimize_picture(&net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm); + optimize_picture(net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm); } } fprintf(stderr, "done\n"); @@ -290,8 +397,7 @@ void run_nightmare(int argc, char **argv) } printf("%d %s\n", e, buff); save_image(im, buff); - //show_image(im, buff); - //cvWaitKey(0); + //show_image(im, buff, 0); if(rotate){ image rot = rotate_image(im, rotate); diff --git a/image.darknet/inst/include/darknet/examples/regressor.c b/image.darknet/inst/include/darknet/examples/regressor.c new file mode 100644 index 0000000..20cec0f --- /dev/null +++ b/image.darknet/inst/include/darknet/examples/regressor.c @@ -0,0 +1,240 @@ +#include "darknet.h" +#include +#include + +void train_regressor(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear) +{ + int i; + + float avg_loss = -1; + char *base = basecfg(cfgfile); + printf("%s\n", base); + printf("%d\n", ngpus); + network **nets = calloc(ngpus, sizeof(network*)); + + srand(time(0)); + int seed = rand(); + for(i = 0; i < ngpus; ++i){ + srand(seed); +#ifdef GPU + cuda_set_device(gpus[i]); +#endif + nets[i] = load_network(cfgfile, weightfile, clear); + nets[i]->learning_rate *= ngpus; + } + srand(time(0)); + network *net = nets[0]; + + int imgs = net->batch * net->subdivisions * ngpus; + + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + list *options = read_data_cfg(datacfg); + + char *backup_directory = option_find_str(options, "backup", "/backup/"); + char *train_list = option_find_str(options, "train", "data/train.list"); + int classes = option_find_int(options, "classes", 1); + + list *plist = get_paths(train_list); + char **paths = (char **)list_to_array(plist); + printf("%d\n", plist->size); + int N = plist->size; + clock_t time; + + load_args args = {0}; + args.w = net->w; + args.h = net->h; + args.threads = 32; + args.classes = classes; + + args.min = net->min_ratio*net->w; + args.max = net->max_ratio*net->w; + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.saturation = net->saturation; + args.hue = net->hue; + args.size = net->w; + + args.paths = paths; + args.n = imgs; + args.m = N; + args.type = REGRESSION_DATA; + + data train; + data buffer; + pthread_t load_thread; + args.d = &buffer; + load_thread = load_data(args); + + int epoch = (*net->seen)/N; + while(get_current_batch(net) < net->max_batches || net->max_batches == 0){ + time=clock(); + + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data(args); + + printf("Loaded: %lf seconds\n", sec(clock()-time)); + time=clock(); + + float loss = 0; +#ifdef GPU + if(ngpus == 1){ + loss = train_network(net, train); + } else { + loss = train_networks(nets, ngpus, train, 4); + } +#else + loss = train_network(net, train); +#endif + if(avg_loss == -1) avg_loss = loss; + avg_loss = avg_loss*.9 + loss*.1; + printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen); + free_data(train); + if(*net->seen/N > epoch){ + epoch = *net->seen/N; + char buff[256]; + sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); + save_weights(net, buff); + } + if(get_current_batch(net)%100 == 0){ + char buff[256]; + sprintf(buff, "%s/%s.backup",backup_directory,base); + save_weights(net, buff); + } + } + char buff[256]; + sprintf(buff, "%s/%s.weights", backup_directory, base); + save_weights(net, buff); + + free_network(net); + free_ptrs((void**)paths, plist->size); + free_list(plist); + free(base); +} + +void predict_regressor(char *cfgfile, char *weightfile, char *filename) +{ + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + srand(2222222); + + clock_t time; + char buff[256]; + char *input = buff; + while(1){ + if(filename){ + strncpy(input, filename, 256); + }else{ + printf("Enter Image Path: "); + fflush(stdout); + input = fgets(input, 256, stdin); + if(!input) return; + strtok(input, "\n"); + } + image im = load_image_color(input, 0, 0); + image sized = letterbox_image(im, net->w, net->h); + + float *X = sized.data; + time=clock(); + float *predictions = network_predict(net, X); + printf("Predicted: %f\n", predictions[0]); + printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + free_image(im); + free_image(sized); + if (filename) break; + } +} + + +void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) +{ +#ifdef OPENCV + printf("Regressor Demo\n"); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + + srand(2222222); + list *options = read_data_cfg(datacfg); + int classes = option_find_int(options, "classes", 1); + char *name_list = option_find_str(options, "names", 0); + char **names = get_labels(name_list); + + void * cap = open_video_stream(filename, cam_index, 0,0,0); + if(!cap) error("Couldn't connect to webcam.\n"); + float fps = 0; + + while(1){ + struct timeval tval_before, tval_after, tval_result; + gettimeofday(&tval_before, NULL); + + image in = get_image_from_stream(cap); + image crop = center_crop_image(in, net->w, net->h); + grayscale_image_3c(crop); + + float *predictions = network_predict(net, crop.data); + + printf("\033[2J"); + printf("\033[1;1H"); + printf("\nFPS:%.0f\n",fps); + + int i; + for(i = 0; i < classes; ++i){ + printf("%s: %f\n", names[i], predictions[i]); + } + + show_image(crop, "Regressor", 10); + free_image(in); + free_image(crop); + + gettimeofday(&tval_after, NULL); + timersub(&tval_after, &tval_before, &tval_result); + float curr = 1000000.f/((long int)tval_result.tv_usec); + fps = .9*fps + .1*curr; + } +#endif +} + + +void run_regressor(int argc, char **argv) +{ + if(argc < 4){ + fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); + return; + } + + char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); + int *gpus = 0; + int gpu = 0; + int ngpus = 0; + if(gpu_list){ + printf("%s\n", gpu_list); + int len = strlen(gpu_list); + ngpus = 1; + int i; + for(i = 0; i < len; ++i){ + if (gpu_list[i] == ',') ++ngpus; + } + gpus = calloc(ngpus, sizeof(int)); + for(i = 0; i < ngpus; ++i){ + gpus[i] = atoi(gpu_list); + gpu_list = strchr(gpu_list, ',')+1; + } + } else { + gpu = gpu_index; + gpus = &gpu; + ngpus = 1; + } + + int cam_index = find_int_arg(argc, argv, "-c", 0); + int clear = find_arg(argc, argv, "-clear"); + char *data = argv[3]; + char *cfg = argv[4]; + char *weights = (argc > 5) ? argv[5] : 0; + char *filename = (argc > 6) ? argv[6]: 0; + if(0==strcmp(argv[2], "test")) predict_regressor(data, cfg, weights); + else if(0==strcmp(argv[2], "train")) train_regressor(data, cfg, weights, gpus, ngpus, clear); + else if(0==strcmp(argv[2], "demo")) demo_regressor(data, cfg, weights, cam_index, filename); +} + + diff --git a/image.darknet/inst/include/darknet/src/rnn.c b/image.darknet/inst/include/darknet/examples/rnn.c similarity index 72% rename from image.darknet/inst/include/darknet/src/rnn.c rename to image.darknet/inst/include/darknet/examples/rnn.c index eca6f55..5d49eaa 100644 --- a/image.darknet/inst/include/darknet/src/rnn.c +++ b/image.darknet/inst/include/darknet/examples/rnn.c @@ -1,18 +1,26 @@ -#include "network.h" -#include "cost_layer.h" -#include "utils.h" -#include "blas.h" -#include "parser.h" +#include "darknet.h" -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif +#include typedef struct { float *x; float *y; } float_pair; +unsigned char **load_files(char *filename, int *n) +{ + list *paths = get_paths(filename); + *n = paths->size; + unsigned char **contents = calloc(*n, sizeof(char *)); + int i; + node *x = paths->front; + for(i = 0; i < *n; ++i){ + contents[i] = read_file((char *)x->val); + x = x->next; + } + return contents; +} + int *read_tokenized_data(char *filename, size_t *read) { size_t size = 512; @@ -49,6 +57,7 @@ char **read_tokens(char *filename, size_t *read) size = size*2; d = realloc(d, size*sizeof(char *)); } + if(0==strcmp(line, "")) line = "\n"; d[count-1] = line; } fclose(fp); @@ -57,6 +66,7 @@ char **read_tokens(char *filename, size_t *read) return d; } + float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps) { float *x = calloc(batch * steps * characters, sizeof(float)); @@ -83,6 +93,37 @@ float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size return p; } +float_pair get_seq2seq_data(char **source, char **dest, int n, int characters, size_t len, int batch, int steps) +{ + int i,j; + float *x = calloc(batch * steps * characters, sizeof(float)); + float *y = calloc(batch * steps * characters, sizeof(float)); + for(i = 0; i < batch; ++i){ + int index = rand()%n; + //int slen = strlen(source[index]); + //int dlen = strlen(dest[index]); + for(j = 0; j < steps; ++j){ + unsigned char curr = source[index][j]; + unsigned char next = dest[index][j]; + + x[(j*batch + i)*characters + curr] = 1; + y[(j*batch + i)*characters + next] = 1; + + if(curr > 255 || curr <= 0 || next > 255 || next <= 0){ + /*text[(index+j+2)%len] = 0; + printf("%ld %d %d %d %d\n", index, j, len, (int)text[index+j], (int)text[index+j+1]); + printf("%s", text+index); + */ + error("Bad char"); + } + } + } + float_pair p; + p.x = x; + p.y = y; + return p; +} + float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps) { float *x = calloc(batch * steps * characters, sizeof(float)); @@ -113,19 +154,6 @@ float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, si return p; } -void reset_rnn_state(network net, int b) -{ - int i; - for (i = 0; i < net.n; ++i) { - #ifdef GPU - layer l = net.layers[i]; - if(l.state_gpu){ - fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1); - } - #endif - } -} - void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int tokenized) { srand(time(0)); @@ -135,32 +163,22 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, if(tokenized){ tokens = read_tokenized_data(filename, &size); } else { - FILE *fp = fopen(filename, "rb"); - - fseek(fp, 0, SEEK_END); - size = ftell(fp); - fseek(fp, 0, SEEK_SET); - - text = calloc(size+1, sizeof(char)); - fread(text, 1, size, fp); - fclose(fp); + text = read_file(filename); + size = strlen((const char*)text); } char *backup_directory = "/home/pjreddie/backup/"; char *base = basecfg(cfgfile); fprintf(stderr, "%s\n", base); float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, clear); - int inputs = get_network_input_size(net); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int batch = net.batch; - int steps = net.time_steps; - if(clear) *net.seen = 0; - int i = (*net.seen)/net.batch; + int inputs = net->inputs; + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g, Inputs: %d %d %d\n", net->learning_rate, net->momentum, net->decay, inputs, net->batch, net->time_steps); + int batch = net->batch; + int steps = net->time_steps; + if(clear) *net->seen = 0; + int i = (*net->seen)/net->batch; int streams = batch/steps; size_t *offsets = calloc(streams, sizeof(size_t)); @@ -170,7 +188,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, } clock_t time; - while(get_current_batch(net) < net.max_batches){ + while(get_current_batch(net) < net->max_batches){ i += 1; time=clock(); float_pair p; @@ -180,30 +198,32 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, p = get_rnn_data(text, offsets, inputs, size, streams, steps); } - float loss = train_network_datum(net, p.x, p.y) / (batch); + copy_cpu(net->inputs*net->batch, p.x, 1, net->input, 1); + copy_cpu(net->truths*net->batch, p.y, 1, net->truth, 1); + float loss = train_network_datum(net) / (batch); free(p.x); free(p.y); if (avg_loss < 0) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - int chars = get_current_batch(net)*batch; + size_t chars = get_current_batch(net)*batch; fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds, %f epochs\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), (float) chars/size); for(j = 0; j < streams; ++j){ //printf("%d\n", j); - if(rand()%10 == 0){ + if(rand()%64 == 0){ //fprintf(stderr, "Reset\n"); offsets[j] = rand_size_t()%size; - reset_rnn_state(net, j); + reset_network_state(net, j); } } - if(i%1000==0){ + if(i%10000==0){ char buff[256]; sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); save_weights(net, buff); } - if(i%10==0){ + if(i%100==0){ char buff[256]; sprintf(buff, "%s/%s.backup", backup_directory, base); save_weights(net, buff); @@ -234,14 +254,11 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t char *base = basecfg(cfgfile); fprintf(stderr, "%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int inputs = get_network_input_size(net); + network *net = load_network(cfgfile, weightfile, 0); + int inputs = net->inputs; int i, j; - for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; + for(i = 0; i < net->n; ++i) net->layers[i].temperature = temp; int c = 0; int len = strlen(seed); float *input = calloc(inputs, sizeof(float)); @@ -279,7 +296,7 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t printf("\n"); } -void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file) +void test_tactic_rnn_multi(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file) { char **tokens = 0; if(token_file){ @@ -291,14 +308,56 @@ void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int r char *base = basecfg(cfgfile); fprintf(stderr, "%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); + network *net = load_network(cfgfile, weightfile, 0); + int inputs = net->inputs; + + int i, j; + for(i = 0; i < net->n; ++i) net->layers[i].temperature = temp; + int c = 0; + float *input = calloc(inputs, sizeof(float)); + float *out = 0; + + while(1){ + reset_network_state(net, 0); + while((c = getc(stdin)) != EOF && c != 0){ + input[c] = 1; + out = network_predict(net, input); + input[c] = 0; + } + for(i = 0; i < num; ++i){ + for(j = 0; j < inputs; ++j){ + if (out[j] < .0001) out[j] = 0; + } + int next = sample_array(out, inputs); + if(c == '.' && next == '\n') break; + c = next; + print_symbol(c, tokens); + + input[c] = 1; + out = network_predict(net, input); + input[c] = 0; + } + printf("\n"); + } +} + +void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file) +{ + char **tokens = 0; + if(token_file){ + size_t n; + tokens = read_tokens(token_file, &n); } - int inputs = get_network_input_size(net); + + srand(rseed); + char *base = basecfg(cfgfile); + fprintf(stderr, "%s\n", base); + + network *net = load_network(cfgfile, weightfile, 0); + int inputs = net->inputs; int i, j; - for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; + for(i = 0; i < net->n; ++i) net->layers[i].temperature = temp; int c = 0; float *input = calloc(inputs, sizeof(float)); float *out = 0; @@ -329,11 +388,8 @@ void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed) char *base = basecfg(cfgfile); fprintf(stderr, "%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int inputs = get_network_input_size(net); + network *net = load_network(cfgfile, weightfile, 0); + int inputs = net->inputs; int count = 0; int words = 1; @@ -381,11 +437,8 @@ void valid_char_rnn(char *cfgfile, char *weightfile, char *seed) char *base = basecfg(cfgfile); fprintf(stderr, "%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int inputs = get_network_input_size(net); + network *net = load_network(cfgfile, weightfile, 0); + int inputs = net->inputs; int count = 0; int words = 1; @@ -413,7 +466,7 @@ void valid_char_rnn(char *cfgfile, char *weightfile, char *seed) input[c] = 0; sum += log(out[next])/log2; c = next; - printf("%d Perplexity: %4.4f Word Perplexity: %4.4f\n", count, pow(2, -sum/count), pow(2, -sum/words)); + printf("%d BPC: %4.4f Perplexity: %4.4f Word Perplexity: %4.4f\n", count, -sum/count, pow(2, -sum/count), pow(2, -sum/words)); } } @@ -422,11 +475,8 @@ void vec_char_rnn(char *cfgfile, char *weightfile, char *seed) char *base = basecfg(cfgfile); fprintf(stderr, "%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - int inputs = get_network_input_size(net); + network *net = load_network(cfgfile, weightfile, 0); + int inputs = net->inputs; int c; int seed_len = strlen(seed); @@ -434,7 +484,7 @@ void vec_char_rnn(char *cfgfile, char *weightfile, char *seed) int i; char *line; while((line=fgetl(stdin)) != 0){ - reset_rnn_state(net, 0); + reset_network_state(net, 0); for(i = 0; i < seed_len; ++i){ c = seed[i]; input[(int)c] = 1; @@ -454,7 +504,7 @@ void vec_char_rnn(char *cfgfile, char *weightfile, char *seed) network_predict(net, input); input[(int)c] = 0; - layer l = net.layers[0]; + layer l = net->layers[0]; #ifdef GPU cuda_pull_array(l.output_gpu, l.output, l.outputs); #endif diff --git a/image.darknet/inst/include/darknet/src/rnn_vid.c b/image.darknet/inst/include/darknet/examples/rnn_vid.c similarity index 96% rename from image.darknet/inst/include/darknet/src/rnn_vid.c rename to image.darknet/inst/include/darknet/examples/rnn_vid.c index 36912d6..e887923 100644 --- a/image.darknet/inst/include/darknet/src/rnn_vid.c +++ b/image.darknet/inst/include/darknet/examples/rnn_vid.c @@ -1,11 +1,6 @@ -#include "network.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "blas.h" +#include "darknet.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" image get_image_from_stream(CvCapture *cap); image ipl_to_image(IplImage* src); @@ -104,7 +99,9 @@ void train_vid_rnn(char *cfgfile, char *weightfile) time=clock(); float_pair p = get_rnn_vid_data(extractor, paths, N, batch, steps); - float loss = train_network_datum(net, p.x, p.y) / (net.batch); + copy_cpu(net.inputs*net.batch, p.x, 1, net.input, 1); + copy_cpu(net.truths*net.batch, p.y, 1, net.truth, 1); + float loss = train_network_datum(net) / (net.batch); free(p.x); diff --git a/image.darknet/inst/include/darknet/examples/segmenter.c b/image.darknet/inst/include/darknet/examples/segmenter.c new file mode 100644 index 0000000..2e7cea0 --- /dev/null +++ b/image.darknet/inst/include/darknet/examples/segmenter.c @@ -0,0 +1,255 @@ +#include "darknet.h" +#include +#include + +void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int display) +{ + int i; + + float avg_loss = -1; + char *base = basecfg(cfgfile); + printf("%s\n", base); + printf("%d\n", ngpus); + network **nets = calloc(ngpus, sizeof(network*)); + + srand(time(0)); + int seed = rand(); + for(i = 0; i < ngpus; ++i){ + srand(seed); +#ifdef GPU + cuda_set_device(gpus[i]); +#endif + nets[i] = load_network(cfgfile, weightfile, clear); + nets[i]->learning_rate *= ngpus; + } + srand(time(0)); + network *net = nets[0]; + image pred = get_network_image(net); + + int div = net->w/pred.w; + assert(pred.w * div == net->w); + assert(pred.h * div == net->h); + + int imgs = net->batch * net->subdivisions * ngpus; + + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + list *options = read_data_cfg(datacfg); + + char *backup_directory = option_find_str(options, "backup", "/backup/"); + char *train_list = option_find_str(options, "train", "data/train.list"); + + list *plist = get_paths(train_list); + char **paths = (char **)list_to_array(plist); + printf("%d\n", plist->size); + int N = plist->size; + + load_args args = {0}; + args.w = net->w; + args.h = net->h; + args.threads = 32; + args.scale = div; + + args.min = net->min_crop; + args.max = net->max_crop; + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.saturation = net->saturation; + args.hue = net->hue; + args.size = net->w; + args.classes = 80; + + args.paths = paths; + args.n = imgs; + args.m = N; + args.type = SEGMENTATION_DATA; + + data train; + data buffer; + pthread_t load_thread; + args.d = &buffer; + load_thread = load_data(args); + + int epoch = (*net->seen)/N; + while(get_current_batch(net) < net->max_batches || net->max_batches == 0){ + double time = what_time_is_it_now(); + + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data(args); + + printf("Loaded: %lf seconds\n", what_time_is_it_now()-time); + time = what_time_is_it_now(); + + float loss = 0; +#ifdef GPU + if(ngpus == 1){ + loss = train_network(net, train); + } else { + loss = train_networks(nets, ngpus, train, 4); + } +#else + loss = train_network(net, train); +#endif + if(display){ + image tr = float_to_image(net->w/div, net->h/div, 80, train.y.vals[net->batch*(net->subdivisions-1)]); + image im = float_to_image(net->w, net->h, net->c, train.X.vals[net->batch*(net->subdivisions-1)]); + image mask = mask_to_rgb(tr); + image prmask = mask_to_rgb(pred); + show_image(im, "input", 1); + show_image(prmask, "pred", 1); + show_image(mask, "truth", 100); + free_image(mask); + free_image(prmask); + } + if(avg_loss == -1) avg_loss = loss; + avg_loss = avg_loss*.9 + loss*.1; + printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen); + free_data(train); + if(*net->seen/N > epoch){ + epoch = *net->seen/N; + char buff[256]; + sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); + save_weights(net, buff); + } + if(get_current_batch(net)%100 == 0){ + char buff[256]; + sprintf(buff, "%s/%s.backup",backup_directory,base); + save_weights(net, buff); + } + } + char buff[256]; + sprintf(buff, "%s/%s.weights", backup_directory, base); + save_weights(net, buff); + + free_network(net); + free_ptrs((void**)paths, plist->size); + free_list(plist); + free(base); +} + +void predict_segmenter(char *datafile, char *cfg, char *weights, char *filename) +{ + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); + srand(2222222); + + clock_t time; + char buff[256]; + char *input = buff; + while(1){ + if(filename){ + strncpy(input, filename, 256); + }else{ + printf("Enter Image Path: "); + fflush(stdout); + input = fgets(input, 256, stdin); + if(!input) return; + strtok(input, "\n"); + } + image im = load_image_color(input, 0, 0); + image sized = letterbox_image(im, net->w, net->h); + + float *X = sized.data; + time=clock(); + float *predictions = network_predict(net, X); + image pred = get_network_image(net); + image prmask = mask_to_rgb(pred); + printf("Predicted: %f\n", predictions[0]); + printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + show_image(sized, "orig", 1); + show_image(prmask, "pred", 0); + free_image(im); + free_image(sized); + free_image(prmask); + if (filename) break; + } +} + + +void demo_segmenter(char *datacfg, char *cfg, char *weights, int cam_index, const char *filename) +{ +#ifdef OPENCV + printf("Classifier Demo\n"); + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); + + srand(2222222); + void * cap = open_video_stream(filename, cam_index, 0,0,0); + + if(!cap) error("Couldn't connect to webcam.\n"); + float fps = 0; + + while(1){ + struct timeval tval_before, tval_after, tval_result; + gettimeofday(&tval_before, NULL); + + image in = get_image_from_stream(cap); + image in_s = letterbox_image(in, net->w, net->h); + + network_predict(net, in_s.data); + + printf("\033[2J"); + printf("\033[1;1H"); + printf("\nFPS:%.0f\n",fps); + + image pred = get_network_image(net); + image prmask = mask_to_rgb(pred); + show_image(prmask, "Segmenter", 10); + + free_image(in_s); + free_image(in); + free_image(prmask); + + gettimeofday(&tval_after, NULL); + timersub(&tval_after, &tval_before, &tval_result); + float curr = 1000000.f/((long int)tval_result.tv_usec); + fps = .9*fps + .1*curr; + } +#endif +} + + +void run_segmenter(int argc, char **argv) +{ + if(argc < 4){ + fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); + return; + } + + char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); + int *gpus = 0; + int gpu = 0; + int ngpus = 0; + if(gpu_list){ + printf("%s\n", gpu_list); + int len = strlen(gpu_list); + ngpus = 1; + int i; + for(i = 0; i < len; ++i){ + if (gpu_list[i] == ',') ++ngpus; + } + gpus = calloc(ngpus, sizeof(int)); + for(i = 0; i < ngpus; ++i){ + gpus[i] = atoi(gpu_list); + gpu_list = strchr(gpu_list, ',')+1; + } + } else { + gpu = gpu_index; + gpus = &gpu; + ngpus = 1; + } + + int cam_index = find_int_arg(argc, argv, "-c", 0); + int clear = find_arg(argc, argv, "-clear"); + int display = find_arg(argc, argv, "-display"); + char *data = argv[3]; + char *cfg = argv[4]; + char *weights = (argc > 5) ? argv[5] : 0; + char *filename = (argc > 6) ? argv[6]: 0; + if(0==strcmp(argv[2], "test")) predict_segmenter(data, cfg, weights, filename); + else if(0==strcmp(argv[2], "train")) train_segmenter(data, cfg, weights, gpus, ngpus, clear, display); + else if(0==strcmp(argv[2], "demo")) demo_segmenter(data, cfg, weights, cam_index, filename); +} + + diff --git a/image.darknet/inst/include/darknet/src/super.c b/image.darknet/inst/include/darknet/examples/super.c similarity index 78% rename from image.darknet/inst/include/darknet/src/super.c rename to image.darknet/inst/include/darknet/examples/super.c index 63e9860..d34406b 100644 --- a/image.darknet/inst/include/darknet/src/super.c +++ b/image.darknet/inst/include/darknet/examples/super.c @@ -1,13 +1,6 @@ -#include "network.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" +#include "darknet.h" -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -void train_super(char *cfgfile, char *weightfile) +void train_super(char *cfgfile, char *weightfile, int clear) { char *train_images = "/data/imagenet/imagenet1k.train.list"; char *backup_directory = "/home/pjreddie/backup/"; @@ -15,13 +8,10 @@ void train_super(char *cfgfile, char *weightfile) char *base = basecfg(cfgfile); printf("%s\n", base); float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; + network *net = load_network(cfgfile, weightfile, clear); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + int imgs = net->batch*net->subdivisions; + int i = *net->seen/imgs; data train, buffer; @@ -30,8 +20,8 @@ void train_super(char *cfgfile, char *weightfile) char **paths = (char **)list_to_array(plist); load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.scale = 4; args.paths = paths; args.n = imgs; @@ -42,7 +32,7 @@ void train_super(char *cfgfile, char *weightfile) pthread_t load_thread = load_data_in_thread(args); clock_t time; //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ + while(get_current_batch(net) < net->max_batches){ i += 1; time=clock(); pthread_join(load_thread, 0); @@ -76,11 +66,8 @@ void train_super(char *cfgfile, char *weightfile) void test_super(char *cfgfile, char *weightfile, char *filename) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); srand(2222222); clock_t time; @@ -97,7 +84,7 @@ void test_super(char *cfgfile, char *weightfile, char *filename) strtok(input, "\n"); } image im = load_image_color(input, 0, 0); - resize_network(&net, im.w, im.h); + resize_network(net, im.w, im.h); printf("%d %d\n", im.w, im.h); float *X = im.data; @@ -106,6 +93,7 @@ void test_super(char *cfgfile, char *weightfile, char *filename) image out = get_network_image(net); printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); save_image(out, "out"); + show_image(out, "out", 0); free_image(im); if (filename) break; @@ -123,7 +111,8 @@ void run_super(int argc, char **argv) char *cfg = argv[3]; char *weights = (argc > 4) ? argv[4] : 0; char *filename = (argc > 5) ? argv[5] : 0; - if(0==strcmp(argv[2], "train")) train_super(cfg, weights); + int clear = find_arg(argc, argv, "-clear"); + if(0==strcmp(argv[2], "train")) train_super(cfg, weights, clear); else if(0==strcmp(argv[2], "test")) test_super(cfg, weights, filename); /* else if(0==strcmp(argv[2], "valid")) validate_super(cfg, weights); diff --git a/image.darknet/inst/include/darknet/src/swag.c b/image.darknet/inst/include/darknet/examples/swag.c similarity index 92% rename from image.darknet/inst/include/darknet/src/swag.c rename to image.darknet/inst/include/darknet/examples/swag.c index 2cb3093..c22d785 100644 --- a/image.darknet/inst/include/darknet/src/swag.c +++ b/image.darknet/inst/include/darknet/examples/swag.c @@ -1,13 +1,5 @@ -#include "network.h" -#include "detection_layer.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "box.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif +#include "darknet.h" +#include void train_swag(char *cfgfile, char *weightfile) { diff --git a/image.darknet/inst/include/darknet/src/tag.c b/image.darknet/inst/include/darknet/examples/tag.c similarity index 72% rename from image.darknet/inst/include/darknet/src/tag.c rename to image.darknet/inst/include/darknet/examples/tag.c index 1e43e7d..4caf8cb 100644 --- a/image.darknet/inst/include/darknet/src/tag.c +++ b/image.darknet/inst/include/darknet/examples/tag.c @@ -1,10 +1,4 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif +#include "darknet.h" void train_tag(char *cfgfile, char *weightfile, int clear) { @@ -13,12 +7,8 @@ void train_tag(char *cfgfile, char *weightfile, int clear) char *base = basecfg(cfgfile); char *backup_directory = "/home/pjreddie/backup/"; printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - if(clear) *net.seen = 0; - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + network *net = load_network(cfgfile, weightfile, clear); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); int imgs = 1024; list *plist = get_paths("/home/pjreddie/tag/train.list"); char **paths = (char **)list_to_array(plist); @@ -30,30 +20,30 @@ void train_tag(char *cfgfile, char *weightfile, int clear) data buffer; load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; - args.min = net.w; - args.max = net.max_crop; - args.size = net.w; + args.min = net->w; + args.max = net->max_crop; + args.size = net->w; args.paths = paths; - args.classes = net.outputs; + args.classes = net->outputs; args.n = imgs; args.m = N; args.d = &buffer; args.type = TAG_DATA; - args.angle = net.angle; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; + args.angle = net->angle; + args.exposure = net->exposure; + args.saturation = net->saturation; + args.hue = net->hue; - fprintf(stderr, "%d classes\n", net.outputs); + fprintf(stderr, "%d classes\n", net->outputs); load_thread = load_data_in_thread(args); - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ + int epoch = (*net->seen)/N; + while(get_current_batch(net) < net->max_batches || net->max_batches == 0){ time=clock(); pthread_join(load_thread, 0); train = buffer; @@ -64,10 +54,10 @@ void train_tag(char *cfgfile, char *weightfile, int clear) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen); free_data(train); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; + if(*net->seen/N > epoch){ + epoch = *net->seen/N; char buff[256]; sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); save_weights(net, buff); @@ -92,11 +82,8 @@ void train_tag(char *cfgfile, char *weightfile, int clear) void test_tag(char *cfgfile, char *weightfile, char *filename) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); srand(2222222); int i = 0; char **names = get_labels("data/tags.txt"); @@ -104,7 +91,7 @@ void test_tag(char *cfgfile, char *weightfile, char *filename) int indexes[10]; char buff[256]; char *input = buff; - int size = net.w; + int size = net->w; while(1){ if(filename){ strncpy(input, filename, 256); @@ -117,7 +104,7 @@ void test_tag(char *cfgfile, char *weightfile, char *filename) } image im = load_image_color(input, 0, 0); image r = resize_min(im, size); - resize_network(&net, r.w, r.h); + resize_network(net, r.w, r.h); printf("%d %d\n", r.w, r.h); float *X = r.data; diff --git a/image.darknet/inst/include/darknet/src/voxel.c b/image.darknet/inst/include/darknet/examples/voxel.c similarity index 96% rename from image.darknet/inst/include/darknet/src/voxel.c rename to image.darknet/inst/include/darknet/examples/voxel.c index 1b53880..01ea9bb 100644 --- a/image.darknet/inst/include/darknet/src/voxel.c +++ b/image.darknet/inst/include/darknet/examples/voxel.c @@ -1,12 +1,4 @@ -#include "network.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -image get_image_from_stream(CvCapture *cap); -#endif +#include "darknet.h" void extract_voxel(char *lfile, char *rfile, char *prefix) { diff --git a/image.darknet/inst/include/darknet/src/writing.c b/image.darknet/inst/include/darknet/examples/writing.c similarity index 90% rename from image.darknet/inst/include/darknet/src/writing.c rename to image.darknet/inst/include/darknet/examples/writing.c index 0a76d48..1b6ff83 100644 --- a/image.darknet/inst/include/darknet/src/writing.c +++ b/image.darknet/inst/include/darknet/examples/writing.c @@ -1,10 +1,4 @@ -#include "network.h" -#include "utils.h" -#include "parser.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif +#include "darknet.h" void train_writing(char *cfgfile, char *weightfile) { @@ -69,11 +63,11 @@ void train_writing(char *cfgfile, char *weightfile) if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); free_data(train); if(get_current_batch(net)%100 == 0){ char buff[256]; - sprintf(buff, "%s/%s_batch_%d.weights", backup_directory, base, get_current_batch(net)); + sprintf(buff, "%s/%s_batch_%ld.weights", backup_directory, base, get_current_batch(net)); save_weights(net, buff); } if(*net.seen/N > epoch){ diff --git a/image.darknet/inst/include/darknet/src/yolo.c b/image.darknet/inst/include/darknet/examples/yolo.c similarity index 68% rename from image.darknet/inst/include/darknet/src/yolo.c rename to image.darknet/inst/include/darknet/examples/yolo.c index ee5f73b..4ddb69a 100644 --- a/image.darknet/inst/include/darknet/src/yolo.c +++ b/image.darknet/inst/include/darknet/examples/yolo.c @@ -1,14 +1,4 @@ -#include "network.h" -#include "detection_layer.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "box.h" -#include "demo.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif +#include "darknet.h" char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; @@ -20,17 +10,14 @@ void train_yolo(char *cfgfile, char *weightfile) char *base = basecfg(cfgfile); printf("%s\n", base); float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; + network *net = load_network(cfgfile, weightfile, 0); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); + int imgs = net->batch*net->subdivisions; + int i = *net->seen/imgs; data train, buffer; - layer l = net.layers[net.n - 1]; + layer l = net->layers[net->n - 1]; int side = l.side; int classes = l.classes; @@ -41,8 +28,8 @@ void train_yolo(char *cfgfile, char *weightfile) char **paths = (char **)list_to_array(plist); load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.paths = paths; args.n = imgs; args.m = plist->size; @@ -52,15 +39,15 @@ void train_yolo(char *cfgfile, char *weightfile) args.d = &buffer; args.type = REGION_DATA; - args.angle = net.angle; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; + args.angle = net->angle; + args.exposure = net->exposure; + args.saturation = net->saturation; + args.hue = net->hue; pthread_t load_thread = load_data_in_thread(args); clock_t time; //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ + while(get_current_batch(net) < net->max_batches){ i += 1; time=clock(); pthread_join(load_thread, 0); @@ -87,14 +74,14 @@ void train_yolo(char *cfgfile, char *weightfile) save_weights(net, buff); } -void print_yolo_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h) +void print_yolo_detections(FILE **fps, char *id, int total, int classes, int w, int h, detection *dets) { int i, j; for(i = 0; i < total; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; + float xmin = dets[i].bbox.x - dets[i].bbox.w/2.; + float xmax = dets[i].bbox.x + dets[i].bbox.w/2.; + float ymin = dets[i].bbox.y - dets[i].bbox.h/2.; + float ymax = dets[i].bbox.y + dets[i].bbox.h/2.; if (xmin < 0) xmin = 0; if (ymin < 0) ymin = 0; @@ -102,20 +89,17 @@ void print_yolo_detections(FILE **fps, char *id, box *boxes, float **probs, int if (ymax > h) ymax = h; for(j = 0; j < classes; ++j){ - if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j], + if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j], xmin, ymin, xmax, ymax); } } } -void validate_yolo(char *cfgfile, char *weightfile) +void validate_yolo(char *cfg, char *weights) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); srand(time(0)); char *base = "results/comp4_det_test_"; @@ -124,7 +108,7 @@ void validate_yolo(char *cfgfile, char *weightfile) //list *plist = get_paths("data/voc.2012.test"); char **paths = (char **)list_to_array(plist); - layer l = net.layers[net.n-1]; + layer l = net->layers[net->n-1]; int classes = l.classes; int j; @@ -134,9 +118,6 @@ void validate_yolo(char *cfgfile, char *weightfile) snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); fps[j] = fopen(buff, "w"); } - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); int m = plist->size; int i=0; @@ -154,8 +135,8 @@ void validate_yolo(char *cfgfile, char *weightfile) pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.type = IMAGE_DATA; for(t = 0; t < nthreads; ++t){ @@ -185,9 +166,11 @@ void validate_yolo(char *cfgfile, char *weightfile) network_predict(net, X); int w = val[t].w; int h = val[t].h; - get_detection_boxes(l, w, h, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, classes, iou_thresh); - print_yolo_detections(fps, id, boxes, probs, l.side*l.side*l.n, classes, w, h); + int nboxes = 0; + detection *dets = get_network_boxes(net, w, h, thresh, 0, 0, 0, &nboxes); + if (nms) do_nms_sort(dets, l.side*l.side*l.n, classes, iou_thresh); + print_yolo_detections(fps, id, l.side*l.side*l.n, classes, w, h, dets); + free_detections(dets, nboxes); free(id); free_image(val[t]); free_image(val_resized[t]); @@ -196,21 +179,18 @@ void validate_yolo(char *cfgfile, char *weightfile) fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); } -void validate_yolo_recall(char *cfgfile, char *weightfile) +void validate_yolo_recall(char *cfg, char *weights) { - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + network *net = load_network(cfg, weights, 0); + set_batch_network(net, 1); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); srand(time(0)); char *base = "results/comp4_det_test_"; list *plist = get_paths("data/voc.2007.test"); char **paths = (char **)list_to_array(plist); - layer l = net.layers[net.n-1]; + layer l = net->layers[net->n-1]; int classes = l.classes; int side = l.side; @@ -221,9 +201,6 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); fps[j] = fopen(buff, "w"); } - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); int m = plist->size; int i=0; @@ -240,11 +217,13 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) for(i = 0; i < m; ++i){ char *path = paths[i]; image orig = load_image_color(path, 0, 0); - image sized = resize_image(orig, net.w, net.h); + image sized = resize_image(orig, net->w, net->h); char *id = basecfg(path); network_predict(net, sized.data); - get_detection_boxes(l, orig.w, orig.h, thresh, probs, boxes, 1); - if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms); + + int nboxes = 0; + detection *dets = get_network_boxes(net, orig.w, orig.h, thresh, 0, 0, 1, &nboxes); + if (nms) do_nms_obj(dets, side*side*l.n, 1, nms); char labelpath[4096]; find_replace(path, "images", "labels", labelpath); @@ -255,7 +234,7 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); for(k = 0; k < side*side*l.n; ++k){ - if(probs[k][0] > thresh){ + if(dets[k].objectness > thresh){ ++proposals; } } @@ -264,8 +243,8 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h}; float best_iou = 0; for(k = 0; k < side*side*l.n; ++k){ - float iou = box_iou(boxes[k], t); - if(probs[k][0] > thresh && iou > best_iou){ + float iou = box_iou(dets[k].bbox, t); + if(dets[k].objectness > thresh && iou > best_iou){ best_iou = iou; } } @@ -276,6 +255,7 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) } fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); + free_detections(dets, nboxes); free(id); free_image(orig); free_image(sized); @@ -285,21 +265,14 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) { image **alphabet = load_alphabet(); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - detection_layer l = net.layers[net.n-1]; - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + layer l = net->layers[net->n-1]; + set_batch_network(net, 1); srand(2222222); clock_t time; char buff[256]; char *input = buff; - int j; float nms=.4; - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); while(1){ if(filename){ strncpy(input, filename, 256); @@ -311,24 +284,22 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) strtok(input, "\n"); } image im = load_image_color(input,0,0); - image sized = resize_image(im, net.w, net.h); + image sized = resize_image(im, net->w, net->h); float *X = sized.data; time=clock(); network_predict(net, X); printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms); - //draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20); - draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20); - save_image(im, "predictions"); - show_image(im, "predictions"); + int nboxes = 0; + detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes); + if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms); + + draw_detections(im, dets, l.side*l.side*l.n, thresh, voc_names, alphabet, 20); + save_image(im, "predictions"); + show_image(im, "predictions", 0); + free_detections(dets, nboxes); free_image(im); free_image(sized); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif if (filename) break; } } @@ -344,6 +315,7 @@ void run_yolo(int argc, char **argv) return; } + int avg = find_int_arg(argc, argv, "-avg", 1); char *cfg = argv[3]; char *weights = (argc > 4) ? argv[4] : 0; char *filename = (argc > 5) ? argv[5]: 0; @@ -351,5 +323,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights); else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); - else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, voc_names, 20, frame_skip, prefix, .5); + else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, voc_names, 20, frame_skip, prefix, avg, .5, 0,0,0,0); } diff --git a/image.darknet/inst/include/darknet/include/darknet.h b/image.darknet/inst/include/darknet/include/darknet.h new file mode 100644 index 0000000..4390c61 --- /dev/null +++ b/image.darknet/inst/include/darknet/include/darknet.h @@ -0,0 +1,805 @@ +#ifndef DARKNET_API +#define DARKNET_API +#include +#include +#include +#include + +#ifdef GPU + #define BLOCK 512 + + #include "cuda_runtime.h" + #include "curand.h" + #include "cublas_v2.h" + + #ifdef CUDNN + #include "cudnn.h" + #endif +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +#define SECRET_NUM -1234 +extern int gpu_index; + +typedef struct{ + int classes; + char **names; +} metadata; + +metadata get_metadata(char *file); + +typedef struct{ + int *leaf; + int n; + int *parent; + int *child; + int *group; + char **name; + + int groups; + int *group_size; + int *group_offset; +} tree; +tree *read_tree(char *filename); + +typedef enum{ + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU +} ACTIVATION; + +typedef enum{ + PNG, BMP, TGA, JPG +} IMTYPE; + +typedef enum{ + MULT, ADD, SUB, DIV +} BINARY_ACTIVATION; + +typedef enum { + CONVOLUTIONAL, + DECONVOLUTIONAL, + CONNECTED, + MAXPOOL, + SOFTMAX, + DETECTION, + DROPOUT, + CROP, + ROUTE, + COST, + NORMALIZATION, + AVGPOOL, + LOCAL, + SHORTCUT, + ACTIVE, + RNN, + GRU, + LSTM, + CRNN, + BATCHNORM, + NETWORK, + XNOR, + REGION, + YOLO, + ISEG, + REORG, + UPSAMPLE, + LOGXENT, + L2NORM, + BLANK +} LAYER_TYPE; + +typedef enum{ + SSE, MASKED, L1, SEG, SMOOTH,WGAN +} COST_TYPE; + +typedef struct{ + int batch; + float learning_rate; + float momentum; + float decay; + int adam; + float B1; + float B2; + float eps; + int t; +} update_args; + +struct network; +typedef struct network network; + +struct layer; +typedef struct layer layer; + +struct layer{ + LAYER_TYPE type; + ACTIVATION activation; + COST_TYPE cost_type; + void (*forward) (struct layer, struct network); + void (*backward) (struct layer, struct network); + void (*update) (struct layer, update_args); + void (*forward_gpu) (struct layer, struct network); + void (*backward_gpu) (struct layer, struct network); + void (*update_gpu) (struct layer, update_args); + int batch_normalize; + int shortcut; + int batch; + int forced; + int flipped; + int inputs; + int outputs; + int nweights; + int nbiases; + int extra; + int truths; + int h,w,c; + int out_h, out_w, out_c; + int n; + int max_boxes; + int groups; + int size; + int side; + int stride; + int reverse; + int flatten; + int spatial; + int pad; + int sqrt; + int flip; + int index; + int binary; + int xnor; + int steps; + int hidden; + int truth; + float smooth; + float dot; + float angle; + float jitter; + float saturation; + float exposure; + float shift; + float ratio; + float learning_rate_scale; + float clip; + int noloss; + int softmax; + int classes; + int coords; + int background; + int rescore; + int objectness; + int joint; + int noadjust; + int reorg; + int log; + int tanh; + int *mask; + int total; + + float alpha; + float beta; + float kappa; + + float coord_scale; + float object_scale; + float noobject_scale; + float mask_scale; + float class_scale; + int bias_match; + int random; + float ignore_thresh; + float truth_thresh; + float thresh; + float focus; + int classfix; + int absolute; + + int onlyforward; + int stopbackward; + int dontload; + int dontsave; + int dontloadscales; + int numload; + + float temperature; + float probability; + float scale; + + char * cweights; + int * indexes; + int * input_layers; + int * input_sizes; + int * map; + int * counts; + float ** sums; + float * rand; + float * cost; + float * state; + float * prev_state; + float * forgot_state; + float * forgot_delta; + float * state_delta; + float * combine_cpu; + float * combine_delta_cpu; + + float * concat; + float * concat_delta; + + float * binary_weights; + + float * biases; + float * bias_updates; + + float * scales; + float * scale_updates; + + float * weights; + float * weight_updates; + + float * delta; + float * output; + float * loss; + float * squared; + float * norms; + + float * spatial_mean; + float * mean; + float * variance; + + float * mean_delta; + float * variance_delta; + + float * rolling_mean; + float * rolling_variance; + + float * x; + float * x_norm; + + float * m; + float * v; + + float * bias_m; + float * bias_v; + float * scale_m; + float * scale_v; + + + float *z_cpu; + float *r_cpu; + float *h_cpu; + float * prev_state_cpu; + + float *temp_cpu; + float *temp2_cpu; + float *temp3_cpu; + + float *dh_cpu; + float *hh_cpu; + float *prev_cell_cpu; + float *cell_cpu; + float *f_cpu; + float *i_cpu; + float *g_cpu; + float *o_cpu; + float *c_cpu; + float *dc_cpu; + + float * binary_input; + + struct layer *input_layer; + struct layer *self_layer; + struct layer *output_layer; + + struct layer *reset_layer; + struct layer *update_layer; + struct layer *state_layer; + + struct layer *input_gate_layer; + struct layer *state_gate_layer; + struct layer *input_save_layer; + struct layer *state_save_layer; + struct layer *input_state_layer; + struct layer *state_state_layer; + + struct layer *input_z_layer; + struct layer *state_z_layer; + + struct layer *input_r_layer; + struct layer *state_r_layer; + + struct layer *input_h_layer; + struct layer *state_h_layer; + + struct layer *wz; + struct layer *uz; + struct layer *wr; + struct layer *ur; + struct layer *wh; + struct layer *uh; + struct layer *uo; + struct layer *wo; + struct layer *uf; + struct layer *wf; + struct layer *ui; + struct layer *wi; + struct layer *ug; + struct layer *wg; + + tree *softmax_tree; + + size_t workspace_size; + +#ifdef GPU + int *indexes_gpu; + + float *z_gpu; + float *r_gpu; + float *h_gpu; + + float *temp_gpu; + float *temp2_gpu; + float *temp3_gpu; + + float *dh_gpu; + float *hh_gpu; + float *prev_cell_gpu; + float *cell_gpu; + float *f_gpu; + float *i_gpu; + float *g_gpu; + float *o_gpu; + float *c_gpu; + float *dc_gpu; + + float *m_gpu; + float *v_gpu; + float *bias_m_gpu; + float *scale_m_gpu; + float *bias_v_gpu; + float *scale_v_gpu; + + float * combine_gpu; + float * combine_delta_gpu; + + float * prev_state_gpu; + float * forgot_state_gpu; + float * forgot_delta_gpu; + float * state_gpu; + float * state_delta_gpu; + float * gate_gpu; + float * gate_delta_gpu; + float * save_gpu; + float * save_delta_gpu; + float * concat_gpu; + float * concat_delta_gpu; + + float * binary_input_gpu; + float * binary_weights_gpu; + + float * mean_gpu; + float * variance_gpu; + + float * rolling_mean_gpu; + float * rolling_variance_gpu; + + float * variance_delta_gpu; + float * mean_delta_gpu; + + float * x_gpu; + float * x_norm_gpu; + float * weights_gpu; + float * weight_updates_gpu; + float * weight_change_gpu; + + float * biases_gpu; + float * bias_updates_gpu; + float * bias_change_gpu; + + float * scales_gpu; + float * scale_updates_gpu; + float * scale_change_gpu; + + float * output_gpu; + float * loss_gpu; + float * delta_gpu; + float * rand_gpu; + float * squared_gpu; + float * norms_gpu; +#ifdef CUDNN + cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; + cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; + cudnnTensorDescriptor_t normTensorDesc; + cudnnFilterDescriptor_t weightDesc; + cudnnFilterDescriptor_t dweightDesc; + cudnnConvolutionDescriptor_t convDesc; + cudnnConvolutionFwdAlgo_t fw_algo; + cudnnConvolutionBwdDataAlgo_t bd_algo; + cudnnConvolutionBwdFilterAlgo_t bf_algo; +#endif +#endif +}; + +void free_layer(layer); + +typedef enum { + CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM +} learning_rate_policy; + +typedef struct network{ + int n; + int batch; + size_t *seen; + int *t; + float epoch; + int subdivisions; + layer *layers; + float *output; + learning_rate_policy policy; + + float learning_rate; + float momentum; + float decay; + float gamma; + float scale; + float power; + int time_steps; + int step; + int max_batches; + float *scales; + int *steps; + int num_steps; + int burn_in; + + int adam; + float B1; + float B2; + float eps; + + int inputs; + int outputs; + int truths; + int notruth; + int h, w, c; + int max_crop; + int min_crop; + float max_ratio; + float min_ratio; + int center; + float angle; + float aspect; + float exposure; + float saturation; + float hue; + int random; + + int gpu_index; + tree *hierarchy; + + float *input; + float *truth; + float *delta; + float *workspace; + int train; + int index; + float *cost; + float clip; + +#ifdef GPU + float *input_gpu; + float *truth_gpu; + float *delta_gpu; + float *output_gpu; +#endif + +} network; + +typedef struct { + int w; + int h; + float scale; + float rad; + float dx; + float dy; + float aspect; +} augment_args; + +typedef struct { + int w; + int h; + int c; + float *data; +} image; + +typedef struct{ + float x, y, w, h; +} box; + +typedef struct detection{ + box bbox; + int classes; + float *prob; + float *mask; + float objectness; + int sort_class; +} detection; + +typedef struct matrix{ + int rows, cols; + float **vals; +} matrix; + + +typedef struct{ + int w, h; + matrix X; + matrix y; + int shallow; + int *num_boxes; + box **boxes; +} data; + +typedef enum { + CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA, ISEG_DATA +} data_type; + +typedef struct load_args{ + int threads; + char **paths; + char *path; + int n; + int m; + char **labels; + int h; + int w; + int out_w; + int out_h; + int nh; + int nw; + int num_boxes; + int min, max, size; + int classes; + int background; + int scale; + int center; + int coords; + float jitter; + float angle; + float aspect; + float saturation; + float exposure; + float hue; + data *d; + image *im; + image *resized; + data_type type; + tree *hierarchy; +} load_args; + +typedef struct{ + int id; + float x,y,w,h; + float left, right, top, bottom; +} box_label; + + +network *load_network(char *cfg, char *weights, int clear); +load_args get_base_args(network *net); + +void free_data(data d); + +typedef struct node{ + void *val; + struct node *next; + struct node *prev; +} node; + +typedef struct list{ + int size; + node *front; + node *back; +} list; + +pthread_t load_data(load_args args); +list *read_data_cfg(char *filename); +list *read_cfg(char *filename); +unsigned char *read_file(char *filename); +data resize_data(data orig, int w, int h); +data *tile_data(data orig, int divs, int size); +data select_data(data *orig, int *inds); + +void forward_network(network *net); +void backward_network(network *net); +void update_network(network *net); + + +float dot_cpu(int N, float *X, int INCX, float *Y, int INCY); +void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); +void copy_cpu(int N, float *X, int INCX, float *Y, int INCY); +void scal_cpu(int N, float ALPHA, float *X, int INCX); +void fill_cpu(int N, float ALPHA, float * X, int INCX); +void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial); +void softmax(float *input, int n, float temp, int stride, float *output); + +int best_3d_shift_r(image a, image b, int min, int max); +#ifdef GPU +void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); +void fill_gpu(int N, float ALPHA, float * X, int INCX); +void scal_gpu(int N, float ALPHA, float * X, int INCX); +void copy_gpu(int N, float * X, int INCX, float * Y, int INCY); + +void cuda_set_device(int n); +void cuda_free(float *x_gpu); +float *cuda_make_array(float *x, size_t n); +void cuda_pull_array(float *x_gpu, float *x, size_t n); +float cuda_mag_array(float *x_gpu, size_t n); +void cuda_push_array(float *x_gpu, float *x, size_t n); + +void forward_network_gpu(network *net); +void backward_network_gpu(network *net); +void update_network_gpu(network *net); + +float train_networks(network **nets, int n, data d, int interval); +void sync_nets(network **nets, int n, int interval); +void harmless_update_network_gpu(network *net); +#endif +image get_label(image **characters, char *string, int size); +void draw_label(image a, int r, int c, image label, const float *rgb); +void save_image(image im, const char *name); +void save_image_options(image im, const char *name, IMTYPE f, int quality); +void get_next_batch(data d, int n, int offset, float *X, float *y); +void grayscale_image_3c(image im); +void normalize_image(image p); +void matrix_to_csv(matrix m); +float train_network_sgd(network *net, data d, int n); +void rgbgr_image(image im); +data copy_data(data d); +data concat_data(data d1, data d2); +data load_cifar10_data(char *filename); +float matrix_topk_accuracy(matrix truth, matrix guess, int k); +void matrix_add_matrix(matrix from, matrix to); +void scale_matrix(matrix m, float scale); +matrix csv_to_matrix(char *filename); +float *network_accuracies(network *net, data d, int n); +float train_network_datum(network *net); +image make_random_image(int w, int h, int c); + +void denormalize_connected_layer(layer l); +void denormalize_convolutional_layer(layer l); +void statistics_connected_layer(layer l); +void rescale_weights(layer l, float scale, float trans); +void rgbgr_weights(layer l); +image *get_weights(layer l); + +void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, int avg, float hier_thresh, int w, int h, int fps, int fullscreen); +void get_detection_detections(layer l, int w, int h, float thresh, detection *dets); + +char *option_find_str(list *l, char *key, char *def); +int option_find_int(list *l, char *key, int def); +int option_find_int_quiet(list *l, char *key, int def); + +network *parse_network_cfg(char *filename); +void save_weights(network *net, char *filename); +void load_weights(network *net, char *filename); +void save_weights_upto(network *net, char *filename, int cutoff); +void load_weights_upto(network *net, char *filename, int start, int cutoff); + +void zero_objectness(layer l); +void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets); +int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets); +void free_network(network *net); +void set_batch_network(network *net, int b); +void set_temp_network(network *net, float t); +image load_image(char *filename, int w, int h, int c); +image load_image_color(char *filename, int w, int h); +image make_image(int w, int h, int c); +image resize_image(image im, int w, int h); +void censor_image(image im, int dx, int dy, int w, int h); +image letterbox_image(image im, int w, int h); +image crop_image(image im, int dx, int dy, int w, int h); +image center_crop_image(image im, int w, int h); +image resize_min(image im, int min); +image resize_max(image im, int max); +image threshold_image(image im, float thresh); +image mask_to_rgb(image mask); +int resize_network(network *net, int w, int h); +void free_matrix(matrix m); +void test_resize(char *filename); +int show_image(image p, const char *name, int ms); +image copy_image(image p); +void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b); +float get_current_rate(network *net); +void composite_3d(char *f1, char *f2, char *out, int delta); +data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); +size_t get_current_batch(network *net); +void constrain_image(image im); +image get_network_image_layer(network *net, int i); +layer get_network_output_layer(network *net); +void top_predictions(network *net, int n, int *index); +void flip_image(image a); +image float_to_image(int w, int h, int c, float *data); +void ghost_image(image source, image dest, int dx, int dy); +float network_accuracy(network *net, data d); +void random_distort_image(image im, float hue, float saturation, float exposure); +void fill_image(image m, float s); +image grayscale_image(image im); +void rotate_image_cw(image im, int times); +double what_time_is_it_now(); +image rotate_image(image m, float rad); +void visualize_network(network *net); +float box_iou(box a, box b); +data load_all_cifar10(); +box_label *read_boxes(char *filename, int *n); +box float_to_box(float *f, int stride); +void draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes); + +matrix network_predict_data(network *net, data test); +image **load_alphabet(); +image get_network_image(network *net); +float *network_predict(network *net, float *input); + +int network_width(network *net); +int network_height(network *net); +float *network_predict_image(network *net, image im); +void network_detect(network *net, image im, float thresh, float hier_thresh, float nms, detection *dets); +detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num); +void free_detections(detection *dets, int n); + +void reset_network_state(network *net, int b); + +char **get_labels(char *filename); +void do_nms_obj(detection *dets, int total, int classes, float thresh); +void do_nms_sort(detection *dets, int total, int classes, float thresh); + +matrix make_matrix(int rows, int cols); + +#ifdef OPENCV +void *open_video_stream(const char *f, int c, int w, int h, int fps); +image get_image_from_stream(void *p); +void make_window(char *name, int w, int h, int fullscreen); +#endif + +void free_image(image m); +float train_network(network *net, data d); +pthread_t load_data_in_thread(load_args args); +void load_data_blocking(load_args args); +list *get_paths(char *filename); +void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride); +void change_leaves(tree *t, char *leaf_list); + +int find_int_arg(int argc, char **argv, char *arg, int def); +float find_float_arg(int argc, char **argv, char *arg, float def); +int find_arg(int argc, char* argv[], char *arg); +char *find_char_arg(int argc, char **argv, char *arg, char *def); +char *basecfg(char *cfgfile); +void find_replace(char *str, char *orig, char *rep, char *output); +void free_ptrs(void **ptrs, int n); +char *fgetl(FILE *fp); +void strip(char *s); +float sec(clock_t clocks); +void **list_to_array(list *l); +void top_k(float *a, int n, int k, int *index); +int *read_map(char *filename); +void error(const char *s); +int max_index(float *a, int n); +int max_int_index(int *a, int n); +int sample_array(float *a, int n); +int *random_index_order(int min, int max); +void free_list(list *l); +float mse_array(float *a, int n); +float variance_array(float *a, int n); +float mag_array(float *a, int n); +void scale_array(float *a, int n, float s); +float mean_array(float *a, int n); +float sum_array(float *a, int n); +void normalize_array(float *a, int n); +int *read_intlist(char *s, int *n, int d); +size_t rand_size_t(); +float rand_normal(); +float rand_uniform(float min, float max); + +#ifdef __cplusplus +} +#endif +#endif diff --git a/image.darknet/inst/include/darknet/python/darknet.py b/image.darknet/inst/include/darknet/python/darknet.py new file mode 100644 index 0000000..88d84cd --- /dev/null +++ b/image.darknet/inst/include/darknet/python/darknet.py @@ -0,0 +1,156 @@ +from ctypes import * +import math +import random + +def sample(probs): + s = sum(probs) + probs = [a/s for a in probs] + r = random.uniform(0, 1) + for i in range(len(probs)): + r = r - probs[i] + if r <= 0: + return i + return len(probs)-1 + +def c_array(ctype, values): + arr = (ctype*len(values))() + arr[:] = values + return arr + +class BOX(Structure): + _fields_ = [("x", c_float), + ("y", c_float), + ("w", c_float), + ("h", c_float)] + +class DETECTION(Structure): + _fields_ = [("bbox", BOX), + ("classes", c_int), + ("prob", POINTER(c_float)), + ("mask", POINTER(c_float)), + ("objectness", c_float), + ("sort_class", c_int)] + + +class IMAGE(Structure): + _fields_ = [("w", c_int), + ("h", c_int), + ("c", c_int), + ("data", POINTER(c_float))] + +class METADATA(Structure): + _fields_ = [("classes", c_int), + ("names", POINTER(c_char_p))] + + + +#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL) +lib = CDLL("libdarknet.so", RTLD_GLOBAL) +lib.network_width.argtypes = [c_void_p] +lib.network_width.restype = c_int +lib.network_height.argtypes = [c_void_p] +lib.network_height.restype = c_int + +predict = lib.network_predict +predict.argtypes = [c_void_p, POINTER(c_float)] +predict.restype = POINTER(c_float) + +set_gpu = lib.cuda_set_device +set_gpu.argtypes = [c_int] + +make_image = lib.make_image +make_image.argtypes = [c_int, c_int, c_int] +make_image.restype = IMAGE + +get_network_boxes = lib.get_network_boxes +get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)] +get_network_boxes.restype = POINTER(DETECTION) + +make_network_boxes = lib.make_network_boxes +make_network_boxes.argtypes = [c_void_p] +make_network_boxes.restype = POINTER(DETECTION) + +free_detections = lib.free_detections +free_detections.argtypes = [POINTER(DETECTION), c_int] + +free_ptrs = lib.free_ptrs +free_ptrs.argtypes = [POINTER(c_void_p), c_int] + +network_predict = lib.network_predict +network_predict.argtypes = [c_void_p, POINTER(c_float)] + +reset_rnn = lib.reset_rnn +reset_rnn.argtypes = [c_void_p] + +load_net = lib.load_network +load_net.argtypes = [c_char_p, c_char_p, c_int] +load_net.restype = c_void_p + +do_nms_obj = lib.do_nms_obj +do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] + +do_nms_sort = lib.do_nms_sort +do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] + +free_image = lib.free_image +free_image.argtypes = [IMAGE] + +letterbox_image = lib.letterbox_image +letterbox_image.argtypes = [IMAGE, c_int, c_int] +letterbox_image.restype = IMAGE + +load_meta = lib.get_metadata +lib.get_metadata.argtypes = [c_char_p] +lib.get_metadata.restype = METADATA + +load_image = lib.load_image_color +load_image.argtypes = [c_char_p, c_int, c_int] +load_image.restype = IMAGE + +rgbgr_image = lib.rgbgr_image +rgbgr_image.argtypes = [IMAGE] + +predict_image = lib.network_predict_image +predict_image.argtypes = [c_void_p, IMAGE] +predict_image.restype = POINTER(c_float) + +def classify(net, meta, im): + out = predict_image(net, im) + res = [] + for i in range(meta.classes): + res.append((meta.names[i], out[i])) + res = sorted(res, key=lambda x: -x[1]) + return res + +def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45): + im = load_image(image, 0, 0) + num = c_int(0) + pnum = pointer(num) + predict_image(net, im) + dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum) + num = pnum[0] + if (nms): do_nms_obj(dets, num, meta.classes, nms); + + res = [] + for j in range(num): + for i in range(meta.classes): + if dets[j].prob[i] > 0: + b = dets[j].bbox + res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h))) + res = sorted(res, key=lambda x: -x[1]) + free_image(im) + free_detections(dets, num) + return res + +if __name__ == "__main__": + #net = load_net("cfg/densenet201.cfg", "/home/pjreddie/trained/densenet201.weights", 0) + #im = load_image("data/wolf.jpg", 0, 0) + #meta = load_meta("cfg/imagenet1k.data") + #r = classify(net, meta, im) + #print r[:10] + net = load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0) + meta = load_meta("cfg/coco.data") + r = detect(net, meta, "data/dog.jpg") + print r + + diff --git a/image.darknet/inst/include/darknet/python/proverbot.py b/image.darknet/inst/include/darknet/python/proverbot.py new file mode 100644 index 0000000..095aae8 --- /dev/null +++ b/image.darknet/inst/include/darknet/python/proverbot.py @@ -0,0 +1,37 @@ +from darknet import * + +def predict_tactic(net, s): + prob = 0 + d = c_array(c_float, [0.0]*256) + tac = '' + if not len(s): + s = '\n' + for c in s[:-1]: + d[ord(c)] = 1 + pred = predict(net, d) + d[ord(c)] = 0 + c = s[-1] + while 1: + d[ord(c)] = 1 + pred = predict(net, d) + d[ord(c)] = 0 + pred = [pred[i] for i in range(256)] + ind = sample(pred) + c = chr(ind) + prob += math.log(pred[ind]) + if len(tac) and tac[-1] == '.': + break + tac = tac + c + return (tac, prob) + +def predict_tactics(net, s, n): + tacs = [] + for i in range(n): + reset_rnn(net) + tacs.append(predict_tactic(net, s)) + tacs = sorted(tacs, key=lambda x: -x[1]) + return tacs + +net = load_net("cfg/coq.test.cfg", "/home/pjreddie/backup/coq.backup", 0) +t = predict_tactics(net, "+++++\n", 10) +print t diff --git a/image.darknet/inst/include/darknet/scripts/get_coco_dataset.sh b/image.darknet/inst/include/darknet/scripts/get_coco_dataset.sh new file mode 100644 index 0000000..2846301 --- /dev/null +++ b/image.darknet/inst/include/darknet/scripts/get_coco_dataset.sh @@ -0,0 +1,31 @@ +#!/bin/bash + +# Clone COCO API +git clone https://github.com/pdollar/coco +cd coco + +mkdir images +cd images + +# Download Images +wget -c https://pjreddie.com/media/files/train2014.zip +wget -c https://pjreddie.com/media/files/val2014.zip + +# Unzip +unzip -q train2014.zip +unzip -q val2014.zip + +cd .. + +# Download COCO Metadata +wget -c https://pjreddie.com/media/files/instances_train-val2014.zip +wget -c https://pjreddie.com/media/files/coco/5k.part +wget -c https://pjreddie.com/media/files/coco/trainvalno5k.part +wget -c https://pjreddie.com/media/files/coco/labels.tgz +tar xzf labels.tgz +unzip -q instances_train-val2014.zip + +# Set Up Image Lists +paste <(awk "{print \"$PWD\"}" <5k.part) 5k.part | tr -d '\t' > 5k.txt +paste <(awk "{print \"$PWD\"}" trainvalno5k.txt + diff --git a/image.darknet/inst/include/darknet/scripts/voc_label.py b/image.darknet/inst/include/darknet/scripts/voc_label.py index d1e8823..679fc36 100644 --- a/image.darknet/inst/include/darknet/scripts/voc_label.py +++ b/image.darknet/inst/include/darknet/scripts/voc_label.py @@ -10,10 +10,10 @@ def convert(size, box): - dw = 1./size[0] - dh = 1./size[1] - x = (box[0] + box[1])/2.0 - y = (box[2] + box[3])/2.0 + dw = 1./(size[0]) + dh = 1./(size[1]) + x = (box[0] + box[1])/2.0 - 1 + y = (box[2] + box[3])/2.0 - 1 w = box[1] - box[0] h = box[3] - box[2] x = x*dw @@ -34,7 +34,7 @@ def convert_annotation(year, image_id): for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text - if cls not in classes or int(difficult) == 1: + if cls not in classes or int(difficult)==1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') @@ -54,3 +54,6 @@ def convert_annotation(year, image_id): convert_annotation(year, image_id) list_file.close() +os.system("cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt") +os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt") + diff --git a/image.darknet/inst/include/darknet/src/activation_kernels.cu b/image.darknet/inst/include/darknet/src/activation_kernels.cu index 994e206..4dc5804 100644 --- a/image.darknet/inst/include/darknet/src/activation_kernels.cu +++ b/image.darknet/inst/include/darknet/src/activation_kernels.cu @@ -10,8 +10,8 @@ extern "C" { __device__ float lhtan_activate_kernel(float x) { - if(x < 0) return .001*x; - if(x > 1) return .001*(x-1) + 1; + if(x < 0) return .001f*x; + if(x > 1) return .001f*(x-1.f) + 1.f; return x; } __device__ float lhtan_gradient_kernel(float x) @@ -27,25 +27,26 @@ __device__ float hardtan_activate_kernel(float x) return x; } __device__ float linear_activate_kernel(float x){return x;} -__device__ float logistic_activate_kernel(float x){return 1./(1. + exp(-x));} -__device__ float loggy_activate_kernel(float x){return 2./(1. + exp(-x)) - 1;} +__device__ float logistic_activate_kernel(float x){return 1.f/(1.f + expf(-x));} +__device__ float loggy_activate_kernel(float x){return 2.f/(1.f + expf(-x)) - 1;} __device__ float relu_activate_kernel(float x){return x*(x>0);} -__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} -__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01*x;} -__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1*x;} -__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1*x;} -__device__ float tanh_activate_kernel(float x){return (2/(1 + exp(-2*x)) - 1);} +__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);} +__device__ float selu_activate_kernel(float x){return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x)-1);} +__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;} +__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1f*x;} +__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1f*x;} +__device__ float tanh_activate_kernel(float x){return (2.f/(1 + expf(-2*x)) - 1);} __device__ float plse_activate_kernel(float x) { - if(x < -4) return .01 * (x + 4); - if(x > 4) return .01 * (x - 4) + 1; - return .125*x + .5; + if(x < -4) return .01f * (x + 4); + if(x > 4) return .01f * (x - 4) + 1; + return .125f*x + .5f; } __device__ float stair_activate_kernel(float x) { - int n = floor(x); - if (n%2 == 0) return floor(x/2.); - else return (x - n) + floor(x/2.); + int n = floorf(x); + if (n%2 == 0) return floorf(x/2); + else return (x - n) + floorf(x/2); } @@ -58,19 +59,20 @@ __device__ float linear_gradient_kernel(float x){return 1;} __device__ float logistic_gradient_kernel(float x){return (1-x)*x;} __device__ float loggy_gradient_kernel(float x) { - float y = (x+1.)/2.; + float y = (x+1)/2; return 2*(1-y)*y; } __device__ float relu_gradient_kernel(float x){return (x>0);} __device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);} -__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01;} -__device__ float ramp_gradient_kernel(float x){return (x>0)+.1;} -__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1;} +__device__ float selu_gradient_kernel(float x){return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732);} +__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01f;} +__device__ float ramp_gradient_kernel(float x){return (x>0)+.1f;} +__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1f;} __device__ float tanh_gradient_kernel(float x){return 1-x*x;} -__device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01 : .125;} +__device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01f : .125f;} __device__ float stair_gradient_kernel(float x) { - if (floor(x) == x) return 0; + if (floorf(x) == x) return 0; return 1; } @@ -87,6 +89,8 @@ __device__ float activate_kernel(float x, ACTIVATION a) return relu_activate_kernel(x); case ELU: return elu_activate_kernel(x); + case SELU: + return selu_activate_kernel(x); case RELIE: return relie_activate_kernel(x); case RAMP: @@ -120,6 +124,8 @@ __device__ float gradient_kernel(float x, ACTIVATION a) return relu_gradient_kernel(x); case ELU: return elu_gradient_kernel(x); + case SELU: + return selu_gradient_kernel(x); case RELIE: return relie_gradient_kernel(x); case RAMP: @@ -140,6 +146,41 @@ __device__ float gradient_kernel(float x, ACTIVATION a) return 0; } +__global__ void binary_gradient_array_kernel(float *x, float *dy, int n, int s, BINARY_ACTIVATION a, float *dx) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + int i = id % s; + int b = id / s; + float x1 = x[b*s + i]; + float x2 = x[b*s + s/2 + i]; + if(id < n) { + float de = dy[id]; + dx[b*s + i] = x2*de; + dx[b*s + s/2 + i] = x1*de; + } +} + +extern "C" void binary_gradient_array_gpu(float *x, float *dx, int n, int size, BINARY_ACTIVATION a, float *y) +{ + binary_gradient_array_kernel<<>>(x, dx, n/2, size, a, y); + check_error(cudaPeekAtLastError()); +} +__global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTIVATION a, float *y) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + int i = id % s; + int b = id / s; + float x1 = x[b*s + i]; + float x2 = x[b*s + s/2 + i]; + if(id < n) y[id] = x1*x2; +} + +extern "C" void binary_activate_array_gpu(float *x, int n, int size, BINARY_ACTIVATION a, float *y) +{ + binary_activate_array_kernel<<>>(x, n/2, size, a, y); + check_error(cudaPeekAtLastError()); +} + __global__ void activate_array_kernel(float *x, int n, ACTIVATION a) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -152,13 +193,13 @@ __global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delt if(i < n) delta[i] *= gradient_kernel(x[i], a); } -extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) +extern "C" void activate_array_gpu(float *x, int n, ACTIVATION a) { activate_array_kernel<<>>(x, n, a); check_error(cudaPeekAtLastError()); } -extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta) +extern "C" void gradient_array_gpu(float *x, int n, ACTIVATION a, float *delta) { gradient_array_kernel<<>>(x, n, a, delta); check_error(cudaPeekAtLastError()); diff --git a/image.darknet/inst/include/darknet/src/activation_layer.c b/image.darknet/inst/include/darknet/src/activation_layer.c index 3430dac..b4ba953 100644 --- a/image.darknet/inst/include/darknet/src/activation_layer.c +++ b/image.darknet/inst/include/darknet/src/activation_layer.c @@ -35,29 +35,29 @@ layer make_activation_layer(int batch, int inputs, ACTIVATION activation) return l; } -void forward_activation_layer(layer l, network_state state) +void forward_activation_layer(layer l, network net) { - copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); activate_array(l.output, l.outputs*l.batch, l.activation); } -void backward_activation_layer(layer l, network_state state) +void backward_activation_layer(layer l, network net) { gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); - copy_cpu(l.outputs*l.batch, l.delta, 1, state.delta, 1); + copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1); } #ifdef GPU -void forward_activation_layer_gpu(layer l, network_state state) +void forward_activation_layer_gpu(layer l, network net) { - copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); } -void backward_activation_layer_gpu(layer l, network_state state) +void backward_activation_layer_gpu(layer l, network net) { - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/inst/include/darknet/src/activation_layer.h b/image.darknet/inst/include/darknet/src/activation_layer.h index a09756a..42118a8 100644 --- a/image.darknet/inst/include/darknet/src/activation_layer.h +++ b/image.darknet/inst/include/darknet/src/activation_layer.h @@ -7,12 +7,12 @@ layer make_activation_layer(int batch, int inputs, ACTIVATION activation); -void forward_activation_layer(layer l, network_state state); -void backward_activation_layer(layer l, network_state state); +void forward_activation_layer(layer l, network net); +void backward_activation_layer(layer l, network net); #ifdef GPU -void forward_activation_layer_gpu(layer l, network_state state); -void backward_activation_layer_gpu(layer l, network_state state); +void forward_activation_layer_gpu(layer l, network net); +void backward_activation_layer_gpu(layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/activations.c b/image.darknet/inst/include/darknet/src/activations.c index 0cbb2f5..da1a17a 100644 --- a/image.darknet/inst/include/darknet/src/activations.c +++ b/image.darknet/inst/include/darknet/src/activations.c @@ -16,6 +16,8 @@ char *get_activation_string(ACTIVATION a) return "relu"; case ELU: return "elu"; + case SELU: + return "selu"; case RELIE: return "relie"; case RAMP: @@ -46,6 +48,7 @@ ACTIVATION get_activation(char *s) if (strcmp(s, "loggy")==0) return LOGGY; if (strcmp(s, "relu")==0) return RELU; if (strcmp(s, "elu")==0) return ELU; + if (strcmp(s, "selu")==0) return SELU; if (strcmp(s, "relie")==0) return RELIE; if (strcmp(s, "plse")==0) return PLSE; if (strcmp(s, "hardtan")==0) return HARDTAN; @@ -72,6 +75,8 @@ float activate(float x, ACTIVATION a) return relu_activate(x); case ELU: return elu_activate(x); + case SELU: + return selu_activate(x); case RELIE: return relie_activate(x); case RAMP: @@ -113,6 +118,8 @@ float gradient(float x, ACTIVATION a) return relu_gradient(x); case ELU: return elu_gradient(x); + case SELU: + return selu_gradient(x); case RELIE: return relie_gradient(x); case RAMP: diff --git a/image.darknet/inst/include/darknet/src/activations.h b/image.darknet/inst/include/darknet/src/activations.h index 1c36ff5..9780d2c 100644 --- a/image.darknet/inst/include/darknet/src/activations.h +++ b/image.darknet/inst/include/darknet/src/activations.h @@ -1,12 +1,9 @@ #ifndef ACTIVATIONS_H #define ACTIVATIONS_H +#include "darknet.h" #include "cuda.h" #include "math.h" -typedef enum{ - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN -}ACTIVATION; - ACTIVATION get_activation(char *s); char *get_activation_string(ACTIVATION a); @@ -15,8 +12,8 @@ float gradient(float x, ACTIVATION a); void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta); void activate_array(float *x, const int n, const ACTIVATION a); #ifdef GPU -void activate_array_ongpu(float *x, int n, ACTIVATION a); -void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); +void activate_array_gpu(float *x, int n, ACTIVATION a); +void gradient_array_gpu(float *x, int n, ACTIVATION a, float *delta); #endif static inline float stair_activate(float x) @@ -36,6 +33,7 @@ static inline float logistic_activate(float x){return 1./(1. + exp(-x));} static inline float loggy_activate(float x){return 2./(1. + exp(-x)) - 1;} static inline float relu_activate(float x){return x*(x>0);} static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} +static inline float selu_activate(float x){return (x >= 0)*1.0507*x + (x < 0)*1.0507*1.6732*(exp(x)-1);} static inline float relie_activate(float x){return (x>0) ? x : .01*x;} static inline float ramp_activate(float x){return x*(x>0)+.1*x;} static inline float leaky_activate(float x){return (x>0) ? x : .1*x;} @@ -78,6 +76,7 @@ static inline float stair_gradient(float x) } static inline float relu_gradient(float x){return (x>0);} static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);} +static inline float selu_gradient(float x){return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732);} static inline float relie_gradient(float x){return (x>0) ? 1 : .01;} static inline float ramp_gradient(float x){return (x>0)+.1;} static inline float leaky_gradient(float x){return (x>0) ? 1 : .1;} diff --git a/image.darknet/inst/include/darknet/src/avgpool_layer.c b/image.darknet/inst/include/darknet/src/avgpool_layer.c index b6932fe..83034db 100644 --- a/image.darknet/inst/include/darknet/src/avgpool_layer.c +++ b/image.darknet/inst/include/darknet/src/avgpool_layer.c @@ -37,7 +37,7 @@ void resize_avgpool_layer(avgpool_layer *l, int w, int h) l->inputs = h*w*l->c; } -void forward_avgpool_layer(const avgpool_layer l, network_state state) +void forward_avgpool_layer(const avgpool_layer l, network net) { int b,i,k; @@ -47,14 +47,14 @@ void forward_avgpool_layer(const avgpool_layer l, network_state state) l.output[out_index] = 0; for(i = 0; i < l.h*l.w; ++i){ int in_index = i + l.h*l.w*(k + b*l.c); - l.output[out_index] += state.input[in_index]; + l.output[out_index] += net.input[in_index]; } l.output[out_index] /= l.h*l.w; } } } -void backward_avgpool_layer(const avgpool_layer l, network_state state) +void backward_avgpool_layer(const avgpool_layer l, network net) { int b,i,k; @@ -63,7 +63,7 @@ void backward_avgpool_layer(const avgpool_layer l, network_state state) int out_index = k + b*l.c; for(i = 0; i < l.h*l.w; ++i){ int in_index = i + l.h*l.w*(k + b*l.c); - state.delta[in_index] += l.delta[out_index] / (l.h*l.w); + net.delta[in_index] += l.delta[out_index] / (l.h*l.w); } } } diff --git a/image.darknet/inst/include/darknet/src/avgpool_layer.h b/image.darknet/inst/include/darknet/src/avgpool_layer.h index f8329ae..3bd356c 100644 --- a/image.darknet/inst/include/darknet/src/avgpool_layer.h +++ b/image.darknet/inst/include/darknet/src/avgpool_layer.h @@ -11,12 +11,12 @@ typedef layer avgpool_layer; image get_avgpool_image(avgpool_layer l); avgpool_layer make_avgpool_layer(int batch, int w, int h, int c); void resize_avgpool_layer(avgpool_layer *l, int w, int h); -void forward_avgpool_layer(const avgpool_layer l, network_state state); -void backward_avgpool_layer(const avgpool_layer l, network_state state); +void forward_avgpool_layer(const avgpool_layer l, network net); +void backward_avgpool_layer(const avgpool_layer l, network net); #ifdef GPU -void forward_avgpool_layer_gpu(avgpool_layer l, network_state state); -void backward_avgpool_layer_gpu(avgpool_layer l, network_state state); +void forward_avgpool_layer_gpu(avgpool_layer l, network net); +void backward_avgpool_layer_gpu(avgpool_layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/avgpool_layer_kernels.cu b/image.darknet/inst/include/darknet/src/avgpool_layer_kernels.cu index b7e2770..a7eca3a 100644 --- a/image.darknet/inst/include/darknet/src/avgpool_layer_kernels.cu +++ b/image.darknet/inst/include/darknet/src/avgpool_layer_kernels.cu @@ -43,19 +43,19 @@ __global__ void backward_avgpool_layer_kernel(int n, int w, int h, int c, float } } -extern "C" void forward_avgpool_layer_gpu(avgpool_layer layer, network_state state) +extern "C" void forward_avgpool_layer_gpu(avgpool_layer layer, network net) { size_t n = layer.c*layer.batch; - forward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.input, layer.output_gpu); + forward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, net.input_gpu, layer.output_gpu); check_error(cudaPeekAtLastError()); } -extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network_state state) +extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network net) { size_t n = layer.c*layer.batch; - backward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.delta, layer.delta_gpu); + backward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, net.delta_gpu, layer.delta_gpu); check_error(cudaPeekAtLastError()); } diff --git a/image.darknet/inst/include/darknet/src/batchnorm_layer.c b/image.darknet/inst/include/darknet/src/batchnorm_layer.c index b53548b..ebff387 100644 --- a/image.darknet/inst/include/darknet/src/batchnorm_layer.c +++ b/image.darknet/inst/include/darknet/src/batchnorm_layer.c @@ -1,3 +1,4 @@ +#include "convolutional_layer.h" #include "batchnorm_layer.h" #include "blas.h" #include @@ -5,55 +6,67 @@ layer make_batchnorm_layer(int batch, int w, int h, int c) { fprintf(stderr, "Batch Normalization Layer: %d x %d x %d image\n", w,h,c); - layer layer = {0}; - layer.type = BATCHNORM; - layer.batch = batch; - layer.h = layer.out_h = h; - layer.w = layer.out_w = w; - layer.c = layer.out_c = c; - layer.output = calloc(h * w * c * batch, sizeof(float)); - layer.delta = calloc(h * w * c * batch, sizeof(float)); - layer.inputs = w*h*c; - layer.outputs = layer.inputs; - - layer.scales = calloc(c, sizeof(float)); - layer.scale_updates = calloc(c, sizeof(float)); + layer l = {0}; + l.type = BATCHNORM; + l.batch = batch; + l.h = l.out_h = h; + l.w = l.out_w = w; + l.c = l.out_c = c; + l.output = calloc(h * w * c * batch, sizeof(float)); + l.delta = calloc(h * w * c * batch, sizeof(float)); + l.inputs = w*h*c; + l.outputs = l.inputs; + + l.scales = calloc(c, sizeof(float)); + l.scale_updates = calloc(c, sizeof(float)); + l.biases = calloc(c, sizeof(float)); + l.bias_updates = calloc(c, sizeof(float)); int i; for(i = 0; i < c; ++i){ - layer.scales[i] = 1; + l.scales[i] = 1; } - layer.mean = calloc(c, sizeof(float)); - layer.variance = calloc(c, sizeof(float)); + l.mean = calloc(c, sizeof(float)); + l.variance = calloc(c, sizeof(float)); - layer.rolling_mean = calloc(c, sizeof(float)); - layer.rolling_variance = calloc(c, sizeof(float)); + l.rolling_mean = calloc(c, sizeof(float)); + l.rolling_variance = calloc(c, sizeof(float)); - layer.forward = forward_batchnorm_layer; - layer.backward = backward_batchnorm_layer; + l.forward = forward_batchnorm_layer; + l.backward = backward_batchnorm_layer; #ifdef GPU - layer.forward_gpu = forward_batchnorm_layer_gpu; - layer.backward_gpu = backward_batchnorm_layer_gpu; + l.forward_gpu = forward_batchnorm_layer_gpu; + l.backward_gpu = backward_batchnorm_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, h * w * c * batch); + l.delta_gpu = cuda_make_array(l.delta, h * w * c * batch); + + l.biases_gpu = cuda_make_array(l.biases, c); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, c); - layer.output_gpu = cuda_make_array(layer.output, h * w * c * batch); - layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch); + l.scales_gpu = cuda_make_array(l.scales, c); + l.scale_updates_gpu = cuda_make_array(l.scale_updates, c); - layer.scales_gpu = cuda_make_array(layer.scales, c); - layer.scale_updates_gpu = cuda_make_array(layer.scale_updates, c); + l.mean_gpu = cuda_make_array(l.mean, c); + l.variance_gpu = cuda_make_array(l.variance, c); - layer.mean_gpu = cuda_make_array(layer.mean, c); - layer.variance_gpu = cuda_make_array(layer.variance, c); + l.rolling_mean_gpu = cuda_make_array(l.mean, c); + l.rolling_variance_gpu = cuda_make_array(l.variance, c); - layer.rolling_mean_gpu = cuda_make_array(layer.mean, c); - layer.rolling_variance_gpu = cuda_make_array(layer.variance, c); + l.mean_delta_gpu = cuda_make_array(l.mean, c); + l.variance_delta_gpu = cuda_make_array(l.variance, c); - layer.mean_delta_gpu = cuda_make_array(layer.mean, c); - layer.variance_delta_gpu = cuda_make_array(layer.variance, c); + l.x_gpu = cuda_make_array(l.output, l.batch*l.outputs); + l.x_norm_gpu = cuda_make_array(l.output, l.batch*l.outputs); + #ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); + cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); - layer.x_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); - layer.x_norm_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); + #endif #endif - return layer; + return l; } void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) @@ -108,7 +121,7 @@ void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_del for(f = 0; f < filters; ++f){ for(k = 0; k < spatial; ++k){ int index = j*filters*spatial + f*spatial + k; - delta[index] = delta[index] * 1./(sqrt(variance[f]) + .00001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); + delta[index] = delta[index] * 1./(sqrt(variance[f] + .00001f)) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); } } } @@ -119,33 +132,35 @@ void resize_batchnorm_layer(layer *layer, int w, int h) fprintf(stderr, "Not implemented\n"); } -void forward_batchnorm_layer(layer l, network_state state) +void forward_batchnorm_layer(layer l, network net) { - if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); - if(l.type == CONNECTED){ - l.out_c = l.outputs; - l.out_h = l.out_w = 1; - } - if(state.train){ + if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); + if(net.train){ mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean); variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance); - scal_cpu(l.out_c, .9, l.rolling_mean, 1); - axpy_cpu(l.out_c, .1, l.mean, 1, l.rolling_mean, 1); - scal_cpu(l.out_c, .9, l.rolling_variance, 1); - axpy_cpu(l.out_c, .1, l.variance, 1, l.rolling_variance, 1); + scal_cpu(l.out_c, .99, l.rolling_mean, 1); + axpy_cpu(l.out_c, .01, l.mean, 1, l.rolling_mean, 1); + scal_cpu(l.out_c, .99, l.rolling_variance, 1); + axpy_cpu(l.out_c, .01, l.variance, 1, l.rolling_variance, 1); - copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w); copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1); } else { normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w); } scale_bias(l.output, l.scales, l.batch, l.out_c, l.out_h*l.out_w); + add_bias(l.output, l.biases, l.batch, l.out_c, l.out_h*l.out_w); } -void backward_batchnorm_layer(const layer l, network_state state) +void backward_batchnorm_layer(layer l, network net) { + if(!net.train){ + l.mean = l.rolling_mean; + l.variance = l.rolling_variance; + } + backward_bias(l.bias_updates, l.delta, l.batch, l.out_c, l.out_w*l.out_h); backward_scale_cpu(l.x_norm, l.delta, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates); scale_bias(l.delta, l.scales, l.batch, l.out_c, l.out_h*l.out_w); @@ -153,7 +168,7 @@ void backward_batchnorm_layer(const layer l, network_state state) mean_delta_cpu(l.delta, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta); variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta); normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.out_c, l.out_w*l.out_h, l.delta); - if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, state.delta, 1); + if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1); } #ifdef GPU @@ -171,34 +186,86 @@ void push_batchnorm_layer(layer l) cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.c); } -void forward_batchnorm_layer_gpu(layer l, network_state state) +void forward_batchnorm_layer_gpu(layer l, network net) { - if(l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - if(l.type == CONNECTED){ - l.out_c = l.outputs; - l.out_h = l.out_w = 1; - } - if (state.train) { + if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + if (net.train) { +#ifdef CUDNN + float one = 1; + float zero = 0; + cudnnBatchNormalizationForwardTraining(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + l.dstTensorDesc, + l.x_gpu, + l.dstTensorDesc, + l.output_gpu, + l.normTensorDesc, + l.scales_gpu, + l.biases_gpu, + .01, + l.rolling_mean_gpu, + l.rolling_variance_gpu, + .00001, + l.mean_gpu, + l.variance_gpu); +#else fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); - scal_ongpu(l.out_c, .99, l.rolling_mean_gpu, 1); - axpy_ongpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); - scal_ongpu(l.out_c, .99, l.rolling_variance_gpu, 1); - axpy_ongpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); + scal_gpu(l.out_c, .99, l.rolling_mean_gpu, 1); + axpy_gpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); + scal_gpu(l.out_c, .99, l.rolling_variance_gpu, 1); + axpy_gpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); + + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); +#endif } else { normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); } - scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); } -void backward_batchnorm_layer_gpu(const layer l, network_state state) +void backward_batchnorm_layer_gpu(layer l, network net) { + if(!net.train){ + l.mean_gpu = l.rolling_mean_gpu; + l.variance_gpu = l.rolling_variance_gpu; + } +#ifdef CUDNN + float one = 1; + float zero = 0; + cudnnBatchNormalizationBackward(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + &one, + &one, + l.dstTensorDesc, + l.x_gpu, + l.dstTensorDesc, + l.delta_gpu, + l.dstTensorDesc, + l.x_norm_gpu, + l.normTensorDesc, + l.scales_gpu, + l.scale_updates_gpu, + l.bias_updates_gpu, + .00001, + l.mean_gpu, + l.variance_gpu); + copy_gpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1); +#else + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h); backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu); scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); @@ -206,6 +273,7 @@ void backward_batchnorm_layer_gpu(const layer l, network_state state) fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu); fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu); normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); - if(l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); +#endif + if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/inst/include/darknet/src/batchnorm_layer.h b/image.darknet/inst/include/darknet/src/batchnorm_layer.h index 99d1d0f..25a18a3 100644 --- a/image.darknet/inst/include/darknet/src/batchnorm_layer.h +++ b/image.darknet/inst/include/darknet/src/batchnorm_layer.h @@ -6,12 +6,12 @@ #include "network.h" layer make_batchnorm_layer(int batch, int w, int h, int c); -void forward_batchnorm_layer(layer l, network_state state); -void backward_batchnorm_layer(layer l, network_state state); +void forward_batchnorm_layer(layer l, network net); +void backward_batchnorm_layer(layer l, network net); #ifdef GPU -void forward_batchnorm_layer_gpu(layer l, network_state state); -void backward_batchnorm_layer_gpu(layer l, network_state state); +void forward_batchnorm_layer_gpu(layer l, network net); +void backward_batchnorm_layer_gpu(layer l, network net); void pull_batchnorm_layer(layer l); void push_batchnorm_layer(layer l); #endif diff --git a/image.darknet/inst/include/darknet/src/blas.c b/image.darknet/inst/include/darknet/src/blas.c index 31bd86b..9e16044 100644 --- a/image.darknet/inst/include/darknet/src/blas.c +++ b/image.darknet/inst/include/darknet/src/blas.c @@ -1,5 +1,6 @@ #include "blas.h" -#include "math.h" + +#include #include #include #include @@ -54,7 +55,17 @@ void weighted_sum_cpu(float *a, float *b, float *s, int n, float *c) } } -void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) +void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc) +{ + int i; + for(i = 0; i < n; ++i){ + if(da) da[i] += dc[i] * s[i]; + if(db) db[i] += dc[i] * (1-s[i]); + ds[i] += dc[i] * (a[i] - b[i]); + } +} + +void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out) { int stride = w1/w2; int sample = w2/w1; @@ -73,7 +84,7 @@ void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, for(i = 0; i < minw; ++i){ int out_index = i*sample + w2*(j*sample + h2*(k + c2*b)); int add_index = i*stride + w1*(j*stride + h1*(k + c1*b)); - out[out_index] += add[add_index]; + out[out_index] = s1*out[out_index] + s2*add[add_index]; } } } @@ -112,6 +123,27 @@ void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, fl } } +void l2normalize_cpu(float *x, float *dx, int batch, int filters, int spatial) +{ + int b,f,i; + for(b = 0; b < batch; ++b){ + for(i = 0; i < spatial; ++i){ + float sum = 0; + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + sum += powf(x[index], 2); + } + sum = sqrtf(sum); + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + x[index] /= sum; + dx[index] = (1 - x[index]) / sum; + } + } + } +} + + void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial) { int b, f, i; @@ -161,12 +193,48 @@ void fill_cpu(int N, float ALPHA, float *X, int INCX) for(i = 0; i < N; ++i) X[i*INCX] = ALPHA; } +void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i, j; + int index = 0; + for(j = 0; j < B; ++j) { + for(i = 0; i < NX; ++i){ + if(X) X[j*NX + i] += OUT[index]; + ++index; + } + for(i = 0; i < NY; ++i){ + if(Y) Y[j*NY + i] += OUT[index]; + ++index; + } + } +} + +void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i, j; + int index = 0; + for(j = 0; j < B; ++j) { + for(i = 0; i < NX; ++i){ + OUT[index++] = X[j*NX + i]; + } + for(i = 0; i < NY; ++i){ + OUT[index++] = Y[j*NY + i]; + } + } +} + void copy_cpu(int N, float *X, int INCX, float *Y, int INCY) { int i; for(i = 0; i < N; ++i) Y[i*INCY] = X[i*INCX]; } +void mult_add_into_cpu(int N, float *X, float *Y, float *Z) +{ + int i; + for(i = 0; i < N; ++i) Z[i] += X[i]*Y[i]; +} + void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error) { int i; @@ -179,11 +247,43 @@ void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error) } else { error[i] = 2*abs_val - 1; - delta[i] = (diff < 0) ? -1 : 1; + delta[i] = (diff < 0) ? 1 : -1; } } } +void l1_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float diff = truth[i] - pred[i]; + error[i] = fabs(diff); + delta[i] = diff > 0 ? 1 : -1; + } +} + +void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float t = truth[i]; + float p = pred[i]; + error[i] = (t) ? -log(p) : 0; + delta[i] = t-p; + } +} + +void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float t = truth[i]; + float p = pred[i]; + error[i] = -t*log(p) - (1-t)*log(1-p); + delta[i] = t-p; + } +} + void l2_cpu(int n, float *pred, float *truth, float *delta, float *error) { int i; @@ -202,21 +302,50 @@ float dot_cpu(int N, float *X, int INCX, float *Y, int INCY) return dot; } -void softmax(float *input, int n, float temp, float *output) +void softmax(float *input, int n, float temp, int stride, float *output) { int i; float sum = 0; float largest = -FLT_MAX; for(i = 0; i < n; ++i){ - if(input[i] > largest) largest = input[i]; + if(input[i*stride] > largest) largest = input[i*stride]; } for(i = 0; i < n; ++i){ - float e = exp(input[i]/temp - largest/temp); + float e = exp(input[i*stride]/temp - largest/temp); sum += e; - output[i] = e; + output[i*stride] = e; } for(i = 0; i < n; ++i){ - output[i] /= sum; + output[i*stride] /= sum; } } + +void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + int g, b; + for(b = 0; b < batch; ++b){ + for(g = 0; g < groups; ++g){ + softmax(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); + } + } +} + +void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) +{ + int i, j, k, b; + for(b = 0; b < batch; ++b){ + for(k = 0; k < c; ++k){ + for(j = 0; j < h*stride; ++j){ + for(i = 0; i < w*stride; ++i){ + int in_index = b*w*h*c + k*w*h + (j/stride)*w + i/stride; + int out_index = b*w*h*c*stride*stride + k*w*h*stride*stride + j*w*stride + i; + if(forward) out[out_index] = scale*in[in_index]; + else in[in_index] += scale*out[out_index]; + } + } + } + } +} + + diff --git a/image.darknet/inst/include/darknet/src/blas.h b/image.darknet/inst/include/darknet/src/blas.h index 3d6ee7d..707291d 100644 --- a/image.darknet/inst/include/darknet/src/blas.h +++ b/image.darknet/inst/include/darknet/src/blas.h @@ -1,5 +1,7 @@ #ifndef BLAS_H #define BLAS_H +#include "darknet.h" + void flatten(float *x, int size, int layers, int batch, int forward); void pm(int M, int N, float *A); float *random_matrix(int rows, int cols); @@ -8,53 +10,60 @@ void reorg_cpu(float *x, int w, int h, int c, int batch, int stride, int forward void test_blas(); +void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT); +void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT); +void mult_add_into_cpu(int N, float *X, float *Y, float *Z); + void const_cpu(int N, float ALPHA, float *X, int INCX); -void constrain_ongpu(int N, float ALPHA, float * X, int INCX); +void constrain_gpu(int N, float ALPHA, float * X, int INCX); void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); void mul_cpu(int N, float *X, int INCX, float *Y, int INCY); -void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); -void copy_cpu(int N, float *X, int INCX, float *Y, int INCY); -void scal_cpu(int N, float ALPHA, float *X, int INCX); -void fill_cpu(int N, float ALPHA, float * X, int INCX); -float dot_cpu(int N, float *X, int INCX, float *Y, int INCY); -void test_gpu_blas(); -void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); +int test_gpu_blas(); +void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out); void mean_cpu(float *x, int batch, int filters, int spatial, float *mean); void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); -void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial); void scale_bias(float *output, float *scales, int batch, int n, int size); void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta); void variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta); void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta); +void l2normalize_cpu(float *x, float *dx, int batch, int filters, int spatial); void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error); void l2_cpu(int n, float *pred, float *truth, float *delta, float *error); +void l1_cpu(int n, float *pred, float *truth, float *delta, float *error); +void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error); +void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error); void weighted_sum_cpu(float *a, float *b, float *s, int num, float *c); +void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc); -void softmax(float *input, int n, float temp, float *output); +void softmax(float *input, int n, float temp, int stride, float *output); +void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output); +void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out); #ifdef GPU #include "cuda.h" - -void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); -void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); -void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY); -void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); -void scal_ongpu(int N, float ALPHA, float * X, int INCX); -void supp_ongpu(int N, float ALPHA, float * X, int INCX); -void mask_ongpu(int N, float * X, float mask_num, float * mask); -void const_ongpu(int N, float ALPHA, float *X, int INCX); -void pow_ongpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); -void mul_ongpu(int N, float *X, int INCX, float *Y, int INCY); -void fill_ongpu(int N, float ALPHA, float * X, int INCX); +#include "tree.h" + +void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); +void axpy_gpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); +void copy_gpu(int N, float * X, int INCX, float * Y, int INCY); +void copy_gpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); +void add_gpu(int N, float ALPHA, float * X, int INCX); +void supp_gpu(int N, float ALPHA, float * X, int INCX); +void mask_gpu(int N, float * X, float mask_num, float * mask, float val); +void scale_mask_gpu(int N, float * X, float mask_num, float * mask, float scale); +void const_gpu(int N, float ALPHA, float *X, int INCX); +void pow_gpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); +void mul_gpu(int N, float *X, int INCX, float *Y, int INCY); void mean_gpu(float *x, int batch, int filters, int spatial, float *mean); void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial); +void l2normalize_gpu(float *x, float *dx, int batch, int filters, int spatial); void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta); @@ -63,25 +72,34 @@ void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *varianc void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); -void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); +void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void add_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); +void logistic_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error); +void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error); void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error); void l2_gpu(int n, float *pred, float *truth, float *delta, float *error); +void l1_gpu(int n, float *pred, float *truth, float *delta, float *error); +void wgan_gpu(int n, float *pred, float *truth, float *delta, float *error); void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc); void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c); void mult_add_into_gpu(int num, float *a, float *b, float *c); +void inter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT); +void deinter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT); -void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out); +void reorg_gpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out); -void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output); +void softmax_gpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output); +void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t); void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t); -void flatten_ongpu(float *x, int spatial, int layers, int batch, int forward, float *out); +void flatten_gpu(float *x, int spatial, int layers, int batch, int forward, float *out); +void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier); +void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/blas_kernels.cu b/image.darknet/inst/include/darknet/src/blas_kernels.cu index d940176..47e8217 100644 --- a/image.darknet/inst/include/darknet/src/blas_kernels.cu +++ b/image.darknet/inst/include/darknet/src/blas_kernels.cu @@ -53,24 +53,40 @@ void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, check_error(cudaPeekAtLastError()); } -__global__ void add_bias_kernel(float *output, float *biases, int n, int size) +__global__ void add_bias_kernel(float *output, float *biases, int batch, int n, int size) { - int offset = blockIdx.x * blockDim.x + threadIdx.x; - int filter = blockIdx.y; - int batch = blockIdx.z; + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= n*size*batch) return; + int i = index % size; + index /= size; + int j = index % n; + index /= n; + int k = index; - if(offset < size) output[(batch*n+filter)*size + offset] += biases[filter]; + output[(k*n+j)*size + i] += biases[j]; } void add_bias_gpu(float *output, float *biases, int batch, int n, int size) { - dim3 dimGrid((size-1)/BLOCK + 1, n, batch); - dim3 dimBlock(BLOCK, 1, 1); + int num = n*size*batch; - add_bias_kernel<<>>(output, biases, n, size); + add_bias_kernel<<>>(output, biases, batch, n, size); check_error(cudaPeekAtLastError()); } +__global__ void backward_bias_conn_kernel(float *bias_updates, float *delta, int batch, int n) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= n) return; + int b; + float sum = 0; + for(b = 0; b < batch; ++b){ + int i = b*n + index; + sum += delta[i]; + } + bias_updates[index] += sum; +} + __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size) { __shared__ float part[BLOCK]; @@ -91,6 +107,16 @@ __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batc } } +void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) +{ + if(size == 1){ + backward_bias_conn_kernel<<>>(bias_updates, delta, batch, n); + }else{ + backward_bias_kernel<<>>(bias_updates, delta, batch, n, size); + } + check_error(cudaPeekAtLastError()); +} + /* __global__ void dot_kernel(float *output, float scale, int batch, int n, int size, float *delta) { @@ -133,20 +159,16 @@ void dot_error_gpu(layer l) } */ -void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) -{ - backward_bias_kernel<<>>(bias_updates, delta, batch, n, size); - check_error(cudaPeekAtLastError()); -} - __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (index >= N) return; + + float mhat = m[index] / (1.f - powf(B1, t)); + float vhat = v[index] / (1.f - powf(B2, t)); - x[index] = x[index] - (rate * sqrt(1.-pow(B2, t)) / (1.-pow(B1, t)) * m[index] / (sqrt(v[index]) + eps)); - //if(index == 0) printf("%f %f %f %f\n", m[index], v[index], (rate * sqrt(1.-pow(B2, t)) / (1.-pow(B1, t)) * m[index] / (sqrt(v[index]) + eps))); + x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps); } extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) @@ -155,13 +177,27 @@ extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2 check_error(cudaPeekAtLastError()); } +extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t) +{ + scal_gpu(n, B1, m, 1); + scal_gpu(n, B2, v, 1); + axpy_gpu(n, -decay*batch, w, 1, d, 1); + + axpy_gpu(n, (1-B1), d, 1, m, 1); + mul_gpu(n, d, 1, d, 1); + axpy_gpu(n, (1-B2), d, 1, v, 1); + + adam_gpu(n, w, m, v, B1, B2, rate, eps, t); + fill_gpu(n, 0, d, 1); +} + __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial) { int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (index >= N) return; int f = (index/spatial)%filters; - x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .000001f); + x[index] = (x[index] - mean[f])/(sqrtf(variance[f] + .00001f)); } __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) @@ -170,7 +206,7 @@ __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *vari if (index >= N) return; int f = (index/spatial)%filters; - delta[index] = delta[index] * 1./(sqrt(variance[f]) + .000001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); + delta[index] = delta[index] * 1.f/(sqrtf(variance[f] + .00001f)) + variance_delta[f] * 2.f * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); } extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) @@ -192,7 +228,7 @@ __global__ void variance_delta_kernel(float *x, float *delta, float *mean, floa variance_delta[i] += delta[index]*(x[index] - mean[i]); } } - variance_delta[i] *= -.5 * pow(variance[i] + .000001f, (float)(-3./2.)); + variance_delta[i] *= -.5f * powf(variance[i] + .00001f, (float)(-3.f/2.f)); } __global__ void accumulate_kernel(float *x, int n, int groups, float *sum) @@ -224,12 +260,14 @@ __global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, } } + __syncthreads(); + if(id == 0){ mean_delta[filter] = 0; for(i = 0; i < threads; ++i){ mean_delta[filter] += local[i]; } - mean_delta[filter] *= (-1./sqrt(variance[filter] + .000001f)); + mean_delta[filter] *= (-1.f/sqrtf(variance[filter] + .00001f)); } } @@ -252,12 +290,14 @@ __global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, } } + __syncthreads(); + if(id == 0){ variance_delta[filter] = 0; for(i = 0; i < threads; ++i){ variance_delta[filter] += local[i]; } - variance_delta[filter] *= -.5 * pow(variance[filter] + .000001f, (float)(-3./2.)); + variance_delta[filter] *= -.5f * powf(variance[filter] + .00001f, (float)(-3.f/2.f)); } } @@ -274,7 +314,7 @@ __global__ void mean_delta_kernel(float *delta, float *variance, int batch, int mean_delta[i] += delta[index]; } } - mean_delta[i] *= (-1./sqrt(variance[i] + .000001f)); + mean_delta[i] *= (-1.f/sqrtf(variance[i] + .00001f)); } extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) @@ -297,7 +337,7 @@ extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, flo __global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean) { - float scale = 1./(batch * spatial); + float scale = 1.f/(batch * spatial); int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i >= filters) return; int j,k; @@ -313,7 +353,7 @@ __global__ void mean_kernel(float *x, int batch, int filters, int spatial, floa __global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance) { - float scale = 1./(batch * spatial - 1); + float scale = 1.f/(batch * spatial - 1); int j,k; int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i >= filters) return; @@ -321,7 +361,7 @@ __global__ void variance_kernel(float *x, float *mean, int batch, int filters, i for(j = 0; j < batch; ++j){ for(k = 0; k < spatial; ++k){ int index = j*filters*spatial + i*spatial + k; - variance[i] += pow((x[index] - mean[i]), 2); + variance[i] += powf((x[index] - mean[i]), 2); } } variance[i] *= scale; @@ -391,22 +431,22 @@ __global__ void supp_kernel(int N, float ALPHA, float *X, int INCX) } } -__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX) +__global__ void add_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if(i < N) X[i*INCX] *= ALPHA; + if(i < N) X[i*INCX] += ALPHA; } -__global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) +__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if(i < N) X[i*INCX] = ALPHA; + if(i < N) X[i*INCX] *= ALPHA; } -__global__ void mask_kernel(int n, float *x, float mask_num, float *mask) +__global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if(i < n && mask[i] == mask_num) x[i] = mask_num; + if(i < N) X[i*INCX] = ALPHA; } __global__ void copy_kernel(int N, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY) @@ -429,6 +469,35 @@ extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, check_error(cudaPeekAtLastError()); } +__global__ void l2norm_kernel(int N, float *x, float *dx, int batch, int filters, int spatial) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= N) return; + int b = index / spatial; + int i = index % spatial; + int f; + float sum = 0; + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + sum += powf(x[index], 2); + } + sum = sqrtf(sum); + if(sum == 0) sum = 1; + //printf("%f\n", sum); + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + x[index] /= sum; + dx[index] = (1 - x[index]) / sum; + } +} + +extern "C" void l2normalize_gpu(float *x, float *dx, int batch, int filters, int spatial) +{ + size_t N = batch*spatial; + l2norm_kernel<<>>(N, x, dx, batch, filters, spatial); + check_error(cudaPeekAtLastError()); +} + __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, float *mean) { const int threads = BLOCK; @@ -447,6 +516,8 @@ __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, } } + __syncthreads(); + if(id == 0){ mean[filter] = 0; for(i = 0; i < threads; ++i){ @@ -471,10 +542,12 @@ __global__ void fast_variance_kernel(float *x, float *mean, int batch, int filt for(i = 0; i < spatial; i += threads){ int index = j*spatial*filters + filter*spatial + i + id; - local[id] += (i+id < spatial) ? pow((x[index] - mean[filter]), 2) : 0; + local[id] += (i+id < spatial) ? powf((x[index] - mean[filter]), 2) : 0; } } + __syncthreads(); + if(id == 0){ variance[filter] = 0; for(i = 0; i < threads; ++i){ @@ -509,35 +582,35 @@ extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int check_error(cudaPeekAtLastError()); } -extern "C" void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) +extern "C" void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) { - axpy_ongpu_offset(N, ALPHA, X, 0, INCX, Y, 0, INCY); + axpy_gpu_offset(N, ALPHA, X, 0, INCX, Y, 0, INCY); } -extern "C" void pow_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) +extern "C" void pow_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) { pow_kernel<<>>(N, ALPHA, X, INCX, Y, INCY); check_error(cudaPeekAtLastError()); } -extern "C" void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) +extern "C" void axpy_gpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) { axpy_kernel<<>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY); check_error(cudaPeekAtLastError()); } -extern "C" void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY) +extern "C" void copy_gpu(int N, float * X, int INCX, float * Y, int INCY) { - copy_ongpu_offset(N, X, 0, INCX, Y, 0, INCY); + copy_gpu_offset(N, X, 0, INCX, Y, 0, INCY); } -extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY) +extern "C" void mul_gpu(int N, float * X, int INCX, float * Y, int INCY) { mul_kernel<<>>(N, X, INCX, Y, INCY); check_error(cudaPeekAtLastError()); } -extern "C" void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) +extern "C" void copy_gpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) { copy_kernel<<>>(N, X, OFFX, INCX, Y, OFFY, INCY); check_error(cudaPeekAtLastError()); @@ -560,58 +633,82 @@ __global__ void flatten_kernel(int N, float *x, int spatial, int layers, int bat else out[i1] = x[i2]; } -extern "C" void flatten_ongpu(float *x, int spatial, int layers, int batch, int forward, float *out) +extern "C" void flatten_gpu(float *x, int spatial, int layers, int batch, int forward, float *out) { int size = spatial*batch*layers; flatten_kernel<<>>(size, x, spatial, layers, batch, forward, out); check_error(cudaPeekAtLastError()); } -extern "C" void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out) +extern "C" void reorg_gpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out) { int size = w*h*c*batch; reorg_kernel<<>>(size, x, w, h, c, batch, stride, forward, out); check_error(cudaPeekAtLastError()); } -extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask) +__global__ void mask_kernel(int n, float *x, float mask_num, float *mask, float val) { - mask_kernel<<>>(N, X, mask_num, mask); + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n && mask[i] == mask_num) x[i] = val; +} + +extern "C" void mask_gpu(int N, float * X, float mask_num, float * mask, float val) +{ + mask_kernel<<>>(N, X, mask_num, mask, val); + check_error(cudaPeekAtLastError()); +} + +__global__ void scale_mask_kernel(int n, float *x, float mask_num, float *mask, float scale) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n && mask[i] == mask_num) x[i] *= scale; +} + +extern "C" void scale_mask_gpu(int N, float * X, float mask_num, float * mask, float scale) +{ + scale_mask_kernel<<>>(N, X, mask_num, mask, scale); check_error(cudaPeekAtLastError()); } -extern "C" void const_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void const_gpu(int N, float ALPHA, float * X, int INCX) { const_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -extern "C" void constrain_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void constrain_gpu(int N, float ALPHA, float * X, int INCX) { constrain_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void add_gpu(int N, float ALPHA, float * X, int INCX) +{ + add_kernel<<>>(N, ALPHA, X, INCX); + check_error(cudaPeekAtLastError()); +} + +extern "C" void scal_gpu(int N, float ALPHA, float * X, int INCX) { scal_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void supp_gpu(int N, float ALPHA, float * X, int INCX) { supp_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) +extern "C" void fill_gpu(int N, float ALPHA, float * X, int INCX) { fill_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } -__global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) +__global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; @@ -625,10 +722,11 @@ __global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stri int out_index = i*sample + w2*(j*sample + h2*(k + c2*b)); int add_index = i*stride + w1*(j*stride + h1*(k + c1*b)); - out[out_index] += add[add_index]; + out[out_index] = s1*out[out_index] + s2*add[add_index]; + //out[out_index] += add[add_index]; } -extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) +extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out) { int minw = (w1 < w2) ? w1 : w2; int minh = (h1 < h2) ? h1 : h2; @@ -642,7 +740,7 @@ extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int if(sample < 1) sample = 1; int size = batch * minw * minh * minc; - shortcut_kernel<<>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); + shortcut_kernel<<>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, s1, s2, out); check_error(cudaPeekAtLastError()); } @@ -651,14 +749,14 @@ __global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ float diff = truth[i] - pred[i]; - float abs_val = abs(diff); + float abs_val = fabsf(diff); if(abs_val < 1) { error[i] = diff * diff; delta[i] = diff; } else { error[i] = 2*abs_val - 1; - delta[i] = (diff < 0) ? -1 : 1; + delta[i] = (diff > 0) ? 1 : -1; } } } @@ -669,6 +767,40 @@ extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, fl check_error(cudaPeekAtLastError()); } +__global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float t = truth[i]; + float p = pred[i]; + error[i] = (t) ? -log(p) : 0; + delta[i] = t-p; + } +} + +extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + softmax_x_ent_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + +__global__ void logistic_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float t = truth[i]; + float p = pred[i]; + error[i] = -t*log(p+.0000001) - (1-t)*log(1-p+.0000001); + delta[i] = t-p; + } +} + +extern "C" void logistic_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + logistic_x_ent_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + __global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float *error) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -685,6 +817,38 @@ extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *er check_error(cudaPeekAtLastError()); } +__global__ void l1_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float diff = truth[i] - pred[i]; + error[i] = abs(diff); + delta[i] = (diff > 0) ? 1 : -1; + } +} + +extern "C" void l1_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + l1_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + +__global__ void wgan_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + error[i] = truth[i] ? -pred[i] : pred[i]; + delta[i] = (truth[i] > 0) ? 1 : -1; + } +} + +extern "C" void wgan_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + wgan_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + + __global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float *c) @@ -695,6 +859,46 @@ __global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float * } } +__global__ void deinter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < (NX+NY)*B){ + int b = i / (NX+NY); + int j = i % (NX+NY); + if (j < NX){ + if(X) X[b*NX + j] += OUT[i]; + } else { + if(Y) Y[b*NY + j - NX] += OUT[i]; + } + } +} + +extern "C" void deinter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + deinter_kernel<<>>(NX, X, NY, Y, B, OUT); + check_error(cudaPeekAtLastError()); +} + +__global__ void inter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < (NX+NY)*B){ + int b = i / (NX+NY); + int j = i % (NX+NY); + if (j < NX){ + OUT[i] = X[b*NX + j]; + } else { + OUT[i] = Y[b*NY + j - NX]; + } + } +} + +extern "C" void inter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + inter_kernel<<>>(NX, X, NY, Y, B, OUT); + check_error(cudaPeekAtLastError()); +} + extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c) { weighted_sum_kernel<<>>(num, a, b, s, c); @@ -706,8 +910,8 @@ __global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n){ if(da) da[i] += dc[i] * s[i]; - db[i] += dc[i] * (1-s[i]); - ds[i] += dc[i] * a[i] + dc[i] * -b[i]; + if(db) db[i] += dc[i] * (1-s[i]); + ds[i] += dc[i] * (a[i] - b[i]); } } @@ -732,36 +936,100 @@ extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c) } -__device__ void softmax_device(int n, float *input, float temp, float *output) +__device__ void softmax_device(float *input, int n, float temp, int stride, float *output) { int i; float sum = 0; float largest = -INFINITY; for(i = 0; i < n; ++i){ - int val = input[i]; + int val = input[i*stride]; largest = (val>largest) ? val : largest; } for(i = 0; i < n; ++i){ - float e = exp(input[i]/temp - largest/temp); + float e = expf(input[i*stride]/temp - largest/temp); sum += e; - output[i] = e; + output[i*stride] = e; } for(i = 0; i < n; ++i){ - output[i] /= sum; + output[i*stride] /= sum; } } -__global__ void softmax_kernel(int n, int offset, int batch, float *input, float temp, float *output) + +__global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset) { - int b = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if(b >= batch) return; - softmax_device(n, input + b*offset, temp, output + b*offset); + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= spatial*batch*groups) return; + int s = id % spatial; + id = id / spatial; + int g = id % groups; + int b = id / groups; + int goff = group_offset[g]*spatial; + int boff = b*stride; + softmax_device(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s); +} + +extern "C" void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier) +{ + int *tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); + int *tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); + /* + static int *tree_groups_size = 0; + static int *tree_groups_offset = 0; + if(!tree_groups_size){ + tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); + tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); + } + */ + int num = spatial*batch*hier.groups; + softmax_tree_kernel<<>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); + check_error(cudaPeekAtLastError()); + cuda_free((float *)tree_groups_size); + cuda_free((float *)tree_groups_offset); +} + +__global__ void softmax_kernel(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= batch*groups) return; + int b = id / groups; + int g = id % groups; + softmax_device(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); } -extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output) +extern "C" void softmax_gpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + softmax_kernel<<>>(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); + check_error(cudaPeekAtLastError()); +} + + +__global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) +{ + size_t i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i >= N) return; + int out_index = i; + int out_w = i%(w*stride); + i = i/(w*stride); + int out_h = i%(h*stride); + i = i/(h*stride); + int out_c = i%c; + i = i/c; + int b = i%batch; + + int in_w = out_w / stride; + int in_h = out_h / stride; + int in_c = out_c; + + int in_index = b*w*h*c + in_c*w*h + in_h*w + in_w; + + + if(forward) out[out_index] += scale * x[in_index]; + else atomicAdd(x+in_index, scale * out[out_index]); +} +extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { - int inputs = n; - int batch = groups; - softmax_kernel<<>>(inputs, offset, batch, input, temp, output); + size_t size = w*h*c*batch*stride*stride; + upsample_kernel<<>>(size, in, w, h, c, batch, stride, forward, scale, out); check_error(cudaPeekAtLastError()); } diff --git a/image.darknet/inst/include/darknet/src/box.c b/image.darknet/inst/include/darknet/src/box.c index 39dea06..8a1772c 100644 --- a/image.darknet/inst/include/darknet/src/box.c +++ b/image.darknet/inst/include/darknet/src/box.c @@ -3,13 +3,98 @@ #include #include -box float_to_box(float *f) +int nms_comparator(const void *pa, const void *pb) { - box b; + detection a = *(detection *)pa; + detection b = *(detection *)pb; + float diff = 0; + if(b.sort_class >= 0){ + diff = a.prob[b.sort_class] - b.prob[b.sort_class]; + } else { + diff = a.objectness - b.objectness; + } + if(diff < 0) return 1; + else if(diff > 0) return -1; + return 0; +} + +void do_nms_obj(detection *dets, int total, int classes, float thresh) +{ + int i, j, k; + k = total-1; + for(i = 0; i <= k; ++i){ + if(dets[i].objectness == 0){ + detection swap = dets[i]; + dets[i] = dets[k]; + dets[k] = swap; + --k; + --i; + } + } + total = k+1; + + for(i = 0; i < total; ++i){ + dets[i].sort_class = -1; + } + + qsort(dets, total, sizeof(detection), nms_comparator); + for(i = 0; i < total; ++i){ + if(dets[i].objectness == 0) continue; + box a = dets[i].bbox; + for(j = i+1; j < total; ++j){ + if(dets[j].objectness == 0) continue; + box b = dets[j].bbox; + if (box_iou(a, b) > thresh){ + dets[j].objectness = 0; + for(k = 0; k < classes; ++k){ + dets[j].prob[k] = 0; + } + } + } + } +} + + +void do_nms_sort(detection *dets, int total, int classes, float thresh) +{ + int i, j, k; + k = total-1; + for(i = 0; i <= k; ++i){ + if(dets[i].objectness == 0){ + detection swap = dets[i]; + dets[i] = dets[k]; + dets[k] = swap; + --k; + --i; + } + } + total = k+1; + + for(k = 0; k < classes; ++k){ + for(i = 0; i < total; ++i){ + dets[i].sort_class = k; + } + qsort(dets, total, sizeof(detection), nms_comparator); + for(i = 0; i < total; ++i){ + if(dets[i].prob[k] == 0) continue; + box a = dets[i].bbox; + for(j = i+1; j < total; ++j){ + box b = dets[j].bbox; + if (box_iou(a, b) > thresh){ + dets[j].prob[k] = 0; + } + } + } + } +} + +box float_to_box(float *f, int stride) +{ + box b = {0}; b.x = f[0]; - b.y = f[1]; - b.w = f[2]; - b.h = f[3]; + b.y = f[1*stride]; + b.w = f[2*stride]; + b.h = f[3*stride]; return b; } @@ -230,79 +315,6 @@ dbox diou(box a, box b) return dd; } -typedef struct{ - int index; - int class; - float **probs; -} sortable_bbox; - -int nms_comparator(const void *pa, const void *pb) -{ - sortable_bbox a = *(sortable_bbox *)pa; - sortable_bbox b = *(sortable_bbox *)pb; - float diff = a.probs[a.index][b.class] - b.probs[b.index][b.class]; - if(diff < 0) return 1; - else if(diff > 0) return -1; - return 0; -} - -void do_nms_obj(box *boxes, float **probs, int total, int classes, float thresh) -{ - int i, j, k; - sortable_bbox *s = calloc(total, sizeof(sortable_bbox)); - - for(i = 0; i < total; ++i){ - s[i].index = i; - s[i].class = classes; - s[i].probs = probs; - } - - qsort(s, total, sizeof(sortable_bbox), nms_comparator); - for(i = 0; i < total; ++i){ - if(probs[s[i].index][classes] == 0) continue; - box a = boxes[s[i].index]; - for(j = i+1; j < total; ++j){ - box b = boxes[s[j].index]; - if (box_iou(a, b) > thresh){ - for(k = 0; k < classes+1; ++k){ - probs[s[j].index][k] = 0; - } - } - } - } - free(s); -} - - -void do_nms_sort(box *boxes, float **probs, int total, int classes, float thresh) -{ - int i, j, k; - sortable_bbox *s = calloc(total, sizeof(sortable_bbox)); - - for(i = 0; i < total; ++i){ - s[i].index = i; - s[i].class = 0; - s[i].probs = probs; - } - - for(k = 0; k < classes; ++k){ - for(i = 0; i < total; ++i){ - s[i].class = k; - } - qsort(s, total, sizeof(sortable_bbox), nms_comparator); - for(i = 0; i < total; ++i){ - if(probs[s[i].index][k] == 0) continue; - box a = boxes[s[i].index]; - for(j = i+1; j < total; ++j){ - box b = boxes[s[j].index]; - if (box_iou(a, b) > thresh){ - probs[s[j].index][k] = 0; - } - } - } - } - free(s); -} void do_nms(box *boxes, float **probs, int total, int classes, float thresh) { diff --git a/image.darknet/inst/include/darknet/src/box.h b/image.darknet/inst/include/darknet/src/box.h index c65589b..dda3e59 100644 --- a/image.darknet/inst/include/darknet/src/box.h +++ b/image.darknet/inst/include/darknet/src/box.h @@ -1,21 +1,13 @@ #ifndef BOX_H #define BOX_H - -typedef struct{ - float x, y, w, h; -} box; +#include "darknet.h" typedef struct{ float dx, dy, dw, dh; } dbox; -box float_to_box(float *f); -float box_iou(box a, box b); float box_rmse(box a, box b); dbox diou(box a, box b); -void do_nms(box *boxes, float **probs, int total, int classes, float thresh); -void do_nms_sort(box *boxes, float **probs, int total, int classes, float thresh); -void do_nms_obj(box *boxes, float **probs, int total, int classes, float thresh); box decode_box(box b, box anchor); box encode_box(box b, box anchor); diff --git a/image.darknet/inst/include/darknet/src/classifier.h b/image.darknet/inst/include/darknet/src/classifier.h index 3c89f49..8b13789 100644 --- a/image.darknet/inst/include/darknet/src/classifier.h +++ b/image.darknet/inst/include/darknet/src/classifier.h @@ -1,2 +1 @@ -list *read_data_cfg(char *filename); diff --git a/image.darknet/inst/include/darknet/src/col2im.h b/image.darknet/inst/include/darknet/src/col2im.h index 0237497..3fbe053 100644 --- a/image.darknet/inst/include/darknet/src/col2im.h +++ b/image.darknet/inst/include/darknet/src/col2im.h @@ -6,7 +6,7 @@ void col2im_cpu(float* data_col, int ksize, int stride, int pad, float* data_im); #ifdef GPU -void col2im_ongpu(float *data_col, +void col2im_gpu(float *data_col, int channels, int height, int width, int ksize, int stride, int pad, float *data_im); #endif diff --git a/image.darknet/inst/include/darknet/src/col2im_kernels.cu b/image.darknet/inst/include/darknet/src/col2im_kernels.cu index aed2df9..ba45e0f 100644 --- a/image.darknet/inst/include/darknet/src/col2im_kernels.cu +++ b/image.darknet/inst/include/darknet/src/col2im_kernels.cu @@ -41,7 +41,7 @@ __global__ void col2im_gpu_kernel(const int n, const float* data_col, } } -void col2im_ongpu(float *data_col, +void col2im_gpu(float *data_col, int channels, int height, int width, int ksize, int stride, int pad, float *data_im){ // We are going to launch channels * height_col * width_col kernels, each diff --git a/image.darknet/inst/include/darknet/src/compare.c b/image.darknet/inst/include/darknet/src/compare.c index 4fd266c..d2d2b3b 100644 --- a/image.darknet/inst/include/darknet/src/compare.c +++ b/image.darknet/inst/include/darknet/src/compare.c @@ -54,7 +54,7 @@ void train_compare(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%.3f: %f, %f avg, %lf seconds, %d images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if(i%100 == 0){ char buff[256]; diff --git a/image.darknet/inst/include/darknet/src/connected_layer.c b/image.darknet/inst/include/darknet/src/connected_layer.c index b678ed0..353f4e5 100644 --- a/image.darknet/inst/include/darknet/src/connected_layer.c +++ b/image.darknet/inst/include/darknet/src/connected_layer.c @@ -1,4 +1,5 @@ #include "connected_layer.h" +#include "convolutional_layer.h" #include "batchnorm_layer.h" #include "utils.h" #include "cuda.h" @@ -10,10 +11,11 @@ #include #include -connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize) +layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam) { int i; - connected_layer l = {0}; + layer l = {0}; + l.learning_rate_scale = 1; l.type = CONNECTED; l.inputs = inputs; @@ -50,6 +52,14 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.biases[i] = 0; } + if(adam){ + l.m = calloc(l.inputs*l.outputs, sizeof(float)); + l.v = calloc(l.inputs*l.outputs, sizeof(float)); + l.bias_m = calloc(l.outputs, sizeof(float)); + l.scale_m = calloc(l.outputs, sizeof(float)); + l.bias_v = calloc(l.outputs, sizeof(float)); + l.scale_v = calloc(l.outputs, sizeof(float)); + } if(batch_normalize){ l.scales = calloc(outputs, sizeof(float)); l.scale_updates = calloc(outputs, sizeof(float)); @@ -82,10 +92,16 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.output_gpu = cuda_make_array(l.output, outputs*batch); l.delta_gpu = cuda_make_array(l.delta, outputs*batch); - if(batch_normalize){ - l.scales_gpu = cuda_make_array(l.scales, outputs); - l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs); + if (adam) { + l.m_gpu = cuda_make_array(0, inputs*outputs); + l.v_gpu = cuda_make_array(0, inputs*outputs); + l.bias_m_gpu = cuda_make_array(0, outputs); + l.bias_v_gpu = cuda_make_array(0, outputs); + l.scale_m_gpu = cuda_make_array(0, outputs); + l.scale_v_gpu = cuda_make_array(0, outputs); + } + if(batch_normalize){ l.mean_gpu = cuda_make_array(l.mean, outputs); l.variance_gpu = cuda_make_array(l.variance, outputs); @@ -95,8 +111,17 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.mean_delta_gpu = cuda_make_array(l.mean, outputs); l.variance_delta_gpu = cuda_make_array(l.variance, outputs); + l.scales_gpu = cuda_make_array(l.scales, outputs); + l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs); + l.x_gpu = cuda_make_array(l.output, l.batch*outputs); l.x_norm_gpu = cuda_make_array(l.output, l.batch*outputs); +#ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); + cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); +#endif } #endif l.activation = activation; @@ -104,8 +129,12 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT return l; } -void update_connected_layer(connected_layer l, int batch, float learning_rate, float momentum, float decay) +void update_connected_layer(layer l, update_args a) { + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.outputs, momentum, l.bias_updates, 1); @@ -119,63 +148,39 @@ void update_connected_layer(connected_layer l, int batch, float learning_rate, f scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1); } -void forward_connected_layer(connected_layer l, network_state state) +void forward_connected_layer(layer l, network net) { - int i; fill_cpu(l.outputs*l.batch, 0, l.output, 1); int m = l.batch; int k = l.inputs; int n = l.outputs; - float *a = state.input; + float *a = net.input; float *b = l.weights; float *c = l.output; gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); if(l.batch_normalize){ - if(state.train){ - mean_cpu(l.output, l.batch, l.outputs, 1, l.mean); - variance_cpu(l.output, l.mean, l.batch, l.outputs, 1, l.variance); - - scal_cpu(l.outputs, .95, l.rolling_mean, 1); - axpy_cpu(l.outputs, .05, l.mean, 1, l.rolling_mean, 1); - scal_cpu(l.outputs, .95, l.rolling_variance, 1); - axpy_cpu(l.outputs, .05, l.variance, 1, l.rolling_variance, 1); - - copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); - normalize_cpu(l.output, l.mean, l.variance, l.batch, l.outputs, 1); - copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1); - } else { - normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.outputs, 1); - } - scale_bias(l.output, l.scales, l.batch, l.outputs, 1); - } - for(i = 0; i < l.batch; ++i){ - axpy_cpu(l.outputs, 1, l.biases, 1, l.output + i*l.outputs, 1); + forward_batchnorm_layer(l, net); + } else { + add_bias(l.output, l.biases, l.batch, l.outputs, 1); } activate_array(l.output, l.outputs*l.batch, l.activation); } -void backward_connected_layer(connected_layer l, network_state state) +void backward_connected_layer(layer l, network net) { - int i; gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); - for(i = 0; i < l.batch; ++i){ - axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1); - } - if(l.batch_normalize){ - backward_scale_cpu(l.x_norm, l.delta, l.batch, l.outputs, 1, l.scale_updates); - - scale_bias(l.delta, l.scales, l.batch, l.outputs, 1); - mean_delta_cpu(l.delta, l.variance, l.batch, l.outputs, 1, l.mean_delta); - variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.outputs, 1, l.variance_delta); - normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.outputs, 1, l.delta); + if(l.batch_normalize){ + backward_batchnorm_layer(l, net); + } else { + backward_bias(l.bias_updates, l.delta, l.batch, l.outputs, 1); } int m = l.outputs; int k = l.batch; int n = l.inputs; float *a = l.delta; - float *b = state.input; + float *b = net.input; float *c = l.weight_updates; gemm(1,0,m,n,k,1,a,m,b,n,1,c,n); @@ -185,7 +190,7 @@ void backward_connected_layer(connected_layer l, network_state state) a = l.delta; b = l.weights; - c = state.delta; + c = net.delta; if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); } @@ -213,11 +218,11 @@ void statistics_connected_layer(layer l) printf("Scales "); print_statistics(l.scales, l.outputs); /* - printf("Rolling Mean "); - print_statistics(l.rolling_mean, l.outputs); - printf("Rolling Variance "); - print_statistics(l.rolling_variance, l.outputs); - */ + printf("Rolling Mean "); + print_statistics(l.rolling_mean, l.outputs); + printf("Rolling Variance "); + print_statistics(l.rolling_variance, l.outputs); + */ } printf("Biases "); print_statistics(l.biases, l.outputs); @@ -227,7 +232,7 @@ void statistics_connected_layer(layer l) #ifdef GPU -void pull_connected_layer(connected_layer l) +void pull_connected_layer(layer l) { cuda_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs); cuda_pull_array(l.biases_gpu, l.biases, l.outputs); @@ -240,7 +245,7 @@ void pull_connected_layer(connected_layer l) } } -void push_connected_layer(connected_layer l) +void push_connected_layer(layer l) { cuda_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs); cuda_push_array(l.biases_gpu, l.biases, l.outputs); @@ -253,62 +258,70 @@ void push_connected_layer(connected_layer l) } } -void update_connected_layer_gpu(connected_layer l, int batch, float learning_rate, float momentum, float decay) +void update_connected_layer_gpu(layer l, update_args a) { - axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); - scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1); + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + if(a.adam){ + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.inputs*l.outputs, batch, a.t); + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t); + if(l.scales_gpu){ + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t); + } + }else{ + axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1); - if(l.batch_normalize){ - axpy_ongpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); - scal_ongpu(l.outputs, momentum, l.scale_updates_gpu, 1); - } + if(l.batch_normalize){ + axpy_gpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_gpu(l.outputs, momentum, l.scale_updates_gpu, 1); + } - axpy_ongpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); - axpy_ongpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); - scal_ongpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1); + axpy_gpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1); + } } -void forward_connected_layer_gpu(connected_layer l, network_state state) +void forward_connected_layer_gpu(layer l, network net) { - int i; - fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); int m = l.batch; int k = l.inputs; int n = l.outputs; - float * a = state.input; + float * a = net.input_gpu; float * b = l.weights_gpu; float * c = l.output_gpu; - gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n); - if(l.batch_normalize){ - forward_batchnorm_layer_gpu(l, state); - } - for(i = 0; i < l.batch; ++i){ - axpy_ongpu(l.outputs, 1, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1); + gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); + + if (l.batch_normalize) { + forward_batchnorm_layer_gpu(l, net); + } else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1); } - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); } -void backward_connected_layer_gpu(connected_layer l, network_state state) +void backward_connected_layer_gpu(layer l, network net) { - int i; - constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - for(i = 0; i < l.batch; ++i){ - axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1); - } - + constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if(l.batch_normalize){ - backward_batchnorm_layer_gpu(l, state); + backward_batchnorm_layer_gpu(l, net); + } else { + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.outputs, 1); } int m = l.outputs; int k = l.batch; int n = l.inputs; float * a = l.delta_gpu; - float * b = state.input; + float * b = net.input_gpu; float * c = l.weight_updates_gpu; - gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n); + gemm_gpu(1,0,m,n,k,1,a,m,b,n,1,c,n); m = l.batch; k = l.outputs; @@ -316,8 +329,8 @@ void backward_connected_layer_gpu(connected_layer l, network_state state) a = l.delta_gpu; b = l.weights_gpu; - c = state.delta; + c = net.delta_gpu; - if(c) gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n); + if(c) gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); } #endif diff --git a/image.darknet/inst/include/darknet/src/connected_layer.h b/image.darknet/inst/include/darknet/src/connected_layer.h index 23797b1..6727a96 100644 --- a/image.darknet/inst/include/darknet/src/connected_layer.h +++ b/image.darknet/inst/include/darknet/src/connected_layer.h @@ -5,22 +5,18 @@ #include "layer.h" #include "network.h" -typedef layer connected_layer; +layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam); -connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize); - -void forward_connected_layer(connected_layer layer, network_state state); -void backward_connected_layer(connected_layer layer, network_state state); -void update_connected_layer(connected_layer layer, int batch, float learning_rate, float momentum, float decay); -void denormalize_connected_layer(layer l); -void statistics_connected_layer(layer l); +void forward_connected_layer(layer l, network net); +void backward_connected_layer(layer l, network net); +void update_connected_layer(layer l, update_args a); #ifdef GPU -void forward_connected_layer_gpu(connected_layer layer, network_state state); -void backward_connected_layer_gpu(connected_layer layer, network_state state); -void update_connected_layer_gpu(connected_layer layer, int batch, float learning_rate, float momentum, float decay); -void push_connected_layer(connected_layer layer); -void pull_connected_layer(connected_layer layer); +void forward_connected_layer_gpu(layer l, network net); +void backward_connected_layer_gpu(layer l, network net); +void update_connected_layer_gpu(layer l, update_args a); +void push_connected_layer(layer l); +void pull_connected_layer(layer l); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/convolutional_kernels.cu b/image.darknet/inst/include/darknet/src/convolutional_kernels.cu index fcaea03..4a1047b 100644 --- a/image.darknet/inst/include/darknet/src/convolutional_kernels.cu +++ b/image.darknet/inst/include/darknet/src/convolutional_kernels.cu @@ -33,7 +33,7 @@ __global__ void binarize_input_kernel(float *input, int n, int size, float *bina int i = 0; float mean = 0; for(i = 0; i < n; ++i){ - mean += abs(input[i*size + s]); + mean += fabsf(input[i*size + s]); } mean = mean / n; for(i = 0; i < n; ++i){ @@ -55,7 +55,7 @@ __global__ void binarize_weights_kernel(float *weights, int n, int size, float * int i = 0; float mean = 0; for(i = 0; i < size; ++i){ - mean += abs(weights[f*size + i]); + mean += fabsf(weights[f*size + i]); } mean = mean / size; for(i = 0; i < size; ++i){ @@ -70,19 +70,19 @@ void binarize_weights_gpu(float *weights, int n, int size, float *binary) check_error(cudaPeekAtLastError()); } -void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) +void forward_convolutional_layer_gpu(convolutional_layer l, network net) { - fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); if(l.binary){ - binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + binarize_weights_gpu(l.weights_gpu, l.n, l.c/l.groups*l.size*l.size, l.binary_weights_gpu); swap_binary(&l); } if(l.xnor){ - binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + binarize_weights_gpu(l.weights_gpu, l.n, l.c/l.groups*l.size*l.size, l.binary_weights_gpu); swap_binary(&l); - binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); - state.input = l.binary_input_gpu; + binarize_gpu(net.input_gpu, l.c*l.h*l.w*l.batch, l.binary_input_gpu); + net.input_gpu = l.binary_input_gpu; } #ifdef CUDNN @@ -90,74 +90,126 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) cudnnConvolutionForward(cudnn_handle(), &one, l.srcTensorDesc, - state.input, + net.input_gpu, l.weightDesc, l.weights_gpu, l.convDesc, l.fw_algo, - state.workspace, + net.workspace, l.workspace_size, &one, l.dstTensorDesc, l.output_gpu); #else - int i; - int m = l.n; - int k = l.size*l.size*l.c; + int i, j; + int m = l.n/l.groups; + int k = l.size*l.size*l.c/l.groups; int n = l.out_w*l.out_h; for(i = 0; i < l.batch; ++i){ - im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); - float * a = l.weights_gpu; - float * b = state.workspace; - float * c = l.output_gpu; - gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n); + for(j = 0; j < l.groups; ++j){ + float *a = l.weights_gpu + j*l.nweights/l.groups; + float *b = net.workspace; + float *c = l.output_gpu + (i*l.groups + j)*n*m; + float *im = net.input_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w; + + if (l.size == 1){ + b = im; + } else { + im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + } + gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); + } } #endif if (l.batch_normalize) { - forward_batchnorm_layer_gpu(l, state); + forward_batchnorm_layer_gpu(l, net); + } else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); } -void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) +__global__ void smooth_kernel(float *x, int n, int w, int h, int c, int size, float rate, float *delta) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id >= n) return; + + int j = id % w; + id /= w; + int i = id % h; + id /= h; + int k = id % c; + id /= c; + int b = id; + + int w_offset = -(size/2.f); + int h_offset = -(size/2.f); + + int out_index = j + w*(i + h*(k + c*b)); + int l, m; + for(l = 0; l < size; ++l){ + for(m = 0; m < size; ++m){ + int cur_h = h_offset + i + l; + int cur_w = w_offset + j + m; + int index = cur_w + w*(cur_h + h*(k + b*c)); + int valid = (cur_h >= 0 && cur_h < h && + cur_w >= 0 && cur_w < w); + delta[out_index] += valid ? rate*(x[index] - x[out_index]) : 0; + } + } +} + +extern "C" void smooth_layer(layer l, int size, float rate) { - //constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + int h = l.out_h; + int w = l.out_w; + int c = l.out_c; + + size_t n = h*w*c*l.batch; + + smooth_kernel<<>>(l.output_gpu, n, l.w, l.h, l.c, size, rate, l.delta_gpu); + check_error(cudaPeekAtLastError()); +} + +void backward_convolutional_layer_gpu(convolutional_layer l, network net) +{ + if(l.smooth){ + smooth_layer(l, 5, l.smooth); + } + //constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); if(l.batch_normalize){ - backward_batchnorm_layer_gpu(l, state); - //axpy_ongpu(l.outputs*l.batch, -state.net.decay, l.x_gpu, 1, l.delta_gpu, 1); + backward_batchnorm_layer_gpu(l, net); } else { - //axpy_ongpu(l.outputs*l.batch, -state.net.decay, l.output_gpu, 1, l.delta_gpu, 1); + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); } - float *original_input = state.input; + float *original_input = net.input_gpu; - if(l.xnor) state.input = l.binary_input_gpu; + if(l.xnor) net.input_gpu = l.binary_input_gpu; #ifdef CUDNN float one = 1; cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc, - state.input, + net.input_gpu, l.ddstTensorDesc, l.delta_gpu, l.convDesc, l.bf_algo, - state.workspace, + net.workspace, l.workspace_size, &one, l.dweightDesc, l.weight_updates_gpu); - if(state.delta){ + if(net.delta_gpu){ if(l.binary || l.xnor) swap_binary(&l); cudnnConvolutionBackwardData(cudnn_handle(), &one, @@ -167,108 +219,111 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.delta_gpu, l.convDesc, l.bd_algo, - state.workspace, + net.workspace, l.workspace_size, &one, l.dsrcTensorDesc, - state.delta); + net.delta_gpu); if(l.binary || l.xnor) swap_binary(&l); - if(l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); + if(l.xnor) gradient_array_gpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, net.delta_gpu); } #else - int m = l.n; - int n = l.size*l.size*l.c; + int m = l.n/l.groups; + int n = l.size*l.size*l.c/l.groups; int k = l.out_w*l.out_h; - int i; + int i, j; for(i = 0; i < l.batch; ++i){ - float * a = l.delta_gpu; - float * b = state.workspace; - float * c = l.weight_updates_gpu; - - im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); - gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n); - - if(state.delta){ - if(l.binary || l.xnor) swap_binary(&l); - float * a = l.weights_gpu; - float * b = l.delta_gpu; - float * c = state.workspace; - - gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k); - - col2im_ongpu(state.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta + i*l.c*l.h*l.w); - if(l.binary || l.xnor) { - swap_binary(&l); + for(j = 0; j < l.groups; ++j){ + float *a = l.delta_gpu + (i*l.groups + j)*m*k; + float *b = net.workspace; + float *c = l.weight_updates_gpu + j*l.nweights/l.groups; + + float *im = net.input_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w; + float *imd = net.delta_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w; + + im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); + + if (net.delta_gpu) { + if (l.binary || l.xnor) swap_binary(&l); + a = l.weights_gpu + j*l.nweights/l.groups; + b = l.delta_gpu + (i*l.groups + j)*m*k; + c = net.workspace; + if (l.size == 1) { + c = imd; + } + + gemm_gpu(1,0,n,k,m,1,a,n,b,k,0,c,k); + + if (l.size != 1) { + col2im_gpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd); + } + if(l.binary || l.xnor) { + swap_binary(&l); + } } - if(l.xnor) gradient_array_ongpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, state.delta + i*l.c*l.h*l.w); + if(l.xnor) gradient_array_gpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, net.delta_gpu + i*l.c*l.h*l.w); } } #endif } -void pull_convolutional_layer(convolutional_layer layer) +void pull_convolutional_layer(layer l) { - cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.biases_gpu, layer.biases, layer.n); - cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); - if (layer.batch_normalize){ - cuda_pull_array(layer.scales_gpu, layer.scales, layer.n); - cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); - cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); - } - if (layer.adam){ - cuda_pull_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); + cuda_pull_array(l.weights_gpu, l.weights, l.nweights); + cuda_pull_array(l.biases_gpu, l.biases, l.n); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_pull_array(l.scales_gpu, l.scales, l.n); + cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.n); } } -void push_convolutional_layer(convolutional_layer layer) +void push_convolutional_layer(layer l) { - cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.biases_gpu, layer.biases, layer.n); - cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); - if (layer.batch_normalize){ - cuda_push_array(layer.scales_gpu, layer.scales, layer.n); - cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); - cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); - } - if (layer.adam){ - cuda_push_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); + cuda_push_array(l.weights_gpu, l.weights, l.nweights); + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_push_array(l.scales_gpu, l.scales, l.n); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); } } -void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay) +void update_convolutional_layer_gpu(layer l, update_args a) { - int size = layer.size*layer.size*layer.c*layer.n; - axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); - scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); - - if(layer.scales_gpu){ - axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1); - scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1); - } - - if(layer.adam){ - scal_ongpu(size, layer.B1, layer.m_gpu, 1); - scal_ongpu(size, layer.B2, layer.v_gpu, 1); - - axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + if(a.adam){ + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + if(l.scales_gpu){ + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + } + }else{ + axpy_gpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(l.nweights, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(l.nweights, momentum, l.weight_updates_gpu, 1); - axpy_ongpu(size, -(1-layer.B1), layer.weight_updates_gpu, 1, layer.m_gpu, 1); - mul_ongpu(size, layer.weight_updates_gpu, 1, layer.weight_updates_gpu, 1); - axpy_ongpu(size, (1-layer.B2), layer.weight_updates_gpu, 1, layer.v_gpu, 1); + axpy_gpu(l.n, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.n, momentum, l.bias_updates_gpu, 1); - adam_gpu(size, layer.weights_gpu, layer.m_gpu, layer.v_gpu, layer.B1, layer.B2, learning_rate/batch, layer.eps, layer.t+1); - fill_ongpu(size, 0, layer.weight_updates_gpu, 1); - }else{ - axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); - axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); - scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); + if(l.scales_gpu){ + axpy_gpu(l.n, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_gpu(l.n, momentum, l.scale_updates_gpu, 1); + } + } + if(l.clip){ + constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); } } diff --git a/image.darknet/inst/include/darknet/src/convolutional_layer.c b/image.darknet/inst/include/darknet/src/convolutional_layer.c index 37211ab..1fb58b0 100644 --- a/image.darknet/inst/include/darknet/src/convolutional_layer.c +++ b/image.darknet/inst/include/darknet/src/convolutional_layer.c @@ -12,22 +12,17 @@ #include "xnor_layer.h" #endif -#ifndef AI2 -#define AI2 0 -void forward_xnor_layer(layer l, network_state state); -#endif - void swap_binary(convolutional_layer *l) { float *swap = l->weights; l->weights = l->binary_weights; l->binary_weights = swap; - #ifdef GPU +#ifdef GPU swap = l->weights_gpu; l->weights_gpu = l->binary_weights_gpu; l->binary_weights_gpu = swap; - #endif +#endif } void binarize_weights(float *weights, int n, int size, float *binary) @@ -80,23 +75,15 @@ int convolutional_out_width(convolutional_layer l) image get_convolutional_image(convolutional_layer l) { - int h,w,c; - h = convolutional_out_height(l); - w = convolutional_out_width(l); - c = l.n; - return float_to_image(w,h,c,l.output); + return float_to_image(l.out_w,l.out_h,l.out_c,l.output); } image get_convolutional_delta(convolutional_layer l) { - int h,w,c; - h = convolutional_out_height(l); - w = convolutional_out_width(l); - c = l.n; - return float_to_image(w,h,c,l.delta); + return float_to_image(l.out_w,l.out_h,l.out_c,l.delta); } -size_t get_workspace_size(layer l){ +static size_t get_workspace_size(layer l){ #ifdef CUDNN if(gpu_index >= 0){ size_t most = 0; @@ -127,8 +114,8 @@ size_t get_workspace_size(layer l){ if (s > most) most = s; return most; } - #endif - return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float); +#endif + return (size_t)l.out_h*l.out_w*l.size*l.size*l.c/l.groups*sizeof(float); } #ifdef GPU @@ -137,46 +124,62 @@ void cudnn_convolutional_setup(layer *l) { cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetFilter4dDescriptor(l->dweightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); + + cudnnSetFilter4dDescriptor(l->dweightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size); + cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size); + #if CUDNN_MAJOR >= 6 + cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT); + #else cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION); + #endif + + #if CUDNN_MAJOR >= 7 + cudnnSetConvolutionGroupCount(l->convDesc, l->groups); + #else + if(l->groups > 1){ + error("CUDNN < 7 doesn't support groups, please upgrade!"); + } + #endif + cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), l->srcTensorDesc, l->weightDesc, l->convDesc, l->dstTensorDesc, - CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, - 0, + CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, + 2000000000, &l->fw_algo); cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), l->weightDesc, l->ddstTensorDesc, l->convDesc, l->dsrcTensorDesc, - CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, - 0, + CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, + 2000000000, &l->bd_algo); cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), l->srcTensorDesc, l->ddstTensorDesc, l->convDesc, l->dweightDesc, - CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, - 0, + CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, + 2000000000, &l->bf_algo); } #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam) +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam) { int i; convolutional_layer l = {0}; l.type = CONVOLUTIONAL; + l.groups = groups; l.h = h; l.w = w; l.c = c; @@ -189,17 +192,23 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.pad = padding; l.batch_normalize = batch_normalize; - l.weights = calloc(c*n*size*size, sizeof(float)); - l.weight_updates = calloc(c*n*size*size, sizeof(float)); + l.weights = calloc(c/groups*n*size*size, sizeof(float)); + l.weight_updates = calloc(c/groups*n*size*size, sizeof(float)); l.biases = calloc(n, sizeof(float)); l.bias_updates = calloc(n, sizeof(float)); + l.nweights = c/groups*n*size*size; + l.nbiases = n; + // float scale = 1./sqrt(size*size*c); - float scale = sqrt(2./(size*size*c)); - for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1); - int out_h = convolutional_out_height(l); + float scale = sqrt(2./(size*size*c/l.groups)); + //printf("convscale %f\n", scale); + //scale = .02; + //for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1); + for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal(); int out_w = convolutional_out_width(l); + int out_h = convolutional_out_height(l); l.out_h = out_h; l.out_w = out_w; l.out_c = n; @@ -213,12 +222,12 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.backward = backward_convolutional_layer; l.update = update_convolutional_layer; if(binary){ - l.binary_weights = calloc(c*n*size*size, sizeof(float)); - l.cweights = calloc(c*n*size*size, sizeof(char)); + l.binary_weights = calloc(l.nweights, sizeof(float)); + l.cweights = calloc(l.nweights, sizeof(char)); l.scales = calloc(n, sizeof(float)); } if(xnor){ - l.binary_weights = calloc(c*n*size*size, sizeof(float)); + l.binary_weights = calloc(l.nweights, sizeof(float)); l.binary_input = calloc(l.inputs*l.batch, sizeof(float)); } @@ -241,9 +250,12 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.x_norm = calloc(l.batch*l.outputs, sizeof(float)); } if(adam){ - l.adam = 1; - l.m = calloc(c*n*size*size, sizeof(float)); - l.v = calloc(c*n*size*size, sizeof(float)); + l.m = calloc(l.nweights, sizeof(float)); + l.v = calloc(l.nweights, sizeof(float)); + l.bias_m = calloc(n, sizeof(float)); + l.scale_m = calloc(n, sizeof(float)); + l.bias_v = calloc(n, sizeof(float)); + l.scale_v = calloc(n, sizeof(float)); } #ifdef GPU @@ -253,12 +265,16 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int if(gpu_index >= 0){ if (adam) { - l.m_gpu = cuda_make_array(l.m, c*n*size*size); - l.v_gpu = cuda_make_array(l.v, c*n*size*size); + l.m_gpu = cuda_make_array(l.m, l.nweights); + l.v_gpu = cuda_make_array(l.v, l.nweights); + l.bias_m_gpu = cuda_make_array(l.bias_m, n); + l.bias_v_gpu = cuda_make_array(l.bias_v, n); + l.scale_m_gpu = cuda_make_array(l.scale_m, n); + l.scale_v_gpu = cuda_make_array(l.scale_v, n); } - l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); - l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); + l.weights_gpu = cuda_make_array(l.weights, l.nweights); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); l.biases_gpu = cuda_make_array(l.biases, n); l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); @@ -267,10 +283,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); if(binary){ - l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); } if(xnor){ - l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch); } @@ -291,6 +307,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); } #ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normTensorDesc); cudnnCreateTensorDescriptor(&l.srcTensorDesc); cudnnCreateTensorDescriptor(&l.dstTensorDesc); cudnnCreateFilterDescriptor(&l.weightDesc); @@ -305,7 +322,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.workspace_size = get_workspace_size(l); l.activation = activation; - fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); + fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BFLOPs\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, (2.0 * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w)/1000000000.); return l; } @@ -315,8 +332,8 @@ void denormalize_convolutional_layer(convolutional_layer l) int i, j; for(i = 0; i < l.n; ++i){ float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001); - for(j = 0; j < l.c*l.size*l.size; ++j){ - l.weights[i*l.c*l.size*l.size + j] *= scale; + for(j = 0; j < l.c/l.groups*l.size*l.size; ++j){ + l.weights[i*l.c/l.groups*l.size*l.size + j] *= scale; } l.biases[i] -= l.rolling_mean[i] * scale; l.scales[i] = 1; @@ -325,6 +342,7 @@ void denormalize_convolutional_layer(convolutional_layer l) } } +/* void test_convolutional_layer() { convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0); @@ -344,10 +362,10 @@ void test_convolutional_layer() 3,3,3,3,3, 3,3,3,3,3, 3,3,3,3,3}; - network_state state = {0}; - state.input = data; - forward_convolutional_layer(l, state); + //net.input = data; + //forward_convolutional_layer(l); } +*/ void resize_convolutional_layer(convolutional_layer *l, int w, int h) { @@ -424,88 +442,106 @@ void backward_bias(float *bias_updates, float *delta, int batch, int n, int size } } -void forward_convolutional_layer(convolutional_layer l, network_state state) +void forward_convolutional_layer(convolutional_layer l, network net) { - int out_h = convolutional_out_height(l); - int out_w = convolutional_out_width(l); - int i; + int i, j; fill_cpu(l.outputs*l.batch, 0, l.output, 1); if(l.xnor){ - binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); + binarize_weights(l.weights, l.n, l.c/l.groups*l.size*l.size, l.binary_weights); swap_binary(&l); - binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input); - state.input = l.binary_input; + binarize_cpu(net.input, l.c*l.h*l.w*l.batch, l.binary_input); + net.input = l.binary_input; } - int m = l.n; - int k = l.size*l.size*l.c; - int n = out_h*out_w; - - - float *a = l.weights; - float *b = state.workspace; - float *c = l.output; - + int m = l.n/l.groups; + int k = l.size*l.size*l.c/l.groups; + int n = l.out_w*l.out_h; for(i = 0; i < l.batch; ++i){ - im2col_cpu(state.input, l.c, l.h, l.w, - l.size, l.stride, l.pad, b); - gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); - c += n*m; - state.input += l.c*l.h*l.w; + for(j = 0; j < l.groups; ++j){ + float *a = l.weights + j*l.nweights/l.groups; + float *b = net.workspace; + float *c = l.output + (i*l.groups + j)*n*m; + float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w; + + if (l.size == 1) { + b = im; + } else { + im2col_cpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + } + gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + } } if(l.batch_normalize){ - forward_batchnorm_layer(l, state); + forward_batchnorm_layer(l, net); + } else { + add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w); } - add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); - activate_array(l.output, m*n*l.batch, l.activation); + activate_array(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); } -void backward_convolutional_layer(convolutional_layer l, network_state state) +void backward_convolutional_layer(convolutional_layer l, network net) { - int i; - int m = l.n; - int n = l.size*l.size*l.c; - int k = convolutional_out_height(l)* - convolutional_out_width(l); + int i, j; + int m = l.n/l.groups; + int n = l.size*l.size*l.c/l.groups; + int k = l.out_w*l.out_h; - gradient_array(l.output, m*k*l.batch, l.activation, l.delta); - backward_bias(l.bias_updates, l.delta, l.batch, l.n, k); + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); if(l.batch_normalize){ - backward_batchnorm_layer(l, state); + backward_batchnorm_layer(l, net); + } else { + backward_bias(l.bias_updates, l.delta, l.batch, l.n, k); } for(i = 0; i < l.batch; ++i){ - float *a = l.delta + i*m*k; - float *b = state.workspace; - float *c = l.weight_updates; - - float *im = state.input+i*l.c*l.h*l.w; + for(j = 0; j < l.groups; ++j){ + float *a = l.delta + (i*l.groups + j)*m*k; + float *b = net.workspace; + float *c = l.weight_updates + j*l.nweights/l.groups; + + float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w; + float *imd = net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w; + + if(l.size == 1){ + b = im; + } else { + im2col_cpu(im, l.c/l.groups, l.h, l.w, + l.size, l.stride, l.pad, b); + } - im2col_cpu(im, l.c, l.h, l.w, - l.size, l.stride, l.pad, b); - gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); + gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); - if(state.delta){ - a = l.weights; - b = l.delta + i*m*k; - c = state.workspace; + if (net.delta) { + a = l.weights + j*l.nweights/l.groups; + b = l.delta + (i*l.groups + j)*m*k; + c = net.workspace; + if (l.size == 1) { + c = imd; + } - gemm(1,0,n,k,m,1,a,n,b,k,0,c,k); + gemm(1,0,n,k,m,1,a,n,b,k,0,c,k); - col2im_cpu(state.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w); + if (l.size != 1) { + col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd); + } + } } } } -void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate, float momentum, float decay) +void update_convolutional_layer(convolutional_layer l, update_args a) { - int size = l.size*l.size*l.c*l.n; + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.n, momentum, l.bias_updates, 1); @@ -514,9 +550,9 @@ void update_convolutional_layer(convolutional_layer l, int batch, float learning scal_cpu(l.n, momentum, l.scale_updates, 1); } - axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1); - axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1); - scal_cpu(size, momentum, l.weight_updates, 1); + axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(l.nweights, learning_rate/batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(l.nweights, momentum, l.weight_updates, 1); } @@ -524,7 +560,7 @@ image get_convolutional_weight(convolutional_layer l, int i) { int h = l.size; int w = l.size; - int c = l.c; + int c = l.c/l.groups; return float_to_image(w,h,c,l.weights+i*h*w*c); } @@ -558,8 +594,14 @@ image *get_weights(convolutional_layer l) int i; for(i = 0; i < l.n; ++i){ weights[i] = copy_image(get_convolutional_weight(l, i)); - //normalize_image(weights[i]); + normalize_image(weights[i]); + /* + char buff[256]; + sprintf(buff, "filter%d", i); + save_image(weights[i], buff); + */ } + //error("hey"); return weights; } diff --git a/image.darknet/inst/include/darknet/src/convolutional_layer.h b/image.darknet/inst/include/darknet/src/convolutional_layer.h index 970aa10..6c261f5 100644 --- a/image.darknet/inst/include/darknet/src/convolutional_layer.h +++ b/image.darknet/inst/include/darknet/src/convolutional_layer.h @@ -10,31 +10,31 @@ typedef layer convolutional_layer; #ifdef GPU -void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state); -void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state); -void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay); +void forward_convolutional_layer_gpu(convolutional_layer layer, network net); +void backward_convolutional_layer_gpu(convolutional_layer layer, network net); +void update_convolutional_layer_gpu(convolutional_layer layer, update_args a); void push_convolutional_layer(convolutional_layer layer); void pull_convolutional_layer(convolutional_layer layer); void add_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); +void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t); #ifdef CUDNN void cudnn_convolutional_setup(layer *l); #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam); -void denormalize_convolutional_layer(convolutional_layer l); +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); -void forward_convolutional_layer(const convolutional_layer layer, network_state state); -void update_convolutional_layer(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay); +void forward_convolutional_layer(const convolutional_layer layer, network net); +void update_convolutional_layer(convolutional_layer layer, update_args a); image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_weights); void binarize_weights(float *weights, int n, int size, float *binary); void swap_binary(convolutional_layer *l); void binarize_weights2(float *weights, int n, int size, char *binary, float *scales); -void backward_convolutional_layer(convolutional_layer layer, network_state state); +void backward_convolutional_layer(convolutional_layer layer, network net); void add_bias(float *output, float *biases, int batch, int n, int size); void backward_bias(float *bias_updates, float *delta, int batch, int n, int size); @@ -45,8 +45,6 @@ image get_convolutional_weight(convolutional_layer layer, int i); int convolutional_out_height(convolutional_layer layer); int convolutional_out_width(convolutional_layer layer); -void rescale_weights(convolutional_layer l, float scale, float trans); -void rgbgr_weights(convolutional_layer l); #endif diff --git a/image.darknet/inst/include/darknet/src/cost_layer.c b/image.darknet/inst/include/darknet/src/cost_layer.c index 39d2398..2138ff2 100644 --- a/image.darknet/inst/include/darknet/src/cost_layer.c +++ b/image.darknet/inst/include/darknet/src/cost_layer.c @@ -9,9 +9,12 @@ COST_TYPE get_cost_type(char *s) { + if (strcmp(s, "seg")==0) return SEG; if (strcmp(s, "sse")==0) return SSE; if (strcmp(s, "masked")==0) return MASKED; if (strcmp(s, "smooth")==0) return SMOOTH; + if (strcmp(s, "L1")==0) return L1; + if (strcmp(s, "wgan")==0) return WGAN; fprintf(stderr, "Couldn't find cost type %s, going with SSE\n", s); return SSE; } @@ -19,12 +22,18 @@ COST_TYPE get_cost_type(char *s) char *get_cost_string(COST_TYPE a) { switch(a){ + case SEG: + return "seg"; case SSE: return "sse"; case MASKED: return "masked"; case SMOOTH: return "smooth"; + case L1: + return "L1"; + case WGAN: + return "wgan"; } return "sse"; } @@ -70,26 +79,28 @@ void resize_cost_layer(cost_layer *l, int inputs) #endif } -void forward_cost_layer(cost_layer l, network_state state) +void forward_cost_layer(cost_layer l, network net) { - if (!state.truth) return; + if (!net.truth) return; if(l.cost_type == MASKED){ int i; for(i = 0; i < l.batch*l.inputs; ++i){ - if(state.truth[i] == SECRET_NUM) state.input[i] = SECRET_NUM; + if(net.truth[i] == SECRET_NUM) net.input[i] = SECRET_NUM; } } if(l.cost_type == SMOOTH){ - smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output); + smooth_l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output); + }else if(l.cost_type == L1){ + l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output); } else { - l2_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output); + l2_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output); } l.cost[0] = sum_array(l.output, l.batch*l.inputs); } -void backward_cost_layer(const cost_layer l, network_state state) +void backward_cost_layer(const cost_layer l, network net) { - axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, state.delta, 1); + axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, net.delta, 1); } #ifdef GPU @@ -113,17 +124,30 @@ int float_abs_compare (const void * a, const void * b) return (fa > fb) - (fa < fb); } -void forward_cost_layer_gpu(cost_layer l, network_state state) +void forward_cost_layer_gpu(cost_layer l, network net) { - if (!state.truth) return; - if (l.cost_type == MASKED) { - mask_ongpu(l.batch*l.inputs, state.input, SECRET_NUM, state.truth); + if (!net.truth) return; + if(l.smooth){ + scal_gpu(l.batch*l.inputs, (1-l.smooth), net.truth_gpu, 1); + add_gpu(l.batch*l.inputs, l.smooth * 1./l.inputs, net.truth_gpu, 1); } if(l.cost_type == SMOOTH){ - smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu); + smooth_l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } else if (l.cost_type == L1){ + l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } else if (l.cost_type == WGAN){ + wgan_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); } else { - l2_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu); + l2_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } + + if (l.cost_type == SEG && l.noobject_scale != 1) { + scale_mask_gpu(l.batch*l.inputs, l.delta_gpu, 0, net.truth_gpu, l.noobject_scale); + scale_mask_gpu(l.batch*l.inputs, l.output_gpu, 0, net.truth_gpu, l.noobject_scale); + } + if (l.cost_type == MASKED) { + mask_gpu(l.batch*l.inputs, net.delta_gpu, SECRET_NUM, net.truth_gpu, 0); } if(l.ratio){ @@ -133,16 +157,20 @@ void forward_cost_layer_gpu(cost_layer l, network_state state) float thresh = l.delta[n]; thresh = 0; printf("%f\n", thresh); - supp_ongpu(l.batch*l.inputs, thresh, l.delta_gpu, 1); + supp_gpu(l.batch*l.inputs, thresh, l.delta_gpu, 1); + } + + if(l.thresh){ + supp_gpu(l.batch*l.inputs, l.thresh*1./l.inputs, l.delta_gpu, 1); } cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs); l.cost[0] = sum_array(l.output, l.batch*l.inputs); } -void backward_cost_layer_gpu(const cost_layer l, network_state state) +void backward_cost_layer_gpu(const cost_layer l, network net) { - axpy_ongpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, state.delta, 1); + axpy_gpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/inst/include/darknet/src/cost_layer.h b/image.darknet/inst/include/darknet/src/cost_layer.h index a692831..ceb64de 100644 --- a/image.darknet/inst/include/darknet/src/cost_layer.h +++ b/image.darknet/inst/include/darknet/src/cost_layer.h @@ -8,13 +8,13 @@ typedef layer cost_layer; COST_TYPE get_cost_type(char *s); char *get_cost_string(COST_TYPE a); cost_layer make_cost_layer(int batch, int inputs, COST_TYPE type, float scale); -void forward_cost_layer(const cost_layer l, network_state state); -void backward_cost_layer(const cost_layer l, network_state state); +void forward_cost_layer(const cost_layer l, network net); +void backward_cost_layer(const cost_layer l, network net); void resize_cost_layer(cost_layer *l, int inputs); #ifdef GPU -void forward_cost_layer_gpu(cost_layer l, network_state state); -void backward_cost_layer_gpu(const cost_layer l, network_state state); +void forward_cost_layer_gpu(cost_layer l, network net); +void backward_cost_layer_gpu(const cost_layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/crnn_layer.c b/image.darknet/inst/include/darknet/src/crnn_layer.c index 5495880..7dd29f6 100644 --- a/image.darknet/inst/include/darknet/src/crnn_layer.c +++ b/image.darknet/inst/include/darknet/src/crnn_layer.c @@ -48,17 +48,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.input_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0); l.input_layer->batch = batch; l.self_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0); l.self_layer->batch = batch; l.output_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0); l.output_layer->batch = batch; l.output = l.output_layer->output; @@ -81,17 +81,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou return l; } -void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay) +void update_crnn_layer(layer l, update_args a) { - update_convolutional_layer(*(l.input_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer(*(l.self_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer(*(l.output_layer), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.input_layer), a); + update_convolutional_layer(*(l.self_layer), a); + update_convolutional_layer(*(l.output_layer), a); } -void forward_crnn_layer(layer l, network_state state) +void forward_crnn_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -100,17 +100,17 @@ void forward_crnn_layer(layer l, network_state state) fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1); fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1); fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1); - if(state.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1); + if(net.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1); for (i = 0; i < l.steps; ++i) { - s.input = state.input; + s.input = net.input; forward_convolutional_layer(input_layer, s); s.input = l.state; forward_convolutional_layer(self_layer, s); float *old_state = l.state; - if(state.train) l.state += l.hidden*l.batch; + if(net.train) l.state += l.hidden*l.batch; if(l.shortcut){ copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1); }else{ @@ -122,17 +122,16 @@ void forward_crnn_layer(layer l, network_state state) s.input = l.state; forward_convolutional_layer(output_layer, s); - state.input += l.inputs*l.batch; + net.input += l.inputs*l.batch; increment_layer(&input_layer, 1); increment_layer(&self_layer, 1); increment_layer(&output_layer, 1); } } -void backward_crnn_layer(layer l, network_state state) +void backward_crnn_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -168,8 +167,8 @@ void backward_crnn_layer(layer l, network_state state) copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1); if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1); - s.input = state.input + i*l.inputs*l.batch; - if(state.delta) s.delta = state.delta + i*l.inputs*l.batch; + s.input = net.input + i*l.inputs*l.batch; + if(net.delta) s.delta = net.delta + i*l.inputs*l.batch; else s.delta = 0; backward_convolutional_layer(input_layer, s); @@ -195,58 +194,57 @@ void push_crnn_layer(layer l) push_convolutional_layer(*(l.output_layer)); } -void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_crnn_layer_gpu(layer l, update_args a) { - update_convolutional_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.input_layer), a); + update_convolutional_layer_gpu(*(l.self_layer), a); + update_convolutional_layer_gpu(*(l.output_layer), a); } -void forward_crnn_layer_gpu(layer l, network_state state) +void forward_crnn_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); - fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1); - if(state.train) fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); + fill_gpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); + fill_gpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1); + if(net.train) fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1); for (i = 0; i < l.steps; ++i) { - s.input = state.input; + s.input_gpu = net.input_gpu; forward_convolutional_layer_gpu(input_layer, s); - s.input = l.state_gpu; + s.input_gpu = l.state_gpu; forward_convolutional_layer_gpu(self_layer, s); float *old_state = l.state_gpu; - if(state.train) l.state_gpu += l.hidden*l.batch; + if(net.train) l.state_gpu += l.hidden*l.batch; if(l.shortcut){ - copy_ongpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1); + copy_gpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1); }else{ - fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); + fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1); } - axpy_ongpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); - axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); - s.input = l.state_gpu; + s.input_gpu = l.state_gpu; forward_convolutional_layer_gpu(output_layer, s); - state.input += l.inputs*l.batch; + net.input_gpu += l.inputs*l.batch; increment_layer(&input_layer, 1); increment_layer(&self_layer, 1); increment_layer(&output_layer, 1); } } -void backward_crnn_layer_gpu(layer l, network_state state) +void backward_crnn_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -256,25 +254,25 @@ void backward_crnn_layer_gpu(layer l, network_state state) increment_layer(&output_layer, l.steps - 1); l.state_gpu += l.hidden*l.batch*l.steps; for (i = l.steps-1; i >= 0; --i) { - copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); - axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + copy_gpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); - s.input = l.state_gpu; - s.delta = self_layer.delta_gpu; + s.input_gpu = l.state_gpu; + s.delta_gpu = self_layer.delta_gpu; backward_convolutional_layer_gpu(output_layer, s); l.state_gpu -= l.hidden*l.batch; - s.input = l.state_gpu; - s.delta = self_layer.delta_gpu - l.hidden*l.batch; - if (i == 0) s.delta = 0; + s.input_gpu = l.state_gpu; + s.delta_gpu = self_layer.delta_gpu - l.hidden*l.batch; + if (i == 0) s.delta_gpu = 0; backward_convolutional_layer_gpu(self_layer, s); - copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); - if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1); - s.input = state.input + i*l.inputs*l.batch; - if(state.delta) s.delta = state.delta + i*l.inputs*l.batch; - else s.delta = 0; + copy_gpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); + if (i > 0 && l.shortcut) axpy_gpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1); + s.input_gpu = net.input_gpu + i*l.inputs*l.batch; + if(net.delta_gpu) s.delta_gpu = net.delta_gpu + i*l.inputs*l.batch; + else s.delta_gpu = 0; backward_convolutional_layer_gpu(input_layer, s); increment_layer(&input_layer, -1); diff --git a/image.darknet/inst/include/darknet/src/crnn_layer.h b/image.darknet/inst/include/darknet/src/crnn_layer.h index 0da942e..515f378 100644 --- a/image.darknet/inst/include/darknet/src/crnn_layer.h +++ b/image.darknet/inst/include/darknet/src/crnn_layer.h @@ -8,14 +8,14 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize); -void forward_crnn_layer(layer l, network_state state); -void backward_crnn_layer(layer l, network_state state); -void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_crnn_layer(layer l, network net); +void backward_crnn_layer(layer l, network net); +void update_crnn_layer(layer l, update_args a); #ifdef GPU -void forward_crnn_layer_gpu(layer l, network_state state); -void backward_crnn_layer_gpu(layer l, network_state state); -void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_crnn_layer_gpu(layer l, network net); +void backward_crnn_layer_gpu(layer l, network net); +void update_crnn_layer_gpu(layer l, update_args a); void push_crnn_layer(layer l); void pull_crnn_layer(layer l); #endif diff --git a/image.darknet/inst/include/darknet/src/crop_layer.c b/image.darknet/inst/include/darknet/src/crop_layer.c index 11c59b4..3b91852 100644 --- a/image.darknet/inst/include/darknet/src/crop_layer.c +++ b/image.darknet/inst/include/darknet/src/crop_layer.c @@ -10,8 +10,8 @@ image get_crop_image(crop_layer l) return float_to_image(w,h,c,l.output); } -void backward_crop_layer(const crop_layer l, network_state state){} -void backward_crop_layer_gpu(const crop_layer l, network_state state){} +void backward_crop_layer(const crop_layer l, network net){} +void backward_crop_layer_gpu(const crop_layer l, network net){} crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure) { @@ -64,7 +64,7 @@ void resize_crop_layer(layer *l, int w, int h) } -void forward_crop_layer(const crop_layer l, network_state state) +void forward_crop_layer(const crop_layer l, network net) { int i,j,c,b,row,col; int index; @@ -78,7 +78,7 @@ void forward_crop_layer(const crop_layer l, network_state state) scale = 1; trans = 0; } - if(!state.train){ + if(!net.train){ flip = 0; dh = (l.h - l.out_h)/2; dw = (l.w - l.out_w)/2; @@ -94,7 +94,7 @@ void forward_crop_layer(const crop_layer l, network_state state) } row = i + dh; index = col+l.w*(row+l.h*(c + l.c*b)); - l.output[count++] = state.input[index]*scale + trans; + l.output[count++] = net.input[index]*scale + trans; } } } diff --git a/image.darknet/inst/include/darknet/src/crop_layer.h b/image.darknet/inst/include/darknet/src/crop_layer.h index 3aa2d3d..3b5883c 100644 --- a/image.darknet/inst/include/darknet/src/crop_layer.h +++ b/image.darknet/inst/include/darknet/src/crop_layer.h @@ -9,11 +9,11 @@ typedef layer crop_layer; image get_crop_image(crop_layer l); crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure); -void forward_crop_layer(const crop_layer l, network_state state); +void forward_crop_layer(const crop_layer l, network net); void resize_crop_layer(layer *l, int w, int h); #ifdef GPU -void forward_crop_layer_gpu(crop_layer l, network_state state); +void forward_crop_layer_gpu(crop_layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/crop_layer_kernels.cu b/image.darknet/inst/include/darknet/src/crop_layer_kernels.cu index 8a08630..b5b9f55 100644 --- a/image.darknet/inst/include/darknet/src/crop_layer_kernels.cu +++ b/image.darknet/inst/include/darknet/src/crop_layer_kernels.cu @@ -113,9 +113,9 @@ __global__ void levels_image_kernel(float *image, float *rand, int batch, int w, float r3 = rand[8*id + 3]; saturation = r0*(saturation - 1) + 1; - saturation = (r1 > .5) ? 1./saturation : saturation; + saturation = (r1 > .5f) ? 1.f/saturation : saturation; exposure = r2*(exposure - 1) + 1; - exposure = (r3 > .5) ? 1./exposure : exposure; + exposure = (r3 > .5f) ? 1.f/exposure : exposure; size_t offset = id * h * w * 3; image += offset; @@ -131,9 +131,9 @@ __global__ void levels_image_kernel(float *image, float *rand, int batch, int w, } else { shift = 0; } - image[x + w*(y + h*0)] = rgb.x*scale + translate + (rshift - .5)*shift; - image[x + w*(y + h*1)] = rgb.y*scale + translate + (gshift - .5)*shift; - image[x + w*(y + h*2)] = rgb.z*scale + translate + (bshift - .5)*shift; + image[x + w*(y + h*0)] = rgb.x*scale + translate + (rshift - .5f)*shift; + image[x + w*(y + h*1)] = rgb.y*scale + translate + (gshift - .5f)*shift; + image[x + w*(y + h*2)] = rgb.z*scale + translate + (bshift - .5f)*shift; } __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, int c, int h, int w, int crop_height, int crop_width, int train, int flip, float angle, float *output) @@ -141,8 +141,8 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(id >= size) return; - float cx = w/2.; - float cy = h/2.; + float cx = w/2.f; + float cy = h/2.f; int count = id; int j = id % crop_width; @@ -160,11 +160,11 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i float dw = (w - crop_width)*r4; float dh = (h - crop_height)*r5; - flip = (flip && (r6 > .5)); + flip = (flip && (r6 > .5f)); angle = 2*angle*r7 - angle; if(!train){ - dw = (w - crop_width)/2.; - dh = (h - crop_height)/2.; + dw = (w - crop_width)/2.f; + dh = (h - crop_height)/2.f; flip = 0; angle = 0; } @@ -174,17 +174,17 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i float x = (flip) ? w - dw - j - 1 : j + dw; float y = i + dh; - float rx = cos(angle)*(x-cx) - sin(angle)*(y-cy) + cx; - float ry = sin(angle)*(x-cx) + cos(angle)*(y-cy) + cy; + float rx = cosf(angle)*(x-cx) - sinf(angle)*(y-cy) + cx; + float ry = sinf(angle)*(x-cx) + cosf(angle)*(y-cy) + cy; output[count] = bilinear_interpolate_kernel(input, w, h, rx, ry, k); } -extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state) +extern "C" void forward_crop_layer_gpu(crop_layer layer, network net) { cuda_random(layer.rand_gpu, layer.batch*8); - float radians = layer.angle*3.14159265/180.; + float radians = layer.angle*3.14159265f/180.f; float scale = 2; float translate = -1; @@ -195,12 +195,12 @@ extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state) int size = layer.batch * layer.w * layer.h; - levels_image_kernel<<>>(state.input, layer.rand_gpu, layer.batch, layer.w, layer.h, state.train, layer.saturation, layer.exposure, translate, scale, layer.shift); + levels_image_kernel<<>>(net.input_gpu, layer.rand_gpu, layer.batch, layer.w, layer.h, net.train, layer.saturation, layer.exposure, translate, scale, layer.shift); check_error(cudaPeekAtLastError()); size = layer.batch*layer.c*layer.out_w*layer.out_h; - forward_crop_layer_kernel<<>>(state.input, layer.rand_gpu, size, layer.c, layer.h, layer.w, layer.out_h, layer.out_w, state.train, layer.flip, radians, layer.output_gpu); + forward_crop_layer_kernel<<>>(net.input_gpu, layer.rand_gpu, size, layer.c, layer.h, layer.w, layer.out_h, layer.out_w, net.train, layer.flip, radians, layer.output_gpu); check_error(cudaPeekAtLastError()); /* diff --git a/image.darknet/inst/include/darknet/src/cuda.c b/image.darknet/inst/include/darknet/src/cuda.c index 1b51271..48aba6e 100644 --- a/image.darknet/inst/include/darknet/src/cuda.c +++ b/image.darknet/inst/include/darknet/src/cuda.c @@ -5,7 +5,7 @@ int gpu_index = 0; #include "cuda.h" #include "utils.h" #include "blas.h" -#include "assert.h" +#include #include #include @@ -96,6 +96,8 @@ float *cuda_make_array(float *x, size_t n) if(x){ status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); check_error(status); + } else { + fill_gpu(n, 0, x_gpu, 1); } if(!x_gpu) error("Cuda malloc failed\n"); return x_gpu; @@ -128,12 +130,17 @@ float cuda_compare(float *x_gpu, float *x, size_t n, char *s) return err; } -int *cuda_make_int_array(size_t n) +int *cuda_make_int_array(int *x, size_t n) { int *x_gpu; size_t size = sizeof(int)*n; cudaError_t status = cudaMalloc((void **)&x_gpu, size); check_error(status); + if(x){ + status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); + check_error(status); + } + if(!x_gpu) error("Cuda malloc failed\n"); return x_gpu; } @@ -157,4 +164,15 @@ void cuda_pull_array(float *x_gpu, float *x, size_t n) check_error(status); } +float cuda_mag_array(float *x_gpu, size_t n) +{ + float *temp = calloc(n, sizeof(float)); + cuda_pull_array(x_gpu, temp, n); + float m = mag_array(temp, n); + free(temp); + return m; +} +#else +void cuda_set_device(int n){} + #endif diff --git a/image.darknet/inst/include/darknet/src/cuda.h b/image.darknet/inst/include/darknet/src/cuda.h index 29b1eef..a1bc216 100644 --- a/image.darknet/inst/include/darknet/src/cuda.h +++ b/image.darknet/inst/include/darknet/src/cuda.h @@ -1,28 +1,13 @@ #ifndef CUDA_H #define CUDA_H -extern int gpu_index; +#include "darknet.h" #ifdef GPU -#define BLOCK 512 - -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" - -#ifdef CUDNN -#include "cudnn.h" -#endif - void check_error(cudaError_t status); cublasHandle_t blas_handle(); -float *cuda_make_array(float *x, size_t n); -int *cuda_make_int_array(size_t n); -void cuda_push_array(float *x_gpu, float *x, size_t n); -void cuda_pull_array(float *x_gpu, float *x, size_t n); -void cuda_set_device(int n); -void cuda_free(float *x_gpu); +int *cuda_make_int_array(int *x, size_t n); void cuda_random(float *x_gpu, size_t n); float cuda_compare(float *x_gpu, float *x, size_t n, char *s); dim3 cuda_gridsize(size_t n); diff --git a/image.darknet/inst/include/darknet/src/data.c b/image.darknet/inst/include/darknet/src/data.c index 05e5a91..59051b4 100644 --- a/image.darknet/inst/include/darknet/src/data.c +++ b/image.darknet/inst/include/darknet/src/data.c @@ -102,7 +102,7 @@ matrix load_image_paths(char **paths, int n, int w, int h) return X; } -matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) +matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center) { int i; matrix X; @@ -112,7 +112,12 @@ matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, for(i = 0; i < n; ++i){ image im = load_image_color(paths[i], 0, 0); - image crop = random_augment_image(im, angle, aspect, min, max, size); + image crop; + if(center){ + crop = center_crop_image(im, size, size); + } else { + crop = random_augment_image(im, angle, aspect, min, max, size, size); + } int flip = rand()%2; if (flip) flip_image(crop); random_distort_image(crop, hue, saturation, exposure); @@ -122,6 +127,7 @@ matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, show_image(crop, "crop"); cvWaitKey(0); */ + //grayscale_image_3c(crop); free_image(im); X.vals[i] = crop.data; X.cols = crop.h*crop.w*crop.c; @@ -132,14 +138,18 @@ matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, box_label *read_boxes(char *filename, int *n) { - box_label *boxes = calloc(1, sizeof(box_label)); FILE *file = fopen(filename, "r"); if(!file) file_error(filename); float x, y, h, w; int id; int count = 0; + int size = 64; + box_label *boxes = calloc(size, sizeof(box_label)); while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){ - boxes = realloc(boxes, (count+1)*sizeof(box_label)); + if(count == size) { + size = size * 2; + boxes = realloc(boxes, size*sizeof(box_label)); + } boxes[count].id = id; boxes[count].x = x; boxes[count].y = y; @@ -221,7 +231,7 @@ void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, int id; int i; - for (i = 0; i < count && i < 30; ++i) { + for (i = 0; i < count && i < 90; ++i) { x = boxes[i].x; y = boxes[i].y; w = boxes[i].w; @@ -290,6 +300,150 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int free(boxes); } +void load_rle(image im, int *rle, int n) +{ + int count = 0; + int curr = 0; + int i,j; + for(i = 0; i < n; ++i){ + for(j = 0; j < rle[i]; ++j){ + im.data[count++] = curr; + } + curr = 1 - curr; + } + for(; count < im.h*im.w*im.c; ++count){ + im.data[count] = curr; + } +} + +void or_image(image src, image dest, int c) +{ + int i; + for(i = 0; i < src.w*src.h; ++i){ + if(src.data[i]) dest.data[dest.w*dest.h*c + i] = 1; + } +} + +void exclusive_image(image src) +{ + int k, j, i; + int s = src.w*src.h; + for(k = 0; k < src.c-1; ++k){ + for(i = 0; i < s; ++i){ + if (src.data[k*s + i]){ + for(j = k+1; j < src.c; ++j){ + src.data[j*s + i] = 0; + } + } + } + } +} + +box bound_image(image im) +{ + int x,y; + int minx = im.w; + int miny = im.h; + int maxx = 0; + int maxy = 0; + for(y = 0; y < im.h; ++y){ + for(x = 0; x < im.w; ++x){ + if(im.data[y*im.w + x]){ + minx = (x < minx) ? x : minx; + miny = (y < miny) ? y : miny; + maxx = (x > maxx) ? x : maxx; + maxy = (y > maxy) ? y : maxy; + } + } + } + box b = {minx, miny, maxx-minx + 1, maxy-miny + 1}; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); + return b; +} + +void fill_truth_iseg(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + int i = 0; + int j; + image part = make_image(w, h, 1); + while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect); + if(flip) flip_image(sized); + + image mask = resize_image(sized, mw, mh); + truth[i*(mw*mh+1)] = id; + for(j = 0; j < mw*mh; ++j){ + truth[i*(mw*mh + 1) + 1 + j] = mask.data[j]; + } + ++i; + + free_image(mask); + free_image(sized); + free(rle); + } + if(i < num_boxes) truth[i*(mw*mh+1)] = -1; + fclose(file); + free_image(part); +} + +void fill_truth_mask(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + int i = 0; + image part = make_image(w, h, 1); + while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect); + if(flip) flip_image(sized); + box b = bound_image(sized); + if(b.w > 0){ + image crop = crop_image(sized, b.x, b.y, b.w, b.h); + image mask = resize_image(crop, mw, mh); + truth[i*(4 + mw*mh + 1) + 0] = (b.x + b.w/2.)/sized.w; + truth[i*(4 + mw*mh + 1) + 1] = (b.y + b.h/2.)/sized.h; + truth[i*(4 + mw*mh + 1) + 2] = b.w/sized.w; + truth[i*(4 + mw*mh + 1) + 3] = b.h/sized.h; + int j; + for(j = 0; j < mw*mh; ++j){ + truth[i*(4 + mw*mh + 1) + 4 + j] = mask.data[j]; + } + truth[i*(4 + mw*mh + 1) + 4 + mw*mh] = id; + free_image(crop); + free_image(mask); + ++i; + } + free_image(sized); + free(rle); + } + fclose(file); + free_image(part); +} + + void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy) { char labelpath[4096]; @@ -309,6 +463,7 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, float x,y,w,h; int id; int i; + int sub = 0; for (i = 0; i < count; ++i) { x = boxes[i].x; @@ -317,13 +472,16 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, h = boxes[i].h; id = boxes[i].id; - if ((w < .005 || h < .005)) continue; + if ((w < .001 || h < .001)) { + ++sub; + continue; + } - truth[i*5+0] = x; - truth[i*5+1] = y; - truth[i*5+2] = w; - truth[i*5+3] = h; - truth[i*5+4] = id; + truth[(i-sub)*5+0] = x; + truth[(i-sub)*5+1] = y; + truth[(i-sub)*5+2] = w; + truth[(i-sub)*5+3] = h; + truth[(i-sub)*5+4] = id; } free(boxes); } @@ -391,9 +549,10 @@ void fill_truth(char *path, char **labels, int k, float *truth) if(strstr(path, labels[i])){ truth[i] = 1; ++count; + //printf("%s %s %d\n", path, labels[i], i); } } - if(count != 1) printf("Too many or too few labels: %d, %s\n", count, path); + if(count != 1 && (k != 1 || count != 0)) printf("Too many or too few labels: %d, %s\n", count, path); } void fill_hierarchy(float *truth, int k, tree *hierarchy) @@ -428,6 +587,36 @@ void fill_hierarchy(float *truth, int k, tree *hierarchy) } } +matrix load_regression_labels_paths(char **paths, int n, int k) +{ + matrix y = make_matrix(n, k); + int i,j; + for(i = 0; i < n; ++i){ + char labelpath[4096]; + find_replace(paths[i], "images", "labels", labelpath); + find_replace(labelpath, "JPEGImages", "labels", labelpath); + find_replace(labelpath, ".BMP", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPeG", ".txt", labelpath); + find_replace(labelpath, ".Jpeg", ".txt", labelpath); + find_replace(labelpath, ".PNG", ".txt", labelpath); + find_replace(labelpath, ".TIF", ".txt", labelpath); + find_replace(labelpath, ".bmp", ".txt", labelpath); + find_replace(labelpath, ".jpeg", ".txt", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".png", ".txt", labelpath); + find_replace(labelpath, ".tif", ".txt", labelpath); + + FILE *file = fopen(labelpath, "r"); + for(j = 0; j < k; ++j){ + fscanf(file, "%f", &(y.vals[i][j])); + } + fclose(file); + } + return y; +} + matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy) { matrix y = make_matrix(n, k); @@ -445,18 +634,14 @@ matrix load_tags_paths(char **paths, int n, int k) { matrix y = make_matrix(n, k); int i; - int count = 0; + //int count = 0; for(i = 0; i < n; ++i){ char label[4096]; - find_replace(paths[i], "imgs", "labels", label); - find_replace(label, "_iconl.jpeg", ".txt", label); + find_replace(paths[i], "images", "labels", label); + find_replace(label, ".jpg", ".txt", label); FILE *file = fopen(label, "r"); - if(!file){ - find_replace(label, "labels", "labels2", label); - file = fopen(label, "r"); - if(!file) continue; - } - ++count; + if (!file) continue; + //++count; int tag; while(fscanf(file, "%d", &tag) == 1){ if(tag < k){ @@ -465,7 +650,7 @@ matrix load_tags_paths(char **paths, int n, int k) } fclose(file); } - printf("%d/%d\n", count, n); + //printf("%d/%d\n", count, n); return y; } @@ -488,6 +673,195 @@ void free_data(data d) } } +image get_segmentation_image(char *path, int w, int h, int classes) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + image mask = make_image(w, h, classes); + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + image part = make_image(w, h, 1); + while(fscanf(file, "%d %s", &id, buff) == 2){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + or_image(part, mask, id); + free(rle); + } + //exclusive_image(mask); + fclose(file); + free_image(part); + return mask; +} + +image get_segmentation_image2(char *path, int w, int h, int classes) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + image mask = make_image(w, h, classes+1); + int i; + for(i = 0; i < w*h; ++i){ + mask.data[w*h*classes + i] = 1; + } + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + image part = make_image(w, h, 1); + while(fscanf(file, "%d %s", &id, buff) == 2){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + or_image(part, mask, id); + for(i = 0; i < w*h; ++i){ + if(part.data[i]) mask.data[w*h*classes + i] = 0; + } + free(rle); + } + //exclusive_image(mask); + fclose(file); + free_image(part); + return mask; +} + +data load_data_seg(int n, char **paths, int m, int w, int h, int classes, int min, int max, float angle, float aspect, float hue, float saturation, float exposure, int div) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + + d.y.rows = n; + d.y.cols = h*w*classes/div/div; + d.y.vals = calloc(d.X.rows, sizeof(float*)); + + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h); + image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); + + int flip = rand()%2; + if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; + + image mask = get_segmentation_image(random_paths[i], orig.w, orig.h, classes); + //image mask = make_image(orig.w, orig.h, classes+1); + image sized_m = rotate_crop_image(mask, a.rad, a.scale/div, a.w/div, a.h/div, a.dx/div, a.dy/div, a.aspect); + + if(flip) flip_image(sized_m); + d.y.vals[i] = sized_m.data; + + free_image(orig); + free_image(mask); + + /* + image rgb = mask_to_rgb(sized_m, classes); + show_image(rgb, "part"); + show_image(sized, "orig"); + cvWaitKey(0); + free_image(rgb); + */ + } + free(random_paths); + return d; +} + +data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int boxes, int div, int min, int max, float angle, float aspect, float hue, float saturation, float exposure) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + d.y = make_matrix(n, (((w/div)*(h/div))+1)*boxes); + + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h); + image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); + + int flip = rand()%2; + if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; + //show_image(sized, "image"); + + fill_truth_iseg(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, w/div, h/div); + + free_image(orig); + + /* + image rgb = mask_to_rgb(sized_m, classes); + show_image(rgb, "part"); + show_image(sized, "orig"); + cvWaitKey(0); + free_image(rgb); + */ + } + free(random_paths); + return d; +} + +data load_data_mask(int n, char **paths, int m, int w, int h, int classes, int boxes, int coords, int min, int max, float angle, float aspect, float hue, float saturation, float exposure) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + d.y = make_matrix(n, (coords+1)*boxes); + + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h); + image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); + + int flip = rand()%2; + if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; + //show_image(sized, "image"); + + fill_truth_mask(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, 14, 14); + + free_image(orig); + + /* + image rgb = mask_to_rgb(sized_m, classes); + show_image(rgb, "part"); + show_image(sized, "orig"); + cvWaitKey(0); + free_image(rgb); + */ + } + free(random_paths); + return d; +} + data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter, float hue, float saturation, float exposure) { char **random_paths = get_random_paths(paths, n, m); @@ -624,7 +998,7 @@ data load_data_swag(char **paths, int n, int classes, float jitter) d.X.vals = calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*3; - int k = (4+classes)*30; + int k = (4+classes)*90; d.y = make_matrix(1, k); int dw = w*jitter; @@ -673,45 +1047,46 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, in d.y = make_matrix(n, 5*boxes); for(i = 0; i < n; ++i){ image orig = load_image_color(random_paths[i], 0, 0); + image sized = make_image(w, h, orig.c); + fill_image(sized, .5); - int oh = orig.h; - int ow = orig.w; + float dw = jitter * orig.w; + float dh = jitter * orig.h; - int dw = (ow*jitter); - int dh = (oh*jitter); + float new_ar = (orig.w + rand_uniform(-dw, dw)) / (orig.h + rand_uniform(-dh, dh)); + //float scale = rand_uniform(.25, 2); + float scale = 1; - int pleft = rand_uniform(-dw, dw); - int pright = rand_uniform(-dw, dw); - int ptop = rand_uniform(-dh, dh); - int pbot = rand_uniform(-dh, dh); + float nw, nh; - int swidth = ow - pleft - pright; - int sheight = oh - ptop - pbot; + if(new_ar < 1){ + nh = scale * h; + nw = nh * new_ar; + } else { + nw = scale * w; + nh = nw / new_ar; + } - float sx = (float)swidth / ow; - float sy = (float)sheight / oh; + float dx = rand_uniform(0, w - nw); + float dy = rand_uniform(0, h - nh); - int flip = rand()%2; - image cropped = crop_image(orig, pleft, ptop, swidth, sheight); + place_image(orig, nw, nh, dx, dy, sized); - float dx = ((float)pleft/ow)/sx; - float dy = ((float)ptop /oh)/sy; + random_distort_image(sized, hue, saturation, exposure); - image sized = resize_image(cropped, w, h); + int flip = rand()%2; if(flip) flip_image(sized); - random_distort_image(sized, hue, saturation, exposure); d.X.vals[i] = sized.data; - fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy); + + fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, -dx/w, -dy/h, nw/w, nh/h); free_image(orig); - free_image(cropped); } free(random_paths); return d; } - void *load_thread(void *ptr) { //printf("Loading data: %d\n", rand()); @@ -722,12 +1097,20 @@ void *load_thread(void *ptr) if (a.type == OLD_CLASSIFICATION_DATA){ *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); + } else if (a.type == REGRESSION_DATA){ + *a.d = load_data_regression(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); } else if (a.type == CLASSIFICATION_DATA){ - *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.center); } else if (a.type == SUPER_DATA){ *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); } else if (a.type == WRITING_DATA){ *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h); + } else if (a.type == ISEG_DATA){ + *a.d = load_data_iseg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.scale, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + } else if (a.type == INSTANCE_DATA){ + *a.d = load_data_mask(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.coords, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + } else if (a.type == SEGMENTATION_DATA){ + *a.d = load_data_seg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.scale); } else if (a.type == REGION_DATA){ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ @@ -739,6 +1122,9 @@ void *load_thread(void *ptr) } else if (a.type == IMAGE_DATA){ *(a.im) = load_image_color(a.path, 0, 0); *(a.resized) = resize_image(*(a.im), a.w, a.h); + } else if (a.type == LETTERBOX_DATA){ + *(a.im) = load_image_color(a.path, 0, 0); + *(a.resized) = letterbox_image(*(a.im), a.w, a.h); } else if (a.type == TAG_DATA){ *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); } @@ -784,6 +1170,13 @@ void *load_threads(void *ptr) return 0; } +void load_data_blocking(load_args args) +{ + struct load_args *ptr = calloc(1, sizeof(struct load_args)); + *ptr = args; + load_thread(ptr); +} + pthread_t load_data(load_args args) { pthread_t thread; @@ -863,12 +1256,95 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) +data load_data_regression(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) { if(m) paths = get_random_paths(paths, n, m); data d = {0}; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure); + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0); + d.y = load_regression_labels_paths(paths, n, k); + if(m) free(paths); + return d; +} + +data select_data(data *orig, int *inds) +{ + data d = {0}; + d.shallow = 1; + d.w = orig[0].w; + d.h = orig[0].h; + + d.X.rows = orig[0].X.rows; + d.y.rows = orig[0].X.rows; + + d.X.cols = orig[0].X.cols; + d.y.cols = orig[0].y.cols; + + d.X.vals = calloc(orig[0].X.rows, sizeof(float *)); + d.y.vals = calloc(orig[0].y.rows, sizeof(float *)); + int i; + for(i = 0; i < d.X.rows; ++i){ + d.X.vals[i] = orig[inds[i]].X.vals[i]; + d.y.vals[i] = orig[inds[i]].y.vals[i]; + } + return d; +} + +data *tile_data(data orig, int divs, int size) +{ + data *ds = calloc(divs*divs, sizeof(data)); + int i, j; +#pragma omp parallel for + for(i = 0; i < divs*divs; ++i){ + data d; + d.shallow = 0; + d.w = orig.w/divs * size; + d.h = orig.h/divs * size; + d.X.rows = orig.X.rows; + d.X.cols = d.w*d.h*3; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + + d.y = copy_matrix(orig.y); +#pragma omp parallel for + for(j = 0; j < orig.X.rows; ++j){ + int x = (i%divs) * orig.w / divs - (d.w - orig.w/divs)/2; + int y = (i/divs) * orig.h / divs - (d.h - orig.h/divs)/2; + image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[j]); + d.X.vals[j] = crop_image(im, x, y, d.w, d.h).data; + } + ds[i] = d; + } + return ds; +} + +data resize_data(data orig, int w, int h) +{ + data d = {0}; + d.shallow = 0; + d.w = w; + d.h = h; + int i; + d.X.rows = orig.X.rows; + d.X.cols = w*h*3; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + + d.y = copy_matrix(orig.y); +#pragma omp parallel for + for(i = 0; i < orig.X.rows; ++i){ + image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[i]); + d.X.vals[i] = resize_image(im, w, h).data; + } + return d; +} + +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center) +{ + if(m) paths = get_random_paths(paths, n, m); + data d = {0}; + d.shallow = 0; + d.w=size; + d.h=size; + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, center); d.y = load_labels_paths(paths, n, labels, k, hierarchy); if(m) free(paths); return d; @@ -881,7 +1357,7 @@ data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size d.w = size; d.h = size; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure); + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0); d.y = load_tags_paths(paths, n, k); if(m) free(paths); return d; @@ -909,6 +1385,8 @@ data concat_data(data d1, data d2) d.shallow = 1; d.X = concat_matrix(d1.X, d2.X); d.y = concat_matrix(d1.y, d2.y); + d.w = d1.w; + d.h = d1.h; return d; } @@ -962,7 +1440,6 @@ data load_cifar10_data(char *filename) X.vals[i][j] = (double)bytes[j+1]; } } - //translate_data_rows(d, -128); scale_data_rows(d, 1./255); //normalize_data_rows(d); fclose(fp); @@ -985,7 +1462,7 @@ void get_next_batch(data d, int n, int offset, float *X, float *y) for(j = 0; j < n; ++j){ int index = offset + j; memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float)); - memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float)); + if(y) memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float)); } } @@ -1029,7 +1506,6 @@ data load_all_cifar10() fclose(fp); } //normalize_data_rows(d); - //translate_data_rows(d, -128); scale_data_rows(d, 1./255); smooth_data(d); return d; @@ -1113,6 +1589,19 @@ void translate_data_rows(data d, float s) } } +data copy_data(data d) +{ + data c = {0}; + c.w = d.w; + c.h = d.h; + c.shallow = 0; + c.num_boxes = d.num_boxes; + c.boxes = d.boxes; + c.X = copy_matrix(d.X); + c.y = copy_matrix(d.y); + return c; +} + void normalize_data_rows(data d) { int i; diff --git a/image.darknet/inst/include/darknet/src/data.h b/image.darknet/inst/include/darknet/src/data.h index 3f6ef61..781906f 100644 --- a/image.darknet/inst/include/darknet/src/data.h +++ b/image.darknet/inst/include/darknet/src/data.h @@ -2,6 +2,7 @@ #define DATA_H #include +#include "darknet.h" #include "matrix.h" #include "list.h" #include "image.h" @@ -17,93 +18,32 @@ static inline float distance_from_edge(int x, int max) if (dist > 1) dist = 1; return dist; } +void load_data_blocking(load_args args); -typedef struct{ - int w, h; - matrix X; - matrix y; - int shallow; - int *num_boxes; - box **boxes; -} data; - -typedef enum { - CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA -} data_type; - -typedef struct load_args{ - int threads; - char **paths; - char *path; - int n; - int m; - char **labels; - int h; - int w; - int out_w; - int out_h; - int nh; - int nw; - int num_boxes; - int min, max, size; - int classes; - int background; - int scale; - float jitter; - float angle; - float aspect; - float saturation; - float exposure; - float hue; - data *d; - image *im; - image *resized; - data_type type; - tree *hierarchy; -} load_args; - -typedef struct{ - int id; - float x,y,w,h; - float left, right, top, bottom; -} box_label; - -void free_data(data d); - -pthread_t load_data(load_args args); - -pthread_t load_data_in_thread(load_args args); void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); -data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure); data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); -matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); +matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center); +data load_data_regression(char **paths, int n, int m, int classes, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); data load_go(char *filename); -box_label *read_boxes(char *filename, int *n); -data load_cifar10_data(char *filename); -data load_all_cifar10(); data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int out_h); -list *get_paths(char *filename); -char **get_labels(char *filename); void get_random_batch(data d, int n, float *X, float *y); data get_data_part(data d, int part, int total); data get_random_data(data d, int num); -void get_next_batch(data d, int n, int offset, float *X, float *y); data load_categorical_data_csv(char *filename, int target, int k); void normalize_data_rows(data d); void scale_data_rows(data d, float s); void translate_data_rows(data d, float s); void randomize_data(data d); data *split_data(data d, int part, int total); -data concat_data(data d1, data d2); data concat_datas(data *d, int n); void fill_truth(char *path, char **labels, int k, float *truth); diff --git a/image.darknet/inst/include/darknet/src/deconvolutional_kernels.cu b/image.darknet/inst/include/darknet/src/deconvolutional_kernels.cu index d6259fb..8267dcf 100644 --- a/image.darknet/inst/include/darknet/src/deconvolutional_kernels.cu +++ b/image.darknet/inst/include/darknet/src/deconvolutional_kernels.cu @@ -5,6 +5,7 @@ extern "C" { #include "convolutional_layer.h" #include "deconvolutional_layer.h" +#include "batchnorm_layer.h" #include "gemm.h" #include "blas.h" #include "im2col.h" @@ -13,97 +14,126 @@ extern "C" { #include "cuda.h" } -extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state) +extern "C" void forward_deconvolutional_layer_gpu(layer l, network net) { int i; - int out_h = deconvolutional_out_height(layer); - int out_w = deconvolutional_out_width(layer); - int size = out_h*out_w; - int m = layer.size*layer.size*layer.n; - int n = layer.h*layer.w; - int k = layer.c; + int m = l.size*l.size*l.n; + int n = l.h*l.w; + int k = l.c; - fill_ongpu(layer.outputs*layer.batch, 0, layer.output_gpu, 1); + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); - for(i = 0; i < layer.batch; ++i){ - float *a = layer.weights_gpu; - float *b = state.input + i*layer.c*layer.h*layer.w; - float *c = layer.col_image_gpu; + for(i = 0; i < l.batch; ++i){ + float *a = l.weights_gpu; + float *b = net.input_gpu + i*l.c*l.h*l.w; + float *c = net.workspace; - gemm_ongpu(1,0,m,n,k,1,a,m,b,n,0,c,n); + gemm_gpu(1,0,m,n,k,1,a,m,b,n,0,c,n); - col2im_ongpu(c, layer.n, out_h, out_w, layer.size, layer.stride, 0, layer.output_gpu+i*layer.n*size); + col2im_gpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output_gpu+i*l.outputs); } - add_bias_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, size); - activate_array(layer.output_gpu, layer.batch*layer.n*size, layer.activation); + if (l.batch_normalize) { + forward_batchnorm_layer_gpu(l, net); + } else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + } + activate_array_gpu(l.output_gpu, l.batch*l.n*l.out_w*l.out_h, l.activation); } -extern "C" void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state) +extern "C" void backward_deconvolutional_layer_gpu(layer l, network net) { - float alpha = 1./layer.batch; - int out_h = deconvolutional_out_height(layer); - int out_w = deconvolutional_out_width(layer); - int size = out_h*out_w; int i; - gradient_array(layer.output_gpu, size*layer.n*layer.batch, layer.activation, layer.delta_gpu); - backward_bias(layer.bias_updates_gpu, layer.delta, layer.batch, layer.n, size); + //constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + + if(l.batch_normalize){ + backward_batchnorm_layer_gpu(l, net); + } else { + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + } - if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float)); + //if(net.delta_gpu) memset(net.delta_gpu, 0, l.batch*l.h*l.w*l.c*sizeof(float)); - for(i = 0; i < layer.batch; ++i){ - int m = layer.c; - int n = layer.size*layer.size*layer.n; - int k = layer.h*layer.w; + for(i = 0; i < l.batch; ++i){ + int m = l.c; + int n = l.size*l.size*l.n; + int k = l.h*l.w; - float *a = state.input + i*m*n; - float *b = layer.col_image_gpu; - float *c = layer.weight_updates_gpu; + float *a = net.input_gpu + i*m*k; + float *b = net.workspace; + float *c = l.weight_updates_gpu; - im2col_ongpu(layer.delta_gpu + i*layer.n*size, layer.n, out_h, out_w, - layer.size, layer.stride, 0, b); - gemm_ongpu(0,1,m,n,k,alpha,a,k,b,k,1,c,n); + im2col_gpu(l.delta_gpu + i*l.outputs, l.out_c, l.out_h, l.out_w, + l.size, l.stride, l.pad, b); + gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); - if(state.delta){ - int m = layer.c; - int n = layer.h*layer.w; - int k = layer.size*layer.size*layer.n; + if(net.delta_gpu){ + int m = l.c; + int n = l.h*l.w; + int k = l.size*l.size*l.n; - float *a = layer.weights_gpu; - float *b = layer.col_image_gpu; - float *c = state.delta + i*n*m; + float *a = l.weights_gpu; + float *b = net.workspace; + float *c = net.delta_gpu + i*n*m; - gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); } } } -extern "C" void pull_deconvolutional_layer(deconvolutional_layer layer) +extern "C" void pull_deconvolutional_layer(layer l) { - cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.biases_gpu, layer.biases, layer.n); - cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); + cuda_pull_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size); + cuda_pull_array(l.biases_gpu, l.biases, l.n); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size); + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_pull_array(l.scales_gpu, l.scales, l.n); + cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.n); + } } -extern "C" void push_deconvolutional_layer(deconvolutional_layer layer) +extern "C" void push_deconvolutional_layer(layer l) { - cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.biases_gpu, layer.biases, layer.n); - cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); + cuda_push_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size); + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size); + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_push_array(l.scales_gpu, l.scales, l.n); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); + } } -extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay) +void update_deconvolutional_layer_gpu(layer l, update_args a) { - int size = layer.size*layer.size*layer.c*layer.n; + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + if(a.adam){ + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + if(l.scales_gpu){ + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + } + }else{ + axpy_gpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(l.nweights, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(l.nweights, momentum, l.weight_updates_gpu, 1); - axpy_ongpu(layer.n, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); - scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); + axpy_gpu(l.n, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.n, momentum, l.bias_updates_gpu, 1); - axpy_ongpu(size, -decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); - axpy_ongpu(size, learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); - scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); + if(l.scales_gpu){ + axpy_gpu(l.n, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_gpu(l.n, momentum, l.scale_updates_gpu, 1); + } + } } diff --git a/image.darknet/inst/include/darknet/src/deconvolutional_layer.c b/image.darknet/inst/include/darknet/src/deconvolutional_layer.c index fbef9d5..00c0e85 100644 --- a/image.darknet/inst/include/darknet/src/deconvolutional_layer.c +++ b/image.darknet/inst/include/darknet/src/deconvolutional_layer.c @@ -1,52 +1,41 @@ #include "deconvolutional_layer.h" #include "convolutional_layer.h" +#include "batchnorm_layer.h" #include "utils.h" #include "im2col.h" #include "col2im.h" #include "blas.h" #include "gemm.h" + #include #include -int deconvolutional_out_height(deconvolutional_layer l) -{ - int h = l.stride*(l.h - 1) + l.size; - return h; -} -int deconvolutional_out_width(deconvolutional_layer l) -{ - int w = l.stride*(l.w - 1) + l.size; - return w; -} - -int deconvolutional_out_size(deconvolutional_layer l) -{ - return deconvolutional_out_height(l) * deconvolutional_out_width(l); +static size_t get_workspace_size(layer l){ + return (size_t)l.h*l.w*l.size*l.size*l.n*sizeof(float); } -image get_deconvolutional_image(deconvolutional_layer l) +void bilinear_init(layer l) { - int h,w,c; - h = deconvolutional_out_height(l); - w = deconvolutional_out_width(l); - c = l.n; - return float_to_image(w,h,c,l.output); + int i,j,f; + float center = (l.size-1) / 2.; + for(f = 0; f < l.n; ++f){ + for(j = 0; j < l.size; ++j){ + for(i = 0; i < l.size; ++i){ + float val = (1 - fabs(i - center)) * (1 - fabs(j - center)); + int c = f%l.c; + int ind = f*l.size*l.size*l.c + c*l.size*l.size + j*l.size + i; + l.weights[ind] = val; + } + } + } } -image get_deconvolutional_delta(deconvolutional_layer l) -{ - int h,w,c; - h = deconvolutional_out_height(l); - w = deconvolutional_out_width(l); - c = l.n; - return float_to_image(w,h,c,l.delta); -} -deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation) +layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int adam) { int i; - deconvolutional_layer l = {0}; + layer l = {0}; l.type = DECONVOLUTIONAL; l.h = h; @@ -57,82 +46,182 @@ deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, l.stride = stride; l.size = size; + l.nweights = c*n*size*size; + l.nbiases = n; + l.weights = calloc(c*n*size*size, sizeof(float)); l.weight_updates = calloc(c*n*size*size, sizeof(float)); l.biases = calloc(n, sizeof(float)); l.bias_updates = calloc(n, sizeof(float)); - float scale = 1./sqrt(size*size*c); + //float scale = n/(size*size*c); + //printf("scale: %f\n", scale); + float scale = .02; for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal(); + //bilinear_init(l); for(i = 0; i < n; ++i){ - l.biases[i] = scale; + l.biases[i] = 0; } - int out_h = deconvolutional_out_height(l); - int out_w = deconvolutional_out_width(l); + l.pad = padding; - l.out_h = out_h; - l.out_w = out_w; + l.out_h = (l.h - 1) * l.stride + l.size - 2*l.pad; + l.out_w = (l.w - 1) * l.stride + l.size - 2*l.pad; l.out_c = n; l.outputs = l.out_w * l.out_h * l.out_c; l.inputs = l.w * l.h * l.c; - l.col_image = calloc(h*w*size*size*n, sizeof(float)); - l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); - l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float)); + scal_cpu(l.nweights, (float)l.out_w*l.out_h/(l.w*l.h), l.weights, 1); + + l.output = calloc(l.batch*l.outputs, sizeof(float)); + l.delta = calloc(l.batch*l.outputs, sizeof(float)); l.forward = forward_deconvolutional_layer; l.backward = backward_deconvolutional_layer; l.update = update_deconvolutional_layer; - #ifdef GPU - l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); - l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); + l.batch_normalize = batch_normalize; + + if(batch_normalize){ + l.scales = calloc(n, sizeof(float)); + l.scale_updates = calloc(n, sizeof(float)); + for(i = 0; i < n; ++i){ + l.scales[i] = 1; + } + + l.mean = calloc(n, sizeof(float)); + l.variance = calloc(n, sizeof(float)); + + l.mean_delta = calloc(n, sizeof(float)); + l.variance_delta = calloc(n, sizeof(float)); + + l.rolling_mean = calloc(n, sizeof(float)); + l.rolling_variance = calloc(n, sizeof(float)); + l.x = calloc(l.batch*l.outputs, sizeof(float)); + l.x_norm = calloc(l.batch*l.outputs, sizeof(float)); + } + if(adam){ + l.m = calloc(c*n*size*size, sizeof(float)); + l.v = calloc(c*n*size*size, sizeof(float)); + l.bias_m = calloc(n, sizeof(float)); + l.scale_m = calloc(n, sizeof(float)); + l.bias_v = calloc(n, sizeof(float)); + l.scale_v = calloc(n, sizeof(float)); + } + +#ifdef GPU + l.forward_gpu = forward_deconvolutional_layer_gpu; + l.backward_gpu = backward_deconvolutional_layer_gpu; + l.update_gpu = update_deconvolutional_layer_gpu; + + if(gpu_index >= 0){ + + if (adam) { + l.m_gpu = cuda_make_array(l.m, c*n*size*size); + l.v_gpu = cuda_make_array(l.v, c*n*size*size); + l.bias_m_gpu = cuda_make_array(l.bias_m, n); + l.bias_v_gpu = cuda_make_array(l.bias_v, n); + l.scale_m_gpu = cuda_make_array(l.scale_m, n); + l.scale_v_gpu = cuda_make_array(l.scale_v, n); + } + l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); + + l.biases_gpu = cuda_make_array(l.biases, n); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); - l.biases_gpu = cuda_make_array(l.biases, n); - l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); + l.delta_gpu = cuda_make_array(l.delta, l.batch*l.out_h*l.out_w*n); + l.output_gpu = cuda_make_array(l.output, l.batch*l.out_h*l.out_w*n); - l.col_image_gpu = cuda_make_array(l.col_image, h*w*size*size*n); - l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n); - l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); + if(batch_normalize){ + l.mean_gpu = cuda_make_array(0, n); + l.variance_gpu = cuda_make_array(0, n); + + l.rolling_mean_gpu = cuda_make_array(0, n); + l.rolling_variance_gpu = cuda_make_array(0, n); + + l.mean_delta_gpu = cuda_make_array(0, n); + l.variance_delta_gpu = cuda_make_array(0, n); + + l.scales_gpu = cuda_make_array(l.scales, n); + l.scale_updates_gpu = cuda_make_array(0, n); + + l.x_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n); + l.x_norm_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n); + } + } + #ifdef CUDNN + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); + cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); #endif +#endif l.activation = activation; + l.workspace_size = get_workspace_size(l); - fprintf(stderr, "Deconvolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n); + fprintf(stderr, "deconv%5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); return l; } -void resize_deconvolutional_layer(deconvolutional_layer *l, int h, int w) +void denormalize_deconvolutional_layer(layer l) +{ + int i, j; + for(i = 0; i < l.n; ++i){ + float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001); + for(j = 0; j < l.c*l.size*l.size; ++j){ + l.weights[i*l.c*l.size*l.size + j] *= scale; + } + l.biases[i] -= l.rolling_mean[i] * scale; + l.scales[i] = 1; + l.rolling_mean[i] = 0; + l.rolling_variance[i] = 1; + } +} + +void resize_deconvolutional_layer(layer *l, int h, int w) { l->h = h; l->w = w; - int out_h = deconvolutional_out_height(*l); - int out_w = deconvolutional_out_width(*l); - - l->col_image = realloc(l->col_image, - out_h*out_w*l->size*l->size*l->c*sizeof(float)); - l->output = realloc(l->output, - l->batch*out_h * out_w * l->n*sizeof(float)); - l->delta = realloc(l->delta, - l->batch*out_h * out_w * l->n*sizeof(float)); - #ifdef GPU - cuda_free(l->col_image_gpu); + l->out_h = (l->h - 1) * l->stride + l->size - 2*l->pad; + l->out_w = (l->w - 1) * l->stride + l->size - 2*l->pad; + + l->outputs = l->out_h * l->out_w * l->out_c; + l->inputs = l->w * l->h * l->c; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + if(l->batch_normalize){ + l->x = realloc(l->x, l->batch*l->outputs*sizeof(float)); + l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float)); + } + +#ifdef GPU cuda_free(l->delta_gpu); cuda_free(l->output_gpu); - l->col_image_gpu = cuda_make_array(l->col_image, out_h*out_w*l->size*l->size*l->c); - l->delta_gpu = cuda_make_array(l->delta, l->batch*out_h*out_w*l->n); - l->output_gpu = cuda_make_array(l->output, l->batch*out_h*out_w*l->n); + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); + + if(l->batch_normalize){ + cuda_free(l->x_gpu); + cuda_free(l->x_norm_gpu); + + l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs); + l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs); + } + #ifdef CUDNN + cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); #endif +#endif + l->workspace_size = get_workspace_size(*l); } -void forward_deconvolutional_layer(const deconvolutional_layer l, network_state state) +void forward_deconvolutional_layer(const layer l, network net) { int i; - int out_h = deconvolutional_out_height(l); - int out_w = deconvolutional_out_width(l); - int size = out_h*out_w; int m = l.size*l.size*l.n; int n = l.h*l.w; @@ -142,63 +231,80 @@ void forward_deconvolutional_layer(const deconvolutional_layer l, network_state for(i = 0; i < l.batch; ++i){ float *a = l.weights; - float *b = state.input + i*l.c*l.h*l.w; - float *c = l.col_image; + float *b = net.input + i*l.c*l.h*l.w; + float *c = net.workspace; - gemm(1,0,m,n,k,1,a,m,b,n,0,c,n); + gemm_cpu(1,0,m,n,k,1,a,m,b,n,0,c,n); - col2im_cpu(c, l.n, out_h, out_w, l.size, l.stride, 0, l.output+i*l.n*size); + col2im_cpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output+i*l.outputs); + } + if (l.batch_normalize) { + forward_batchnorm_layer(l, net); + } else { + add_bias(l.output, l.biases, l.batch, l.n, l.out_w*l.out_h); } - add_bias(l.output, l.biases, l.batch, l.n, size); - activate_array(l.output, l.batch*l.n*size, l.activation); + activate_array(l.output, l.batch*l.n*l.out_w*l.out_h, l.activation); } -void backward_deconvolutional_layer(deconvolutional_layer l, network_state state) +void backward_deconvolutional_layer(layer l, network net) { - float alpha = 1./l.batch; - int out_h = deconvolutional_out_height(l); - int out_w = deconvolutional_out_width(l); - int size = out_h*out_w; int i; - gradient_array(l.output, size*l.n*l.batch, l.activation, l.delta); - backward_bias(l.bias_updates, l.delta, l.batch, l.n, size); + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + + if(l.batch_normalize){ + backward_batchnorm_layer(l, net); + } else { + backward_bias(l.bias_updates, l.delta, l.batch, l.n, l.out_w*l.out_h); + } + + //if(net.delta) memset(net.delta, 0, l.batch*l.h*l.w*l.c*sizeof(float)); for(i = 0; i < l.batch; ++i){ int m = l.c; int n = l.size*l.size*l.n; int k = l.h*l.w; - float *a = state.input + i*m*n; - float *b = l.col_image; + float *a = net.input + i*m*k; + float *b = net.workspace; float *c = l.weight_updates; - im2col_cpu(l.delta + i*l.n*size, l.n, out_h, out_w, - l.size, l.stride, 0, b); - gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n); + im2col_cpu(l.delta + i*l.outputs, l.out_c, l.out_h, l.out_w, + l.size, l.stride, l.pad, b); + gemm_cpu(0,1,m,n,k,1,a,k,b,k,1,c,n); - if(state.delta){ + if(net.delta){ int m = l.c; int n = l.h*l.w; int k = l.size*l.size*l.n; float *a = l.weights; - float *b = l.col_image; - float *c = state.delta + i*n*m; + float *b = net.workspace; + float *c = net.delta + i*n*m; - gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + gemm_cpu(0,0,m,n,k,1,a,k,b,n,1,c,n); } } } -void update_deconvolutional_layer(deconvolutional_layer l, float learning_rate, float momentum, float decay) +void update_deconvolutional_layer(layer l, update_args a) { + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + int size = l.size*l.size*l.c*l.n; - axpy_cpu(l.n, learning_rate, l.bias_updates, 1, l.biases, 1); + axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.n, momentum, l.bias_updates, 1); - axpy_cpu(size, -decay, l.weights, 1, l.weight_updates, 1); - axpy_cpu(size, learning_rate, l.weight_updates, 1, l.weights, 1); + if(l.scales){ + axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1); + scal_cpu(l.n, momentum, l.scale_updates, 1); + } + + axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1); scal_cpu(size, momentum, l.weight_updates, 1); } diff --git a/image.darknet/inst/include/darknet/src/deconvolutional_layer.h b/image.darknet/inst/include/darknet/src/deconvolutional_layer.h index 2d36e02..b254fb9 100644 --- a/image.darknet/inst/include/darknet/src/deconvolutional_layer.h +++ b/image.darknet/inst/include/darknet/src/deconvolutional_layer.h @@ -7,28 +7,19 @@ #include "layer.h" #include "network.h" -typedef layer deconvolutional_layer; - #ifdef GPU -void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state); -void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state); -void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay); -void push_deconvolutional_layer(deconvolutional_layer layer); -void pull_deconvolutional_layer(deconvolutional_layer layer); +void forward_deconvolutional_layer_gpu(layer l, network net); +void backward_deconvolutional_layer_gpu(layer l, network net); +void update_deconvolutional_layer_gpu(layer l, update_args a); +void push_deconvolutional_layer(layer l); +void pull_deconvolutional_layer(layer l); #endif -deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation); -void resize_deconvolutional_layer(deconvolutional_layer *layer, int h, int w); -void forward_deconvolutional_layer(const deconvolutional_layer layer, network_state state); -void update_deconvolutional_layer(deconvolutional_layer layer, float learning_rate, float momentum, float decay); -void backward_deconvolutional_layer(deconvolutional_layer layer, network_state state); - -image get_deconvolutional_image(deconvolutional_layer layer); -image get_deconvolutional_delta(deconvolutional_layer layer); -image get_deconvolutional_filter(deconvolutional_layer layer, int i); - -int deconvolutional_out_height(deconvolutional_layer layer); -int deconvolutional_out_width(deconvolutional_layer layer); +layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int adam); +void resize_deconvolutional_layer(layer *l, int h, int w); +void forward_deconvolutional_layer(const layer l, network net); +void update_deconvolutional_layer(layer l, update_args a); +void backward_deconvolutional_layer(layer l, network net); #endif diff --git a/image.darknet/inst/include/darknet/src/demo.c b/image.darknet/inst/include/darknet/src/demo.c index 7818bc3..b89efb8 100644 --- a/image.darknet/inst/include/darknet/src/demo.c +++ b/image.darknet/inst/include/darknet/src/demo.c @@ -9,213 +9,339 @@ #include "demo.h" #include -#define FRAMES 3 +#define DEMO 1 #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" -image get_image_from_stream(CvCapture *cap); static char **demo_names; static image **demo_alphabet; static int demo_classes; -static float **probs; -static box *boxes; -static network net; -static image in ; -static image in_s ; -static image det ; -static image det_s; -static image disp = {0}; -static CvCapture * cap; +static network *net; +static image buff [3]; +static image buff_letter[3]; +static int buff_index = 0; +static void * cap; static float fps = 0; static float demo_thresh = 0; -static float demo_hier_thresh = .5; +static float demo_hier = .5; +static int running = 0; -static float *predictions[FRAMES]; +static int demo_frame = 3; static int demo_index = 0; -static image images[FRAMES]; +static float **predictions; static float *avg; +static int demo_done = 0; +static int demo_total = 0; +double demo_time; -void *fetch_in_thread(void *ptr) +detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num); + +int size_network(network *net) { - in = get_image_from_stream(cap); - if(!in.data){ - error("Stream closed."); + int i; + int count = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO || l.type == REGION || l.type == DETECTION){ + count += l.outputs; + } } - in_s = resize_image(in, net.w, net.h); - return 0; + return count; +} + +void remember_network(network *net) +{ + int i; + int count = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO || l.type == REGION || l.type == DETECTION){ + memcpy(predictions[demo_index] + count, net->layers[i].output, sizeof(float) * l.outputs); + count += l.outputs; + } + } +} + +detection *avg_predictions(network *net, int *nboxes) +{ + int i, j; + int count = 0; + fill_cpu(demo_total, 0, avg, 1); + for(j = 0; j < demo_frame; ++j){ + axpy_cpu(demo_total, 1./demo_frame, predictions[j], 1, avg, 1); + } + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO || l.type == REGION || l.type == DETECTION){ + memcpy(l.output, avg + count, sizeof(float) * l.outputs); + count += l.outputs; + } + } + detection *dets = get_network_boxes(net, buff[0].w, buff[0].h, demo_thresh, demo_hier, 0, 1, nboxes); + return dets; } void *detect_in_thread(void *ptr) { + running = 1; float nms = .4; - layer l = net.layers[net.n-1]; - float *X = det_s.data; - float *prediction = network_predict(net, X); - - memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float)); - mean_arrays(predictions, FRAMES, l.outputs, avg); - l.output = avg; - - free_image(det_s); - if(l.type == DETECTION){ - get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0); - } else if (l.type == REGION){ - get_region_boxes(l, 1, 1, demo_thresh, probs, boxes, 0, 0, demo_hier_thresh); - } else { - error("Last layer must produce detections\n"); + layer l = net->layers[net->n-1]; + float *X = buff_letter[(buff_index+2)%3].data; + network_predict(net, X); + + /* + if(l.type == DETECTION){ + get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0); + } else */ + remember_network(net); + detection *dets = 0; + int nboxes = 0; + dets = avg_predictions(net, &nboxes); + + + /* + int i,j; + box zero = {0}; + int classes = l.classes; + for(i = 0; i < demo_detections; ++i){ + avg[i].objectness = 0; + avg[i].bbox = zero; + memset(avg[i].prob, 0, classes*sizeof(float)); + for(j = 0; j < demo_frame; ++j){ + axpy_cpu(classes, 1./demo_frame, dets[j][i].prob, 1, avg[i].prob, 1); + avg[i].objectness += dets[j][i].objectness * 1./demo_frame; + avg[i].bbox.x += dets[j][i].bbox.x * 1./demo_frame; + avg[i].bbox.y += dets[j][i].bbox.y * 1./demo_frame; + avg[i].bbox.w += dets[j][i].bbox.w * 1./demo_frame; + avg[i].bbox.h += dets[j][i].bbox.h * 1./demo_frame; + } + //copy_cpu(classes, dets[0][i].prob, 1, avg[i].prob, 1); + //avg[i].objectness = dets[0][i].objectness; } - if (nms > 0) do_nms(boxes, probs, l.w*l.h*l.n, l.classes, nms); + */ + + if (nms > 0) do_nms_obj(dets, nboxes, l.classes, nms); + printf("\033[2J"); printf("\033[1;1H"); printf("\nFPS:%.1f\n",fps); printf("Objects:\n\n"); + image display = buff[(buff_index+2) % 3]; + draw_detections(display, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes); + free_detections(dets, nboxes); - images[demo_index] = det; - det = images[(demo_index + FRAMES/2 + 1)%FRAMES]; - demo_index = (demo_index + 1)%FRAMES; - - draw_detections(det, l.w*l.h*l.n, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes); + demo_index = (demo_index + 1)%demo_frame; + running = 0; + return 0; +} +void *fetch_in_thread(void *ptr) +{ + free_image(buff[buff_index]); + buff[buff_index] = get_image_from_stream(cap); + if(buff[buff_index].data == 0) { + demo_done = 1; + return 0; + } + letterbox_image_into(buff[buff_index], net->w, net->h, buff_letter[buff_index]); return 0; } -double get_wall_time() +void *display_in_thread(void *ptr) { - struct timeval time; - if (gettimeofday(&time,NULL)){ + int c = show_image(buff[(buff_index + 1)%3], "Demo", 1); + if (c != -1) c = c%256; + if (c == 27) { + demo_done = 1; return 0; + } else if (c == 82) { + demo_thresh += .02; + } else if (c == 84) { + demo_thresh -= .02; + if(demo_thresh <= .02) demo_thresh = .02; + } else if (c == 83) { + demo_hier += .02; + } else if (c == 81) { + demo_hier -= .02; + if(demo_hier <= .0) demo_hier = .0; } - return (double)time.tv_sec + (double)time.tv_usec * .000001; + return 0; } -void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, float hier_thresh) +void *display_loop(void *ptr) { - //skip = frame_skip; + while(1){ + display_in_thread(0); + } +} + +void *detect_loop(void *ptr) +{ + while(1){ + detect_in_thread(0); + } +} + +void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg_frames, float hier, int w, int h, int frames, int fullscreen) +{ + //demo_frame = avg_frames; image **alphabet = load_alphabet(); - int delay = frame_skip; demo_names = names; demo_alphabet = alphabet; demo_classes = classes; demo_thresh = thresh; - demo_hier_thresh = hier_thresh; + demo_hier = hier; printf("Demo\n"); - net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + pthread_t detect_thread; + pthread_t fetch_thread; srand(2222222); + int i; + demo_total = size_network(net); + predictions = calloc(demo_frame, sizeof(float*)); + for (i = 0; i < demo_frame; ++i){ + predictions[i] = calloc(demo_total, sizeof(float)); + } + avg = calloc(demo_total, sizeof(float)); + if(filename){ printf("video file: %s\n", filename); - cap = cvCaptureFromFile(filename); + cap = open_video_stream(filename, 0, 0, 0, 0); }else{ - cap = cvCaptureFromCAM(cam_index); + cap = open_video_stream(0, cam_index, w, h, frames); } if(!cap) error("Couldn't connect to webcam.\n"); - layer l = net.layers[net.n-1]; - int j; - - avg = (float *) calloc(l.outputs, sizeof(float)); - for(j = 0; j < FRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); - for(j = 0; j < FRAMES; ++j) images[j] = make_image(1,1,3); - - boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box)); - probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); - for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float)); - - pthread_t fetch_thread; - pthread_t detect_thread; - - fetch_in_thread(0); - det = in; - det_s = in_s; - - fetch_in_thread(0); - detect_in_thread(0); - disp = det; - det = in; - det_s = in_s; - - for(j = 0; j < FRAMES/2; ++j){ - fetch_in_thread(0); - detect_in_thread(0); - disp = det; - det = in; - det_s = in_s; - } + buff[0] = get_image_from_stream(cap); + buff[1] = copy_image(buff[0]); + buff[2] = copy_image(buff[0]); + buff_letter[0] = letterbox_image(buff[0], net->w, net->h); + buff_letter[1] = letterbox_image(buff[0], net->w, net->h); + buff_letter[2] = letterbox_image(buff[0], net->w, net->h); int count = 0; if(!prefix){ - cvNamedWindow("Demo", CV_WINDOW_NORMAL); - cvMoveWindow("Demo", 0, 0); - cvResizeWindow("Demo", 1352, 1013); + make_window("Demo", 1352, 1013, fullscreen); } - double before = get_wall_time(); - - while(1){ - ++count; - if(1){ - if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); - if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); - - if(!prefix){ - show_image(disp, "Demo"); - int c = cvWaitKey(1); - if (c == 10){ - if(frame_skip == 0) frame_skip = 60; - else if(frame_skip == 4) frame_skip = 0; - else if(frame_skip == 60) frame_skip = 4; - else frame_skip = 0; - } - }else{ - char buff[256]; - sprintf(buff, "%s_%08d", prefix, count); - save_image(disp, buff); - } - - pthread_join(fetch_thread, 0); - pthread_join(detect_thread, 0); - - if(delay == 0){ - free_image(disp); - disp = det; - } - det = in; - det_s = in_s; - }else { - fetch_in_thread(0); - det = in; - det_s = in_s; - detect_in_thread(0); - if(delay == 0) { - free_image(disp); - disp = det; - } - show_image(disp, "Demo"); - cvWaitKey(1); - } - --delay; - if(delay < 0){ - delay = frame_skip; - - double after = get_wall_time(); - float curr = 1./(after - before); - fps = curr; - before = after; + demo_time = what_time_is_it_now(); + + while(!demo_done){ + buff_index = (buff_index + 1) %3; + if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); + if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); + if(!prefix){ + fps = 1./(what_time_is_it_now() - demo_time); + demo_time = what_time_is_it_now(); + display_in_thread(0); + }else{ + char name[256]; + sprintf(name, "%s_%08d", prefix, count); + save_image(buff[(buff_index + 1)%3], name); } + pthread_join(fetch_thread, 0); + pthread_join(detect_thread, 0); + ++count; } } + +/* + void demo_compare(char *cfg1, char *weight1, char *cfg2, char *weight2, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg_frames, float hier, int w, int h, int frames, int fullscreen) + { + demo_frame = avg_frames; + predictions = calloc(demo_frame, sizeof(float*)); + image **alphabet = load_alphabet(); + demo_names = names; + demo_alphabet = alphabet; + demo_classes = classes; + demo_thresh = thresh; + demo_hier = hier; + printf("Demo\n"); + net = load_network(cfg1, weight1, 0); + set_batch_network(net, 1); + pthread_t detect_thread; + pthread_t fetch_thread; + + srand(2222222); + + if(filename){ + printf("video file: %s\n", filename); + cap = cvCaptureFromFile(filename); + }else{ + cap = cvCaptureFromCAM(cam_index); + + if(w){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w); + } + if(h){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h); + } + if(frames){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FPS, frames); + } + } + + if(!cap) error("Couldn't connect to webcam.\n"); + + layer l = net->layers[net->n-1]; + demo_detections = l.n*l.w*l.h; + int j; + + avg = (float *) calloc(l.outputs, sizeof(float)); + for(j = 0; j < demo_frame; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); + + boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box)); + probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); + for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes+1, sizeof(float)); + + buff[0] = get_image_from_stream(cap); + buff[1] = copy_image(buff[0]); + buff[2] = copy_image(buff[0]); + buff_letter[0] = letterbox_image(buff[0], net->w, net->h); + buff_letter[1] = letterbox_image(buff[0], net->w, net->h); + buff_letter[2] = letterbox_image(buff[0], net->w, net->h); + ipl = cvCreateImage(cvSize(buff[0].w,buff[0].h), IPL_DEPTH_8U, buff[0].c); + + int count = 0; + if(!prefix){ + cvNamedWindow("Demo", CV_WINDOW_NORMAL); + if(fullscreen){ + cvSetWindowProperty("Demo", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + } else { + cvMoveWindow("Demo", 0, 0); + cvResizeWindow("Demo", 1352, 1013); + } + } + + demo_time = what_time_is_it_now(); + + while(!demo_done){ +buff_index = (buff_index + 1) %3; +if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); +if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); +if(!prefix){ + fps = 1./(what_time_is_it_now() - demo_time); + demo_time = what_time_is_it_now(); + display_in_thread(0); +}else{ + char name[256]; + sprintf(name, "%s_%08d", prefix, count); + save_image(buff[(buff_index + 1)%3], name); +} +pthread_join(fetch_thread, 0); +pthread_join(detect_thread, 0); +++count; +} +} +*/ #else -void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, float hier_thresh) +void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg, float hier, int w, int h, int frames, int fullscreen) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/image.darknet/inst/include/darknet/src/demo.h b/image.darknet/inst/include/darknet/src/demo.h index c3d6a61..86e4654 100644 --- a/image.darknet/inst/include/darknet/src/demo.h +++ b/image.darknet/inst/include/darknet/src/demo.h @@ -1,7 +1,6 @@ -#ifndef DEMO -#define DEMO +#ifndef DEMO_H +#define DEMO_H #include "image.h" -void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, float hier_thresh); #endif diff --git a/image.darknet/inst/include/darknet/src/detection_layer.c b/image.darknet/inst/include/darknet/src/detection_layer.c index cd98b4b..d0e0194 100644 --- a/image.darknet/inst/include/darknet/src/detection_layer.c +++ b/image.darknet/inst/include/darknet/src/detection_layer.c @@ -5,6 +5,7 @@ #include "box.h" #include "cuda.h" #include "utils.h" + #include #include #include @@ -46,11 +47,11 @@ detection_layer make_detection_layer(int batch, int inputs, int n, int side, int return l; } -void forward_detection_layer(const detection_layer l, network_state state) +void forward_detection_layer(const detection_layer l, network net) { int locations = l.side*l.side; int i,j; - memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); //if(l.reorg) reorg(l.output, l.w*l.h, size*l.n, l.batch, 1); int b; if (l.softmax){ @@ -58,12 +59,12 @@ void forward_detection_layer(const detection_layer l, network_state state) int index = b*l.inputs; for (i = 0; i < locations; ++i) { int offset = i*l.classes; - softmax(l.output + index + offset, l.classes, 1, + softmax(l.output + index + offset, l.classes, 1, 1, l.output + index + offset); } } } - if(state.train){ + if(net.train){ float avg_iou = 0; float avg_cat = 0; float avg_allcat = 0; @@ -77,7 +78,7 @@ void forward_detection_layer(const detection_layer l, network_state state) int index = b*l.inputs; for (i = 0; i < locations; ++i) { int truth_index = (b*locations + i)*(1+l.coords+l.classes); - int is_obj = state.truth[truth_index]; + int is_obj = net.truth[truth_index]; for (j = 0; j < l.n; ++j) { int p_index = index + locations*l.classes + i*l.n + j; l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]); @@ -95,19 +96,19 @@ void forward_detection_layer(const detection_layer l, network_state state) int class_index = index + i*l.classes; for(j = 0; j < l.classes; ++j) { - l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]); - *(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2); - if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; + l.delta[class_index+j] = l.class_scale * (net.truth[truth_index+1+j] - l.output[class_index+j]); + *(l.cost) += l.class_scale * pow(net.truth[truth_index+1+j] - l.output[class_index+j], 2); + if(net.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; avg_allcat += l.output[class_index+j]; } - box truth = float_to_box(state.truth + truth_index + 1 + l.classes); + box truth = float_to_box(net.truth + truth_index + 1 + l.classes, 1); truth.x /= l.side; truth.y /= l.side; for(j = 0; j < l.n; ++j){ int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords; - box out = float_to_box(l.output + box_index); + box out = float_to_box(l.output + box_index, 1); out.x /= l.side; out.y /= l.side; @@ -139,14 +140,14 @@ void forward_detection_layer(const detection_layer l, network_state state) best_index = 0; } } - if(l.random && *(state.net.seen) < 64000){ + if(l.random && *(net.seen) < 64000){ best_index = rand()%l.n; } int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords; int tbox_index = truth_index + 1 + l.classes; - box out = float_to_box(l.output + box_index); + box out = float_to_box(l.output + box_index, 1); out.x /= l.side; out.y /= l.side; if (l.sqrt) { @@ -166,13 +167,13 @@ void forward_detection_layer(const detection_layer l, network_state state) l.delta[p_index] = l.object_scale * (iou - l.output[p_index]); } - l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]); - l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]); - l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]); - l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]); + l.delta[box_index+0] = l.coord_scale*(net.truth[tbox_index + 0] - l.output[box_index + 0]); + l.delta[box_index+1] = l.coord_scale*(net.truth[tbox_index + 1] - l.output[box_index + 1]); + l.delta[box_index+2] = l.coord_scale*(net.truth[tbox_index + 2] - l.output[box_index + 2]); + l.delta[box_index+3] = l.coord_scale*(net.truth[tbox_index + 3] - l.output[box_index + 3]); if(l.sqrt){ - l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]); - l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]); + l.delta[box_index+2] = l.coord_scale*(sqrt(net.truth[tbox_index + 2]) - l.output[box_index + 2]); + l.delta[box_index+3] = l.coord_scale*(sqrt(net.truth[tbox_index + 3]) - l.output[box_index + 3]); } *(l.cost) += pow(1-iou, 2); @@ -216,12 +217,12 @@ void forward_detection_layer(const detection_layer l, network_state state) } } -void backward_detection_layer(const detection_layer l, network_state state) +void backward_detection_layer(const detection_layer l, network net) { - axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); } -void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness) +void get_detection_detections(layer l, int w, int h, float thresh, detection *dets) { int i,j,n; float *predictions = l.output; @@ -234,17 +235,17 @@ void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box int p_index = l.side*l.side*l.classes + i*l.n + n; float scale = predictions[p_index]; int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4; - boxes[index].x = (predictions[box_index + 0] + col) / l.side * w; - boxes[index].y = (predictions[box_index + 1] + row) / l.side * h; - boxes[index].w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w; - boxes[index].h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h; + box b; + b.x = (predictions[box_index + 0] + col) / l.side * w; + b.y = (predictions[box_index + 1] + row) / l.side * h; + b.w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w; + b.h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h; + dets[index].bbox = b; + dets[index].objectness = scale; for(j = 0; j < l.classes; ++j){ int class_index = i*l.classes; float prob = scale*predictions[class_index+j]; - probs[index][j] = (prob > thresh) ? prob : 0; - } - if(only_objectness){ - probs[index][0] = scale; + dets[index].prob[j] = (prob > thresh) ? prob : 0; } } } @@ -252,36 +253,23 @@ void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box #ifdef GPU -void forward_detection_layer_gpu(const detection_layer l, network_state state) +void forward_detection_layer_gpu(const detection_layer l, network net) { - if(!state.train){ - copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); + if(!net.train){ + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); return; } - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); - float *truth_cpu = 0; - if(state.truth){ - int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes); - truth_cpu = calloc(num_truth, sizeof(float)); - cuda_pull_array(state.truth, truth_cpu, num_truth); - } - cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); - network_state cpu_state = state; - cpu_state.train = state.train; - cpu_state.truth = truth_cpu; - cpu_state.input = in_cpu; - forward_detection_layer(l, cpu_state); + cuda_pull_array(net.input_gpu, net.input, l.batch*l.inputs); + forward_detection_layer(l, net); cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs); - free(cpu_state.input); - if(cpu_state.truth) free(cpu_state.truth); } -void backward_detection_layer_gpu(detection_layer l, network_state state) +void backward_detection_layer_gpu(detection_layer l, network net) { - axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); - //copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1); + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); + //copy_gpu(l.batch*l.inputs, l.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/inst/include/darknet/src/detection_layer.h b/image.darknet/inst/include/darknet/src/detection_layer.h index e847a09..1c81853 100644 --- a/image.darknet/inst/include/darknet/src/detection_layer.h +++ b/image.darknet/inst/include/darknet/src/detection_layer.h @@ -7,13 +7,12 @@ typedef layer detection_layer; detection_layer make_detection_layer(int batch, int inputs, int n, int size, int classes, int coords, int rescore); -void forward_detection_layer(const detection_layer l, network_state state); -void backward_detection_layer(const detection_layer l, network_state state); -void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness); +void forward_detection_layer(const detection_layer l, network net); +void backward_detection_layer(const detection_layer l, network net); #ifdef GPU -void forward_detection_layer_gpu(const detection_layer l, network_state state); -void backward_detection_layer_gpu(detection_layer l, network_state state); +void forward_detection_layer_gpu(const detection_layer l, network net); +void backward_detection_layer_gpu(detection_layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/detector.c b/image.darknet/inst/include/darknet/src/detector.c deleted file mode 100644 index 1416c05..0000000 --- a/image.darknet/inst/include/darknet/src/detector.c +++ /dev/null @@ -1,552 +0,0 @@ -#include "network.h" -#include "region_layer.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "box.h" -#include "demo.h" -#include "option_list.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif -static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; - -void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear) -{ - list *options = read_data_cfg(datacfg); - char *train_images = option_find_str(options, "train", "data/train.list"); - char *backup_directory = option_find_str(options, "backup", "/backup/"); - - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - float avg_loss = -1; - network *nets = calloc(ngpus, sizeof(network)); - - srand(time(0)); - int seed = rand(); - int i; - for(i = 0; i < ngpus; ++i){ - srand(seed); -#ifdef GPU - cuda_set_device(gpus[i]); -#endif - nets[i] = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&nets[i], weightfile); - } - if(clear) *nets[i].seen = 0; - nets[i].learning_rate *= ngpus; - } - srand(time(0)); - network net = nets[0]; - - int imgs = net.batch * net.subdivisions * ngpus; - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - data train, buffer; - - layer l = net.layers[net.n - 1]; - - int classes = l.classes; - float jitter = l.jitter; - - list *plist = get_paths(train_images); - //int N = plist->size; - char **paths = (char **)list_to_array(plist); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.n = imgs; - args.m = plist->size; - args.classes = classes; - args.jitter = jitter; - args.num_boxes = l.max_boxes; - args.d = &buffer; - args.type = DETECTION_DATA; - args.threads = 8; - - args.angle = net.angle; - args.exposure = net.exposure; - args.saturation = net.saturation; - args.hue = net.hue; - - pthread_t load_thread = load_data(args); - clock_t time; - int count = 0; - //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ - if(l.random && count++%10 == 0){ - printf("Resizing\n"); - int dim = (rand() % 10 + 10) * 32; - if (get_current_batch(net)+200 > net.max_batches) dim = 608; - //int dim = (rand() % 4 + 16) * 32; - printf("%d\n", dim); - args.w = dim; - args.h = dim; - - pthread_join(load_thread, 0); - train = buffer; - free_data(train); - load_thread = load_data(args); - - for(i = 0; i < ngpus; ++i){ - resize_network(nets + i, dim, dim); - } - net = nets[0]; - } - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data(args); - - /* - int k; - for(k = 0; k < l.max_boxes; ++k){ - box b = float_to_box(train.y.vals[10] + 1 + k*5); - if(!b.x) break; - printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h); - } - image im = float_to_image(448, 448, 3, train.X.vals[10]); - int k; - for(k = 0; k < l.max_boxes; ++k){ - box b = float_to_box(train.y.vals[10] + 1 + k*5); - printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h); - draw_bbox(im, b, 8, 1,0,0); - } - save_image(im, "truth11"); - */ - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - - time=clock(); - float loss = 0; -#ifdef GPU - if(ngpus == 1){ - loss = train_network(net, train); - } else { - loss = train_networks(nets, ngpus, train, 4); - } -#else - loss = train_network(net, train); -#endif - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - i = get_current_batch(net); - printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); - if(i%1000==0 || (i < 1000 && i%100 == 0)){ -#ifdef GPU - if(ngpus != 1) sync_nets(nets, ngpus, 0); -#endif - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - free_data(train); - } -#ifdef GPU - if(ngpus != 1) sync_nets(nets, ngpus, 0); -#endif - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - - -static int get_coco_image_id(char *filename) -{ - char *p = strrchr(filename, '_'); - return atoi(p+1); -} - -static void print_cocos(FILE *fp, char *image_path, box *boxes, float **probs, int num_boxes, int classes, int w, int h) -{ - int i, j; - int image_id = get_coco_image_id(image_path); - for(i = 0; i < num_boxes; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - float bx = xmin; - float by = ymin; - float bw = xmax - xmin; - float bh = ymax - ymin; - - for(j = 0; j < classes; ++j){ - if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]); - } - } -} - -void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h) -{ - int i, j; - for(i = 0; i < total; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - for(j = 0; j < classes; ++j){ - if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j], - xmin, ymin, xmax, ymax); - } - } -} - -void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h) -{ - int i, j; - for(i = 0; i < total; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - for(j = 0; j < classes; ++j){ - int class = j; - if (probs[i][class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class], - xmin, ymin, xmax, ymax); - } - } -} - -void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile) -{ - int j; - list *options = read_data_cfg(datacfg); - char *valid_images = option_find_str(options, "valid", "data/train.list"); - char *name_list = option_find_str(options, "names", "data/names.list"); - char *prefix = option_find_str(options, "results", "results"); - char **names = get_labels(name_list); - char *mapf = option_find_str(options, "map", 0); - int *map = 0; - if (mapf) map = read_map(mapf); - - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - list *plist = get_paths(valid_images); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - - char buff[1024]; - char *type = option_find_str(options, "eval", "voc"); - FILE *fp = 0; - FILE **fps = 0; - int coco = 0; - int imagenet = 0; - if(0==strcmp(type, "coco")){ - if(!outfile) outfile = "coco_results"; - snprintf(buff, 1024, "%s/%s.json", prefix, outfile); - fp = fopen(buff, "w"); - fprintf(fp, "[\n"); - coco = 1; - } else if(0==strcmp(type, "imagenet")){ - if(!outfile) outfile = "imagenet-detection"; - snprintf(buff, 1024, "%s/%s.txt", prefix, outfile); - fp = fopen(buff, "w"); - imagenet = 1; - classes = 200; - } else { - if(!outfile) outfile = "comp4_det_test_"; - fps = calloc(classes, sizeof(FILE *)); - for(j = 0; j < classes; ++j){ - snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]); - fps[j] = fopen(buff, "w"); - } - } - - - box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); - float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); - for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - int t; - - float thresh = .005; - float nms = .45; - - int nthreads = 4; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.type = IMAGE_DATA; - - for(t = 0; t < nthreads; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - time_t start = time(0); - for(i = nthreads; i < m+nthreads; i += nthreads){ - fprintf(stderr, "%d\n", i); - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - pthread_join(thr[t], 0); - val[t] = buf[t]; - val_resized[t] = buf_resized[t]; - } - for(t = 0; t < nthreads && i+t < m; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - char *path = paths[i+t-nthreads]; - char *id = basecfg(path); - float *X = val_resized[t].data; - network_predict(net, X); - int w = val[t].w; - int h = val[t].h; - get_region_boxes(l, w, h, thresh, probs, boxes, 0, map, .5); - if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms); - if (coco){ - print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h); - } else if (imagenet){ - print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h); - } else { - print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h); - } - free(id); - free_image(val[t]); - free_image(val_resized[t]); - } - } - for(j = 0; j < classes; ++j){ - if(fps) fclose(fps[j]); - } - if(coco){ - fseek(fp, -2, SEEK_CUR); - fprintf(fp, "\n]\n"); - fclose(fp); - } - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); -} - -void validate_detector_recall(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - list *plist = get_paths("data/voc.2007.test"); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - - int j, k; - box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); - float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); - for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - - float thresh = .001; - float iou_thresh = .5; - float nms = .4; - - int total = 0; - int correct = 0; - int proposals = 0; - float avg_iou = 0; - - for(i = 0; i < m; ++i){ - char *path = paths[i]; - image orig = load_image_color(path, 0, 0); - image sized = resize_image(orig, net.w, net.h); - char *id = basecfg(path); - network_predict(net, sized.data); - get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0, .5); - if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms); - - char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - - int num_labels = 0; - box_label *truth = read_boxes(labelpath, &num_labels); - for(k = 0; k < l.w*l.h*l.n; ++k){ - if(probs[k][0] > thresh){ - ++proposals; - } - } - for (j = 0; j < num_labels; ++j) { - ++total; - box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h}; - float best_iou = 0; - for(k = 0; k < l.w*l.h*l.n; ++k){ - float iou = box_iou(boxes[k], t); - if(probs[k][0] > thresh && iou > best_iou){ - best_iou = iou; - } - } - avg_iou += best_iou; - if(best_iou > iou_thresh){ - ++correct; - } - } - - fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); - free(id); - free_image(orig); - free_image(sized); - } -} - -void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh) -{ - list *options = read_data_cfg(datacfg); - char *name_list = option_find_str(options, "names", "data/names.list"); - char **names = get_labels(name_list); - - image **alphabet = load_alphabet(); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - srand(2222222); - clock_t time; - char buff[256]; - char *input = buff; - int j; - float nms=.4; - while(1){ - if(filename){ - strncpy(input, filename, 256); - } else { - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input,0,0); - image sized = resize_image(im, net.w, net.h); - layer l = net.layers[net.n-1]; - - box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); - float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); - for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes + 1, sizeof(float *)); - - float *X = sized.data; - time=clock(); - network_predict(net, X); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0, hier_thresh); - if (l.softmax_tree && nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms); - else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms); - draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes); - save_image(im, "predictions"); - show_image(im, "predictions"); - - free_image(im); - free_image(sized); - free(boxes); - free_ptrs((void **)probs, l.w*l.h*l.n); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif - if (filename) break; - } -} - -void run_detector(int argc, char **argv) -{ - char *prefix = find_char_arg(argc, argv, "-prefix", 0); - float thresh = find_float_arg(argc, argv, "-thresh", .24); - float hier_thresh = find_float_arg(argc, argv, "-hier", .5); - int cam_index = find_int_arg(argc, argv, "-c", 0); - int frame_skip = find_int_arg(argc, argv, "-s", 0); - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); - char *outfile = find_char_arg(argc, argv, "-out", 0); - int *gpus = 0; - int gpu = 0; - int ngpus = 0; - if(gpu_list){ - printf("%s\n", gpu_list); - int len = strlen(gpu_list); - ngpus = 1; - int i; - for(i = 0; i < len; ++i){ - if (gpu_list[i] == ',') ++ngpus; - } - gpus = calloc(ngpus, sizeof(int)); - for(i = 0; i < ngpus; ++i){ - gpus[i] = atoi(gpu_list); - gpu_list = strchr(gpu_list, ',')+1; - } - } else { - gpu = gpu_index; - gpus = &gpu; - ngpus = 1; - } - - int clear = find_arg(argc, argv, "-clear"); - - char *datacfg = argv[3]; - char *cfg = argv[4]; - char *weights = (argc > 5) ? argv[5] : 0; - char *filename = (argc > 6) ? argv[6]: 0; - if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh); - else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear); - else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); - else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights); - else if(0==strcmp(argv[2], "demo")) { - list *options = read_data_cfg(datacfg); - int classes = option_find_int(options, "classes", 20); - char *name_list = option_find_str(options, "names", "data/names.list"); - char **names = get_labels(name_list); - demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, hier_thresh); - } -} diff --git a/image.darknet/inst/include/darknet/src/dropout_layer.c b/image.darknet/inst/include/darknet/src/dropout_layer.c index b1381e6..780554f 100644 --- a/image.darknet/inst/include/darknet/src/dropout_layer.c +++ b/image.darknet/inst/include/darknet/src/dropout_layer.c @@ -35,26 +35,26 @@ void resize_dropout_layer(dropout_layer *l, int inputs) #endif } -void forward_dropout_layer(dropout_layer l, network_state state) +void forward_dropout_layer(dropout_layer l, network net) { int i; - if (!state.train) return; + if (!net.train) return; for(i = 0; i < l.batch * l.inputs; ++i){ float r = rand_uniform(0, 1); l.rand[i] = r; - if(r < l.probability) state.input[i] = 0; - else state.input[i] *= l.scale; + if(r < l.probability) net.input[i] = 0; + else net.input[i] *= l.scale; } } -void backward_dropout_layer(dropout_layer l, network_state state) +void backward_dropout_layer(dropout_layer l, network net) { int i; - if(!state.delta) return; + if(!net.delta) return; for(i = 0; i < l.batch * l.inputs; ++i){ float r = l.rand[i]; - if(r < l.probability) state.delta[i] = 0; - else state.delta[i] *= l.scale; + if(r < l.probability) net.delta[i] = 0; + else net.delta[i] *= l.scale; } } diff --git a/image.darknet/inst/include/darknet/src/dropout_layer.h b/image.darknet/inst/include/darknet/src/dropout_layer.h index 691cfc5..01f94d4 100644 --- a/image.darknet/inst/include/darknet/src/dropout_layer.h +++ b/image.darknet/inst/include/darknet/src/dropout_layer.h @@ -8,13 +8,13 @@ typedef layer dropout_layer; dropout_layer make_dropout_layer(int batch, int inputs, float probability); -void forward_dropout_layer(dropout_layer l, network_state state); -void backward_dropout_layer(dropout_layer l, network_state state); +void forward_dropout_layer(dropout_layer l, network net); +void backward_dropout_layer(dropout_layer l, network net); void resize_dropout_layer(dropout_layer *l, int inputs); #ifdef GPU -void forward_dropout_layer_gpu(dropout_layer l, network_state state); -void backward_dropout_layer_gpu(dropout_layer l, network_state state); +void forward_dropout_layer_gpu(dropout_layer l, network net); +void backward_dropout_layer_gpu(dropout_layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/dropout_layer_kernels.cu b/image.darknet/inst/include/darknet/src/dropout_layer_kernels.cu index 7e51bd5..bd12b67 100644 --- a/image.darknet/inst/include/darknet/src/dropout_layer_kernels.cu +++ b/image.darknet/inst/include/darknet/src/dropout_layer_kernels.cu @@ -14,9 +14,9 @@ __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand if(id < size) input[id] = (rand[id] < prob) ? 0 : input[id]*scale; } -void forward_dropout_layer_gpu(dropout_layer layer, network_state state) +void forward_dropout_layer_gpu(dropout_layer layer, network net) { - if (!state.train) return; + if (!net.train) return; int size = layer.inputs*layer.batch; cuda_random(layer.rand_gpu, size); /* @@ -27,15 +27,15 @@ void forward_dropout_layer_gpu(dropout_layer layer, network_state state) cuda_push_array(layer.rand_gpu, layer.rand, size); */ - yoloswag420blazeit360noscope<<>>(state.input, size, layer.rand_gpu, layer.probability, layer.scale); + yoloswag420blazeit360noscope<<>>(net.input_gpu, size, layer.rand_gpu, layer.probability, layer.scale); check_error(cudaPeekAtLastError()); } -void backward_dropout_layer_gpu(dropout_layer layer, network_state state) +void backward_dropout_layer_gpu(dropout_layer layer, network net) { - if(!state.delta) return; + if(!net.delta_gpu) return; int size = layer.inputs*layer.batch; - yoloswag420blazeit360noscope<<>>(state.delta, size, layer.rand_gpu, layer.probability, layer.scale); + yoloswag420blazeit360noscope<<>>(net.delta_gpu, size, layer.rand_gpu, layer.probability, layer.scale); check_error(cudaPeekAtLastError()); } diff --git a/image.darknet/inst/include/darknet/src/gemm.c b/image.darknet/inst/include/darknet/src/gemm.c index 3003be0..648027f 100644 --- a/image.darknet/inst/include/darknet/src/gemm.c +++ b/image.darknet/inst/include/darknet/src/gemm.c @@ -77,6 +77,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, float *C, int ldc) { int i,j,k; + #pragma omp parallel for for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ register float A_PART = ALPHA*A[i*lda+k]; @@ -93,6 +94,7 @@ void gemm_nt(int M, int N, int K, float ALPHA, float *C, int ldc) { int i,j,k; + #pragma omp parallel for for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ register float sum = 0; @@ -110,6 +112,7 @@ void gemm_tn(int M, int N, int K, float ALPHA, float *C, int ldc) { int i,j,k; + #pragma omp parallel for for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ register float A_PART = ALPHA*A[k*lda+i]; @@ -126,6 +129,7 @@ void gemm_tt(int M, int N, int K, float ALPHA, float *C, int ldc) { int i,j,k; + #pragma omp parallel for for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ register float sum = 0; @@ -165,7 +169,7 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, #include -void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, +void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, float *A_gpu, int lda, float *B_gpu, int ldb, float BETA, @@ -177,24 +181,6 @@ void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, check_error(status); } -void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float BETA, - float *C, int ldc) -{ - float *A_gpu = cuda_make_array(A, (TA ? lda*K:lda*M)); - float *B_gpu = cuda_make_array(B, (TB ? ldb*N : ldb*K)); - float *C_gpu = cuda_make_array(C, ldc*M); - - gemm_ongpu(TA, TB, M, N, K, ALPHA, A_gpu, lda, B_gpu, ldb, BETA, C_gpu, ldc); - - cuda_pull_array(C_gpu, C, ldc*M); - cuda_free(A_gpu); - cuda_free(B_gpu); - cuda_free(C_gpu); -} - #include #include #include @@ -224,7 +210,7 @@ void time_gpu_random_matrix(int TA, int TB, int m, int k, int n) free(c); } -void time_ongpu(int TA, int TB, int m, int k, int n) +void time_gpu(int TA, int TB, int m, int k, int n) { int iter = 10; float *a = random_matrix(m,k); @@ -242,7 +228,7 @@ void time_ongpu(int TA, int TB, int m, int k, int n) int i; clock_t start = clock(), end; for(i = 0; i= m.n){ - m.n *= 2; - m.data = realloc(m.data, m.n*sizeof(char*)); - } - m.data[count] = line; - ++count; - } - printf("%d\n", count); - m.n = count; - m.data = realloc(m.data, count*sizeof(char*)); - return m; -} - -void string_to_board(char *s, float *board) -{ - int i, j; - //memset(board, 0, 1*19*19*sizeof(float)); - int count = 0; - for(i = 0; i < 91; ++i){ - char c = s[i]; - for(j = 0; j < 4; ++j){ - int me = (c >> (2*j)) & 1; - int you = (c >> (2*j + 1)) & 1; - if (me) board[count] = 1; - else if (you) board[count] = -1; - else board[count] = 0; - ++count; - if(count >= 19*19) break; - } - } -} - -void board_to_string(char *s, float *board) -{ - int i, j; - memset(s, 0, (19*19/4+1)*sizeof(char)); - int count = 0; - for(i = 0; i < 91; ++i){ - for(j = 0; j < 4; ++j){ - int me = (board[count] == 1); - int you = (board[count] == -1); - if (me) s[i] = s[i] | (1<<(2*j)); - if (you) s[i] = s[i] | (1<<(2*j + 1)); - ++count; - if(count >= 19*19) break; - } - } -} - -void random_go_moves(moves m, float *boards, float *labels, int n) -{ - int i; - memset(labels, 0, 19*19*n*sizeof(float)); - for(i = 0; i < n; ++i){ - char *b = m.data[rand()%m.n]; - int row = b[0]; - int col = b[1]; - labels[col + 19*(row + i*19)] = 1; - string_to_board(b+2, boards+i*19*19); - boards[col + 19*(row + i*19)] = 0; - - int flip = rand()%2; - int rotate = rand()%4; - image in = float_to_image(19, 19, 1, boards+i*19*19); - image out = float_to_image(19, 19, 1, labels+i*19*19); - if(flip){ - flip_image(in); - flip_image(out); - } - rotate_image_cw(in, rotate); - rotate_image_cw(out, rotate); - } -} - - -void train_go(char *cfgfile, char *weightfile) -{ - srand(time(0)); - float avg_loss = -1; - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - - char *backup_directory = "/home/pjreddie/backup/"; - - char buff[256]; - float *board = calloc(19*19*net.batch, sizeof(float)); - float *move = calloc(19*19*net.batch, sizeof(float)); - moves m = load_go_moves("/home/pjreddie/backup/go.train"); - //moves m = load_go_moves("games.txt"); - - int N = m.n; - int epoch = (*net.seen)/N; - while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ - clock_t time=clock(); - - random_go_moves(m, board, move, net.batch); - float loss = train_network_datum(net, board, move) / net.batch; - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory,base, epoch); - save_weights(net, buff); - - } - if(get_current_batch(net)%100 == 0){ - char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); - save_weights(net, buff); - } - if(get_current_batch(net)%10000 == 0){ - char buff[256]; - sprintf(buff, "%s/%s_%d.backup",backup_directory,base,get_current_batch(net)); - save_weights(net, buff); - } - } - sprintf(buff, "%s/%s.weights", backup_directory, base); - save_weights(net, buff); - - free_network(net); - free(base); -} - -void propagate_liberty(float *board, int *lib, int *visited, int row, int col, int side) -{ - if (row < 0 || row > 18 || col < 0 || col > 18) return; - int index = row*19 + col; - if (board[index] != side) return; - if (visited[index]) return; - visited[index] = 1; - lib[index] += 1; - propagate_liberty(board, lib, visited, row+1, col, side); - propagate_liberty(board, lib, visited, row-1, col, side); - propagate_liberty(board, lib, visited, row, col+1, side); - propagate_liberty(board, lib, visited, row, col-1, side); -} - - -int *calculate_liberties(float *board) -{ - int *lib = calloc(19*19, sizeof(int)); - int visited[361]; - int i, j; - for(j = 0; j < 19; ++j){ - for(i = 0; i < 19; ++i){ - memset(visited, 0, 19*19*sizeof(int)); - int index = j*19 + i; - if(board[index] == 0){ - if ((i > 0) && board[index - 1]) propagate_liberty(board, lib, visited, j, i-1, board[index-1]); - if ((i < 18) && board[index + 1]) propagate_liberty(board, lib, visited, j, i+1, board[index+1]); - if ((j > 0) && board[index - 19]) propagate_liberty(board, lib, visited, j-1, i, board[index-19]); - if ((j < 18) && board[index + 19]) propagate_liberty(board, lib, visited, j+1, i, board[index+19]); - } - } - } - return lib; -} - -void print_board(float *board, int swap, int *indexes) -{ - //FILE *stream = stdout; - FILE *stream = stderr; - int i,j,n; - fprintf(stream, "\n\n"); - fprintf(stream, " "); - for(i = 0; i < 19; ++i){ - fprintf(stream, "%c ", 'A' + i + 1*(i > 7 && noi)); - } - fprintf(stream, "\n"); - for(j = 0; j < 19; ++j){ - fprintf(stream, "%2d", (inverted) ? 19-j : j+1); - for(i = 0; i < 19; ++i){ - int index = j*19 + i; - if(indexes){ - int found = 0; - for(n = 0; n < nind; ++n){ - if(index == indexes[n]){ - found = 1; - /* - if(n == 0) fprintf(stream, "\uff11"); - else if(n == 1) fprintf(stream, "\uff12"); - else if(n == 2) fprintf(stream, "\uff13"); - else if(n == 3) fprintf(stream, "\uff14"); - else if(n == 4) fprintf(stream, "\uff15"); - */ - if(n == 0) fprintf(stream, " 1"); - else if(n == 1) fprintf(stream, " 2"); - else if(n == 2) fprintf(stream, " 3"); - else if(n == 3) fprintf(stream, " 4"); - else if(n == 4) fprintf(stream, " 5"); - } - } - if(found) continue; - } - //if(board[index]*-swap > 0) fprintf(stream, "\u25C9 "); - //else if(board[index]*-swap < 0) fprintf(stream, "\u25EF "); - if(board[index]*-swap > 0) fprintf(stream, " O"); - else if(board[index]*-swap < 0) fprintf(stream, " X"); - else fprintf(stream, " "); - } - fprintf(stream, "\n"); - } -} - -void flip_board(float *board) -{ - int i; - for(i = 0; i < 19*19; ++i){ - board[i] = -board[i]; - } -} - -void predict_move(network net, float *board, float *move, int multi) -{ - float *output = network_predict(net, board); - copy_cpu(19*19, output, 1, move, 1); - int i; - if(multi){ - image bim = float_to_image(19, 19, 1, board); - for(i = 1; i < 8; ++i){ - rotate_image_cw(bim, i); - if(i >= 4) flip_image(bim); - - float *output = network_predict(net, board); - image oim = float_to_image(19, 19, 1, output); - - if(i >= 4) flip_image(oim); - rotate_image_cw(oim, -i); - - axpy_cpu(19*19, 1, output, 1, move, 1); - - if(i >= 4) flip_image(bim); - rotate_image_cw(bim, -i); - } - scal_cpu(19*19, 1./8., move, 1); - } - for(i = 0; i < 19*19; ++i){ - if(board[i]) move[i] = 0; - } -} - -void remove_connected(float *b, int *lib, int p, int r, int c) -{ - if (r < 0 || r >= 19 || c < 0 || c >= 19) return; - if (b[r*19 + c] != p) return; - if (lib[r*19 + c] != 1) return; - b[r*19 + c] = 0; - remove_connected(b, lib, p, r+1, c); - remove_connected(b, lib, p, r-1, c); - remove_connected(b, lib, p, r, c+1); - remove_connected(b, lib, p, r, c-1); -} - - -void move_go(float *b, int p, int r, int c) -{ - int *l = calculate_liberties(b); - b[r*19 + c] = p; - remove_connected(b, l, -p, r+1, c); - remove_connected(b, l, -p, r-1, c); - remove_connected(b, l, -p, r, c+1); - remove_connected(b, l, -p, r, c-1); - free(l); -} - -int makes_safe_go(float *b, int *lib, int p, int r, int c){ - if (r < 0 || r >= 19 || c < 0 || c >= 19) return 0; - if (b[r*19 + c] == -p){ - if (lib[r*19 + c] > 1) return 0; - else return 1; - } - if (b[r*19 + c] == 0) return 1; - if (lib[r*19 + c] > 1) return 1; - return 0; -} - -int suicide_go(float *b, int p, int r, int c) -{ - int *l = calculate_liberties(b); - int safe = 0; - safe = safe || makes_safe_go(b, l, p, r+1, c); - safe = safe || makes_safe_go(b, l, p, r-1, c); - safe = safe || makes_safe_go(b, l, p, r, c+1); - safe = safe || makes_safe_go(b, l, p, r, c-1); - free(l); - return !safe; -} - -int legal_go(float *b, char *ko, int p, int r, int c) -{ - if (b[r*19 + c]) return 0; - char curr[91]; - char next[91]; - board_to_string(curr, b); - move_go(b, p, r, c); - board_to_string(next, b); - string_to_board(curr, b); - if(memcmp(next, ko, 91) == 0) return 0; - return 1; -} - -int generate_move(network net, int player, float *board, int multi, float thresh, float temp, char *ko, int print) -{ - int i, j; - for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; - - float move[361]; - if (player < 0) flip_board(board); - predict_move(net, board, move, multi); - if (player < 0) flip_board(board); - - - for(i = 0; i < 19; ++i){ - for(j = 0; j < 19; ++j){ - if (!legal_go(board, ko, player, i, j)) move[i*19 + j] = 0; - } - } - - int indexes[nind]; - top_k(move, 19*19, nind, indexes); - if(thresh > move[indexes[0]]) thresh = move[indexes[nind-1]]; - - for(i = 0; i < 19; ++i){ - for(j = 0; j < 19; ++j){ - if (move[i*19 + j] < thresh) move[i*19 + j] = 0; - } - } - - - int max = max_index(move, 19*19); - int row = max / 19; - int col = max % 19; - int index = sample_array(move, 19*19); - - if(print){ - top_k(move, 19*19, nind, indexes); - for(i = 0; i < nind; ++i){ - if (!move[indexes[i]]) indexes[i] = -1; - } - print_board(board, player, indexes); - for(i = 0; i < nind; ++i){ - fprintf(stderr, "%d: %f\n", i+1, move[indexes[i]]); - } - } - - if(suicide_go(board, player, row, col)){ - return -1; - } - if(suicide_go(board, player, index/19, index%19)) index = max; - return index; -} - -void valid_go(char *cfgfile, char *weightfile, int multi) -{ - srand(time(0)); - char *base = basecfg(cfgfile); - printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - - float *board = calloc(19*19, sizeof(float)); - float *move = calloc(19*19, sizeof(float)); - moves m = load_go_moves("/home/pjreddie/backup/go.test"); - - int N = m.n; - int i; - int correct = 0; - for(i = 0; i = 'A' && c <= 'Z') c = c - 'A'; - if(c >= 'a' && c <= 'z') c = c - 'a'; - if(c >= 8) --c; - r = 19 - r; - fprintf(stderr, "move: %d %d\n", r, c); - - char *swap = two; - two = one; - one = swap; - move_go(board, player, r, c); - board_to_string(one, board); - - printf("=%s \n\n", ids); - print_board(board, 1, 0); - } else if (!strcmp(buff, "genmove")){ - char color[256]; - scanf("%s", color); - int player = (color[0] == 'b' || color[0] == 'B') ? 1 : -1; - - int index = generate_move(net, player, board, multi, .1, .7, two, 1); - if(passed || index < 0){ - printf("=%s pass\n\n", ids); - passed = 0; - } else { - int row = index / 19; - int col = index % 19; - - char *swap = two; - two = one; - one = swap; - - move_go(board, player, row, col); - board_to_string(one, board); - row = 19 - row; - if (col >= 8) ++col; - printf("=%s %c%d\n\n", ids, 'A' + col, row); - print_board(board, 1, 0); - } - - } else if (!strcmp(buff, "p")){ - //print_board(board, 1, 0); - } else if (!strcmp(buff, "final_status_list")){ - char type[256]; - scanf("%s", type); - fprintf(stderr, "final_status\n"); - char *line = fgetl(stdin); - free(line); - if(type[0] == 'd' || type[0] == 'D'){ - FILE *f = fopen("game.txt", "w"); - int i, j; - int count = 2; - fprintf(f, "boardsize 19\n"); - fprintf(f, "clear_board\n"); - for(j = 0; j < 19; ++j){ - for(i = 0; i < 19; ++i){ - if(board[j*19 + i] == 1) fprintf(f, "play black %c%d\n", 'A'+i+(i>=8), 19-j); - if(board[j*19 + i] == -1) fprintf(f, "play white %c%d\n", 'A'+i+(i>=8), 19-j); - if(board[j*19 + i]) ++count; - } - } - fprintf(f, "final_status_list dead\n"); - fclose(f); - FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); - for(i = 0; i < count; ++i){ - free(fgetl(p)); - free(fgetl(p)); - } - char *l = 0; - while((l = fgetl(p))){ - printf("%s\n", l); - free(l); - } - } else { - printf("?%s unknown command\n\n", ids); - } - } else { - char *line = fgetl(stdin); - free(line); - printf("?%s unknown command\n\n", ids); - } - fflush(stdout); - fflush(stderr); - } -} - -void test_go(char *cfg, char *weights, int multi) -{ - network net = parse_network_cfg(cfg); - if(weights){ - load_weights(&net, weights); - } - srand(time(0)); - set_batch_network(&net, 1); - float *board = calloc(19*19, sizeof(float)); - float *move = calloc(19*19, sizeof(float)); - int color = 1; - while(1){ - float *output = network_predict(net, board); - copy_cpu(19*19, output, 1, move, 1); - int i; - if(multi){ - image bim = float_to_image(19, 19, 1, board); - for(i = 1; i < 8; ++i){ - rotate_image_cw(bim, i); - if(i >= 4) flip_image(bim); - - float *output = network_predict(net, board); - image oim = float_to_image(19, 19, 1, output); - - if(i >= 4) flip_image(oim); - rotate_image_cw(oim, -i); - - axpy_cpu(19*19, 1, output, 1, move, 1); - - if(i >= 4) flip_image(bim); - rotate_image_cw(bim, -i); - } - scal_cpu(19*19, 1./8., move, 1); - } - for(i = 0; i < 19*19; ++i){ - if(board[i]) move[i] = 0; - } - - int indexes[nind]; - int row, col; - top_k(move, 19*19, nind, indexes); - print_board(board, color, indexes); - for(i = 0; i < nind; ++i){ - int index = indexes[i]; - row = index / 19; - col = index % 19; - printf("%d: %c %d, %.2f%%\n", i+1, col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100); - } - //if(color == 1) printf("\u25EF Enter move: "); - //else printf("\u25C9 Enter move: "); - if(color == 1) printf("X Enter move: "); - else printf("O Enter move: "); - - char c; - char *line = fgetl(stdin); - int picked = 1; - int dnum = sscanf(line, "%d", &picked); - int cnum = sscanf(line, "%c", &c); - if (strlen(line) == 0 || dnum) { - --picked; - if (picked < nind){ - int index = indexes[picked]; - row = index / 19; - col = index % 19; - board[row*19 + col] = 1; - } - } else if (cnum){ - if (c <= 'T' && c >= 'A'){ - int num = sscanf(line, "%c %d", &c, &row); - row = (inverted)?19 - row : row-1; - col = c - 'A'; - if (col > 7 && noi) col -= 1; - if (num == 2) board[row*19 + col] = 1; - } else if (c == 'p') { - // Pass - } else if(c=='b' || c == 'w'){ - char g; - int num = sscanf(line, "%c %c %d", &g, &c, &row); - row = (inverted)?19 - row : row-1; - col = c - 'A'; - if (col > 7 && noi) col -= 1; - if (num == 3) board[row*19 + col] = (g == 'b') ? color : -color; - } else if(c == 'c'){ - char g; - int num = sscanf(line, "%c %c %d", &g, &c, &row); - row = (inverted)?19 - row : row-1; - col = c - 'A'; - if (col > 7 && noi) col -= 1; - if (num == 3) board[row*19 + col] = 0; - } - } - free(line); - flip_board(board); - color = -color; - } -} - -float score_game(float *board) -{ - FILE *f = fopen("game.txt", "w"); - int i, j; - int count = 3; - fprintf(f, "komi 6.5\n"); - fprintf(f, "boardsize 19\n"); - fprintf(f, "clear_board\n"); - for(j = 0; j < 19; ++j){ - for(i = 0; i < 19; ++i){ - if(board[j*19 + i] == 1) fprintf(f, "play black %c%d\n", 'A'+i+(i>=8), 19-j); - if(board[j*19 + i] == -1) fprintf(f, "play white %c%d\n", 'A'+i+(i>=8), 19-j); - if(board[j*19 + i]) ++count; - } - } - fprintf(f, "final_score\n"); - fclose(f); - FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); - for(i = 0; i < count; ++i){ - free(fgetl(p)); - free(fgetl(p)); - } - char *l = 0; - float score = 0; - char player = 0; - while((l = fgetl(p))){ - fprintf(stderr, "%s \t", l); - int n = sscanf(l, "= %c+%f", &player, &score); - free(l); - if (n == 2) break; - } - if(player == 'W') score = -score; - pclose(p); - return score; -} - -void self_go(char *filename, char *weightfile, char *f2, char *w2, int multi) -{ - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } - - network net2 = net; - if(f2){ - net2 = parse_network_cfg(f2); - if(w2){ - load_weights(&net2, w2); - } - } - srand(time(0)); - char boards[300][93]; - int count = 0; - set_batch_network(&net, 1); - set_batch_network(&net2, 1); - float *board = calloc(19*19, sizeof(float)); - char *one = calloc(91, sizeof(char)); - char *two = calloc(91, sizeof(char)); - int done = 0; - int player = 1; - int p1 = 0; - int p2 = 0; - int total = 0; - while(1){ - if (done || count >= 300){ - float score = score_game(board); - int i = (score > 0)? 0 : 1; - if((score > 0) == (total%2==0)) ++p1; - else ++p2; - ++total; - fprintf(stderr, "Total: %d, Player 1: %f, Player 2: %f\n", total, (float)p1/total, (float)p2/total); - int j; - for(; i < count; i += 2){ - for(j = 0; j < 93; ++j){ - printf("%c", boards[i][j]); - } - printf("\n"); - } - memset(board, 0, 19*19*sizeof(float)); - player = 1; - done = 0; - count = 0; - fflush(stdout); - fflush(stderr); - } - //print_board(board, 1, 0); - //sleep(1); - network use = ((total%2==0) == (player==1)) ? net : net2; - int index = generate_move(use, player, board, multi, .1, .7, two, 0); - if(index < 0){ - done = 1; - continue; - } - int row = index / 19; - int col = index % 19; - - char *swap = two; - two = one; - one = swap; - - if(player < 0) flip_board(board); - boards[count][0] = row; - boards[count][1] = col; - board_to_string(boards[count] + 2, board); - if(player < 0) flip_board(board); - ++count; - - move_go(board, player, row, col); - board_to_string(one, board); - - player = -player; - } -} - -void run_go(int argc, char **argv) -{ - //boards_go(); - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *c2 = (argc > 5) ? argv[5] : 0; - char *w2 = (argc > 6) ? argv[6] : 0; - int multi = find_arg(argc, argv, "-multi"); - if(0==strcmp(argv[2], "train")) train_go(cfg, weights); - else if(0==strcmp(argv[2], "valid")) valid_go(cfg, weights, multi); - else if(0==strcmp(argv[2], "self")) self_go(cfg, weights, c2, w2, multi); - else if(0==strcmp(argv[2], "test")) test_go(cfg, weights, multi); - else if(0==strcmp(argv[2], "engine")) engine_go(cfg, weights, multi); -} - - diff --git a/image.darknet/inst/include/darknet/src/gru_layer.c b/image.darknet/inst/include/darknet/src/gru_layer.c index b78e868..b6601d8 100644 --- a/image.darknet/inst/include/darknet/src/gru_layer.c +++ b/image.darknet/inst/include/darknet/src/gru_layer.c @@ -26,7 +26,7 @@ static void increment_layer(layer *l, int steps) #endif } -layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize) +layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam) { fprintf(stderr, "GRU Layer: %d inputs, %d outputs\n", inputs, outputs); batch = batch / steps; @@ -36,39 +36,37 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no l.steps = steps; l.inputs = inputs; - l.input_z_layer = malloc(sizeof(layer)); + l.uz = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_z_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); - l.input_z_layer->batch = batch; + *(l.uz) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uz->batch = batch; - l.state_z_layer = malloc(sizeof(layer)); + l.wz = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.state_z_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); - l.state_z_layer->batch = batch; + *(l.wz) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wz->batch = batch; - - - l.input_r_layer = malloc(sizeof(layer)); + l.ur = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_r_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); - l.input_r_layer->batch = batch; + *(l.ur) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.ur->batch = batch; - l.state_r_layer = malloc(sizeof(layer)); + l.wr = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.state_r_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); - l.state_r_layer->batch = batch; + *(l.wr) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wr->batch = batch; - l.input_h_layer = malloc(sizeof(layer)); + l.uh = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_h_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); - l.input_h_layer->batch = batch; + *(l.uh) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uh->batch = batch; - l.state_h_layer = malloc(sizeof(layer)); + l.wh = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.state_h_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); - l.state_h_layer->batch = batch; + *(l.wh) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wh->batch = batch; l.batch_normalize = batch_normalize; @@ -94,68 +92,80 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no l.backward_gpu = backward_gru_layer_gpu; l.update_gpu = update_gru_layer_gpu; - l.forgot_state_gpu = cuda_make_array(l.output, batch*outputs); - l.forgot_delta_gpu = cuda_make_array(l.output, batch*outputs); - l.prev_state_gpu = cuda_make_array(l.output, batch*outputs); - l.state_gpu = cuda_make_array(l.output, batch*outputs); - l.output_gpu = cuda_make_array(l.output, batch*outputs*steps); - l.delta_gpu = cuda_make_array(l.delta, batch*outputs*steps); - l.r_gpu = cuda_make_array(l.output_gpu, batch*outputs); - l.z_gpu = cuda_make_array(l.output_gpu, batch*outputs); - l.h_gpu = cuda_make_array(l.output_gpu, batch*outputs); + l.forgot_state_gpu = cuda_make_array(0, batch*outputs); + l.forgot_delta_gpu = cuda_make_array(0, batch*outputs); + l.prev_state_gpu = cuda_make_array(0, batch*outputs); + l.state_gpu = cuda_make_array(0, batch*outputs); + l.output_gpu = cuda_make_array(0, batch*outputs*steps); + l.delta_gpu = cuda_make_array(0, batch*outputs*steps); + l.r_gpu = cuda_make_array(0, batch*outputs); + l.z_gpu = cuda_make_array(0, batch*outputs); + l.h_gpu = cuda_make_array(0, batch*outputs); + +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.uz->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uz->out_c, l.uz->out_h, l.uz->out_w); + cudnnSetTensor4dDescriptor(l.uh->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uh->out_c, l.uh->out_h, l.uh->out_w); + cudnnSetTensor4dDescriptor(l.ur->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ur->out_c, l.ur->out_h, l.ur->out_w); + cudnnSetTensor4dDescriptor(l.wz->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wz->out_c, l.wz->out_h, l.wz->out_w); + cudnnSetTensor4dDescriptor(l.wh->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wh->out_c, l.wh->out_h, l.wh->out_w); + cudnnSetTensor4dDescriptor(l.wr->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wr->out_c, l.wr->out_h, l.wr->out_w); +#endif #endif return l; } -void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay) +void update_gru_layer(layer l, update_args a) { - update_connected_layer(*(l.input_layer), batch, learning_rate, momentum, decay); - update_connected_layer(*(l.self_layer), batch, learning_rate, momentum, decay); - update_connected_layer(*(l.output_layer), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.ur), a); + update_connected_layer(*(l.uz), a); + update_connected_layer(*(l.uh), a); + update_connected_layer(*(l.wr), a); + update_connected_layer(*(l.wz), a); + update_connected_layer(*(l.wh), a); } -void forward_gru_layer(layer l, network_state state) +void forward_gru_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; - layer input_z_layer = *(l.input_z_layer); - layer input_r_layer = *(l.input_r_layer); - layer input_h_layer = *(l.input_h_layer); - - layer state_z_layer = *(l.state_z_layer); - layer state_r_layer = *(l.state_r_layer); - layer state_h_layer = *(l.state_h_layer); - - fill_cpu(l.outputs * l.batch * l.steps, 0, input_z_layer.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, input_r_layer.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, input_h_layer.delta, 1); - - fill_cpu(l.outputs * l.batch * l.steps, 0, state_z_layer.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, state_r_layer.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, state_h_layer.delta, 1); - if(state.train) { + layer uz = *(l.uz); + layer ur = *(l.ur); + layer uh = *(l.uh); + + layer wz = *(l.wz); + layer wr = *(l.wr); + layer wh = *(l.wh); + + fill_cpu(l.outputs * l.batch * l.steps, 0, uz.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ur.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, uh.delta, 1); + + fill_cpu(l.outputs * l.batch * l.steps, 0, wz.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wr.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wh.delta, 1); + if(net.train) { fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1); copy_cpu(l.outputs*l.batch, l.state, 1, l.prev_state, 1); } for (i = 0; i < l.steps; ++i) { s.input = l.state; - forward_connected_layer(state_z_layer, s); - forward_connected_layer(state_r_layer, s); + forward_connected_layer(wz, s); + forward_connected_layer(wr, s); - s.input = state.input; - forward_connected_layer(input_z_layer, s); - forward_connected_layer(input_r_layer, s); - forward_connected_layer(input_h_layer, s); + s.input = net.input; + forward_connected_layer(uz, s); + forward_connected_layer(ur, s); + forward_connected_layer(uh, s); - copy_cpu(l.outputs*l.batch, input_z_layer.output, 1, l.z_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, state_z_layer.output, 1, l.z_cpu, 1); + copy_cpu(l.outputs*l.batch, uz.output, 1, l.z_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, wz.output, 1, l.z_cpu, 1); - copy_cpu(l.outputs*l.batch, input_r_layer.output, 1, l.r_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, state_r_layer.output, 1, l.r_cpu, 1); + copy_cpu(l.outputs*l.batch, ur.output, 1, l.r_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, wr.output, 1, l.r_cpu, 1); activate_array(l.z_cpu, l.outputs*l.batch, LOGISTIC); activate_array(l.r_cpu, l.outputs*l.batch, LOGISTIC); @@ -164,34 +174,34 @@ void forward_gru_layer(layer l, network_state state) mul_cpu(l.outputs*l.batch, l.r_cpu, 1, l.forgot_state, 1); s.input = l.forgot_state; - forward_connected_layer(state_h_layer, s); + forward_connected_layer(wh, s); - copy_cpu(l.outputs*l.batch, input_h_layer.output, 1, l.h_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, state_h_layer.output, 1, l.h_cpu, 1); + copy_cpu(l.outputs*l.batch, uh.output, 1, l.h_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, wh.output, 1, l.h_cpu, 1); - #ifdef USET - activate_array(l.h_cpu, l.outputs*l.batch, TANH); - #else - activate_array(l.h_cpu, l.outputs*l.batch, LOGISTIC); - #endif + if(l.tanh){ + activate_array(l.h_cpu, l.outputs*l.batch, TANH); + } else { + activate_array(l.h_cpu, l.outputs*l.batch, LOGISTIC); + } weighted_sum_cpu(l.state, l.h_cpu, l.z_cpu, l.outputs*l.batch, l.output); copy_cpu(l.outputs*l.batch, l.output, 1, l.state, 1); - state.input += l.inputs*l.batch; + net.input += l.inputs*l.batch; l.output += l.outputs*l.batch; - increment_layer(&input_z_layer, 1); - increment_layer(&input_r_layer, 1); - increment_layer(&input_h_layer, 1); + increment_layer(&uz, 1); + increment_layer(&ur, 1); + increment_layer(&uh, 1); - increment_layer(&state_z_layer, 1); - increment_layer(&state_r_layer, 1); - increment_layer(&state_h_layer, 1); + increment_layer(&wz, 1); + increment_layer(&wr, 1); + increment_layer(&wh, 1); } } -void backward_gru_layer(layer l, network_state state) +void backward_gru_layer(layer l, network net) { } @@ -205,191 +215,192 @@ void push_gru_layer(layer l) { } -void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_gru_layer_gpu(layer l, update_args a) { - update_connected_layer_gpu(*(l.input_r_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.input_z_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.input_h_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.state_r_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.state_z_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.state_h_layer), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.ur), a); + update_connected_layer_gpu(*(l.uz), a); + update_connected_layer_gpu(*(l.uh), a); + update_connected_layer_gpu(*(l.wr), a); + update_connected_layer_gpu(*(l.wz), a); + update_connected_layer_gpu(*(l.wh), a); } -void forward_gru_layer_gpu(layer l, network_state state) +void forward_gru_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = {0}; + s.train = net.train; int i; - layer input_z_layer = *(l.input_z_layer); - layer input_r_layer = *(l.input_r_layer); - layer input_h_layer = *(l.input_h_layer); - - layer state_z_layer = *(l.state_z_layer); - layer state_r_layer = *(l.state_r_layer); - layer state_h_layer = *(l.state_h_layer); - - fill_ongpu(l.outputs * l.batch * l.steps, 0, input_z_layer.delta_gpu, 1); - fill_ongpu(l.outputs * l.batch * l.steps, 0, input_r_layer.delta_gpu, 1); - fill_ongpu(l.outputs * l.batch * l.steps, 0, input_h_layer.delta_gpu, 1); - - fill_ongpu(l.outputs * l.batch * l.steps, 0, state_z_layer.delta_gpu, 1); - fill_ongpu(l.outputs * l.batch * l.steps, 0, state_r_layer.delta_gpu, 1); - fill_ongpu(l.outputs * l.batch * l.steps, 0, state_h_layer.delta_gpu, 1); - if(state.train) { - fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1); + layer uz = *(l.uz); + layer ur = *(l.ur); + layer uh = *(l.uh); + + layer wz = *(l.wz); + layer wr = *(l.wr); + layer wh = *(l.wh); + + fill_gpu(l.outputs * l.batch * l.steps, 0, uz.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, ur.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, uh.delta_gpu, 1); + + fill_gpu(l.outputs * l.batch * l.steps, 0, wz.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wr.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wh.delta_gpu, 1); + if(net.train) { + fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1); } for (i = 0; i < l.steps; ++i) { - s.input = l.state_gpu; - forward_connected_layer_gpu(state_z_layer, s); - forward_connected_layer_gpu(state_r_layer, s); + s.input_gpu = l.state_gpu; + forward_connected_layer_gpu(wz, s); + forward_connected_layer_gpu(wr, s); - s.input = state.input; - forward_connected_layer_gpu(input_z_layer, s); - forward_connected_layer_gpu(input_r_layer, s); - forward_connected_layer_gpu(input_h_layer, s); + s.input_gpu = net.input_gpu; + forward_connected_layer_gpu(uz, s); + forward_connected_layer_gpu(ur, s); + forward_connected_layer_gpu(uh, s); + copy_gpu(l.outputs*l.batch, uz.output_gpu, 1, l.z_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wz.output_gpu, 1, l.z_gpu, 1); - copy_ongpu(l.outputs*l.batch, input_z_layer.output_gpu, 1, l.z_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_z_layer.output_gpu, 1, l.z_gpu, 1); + copy_gpu(l.outputs*l.batch, ur.output_gpu, 1, l.r_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wr.output_gpu, 1, l.r_gpu, 1); - copy_ongpu(l.outputs*l.batch, input_r_layer.output_gpu, 1, l.r_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_r_layer.output_gpu, 1, l.r_gpu, 1); + activate_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1); + mul_gpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); + s.input_gpu = l.forgot_state_gpu; + forward_connected_layer_gpu(wh, s); - s.input = l.forgot_state_gpu; - forward_connected_layer_gpu(state_h_layer, s); + copy_gpu(l.outputs*l.batch, uh.output_gpu, 1, l.h_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wh.output_gpu, 1, l.h_gpu, 1); - copy_ongpu(l.outputs*l.batch, input_h_layer.output_gpu, 1, l.h_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_h_layer.output_gpu, 1, l.h_gpu, 1); - - #ifdef USET - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); - #else - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); - #endif + if(l.tanh){ + activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH); + } else { + activate_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); + } weighted_sum_gpu(l.state_gpu, l.h_gpu, l.z_gpu, l.outputs*l.batch, l.output_gpu); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.state_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.state_gpu, 1); - - state.input += l.inputs*l.batch; + net.input_gpu += l.inputs*l.batch; l.output_gpu += l.outputs*l.batch; - increment_layer(&input_z_layer, 1); - increment_layer(&input_r_layer, 1); - increment_layer(&input_h_layer, 1); + increment_layer(&uz, 1); + increment_layer(&ur, 1); + increment_layer(&uh, 1); - increment_layer(&state_z_layer, 1); - increment_layer(&state_r_layer, 1); - increment_layer(&state_h_layer, 1); + increment_layer(&wz, 1); + increment_layer(&wr, 1); + increment_layer(&wh, 1); } } -void backward_gru_layer_gpu(layer l, network_state state) +void backward_gru_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = {0}; + s.train = net.train; int i; - layer input_z_layer = *(l.input_z_layer); - layer input_r_layer = *(l.input_r_layer); - layer input_h_layer = *(l.input_h_layer); + layer uz = *(l.uz); + layer ur = *(l.ur); + layer uh = *(l.uh); - layer state_z_layer = *(l.state_z_layer); - layer state_r_layer = *(l.state_r_layer); - layer state_h_layer = *(l.state_h_layer); + layer wz = *(l.wz); + layer wr = *(l.wr); + layer wh = *(l.wh); - increment_layer(&input_z_layer, l.steps - 1); - increment_layer(&input_r_layer, l.steps - 1); - increment_layer(&input_h_layer, l.steps - 1); + increment_layer(&uz, l.steps - 1); + increment_layer(&ur, l.steps - 1); + increment_layer(&uh, l.steps - 1); - increment_layer(&state_z_layer, l.steps - 1); - increment_layer(&state_r_layer, l.steps - 1); - increment_layer(&state_h_layer, l.steps - 1); + increment_layer(&wz, l.steps - 1); + increment_layer(&wr, l.steps - 1); + increment_layer(&wh, l.steps - 1); - state.input += l.inputs*l.batch*(l.steps-1); - if(state.delta) state.delta += l.inputs*l.batch*(l.steps-1); + net.input_gpu += l.inputs*l.batch*(l.steps-1); + if(net.delta_gpu) net.delta_gpu += l.inputs*l.batch*(l.steps-1); l.output_gpu += l.outputs*l.batch*(l.steps-1); l.delta_gpu += l.outputs*l.batch*(l.steps-1); + float *end_state = l.output_gpu; for (i = l.steps-1; i >= 0; --i) { - if(i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1); + if(i != 0) copy_gpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1); + else copy_gpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.state_gpu, 1); float *prev_delta_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; - copy_ongpu(l.outputs*l.batch, input_z_layer.output_gpu, 1, l.z_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_z_layer.output_gpu, 1, l.z_gpu, 1); - - copy_ongpu(l.outputs*l.batch, input_r_layer.output_gpu, 1, l.r_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_r_layer.output_gpu, 1, l.r_gpu, 1); - - activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); - - copy_ongpu(l.outputs*l.batch, input_h_layer.output_gpu, 1, l.h_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, state_h_layer.output_gpu, 1, l.h_gpu, 1); - - #ifdef USET - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); - #else - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); - #endif - - weighted_delta_gpu(l.prev_state_gpu, l.h_gpu, l.z_gpu, prev_delta_gpu, input_h_layer.delta_gpu, input_z_layer.delta_gpu, l.outputs*l.batch, l.delta_gpu); - - #ifdef USET - gradient_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH, input_h_layer.delta_gpu); - #else - gradient_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC, input_h_layer.delta_gpu); - #endif - - copy_ongpu(l.outputs*l.batch, input_h_layer.delta_gpu, 1, state_h_layer.delta_gpu, 1); - - copy_ongpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.forgot_state_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); - fill_ongpu(l.outputs*l.batch, 0, l.forgot_delta_gpu, 1); - - s.input = l.forgot_state_gpu; - s.delta = l.forgot_delta_gpu; - - backward_connected_layer_gpu(state_h_layer, s); + copy_gpu(l.outputs*l.batch, uz.output_gpu, 1, l.z_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wz.output_gpu, 1, l.z_gpu, 1); + + copy_gpu(l.outputs*l.batch, ur.output_gpu, 1, l.r_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wr.output_gpu, 1, l.r_gpu, 1); + + activate_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, uh.output_gpu, 1, l.h_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wh.output_gpu, 1, l.h_gpu, 1); + + if(l.tanh){ + activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH); + } else { + activate_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); + } + + weighted_delta_gpu(l.state_gpu, l.h_gpu, l.z_gpu, prev_delta_gpu, uh.delta_gpu, uz.delta_gpu, l.outputs*l.batch, l.delta_gpu); + + if(l.tanh){ + gradient_array_gpu(l.h_gpu, l.outputs*l.batch, TANH, uh.delta_gpu); + } else { + gradient_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC, uh.delta_gpu); + } + + copy_gpu(l.outputs*l.batch, uh.delta_gpu, 1, wh.delta_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1); + mul_gpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); + fill_gpu(l.outputs*l.batch, 0, l.forgot_delta_gpu, 1); + + s.input_gpu = l.forgot_state_gpu; + s.delta_gpu = l.forgot_delta_gpu; + + backward_connected_layer_gpu(wh, s); if(prev_delta_gpu) mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.r_gpu, prev_delta_gpu); - mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.prev_state_gpu, input_r_layer.delta_gpu); - - gradient_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC, input_r_layer.delta_gpu); - copy_ongpu(l.outputs*l.batch, input_r_layer.delta_gpu, 1, state_r_layer.delta_gpu, 1); - - gradient_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, input_z_layer.delta_gpu); - copy_ongpu(l.outputs*l.batch, input_z_layer.delta_gpu, 1, state_z_layer.delta_gpu, 1); - - s.input = l.prev_state_gpu; - s.delta = prev_delta_gpu; - - backward_connected_layer_gpu(state_r_layer, s); - backward_connected_layer_gpu(state_z_layer, s); - - s.input = state.input; - s.delta = state.delta; - - backward_connected_layer_gpu(input_h_layer, s); - backward_connected_layer_gpu(input_r_layer, s); - backward_connected_layer_gpu(input_z_layer, s); - - - state.input -= l.inputs*l.batch; - if(state.delta) state.delta -= l.inputs*l.batch; + mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.state_gpu, ur.delta_gpu); + + gradient_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC, ur.delta_gpu); + copy_gpu(l.outputs*l.batch, ur.delta_gpu, 1, wr.delta_gpu, 1); + + gradient_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, uz.delta_gpu); + copy_gpu(l.outputs*l.batch, uz.delta_gpu, 1, wz.delta_gpu, 1); + + s.input_gpu = l.state_gpu; + s.delta_gpu = prev_delta_gpu; + + backward_connected_layer_gpu(wr, s); + backward_connected_layer_gpu(wz, s); + + s.input_gpu = net.input_gpu; + s.delta_gpu = net.delta_gpu; + + backward_connected_layer_gpu(uh, s); + backward_connected_layer_gpu(ur, s); + backward_connected_layer_gpu(uz, s); + + + net.input_gpu -= l.inputs*l.batch; + if(net.delta_gpu) net.delta_gpu -= l.inputs*l.batch; l.output_gpu -= l.outputs*l.batch; l.delta_gpu -= l.outputs*l.batch; - increment_layer(&input_z_layer, -1); - increment_layer(&input_r_layer, -1); - increment_layer(&input_h_layer, -1); + increment_layer(&uz, -1); + increment_layer(&ur, -1); + increment_layer(&uh, -1); - increment_layer(&state_z_layer, -1); - increment_layer(&state_r_layer, -1); - increment_layer(&state_h_layer, -1); + increment_layer(&wz, -1); + increment_layer(&wr, -1); + increment_layer(&wh, -1); } + copy_gpu(l.outputs*l.batch, end_state, 1, l.state_gpu, 1); } #endif diff --git a/image.darknet/inst/include/darknet/src/gru_layer.h b/image.darknet/inst/include/darknet/src/gru_layer.h index 9e19cee..9067942 100644 --- a/image.darknet/inst/include/darknet/src/gru_layer.h +++ b/image.darknet/inst/include/darknet/src/gru_layer.h @@ -6,16 +6,16 @@ #include "layer.h" #include "network.h" -layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); +layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam); -void forward_gru_layer(layer l, network_state state); -void backward_gru_layer(layer l, network_state state); -void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_gru_layer(layer l, network state); +void backward_gru_layer(layer l, network state); +void update_gru_layer(layer l, update_args a); #ifdef GPU -void forward_gru_layer_gpu(layer l, network_state state); -void backward_gru_layer_gpu(layer l, network_state state); -void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_gru_layer_gpu(layer l, network state); +void backward_gru_layer_gpu(layer l, network state); +void update_gru_layer_gpu(layer l, update_args a); void push_gru_layer(layer l); void pull_gru_layer(layer l); #endif diff --git a/image.darknet/inst/include/darknet/src/im2col.h b/image.darknet/inst/include/darknet/src/im2col.h index f0ddeee..02c4247 100644 --- a/image.darknet/inst/include/darknet/src/im2col.h +++ b/image.darknet/inst/include/darknet/src/im2col.h @@ -7,7 +7,7 @@ void im2col_cpu(float* data_im, #ifdef GPU -void im2col_ongpu(float *im, +void im2col_gpu(float *im, int channels, int height, int width, int ksize, int stride, int pad,float *data_col); diff --git a/image.darknet/inst/include/darknet/src/im2col_kernels.cu b/image.darknet/inst/include/darknet/src/im2col_kernels.cu index d42d600..07b5e67 100644 --- a/image.darknet/inst/include/darknet/src/im2col_kernels.cu +++ b/image.darknet/inst/include/darknet/src/im2col_kernels.cu @@ -45,7 +45,7 @@ __global__ void im2col_gpu_kernel(const int n, const float* data_im, } } -void im2col_ongpu(float *im, +void im2col_gpu(float *im, int channels, int height, int width, int ksize, int stride, int pad, float *data_col){ // We are going to launch channels * height_col * width_col kernels, each diff --git a/image.darknet/inst/include/darknet/src/image.c b/image.darknet/inst/include/darknet/src/image.c index 5a90efd..4a2c6ba 100644 --- a/image.darknet/inst/include/darknet/src/image.c +++ b/image.darknet/inst/include/darknet/src/image.c @@ -10,12 +10,6 @@ #define STB_IMAGE_WRITE_IMPLEMENTATION #include "stb_image_write.h" -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#endif - - int windows = 0; float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} }; @@ -31,6 +25,70 @@ float get_color(int c, int x, int max) return r; } +image mask_to_rgb(image mask) +{ + int n = mask.c; + image im = make_image(mask.w, mask.h, 3); + int i, j; + for(j = 0; j < n; ++j){ + int offset = j*123457 % n; + float red = get_color(2,offset,n); + float green = get_color(1,offset,n); + float blue = get_color(0,offset,n); + for(i = 0; i < im.w*im.h; ++i){ + im.data[i + 0*im.w*im.h] += mask.data[j*im.h*im.w + i]*red; + im.data[i + 1*im.w*im.h] += mask.data[j*im.h*im.w + i]*green; + im.data[i + 2*im.w*im.h] += mask.data[j*im.h*im.w + i]*blue; + } + } + return im; +} + +static float get_pixel(image m, int x, int y, int c) +{ + assert(x < m.w && y < m.h && c < m.c); + return m.data[c*m.h*m.w + y*m.w + x]; +} +static float get_pixel_extend(image m, int x, int y, int c) +{ + if(x < 0 || x >= m.w || y < 0 || y >= m.h) return 0; + /* + if(x < 0) x = 0; + if(x >= m.w) x = m.w-1; + if(y < 0) y = 0; + if(y >= m.h) y = m.h-1; + */ + if(c < 0 || c >= m.c) return 0; + return get_pixel(m, x, y, c); +} +static void set_pixel(image m, int x, int y, int c, float val) +{ + if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return; + assert(x < m.w && y < m.h && c < m.c); + m.data[c*m.h*m.w + y*m.w + x] = val; +} +static void add_pixel(image m, int x, int y, int c, float val) +{ + assert(x < m.w && y < m.h && c < m.c); + m.data[c*m.h*m.w + y*m.w + x] += val; +} + +static float bilinear_interpolate(image im, float x, float y, int c) +{ + int ix = (int) floorf(x); + int iy = (int) floorf(y); + + float dx = x - ix; + float dy = y - iy; + + float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) + + dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) + + (1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) + + dy * dx * get_pixel_extend(im, ix+1, iy+1, c); + return val; +} + + void composite_image(image source, image dest, int dx, int dy) { int x,y,k; @@ -73,6 +131,7 @@ image tile_images(image a, image b, int dx) image get_label(image **characters, char *string, int size) { + size = size/10; if(size > 7) size = 7; image label = make_empty_image(0,0,0); while(*string){ @@ -177,23 +236,36 @@ image **load_alphabet() return alphabets; } -void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes) +void draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes) { - int i; + int i,j; for(i = 0; i < num; ++i){ - int class = max_index(probs[i], classes); - float prob = probs[i][class]; - if(prob > thresh){ - - int width = im.h * .012; - - if(0){ - width = pow(prob, 1./2.)*10+1; - alphabet = 0; + char labelstr[4096] = {0}; + int class = -1; + for(j = 0; j < classes; ++j){ + if (dets[i].prob[j] > thresh){ + if (class < 0) { + strcat(labelstr, names[j]); + class = j; + } else { + strcat(labelstr, ", "); + strcat(labelstr, names[j]); + } + printf("%s: %.0f%%\n", names[j], dets[i].prob[j]*100); } + } + if(class >= 0){ + int width = im.h * .006; - printf("%s: %.0f%%\n", names[class], prob*100); + /* + if(0){ + width = pow(prob, 1./2.)*10+1; + alphabet = 0; + } + */ + + //printf("%d %s: %.0f%%\n", i, names[class], prob*100); int offset = class*123457 % classes; float red = get_color(2,offset,classes); float green = get_color(1,offset,classes); @@ -205,7 +277,8 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, rgb[0] = red; rgb[1] = green; rgb[2] = blue; - box b = boxes[i]; + box b = dets[i].bbox; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); int left = (b.x-b.w/2.)*im.w; int right = (b.x+b.w/2.)*im.w; @@ -219,8 +292,18 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, draw_box_width(im, left, top, right, bot, width, red, green, blue); if (alphabet) { - image label = get_label(alphabet, names[class], (im.h*.03)/10); + image label = get_label(alphabet, labelstr, (im.h*.03)); draw_label(im, top + width, left, label, rgb); + free_image(label); + } + if (dets[i].mask){ + image mask = float_to_image(14, 14, 1, dets[i].mask); + image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h); + image tmask = threshold_image(resized_mask, .5); + embed_image(tmask, im, left, top); + free_image(mask); + free_image(resized_mask); + free_image(tmask); } } } @@ -294,6 +377,54 @@ image image_distance(image a, image b) return dist; } +void ghost_image(image source, image dest, int dx, int dy) +{ + int x,y,k; + float max_dist = sqrt((-source.w/2. + .5)*(-source.w/2. + .5)); + for(k = 0; k < source.c; ++k){ + for(y = 0; y < source.h; ++y){ + for(x = 0; x < source.w; ++x){ + float dist = sqrt((x - source.w/2. + .5)*(x - source.w/2. + .5) + (y - source.h/2. + .5)*(y - source.h/2. + .5)); + float alpha = (1 - dist/max_dist); + if(alpha < 0) alpha = 0; + float v1 = get_pixel(source, x,y,k); + float v2 = get_pixel(dest, dx+x,dy+y,k); + float val = alpha*v1 + (1-alpha)*v2; + set_pixel(dest, dx+x, dy+y, k, val); + } + } + } +} + +void blocky_image(image im, int s) +{ + int i,j,k; + for(k = 0; k < im.c; ++k){ + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)]; + } + } + } +} + +void censor_image(image im, int dx, int dy, int w, int h) +{ + int i,j,k; + int s = 32; + if(dx < 0) dx = 0; + if(dy < 0) dy = 0; + + for(k = 0; k < im.c; ++k){ + for(j = dy; j < dy + h && j < im.h; ++j){ + for(i = dx; i < dx + w && i < im.w; ++i){ + im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)]; + //im.data[i + j*im.w + k*im.w*im.h] = 0; + } + } + } +} + void embed_image(image source, image dest, int dx, int dy) { int x,y,k; @@ -380,6 +511,11 @@ void normalize_image2(image p) free(max); } +void copy_image_into(image src, image dest) +{ + memcpy(dest.data, src.data, src.h*src.w*src.c*sizeof(float)); +} + image copy_image(image p) { image copy = p; @@ -398,145 +534,27 @@ void rgbgr_image(image im) } } -#ifdef OPENCV -void show_image_cv(image p, const char *name) -{ - int x,y,k; - image copy = copy_image(p); - constrain_image(copy); - if(p.c == 3) rgbgr_image(copy); - //normalize_image(copy); - - char buff[256]; - //sprintf(buff, "%s (%d)", name, windows); - sprintf(buff, "%s", name); - - IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); - int step = disp->widthStep; - cvNamedWindow(buff, CV_WINDOW_NORMAL); - //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); - ++windows; - for(y = 0; y < p.h; ++y){ - for(x = 0; x < p.w; ++x){ - for(k= 0; k < p.c; ++k){ - disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255); - } - } - } - free_image(copy); - if(0){ - int w = 448; - int h = w*p.h/p.w; - if(h > 1000){ - h = 1000; - w = h*p.w/p.h; - } - IplImage *buffer = disp; - disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels); - cvResize(buffer, disp, CV_INTER_LINEAR); - cvReleaseImage(&buffer); - } - cvShowImage(buff, disp); - cvReleaseImage(&disp); -} -#endif - -void show_image(image p, const char *name) +int show_image(image p, const char *name, int ms) { #ifdef OPENCV - show_image_cv(p, name); + int c = show_image_cv(p, name, ms); + return c; #else fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name); save_image(p, name); + return -1; #endif } -#ifdef OPENCV - -image ipl_to_image(IplImage* src) -{ - unsigned char *data = (unsigned char *)src->imageData; - int h = src->height; - int w = src->width; - int c = src->nChannels; - int step = src->widthStep; - image out = make_image(w, h, c); - int i, j, k, count=0;; - - for(k= 0; k < c; ++k){ - for(i = 0; i < h; ++i){ - for(j = 0; j < w; ++j){ - out.data[count++] = data[i*step + j*c + k]/255.; - } - } - } - return out; -} - -image load_image_cv(char *filename, int channels) -{ - IplImage* src = 0; - int flag = -1; - if (channels == 0) flag = -1; - else if (channels == 1) flag = 0; - else if (channels == 3) flag = 1; - else { - fprintf(stderr, "OpenCV can't force load with %d channels\n", channels); - } - - if( (src = cvLoadImage(filename, flag)) == 0 ) - { - fprintf(stderr, "Cannot load image \"%s\"\n", filename); - char buff[256]; - sprintf(buff, "echo %s >> bad.list", filename); - system(buff); - return make_image(10,10,3); - //exit(0); - } - image out = ipl_to_image(src); - cvReleaseImage(&src); - rgbgr_image(out); - return out; -} - -image get_image_from_stream(CvCapture *cap) -{ - IplImage* src = cvQueryFrame(cap); - if (!src) return make_empty_image(0,0,0); - image im = ipl_to_image(src); - rgbgr_image(im); - return im; -} - -void save_image_jpg(image p, const char *name) -{ - image copy = copy_image(p); - if(p.c == 3) rgbgr_image(copy); - int x,y,k; - - char buff[256]; - sprintf(buff, "%s.jpg", name); - - IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); - int step = disp->widthStep; - for(y = 0; y < p.h; ++y){ - for(x = 0; x < p.w; ++x){ - for(k= 0; k < p.c; ++k){ - disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255); - } - } - } - cvSaveImage(buff, disp,0); - cvReleaseImage(&disp); - free_image(copy); -} -#endif - -void save_image_png(image im, const char *name) +void save_image_options(image im, const char *name, IMTYPE f, int quality) { char buff[256]; //sprintf(buff, "%s (%d)", name, windows); - sprintf(buff, "%s.png", name); + if(f == PNG) sprintf(buff, "%s.png", name); + else if (f == BMP) sprintf(buff, "%s.bmp", name); + else if (f == TGA) sprintf(buff, "%s.tga", name); + else if (f == JPG) sprintf(buff, "%s.jpg", name); + else sprintf(buff, "%s.png", name); unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char)); int i,k; for(k = 0; k < im.c; ++k){ @@ -544,21 +562,20 @@ void save_image_png(image im, const char *name) data[i*im.c+k] = (unsigned char) (255*im.data[i + k*im.w*im.h]); } } - int success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c); + int success = 0; + if(f == PNG) success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c); + else if (f == BMP) success = stbi_write_bmp(buff, im.w, im.h, im.c, data); + else if (f == TGA) success = stbi_write_tga(buff, im.w, im.h, im.c, data); + else if (f == JPG) success = stbi_write_jpg(buff, im.w, im.h, im.c, data, quality); free(data); if(!success) fprintf(stderr, "Failed to write image %s\n", buff); } void save_image(image im, const char *name) { -#ifdef OPENCV - save_image_jpg(im, name); -#else - save_image_png(im, name); -#endif + save_image_options(im, name, JPG, 80); } - void show_image_layers(image p, char *name) { int i; @@ -566,7 +583,7 @@ void show_image_layers(image p, char *name) for(i = 0; i < p.c; ++i){ sprintf(buff, "%s - Layer %d", name, i); image layer = get_image_layer(p, i); - show_image(layer, buff); + show_image(layer, buff, 1); free_image(layer); } } @@ -574,7 +591,7 @@ void show_image_layers(image p, char *name) void show_image_collapsed(image p, char *name) { image c = collapse_image_layers(p, 1); - show_image(c, name); + show_image(c, name, 1); free_image(c); } @@ -613,6 +630,29 @@ image float_to_image(int w, int h, int c, float *data) return out; } +void place_image(image im, int w, int h, int dx, int dy, image canvas) +{ + int x, y, c; + for(c = 0; c < im.c; ++c){ + for(y = 0; y < h; ++y){ + for(x = 0; x < w; ++x){ + float rx = ((float)x / w) * im.w; + float ry = ((float)y / h) * im.h; + float val = bilinear_interpolate(im, rx, ry, c); + set_pixel(canvas, x + dx, y + dy, c, val); + } + } + } +} + +image center_crop_image(image im, int w, int h) +{ + int m = (im.w < im.h) ? im.w : im.h; + image c = crop_image(im, (im.w - m) / 2, (im.h - m)/2, m, m); + image r = resize_image(c, w, h); + free_image(c); + return r; +} image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect) { @@ -652,6 +692,12 @@ image rotate_image(image im, float rad) return rot; } +void fill_image(image m, float s) +{ + int i; + for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] = s; +} + void translate_image(image m, float s) { int i; @@ -676,9 +722,7 @@ image crop_image(image im, int dx, int dy, int w, int h) float val = 0; r = constrain_int(r, 0, im.h-1); c = constrain_int(c, 0, im.w-1); - if (r >= 0 && r < im.h && c >= 0 && c < im.w) { - val = get_pixel(im, c, r, k); - } + val = get_pixel(im, c, r, k); set_pixel(cropped, i, j, k, val); } } @@ -746,11 +790,44 @@ void composite_3d(char *f1, char *f2, char *out, int delta) for(i = 0; i < c.w*c.h; ++i){ c.data[i] = a.data[i]; } -#ifdef OPENCV - save_image_jpg(c, out); -#else save_image(c, out); -#endif +} + +void letterbox_image_into(image im, int w, int h, image boxed) +{ + int new_w = im.w; + int new_h = im.h; + if (((float)w/im.w) < ((float)h/im.h)) { + new_w = w; + new_h = (im.h * w)/im.w; + } else { + new_h = h; + new_w = (im.w * h)/im.h; + } + image resized = resize_image(im, new_w, new_h); + embed_image(resized, boxed, (w-new_w)/2, (h-new_h)/2); + free_image(resized); +} + +image letterbox_image(image im, int w, int h) +{ + int new_w = im.w; + int new_h = im.h; + if (((float)w/im.w) < ((float)h/im.h)) { + new_w = w; + new_h = (im.h * w)/im.w; + } else { + new_h = h; + new_w = (im.w * h)/im.h; + } + image resized = resize_image(im, new_w, new_h); + image boxed = make_image(w, h, im.c); + fill_image(boxed, .5); + //int i; + //for(i = 0; i < boxed.w*boxed.h*boxed.c; ++i) boxed.data[i] = 0; + embed_image(resized, boxed, (w-new_w)/2, (h-new_h)/2); + free_image(resized); + return boxed; } image resize_max(image im, int max) @@ -793,8 +870,9 @@ image random_crop_image(image im, int w, int h) return crop; } -image random_augment_image(image im, float angle, float aspect, int low, int high, int size) +augment_args random_augment_args(image im, float angle, float aspect, int low, int high, int w, int h) { + augment_args a = {0}; aspect = rand_scale(aspect); int r = rand_int(low, high); int min = (im.h < im.w*aspect) ? im.h : im.w*aspect; @@ -802,15 +880,27 @@ image random_augment_image(image im, float angle, float aspect, int low, int hig float rad = rand_uniform(-angle, angle) * TWO_PI / 360.; - float dx = (im.w*scale/aspect - size) / 2.; - float dy = (im.h*scale - size) / 2.; - if(dx < 0) dx = 0; - if(dy < 0) dy = 0; + float dx = (im.w*scale/aspect - w) / 2.; + float dy = (im.h*scale - w) / 2.; + //if(dx < 0) dx = 0; + //if(dy < 0) dy = 0; dx = rand_uniform(-dx, dx); dy = rand_uniform(-dy, dy); - image crop = rotate_crop_image(im, rad, scale, size, size, dx, dy, aspect); + a.rad = rad; + a.scale = scale; + a.w = w; + a.h = h; + a.dx = dx; + a.dy = dy; + a.aspect = aspect; + return a; +} +image random_augment_image(image im, float angle, float aspect, int low, int high, int w, int h) +{ + augment_args a = random_augment_args(im, angle, aspect, low, high, w, h); + image crop = rotate_crop_image(im, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); return crop; } @@ -824,6 +914,52 @@ float three_way_min(float a, float b, float c) return (a < b) ? ( (a < c) ? a : c) : ( (b < c) ? b : c) ; } +void yuv_to_rgb(image im) +{ + assert(im.c == 3); + int i, j; + float r, g, b; + float y, u, v; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + y = get_pixel(im, i , j, 0); + u = get_pixel(im, i , j, 1); + v = get_pixel(im, i , j, 2); + + r = y + 1.13983*v; + g = y + -.39465*u + -.58060*v; + b = y + 2.03211*u; + + set_pixel(im, i, j, 0, r); + set_pixel(im, i, j, 1, g); + set_pixel(im, i, j, 2, b); + } + } +} + +void rgb_to_yuv(image im) +{ + assert(im.c == 3); + int i, j; + float r, g, b; + float y, u, v; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + r = get_pixel(im, i , j, 0); + g = get_pixel(im, i , j, 1); + b = get_pixel(im, i , j, 2); + + y = .299*r + .587*g + .114*b; + u = -.14713*r + -.28886*g + .436*b; + v = .615*r + -.51499*g + -.10001*b; + + set_pixel(im, i, j, 0, y); + set_pixel(im, i, j, 1, u); + set_pixel(im, i, j, 2, v); + } + } +} + // http://www.cs.rit.edu/~ncs/color/t_convert.html void rgb_to_hsv(image im) { @@ -903,12 +1039,30 @@ void hsv_to_rgb(image im) } } +void grayscale_image_3c(image im) +{ + assert(im.c == 3); + int i, j, k; + float scale[] = {0.299, 0.587, 0.114}; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + float val = 0; + for(k = 0; k < 3; ++k){ + val += scale[k]*get_pixel(im, i, j, k); + } + im.data[0*im.h*im.w + im.w*j + i] = val; + im.data[1*im.h*im.w + im.w*j + i] = val; + im.data[2*im.h*im.w + im.w*j + i] = val; + } + } +} + image grayscale_image(image im) { assert(im.c == 3); int i, j, k; image gray = make_image(im.w, im.h, 1); - float scale[] = {0.587, 0.299, 0.114}; + float scale[] = {0.299, 0.587, 0.114}; for(k = 0; k < im.c; ++k){ for(j = 0; j < im.h; ++j){ for(i = 0; i < im.w; ++i){ @@ -1042,21 +1196,6 @@ void saturate_exposure_image(image im, float sat, float exposure) constrain_image(im); } -float bilinear_interpolate(image im, float x, float y, int c) -{ - int ix = (int) floorf(x); - int iy = (int) floorf(y); - - float dx = x - ix; - float dy = y - iy; - - float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) + - dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) + - (1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) + - dy * dx * get_pixel_extend(im, ix+1, iy+1, c); - return val; -} - image resize_image(image im, int w, int h) { image resized = make_image(w, h, im.c); @@ -1119,16 +1258,16 @@ void test_resize(char *filename) distort_image(c4, .1, .66666, 1.5); - show_image(im, "Original"); - show_image(gray, "Gray"); - show_image(c1, "C1"); - show_image(c2, "C2"); - show_image(c3, "C3"); - show_image(c4, "C4"); + show_image(im, "Original", 1); + show_image(gray, "Gray", 1); + show_image(c1, "C1", 1); + show_image(c2, "C2", 1); + show_image(c3, "C3", 1); + show_image(c4, "C4", 1); #ifdef OPENCV while(1){ - image aug = random_augment_image(im, 0, .75, 320, 448, 320); - show_image(aug, "aug"); + image aug = random_augment_image(im, 0, .75, 320, 448, 320, 320); + show_image(aug, "aug", 1); free_image(aug); @@ -1143,10 +1282,9 @@ void test_resize(char *filename) float dhue = rand_uniform(-hue, hue); distort_image(c, dhue, dsat, dexp); - show_image(c, "rand"); + show_image(c, "rand", 1); printf("%f %f %f\n", dhue, dsat, dexp); free_image(c); - cvWaitKey(0); } #endif } @@ -1206,33 +1344,6 @@ image get_image_layer(image m, int l) } return out; } - -float get_pixel(image m, int x, int y, int c) -{ - assert(x < m.w && y < m.h && c < m.c); - return m.data[c*m.h*m.w + y*m.w + x]; -} -float get_pixel_extend(image m, int x, int y, int c) -{ - if(x < 0) x = 0; - if(x >= m.w) x = m.w-1; - if(y < 0) y = 0; - if(y >= m.h) y = m.h-1; - if(c < 0 || c >= m.c) return 0; - return get_pixel(m, x, y, c); -} -void set_pixel(image m, int x, int y, int c, float val) -{ - if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return; - assert(x < m.w && y < m.h && c < m.c); - m.data[c*m.h*m.w + y*m.w + x] = val; -} -void add_pixel(image m, int x, int y, int c, float val) -{ - assert(x < m.w && y < m.h && c < m.c); - m.data[c*m.h*m.w + y*m.w + x] += val; -} - void print_image(image m) { int i, j, k; @@ -1325,7 +1436,7 @@ void show_image_normalized(image im, const char *name) { image c = copy_image(im); normalize_image(c); - show_image(c, name); + show_image(c, name, 1); free_image(c); } @@ -1343,7 +1454,7 @@ void show_images(image *ims, int n, char *window) */ normalize_image(m); save_image(m, window); - show_image(m, window); + show_image(m, window, 1); free_image(m); } diff --git a/image.darknet/inst/include/darknet/src/image.h b/image.darknet/inst/include/darknet/src/image.h index 39c3962..3392bb9 100644 --- a/image.darknet/inst/include/darknet/src/image.h +++ b/image.darknet/inst/include/darknet/src/image.h @@ -7,81 +7,63 @@ #include #include #include "box.h" +#include "darknet.h" -typedef struct { - int h; - int w; - int c; - float *data; -} image; +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef OPENCV +void *open_video_stream(const char *f, int c, int w, int h, int fps); +image get_image_from_stream(void *p); +image load_image_cv(char *filename, int channels); +int show_image_cv(image im, const char* name, int ms); +#endif float get_color(int c, int x, int max); -void flip_image(image a); void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b); -void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b); void draw_bbox(image a, box bbox, int w, float r, float g, float b); -void draw_label(image a, int r, int c, image label, const float *rgb); void write_label(image a, int r, int c, image *characters, char *string, float *rgb); -void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **labels, int classes); image image_distance(image a, image b); void scale_image(image m, float s); -image crop_image(image im, int dx, int dy, int w, int h); +image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect); image random_crop_image(image im, int w, int h); -image random_augment_image(image im, float angle, float aspect, int low, int high, int size); -void random_distort_image(image im, float hue, float saturation, float exposure); -image resize_image(image im, int w, int h); -image resize_min(image im, int min); +image random_augment_image(image im, float angle, float aspect, int low, int high, int w, int h); +augment_args random_augment_args(image im, float angle, float aspect, int low, int high, int w, int h); +void letterbox_image_into(image im, int w, int h, image boxed); image resize_max(image im, int max); void translate_image(image m, float s); -void normalize_image(image p); -image rotate_image(image m, float rad); -void rotate_image_cw(image im, int times); void embed_image(image source, image dest, int dx, int dy); +void place_image(image im, int w, int h, int dx, int dy, image canvas); void saturate_image(image im, float sat); void exposure_image(image im, float sat); void distort_image(image im, float hue, float sat, float val); void saturate_exposure_image(image im, float sat, float exposure); +void rgb_to_hsv(image im); void hsv_to_rgb(image im); -void rgbgr_image(image im); -void constrain_image(image im); -void composite_3d(char *f1, char *f2, char *out, int delta); -int best_3d_shift_r(image a, image b, int min, int max); +void yuv_to_rgb(image im); +void rgb_to_yuv(image im); -image grayscale_image(image im); -image threshold_image(image im, float thresh); image collapse_image_layers(image source, int border); image collapse_images_horz(image *ims, int n); image collapse_images_vert(image *ims, int n); -void show_image(image p, const char *name); void show_image_normalized(image im, const char *name); -void save_image_png(image im, const char *name); -void save_image(image p, const char *name); void show_images(image *ims, int n, char *window); void show_image_layers(image p, char *name); void show_image_collapsed(image p, char *name); void print_image(image m); -image make_image(int w, int h, int c); -image make_random_image(int w, int h, int c); image make_empty_image(int w, int h, int c); -image float_to_image(int w, int h, int c, float *data); -image copy_image(image p); -image load_image(char *filename, int w, int h, int c); -image load_image_color(char *filename, int w, int h); -image **load_alphabet(); - -float get_pixel(image m, int x, int y, int c); -float get_pixel_extend(image m, int x, int y, int c); -void set_pixel(image m, int x, int y, int c, float val); -void add_pixel(image m, int x, int y, int c, float val); -float bilinear_interpolate(image im, float x, float y, int c); +void copy_image_into(image src, image dest); image get_image_layer(image m, int l); -void free_image(image m); -void test_resize(char *filename); +#ifdef __cplusplus +} +#endif + #endif diff --git a/image.darknet/inst/include/darknet/src/image_opencv.cpp b/image.darknet/inst/include/darknet/src/image_opencv.cpp new file mode 100644 index 0000000..7511280 --- /dev/null +++ b/image.darknet/inst/include/darknet/src/image_opencv.cpp @@ -0,0 +1,135 @@ +#ifdef OPENCV + +#include "stdio.h" +#include "stdlib.h" +#include "opencv2/opencv.hpp" +#include "image.h" + +using namespace cv; + +extern "C" { + +IplImage *image_to_ipl(image im) +{ + int x,y,c; + IplImage *disp = cvCreateImage(cvSize(im.w,im.h), IPL_DEPTH_8U, im.c); + int step = disp->widthStep; + for(y = 0; y < im.h; ++y){ + for(x = 0; x < im.w; ++x){ + for(c= 0; c < im.c; ++c){ + float val = im.data[c*im.h*im.w + y*im.w + x]; + disp->imageData[y*step + x*im.c + c] = (unsigned char)(val*255); + } + } + } + return disp; +} + +image ipl_to_image(IplImage* src) +{ + int h = src->height; + int w = src->width; + int c = src->nChannels; + image im = make_image(w, h, c); + unsigned char *data = (unsigned char *)src->imageData; + int step = src->widthStep; + int i, j, k; + + for(i = 0; i < h; ++i){ + for(k= 0; k < c; ++k){ + for(j = 0; j < w; ++j){ + im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.; + } + } + } + return im; +} + +Mat image_to_mat(image im) +{ + image copy = copy_image(im); + constrain_image(copy); + if(im.c == 3) rgbgr_image(copy); + + IplImage *ipl = image_to_ipl(copy); + Mat m = cvarrToMat(ipl, true); + cvReleaseImage(&ipl); + free_image(copy); + return m; +} + +image mat_to_image(Mat m) +{ + IplImage ipl = m; + image im = ipl_to_image(&ipl); + rgbgr_image(im); + return im; +} + +void *open_video_stream(const char *f, int c, int w, int h, int fps) +{ + VideoCapture *cap; + if(f) cap = new VideoCapture(f); + else cap = new VideoCapture(c); + if(!cap->isOpened()) return 0; + if(w) cap->set(CV_CAP_PROP_FRAME_WIDTH, w); + if(h) cap->set(CV_CAP_PROP_FRAME_HEIGHT, w); + if(fps) cap->set(CV_CAP_PROP_FPS, w); + return (void *) cap; +} + +image get_image_from_stream(void *p) +{ + VideoCapture *cap = (VideoCapture *)p; + Mat m; + *cap >> m; + if(m.empty()) return make_empty_image(0,0,0); + return mat_to_image(m); +} + +image load_image_cv(char *filename, int channels) +{ + int flag = -1; + if (channels == 0) flag = -1; + else if (channels == 1) flag = 0; + else if (channels == 3) flag = 1; + else { + fprintf(stderr, "OpenCV can't force load with %d channels\n", channels); + } + Mat m; + m = imread(filename, flag); + if(!m.data){ + fprintf(stderr, "Cannot load image \"%s\"\n", filename); + char buff[256]; + sprintf(buff, "echo %s >> bad.list", filename); + system(buff); + return make_image(10,10,3); + //exit(0); + } + image im = mat_to_image(m); + return im; +} + +int show_image_cv(image im, const char* name, int ms) +{ + Mat m = image_to_mat(im); + imshow(name, m); + int c = waitKey(ms); + if (c != -1) c = c%256; + return c; +} + +void make_window(char *name, int w, int h, int fullscreen) +{ + namedWindow(name, WINDOW_NORMAL); + if (fullscreen) { + setWindowProperty(name, CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + } else { + resizeWindow(name, w, h); + if(strcmp(name, "Demo") == 0) moveWindow(name, 0, 0); + } +} + +} + +#endif diff --git a/image.darknet/inst/include/darknet/src/iseg_layer.c b/image.darknet/inst/include/darknet/src/iseg_layer.c new file mode 100644 index 0000000..2bf03a8 --- /dev/null +++ b/image.darknet/inst/include/darknet/src/iseg_layer.c @@ -0,0 +1,225 @@ +#include "iseg_layer.h" +#include "activations.h" +#include "blas.h" +#include "box.h" +#include "cuda.h" +#include "utils.h" + +#include +#include +#include +#include + +layer make_iseg_layer(int batch, int w, int h, int classes, int ids) +{ + layer l = {0}; + l.type = ISEG; + + l.h = h; + l.w = w; + l.c = classes + ids; + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; + l.classes = classes; + l.batch = batch; + l.extra = ids; + l.cost = calloc(1, sizeof(float)); + l.outputs = h*w*l.c; + l.inputs = l.outputs; + l.truths = 90*(l.w*l.h+1); + l.delta = calloc(batch*l.outputs, sizeof(float)); + l.output = calloc(batch*l.outputs, sizeof(float)); + + l.counts = calloc(90, sizeof(int)); + l.sums = calloc(90, sizeof(float*)); + if(ids){ + int i; + for(i = 0; i < 90; ++i){ + l.sums[i] = calloc(ids, sizeof(float)); + } + } + + l.forward = forward_iseg_layer; + l.backward = backward_iseg_layer; +#ifdef GPU + l.forward_gpu = forward_iseg_layer_gpu; + l.backward_gpu = backward_iseg_layer_gpu; + l.output_gpu = cuda_make_array(l.output, batch*l.outputs); + l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); +#endif + + fprintf(stderr, "iseg\n"); + srand(0); + + return l; +} + +void resize_iseg_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + + l->outputs = h*w*l->c; + l->inputs = l->outputs; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); +#endif +} + +void forward_iseg_layer(const layer l, network net) +{ + + double time = what_time_is_it_now(); + int i,b,j,k; + int ids = l.extra; + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + +#ifndef GPU + for (b = 0; b < l.batch; ++b){ + int index = b*l.outputs; + activate_array(l.output + index, l.classes*l.w*l.h, LOGISTIC); + } +#endif + + for (b = 0; b < l.batch; ++b){ + // a priori, each pixel has no class + for(i = 0; i < l.classes; ++i){ + for(k = 0; k < l.w*l.h; ++k){ + int index = b*l.outputs + i*l.w*l.h + k; + l.delta[index] = 0 - l.output[index]; + } + } + + // a priori, embedding should be small magnitude + for(i = 0; i < ids; ++i){ + for(k = 0; k < l.w*l.h; ++k){ + int index = b*l.outputs + (i+l.classes)*l.w*l.h + k; + l.delta[index] = .1 * (0 - l.output[index]); + } + } + + + memset(l.counts, 0, 90*sizeof(int)); + for(i = 0; i < 90; ++i){ + fill_cpu(ids, 0, l.sums[i], 1); + + int c = net.truth[b*l.truths + i*(l.w*l.h+1)]; + if(c < 0) break; + // add up metric embeddings for each instance + for(k = 0; k < l.w*l.h; ++k){ + int index = b*l.outputs + c*l.w*l.h + k; + float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k]; + if(v){ + l.delta[index] = v - l.output[index]; + axpy_cpu(ids, 1, l.output + b*l.outputs + l.classes*l.w*l.h + k, l.w*l.h, l.sums[i], 1); + ++l.counts[i]; + } + } + } + + float *mse = calloc(90, sizeof(float)); + for(i = 0; i < 90; ++i){ + int c = net.truth[b*l.truths + i*(l.w*l.h+1)]; + if(c < 0) break; + for(k = 0; k < l.w*l.h; ++k){ + float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k]; + if(v){ + int z; + float sum = 0; + for(z = 0; z < ids; ++z){ + int index = b*l.outputs + (l.classes + z)*l.w*l.h + k; + sum += pow(l.sums[i][z]/l.counts[i] - l.output[index], 2); + } + mse[i] += sum; + } + } + mse[i] /= l.counts[i]; + } + + // Calculate average embedding + for(i = 0; i < 90; ++i){ + if(!l.counts[i]) continue; + scal_cpu(ids, 1.f/l.counts[i], l.sums[i], 1); + if(b == 0 && net.gpu_index == 0){ + printf("%4d, %6.3f, ", l.counts[i], mse[i]); + for(j = 0; j < ids; ++j){ + printf("%6.3f,", l.sums[i][j]); + } + printf("\n"); + } + } + free(mse); + + // Calculate embedding loss + for(i = 0; i < 90; ++i){ + if(!l.counts[i]) continue; + for(k = 0; k < l.w*l.h; ++k){ + float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k]; + if(v){ + for(j = 0; j < 90; ++j){ + if(!l.counts[j])continue; + int z; + for(z = 0; z < ids; ++z){ + int index = b*l.outputs + (l.classes + z)*l.w*l.h + k; + float diff = l.sums[j][z] - l.output[index]; + if (j == i) l.delta[index] += diff < 0? -.1 : .1; + else l.delta[index] += -(diff < 0? -.1 : .1); + } + } + } + } + } + + for(i = 0; i < ids; ++i){ + for(k = 0; k < l.w*l.h; ++k){ + int index = b*l.outputs + (i+l.classes)*l.w*l.h + k; + l.delta[index] *= .01; + } + } + } + + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + printf("took %lf sec\n", what_time_is_it_now() - time); +} + +void backward_iseg_layer(const layer l, network net) +{ + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void forward_iseg_layer_gpu(const layer l, network net) +{ + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + int b; + for (b = 0; b < l.batch; ++b){ + activate_array_gpu(l.output_gpu + b*l.outputs, l.classes*l.w*l.h, LOGISTIC); + //if(l.extra) activate_array_gpu(l.output_gpu + b*l.outputs + l.classes*l.w*l.h, l.extra*l.w*l.h, LOGISTIC); + } + + cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); + forward_iseg_layer(l, net); + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); +} + +void backward_iseg_layer_gpu(const layer l, network net) +{ + int b; + for (b = 0; b < l.batch; ++b){ + //if(l.extra) gradient_array_gpu(l.output_gpu + b*l.outputs + l.classes*l.w*l.h, l.extra*l.w*l.h, LOGISTIC, l.delta_gpu + b*l.outputs + l.classes*l.w*l.h); + } + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif + diff --git a/image.darknet/inst/include/darknet/src/iseg_layer.h b/image.darknet/inst/include/darknet/src/iseg_layer.h new file mode 100644 index 0000000..dd8e64e --- /dev/null +++ b/image.darknet/inst/include/darknet/src/iseg_layer.h @@ -0,0 +1,19 @@ +#ifndef ISEG_LAYER_H +#define ISEG_LAYER_H + +#include "darknet.h" +#include "layer.h" +#include "network.h" + +layer make_iseg_layer(int batch, int w, int h, int classes, int ids); +void forward_iseg_layer(const layer l, network net); +void backward_iseg_layer(const layer l, network net); +void resize_iseg_layer(layer *l, int w, int h); +int iseg_num_detections(layer l, float thresh); + +#ifdef GPU +void forward_iseg_layer_gpu(const layer l, network net); +void backward_iseg_layer_gpu(layer l, network net); +#endif + +#endif diff --git a/image.darknet/inst/include/darknet/src/l2norm_layer.c b/image.darknet/inst/include/darknet/src/l2norm_layer.c new file mode 100644 index 0000000..d099479 --- /dev/null +++ b/image.darknet/inst/include/darknet/src/l2norm_layer.c @@ -0,0 +1,63 @@ +#include "l2norm_layer.h" +#include "activations.h" +#include "blas.h" +#include "cuda.h" + +#include +#include +#include +#include +#include + +layer make_l2norm_layer(int batch, int inputs) +{ + fprintf(stderr, "l2norm %4d\n", inputs); + layer l = {0}; + l.type = L2NORM; + l.batch = batch; + l.inputs = inputs; + l.outputs = inputs; + l.output = calloc(inputs*batch, sizeof(float)); + l.scales = calloc(inputs*batch, sizeof(float)); + l.delta = calloc(inputs*batch, sizeof(float)); + + l.forward = forward_l2norm_layer; + l.backward = backward_l2norm_layer; + #ifdef GPU + l.forward_gpu = forward_l2norm_layer_gpu; + l.backward_gpu = backward_l2norm_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.scales_gpu = cuda_make_array(l.output, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + #endif + return l; +} + +void forward_l2norm_layer(const layer l, network net) +{ + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + l2normalize_cpu(l.output, l.scales, l.batch, l.out_c, l.out_w*l.out_h); +} + +void backward_l2norm_layer(const layer l, network net) +{ + //axpy_cpu(l.inputs*l.batch, 1, l.scales, 1, l.delta, 1); + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void forward_l2norm_layer_gpu(const layer l, network net) +{ + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + l2normalize_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_w*l.out_h); +} + +void backward_l2norm_layer_gpu(const layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.scales_gpu, 1, l.delta_gpu, 1); + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} + +#endif diff --git a/image.darknet/inst/include/darknet/src/l2norm_layer.h b/image.darknet/inst/include/darknet/src/l2norm_layer.h new file mode 100644 index 0000000..1ca6f71 --- /dev/null +++ b/image.darknet/inst/include/darknet/src/l2norm_layer.h @@ -0,0 +1,15 @@ +#ifndef L2NORM_LAYER_H +#define L2NORM_LAYER_H +#include "layer.h" +#include "network.h" + +layer make_l2norm_layer(int batch, int inputs); +void forward_l2norm_layer(const layer l, network net); +void backward_l2norm_layer(const layer l, network net); + +#ifdef GPU +void forward_l2norm_layer_gpu(const layer l, network net); +void backward_l2norm_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/image.darknet/inst/include/darknet/src/layer.c b/image.darknet/inst/include/darknet/src/layer.c index 622cf26..c27b477 100644 --- a/image.darknet/inst/include/darknet/src/layer.c +++ b/image.darknet/inst/include/darknet/src/layer.c @@ -1,5 +1,6 @@ #include "layer.h" #include "cuda.h" + #include void free_layer(layer l) @@ -32,7 +33,6 @@ void free_layer(layer l) if(l.scale_updates) free(l.scale_updates); if(l.weights) free(l.weights); if(l.weight_updates) free(l.weight_updates); - if(l.col_image) free(l.col_image); if(l.delta) free(l.delta); if(l.output) free(l.output); if(l.squared) free(l.squared); @@ -80,7 +80,6 @@ void free_layer(layer l) if(l.rolling_variance_gpu) cuda_free(l.rolling_variance_gpu); if(l.variance_delta_gpu) cuda_free(l.variance_delta_gpu); if(l.mean_delta_gpu) cuda_free(l.mean_delta_gpu); - if(l.col_image_gpu) cuda_free(l.col_image_gpu); if(l.x_gpu) cuda_free(l.x_gpu); if(l.x_norm_gpu) cuda_free(l.x_norm_gpu); if(l.weights_gpu) cuda_free(l.weights_gpu); diff --git a/image.darknet/inst/include/darknet/src/layer.h b/image.darknet/inst/include/darknet/src/layer.h index 806542b..af6cd2a 100644 --- a/image.darknet/inst/include/darknet/src/layer.h +++ b/image.darknet/inst/include/darknet/src/layer.h @@ -1,271 +1 @@ -#ifndef BASE_LAYER_H -#define BASE_LAYER_H - -#include "activations.h" -#include "stddef.h" -#include "tree.h" - -struct network_state; - -struct layer; -typedef struct layer layer; - -typedef enum { - CONVOLUTIONAL, - DECONVOLUTIONAL, - CONNECTED, - MAXPOOL, - SOFTMAX, - DETECTION, - DROPOUT, - CROP, - ROUTE, - COST, - NORMALIZATION, - AVGPOOL, - LOCAL, - SHORTCUT, - ACTIVE, - RNN, - GRU, - CRNN, - BATCHNORM, - NETWORK, - XNOR, - REGION, - REORG, - BLANK -} LAYER_TYPE; - -typedef enum{ - SSE, MASKED, SMOOTH -} COST_TYPE; - -struct layer{ - LAYER_TYPE type; - ACTIVATION activation; - COST_TYPE cost_type; - void (*forward) (struct layer, struct network_state); - void (*backward) (struct layer, struct network_state); - void (*update) (struct layer, int, float, float, float); - void (*forward_gpu) (struct layer, struct network_state); - void (*backward_gpu) (struct layer, struct network_state); - void (*update_gpu) (struct layer, int, float, float, float); - int batch_normalize; - int shortcut; - int batch; - int forced; - int flipped; - int inputs; - int outputs; - int truths; - int h,w,c; - int out_h, out_w, out_c; - int n; - int max_boxes; - int groups; - int size; - int side; - int stride; - int reverse; - int pad; - int sqrt; - int flip; - int index; - int binary; - int xnor; - int steps; - int hidden; - float dot; - float angle; - float jitter; - float saturation; - float exposure; - float shift; - float ratio; - int softmax; - int classes; - int coords; - int background; - int rescore; - int objectness; - int does_cost; - int joint; - int noadjust; - int reorg; - int log; - - int adam; - float B1; - float B2; - float eps; - int t; - - float alpha; - float beta; - float kappa; - - float coord_scale; - float object_scale; - float noobject_scale; - float class_scale; - int bias_match; - int random; - float thresh; - int classfix; - int absolute; - - int dontload; - int dontloadscales; - - float temperature; - float probability; - float scale; - - char * cweights; - int * indexes; - int * input_layers; - int * input_sizes; - int * map; - float * rand; - float * cost; - float * state; - float * prev_state; - float * forgot_state; - float * forgot_delta; - float * state_delta; - - float * concat; - float * concat_delta; - - float * binary_weights; - - float * biases; - float * bias_updates; - - float * scales; - float * scale_updates; - - float * weights; - float * weight_updates; - - float * col_image; - float * delta; - float * output; - float * squared; - float * norms; - - float * spatial_mean; - float * mean; - float * variance; - - float * mean_delta; - float * variance_delta; - - float * rolling_mean; - float * rolling_variance; - - float * x; - float * x_norm; - - float * m; - float * v; - - float * z_cpu; - float * r_cpu; - float * h_cpu; - - float * binary_input; - - struct layer *input_layer; - struct layer *self_layer; - struct layer *output_layer; - - struct layer *input_gate_layer; - struct layer *state_gate_layer; - struct layer *input_save_layer; - struct layer *state_save_layer; - struct layer *input_state_layer; - struct layer *state_state_layer; - - struct layer *input_z_layer; - struct layer *state_z_layer; - - struct layer *input_r_layer; - struct layer *state_r_layer; - - struct layer *input_h_layer; - struct layer *state_h_layer; - - tree *softmax_tree; - - size_t workspace_size; - - #ifdef GPU - int *indexes_gpu; - - float *z_gpu; - float *r_gpu; - float *h_gpu; - - float *m_gpu; - float *v_gpu; - - float * prev_state_gpu; - float * forgot_state_gpu; - float * forgot_delta_gpu; - float * state_gpu; - float * state_delta_gpu; - float * gate_gpu; - float * gate_delta_gpu; - float * save_gpu; - float * save_delta_gpu; - float * concat_gpu; - float * concat_delta_gpu; - - float *binary_input_gpu; - float *binary_weights_gpu; - - float * mean_gpu; - float * variance_gpu; - - float * rolling_mean_gpu; - float * rolling_variance_gpu; - - float * variance_delta_gpu; - float * mean_delta_gpu; - - float * col_image_gpu; - - float * x_gpu; - float * x_norm_gpu; - float * weights_gpu; - float * weight_updates_gpu; - - float * biases_gpu; - float * bias_updates_gpu; - - float * scales_gpu; - float * scale_updates_gpu; - - float * output_gpu; - float * delta_gpu; - float * rand_gpu; - float * squared_gpu; - float * norms_gpu; - #ifdef CUDNN - cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; - cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; - cudnnFilterDescriptor_t weightDesc; - cudnnFilterDescriptor_t dweightDesc; - cudnnConvolutionDescriptor_t convDesc; - cudnnConvolutionFwdAlgo_t fw_algo; - cudnnConvolutionBwdDataAlgo_t bd_algo; - cudnnConvolutionBwdFilterAlgo_t bf_algo; - #endif - #endif -}; - -void free_layer(layer); - -#endif +#include "darknet.h" diff --git a/image.darknet/inst/include/darknet/src/list.h b/image.darknet/inst/include/darknet/src/list.h index fb818c2..6b445c7 100644 --- a/image.darknet/inst/include/darknet/src/list.h +++ b/image.darknet/inst/include/darknet/src/list.h @@ -1,26 +1,13 @@ #ifndef LIST_H #define LIST_H - -typedef struct node{ - void *val; - struct node *next; - struct node *prev; -} node; - -typedef struct list{ - int size; - node *front; - node *back; -} list; +#include "darknet.h" list *make_list(); int list_find(list *l, void *val); void list_insert(list *, void *); -void **list_to_array(list *l); -void free_list(list *l); void free_list_contents(list *l); #endif diff --git a/image.darknet/inst/include/darknet/src/local_layer.c b/image.darknet/inst/include/darknet/src/local_layer.c index 31f0ca6..74f6910 100644 --- a/image.darknet/inst/include/darknet/src/local_layer.c +++ b/image.darknet/inst/include/darknet/src/local_layer.c @@ -57,9 +57,10 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in float scale = sqrt(2./(size*size*c)); for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1,1); - l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float)); l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float)); + + l.workspace_size = out_h*out_w*size*size*c; l.forward = forward_local_layer; l.backward = backward_local_layer; @@ -76,7 +77,6 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in l.biases_gpu = cuda_make_array(l.biases, l.outputs); l.bias_updates_gpu = cuda_make_array(l.bias_updates, l.outputs); - l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c); l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n); l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); @@ -88,7 +88,7 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in return l; } -void forward_local_layer(const local_layer l, network_state state) +void forward_local_layer(const local_layer l, network net) { int out_h = local_out_height(l); int out_w = local_out_width(l); @@ -100,13 +100,13 @@ void forward_local_layer(const local_layer l, network_state state) } for(i = 0; i < l.batch; ++i){ - float *input = state.input + i*l.w*l.h*l.c; + float *input = net.input + i*l.w*l.h*l.c; im2col_cpu(input, l.c, l.h, l.w, - l.size, l.stride, l.pad, l.col_image); + l.size, l.stride, l.pad, net.workspace); float *output = l.output + i*l.outputs; for(j = 0; j < locations; ++j){ float *a = l.weights + j*l.size*l.size*l.c*l.n; - float *b = l.col_image + j; + float *b = net.workspace + j; float *c = output + j; int m = l.n; @@ -119,7 +119,7 @@ void forward_local_layer(const local_layer l, network_state state) activate_array(l.output, l.outputs*l.batch, l.activation); } -void backward_local_layer(local_layer l, network_state state) +void backward_local_layer(local_layer l, network net) { int i, j; int locations = l.out_w*l.out_h; @@ -131,13 +131,13 @@ void backward_local_layer(local_layer l, network_state state) } for(i = 0; i < l.batch; ++i){ - float *input = state.input + i*l.w*l.h*l.c; + float *input = net.input + i*l.w*l.h*l.c; im2col_cpu(input, l.c, l.h, l.w, - l.size, l.stride, l.pad, l.col_image); + l.size, l.stride, l.pad, net.workspace); for(j = 0; j < locations; ++j){ float *a = l.delta + i*l.outputs + j; - float *b = l.col_image + j; + float *b = net.workspace + j; float *c = l.weight_updates + j*l.size*l.size*l.c*l.n; int m = l.n; int n = l.size*l.size*l.c; @@ -146,11 +146,11 @@ void backward_local_layer(local_layer l, network_state state) gemm(0,1,m,n,k,1,a,locations,b,locations,1,c,n); } - if(state.delta){ + if(net.delta){ for(j = 0; j < locations; ++j){ float *a = l.weights + j*l.size*l.size*l.c*l.n; float *b = l.delta + i*l.outputs + j; - float *c = l.col_image + j; + float *c = net.workspace + j; int m = l.size*l.size*l.c; int n = 1; @@ -159,13 +159,18 @@ void backward_local_layer(local_layer l, network_state state) gemm(1,0,m,n,k,1,a,m,b,locations,0,c,locations); } - col2im_cpu(l.col_image, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w); + col2im_cpu(net.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, net.delta+i*l.c*l.h*l.w); } } } -void update_local_layer(local_layer l, int batch, float learning_rate, float momentum, float decay) +void update_local_layer(local_layer l, update_args a) { + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1); @@ -178,7 +183,7 @@ void update_local_layer(local_layer l, int batch, float learning_rate, float mom #ifdef GPU -void forward_local_layer_gpu(const local_layer l, network_state state) +void forward_local_layer_gpu(const local_layer l, network net) { int out_h = local_out_height(l); int out_w = local_out_width(l); @@ -186,83 +191,88 @@ void forward_local_layer_gpu(const local_layer l, network_state state) int locations = out_h * out_w; for(i = 0; i < l.batch; ++i){ - copy_ongpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1); + copy_gpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1); } for(i = 0; i < l.batch; ++i){ - float *input = state.input + i*l.w*l.h*l.c; - im2col_ongpu(input, l.c, l.h, l.w, - l.size, l.stride, l.pad, l.col_image_gpu); + float *input = net.input_gpu + i*l.w*l.h*l.c; + im2col_gpu(input, l.c, l.h, l.w, + l.size, l.stride, l.pad, net.workspace); float *output = l.output_gpu + i*l.outputs; for(j = 0; j < locations; ++j){ float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n; - float *b = l.col_image_gpu + j; + float *b = net.workspace + j; float *c = output + j; int m = l.n; int n = 1; int k = l.size*l.size*l.c; - gemm_ongpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations); + gemm_gpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations); } } - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); } -void backward_local_layer_gpu(local_layer l, network_state state) +void backward_local_layer_gpu(local_layer l, network net) { int i, j; int locations = l.out_w*l.out_h; - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); for(i = 0; i < l.batch; ++i){ - axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1); + axpy_gpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1); } for(i = 0; i < l.batch; ++i){ - float *input = state.input + i*l.w*l.h*l.c; - im2col_ongpu(input, l.c, l.h, l.w, - l.size, l.stride, l.pad, l.col_image_gpu); + float *input = net.input_gpu + i*l.w*l.h*l.c; + im2col_gpu(input, l.c, l.h, l.w, + l.size, l.stride, l.pad, net.workspace); for(j = 0; j < locations; ++j){ float *a = l.delta_gpu + i*l.outputs + j; - float *b = l.col_image_gpu + j; + float *b = net.workspace + j; float *c = l.weight_updates_gpu + j*l.size*l.size*l.c*l.n; int m = l.n; int n = l.size*l.size*l.c; int k = 1; - gemm_ongpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n); + gemm_gpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n); } - if(state.delta){ + if(net.delta_gpu){ for(j = 0; j < locations; ++j){ float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n; float *b = l.delta_gpu + i*l.outputs + j; - float *c = l.col_image_gpu + j; + float *c = net.workspace + j; int m = l.size*l.size*l.c; int n = 1; int k = l.n; - gemm_ongpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations); + gemm_gpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations); } - col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w); + col2im_gpu(net.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, net.delta_gpu+i*l.c*l.h*l.w); } } } -void update_local_layer_gpu(local_layer l, int batch, float learning_rate, float momentum, float decay) +void update_local_layer_gpu(local_layer l, update_args a) { + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; - axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); - scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1); + axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1); - axpy_ongpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); - axpy_ongpu(size, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); - scal_ongpu(size, momentum, l.weight_updates_gpu, 1); + axpy_gpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(size, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(size, momentum, l.weight_updates_gpu, 1); } void pull_local_layer(local_layer l) diff --git a/image.darknet/inst/include/darknet/src/local_layer.h b/image.darknet/inst/include/darknet/src/local_layer.h index 28915d8..776e572 100644 --- a/image.darknet/inst/include/darknet/src/local_layer.h +++ b/image.darknet/inst/include/darknet/src/local_layer.h @@ -10,9 +10,9 @@ typedef layer local_layer; #ifdef GPU -void forward_local_layer_gpu(local_layer layer, network_state state); -void backward_local_layer_gpu(local_layer layer, network_state state); -void update_local_layer_gpu(local_layer layer, int batch, float learning_rate, float momentum, float decay); +void forward_local_layer_gpu(local_layer layer, network net); +void backward_local_layer_gpu(local_layer layer, network net); +void update_local_layer_gpu(local_layer layer, update_args a); void push_local_layer(local_layer layer); void pull_local_layer(local_layer layer); @@ -20,9 +20,9 @@ void pull_local_layer(local_layer layer); local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation); -void forward_local_layer(const local_layer layer, network_state state); -void backward_local_layer(local_layer layer, network_state state); -void update_local_layer(local_layer layer, int batch, float learning_rate, float momentum, float decay); +void forward_local_layer(const local_layer layer, network net); +void backward_local_layer(local_layer layer, network net); +void update_local_layer(local_layer layer, update_args a); void bias_output(float *output, float *biases, int batch, int n, int size); void backward_bias(float *bias_updates, float *delta, int batch, int n, int size); diff --git a/image.darknet/inst/include/darknet/src/logistic_layer.c b/image.darknet/inst/include/darknet/src/logistic_layer.c new file mode 100644 index 0000000..b2b3d6b --- /dev/null +++ b/image.darknet/inst/include/darknet/src/logistic_layer.c @@ -0,0 +1,71 @@ +#include "logistic_layer.h" +#include "activations.h" +#include "blas.h" +#include "cuda.h" + +#include +#include +#include +#include +#include + +layer make_logistic_layer(int batch, int inputs) +{ + fprintf(stderr, "logistic x entropy %4d\n", inputs); + layer l = {0}; + l.type = LOGXENT; + l.batch = batch; + l.inputs = inputs; + l.outputs = inputs; + l.loss = calloc(inputs*batch, sizeof(float)); + l.output = calloc(inputs*batch, sizeof(float)); + l.delta = calloc(inputs*batch, sizeof(float)); + l.cost = calloc(1, sizeof(float)); + + l.forward = forward_logistic_layer; + l.backward = backward_logistic_layer; + #ifdef GPU + l.forward_gpu = forward_logistic_layer_gpu; + l.backward_gpu = backward_logistic_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.loss_gpu = cuda_make_array(l.loss, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + #endif + return l; +} + +void forward_logistic_layer(const layer l, network net) +{ + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + activate_array(l.output, l.outputs*l.batch, LOGISTIC); + if(net.truth){ + logistic_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); + } +} + +void backward_logistic_layer(const layer l, network net) +{ + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void forward_logistic_layer_gpu(const layer l, network net) +{ + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, LOGISTIC); + if(net.truth){ + logistic_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth_gpu, l.delta_gpu, l.loss_gpu); + cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); + } +} + +void backward_logistic_layer_gpu(const layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} + +#endif diff --git a/image.darknet/inst/include/darknet/src/logistic_layer.h b/image.darknet/inst/include/darknet/src/logistic_layer.h new file mode 100644 index 0000000..9c25bee --- /dev/null +++ b/image.darknet/inst/include/darknet/src/logistic_layer.h @@ -0,0 +1,15 @@ +#ifndef LOGISTIC_LAYER_H +#define LOGISTIC_LAYER_H +#include "layer.h" +#include "network.h" + +layer make_logistic_layer(int batch, int inputs); +void forward_logistic_layer(const layer l, network net); +void backward_logistic_layer(const layer l, network net); + +#ifdef GPU +void forward_logistic_layer_gpu(const layer l, network net); +void backward_logistic_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/image.darknet/inst/include/darknet/src/lstm_layer.c b/image.darknet/inst/include/darknet/src/lstm_layer.c new file mode 100644 index 0000000..fb07de2 --- /dev/null +++ b/image.darknet/inst/include/darknet/src/lstm_layer.c @@ -0,0 +1,626 @@ +#include "lstm_layer.h" +#include "connected_layer.h" +#include "utils.h" +#include "cuda.h" +#include "blas.h" +#include "gemm.h" + +#include +#include +#include +#include + +static void increment_layer(layer *l, int steps) +{ + int num = l->outputs*l->batch*steps; + l->output += num; + l->delta += num; + l->x += num; + l->x_norm += num; + +#ifdef GPU + l->output_gpu += num; + l->delta_gpu += num; + l->x_gpu += num; + l->x_norm_gpu += num; +#endif +} + +layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam) +{ + fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs); + batch = batch / steps; + layer l = { 0 }; + l.batch = batch; + l.type = LSTM; + l.steps = steps; + l.inputs = inputs; + + l.uf = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uf) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uf->batch = batch; + + l.ui = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.ui) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.ui->batch = batch; + + l.ug = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.ug) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.ug->batch = batch; + + l.uo = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uo) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uo->batch = batch; + + l.wf = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wf) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wf->batch = batch; + + l.wi = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wi) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wi->batch = batch; + + l.wg = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wg) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wg->batch = batch; + + l.wo = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wo) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wo->batch = batch; + + l.batch_normalize = batch_normalize; + l.outputs = outputs; + + l.output = calloc(outputs*batch*steps, sizeof(float)); + l.state = calloc(outputs*batch, sizeof(float)); + + l.forward = forward_lstm_layer; + l.update = update_lstm_layer; + + l.prev_state_cpu = calloc(batch*outputs, sizeof(float)); + l.prev_cell_cpu = calloc(batch*outputs, sizeof(float)); + l.cell_cpu = calloc(batch*outputs*steps, sizeof(float)); + + l.f_cpu = calloc(batch*outputs, sizeof(float)); + l.i_cpu = calloc(batch*outputs, sizeof(float)); + l.g_cpu = calloc(batch*outputs, sizeof(float)); + l.o_cpu = calloc(batch*outputs, sizeof(float)); + l.c_cpu = calloc(batch*outputs, sizeof(float)); + l.h_cpu = calloc(batch*outputs, sizeof(float)); + l.temp_cpu = calloc(batch*outputs, sizeof(float)); + l.temp2_cpu = calloc(batch*outputs, sizeof(float)); + l.temp3_cpu = calloc(batch*outputs, sizeof(float)); + l.dc_cpu = calloc(batch*outputs, sizeof(float)); + l.dh_cpu = calloc(batch*outputs, sizeof(float)); + +#ifdef GPU + l.forward_gpu = forward_lstm_layer_gpu; + l.backward_gpu = backward_lstm_layer_gpu; + l.update_gpu = update_lstm_layer_gpu; + + l.output_gpu = cuda_make_array(0, batch*outputs*steps); + l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps); + + l.prev_state_gpu = cuda_make_array(0, batch*outputs); + l.prev_cell_gpu = cuda_make_array(0, batch*outputs); + l.cell_gpu = cuda_make_array(0, batch*outputs*steps); + + l.f_gpu = cuda_make_array(0, batch*outputs); + l.i_gpu = cuda_make_array(0, batch*outputs); + l.g_gpu = cuda_make_array(0, batch*outputs); + l.o_gpu = cuda_make_array(0, batch*outputs); + l.c_gpu = cuda_make_array(0, batch*outputs); + l.h_gpu = cuda_make_array(0, batch*outputs); + l.temp_gpu = cuda_make_array(0, batch*outputs); + l.temp2_gpu = cuda_make_array(0, batch*outputs); + l.temp3_gpu = cuda_make_array(0, batch*outputs); + l.dc_gpu = cuda_make_array(0, batch*outputs); + l.dh_gpu = cuda_make_array(0, batch*outputs); +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w); + cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w); + cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w); + cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w); + + cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w); + cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w); + cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w); + cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w); +#endif + +#endif + + return l; +} + +void update_lstm_layer(layer l, update_args a) +{ + update_connected_layer(*(l.wf), a); + update_connected_layer(*(l.wi), a); + update_connected_layer(*(l.wg), a); + update_connected_layer(*(l.wo), a); + update_connected_layer(*(l.uf), a); + update_connected_layer(*(l.ui), a); + update_connected_layer(*(l.ug), a); + update_connected_layer(*(l.uo), a); +} + +void forward_lstm_layer(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1); + + fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1); + if (state.train) { + fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input = l.h_cpu; + forward_connected_layer(wf, s); + forward_connected_layer(wi, s); + forward_connected_layer(wg, s); + forward_connected_layer(wo, s); + + s.input = state.input; + forward_connected_layer(uf, s); + forward_connected_layer(ui, s); + forward_connected_layer(ug, s); + forward_connected_layer(uo, s); + + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1); + activate_array(l.h_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1); + + state.input += l.inputs*l.batch; + l.output += l.outputs*l.batch; + l.cell_cpu += l.outputs*l.batch; + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_lstm_layer(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input += l.inputs*l.batch*(l.steps - 1); + if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1); + + l.output += l.outputs*l.batch*(l.steps - 1); + l.cell_cpu += l.outputs*l.batch*(l.steps - 1); + l.delta += l.outputs*l.batch*(l.steps - 1); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_cpu(l.outputs*l.batch, l.cell_cpu - l.outputs*l.batch, 1, l.prev_cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.cell_cpu, 1, l.c_cpu, 1); + if (i != 0) copy_cpu(l.outputs*l.batch, l.output - l.outputs*l.batch, 1, l.prev_state_cpu, 1); + copy_cpu(l.outputs*l.batch, l.output, 1, l.h_cpu, 1); + + l.dh_cpu = (i == 0) ? 0 : l.delta - l.outputs*l.batch; + + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + + copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1); + + gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu); + axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1); + gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wo.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wo, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uo.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(uo, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wg, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ug.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(ug, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wi.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wi, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ui.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(ui, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1); + gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wf.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wf, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uf.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(uf, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1); + + state.input -= l.inputs*l.batch; + if (state.delta) state.delta -= l.inputs*l.batch; + l.output -= l.outputs*l.batch; + l.cell_cpu -= l.outputs*l.batch; + l.delta -= l.outputs*l.batch; + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } +} + +#ifdef GPU +void update_lstm_layer_gpu(layer l, update_args a) +{ + update_connected_layer_gpu(*(l.wf), a); + update_connected_layer_gpu(*(l.wi), a); + update_connected_layer_gpu(*(l.wg), a); + update_connected_layer_gpu(*(l.wo), a); + update_connected_layer_gpu(*(l.uf), a); + update_connected_layer_gpu(*(l.ui), a); + update_connected_layer_gpu(*(l.ug), a); + update_connected_layer_gpu(*(l.uo), a); +} + +void forward_lstm_layer_gpu(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + fill_gpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1); + + fill_gpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1); + if (state.train) { + fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input_gpu = l.h_gpu; + forward_connected_layer_gpu(wf, s); + forward_connected_layer_gpu(wi, s); + forward_connected_layer_gpu(wg, s); + forward_connected_layer_gpu(wo, s); + + s.input_gpu = state.input_gpu; + forward_connected_layer_gpu(uf, s); + forward_connected_layer_gpu(ui, s); + forward_connected_layer_gpu(ug, s); + forward_connected_layer_gpu(uo, s); + + copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + + copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + + copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + + activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1); + activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH); + mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); + copy_gpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); + + state.input_gpu += l.inputs*l.batch; + l.output_gpu += l.outputs*l.batch; + l.cell_gpu += l.outputs*l.batch; + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_lstm_layer_gpu(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input_gpu += l.inputs*l.batch*(l.steps - 1); + if (state.delta_gpu) state.delta_gpu += l.inputs*l.batch*(l.steps - 1); + + l.output_gpu += l.outputs*l.batch*(l.steps - 1); + l.cell_gpu += l.outputs*l.batch*(l.steps - 1); + l.delta_gpu += l.outputs*l.batch*(l.steps - 1); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_gpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1); + copy_gpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1); + if (i != 0) copy_gpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1); + + l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; + + copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + + copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + + copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + + activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH); + + copy_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1); + mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1); + + gradient_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu); + axpy_gpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH); + mul_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wo, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(uo, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wg, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(ug, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wi, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(ui, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wf, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(uf, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1); + + state.input_gpu -= l.inputs*l.batch; + if (state.delta_gpu) state.delta_gpu -= l.inputs*l.batch; + l.output_gpu -= l.outputs*l.batch; + l.cell_gpu -= l.outputs*l.batch; + l.delta_gpu -= l.outputs*l.batch; + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } +} +#endif diff --git a/image.darknet/inst/include/darknet/src/lstm_layer.h b/image.darknet/inst/include/darknet/src/lstm_layer.h new file mode 100644 index 0000000..b9f07e6 --- /dev/null +++ b/image.darknet/inst/include/darknet/src/lstm_layer.h @@ -0,0 +1,20 @@ +#ifndef LSTM_LAYER_H +#define LSTM_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" +#define USET + +layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam); + +void forward_lstm_layer(layer l, network net); +void update_lstm_layer(layer l, update_args a); + +#ifdef GPU +void forward_lstm_layer_gpu(layer l, network net); +void backward_lstm_layer_gpu(layer l, network net); +void update_lstm_layer_gpu(layer l, update_args a); + +#endif +#endif diff --git a/image.darknet/inst/include/darknet/src/matrix.c b/image.darknet/inst/include/darknet/src/matrix.c index ee14979..799916b 100644 --- a/image.darknet/inst/include/darknet/src/matrix.c +++ b/image.darknet/inst/include/darknet/src/matrix.c @@ -1,5 +1,6 @@ #include "matrix.h" #include "utils.h" +#include "blas.h" #include #include #include @@ -73,6 +74,20 @@ void matrix_add_matrix(matrix from, matrix to) } } +matrix copy_matrix(matrix m) +{ + matrix c = {0}; + c.rows = m.rows; + c.cols = m.cols; + c.vals = calloc(c.rows, sizeof(float *)); + int i; + for(i = 0; i < c.rows; ++i){ + c.vals[i] = calloc(c.cols, sizeof(float)); + copy_cpu(c.cols, m.vals[i], 1, c.vals[i], 1); + } + return c; +} + matrix make_matrix(int rows, int cols) { int i; diff --git a/image.darknet/inst/include/darknet/src/matrix.h b/image.darknet/inst/include/darknet/src/matrix.h index 641b596..879acd7 100644 --- a/image.darknet/inst/include/darknet/src/matrix.h +++ b/image.darknet/inst/include/darknet/src/matrix.h @@ -1,20 +1,11 @@ #ifndef MATRIX_H #define MATRIX_H -typedef struct matrix{ - int rows, cols; - float **vals; -} matrix; +#include "darknet.h" -matrix make_matrix(int rows, int cols); -void free_matrix(matrix m); +matrix copy_matrix(matrix m); void print_matrix(matrix m); -matrix csv_to_matrix(char *filename); -void matrix_to_csv(matrix m); matrix hold_out_matrix(matrix *m, int n); -float matrix_topk_accuracy(matrix truth, matrix guess, int k); -void matrix_add_matrix(matrix from, matrix to); -void scale_matrix(matrix m, float scale); matrix resize_matrix(matrix m, int size); float *pop_column(matrix *m, int c); diff --git a/image.darknet/inst/include/darknet/src/maxpool_layer.c b/image.darknet/inst/include/darknet/src/maxpool_layer.c index 031d116..fb05635 100644 --- a/image.darknet/inst/include/darknet/src/maxpool_layer.c +++ b/image.darknet/inst/include/darknet/src/maxpool_layer.c @@ -27,8 +27,8 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.w = w; l.c = c; l.pad = padding; - l.out_w = (w + 2*padding)/stride; - l.out_h = (h + 2*padding)/stride; + l.out_w = (w + padding - size)/stride + 1; + l.out_h = (h + padding - size)/stride + 1; l.out_c = c; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; @@ -43,7 +43,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s #ifdef GPU l.forward_gpu = forward_maxpool_layer_gpu; l.backward_gpu = backward_maxpool_layer_gpu; - l.indexes_gpu = cuda_make_int_array(output_size); + l.indexes_gpu = cuda_make_int_array(0, output_size); l.output_gpu = cuda_make_array(l.output, output_size); l.delta_gpu = cuda_make_array(l.delta, output_size); #endif @@ -57,8 +57,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->w = w; l->inputs = h*w*l->c; - l->out_w = (w + 2*l->pad)/l->stride; - l->out_h = (h + 2*l->pad)/l->stride; + l->out_w = (w + l->pad - l->size)/l->stride + 1; + l->out_h = (h + l->pad - l->size)/l->stride + 1; l->outputs = l->out_w * l->out_h * l->c; int output_size = l->outputs * l->batch; @@ -70,17 +70,17 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) cuda_free((float *)l->indexes_gpu); cuda_free(l->output_gpu); cuda_free(l->delta_gpu); - l->indexes_gpu = cuda_make_int_array(output_size); + l->indexes_gpu = cuda_make_int_array(0, output_size); l->output_gpu = cuda_make_array(l->output, output_size); l->delta_gpu = cuda_make_array(l->delta, output_size); #endif } -void forward_maxpool_layer(const maxpool_layer l, network_state state) +void forward_maxpool_layer(const maxpool_layer l, network net) { int b,i,j,k,m,n; - int w_offset = -l.pad; - int h_offset = -l.pad; + int w_offset = -l.pad/2; + int h_offset = -l.pad/2; int h = l.out_h; int w = l.out_w; @@ -100,7 +100,7 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c)); int valid = (cur_h >= 0 && cur_h < l.h && cur_w >= 0 && cur_w < l.w); - float val = (valid != 0) ? state.input[index] : -FLT_MAX; + float val = (valid != 0) ? net.input[index] : -FLT_MAX; max_i = (val > max) ? index : max_i; max = (val > max) ? val : max; } @@ -113,7 +113,7 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) } } -void backward_maxpool_layer(const maxpool_layer l, network_state state) +void backward_maxpool_layer(const maxpool_layer l, network net) { int i; int h = l.out_h; @@ -121,7 +121,7 @@ void backward_maxpool_layer(const maxpool_layer l, network_state state) int c = l.c; for(i = 0; i < h*w*c*l.batch; ++i){ int index = l.indexes[i]; - state.delta[index] += l.delta[i]; + net.delta[index] += l.delta[i]; } } diff --git a/image.darknet/inst/include/darknet/src/maxpool_layer.h b/image.darknet/inst/include/darknet/src/maxpool_layer.h index ce56dd8..ceb5190 100644 --- a/image.darknet/inst/include/darknet/src/maxpool_layer.h +++ b/image.darknet/inst/include/darknet/src/maxpool_layer.h @@ -11,12 +11,12 @@ typedef layer maxpool_layer; image get_maxpool_image(maxpool_layer l); maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding); void resize_maxpool_layer(maxpool_layer *l, int w, int h); -void forward_maxpool_layer(const maxpool_layer l, network_state state); -void backward_maxpool_layer(const maxpool_layer l, network_state state); +void forward_maxpool_layer(const maxpool_layer l, network net); +void backward_maxpool_layer(const maxpool_layer l, network net); #ifdef GPU -void forward_maxpool_layer_gpu(maxpool_layer l, network_state state); -void backward_maxpool_layer_gpu(maxpool_layer l, network_state state); +void forward_maxpool_layer_gpu(maxpool_layer l, network net); +void backward_maxpool_layer_gpu(maxpool_layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/maxpool_layer_kernels.cu b/image.darknet/inst/include/darknet/src/maxpool_layer_kernels.cu index 6381cc1..869ef46 100644 --- a/image.darknet/inst/include/darknet/src/maxpool_layer_kernels.cu +++ b/image.darknet/inst/include/darknet/src/maxpool_layer_kernels.cu @@ -9,8 +9,8 @@ extern "C" { __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) { - int h = (in_h + 2*pad)/stride; - int w = (in_w + 2*pad)/stride; + int h = (in_h + pad - size)/stride + 1; + int w = (in_w + pad - size)/stride + 1; int c = in_c; int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -24,8 +24,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c id /= c; int b = id; - int w_offset = -pad; - int h_offset = -pad; + int w_offset = -pad/2; + int h_offset = -pad/2; int out_index = j + w*(i + h*(k + c*b)); float max = -INFINITY; @@ -49,8 +49,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *delta, float *prev_delta, int *indexes) { - int h = (in_h + 2*pad)/stride; - int w = (in_w + 2*pad)/stride; + int h = (in_h + pad - size)/stride + 1; + int w = (in_w + pad - size)/stride + 1; int c = in_c; int area = (size-1)/stride; @@ -66,8 +66,8 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ id /= in_c; int b = id; - int w_offset = -pad; - int h_offset = -pad; + int w_offset = -pad/2; + int h_offset = -pad/2; float d = 0; int l, m; @@ -84,7 +84,7 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ prev_delta[index] += d; } -extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state) +extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network net) { int h = layer.out_h; int w = layer.out_w; @@ -92,15 +92,15 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta size_t n = h*w*c*layer.batch; - forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); + forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, net.input_gpu, layer.output_gpu, layer.indexes_gpu); check_error(cudaPeekAtLastError()); } -extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state state) +extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network net) { size_t n = layer.h*layer.w*layer.c*layer.batch; - backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); + backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, net.delta_gpu, layer.indexes_gpu); check_error(cudaPeekAtLastError()); } diff --git a/image.darknet/inst/include/darknet/src/network.c b/image.darknet/inst/include/darknet/src/network.c index 0914e37..aaab799 100644 --- a/image.darknet/inst/include/darknet/src/network.c +++ b/image.darknet/inst/include/darknet/src/network.c @@ -17,6 +17,7 @@ #include "activation_layer.h" #include "detection_layer.h" #include "region_layer.h" +#include "yolo_layer.h" #include "normalization_layer.h" #include "batchnorm_layer.h" #include "maxpool_layer.h" @@ -26,55 +27,95 @@ #include "softmax_layer.h" #include "dropout_layer.h" #include "route_layer.h" +#include "upsample_layer.h" #include "shortcut_layer.h" +#include "parser.h" +#include "data.h" + +load_args get_base_args(network *net) +{ + load_args args = {0}; + args.w = net->w; + args.h = net->h; + args.size = net->w; + + args.min = net->min_crop; + args.max = net->max_crop; + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.center = net->center; + args.saturation = net->saturation; + args.hue = net->hue; + return args; +} + +network *load_network(char *cfg, char *weights, int clear) +{ + network *net = parse_network_cfg(cfg); + if(weights && weights[0] != 0){ + load_weights(net, weights); + } + if(clear) (*net->seen) = 0; + return net; +} -int get_current_batch(network net) +size_t get_current_batch(network *net) { - int batch_num = (*net.seen)/(net.batch*net.subdivisions); + size_t batch_num = (*net->seen)/(net->batch*net->subdivisions); return batch_num; } -void reset_momentum(network net) +void reset_network_state(network *net, int b) { - if (net.momentum == 0) return; - net.learning_rate = 0; - net.momentum = 0; - net.decay = 0; - #ifdef GPU - //if(net.gpu_index >= 0) update_network_gpu(net); - #endif + int i; + for (i = 0; i < net->n; ++i) { + #ifdef GPU + layer l = net->layers[i]; + if(l.state_gpu){ + fill_gpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1); + } + if(l.h_gpu){ + fill_gpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1); + } + #endif + } } -float get_current_rate(network net) +void reset_rnn(network *net) { - int batch_num = get_current_batch(net); + reset_network_state(net, 0); +} + +float get_current_rate(network *net) +{ + size_t batch_num = get_current_batch(net); int i; float rate; - switch (net.policy) { + if (batch_num < net->burn_in) return net->learning_rate * pow((float)batch_num / net->burn_in, net->power); + switch (net->policy) { case CONSTANT: - return net.learning_rate; + return net->learning_rate; case STEP: - return net.learning_rate * pow(net.scale, batch_num/net.step); + return net->learning_rate * pow(net->scale, batch_num/net->step); case STEPS: - rate = net.learning_rate; - for(i = 0; i < net.num_steps; ++i){ - if(net.steps[i] > batch_num) return rate; - rate *= net.scales[i]; - //if(net.steps[i] > batch_num - 1 && net.scales[i] > 1) reset_momentum(net); + rate = net->learning_rate; + for(i = 0; i < net->num_steps; ++i){ + if(net->steps[i] > batch_num) return rate; + rate *= net->scales[i]; } return rate; case EXP: - return net.learning_rate * pow(net.gamma, batch_num); + return net->learning_rate * pow(net->gamma, batch_num); case POLY: - if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power); - return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power); + return net->learning_rate * pow(1 - (float)batch_num / net->max_batches, net->power); case RANDOM: - return net.learning_rate * pow(rand_uniform(0,1), net.power); + return net->learning_rate * pow(rand_uniform(0,1), net->power); case SIG: - return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step)))); + return net->learning_rate * (1./(1.+exp(net->gamma*(batch_num - net->step)))); default: fprintf(stderr, "Policy is weird!\n"); - return net.learning_rate; + return net->learning_rate; } } @@ -95,6 +136,8 @@ char *get_layer_string(LAYER_TYPE a) return "rnn"; case GRU: return "gru"; + case LSTM: + return "lstm"; case CRNN: return "crnn"; case MAXPOOL: @@ -109,6 +152,8 @@ char *get_layer_string(LAYER_TYPE a) return "detection"; case REGION: return "region"; + case YOLO: + return "yolo"; case DROPOUT: return "dropout"; case CROP: @@ -129,59 +174,75 @@ char *get_layer_string(LAYER_TYPE a) return "none"; } -network make_network(int n) +network *make_network(int n) { - network net = {0}; - net.n = n; - net.layers = calloc(net.n, sizeof(layer)); - net.seen = calloc(1, sizeof(int)); - #ifdef GPU - net.input_gpu = calloc(1, sizeof(float *)); - net.truth_gpu = calloc(1, sizeof(float *)); - #endif + network *net = calloc(1, sizeof(network)); + net->n = n; + net->layers = calloc(net->n, sizeof(layer)); + net->seen = calloc(1, sizeof(size_t)); + net->t = calloc(1, sizeof(int)); + net->cost = calloc(1, sizeof(float)); return net; } -void forward_network(network net, network_state state) +void forward_network(network *netp) { - state.workspace = net.workspace; +#ifdef GPU + if(netp->gpu_index >= 0){ + forward_network_gpu(netp); + return; + } +#endif + network net = *netp; int i; for(i = 0; i < net.n; ++i){ - state.index = i; + net.index = i; layer l = net.layers[i]; if(l.delta){ - scal_cpu(l.outputs * l.batch, 0, l.delta, 1); + fill_cpu(l.outputs * l.batch, 0, l.delta, 1); + } + l.forward(l, net); + net.input = l.output; + if(l.truth) { + net.truth = l.output; } - l.forward(l, state); - state.input = l.output; } + calc_network_cost(netp); } -void update_network(network net) +void update_network(network *netp) { +#ifdef GPU + if(netp->gpu_index >= 0){ + update_network_gpu(netp); + return; + } +#endif + network net = *netp; int i; - int update_batch = net.batch*net.subdivisions; - float rate = get_current_rate(net); + update_args a = {0}; + a.batch = net.batch*net.subdivisions; + a.learning_rate = get_current_rate(netp); + a.momentum = net.momentum; + a.decay = net.decay; + a.adam = net.adam; + a.B1 = net.B1; + a.B2 = net.B2; + a.eps = net.eps; + ++*net.t; + a.t = *net.t; + for(i = 0; i < net.n; ++i){ layer l = net.layers[i]; if(l.update){ - l.update(l, update_batch, rate, net.momentum, net.decay); + l.update(l, a); } } } -float *get_network_output(network net) -{ -#ifdef GPU - if (gpu_index >= 0) return get_network_output_gpu(net); -#endif - int i; - for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break; - return net.layers[i].output; -} - -float get_network_cost(network net) +void calc_network_cost(network *netp) { + network net = *netp; int i; float sum = 0; int count = 0; @@ -191,120 +252,90 @@ float get_network_cost(network net) ++count; } } - return sum/count; + *net.cost = sum/count; } -int get_predicted_class_network(network net) +int get_predicted_class_network(network *net) { - float *out = get_network_output(net); - int k = get_network_output_size(net); - return max_index(out, k); + return max_index(net->output, net->outputs); } -void backward_network(network net, network_state state) +void backward_network(network *netp) { +#ifdef GPU + if(netp->gpu_index >= 0){ + backward_network_gpu(netp); + return; + } +#endif + network net = *netp; int i; - float *original_input = state.input; - float *original_delta = state.delta; - state.workspace = net.workspace; + network orig = net; for(i = net.n-1; i >= 0; --i){ - state.index = i; + layer l = net.layers[i]; + if(l.stopbackward) break; if(i == 0){ - state.input = original_input; - state.delta = original_delta; + net = orig; }else{ layer prev = net.layers[i-1]; - state.input = prev.output; - state.delta = prev.delta; + net.input = prev.output; + net.delta = prev.delta; } - layer l = net.layers[i]; - l.backward(l, state); + net.index = i; + l.backward(l, net); } } -float train_network_datum(network net, float *x, float *y) +float train_network_datum(network *net) { -#ifdef GPU - if(gpu_index >= 0) return train_network_datum_gpu(net, x, y); -#endif - network_state state; - *net.seen += net.batch; - state.index = 0; - state.net = net; - state.input = x; - state.delta = 0; - state.truth = y; - state.train = 1; - forward_network(net, state); - backward_network(net, state); - float error = get_network_cost(net); - if(((*net.seen)/net.batch)%net.subdivisions == 0) update_network(net); + *net->seen += net->batch; + net->train = 1; + forward_network(net); + backward_network(net); + float error = *net->cost; + if(((*net->seen)/net->batch)%net->subdivisions == 0) update_network(net); return error; } -float train_network_sgd(network net, data d, int n) +float train_network_sgd(network *net, data d, int n) { - int batch = net.batch; - float *X = calloc(batch*d.X.cols, sizeof(float)); - float *y = calloc(batch*d.y.cols, sizeof(float)); + int batch = net->batch; int i; float sum = 0; for(i = 0; i < n; ++i){ - get_random_batch(d, batch, X, y); - float err = train_network_datum(net, X, y); + get_random_batch(d, batch, net->input, net->truth); + float err = train_network_datum(net); sum += err; } - free(X); - free(y); return (float)sum/(n*batch); } -float train_network(network net, data d) +float train_network(network *net, data d) { - assert(d.X.rows % net.batch == 0); - int batch = net.batch; + assert(d.X.rows % net->batch == 0); + int batch = net->batch; int n = d.X.rows / batch; - float *X = calloc(batch*d.X.cols, sizeof(float)); - float *y = calloc(batch*d.y.cols, sizeof(float)); int i; float sum = 0; for(i = 0; i < n; ++i){ - get_next_batch(d, batch, i*batch, X, y); - float err = train_network_datum(net, X, y); + get_next_batch(d, batch, i*batch, net->input, net->truth); + float err = train_network_datum(net); sum += err; } - free(X); - free(y); return (float)sum/(n*batch); } - -float train_network_batch(network net, data d, int n) +void set_temp_network(network *net, float t) { - int i,j; - network_state state; - state.index = 0; - state.net = net; - state.train = 1; - state.delta = 0; - float sum = 0; - int batch = 2; - for(i = 0; i < n; ++i){ - for(j = 0; j < batch; ++j){ - int index = rand()%d.X.rows; - state.input = d.X.vals[index]; - state.truth = d.y.vals[index]; - forward_network(net, state); - backward_network(net, state); - sum += get_network_cost(net); - } - update_network(net); + int i; + for(i = 0; i < net->n; ++i){ + net->layers[i].temperature = t; } - return (float)sum/(n*batch); } + void set_batch_network(network *net, int b) { net->batch = b; @@ -315,6 +346,11 @@ void set_batch_network(network *net, int b) if(net->layers[i].type == CONVOLUTIONAL){ cudnn_convolutional_setup(net->layers + i); } + if(net->layers[i].type == DECONVOLUTIONAL){ + layer *l = net->layers + i; + cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, l->out_h, l->out_w); + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); + } #endif } } @@ -323,9 +359,7 @@ int resize_network(network *net, int w, int h) { #ifdef GPU cuda_set_device(net->gpu_index); - if(gpu_index >= 0){ - cuda_free(net->workspace); - } + cuda_free(net->workspace); #endif int i; //if(w == net->w && h == net->h) return 0; @@ -345,8 +379,14 @@ int resize_network(network *net, int w, int h) resize_maxpool_layer(&l, w, h); }else if(l.type == REGION){ resize_region_layer(&l, w, h); + }else if(l.type == YOLO){ + resize_yolo_layer(&l, w, h); }else if(l.type == ROUTE){ resize_route_layer(&l, net); + }else if(l.type == SHORTCUT){ + resize_shortcut_layer(&l, w, h); + }else if(l.type == UPSAMPLE){ + resize_upsample_layer(&l, w, h); }else if(l.type == REORG){ resize_reorg_layer(&l, w, h); }else if(l.type == AVGPOOL){ @@ -359,21 +399,32 @@ int resize_network(network *net, int w, int h) error("Cannot resize this type of layer"); } if(l.workspace_size > workspace_size) workspace_size = l.workspace_size; + if(l.workspace_size > 2000000000) assert(0); inputs = l.outputs; net->layers[i] = l; w = l.out_w; h = l.out_h; if(l.type == AVGPOOL) break; } + layer out = get_network_output_layer(net); + net->inputs = net->layers[0].inputs; + net->outputs = out.outputs; + net->truths = out.outputs; + if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths; + net->output = out.output; + free(net->input); + free(net->truth); + net->input = calloc(net->inputs*net->batch, sizeof(float)); + net->truth = calloc(net->truths*net->batch, sizeof(float)); #ifdef GPU if(gpu_index >= 0){ - if(net->input_gpu) { - cuda_free(*net->input_gpu); - *net->input_gpu = 0; - cuda_free(*net->truth_gpu); - *net->truth_gpu = 0; + cuda_free(net->input_gpu); + cuda_free(net->truth_gpu); + net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch); + net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch); + if(workspace_size){ + net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); } - net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); }else { free(net->workspace); net->workspace = calloc(1, workspace_size); @@ -386,34 +437,25 @@ int resize_network(network *net, int w, int h) return 0; } -int get_network_output_size(network net) +layer get_network_detection_layer(network *net) { int i; - for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break; - return net.layers[i].outputs; -} - -int get_network_input_size(network net) -{ - return net.layers[0].inputs; -} - -detection_layer get_network_detection_layer(network net) -{ - int i; - for(i = 0; i < net.n; ++i){ - if(net.layers[i].type == DETECTION){ - return net.layers[i]; + for(i = 0; i < net->n; ++i){ + if(net->layers[i].type == DETECTION){ + return net->layers[i]; } } fprintf(stderr, "Detection layer not found!!\n"); - detection_layer l = {0}; + layer l = {0}; return l; } -image get_network_image_layer(network net, int i) +image get_network_image_layer(network *net, int i) { - layer l = net.layers[i]; + layer l = net->layers[i]; +#ifdef GPU + //cuda_pull_array(l.output_gpu, l.output, l.outputs); +#endif if (l.out_w && l.out_h && l.out_c){ return float_to_image(l.out_w, l.out_h, l.out_c, l.output); } @@ -421,10 +463,10 @@ image get_network_image_layer(network net, int i) return def; } -image get_network_image(network net) +image get_network_image(network *net) { int i; - for(i = net.n-1; i >= 0; --i){ + for(i = net->n-1; i >= 0; --i){ image m = get_network_image_layer(net, i); if(m.h != 0) return m; } @@ -432,60 +474,134 @@ image get_network_image(network net) return def; } -void visualize_network(network net) +void visualize_network(network *net) { image *prev = 0; int i; char buff[256]; - for(i = 0; i < net.n; ++i){ + for(i = 0; i < net->n; ++i){ sprintf(buff, "Layer %d", i); - layer l = net.layers[i]; + layer l = net->layers[i]; if(l.type == CONVOLUTIONAL){ prev = visualize_convolutional_layer(l, buff, prev); } } } -void top_predictions(network net, int k, int *index) +void top_predictions(network *net, int k, int *index) { - int size = get_network_output_size(net); - float *out = get_network_output(net); - top_k(out, size, k, index); + top_k(net->output, net->outputs, k, index); } -float *network_predict(network net, float *input) +float *network_predict(network *net, float *input) { -#ifdef GPU - if(gpu_index >= 0) return network_predict_gpu(net, input); -#endif - - network_state state; - state.net = net; - state.index = 0; - state.input = input; - state.truth = 0; - state.train = 0; - state.delta = 0; - forward_network(net, state); - float *out = get_network_output(net); + network orig = *net; + net->input = input; + net->truth = 0; + net->train = 0; + net->delta = 0; + forward_network(net); + float *out = net->output; + *net = orig; return out; } -matrix network_predict_data_multi(network net, data test, int n) +int num_detections(network *net, float thresh) +{ + int i; + int s = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO){ + s += yolo_num_detections(l, thresh); + } + if(l.type == DETECTION || l.type == REGION){ + s += l.w*l.h*l.n; + } + } + return s; +} + +detection *make_network_boxes(network *net, float thresh, int *num) +{ + layer l = net->layers[net->n - 1]; + int i; + int nboxes = num_detections(net, thresh); + if(num) *num = nboxes; + detection *dets = calloc(nboxes, sizeof(detection)); + for(i = 0; i < nboxes; ++i){ + dets[i].prob = calloc(l.classes, sizeof(float)); + if(l.coords > 4){ + dets[i].mask = calloc(l.coords-4, sizeof(float)); + } + } + return dets; +} + +void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets) +{ + int j; + for(j = 0; j < net->n; ++j){ + layer l = net->layers[j]; + if(l.type == YOLO){ + int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets); + dets += count; + } + if(l.type == REGION){ + get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets); + dets += l.w*l.h*l.n; + } + if(l.type == DETECTION){ + get_detection_detections(l, w, h, thresh, dets); + dets += l.w*l.h*l.n; + } + } +} + +detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num) +{ + detection *dets = make_network_boxes(net, thresh, num); + fill_network_boxes(net, w, h, thresh, hier, map, relative, dets); + return dets; +} + +void free_detections(detection *dets, int n) +{ + int i; + for(i = 0; i < n; ++i){ + free(dets[i].prob); + if(dets[i].mask) free(dets[i].mask); + } + free(dets); +} + +float *network_predict_image(network *net, image im) +{ + image imr = letterbox_image(im, net->w, net->h); + set_batch_network(net, 1); + float *p = network_predict(net, imr.data); + free_image(imr); + return p; +} + +int network_width(network *net){return net->w;} +int network_height(network *net){return net->h;} + +matrix network_predict_data_multi(network *net, data test, int n) { int i,j,b,m; - int k = get_network_output_size(net); + int k = net->outputs; matrix pred = make_matrix(test.X.rows, k); - float *X = calloc(net.batch*test.X.rows, sizeof(float)); - for(i = 0; i < test.X.rows; i += net.batch){ - for(b = 0; b < net.batch; ++b){ + float *X = calloc(net->batch*test.X.rows, sizeof(float)); + for(i = 0; i < test.X.rows; i += net->batch){ + for(b = 0; b < net->batch; ++b){ if(i+b == test.X.rows) break; memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float)); } for(m = 0; m < n; ++m){ float *out = network_predict(net, X); - for(b = 0; b < net.batch; ++b){ + for(b = 0; b < net->batch; ++b){ if(i+b == test.X.rows) break; for(j = 0; j < k; ++j){ pred.vals[i+b][j] += out[j+b*k]/n; @@ -497,19 +613,19 @@ matrix network_predict_data_multi(network net, data test, int n) return pred; } -matrix network_predict_data(network net, data test) +matrix network_predict_data(network *net, data test) { int i,j,b; - int k = get_network_output_size(net); + int k = net->outputs; matrix pred = make_matrix(test.X.rows, k); - float *X = calloc(net.batch*test.X.cols, sizeof(float)); - for(i = 0; i < test.X.rows; i += net.batch){ - for(b = 0; b < net.batch; ++b){ + float *X = calloc(net->batch*test.X.cols, sizeof(float)); + for(i = 0; i < test.X.rows; i += net->batch){ + for(b = 0; b < net->batch; ++b){ if(i+b == test.X.rows) break; memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float)); } float *out = network_predict(net, X); - for(b = 0; b < net.batch; ++b){ + for(b = 0; b < net->batch; ++b){ if(i+b == test.X.rows) break; for(j = 0; j < k; ++j){ pred.vals[i+b][j] = out[j+b*k]; @@ -520,11 +636,11 @@ matrix network_predict_data(network net, data test) return pred; } -void print_network(network net) +void print_network(network *net) { int i,j; - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; float *output = l.output; int n = l.outputs; float mean = mean_array(output, n); @@ -537,7 +653,7 @@ void print_network(network net) } } -void compare_networks(network n1, network n2, data test) +void compare_networks(network *n1, network *n2, data test) { matrix g1 = network_predict_data(n1, test); matrix g2 = network_predict_data(n2, test); @@ -562,7 +678,7 @@ void compare_networks(network n1, network n2, data test) printf("%f\n", num/den); } -float network_accuracy(network net, data d) +float network_accuracy(network *net, data d) { matrix guess = network_predict_data(net, d); float acc = matrix_topk_accuracy(d.y, guess,1); @@ -570,7 +686,7 @@ float network_accuracy(network net, data d) return acc; } -float *network_accuracies(network net, data d, int n) +float *network_accuracies(network *net, data d, int n) { static float acc[2]; matrix guess = network_predict_data(net, d); @@ -580,7 +696,16 @@ float *network_accuracies(network net, data d, int n) return acc; } -float network_accuracy_multi(network net, data d, int n) +layer get_network_output_layer(network *net) +{ + int i; + for(i = net->n - 1; i >= 0; --i){ + if(net->layers[i].type != COST) break; + } + return net->layers[i]; +} + +float network_accuracy_multi(network *net, data d, int n) { matrix guess = network_predict_data_multi(net, d, n); float acc = matrix_topk_accuracy(d.y, guess,1); @@ -588,17 +713,417 @@ float network_accuracy_multi(network net, data d, int n) return acc; } -void free_network(network net) +void free_network(network *net) { int i; - for(i = 0; i < net.n; ++i){ - free_layer(net.layers[i]); + for(i = 0; i < net->n; ++i){ + free_layer(net->layers[i]); } - free(net.layers); + free(net->layers); + if(net->input) free(net->input); + if(net->truth) free(net->truth); #ifdef GPU - if(*net.input_gpu) cuda_free(*net.input_gpu); - if(*net.truth_gpu) cuda_free(*net.truth_gpu); - if(net.input_gpu) free(net.input_gpu); - if(net.truth_gpu) free(net.truth_gpu); + if(net->input_gpu) cuda_free(net->input_gpu); + if(net->truth_gpu) cuda_free(net->truth_gpu); #endif + free(net); +} + +// Some day... +// ^ What the hell is this comment for? + + +layer network_output_layer(network *net) +{ + int i; + for(i = net->n - 1; i >= 0; --i){ + if(net->layers[i].type != COST) break; + } + return net->layers[i]; } + +int network_inputs(network *net) +{ + return net->layers[0].inputs; +} + +int network_outputs(network *net) +{ + return network_output_layer(net).outputs; +} + +float *network_output(network *net) +{ + return network_output_layer(net).output; +} + +#ifdef GPU + +void forward_network_gpu(network *netp) +{ + network net = *netp; + cuda_set_device(net.gpu_index); + cuda_push_array(net.input_gpu, net.input, net.inputs*net.batch); + if(net.truth){ + cuda_push_array(net.truth_gpu, net.truth, net.truths*net.batch); + } + + int i; + for(i = 0; i < net.n; ++i){ + net.index = i; + layer l = net.layers[i]; + if(l.delta_gpu){ + fill_gpu(l.outputs * l.batch, 0, l.delta_gpu, 1); + } + l.forward_gpu(l, net); + net.input_gpu = l.output_gpu; + net.input = l.output; + if(l.truth) { + net.truth_gpu = l.output_gpu; + net.truth = l.output; + } + } + pull_network_output(netp); + calc_network_cost(netp); +} + +void backward_network_gpu(network *netp) +{ + int i; + network net = *netp; + network orig = net; + cuda_set_device(net.gpu_index); + for(i = net.n-1; i >= 0; --i){ + layer l = net.layers[i]; + if(l.stopbackward) break; + if(i == 0){ + net = orig; + }else{ + layer prev = net.layers[i-1]; + net.input = prev.output; + net.delta = prev.delta; + net.input_gpu = prev.output_gpu; + net.delta_gpu = prev.delta_gpu; + } + net.index = i; + l.backward_gpu(l, net); + } +} + +void update_network_gpu(network *netp) +{ + network net = *netp; + cuda_set_device(net.gpu_index); + int i; + update_args a = {0}; + a.batch = net.batch*net.subdivisions; + a.learning_rate = get_current_rate(netp); + a.momentum = net.momentum; + a.decay = net.decay; + a.adam = net.adam; + a.B1 = net.B1; + a.B2 = net.B2; + a.eps = net.eps; + ++*net.t; + a.t = (*net.t); + + for(i = 0; i < net.n; ++i){ + layer l = net.layers[i]; + if(l.update_gpu){ + l.update_gpu(l, a); + } + } +} + +void harmless_update_network_gpu(network *netp) +{ + network net = *netp; + cuda_set_device(net.gpu_index); + int i; + for(i = 0; i < net.n; ++i){ + layer l = net.layers[i]; + if(l.weight_updates_gpu) fill_gpu(l.nweights, 0, l.weight_updates_gpu, 1); + if(l.bias_updates_gpu) fill_gpu(l.nbiases, 0, l.bias_updates_gpu, 1); + if(l.scale_updates_gpu) fill_gpu(l.nbiases, 0, l.scale_updates_gpu, 1); + } +} + +typedef struct { + network *net; + data d; + float *err; +} train_args; + +void *train_thread(void *ptr) +{ + train_args args = *(train_args*)ptr; + free(ptr); + cuda_set_device(args.net->gpu_index); + *args.err = train_network(args.net, args.d); + return 0; +} + +pthread_t train_network_in_thread(network *net, data d, float *err) +{ + pthread_t thread; + train_args *ptr = (train_args *)calloc(1, sizeof(train_args)); + ptr->net = net; + ptr->d = d; + ptr->err = err; + if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed"); + return thread; +} + +void merge_weights(layer l, layer base) +{ + if (l.type == CONVOLUTIONAL) { + axpy_cpu(l.n, 1, l.bias_updates, 1, base.biases, 1); + axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weights, 1); + if (l.scales) { + axpy_cpu(l.n, 1, l.scale_updates, 1, base.scales, 1); + } + } else if(l.type == CONNECTED) { + axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.biases, 1); + axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weights, 1); + } +} + +void scale_weights(layer l, float s) +{ + if (l.type == CONVOLUTIONAL) { + scal_cpu(l.n, s, l.biases, 1); + scal_cpu(l.nweights, s, l.weights, 1); + if (l.scales) { + scal_cpu(l.n, s, l.scales, 1); + } + } else if(l.type == CONNECTED) { + scal_cpu(l.outputs, s, l.biases, 1); + scal_cpu(l.outputs*l.inputs, s, l.weights, 1); + } +} + + +void pull_weights(layer l) +{ + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + cuda_pull_array(l.biases_gpu, l.bias_updates, l.n); + cuda_pull_array(l.weights_gpu, l.weight_updates, l.nweights); + if(l.scales) cuda_pull_array(l.scales_gpu, l.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_pull_array(l.biases_gpu, l.bias_updates, l.outputs); + cuda_pull_array(l.weights_gpu, l.weight_updates, l.outputs*l.inputs); + } +} + +void push_weights(layer l) +{ + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weights_gpu, l.weights, l.nweights); + if(l.scales) cuda_push_array(l.scales_gpu, l.scales, l.n); + } else if(l.type == CONNECTED){ + cuda_push_array(l.biases_gpu, l.biases, l.outputs); + cuda_push_array(l.weights_gpu, l.weights, l.outputs*l.inputs); + } +} + +void distribute_weights(layer l, layer base) +{ + if (l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL) { + cuda_push_array(l.biases_gpu, base.biases, l.n); + cuda_push_array(l.weights_gpu, base.weights, l.nweights); + if (base.scales) cuda_push_array(l.scales_gpu, base.scales, l.n); + } else if (l.type == CONNECTED) { + cuda_push_array(l.biases_gpu, base.biases, l.outputs); + cuda_push_array(l.weights_gpu, base.weights, l.outputs*l.inputs); + } +} + + +/* + + void pull_updates(layer l) + { + if(l.type == CONVOLUTIONAL){ + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); + } + } + + void push_updates(layer l) + { + if(l.type == CONVOLUTIONAL){ + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); + } + } + + void update_layer(layer l, network net) + { + int update_batch = net.batch*net.subdivisions; + float rate = get_current_rate(net); + l.t = get_current_batch(net); + if(l.update_gpu){ + l.update_gpu(l, update_batch, rate*l.learning_rate_scale, net.momentum, net.decay); + } + } + void merge_updates(layer l, layer base) + { + if (l.type == CONVOLUTIONAL) { + axpy_cpu(l.n, 1, l.bias_updates, 1, base.bias_updates, 1); + axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weight_updates, 1); + if (l.scale_updates) { + axpy_cpu(l.n, 1, l.scale_updates, 1, base.scale_updates, 1); + } + } else if(l.type == CONNECTED) { + axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.bias_updates, 1); + axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weight_updates, 1); + } + } + + void distribute_updates(layer l, layer base) + { + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.n); + cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.nweights); + if(base.scale_updates) cuda_push_array(l.scale_updates_gpu, base.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.outputs); + cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.outputs*l.inputs); + } + } + */ + +/* + void sync_layer(network *nets, int n, int j) + { + int i; + network net = nets[0]; + layer base = net.layers[j]; + scale_weights(base, 0); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i].gpu_index); + layer l = nets[i].layers[j]; + pull_weights(l); + merge_weights(l, base); + } + scale_weights(base, 1./n); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i].gpu_index); + layer l = nets[i].layers[j]; + distribute_weights(l, base); + } + } + */ + +void sync_layer(network **nets, int n, int j) +{ + int i; + network *net = nets[0]; + layer base = net->layers[j]; + scale_weights(base, 0); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i]->gpu_index); + layer l = nets[i]->layers[j]; + pull_weights(l); + merge_weights(l, base); + } + scale_weights(base, 1./n); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i]->gpu_index); + layer l = nets[i]->layers[j]; + distribute_weights(l, base); + } +} + +typedef struct{ + network **nets; + int n; + int j; +} sync_args; + +void *sync_layer_thread(void *ptr) +{ + sync_args args = *(sync_args*)ptr; + sync_layer(args.nets, args.n, args.j); + free(ptr); + return 0; +} + +pthread_t sync_layer_in_thread(network **nets, int n, int j) +{ + pthread_t thread; + sync_args *ptr = (sync_args *)calloc(1, sizeof(sync_args)); + ptr->nets = nets; + ptr->n = n; + ptr->j = j; + if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed"); + return thread; +} + +void sync_nets(network **nets, int n, int interval) +{ + int j; + int layers = nets[0]->n; + pthread_t *threads = (pthread_t *) calloc(layers, sizeof(pthread_t)); + + *(nets[0]->seen) += interval * (n-1) * nets[0]->batch * nets[0]->subdivisions; + for (j = 0; j < n; ++j){ + *(nets[j]->seen) = *(nets[0]->seen); + } + for (j = 0; j < layers; ++j) { + threads[j] = sync_layer_in_thread(nets, n, j); + } + for (j = 0; j < layers; ++j) { + pthread_join(threads[j], 0); + } + free(threads); +} + +float train_networks(network **nets, int n, data d, int interval) +{ + int i; + int batch = nets[0]->batch; + int subdivisions = nets[0]->subdivisions; + assert(batch * subdivisions * n == d.X.rows); + pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t)); + float *errors = (float *) calloc(n, sizeof(float)); + + float sum = 0; + for(i = 0; i < n; ++i){ + data p = get_data_part(d, i, n); + threads[i] = train_network_in_thread(nets[i], p, errors + i); + } + for(i = 0; i < n; ++i){ + pthread_join(threads[i], 0); + //printf("%f\n", errors[i]); + sum += errors[i]; + } + //cudaDeviceSynchronize(); + if (get_current_batch(nets[0]) % interval == 0) { + printf("Syncing... "); + fflush(stdout); + sync_nets(nets, n, interval); + printf("Done!\n"); + } + //cudaDeviceSynchronize(); + free(threads); + free(errors); + return (float)sum/(n); +} + +void pull_network_output(network *net) +{ + layer l = get_network_output_layer(net); + cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch); +} + +#endif diff --git a/image.darknet/inst/include/darknet/src/network.h b/image.darknet/inst/include/darknet/src/network.h index e48cbc2..1b0dfd1 100644 --- a/image.darknet/inst/include/darknet/src/network.h +++ b/image.darknet/inst/include/darknet/src/network.h @@ -1,129 +1,29 @@ // Oh boy, why am I about to do this.... #ifndef NETWORK_H #define NETWORK_H +#include "darknet.h" #include "image.h" #include "layer.h" #include "data.h" #include "tree.h" -typedef enum { - CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM -} learning_rate_policy; - -typedef struct network{ - float *workspace; - int n; - int batch; - int *seen; - float epoch; - int subdivisions; - float momentum; - float decay; - layer *layers; - int outputs; - float *output; - learning_rate_policy policy; - - float learning_rate; - float gamma; - float scale; - float power; - int time_steps; - int step; - int max_batches; - float *scales; - int *steps; - int num_steps; - int burn_in; - - int adam; - float B1; - float B2; - float eps; - - int inputs; - int h, w, c; - int max_crop; - int min_crop; - float angle; - float aspect; - float exposure; - float saturation; - float hue; - - int gpu_index; - tree *hierarchy; - - #ifdef GPU - float **input_gpu; - float **truth_gpu; - #endif -} network; - -typedef struct network_state { - float *truth; - float *input; - float *delta; - float *workspace; - int train; - int index; - network net; -} network_state; #ifdef GPU -float train_networks(network *nets, int n, data d, int interval); -void sync_nets(network *nets, int n, int interval); -float train_network_datum_gpu(network net, float *x, float *y); -float *network_predict_gpu(network net, float *input); -float * get_network_output_gpu_layer(network net, int i); -float * get_network_delta_gpu_layer(network net, int i); -float *get_network_output_gpu(network net); -void forward_network_gpu(network net, network_state state); -void backward_network_gpu(network net, network_state state); -void update_network_gpu(network net); +void pull_network_output(network *net); #endif -float get_current_rate(network net); -int get_current_batch(network net); -void free_network(network net); -void compare_networks(network n1, network n2, data d); +void compare_networks(network *n1, network *n2, data d); char *get_layer_string(LAYER_TYPE a); -network make_network(int n); -void forward_network(network net, network_state state); -void backward_network(network net, network_state state); -void update_network(network net); +network *make_network(int n); -float train_network(network net, data d); -float train_network_batch(network net, data d, int n); -float train_network_sgd(network net, data d, int n); -float train_network_datum(network net, float *x, float *y); -matrix network_predict_data(network net, data test); -float *network_predict(network net, float *input); -float network_accuracy(network net, data d); -float *network_accuracies(network net, data d, int n); -float network_accuracy_multi(network net, data d, int n); -void top_predictions(network net, int n, int *index); -float *get_network_output(network net); -float *get_network_output_layer(network net, int i); -float *get_network_delta_layer(network net, int i); -float *get_network_delta(network net); -int get_network_output_size_layer(network net, int i); -int get_network_output_size(network net); -image get_network_image(network net); -image get_network_image_layer(network net, int i); -int get_predicted_class_network(network net); -void print_network(network net); -void visualize_network(network net); +float network_accuracy_multi(network *net, data d, int n); +int get_predicted_class_network(network *net); +void print_network(network *net); int resize_network(network *net, int w, int h); -void set_batch_network(network *net, int b); -int get_network_input_size(network net); -float get_network_cost(network net); - -int get_network_nuisance(network net); -int get_network_background(network net); +void calc_network_cost(network *net); #endif diff --git a/image.darknet/inst/include/darknet/src/network_kernels.cu b/image.darknet/inst/include/darknet/src/network_kernels.cu deleted file mode 100644 index 313cd6d..0000000 --- a/image.darknet/inst/include/darknet/src/network_kernels.cu +++ /dev/null @@ -1,408 +0,0 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" - -extern "C" { -#include -#include -#include - -#include "network.h" -#include "image.h" -#include "data.h" -#include "utils.h" -#include "parser.h" - -#include "crop_layer.h" -#include "connected_layer.h" -#include "rnn_layer.h" -#include "gru_layer.h" -#include "crnn_layer.h" -#include "detection_layer.h" -#include "region_layer.h" -#include "convolutional_layer.h" -#include "activation_layer.h" -#include "maxpool_layer.h" -#include "reorg_layer.h" -#include "avgpool_layer.h" -#include "normalization_layer.h" -#include "batchnorm_layer.h" -#include "cost_layer.h" -#include "local_layer.h" -#include "softmax_layer.h" -#include "dropout_layer.h" -#include "route_layer.h" -#include "shortcut_layer.h" -#include "blas.h" -} - -float * get_network_output_gpu_layer(network net, int i); -float * get_network_delta_gpu_layer(network net, int i); -float * get_network_output_gpu(network net); - -void forward_network_gpu(network net, network_state state) -{ - state.workspace = net.workspace; - int i; - for(i = 0; i < net.n; ++i){ - state.index = i; - layer l = net.layers[i]; - if(l.delta_gpu){ - fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); - } - l.forward_gpu(l, state); - state.input = l.output_gpu; - } -} - -void backward_network_gpu(network net, network_state state) -{ - state.workspace = net.workspace; - int i; - float * original_input = state.input; - float * original_delta = state.delta; - for(i = net.n-1; i >= 0; --i){ - state.index = i; - layer l = net.layers[i]; - if(i == 0){ - state.input = original_input; - state.delta = original_delta; - }else{ - layer prev = net.layers[i-1]; - state.input = prev.output_gpu; - state.delta = prev.delta_gpu; - } - l.backward_gpu(l, state); - } -} - -void update_network_gpu(network net) -{ - cuda_set_device(net.gpu_index); - int i; - int update_batch = net.batch*net.subdivisions; - float rate = get_current_rate(net); - for(i = 0; i < net.n; ++i){ - layer l = net.layers[i]; - l.t = get_current_batch(net); - if(l.update_gpu){ - l.update_gpu(l, update_batch, rate, net.momentum, net.decay); - } - } -} - -void forward_backward_network_gpu(network net, float *x, float *y) -{ - network_state state; - state.index = 0; - state.net = net; - int x_size = get_network_input_size(net)*net.batch; - int y_size = get_network_output_size(net)*net.batch; - if(net.layers[net.n-1].truths) y_size = net.layers[net.n-1].truths*net.batch; - if(!*net.input_gpu){ - *net.input_gpu = cuda_make_array(x, x_size); - *net.truth_gpu = cuda_make_array(y, y_size); - }else{ - cuda_push_array(*net.input_gpu, x, x_size); - cuda_push_array(*net.truth_gpu, y, y_size); - } - state.input = *net.input_gpu; - state.delta = 0; - state.truth = *net.truth_gpu; - state.train = 1; - forward_network_gpu(net, state); - backward_network_gpu(net, state); -} - -float train_network_datum_gpu(network net, float *x, float *y) -{ - *net.seen += net.batch; - forward_backward_network_gpu(net, x, y); - float error = get_network_cost(net); - if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net); - - return error; -} - -typedef struct { - network net; - data d; - float *err; -} train_args; - -void *train_thread(void *ptr) -{ - train_args args = *(train_args*)ptr; - free(ptr); - cuda_set_device(args.net.gpu_index); - *args.err = train_network(args.net, args.d); - return 0; -} - -pthread_t train_network_in_thread(network net, data d, float *err) -{ - pthread_t thread; - train_args *ptr = (train_args *)calloc(1, sizeof(train_args)); - ptr->net = net; - ptr->d = d; - ptr->err = err; - if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed"); - return thread; -} - -void pull_updates(layer l) -{ - if(l.type == CONVOLUTIONAL){ - cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); - cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.n*l.size*l.size*l.c); - if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n); - } else if(l.type == CONNECTED){ - cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs); - cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); - } -} - -void push_updates(layer l) -{ - if(l.type == CONVOLUTIONAL){ - cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); - cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.n*l.size*l.size*l.c); - if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n); - } else if(l.type == CONNECTED){ - cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs); - cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); - } -} - -void update_layer(layer l, network net) -{ - int update_batch = net.batch*net.subdivisions; - float rate = get_current_rate(net); - l.t = get_current_batch(net); - if(l.update_gpu){ - l.update_gpu(l, update_batch, rate, net.momentum, net.decay); - } -} - -void merge_weights(layer l, layer base) -{ - if (l.type == CONVOLUTIONAL) { - axpy_cpu(l.n, 1, l.biases, 1, base.biases, 1); - axpy_cpu(l.n*l.size*l.size*l.c, 1, l.weights, 1, base.weights, 1); - if (l.scales) { - axpy_cpu(l.n, 1, l.scales, 1, base.scales, 1); - } - } else if(l.type == CONNECTED) { - axpy_cpu(l.outputs, 1, l.biases, 1, base.biases, 1); - axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, base.weights, 1); - } -} - -void scale_weights(layer l, float s) -{ - if (l.type == CONVOLUTIONAL) { - scal_cpu(l.n, s, l.biases, 1); - scal_cpu(l.n*l.size*l.size*l.c, s, l.weights, 1); - if (l.scales) { - scal_cpu(l.n, s, l.scales, 1); - } - } else if(l.type == CONNECTED) { - scal_cpu(l.outputs, s, l.biases, 1); - scal_cpu(l.outputs*l.inputs, s, l.weights, 1); - } -} - - -void pull_weights(layer l) -{ - if(l.type == CONVOLUTIONAL){ - cuda_pull_array(l.biases_gpu, l.biases, l.n); - cuda_pull_array(l.weights_gpu, l.weights, l.n*l.size*l.size*l.c); - if(l.scales) cuda_pull_array(l.scales_gpu, l.scales, l.n); - } else if(l.type == CONNECTED){ - cuda_pull_array(l.biases_gpu, l.biases, l.outputs); - cuda_pull_array(l.weights_gpu, l.weights, l.outputs*l.inputs); - } -} - -void push_weights(layer l) -{ - if(l.type == CONVOLUTIONAL){ - cuda_push_array(l.biases_gpu, l.biases, l.n); - cuda_push_array(l.weights_gpu, l.weights, l.n*l.size*l.size*l.c); - if(l.scales) cuda_push_array(l.scales_gpu, l.scales, l.n); - } else if(l.type == CONNECTED){ - cuda_push_array(l.biases_gpu, l.biases, l.outputs); - cuda_push_array(l.weights_gpu, l.weights, l.outputs*l.inputs); - } -} - -void distribute_weights(layer l, layer base) -{ - if(l.type == CONVOLUTIONAL){ - cuda_push_array(l.biases_gpu, base.biases, l.n); - cuda_push_array(l.weights_gpu, base.weights, l.n*l.size*l.size*l.c); - if(base.scales) cuda_push_array(l.scales_gpu, base.scales, l.n); - } else if(l.type == CONNECTED){ - cuda_push_array(l.biases_gpu, base.biases, l.outputs); - cuda_push_array(l.weights_gpu, base.weights, l.outputs*l.inputs); - } -} - - -void merge_updates(layer l, layer base) -{ - if (l.type == CONVOLUTIONAL) { - axpy_cpu(l.n, 1, l.bias_updates, 1, base.bias_updates, 1); - axpy_cpu(l.n*l.size*l.size*l.c, 1, l.weight_updates, 1, base.weight_updates, 1); - if (l.scale_updates) { - axpy_cpu(l.n, 1, l.scale_updates, 1, base.scale_updates, 1); - } - } else if(l.type == CONNECTED) { - axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.bias_updates, 1); - axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weight_updates, 1); - } -} - -void distribute_updates(layer l, layer base) -{ - if(l.type == CONVOLUTIONAL){ - cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.n); - cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.n*l.size*l.size*l.c); - if(base.scale_updates) cuda_push_array(l.scale_updates_gpu, base.scale_updates, l.n); - } else if(l.type == CONNECTED){ - cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.outputs); - cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.outputs*l.inputs); - } -} - -void sync_layer(network *nets, int n, int j) -{ - //printf("Syncing layer %d\n", j); - int i; - network net = nets[0]; - layer base = net.layers[j]; - cuda_set_device(net.gpu_index); - pull_weights(base); - for (i = 1; i < n; ++i) { - cuda_set_device(nets[i].gpu_index); - layer l = nets[i].layers[j]; - pull_weights(l); - merge_weights(l, base); - } - scale_weights(base, 1./n); - for (i = 0; i < n; ++i) { - cuda_set_device(nets[i].gpu_index); - layer l = nets[i].layers[j]; - distribute_weights(l, base); - } - //printf("Done syncing layer %d\n", j); -} - -typedef struct{ - network *nets; - int n; - int j; -} sync_args; - -void *sync_layer_thread(void *ptr) -{ - sync_args args = *(sync_args*)ptr; - sync_layer(args.nets, args.n, args.j); - free(ptr); - return 0; -} - -pthread_t sync_layer_in_thread(network *nets, int n, int j) -{ - pthread_t thread; - sync_args *ptr = (sync_args *)calloc(1, sizeof(sync_args)); - ptr->nets = nets; - ptr->n = n; - ptr->j = j; - if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed"); - return thread; -} - -void sync_nets(network *nets, int n, int interval) -{ - int j; - int layers = nets[0].n; - pthread_t *threads = (pthread_t *) calloc(layers, sizeof(pthread_t)); - - *nets[0].seen += interval * (n-1) * nets[0].batch * nets[0].subdivisions; - for (j = 0; j < n; ++j){ - *nets[j].seen = *nets[0].seen; - } - for (j = 0; j < layers; ++j) { - threads[j] = sync_layer_in_thread(nets, n, j); - } - for (j = 0; j < layers; ++j) { - pthread_join(threads[j], 0); - } - free(threads); -} - -float train_networks(network *nets, int n, data d, int interval) -{ - int i; - int batch = nets[0].batch; - int subdivisions = nets[0].subdivisions; - assert(batch * subdivisions * n == d.X.rows); - pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t)); - float *errors = (float *) calloc(n, sizeof(float)); - - float sum = 0; - for(i = 0; i < n; ++i){ - data p = get_data_part(d, i, n); - threads[i] = train_network_in_thread(nets[i], p, errors + i); - } - for(i = 0; i < n; ++i){ - pthread_join(threads[i], 0); - //printf("%f\n", errors[i]); - sum += errors[i]; - } - //cudaDeviceSynchronize(); - if (get_current_batch(nets[0]) % interval == 0) { - printf("Syncing... "); - fflush(stdout); - sync_nets(nets, n, interval); - printf("Done!\n"); - } - //cudaDeviceSynchronize(); - free(threads); - free(errors); - return (float)sum/(n); -} - -float *get_network_output_layer_gpu(network net, int i) -{ - layer l = net.layers[i]; - if(l.type != REGION) cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch); - return l.output; -} - -float *get_network_output_gpu(network net) -{ - int i; - for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break; - return get_network_output_layer_gpu(net, i); -} - -float *network_predict_gpu(network net, float *input) -{ - cuda_set_device(net.gpu_index); - int size = get_network_input_size(net) * net.batch; - network_state state; - state.index = 0; - state.net = net; - state.input = cuda_make_array(input, size); - state.truth = 0; - state.train = 0; - state.delta = 0; - forward_network_gpu(net, state); - float *out = get_network_output_gpu(net); - cuda_free(state.input); - return out; -} - diff --git a/image.darknet/inst/include/darknet/src/normalization_layer.c b/image.darknet/inst/include/darknet/src/normalization_layer.c index 069a079..424714f 100644 --- a/image.darknet/inst/include/darknet/src/normalization_layer.c +++ b/image.darknet/inst/include/darknet/src/normalization_layer.c @@ -1,5 +1,6 @@ #include "normalization_layer.h" #include "blas.h" + #include layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa) @@ -62,7 +63,7 @@ void resize_normalization_layer(layer *layer, int w, int h) #endif } -void forward_normalization_layer(const layer layer, network_state state) +void forward_normalization_layer(const layer layer, network net) { int k,b; int w = layer.w; @@ -73,7 +74,7 @@ void forward_normalization_layer(const layer layer, network_state state) for(b = 0; b < layer.batch; ++b){ float *squared = layer.squared + w*h*c*b; float *norms = layer.norms + w*h*c*b; - float *input = state.input + w*h*c*b; + float *input = net.input + w*h*c*b; pow_cpu(w*h*c, 2, input, 1, squared, 1); const_cpu(w*h, layer.kappa, norms, 1); @@ -90,10 +91,10 @@ void forward_normalization_layer(const layer layer, network_state state) } } pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, layer.output, 1); - mul_cpu(w*h*c*layer.batch, state.input, 1, layer.output, 1); + mul_cpu(w*h*c*layer.batch, net.input, 1, layer.output, 1); } -void backward_normalization_layer(const layer layer, network_state state) +void backward_normalization_layer(const layer layer, network net) { // TODO This is approximate ;-) // Also this should add in to delta instead of overwritting. @@ -101,50 +102,50 @@ void backward_normalization_layer(const layer layer, network_state state) int w = layer.w; int h = layer.h; int c = layer.c; - pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, state.delta, 1); - mul_cpu(w*h*c*layer.batch, layer.delta, 1, state.delta, 1); + pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, net.delta, 1); + mul_cpu(w*h*c*layer.batch, layer.delta, 1, net.delta, 1); } #ifdef GPU -void forward_normalization_layer_gpu(const layer layer, network_state state) +void forward_normalization_layer_gpu(const layer layer, network net) { int k,b; int w = layer.w; int h = layer.h; int c = layer.c; - scal_ongpu(w*h*c*layer.batch, 0, layer.squared_gpu, 1); + scal_gpu(w*h*c*layer.batch, 0, layer.squared_gpu, 1); for(b = 0; b < layer.batch; ++b){ float *squared = layer.squared_gpu + w*h*c*b; float *norms = layer.norms_gpu + w*h*c*b; - float *input = state.input + w*h*c*b; - pow_ongpu(w*h*c, 2, input, 1, squared, 1); + float *input = net.input_gpu + w*h*c*b; + pow_gpu(w*h*c, 2, input, 1, squared, 1); - const_ongpu(w*h, layer.kappa, norms, 1); + const_gpu(w*h, layer.kappa, norms, 1); for(k = 0; k < layer.size/2; ++k){ - axpy_ongpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1); + axpy_gpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1); } for(k = 1; k < layer.c; ++k){ - copy_ongpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1); + copy_gpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1); int prev = k - ((layer.size-1)/2) - 1; int next = k + (layer.size/2); - if(prev >= 0) axpy_ongpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1); - if(next < layer.c) axpy_ongpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1); + if(prev >= 0) axpy_gpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1); + if(next < layer.c) axpy_gpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1); } } - pow_ongpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, layer.output_gpu, 1); - mul_ongpu(w*h*c*layer.batch, state.input, 1, layer.output_gpu, 1); + pow_gpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, layer.output_gpu, 1); + mul_gpu(w*h*c*layer.batch, net.input_gpu, 1, layer.output_gpu, 1); } -void backward_normalization_layer_gpu(const layer layer, network_state state) +void backward_normalization_layer_gpu(const layer layer, network net) { // TODO This is approximate ;-) int w = layer.w; int h = layer.h; int c = layer.c; - pow_ongpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, state.delta, 1); - mul_ongpu(w*h*c*layer.batch, layer.delta_gpu, 1, state.delta, 1); + pow_gpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, net.delta_gpu, 1); + mul_gpu(w*h*c*layer.batch, layer.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/inst/include/darknet/src/normalization_layer.h b/image.darknet/inst/include/darknet/src/normalization_layer.h index ab32776..665baa5 100644 --- a/image.darknet/inst/include/darknet/src/normalization_layer.h +++ b/image.darknet/inst/include/darknet/src/normalization_layer.h @@ -7,13 +7,13 @@ layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa); void resize_normalization_layer(layer *layer, int h, int w); -void forward_normalization_layer(const layer layer, network_state state); -void backward_normalization_layer(const layer layer, network_state state); +void forward_normalization_layer(const layer layer, network net); +void backward_normalization_layer(const layer layer, network net); void visualize_normalization_layer(layer layer, char *window); #ifdef GPU -void forward_normalization_layer_gpu(const layer layer, network_state state); -void backward_normalization_layer_gpu(const layer layer, network_state state); +void forward_normalization_layer_gpu(const layer layer, network net); +void backward_normalization_layer_gpu(const layer layer, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/option_list.c b/image.darknet/inst/include/darknet/src/option_list.c index f935af3..2f52781 100644 --- a/image.darknet/inst/include/darknet/src/option_list.c +++ b/image.darknet/inst/include/darknet/src/option_list.c @@ -32,6 +32,23 @@ list *read_data_cfg(char *filename) return options; } +metadata get_metadata(char *file) +{ + metadata m = {0}; + list *options = read_data_cfg(file); + + char *name_list = option_find_str(options, "names", 0); + if(!name_list) name_list = option_find_str(options, "labels", 0); + if(!name_list) { + fprintf(stderr, "No names or labels found\n"); + } else { + m.names = get_labels(name_list); + } + m.classes = option_find_int(options, "classes", 2); + free_list(options); + return m; +} + int read_option(char *s, list *options) { size_t i; diff --git a/image.darknet/inst/include/darknet/src/option_list.h b/image.darknet/inst/include/darknet/src/option_list.h index 054b3fd..844bd87 100644 --- a/image.darknet/inst/include/darknet/src/option_list.h +++ b/image.darknet/inst/include/darknet/src/option_list.h @@ -9,13 +9,9 @@ typedef struct{ } kvp; -list *read_data_cfg(char *filename); int read_option(char *s, list *options); void option_insert(list *l, char *key, char *val); char *option_find(list *l, char *key); -char *option_find_str(list *l, char *key, char *def); -int option_find_int(list *l, char *key, int def); -int option_find_int_quiet(list *l, char *key, int def); float option_find_float(list *l, char *key, float def); float option_find_float_quiet(list *l, char *key, float def); void option_unused(list *l); diff --git a/image.darknet/inst/include/darknet/src/parser.c b/image.darknet/inst/include/darknet/src/parser.c index 3f39a13..c8141c9 100644 --- a/image.darknet/inst/include/darknet/src/parser.c +++ b/image.darknet/inst/include/darknet/src/parser.c @@ -1,14 +1,17 @@ #include #include #include +#include #include "activation_layer.h" +#include "logistic_layer.h" +#include "l2norm_layer.h" #include "activations.h" -#include "assert.h" #include "avgpool_layer.h" #include "batchnorm_layer.h" #include "blas.h" #include "connected_layer.h" +#include "deconvolutional_layer.h" #include "convolutional_layer.h" #include "cost_layer.h" #include "crnn_layer.h" @@ -23,11 +26,15 @@ #include "option_list.h" #include "parser.h" #include "region_layer.h" +#include "yolo_layer.h" +#include "iseg_layer.h" #include "reorg_layer.h" #include "rnn_layer.h" #include "route_layer.h" +#include "upsample_layer.h" #include "shortcut_layer.h" #include "softmax_layer.h" +#include "lstm_layer.h" #include "utils.h" typedef struct{ @@ -45,14 +52,21 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[cost]")==0) return COST; if (strcmp(type, "[detection]")==0) return DETECTION; if (strcmp(type, "[region]")==0) return REGION; + if (strcmp(type, "[yolo]")==0) return YOLO; + if (strcmp(type, "[iseg]")==0) return ISEG; if (strcmp(type, "[local]")==0) return LOCAL; if (strcmp(type, "[conv]")==0 || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL; + if (strcmp(type, "[deconv]")==0 + || strcmp(type, "[deconvolutional]")==0) return DECONVOLUTIONAL; if (strcmp(type, "[activation]")==0) return ACTIVE; + if (strcmp(type, "[logistic]")==0) return LOGXENT; + if (strcmp(type, "[l2norm]")==0) return L2NORM; if (strcmp(type, "[net]")==0 || strcmp(type, "[network]")==0) return NETWORK; if (strcmp(type, "[crnn]")==0) return CRNN; if (strcmp(type, "[gru]")==0) return GRU; + if (strcmp(type, "[lstm]") == 0) return LSTM; if (strcmp(type, "[rnn]")==0) return RNN; if (strcmp(type, "[conn]")==0 || strcmp(type, "[connected]")==0) return CONNECTED; @@ -68,6 +82,7 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[soft]")==0 || strcmp(type, "[softmax]")==0) return SOFTMAX; if (strcmp(type, "[route]")==0) return ROUTE; + if (strcmp(type, "[upsample]")==0) return UPSAMPLE; return BLANK; } @@ -111,7 +126,7 @@ typedef struct size_params{ int c; int index; int time_steps; - network net; + network *net; } size_params; local_layer parse_local(list *options, size_params params) @@ -135,6 +150,32 @@ local_layer parse_local(list *options, size_params params) return layer; } +layer parse_deconvolutional(list *options, size_params params) +{ + int n = option_find_int(options, "filters",1); + int size = option_find_int(options, "size",1); + int stride = option_find_int(options, "stride",1); + + char *activation_s = option_find_str(options, "activation", "logistic"); + ACTIVATION activation = get_activation(activation_s); + + int batch,h,w,c; + h = params.h; + w = params.w; + c = params.c; + batch=params.batch; + if(!(h && w && c)) error("Layer before deconvolutional layer must output image."); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + int pad = option_find_int_quiet(options, "pad",0); + int padding = option_find_int_quiet(options, "padding",0); + if(pad) padding = size/2; + + layer l = make_deconvolutional_layer(batch,h,w,c,n,size,stride,padding, activation, batch_normalize, params.net->adam); + + return l; +} + + convolutional_layer parse_convolutional(list *options, size_params params) { int n = option_find_int(options, "filters",1); @@ -142,6 +183,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int stride = option_find_int(options, "stride",1); int pad = option_find_int_quiet(options, "pad",0); int padding = option_find_int_quiet(options, "padding",0); + int groups = option_find_int_quiet(options, "groups", 1); if(pad) padding = size/2; char *activation_s = option_find_str(options, "activation", "logistic"); @@ -157,14 +199,9 @@ convolutional_layer parse_convolutional(list *options, size_params params) int binary = option_find_int_quiet(options, "binary", 0); int xnor = option_find_int_quiet(options, "xnor", 0); - convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam); + convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,groups,size,stride,padding,activation, batch_normalize, binary, xnor, params.net->adam); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); - if(params.net.adam){ - layer.B1 = params.net.B1; - layer.B2 = params.net.B2; - layer.eps = params.net.eps; - } return layer; } @@ -187,13 +224,11 @@ layer parse_crnn(list *options, size_params params) layer parse_rnn(list *options, size_params params) { int output = option_find_int(options, "output",1); - int hidden = option_find_int(options, "hidden",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); - int logistic = option_find_int_quiet(options, "logistic", 0); - layer l = make_rnn_layer(params.batch, params.inputs, hidden, output, params.time_steps, activation, batch_normalize, logistic); + layer l = make_rnn_layer(params.batch, params.inputs, output, params.time_steps, activation, batch_normalize, params.net->adam); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -205,31 +240,114 @@ layer parse_gru(list *options, size_params params) int output = option_find_int(options, "output",1); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); - layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize); + layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam); + l.tanh = option_find_int_quiet(options, "tanh", 0); + + return l; +} + +layer parse_lstm(list *options, size_params params) +{ + int output = option_find_int(options, "output", 1); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + + layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam); return l; } -connected_layer parse_connected(list *options, size_params params) +layer parse_connected(list *options, size_params params) { int output = option_find_int(options, "output",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); - connected_layer layer = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize); - - return layer; + layer l = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize, params.net->adam); + return l; } -softmax_layer parse_softmax(list *options, size_params params) +layer parse_softmax(list *options, size_params params) { int groups = option_find_int_quiet(options, "groups",1); - softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups); - layer.temperature = option_find_float_quiet(options, "temperature", 1); + layer l = make_softmax_layer(params.batch, params.inputs, groups); + l.temperature = option_find_float_quiet(options, "temperature", 1); char *tree_file = option_find_str(options, "tree", 0); - if (tree_file) layer.softmax_tree = read_tree(tree_file); - return layer; + if (tree_file) l.softmax_tree = read_tree(tree_file); + l.w = params.w; + l.h = params.h; + l.c = params.c; + l.spatial = option_find_float_quiet(options, "spatial", 0); + l.noloss = option_find_int_quiet(options, "noloss", 0); + return l; +} + +int *parse_yolo_mask(char *a, int *num) +{ + int *mask = 0; + if(a){ + int len = strlen(a); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (a[i] == ',') ++n; + } + mask = calloc(n, sizeof(int)); + for(i = 0; i < n; ++i){ + int val = atoi(a); + mask[i] = val; + a = strchr(a, ',')+1; + } + *num = n; + } + return mask; +} + +layer parse_yolo(list *options, size_params params) +{ + int classes = option_find_int(options, "classes", 20); + int total = option_find_int(options, "num", 1); + int num = total; + + char *a = option_find_str(options, "mask", 0); + int *mask = parse_yolo_mask(a, &num); + layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes); + assert(l.outputs == params.inputs); + + l.max_boxes = option_find_int_quiet(options, "max",90); + l.jitter = option_find_float(options, "jitter", .2); + + l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); + l.truth_thresh = option_find_float(options, "truth_thresh", 1); + l.random = option_find_int_quiet(options, "random", 0); + + char *map_file = option_find_str(options, "map", 0); + if (map_file) l.map = read_map(map_file); + + a = option_find_str(options, "anchors", 0); + if(a){ + int len = strlen(a); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (a[i] == ',') ++n; + } + for(i = 0; i < n; ++i){ + float bias = atof(a); + l.biases[i] = bias; + a = strchr(a, ',')+1; + } + } + return l; +} + +layer parse_iseg(list *options, size_params params) +{ + int classes = option_find_int(options, "classes", 20); + int ids = option_find_int(options, "ids", 32); + layer l = make_iseg_layer(params.batch, params.w, params.h, classes, ids); + assert(l.outputs == params.inputs); + return l; } layer parse_region(list *options, size_params params) @@ -245,6 +363,7 @@ layer parse_region(list *options, size_params params) l.sqrt = option_find_int_quiet(options, "sqrt", 0); l.softmax = option_find_int(options, "softmax", 0); + l.background = option_find_int_quiet(options, "background", 0); l.max_boxes = option_find_int_quiet(options, "max",30); l.jitter = option_find_float(options, "jitter", .2); l.rescore = option_find_int_quiet(options, "rescore",0); @@ -257,6 +376,7 @@ layer parse_region(list *options, size_params params) l.coord_scale = option_find_float(options, "coord_scale", 1); l.object_scale = option_find_float(options, "object_scale", 1); l.noobject_scale = option_find_float(options, "noobject_scale", 1); + l.mask_scale = option_find_float(options, "mask_scale", 1); l.class_scale = option_find_float(options, "class_scale", 1); l.bias_match = option_find_int_quiet(options, "bias_match",0); @@ -281,6 +401,7 @@ layer parse_region(list *options, size_params params) } return l; } + detection_layer parse_detection(list *options, size_params params) { int coords = option_find_int(options, "coords", 1); @@ -293,7 +414,7 @@ detection_layer parse_detection(list *options, size_params params) layer.softmax = option_find_int(options, "softmax", 0); layer.sqrt = option_find_int(options, "sqrt", 0); - layer.max_boxes = option_find_int_quiet(options, "max",30); + layer.max_boxes = option_find_int_quiet(options, "max",90); layer.coord_scale = option_find_float(options, "coord_scale", 1); layer.forced = option_find_int(options, "forced", 0); layer.object_scale = option_find_float(options, "object_scale", 1); @@ -312,6 +433,8 @@ cost_layer parse_cost(list *options, size_params params) float scale = option_find_float_quiet(options, "scale",1); cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale); layer.ratio = option_find_float_quiet(options, "ratio",0); + layer.noobject_scale = option_find_float_quiet(options, "noobj", 1); + layer.thresh = option_find_float_quiet(options, "thresh",0); return layer; } @@ -343,6 +466,8 @@ layer parse_reorg(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int reverse = option_find_int_quiet(options, "reverse",0); + int flatten = option_find_int_quiet(options, "flatten",0); + int extra = option_find_int_quiet(options, "extra",0); int batch,h,w,c; h = params.h; @@ -351,7 +476,7 @@ layer parse_reorg(list *options, size_params params) batch=params.batch; if(!(h && w && c)) error("Layer before reorg layer must output image."); - layer layer = make_reorg_layer(batch,w,h,c,stride,reverse); + layer layer = make_reorg_layer(batch,w,h,c,stride,reverse, flatten, extra); return layer; } @@ -359,7 +484,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int size = option_find_int(options, "size",stride); - int padding = option_find_int_quiet(options, "padding", (size-1)/2); + int padding = option_find_int_quiet(options, "padding", size-1); int batch,h,w,c; h = params.h; @@ -411,24 +536,45 @@ layer parse_batchnorm(list *options, size_params params) return l; } -layer parse_shortcut(list *options, size_params params, network net) +layer parse_shortcut(list *options, size_params params, network *net) { - char *l = option_find(options, "from"); + char *l = option_find(options, "from"); int index = atoi(l); if(index < 0) index = params.index + index; int batch = params.batch; - layer from = net.layers[index]; + layer from = net->layers[index]; layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); s.activation = activation; + s.alpha = option_find_float_quiet(options, "alpha", 1); + s.beta = option_find_float_quiet(options, "beta", 1); return s; } +layer parse_l2norm(list *options, size_params params) +{ + layer l = make_l2norm_layer(params.batch, params.inputs); + l.h = l.out_h = params.h; + l.w = l.out_w = params.w; + l.c = l.out_c = params.c; + return l; +} + + +layer parse_logistic(list *options, size_params params) +{ + layer l = make_logistic_layer(params.batch, params.inputs); + l.h = l.out_h = params.h; + l.w = l.out_w = params.w; + l.c = l.out_c = params.c; + return l; +} + layer parse_activation(list *options, size_params params) { char *activation_s = option_find_str(options, "activation", "linear"); @@ -436,19 +582,25 @@ layer parse_activation(list *options, size_params params) layer l = make_activation_layer(params.batch, params.inputs, activation); - l.out_h = params.h; - l.out_w = params.w; - l.out_c = params.c; - l.h = params.h; - l.w = params.w; - l.c = params.c; + l.h = l.out_h = params.h; + l.w = l.out_w = params.w; + l.c = l.out_c = params.c; + + return l; +} + +layer parse_upsample(list *options, size_params params, network *net) +{ + int stride = option_find_int(options, "stride",2); + layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride); + l.scale = option_find_float_quiet(options, "scale", 1); return l; } -route_layer parse_route(list *options, size_params params, network net) +route_layer parse_route(list *options, size_params params, network *net) { - char *l = option_find(options, "layers"); + char *l = option_find(options, "layers"); int len = strlen(l); if(!l) error("Route Layer must specify input layers"); int n = 1; @@ -464,19 +616,19 @@ route_layer parse_route(list *options, size_params params, network net) l = strchr(l, ',')+1; if(index < 0) index = params.index + index; layers[i] = index; - sizes[i] = net.layers[index].outputs; + sizes[i] = net->layers[index].outputs; } int batch = params.batch; route_layer layer = make_route_layer(batch, n, layers, sizes); - convolutional_layer first = net.layers[layers[0]]; + convolutional_layer first = net->layers[layers[0]]; layer.out_w = first.out_w; layer.out_h = first.out_h; layer.out_c = first.out_c; for(i = 1; i < n; ++i){ int index = layers[i]; - convolutional_layer next = net.layers[index]; + convolutional_layer next = net->layers[index]; if(next.out_w == first.out_w && next.out_h == first.out_h){ layer.out_c += next.out_c; }else{ @@ -508,15 +660,17 @@ void parse_net_options(list *options, network *net) net->decay = option_find_float(options, "decay", .0001); int subdivs = option_find_int(options, "subdivisions",1); net->time_steps = option_find_int_quiet(options, "time_steps",1); + net->notruth = option_find_int_quiet(options, "notruth",0); net->batch /= subdivs; net->batch *= net->time_steps; net->subdivisions = subdivs; + net->random = option_find_int_quiet(options, "random", 0); net->adam = option_find_int_quiet(options, "adam", 0); if(net->adam){ net->B1 = option_find_float(options, "B1", .9); net->B2 = option_find_float(options, "B2", .999); - net->eps = option_find_float(options, "eps", .000001); + net->eps = option_find_float(options, "eps", .0000001); } net->h = option_find_int_quiet(options, "height",0); @@ -525,6 +679,10 @@ void parse_net_options(list *options, network *net) net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c); net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2); net->min_crop = option_find_int_quiet(options, "min_crop",net->w); + net->max_ratio = option_find_float_quiet(options, "max_ratio", (float) net->max_crop / net->w); + net->min_ratio = option_find_float_quiet(options, "min_ratio", (float) net->min_crop / net->w); + net->center = option_find_int_quiet(options, "center",0); + net->clip = option_find_float_quiet(options, "clip", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); @@ -537,12 +695,13 @@ void parse_net_options(list *options, network *net) char *policy_s = option_find_str(options, "policy", "constant"); net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); + net->power = option_find_float_quiet(options, "power", 4); if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); net->scale = option_find_float(options, "scale", 1); } else if (net->policy == STEPS){ - char *l = option_find(options, "steps"); - char *p = option_find(options, "scales"); + char *l = option_find(options, "steps"); + char *p = option_find(options, "scales"); if(!l || !p) error("STEPS policy must have steps and scales in cfg file"); int len = strlen(l); @@ -570,7 +729,6 @@ void parse_net_options(list *options, network *net) net->gamma = option_find_float(options, "gamma", 1); net->step = option_find_int(options, "step", 1); } else if (net->policy == POLY || net->policy == RANDOM){ - net->power = option_find_float(options, "power", 1); } net->max_batches = option_find_int(options, "max_batches", 0); } @@ -581,26 +739,26 @@ int is_network(section *s) || strcmp(s->type, "[network]")==0); } -network parse_network_cfg(char *filename) +network *parse_network_cfg(char *filename) { list *sections = read_cfg(filename); node *n = sections->front; if(!n) error("Config file has no sections"); - network net = make_network(sections->size - 1); - net.gpu_index = gpu_index; + network *net = make_network(sections->size - 1); + net->gpu_index = gpu_index; size_params params; section *s = (section *)n->val; list *options = s->options; if(!is_network(s)) error("First section must be [net] or [network]"); - parse_net_options(options, &net); - - params.h = net.h; - params.w = net.w; - params.c = net.c; - params.inputs = net.inputs; - params.batch = net.batch; - params.time_steps = net.time_steps; + parse_net_options(options, net); + + params.h = net->h; + params.w = net->w; + params.c = net->c; + params.inputs = net->inputs; + params.batch = net->batch; + params.time_steps = net->time_steps; params.net = net; size_t workspace_size = 0; @@ -617,14 +775,22 @@ network parse_network_cfg(char *filename) LAYER_TYPE lt = string_to_layer_type(s->type); if(lt == CONVOLUTIONAL){ l = parse_convolutional(options, params); + }else if(lt == DECONVOLUTIONAL){ + l = parse_deconvolutional(options, params); }else if(lt == LOCAL){ l = parse_local(options, params); }else if(lt == ACTIVE){ l = parse_activation(options, params); + }else if(lt == LOGXENT){ + l = parse_logistic(options, params); + }else if(lt == L2NORM){ + l = parse_l2norm(options, params); }else if(lt == RNN){ l = parse_rnn(options, params); }else if(lt == GRU){ l = parse_gru(options, params); + }else if (lt == LSTM) { + l = parse_lstm(options, params); }else if(lt == CRNN){ l = parse_crnn(options, params); }else if(lt == CONNECTED){ @@ -635,11 +801,15 @@ network parse_network_cfg(char *filename) l = parse_cost(options, params); }else if(lt == REGION){ l = parse_region(options, params); + }else if(lt == YOLO){ + l = parse_yolo(options, params); + }else if(lt == ISEG){ + l = parse_iseg(options, params); }else if(lt == DETECTION){ l = parse_detection(options, params); }else if(lt == SOFTMAX){ l = parse_softmax(options, params); - net.hierarchy = l.softmax_tree; + net->hierarchy = l.softmax_tree; }else if(lt == NORMALIZATION){ l = parse_normalization(options, params); }else if(lt == BATCHNORM){ @@ -652,23 +822,33 @@ network parse_network_cfg(char *filename) l = parse_avgpool(options, params); }else if(lt == ROUTE){ l = parse_route(options, params, net); + }else if(lt == UPSAMPLE){ + l = parse_upsample(options, params, net); }else if(lt == SHORTCUT){ l = parse_shortcut(options, params, net); }else if(lt == DROPOUT){ l = parse_dropout(options, params); - l.output = net.layers[count-1].output; - l.delta = net.layers[count-1].delta; + l.output = net->layers[count-1].output; + l.delta = net->layers[count-1].delta; #ifdef GPU - l.output_gpu = net.layers[count-1].output_gpu; - l.delta_gpu = net.layers[count-1].delta_gpu; + l.output_gpu = net->layers[count-1].output_gpu; + l.delta_gpu = net->layers[count-1].delta_gpu; #endif }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } + l.clip = net->clip; + l.truth = option_find_int_quiet(options, "truth", 0); + l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); + l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); + l.dontsave = option_find_int_quiet(options, "dontsave", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); + l.numload = option_find_int_quiet(options, "numload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); + l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); + l.smooth = option_find_float_quiet(options, "smooth", 0); option_unused(options); - net.layers[count] = l; + net->layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; free_section(s); n = n->next; @@ -679,20 +859,30 @@ network parse_network_cfg(char *filename) params.c = l.out_c; params.inputs = l.outputs; } - } + } free_list(sections); - net.outputs = get_network_output_size(net); - net.output = get_network_output(net); + layer out = get_network_output_layer(net); + net->outputs = out.outputs; + net->truths = out.outputs; + if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths; + net->output = out.output; + net->input = calloc(net->inputs*net->batch, sizeof(float)); + net->truth = calloc(net->truths*net->batch, sizeof(float)); +#ifdef GPU + net->output_gpu = out.output_gpu; + net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch); + net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch); +#endif if(workspace_size){ //printf("%ld\n", workspace_size); #ifdef GPU if(gpu_index >= 0){ - net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); + net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); }else { - net.workspace = calloc(1, workspace_size); + net->workspace = calloc(1, workspace_size); } #else - net.workspace = calloc(1, workspace_size); + net->workspace = calloc(1, workspace_size); #endif } return net; @@ -704,7 +894,7 @@ list *read_cfg(char *filename) if(file == 0) file_error(filename); char *line; int nu = 0; - list *sections = make_list(); + list *options = make_list(); section *current = 0; while((line=fgetl(file)) != 0){ ++ nu; @@ -712,7 +902,7 @@ list *read_cfg(char *filename) switch(line[0]){ case '[': current = malloc(sizeof(section)); - list_insert(sections, current); + list_insert(options, current); current->options = make_list(); current->type = line; break; @@ -730,7 +920,7 @@ list *read_cfg(char *filename) } } fclose(file); - return sections; + return options; } void save_convolutional_weights_binary(layer l, FILE *fp) @@ -776,7 +966,7 @@ void save_convolutional_weights(layer l, FILE *fp) pull_convolutional_layer(l); } #endif - int num = l.n*l.c*l.size*l.size; + int num = l.nweights; fwrite(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize){ fwrite(l.scales, sizeof(float), l.n, fp); @@ -784,10 +974,6 @@ void save_convolutional_weights(layer l, FILE *fp) fwrite(l.rolling_variance, sizeof(float), l.n, fp); } fwrite(l.weights, sizeof(float), num, fp); - if(l.adam){ - fwrite(l.m, sizeof(float), num, fp); - fwrite(l.v, sizeof(float), num, fp); - } } void save_batchnorm_weights(layer l, FILE *fp) @@ -818,11 +1004,11 @@ void save_connected_weights(layer l, FILE *fp) } } -void save_weights_upto(network net, char *filename, int cutoff) +void save_weights_upto(network *net, char *filename, int cutoff) { #ifdef GPU - if(net.gpu_index >= 0){ - cuda_set_device(net.gpu_index); + if(net->gpu_index >= 0){ + cuda_set_device(net->gpu_index); } #endif fprintf(stderr, "Saving weights to %s\n", filename); @@ -830,17 +1016,18 @@ void save_weights_upto(network net, char *filename, int cutoff) if(!fp) file_error(filename); int major = 0; - int minor = 1; + int minor = 2; int revision = 0; fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); - fwrite(net.seen, sizeof(int), 1, fp); + fwrite(net->seen, sizeof(size_t), 1, fp); int i; - for(i = 0; i < net.n && i < cutoff; ++i){ - layer l = net.layers[i]; - if(l.type == CONVOLUTIONAL){ + for(i = 0; i < net->n && i < cutoff; ++i){ + layer l = net->layers[i]; + if (l.dontsave) continue; + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ save_convolutional_weights(l, fp); } if(l.type == CONNECTED){ save_connected_weights(l, fp); @@ -850,14 +1037,29 @@ void save_weights_upto(network net, char *filename, int cutoff) save_connected_weights(*(l.input_layer), fp); save_connected_weights(*(l.self_layer), fp); save_connected_weights(*(l.output_layer), fp); - } if(l.type == GRU){ - save_connected_weights(*(l.input_z_layer), fp); - save_connected_weights(*(l.input_r_layer), fp); - save_connected_weights(*(l.input_h_layer), fp); - save_connected_weights(*(l.state_z_layer), fp); - save_connected_weights(*(l.state_r_layer), fp); - save_connected_weights(*(l.state_h_layer), fp); - } if(l.type == CRNN){ + } if (l.type == LSTM) { + save_connected_weights(*(l.wi), fp); + save_connected_weights(*(l.wf), fp); + save_connected_weights(*(l.wo), fp); + save_connected_weights(*(l.wg), fp); + save_connected_weights(*(l.ui), fp); + save_connected_weights(*(l.uf), fp); + save_connected_weights(*(l.uo), fp); + save_connected_weights(*(l.ug), fp); + } if (l.type == GRU) { + if(1){ + save_connected_weights(*(l.wz), fp); + save_connected_weights(*(l.wr), fp); + save_connected_weights(*(l.wh), fp); + save_connected_weights(*(l.uz), fp); + save_connected_weights(*(l.ur), fp); + save_connected_weights(*(l.uh), fp); + }else{ + save_connected_weights(*(l.reset_layer), fp); + save_connected_weights(*(l.update_layer), fp); + save_connected_weights(*(l.state_layer), fp); + } + } if(l.type == CRNN){ save_convolutional_weights(*(l.input_layer), fp); save_convolutional_weights(*(l.self_layer), fp); save_convolutional_weights(*(l.output_layer), fp); @@ -875,9 +1077,9 @@ void save_weights_upto(network net, char *filename, int cutoff) } fclose(fp); } -void save_weights(network net, char *filename) +void save_weights(network *net, char *filename) { - save_weights_upto(net, filename, net.n); + save_weights_upto(net, filename, net->n); } void transpose_matrix(float *a, int rows, int cols) @@ -965,7 +1167,8 @@ void load_convolutional_weights(layer l, FILE *fp) //load_convolutional_weights_binary(l, fp); //return; } - int num = l.n*l.c*l.size*l.size; + if(l.numload) l.n = l.numload; + int num = l.c/l.groups*l.n*l.size*l.size; fread(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize && (!l.dontloadscales)){ fread(l.scales, sizeof(float), l.n, fp); @@ -986,12 +1189,19 @@ void load_convolutional_weights(layer l, FILE *fp) fill_cpu(l.n, 0, l.rolling_mean, 1); fill_cpu(l.n, 0, l.rolling_variance, 1); } + if(0){ + int i; + for(i = 0; i < l.n; ++i){ + printf("%g, ", l.rolling_mean[i]); + } + printf("\n"); + for(i = 0; i < l.n; ++i){ + printf("%g, ", l.rolling_variance[i]); + } + printf("\n"); + } } fread(l.weights, sizeof(float), num, fp); - if(l.adam){ - fread(l.m, sizeof(float), num, fp); - fread(l.v, sizeof(float), num, fp); - } //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1); if (l.flipped) { transpose_matrix(l.weights, l.c*l.size*l.size, l.n); @@ -1005,7 +1215,7 @@ void load_convolutional_weights(layer l, FILE *fp) } -void load_weights_upto(network *net, char *filename, int cutoff) +void load_weights_upto(network *net, char *filename, int start, int cutoff) { #ifdef GPU if(net->gpu_index >= 0){ @@ -1023,14 +1233,20 @@ void load_weights_upto(network *net, char *filename, int cutoff) fread(&major, sizeof(int), 1, fp); fread(&minor, sizeof(int), 1, fp); fread(&revision, sizeof(int), 1, fp); - fread(net->seen, sizeof(int), 1, fp); + if ((major*10 + minor) >= 2 && major < 1000 && minor < 1000){ + fread(net->seen, sizeof(size_t), 1, fp); + } else { + int iseen = 0; + fread(&iseen, sizeof(int), 1, fp); + *net->seen = iseen; + } int transpose = (major > 1000) || (minor > 1000); int i; - for(i = 0; i < net->n && i < cutoff; ++i){ + for(i = start; i < net->n && i < cutoff; ++i){ layer l = net->layers[i]; if (l.dontload) continue; - if(l.type == CONVOLUTIONAL){ + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ load_convolutional_weights(l, fp); } if(l.type == CONNECTED){ @@ -1049,13 +1265,29 @@ void load_weights_upto(network *net, char *filename, int cutoff) load_connected_weights(*(l.self_layer), fp, transpose); load_connected_weights(*(l.output_layer), fp, transpose); } - if(l.type == GRU){ - load_connected_weights(*(l.input_z_layer), fp, transpose); - load_connected_weights(*(l.input_r_layer), fp, transpose); - load_connected_weights(*(l.input_h_layer), fp, transpose); - load_connected_weights(*(l.state_z_layer), fp, transpose); - load_connected_weights(*(l.state_r_layer), fp, transpose); - load_connected_weights(*(l.state_h_layer), fp, transpose); + if (l.type == LSTM) { + load_connected_weights(*(l.wi), fp, transpose); + load_connected_weights(*(l.wf), fp, transpose); + load_connected_weights(*(l.wo), fp, transpose); + load_connected_weights(*(l.wg), fp, transpose); + load_connected_weights(*(l.ui), fp, transpose); + load_connected_weights(*(l.uf), fp, transpose); + load_connected_weights(*(l.uo), fp, transpose); + load_connected_weights(*(l.ug), fp, transpose); + } + if (l.type == GRU) { + if(1){ + load_connected_weights(*(l.wz), fp, transpose); + load_connected_weights(*(l.wr), fp, transpose); + load_connected_weights(*(l.wh), fp, transpose); + load_connected_weights(*(l.uz), fp, transpose); + load_connected_weights(*(l.ur), fp, transpose); + load_connected_weights(*(l.uh), fp, transpose); + }else{ + load_connected_weights(*(l.reset_layer), fp, transpose); + load_connected_weights(*(l.update_layer), fp, transpose); + load_connected_weights(*(l.state_layer), fp, transpose); + } } if(l.type == LOCAL){ int locations = l.out_w*l.out_h; @@ -1075,6 +1307,6 @@ void load_weights_upto(network *net, char *filename, int cutoff) void load_weights(network *net, char *filename) { - load_weights_upto(net, filename, net->n); + load_weights_upto(net, filename, 0, net->n); } diff --git a/image.darknet/inst/include/darknet/src/parser.h b/image.darknet/inst/include/darknet/src/parser.h index 6cff4fb..81aef2c 100644 --- a/image.darknet/inst/include/darknet/src/parser.h +++ b/image.darknet/inst/include/darknet/src/parser.h @@ -1,13 +1,9 @@ #ifndef PARSER_H #define PARSER_H +#include "darknet.h" #include "network.h" -network parse_network_cfg(char *filename); void save_network(network net, char *filename); -void save_weights(network net, char *filename); -void save_weights_upto(network net, char *filename, int cutoff); void save_weights_double(network net, char *filename); -void load_weights(network *net, char *filename); -void load_weights_upto(network *net, char *filename, int cutoff); #endif diff --git a/image.darknet/inst/include/darknet/src/region_layer.c b/image.darknet/inst/include/darknet/src/region_layer.c index f5522c3..179f5e3 100644 --- a/image.darknet/inst/include/darknet/src/region_layer.c +++ b/image.darknet/inst/include/darknet/src/region_layer.c @@ -4,6 +4,7 @@ #include "box.h" #include "cuda.h" #include "utils.h" + #include #include #include @@ -18,6 +19,10 @@ layer make_region_layer(int batch, int w, int h, int n, int classes, int coords) l.batch = batch; l.h = h; l.w = w; + l.c = n*(classes + coords + 1); + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; l.classes = classes; l.coords = coords; l.cost = calloc(1, sizeof(float)); @@ -25,7 +30,7 @@ layer make_region_layer(int batch, int w, int h, int n, int classes, int coords) l.bias_updates = calloc(n*2, sizeof(float)); l.outputs = h*w*n*(classes + coords + 1); l.inputs = l.outputs; - l.truths = 30*(5); + l.truths = 30*(l.coords + 1); l.delta = calloc(batch*l.outputs, sizeof(float)); l.output = calloc(batch*l.outputs, sizeof(float)); int i; @@ -68,19 +73,19 @@ void resize_region_layer(layer *l, int w, int h) #endif } -box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h) +box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h, int stride) { box b; - b.x = (i + logistic_activate(x[index + 0])) / w; - b.y = (j + logistic_activate(x[index + 1])) / h; - b.w = exp(x[index + 2]) * biases[2*n] / w; - b.h = exp(x[index + 3]) * biases[2*n+1] / h; + b.x = (i + x[index + 0*stride]) / w; + b.y = (j + x[index + 1*stride]) / h; + b.w = exp(x[index + 2*stride]) * biases[2*n] / w; + b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; return b; } -float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale) +float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale, int stride) { - box pred = get_region_box(x, biases, n, index, i, j, w, h); + box pred = get_region_box(x, biases, n, index, i, j, w, h, stride); float iou = box_iou(pred, truth); float tx = (truth.x*w - i); @@ -88,34 +93,47 @@ float delta_region_box(box truth, float *x, float *biases, int n, int index, int float tw = log(truth.w*w / biases[2*n]); float th = log(truth.h*h / biases[2*n + 1]); - delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0])); - delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1])); - delta[index + 2] = scale * (tw - x[index + 2]); - delta[index + 3] = scale * (th - x[index + 3]); + delta[index + 0*stride] = scale * (tx - x[index + 0*stride]); + delta[index + 1*stride] = scale * (ty - x[index + 1*stride]); + delta[index + 2*stride] = scale * (tw - x[index + 2*stride]); + delta[index + 3*stride] = scale * (th - x[index + 3*stride]); return iou; } -void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, float *avg_cat) +void delta_region_mask(float *truth, float *x, int n, int index, float *delta, int stride, int scale) +{ + int i; + for(i = 0; i < n; ++i){ + delta[index + i*stride] = scale*(truth[i] - x[index + i*stride]); + } +} + + +void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, int stride, float *avg_cat, int tag) { int i, n; if(hier){ float pred = 1; while(class >= 0){ - pred *= output[index + class]; + pred *= output[index + stride*class]; int g = hier->group[class]; int offset = hier->group_offset[g]; for(i = 0; i < hier->group_size[g]; ++i){ - delta[index + offset + i] = scale * (0 - output[index + offset + i]); + delta[index + stride*(offset + i)] = scale * (0 - output[index + stride*(offset + i)]); } - delta[index + class] = scale * (1 - output[index + class]); + delta[index + stride*class] = scale * (1 - output[index + stride*class]); class = hier->parent[class]; } *avg_cat += pred; } else { + if (delta[index] && tag){ + delta[index + stride*class] = scale * (1 - output[index + stride*class]); + return; + } for(n = 0; n < classes; ++n){ - delta[index + n] = scale * (((n == class)?1 : 0) - output[index + n]); - if(n == class) *avg_cat += output[index + n]; + delta[index + stride*n] = scale * (((n == class)?1 : 0) - output[index + stride*n]); + if(n == class) *avg_cat += output[index + stride*n]; } } } @@ -130,42 +148,45 @@ float tisnan(float x) return (x != x); } -void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output); -void forward_region_layer(const layer l, network_state state) +int entry_index(layer l, int batch, int location, int entry) +{ + int n = location / (l.w*l.h); + int loc = location % (l.w*l.h); + return batch*l.outputs + n*l.w*l.h*(l.coords+l.classes+1) + entry*l.w*l.h + loc; +} + +void forward_region_layer(const layer l, network net) { int i,j,b,t,n; - int size = l.coords + l.classes + 1; - memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + #ifndef GPU - flatten(l.output, l.w*l.h, size*l.n, l.batch, 1); -#endif for (b = 0; b < l.batch; ++b){ - for(i = 0; i < l.h*l.w*l.n; ++i){ - int index = size*i + b*l.outputs; - l.output[index + 4] = logistic_activate(l.output[index + 4]); + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, l.coords); + if(!l.background) activate_array(l.output + index, l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, l.coords + 1); + if(!l.softmax && !l.softmax_tree) activate_array(l.output + index, l.classes*l.w*l.h, LOGISTIC); } } - - -#ifndef GPU if (l.softmax_tree){ - for (b = 0; b < l.batch; ++b){ - for(i = 0; i < l.h*l.w*l.n; ++i){ - int index = size*i + b*l.outputs; - softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5); - } + int i; + int count = l.coords + 1; + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_cpu(net.input + count, group_size, l.batch, l.inputs, l.n*l.w*l.h, 1, l.n*l.w*l.h, l.temperature, l.output + count); + count += group_size; } } else if (l.softmax){ - for (b = 0; b < l.batch; ++b){ - for(i = 0; i < l.h*l.w*l.n; ++i){ - int index = size*i + b*l.outputs; - softmax(l.output + index + 5, l.classes, 1, l.output + index + 5); - } - } + int index = entry_index(l, 0, 0, l.coords + !l.background); + softmax_cpu(net.input + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output + index); } #endif - if(!state.train) return; + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if(!net.train) return; float avg_iou = 0; float recall = 0; float avg_cat = 0; @@ -178,26 +199,29 @@ void forward_region_layer(const layer l, network_state state) if(l.softmax_tree){ int onlyclass = 0; for(t = 0; t < 30; ++t){ - box truth = float_to_box(state.truth + t*5 + b*l.truths); + box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1); if(!truth.x) break; - int class = state.truth[t*5 + b*l.truths + 4]; + int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords]; float maxp = 0; int maxi = 0; if(truth.x > 100000 && truth.y > 100000){ for(n = 0; n < l.n*l.w*l.h; ++n){ - int index = size*n + b*l.outputs + 5; - float scale = l.output[index-1]; - l.delta[index - 1] = l.noobject_scale * ((0 - l.output[index - 1]) * logistic_gradient(l.output[index - 1])); - float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class); + int class_index = entry_index(l, b, n, l.coords + 1); + int obj_index = entry_index(l, b, n, l.coords); + float scale = l.output[obj_index]; + l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]); + float p = scale*get_hierarchy_probability(l.output + class_index, l.softmax_tree, class, l.w*l.h); if(p > maxp){ maxp = p; maxi = n; } } - int index = size*maxi + b*l.outputs + 5; - delta_region_class(l.output, l.delta, index, class, l.classes, l.softmax_tree, l.class_scale, &avg_cat); - if(l.output[index - 1] < .3) l.delta[index - 1] = l.object_scale * ((.3 - l.output[index - 1]) * logistic_gradient(l.output[index - 1])); - else l.delta[index - 1] = 0; + int class_index = entry_index(l, b, maxi, l.coords + 1); + int obj_index = entry_index(l, b, maxi, l.coords); + delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax); + if(l.output[obj_index] < .3) l.delta[obj_index] = l.object_scale * (.3 - l.output[obj_index]); + else l.delta[obj_index] = 0; + l.delta[obj_index] = 0; ++class_count; onlyclass = 1; break; @@ -208,190 +232,276 @@ void forward_region_layer(const layer l, network_state state) for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { - int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs; - box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h); + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h); float best_iou = 0; for(t = 0; t < 30; ++t){ - box truth = float_to_box(state.truth + t*5 + b*l.truths); + box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1); if(!truth.x) break; float iou = box_iou(pred, truth); if (iou > best_iou) { best_iou = iou; } } - avg_anyobj += l.output[index + 4]; - l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4])); + int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, l.coords); + avg_anyobj += l.output[obj_index]; + l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]); + if(l.background) l.delta[obj_index] = l.noobject_scale * (1 - l.output[obj_index]); if (best_iou > l.thresh) { - l.delta[index + 4] = 0; + l.delta[obj_index] = 0; } - if(*(state.net.seen) < 12800){ + if(*(net.seen) < 12800){ box truth = {0}; truth.x = (i + .5)/l.w; truth.y = (j + .5)/l.h; truth.w = l.biases[2*n]/l.w; truth.h = l.biases[2*n+1]/l.h; - delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01); + delta_region_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, l.delta, .01, l.w*l.h); } } } } for(t = 0; t < 30; ++t){ - box truth = float_to_box(state.truth + t*5 + b*l.truths); + box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1); if(!truth.x) break; float best_iou = 0; - int best_index = 0; int best_n = 0; i = (truth.x * l.w); j = (truth.y * l.h); - //printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h); box truth_shift = truth; truth_shift.x = 0; truth_shift.y = 0; - //printf("index %d %d\n",i, j); for(n = 0; n < l.n; ++n){ - int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs; - box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h); + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h); if(l.bias_match){ pred.w = l.biases[2*n]/l.w; pred.h = l.biases[2*n+1]/l.h; } - //printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h); pred.x = 0; pred.y = 0; float iou = box_iou(pred, truth_shift); if (iou > best_iou){ - best_index = index; best_iou = iou; best_n = n; } } - //printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h); - float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale); + int box_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 0); + float iou = delta_region_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, l.delta, l.coord_scale * (2 - truth.w*truth.h), l.w*l.h); + if(l.coords > 4){ + int mask_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 4); + delta_region_mask(net.truth + t*(l.coords + 1) + b*l.truths + 5, l.output, l.coords - 4, mask_index, l.delta, l.w*l.h, l.mask_scale); + } if(iou > .5) recall += 1; avg_iou += iou; - //l.delta[best_index + 4] = iou - l.output[best_index + 4]; - avg_obj += l.output[best_index + 4]; - l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]); + int obj_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords); + avg_obj += l.output[obj_index]; + l.delta[obj_index] = l.object_scale * (1 - l.output[obj_index]); if (l.rescore) { - l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]); + l.delta[obj_index] = l.object_scale * (iou - l.output[obj_index]); + } + if(l.background){ + l.delta[obj_index] = l.object_scale * (0 - l.output[obj_index]); } - - int class = state.truth[t*5 + b*l.truths + 4]; + int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords]; if (l.map) class = l.map[class]; - delta_region_class(l.output, l.delta, best_index + 5, class, l.classes, l.softmax_tree, l.class_scale, &avg_cat); + int class_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords + 1); + delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax); ++count; ++class_count; } } - //printf("\n"); -#ifndef GPU - flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0); -#endif *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count); } -void backward_region_layer(const layer l, network_state state) +void backward_region_layer(const layer l, network net) { - axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); + /* + int b; + int size = l.coords + l.classes + 1; + for (b = 0; b < l.batch*l.n; ++b){ + int index = (b*size + 4)*l.w*l.h; + gradient_array(l.output + index, l.w*l.h, LOGISTIC, l.delta + index); + } + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); + */ +} + +void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +{ + int i; + int new_w=0; + int new_h=0; + if (((float)netw/w) < ((float)neth/h)) { + new_w = netw; + new_h = (h * netw)/w; + } else { + new_h = neth; + new_w = (w * neth)/h; + } + for (i = 0; i < n; ++i){ + box b = dets[i].bbox; + b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); + b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); + b.w *= (float)netw/new_w; + b.h *= (float)neth/new_h; + if(!relative){ + b.x *= w; + b.w *= w; + b.y *= h; + b.h *= h; + } + dets[i].bbox = b; + } } -void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map, float tree_thresh) +void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets) { - int i,j,n; + int i,j,n,z; float *predictions = l.output; + if (l.batch == 2) { + float *flip = l.output + l.outputs; + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w/2; ++i) { + for (n = 0; n < l.n; ++n) { + for(z = 0; z < l.classes + l.coords + 1; ++z){ + int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; + int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); + float swap = flip[i1]; + flip[i1] = flip[i2]; + flip[i2] = swap; + if(z == 0){ + flip[i1] = -flip[i1]; + flip[i2] = -flip[i2]; + } + } + } + } + } + for(i = 0; i < l.outputs; ++i){ + l.output[i] = (l.output[i] + flip[i])/2.; + } + } for (i = 0; i < l.w*l.h; ++i){ int row = i / l.w; int col = i % l.w; for(n = 0; n < l.n; ++n){ - int index = i*l.n + n; - int p_index = index * (l.classes + 5) + 4; - float scale = predictions[p_index]; - int box_index = index * (l.classes + 5); - boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h); - boxes[index].x *= w; - boxes[index].y *= h; - boxes[index].w *= w; - boxes[index].h *= h; - - int class_index = index * (l.classes + 5) + 5; + int index = n*l.w*l.h + i; + for(j = 0; j < l.classes; ++j){ + dets[index].prob[j] = 0; + } + int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); + int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); + int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4); + float scale = l.background ? 1 : predictions[obj_index]; + dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h, l.w*l.h); + dets[index].objectness = scale > thresh ? scale : 0; + if(dets[index].mask){ + for(j = 0; j < l.coords - 4; ++j){ + dets[index].mask[j] = l.output[mask_index + j*l.w*l.h]; + } + } + + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background); if(l.softmax_tree){ - hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0); + hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h); if(map){ for(j = 0; j < 200; ++j){ - float prob = scale*predictions[class_index+map[j]]; - probs[index][j] = (prob > thresh) ? prob : 0; + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]); + float prob = scale*predictions[class_index]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; } } else { - int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh); - probs[index][j] = (scale > thresh) ? scale : 0; - probs[index][l.classes] = scale; + int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h); + dets[index].prob[j] = (scale > thresh) ? scale : 0; } } else { - for(j = 0; j < l.classes; ++j){ - float prob = scale*predictions[class_index+j]; - probs[index][j] = (prob > thresh) ? prob : 0; + if(dets[index].objectness){ + for(j = 0; j < l.classes; ++j){ + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j); + float prob = scale*predictions[class_index]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; + } } } - if(only_objectness){ - probs[index][0] = scale; - } } } + correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative); } #ifdef GPU -void forward_region_layer_gpu(const layer l, network_state state) +void forward_region_layer_gpu(const layer l, network net) { - /* - if(!state.train){ - copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); - return; - } - */ - flatten_ongpu(state.input, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 1, l.output_gpu); - if(l.softmax_tree){ - int i; - int count = 5; - for (i = 0; i < l.softmax_tree->groups; ++i) { - int group_size = l.softmax_tree->group_size[i]; - softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count); - count += group_size; + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + int b, n; + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + if(l.coords > 4){ + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC); + } + index = entry_index(l, b, n*l.w*l.h, l.coords); + if(!l.background) activate_array_gpu(l.output_gpu + index, l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, l.coords + 1); + if(!l.softmax && !l.softmax_tree) activate_array_gpu(l.output_gpu + index, l.classes*l.w*l.h, LOGISTIC); } - }else if (l.softmax){ - softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5); } - - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); - float *truth_cpu = 0; - if(state.truth){ - int num_truth = l.batch*l.truths; - truth_cpu = calloc(num_truth, sizeof(float)); - cuda_pull_array(state.truth, truth_cpu, num_truth); + if (l.softmax_tree){ + int index = entry_index(l, 0, 0, l.coords + 1); + softmax_tree(net.input_gpu + index, l.w*l.h, l.batch*l.n, l.inputs/l.n, 1, l.output_gpu + index, *l.softmax_tree); + } else if (l.softmax) { + int index = entry_index(l, 0, 0, l.coords + !l.background); + softmax_gpu(net.input_gpu + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu + index); } - cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); - network_state cpu_state = state; - cpu_state.train = state.train; - cpu_state.truth = truth_cpu; - cpu_state.input = in_cpu; - forward_region_layer(l, cpu_state); + if(!net.train || l.onlyforward){ + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + return; + } + + cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); + forward_region_layer(l, net); //cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); - free(cpu_state.input); - if(!state.train) return; + if(!net.train) return; cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); - if(cpu_state.truth) free(cpu_state.truth); } -void backward_region_layer_gpu(layer l, network_state state) +void backward_region_layer_gpu(const layer l, network net) { - flatten_ongpu(l.delta_gpu, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 0, state.delta); + int b, n; + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + gradient_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC, l.delta_gpu + index); + if(l.coords > 4){ + index = entry_index(l, b, n*l.w*l.h, 4); + gradient_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC, l.delta_gpu + index); + } + index = entry_index(l, b, n*l.w*l.h, l.coords); + if(!l.background) gradient_array_gpu(l.output_gpu + index, l.w*l.h, LOGISTIC, l.delta_gpu + index); + } + } + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); } #endif +void zero_objectness(layer l) +{ + int i, n; + for (i = 0; i < l.w*l.h; ++i){ + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); + l.output[obj_index] = 0; + } + } +} + diff --git a/image.darknet/inst/include/darknet/src/region_layer.h b/image.darknet/inst/include/darknet/src/region_layer.h index 9a3b7cd..9f12fd1 100644 --- a/image.darknet/inst/include/darknet/src/region_layer.h +++ b/image.darknet/inst/include/darknet/src/region_layer.h @@ -1,18 +1,18 @@ #ifndef REGION_LAYER_H #define REGION_LAYER_H +#include "darknet.h" #include "layer.h" #include "network.h" -layer make_region_layer(int batch, int h, int w, int n, int classes, int coords); -void forward_region_layer(const layer l, network_state state); -void backward_region_layer(const layer l, network_state state); -void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map, float tree_thresh); +layer make_region_layer(int batch, int w, int h, int n, int classes, int coords); +void forward_region_layer(const layer l, network net); +void backward_region_layer(const layer l, network net); void resize_region_layer(layer *l, int w, int h); #ifdef GPU -void forward_region_layer_gpu(const layer l, network_state state); -void backward_region_layer_gpu(layer l, network_state state); +void forward_region_layer_gpu(const layer l, network net); +void backward_region_layer_gpu(layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/reorg_layer.c b/image.darknet/inst/include/darknet/src/reorg_layer.c index 2abca8f..31d6b84 100644 --- a/image.darknet/inst/include/darknet/src/reorg_layer.c +++ b/image.darknet/inst/include/darknet/src/reorg_layer.c @@ -1,18 +1,21 @@ #include "reorg_layer.h" #include "cuda.h" #include "blas.h" + #include -layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse) +layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse, int flatten, int extra) { layer l = {0}; l.type = REORG; l.batch = batch; l.stride = stride; + l.extra = extra; l.h = h; l.w = w; l.c = c; + l.flatten = flatten; if(reverse){ l.out_w = w*stride; l.out_h = h*stride; @@ -23,10 +26,20 @@ layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse) l.out_c = c*(stride*stride); } l.reverse = reverse; - fprintf(stderr, "reorg /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; - int output_size = l.out_h * l.out_w * l.out_c * batch; + if(l.extra){ + l.out_w = l.out_h = l.out_c = 0; + l.outputs = l.inputs + l.extra; + } + + if(extra){ + fprintf(stderr, "reorg %4d -> %4d\n", l.inputs, l.outputs); + } else { + fprintf(stderr, "reorg /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + } + int output_size = l.outputs * batch; l.output = calloc(output_size, sizeof(float)); l.delta = calloc(output_size, sizeof(float)); @@ -75,40 +88,86 @@ void resize_reorg_layer(layer *l, int w, int h) #endif } -void forward_reorg_layer(const layer l, network_state state) +void forward_reorg_layer(const layer l, network net) { - if(l.reverse){ - reorg_cpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output); - }else { - reorg_cpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output); + int i; + if(l.flatten){ + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + if(l.reverse){ + flatten(l.output, l.w*l.h, l.c, l.batch, 0); + }else{ + flatten(l.output, l.w*l.h, l.c, l.batch, 1); + } + } else if (l.extra) { + for(i = 0; i < l.batch; ++i){ + copy_cpu(l.inputs, net.input + i*l.inputs, 1, l.output + i*l.outputs, 1); + } + } else if (l.reverse){ + reorg_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output); + } else { + reorg_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output); } } -void backward_reorg_layer(const layer l, network_state state) +void backward_reorg_layer(const layer l, network net) { - if(l.reverse){ - reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 0, state.delta); + int i; + if(l.flatten){ + memcpy(net.delta, l.delta, l.outputs*l.batch*sizeof(float)); + if(l.reverse){ + flatten(net.delta, l.w*l.h, l.c, l.batch, 1); + }else{ + flatten(net.delta, l.w*l.h, l.c, l.batch, 0); + } + } else if(l.reverse){ + reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 0, net.delta); + } else if (l.extra) { + for(i = 0; i < l.batch; ++i){ + copy_cpu(l.inputs, l.delta + i*l.outputs, 1, net.delta + i*l.inputs, 1); + } }else{ - reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 1, state.delta); + reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 1, net.delta); } } #ifdef GPU -void forward_reorg_layer_gpu(layer l, network_state state) +void forward_reorg_layer_gpu(layer l, network net) { - if(l.reverse){ - reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output_gpu); + int i; + if(l.flatten){ + if(l.reverse){ + flatten_gpu(net.input_gpu, l.w*l.h, l.c, l.batch, 0, l.output_gpu); + }else{ + flatten_gpu(net.input_gpu, l.w*l.h, l.c, l.batch, 1, l.output_gpu); + } + } else if (l.extra) { + for(i = 0; i < l.batch; ++i){ + copy_gpu(l.inputs, net.input_gpu + i*l.inputs, 1, l.output_gpu + i*l.outputs, 1); + } + } else if (l.reverse) { + reorg_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, l.output_gpu); }else { - reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output_gpu); + reorg_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, l.output_gpu); } } -void backward_reorg_layer_gpu(layer l, network_state state) +void backward_reorg_layer_gpu(layer l, network net) { - if(l.reverse){ - reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, state.delta); - }else{ - reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, state.delta); + if(l.flatten){ + if(l.reverse){ + flatten_gpu(l.delta_gpu, l.w*l.h, l.c, l.batch, 1, net.delta_gpu); + }else{ + flatten_gpu(l.delta_gpu, l.w*l.h, l.c, l.batch, 0, net.delta_gpu); + } + } else if (l.extra) { + int i; + for(i = 0; i < l.batch; ++i){ + copy_gpu(l.inputs, l.delta_gpu + i*l.outputs, 1, net.delta_gpu + i*l.inputs, 1); + } + } else if(l.reverse){ + reorg_gpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, net.delta_gpu); + } else { + reorg_gpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, net.delta_gpu); } } #endif diff --git a/image.darknet/inst/include/darknet/src/reorg_layer.h b/image.darknet/inst/include/darknet/src/reorg_layer.h index 21c22cd..e6513a5 100644 --- a/image.darknet/inst/include/darknet/src/reorg_layer.h +++ b/image.darknet/inst/include/darknet/src/reorg_layer.h @@ -6,14 +6,14 @@ #include "layer.h" #include "network.h" -layer make_reorg_layer(int batch, int h, int w, int c, int stride, int reverse); +layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse, int flatten, int extra); void resize_reorg_layer(layer *l, int w, int h); -void forward_reorg_layer(const layer l, network_state state); -void backward_reorg_layer(const layer l, network_state state); +void forward_reorg_layer(const layer l, network net); +void backward_reorg_layer(const layer l, network net); #ifdef GPU -void forward_reorg_layer_gpu(layer l, network_state state); -void backward_reorg_layer_gpu(layer l, network_state state); +void forward_reorg_layer_gpu(layer l, network net); +void backward_reorg_layer_gpu(layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/rnn_layer.c b/image.darknet/inst/include/darknet/src/rnn_layer.c index 83fda13..8c9b457 100644 --- a/image.darknet/inst/include/darknet/src/rnn_layer.c +++ b/image.darknet/inst/include/darknet/src/rnn_layer.c @@ -26,7 +26,7 @@ static void increment_layer(layer *l, int steps) #endif } -layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, ACTIVATION activation, int batch_normalize, int log) +layer make_rnn_layer(int batch, int inputs, int outputs, int steps, ACTIVATION activation, int batch_normalize, int adam) { fprintf(stderr, "RNN Layer: %d inputs, %d outputs\n", inputs, outputs); batch = batch / steps; @@ -34,24 +34,24 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, l.batch = batch; l.type = RNN; l.steps = steps; - l.hidden = hidden; l.inputs = inputs; - l.state = calloc(batch*hidden*(steps+1), sizeof(float)); + l.state = calloc(batch*outputs, sizeof(float)); + l.prev_state = calloc(batch*outputs, sizeof(float)); l.input_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_layer) = make_connected_layer(batch*steps, inputs, hidden, activation, batch_normalize); + *(l.input_layer) = make_connected_layer(batch*steps, inputs, outputs, activation, batch_normalize, adam); l.input_layer->batch = batch; l.self_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.self_layer) = make_connected_layer(batch*steps, hidden, hidden, (log==2)?LOGGY:(log==1?LOGISTIC:activation), batch_normalize); + *(l.self_layer) = make_connected_layer(batch*steps, outputs, outputs, activation, batch_normalize, adam); l.self_layer->batch = batch; l.output_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.output_layer) = make_connected_layer(batch*steps, hidden, outputs, activation, batch_normalize); + *(l.output_layer) = make_connected_layer(batch*steps, outputs, outputs, activation, batch_normalize, adam); l.output_layer->batch = batch; l.outputs = outputs; @@ -65,66 +65,72 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, l.forward_gpu = forward_rnn_layer_gpu; l.backward_gpu = backward_rnn_layer_gpu; l.update_gpu = update_rnn_layer_gpu; - l.state_gpu = cuda_make_array(l.state, batch*hidden*(steps+1)); + l.state_gpu = cuda_make_array(0, batch*outputs); + l.prev_state_gpu = cuda_make_array(0, batch*outputs); l.output_gpu = l.output_layer->output_gpu; l.delta_gpu = l.output_layer->delta_gpu; +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.input_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_layer->out_c, l.input_layer->out_h, l.input_layer->out_w); + cudnnSetTensor4dDescriptor(l.self_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.self_layer->out_c, l.self_layer->out_h, l.self_layer->out_w); + cudnnSetTensor4dDescriptor(l.output_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.output_layer->out_c, l.output_layer->out_h, l.output_layer->out_w); +#endif #endif return l; } -void update_rnn_layer(layer l, int batch, float learning_rate, float momentum, float decay) +void update_rnn_layer(layer l, update_args a) { - update_connected_layer(*(l.input_layer), batch, learning_rate, momentum, decay); - update_connected_layer(*(l.self_layer), batch, learning_rate, momentum, decay); - update_connected_layer(*(l.output_layer), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.input_layer), a); + update_connected_layer(*(l.self_layer), a); + update_connected_layer(*(l.output_layer), a); } -void forward_rnn_layer(layer l, network_state state) +void forward_rnn_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1); - fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1); - fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1); - if(state.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, self_layer.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, input_layer.delta, 1); + if(net.train) fill_cpu(l.outputs * l.batch, 0, l.state, 1); for (i = 0; i < l.steps; ++i) { - s.input = state.input; + s.input = net.input; forward_connected_layer(input_layer, s); s.input = l.state; forward_connected_layer(self_layer, s); float *old_state = l.state; - if(state.train) l.state += l.hidden*l.batch; + if(net.train) l.state += l.outputs*l.batch; if(l.shortcut){ - copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1); + copy_cpu(l.outputs * l.batch, old_state, 1, l.state, 1); }else{ - fill_cpu(l.hidden * l.batch, 0, l.state, 1); + fill_cpu(l.outputs * l.batch, 0, l.state, 1); } - axpy_cpu(l.hidden * l.batch, 1, input_layer.output, 1, l.state, 1); - axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, input_layer.output, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, self_layer.output, 1, l.state, 1); s.input = l.state; forward_connected_layer(output_layer, s); - state.input += l.inputs*l.batch; + net.input += l.inputs*l.batch; increment_layer(&input_layer, 1); increment_layer(&self_layer, 1); increment_layer(&output_layer, 1); } } -void backward_rnn_layer(layer l, network_state state) +void backward_rnn_layer(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = net; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -134,34 +140,34 @@ void backward_rnn_layer(layer l, network_state state) increment_layer(&self_layer, l.steps-1); increment_layer(&output_layer, l.steps-1); - l.state += l.hidden*l.batch*l.steps; + l.state += l.outputs*l.batch*l.steps; for (i = l.steps-1; i >= 0; --i) { - copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1); - axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1); + copy_cpu(l.outputs * l.batch, input_layer.output, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, self_layer.output, 1, l.state, 1); s.input = l.state; s.delta = self_layer.delta; backward_connected_layer(output_layer, s); - l.state -= l.hidden*l.batch; + l.state -= l.outputs*l.batch; /* if(i > 0){ - copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1); - axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1); + copy_cpu(l.outputs * l.batch, input_layer.output - l.outputs*l.batch, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, self_layer.output - l.outputs*l.batch, 1, l.state, 1); }else{ - fill_cpu(l.hidden * l.batch, 0, l.state, 1); + fill_cpu(l.outputs * l.batch, 0, l.state, 1); } */ s.input = l.state; - s.delta = self_layer.delta - l.hidden*l.batch; + s.delta = self_layer.delta - l.outputs*l.batch; if (i == 0) s.delta = 0; backward_connected_layer(self_layer, s); - copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1); - if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1); - s.input = state.input + i*l.inputs*l.batch; - if(state.delta) s.delta = state.delta + i*l.inputs*l.batch; + copy_cpu(l.outputs*l.batch, self_layer.delta, 1, input_layer.delta, 1); + if (i > 0 && l.shortcut) axpy_cpu(l.outputs*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.outputs*l.batch, 1); + s.input = net.input + i*l.inputs*l.batch; + if(net.delta) s.delta = net.delta + i*l.inputs*l.batch; else s.delta = 0; backward_connected_layer(input_layer, s); @@ -187,58 +193,56 @@ void push_rnn_layer(layer l) push_connected_layer(*(l.output_layer)); } -void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_rnn_layer_gpu(layer l, update_args a) { - update_connected_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.input_layer), a); + update_connected_layer_gpu(*(l.self_layer), a); + update_connected_layer_gpu(*(l.output_layer), a); } -void forward_rnn_layer_gpu(layer l, network_state state) +void forward_rnn_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = {0}; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); - fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1); - if(state.train) fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, self_layer.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, input_layer.delta_gpu, 1); + + if(net.train) { + fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1); + } for (i = 0; i < l.steps; ++i) { - s.input = state.input; + s.input_gpu = net.input_gpu; forward_connected_layer_gpu(input_layer, s); - s.input = l.state_gpu; + s.input_gpu = l.state_gpu; forward_connected_layer_gpu(self_layer, s); - float *old_state = l.state_gpu; - if(state.train) l.state_gpu += l.hidden*l.batch; - if(l.shortcut){ - copy_ongpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1); - }else{ - fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); - } - axpy_ongpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); - axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); - s.input = l.state_gpu; + s.input_gpu = l.state_gpu; forward_connected_layer_gpu(output_layer, s); - state.input += l.inputs*l.batch; + net.input_gpu += l.inputs*l.batch; increment_layer(&input_layer, 1); increment_layer(&self_layer, 1); increment_layer(&output_layer, 1); } } -void backward_rnn_layer_gpu(layer l, network_state state) +void backward_rnn_layer_gpu(layer l, network net) { - network_state s = {0}; - s.train = state.train; + network s = {0}; + s.train = net.train; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -246,32 +250,43 @@ void backward_rnn_layer_gpu(layer l, network_state state) increment_layer(&input_layer, l.steps - 1); increment_layer(&self_layer, l.steps - 1); increment_layer(&output_layer, l.steps - 1); - l.state_gpu += l.hidden*l.batch*l.steps; + float *last_input = input_layer.output_gpu; + float *last_self = self_layer.output_gpu; for (i = l.steps-1; i >= 0; --i) { + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); - s.input = l.state_gpu; - s.delta = self_layer.delta_gpu; + s.input_gpu = l.state_gpu; + s.delta_gpu = self_layer.delta_gpu; backward_connected_layer_gpu(output_layer, s); - l.state_gpu -= l.hidden*l.batch; + if(i != 0) { + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1); + }else { + copy_gpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.state_gpu, 1); + } - copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); + copy_gpu(l.outputs*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); - s.input = l.state_gpu; - s.delta = self_layer.delta_gpu - l.hidden*l.batch; - if (i == 0) s.delta = 0; + s.input_gpu = l.state_gpu; + s.delta_gpu = (i > 0) ? self_layer.delta_gpu - l.outputs*l.batch : 0; + if (i == 0) s.delta_gpu = 0; backward_connected_layer_gpu(self_layer, s); - //copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); - if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1); - s.input = state.input + i*l.inputs*l.batch; - if(state.delta) s.delta = state.delta + i*l.inputs*l.batch; - else s.delta = 0; + s.input_gpu = net.input_gpu + i*l.inputs*l.batch; + if(net.delta_gpu) s.delta_gpu = net.delta_gpu + i*l.inputs*l.batch; + else s.delta_gpu = 0; backward_connected_layer_gpu(input_layer, s); increment_layer(&input_layer, -1); increment_layer(&self_layer, -1); increment_layer(&output_layer, -1); } + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, last_input, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, last_self, 1, l.state_gpu, 1); } #endif diff --git a/image.darknet/inst/include/darknet/src/rnn_layer.h b/image.darknet/inst/include/darknet/src/rnn_layer.h index bb9478b..270a63f 100644 --- a/image.darknet/inst/include/darknet/src/rnn_layer.h +++ b/image.darknet/inst/include/darknet/src/rnn_layer.h @@ -7,16 +7,16 @@ #include "network.h" #define USET -layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, ACTIVATION activation, int batch_normalize, int log); +layer make_rnn_layer(int batch, int inputs, int outputs, int steps, ACTIVATION activation, int batch_normalize, int adam); -void forward_rnn_layer(layer l, network_state state); -void backward_rnn_layer(layer l, network_state state); -void update_rnn_layer(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_rnn_layer(layer l, network net); +void backward_rnn_layer(layer l, network net); +void update_rnn_layer(layer l, update_args a); #ifdef GPU -void forward_rnn_layer_gpu(layer l, network_state state); -void backward_rnn_layer_gpu(layer l, network_state state); -void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void forward_rnn_layer_gpu(layer l, network net); +void backward_rnn_layer_gpu(layer l, network net); +void update_rnn_layer_gpu(layer l, update_args a); void push_rnn_layer(layer l); void pull_rnn_layer(layer l); #endif diff --git a/image.darknet/inst/include/darknet/src/route_layer.c b/image.darknet/inst/include/darknet/src/route_layer.c index dce7118..a8970a4 100644 --- a/image.darknet/inst/include/darknet/src/route_layer.c +++ b/image.darknet/inst/include/darknet/src/route_layer.c @@ -1,6 +1,7 @@ #include "route_layer.h" #include "cuda.h" #include "blas.h" + #include route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes) @@ -70,13 +71,13 @@ void resize_route_layer(route_layer *l, network *net) } -void forward_route_layer(const route_layer l, network_state state) +void forward_route_layer(const route_layer l, network net) { int i, j; int offset = 0; for(i = 0; i < l.n; ++i){ int index = l.input_layers[i]; - float *input = state.net.layers[index].output; + float *input = net.layers[index].output; int input_size = l.input_sizes[i]; for(j = 0; j < l.batch; ++j){ copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1); @@ -85,13 +86,13 @@ void forward_route_layer(const route_layer l, network_state state) } } -void backward_route_layer(const route_layer l, network_state state) +void backward_route_layer(const route_layer l, network net) { int i, j; int offset = 0; for(i = 0; i < l.n; ++i){ int index = l.input_layers[i]; - float *delta = state.net.layers[index].delta; + float *delta = net.layers[index].delta; int input_size = l.input_sizes[i]; for(j = 0; j < l.batch; ++j){ axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1); @@ -101,31 +102,31 @@ void backward_route_layer(const route_layer l, network_state state) } #ifdef GPU -void forward_route_layer_gpu(const route_layer l, network_state state) +void forward_route_layer_gpu(const route_layer l, network net) { int i, j; int offset = 0; for(i = 0; i < l.n; ++i){ int index = l.input_layers[i]; - float *input = state.net.layers[index].output_gpu; + float *input = net.layers[index].output_gpu; int input_size = l.input_sizes[i]; for(j = 0; j < l.batch; ++j){ - copy_ongpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1); + copy_gpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1); } offset += input_size; } } -void backward_route_layer_gpu(const route_layer l, network_state state) +void backward_route_layer_gpu(const route_layer l, network net) { int i, j; int offset = 0; for(i = 0; i < l.n; ++i){ int index = l.input_layers[i]; - float *delta = state.net.layers[index].delta_gpu; + float *delta = net.layers[index].delta_gpu; int input_size = l.input_sizes[i]; for(j = 0; j < l.batch; ++j){ - axpy_ongpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1); + axpy_gpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1); } offset += input_size; } diff --git a/image.darknet/inst/include/darknet/src/route_layer.h b/image.darknet/inst/include/darknet/src/route_layer.h index 45467d9..1d40330 100644 --- a/image.darknet/inst/include/darknet/src/route_layer.h +++ b/image.darknet/inst/include/darknet/src/route_layer.h @@ -6,13 +6,13 @@ typedef layer route_layer; route_layer make_route_layer(int batch, int n, int *input_layers, int *input_size); -void forward_route_layer(const route_layer l, network_state state); -void backward_route_layer(const route_layer l, network_state state); +void forward_route_layer(const route_layer l, network net); +void backward_route_layer(const route_layer l, network net); void resize_route_layer(route_layer *l, network *net); #ifdef GPU -void forward_route_layer_gpu(const route_layer l, network_state state); -void backward_route_layer_gpu(const route_layer l, network_state state); +void forward_route_layer_gpu(const route_layer l, network net); +void backward_route_layer_gpu(const route_layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/shortcut_layer.c b/image.darknet/inst/include/darknet/src/shortcut_layer.c index 8bca50f..49d17f5 100644 --- a/image.darknet/inst/include/darknet/src/shortcut_layer.c +++ b/image.darknet/inst/include/darknet/src/shortcut_layer.c @@ -1,12 +1,14 @@ #include "shortcut_layer.h" #include "cuda.h" #include "blas.h" +#include "activations.h" + #include #include layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2) { - fprintf(stderr,"Shortcut Layer: %d\n", index); + fprintf(stderr, "res %3d %4d x%4d x%4d -> %4d x%4d x%4d\n",index, w2,h2,c2, w,h,c); layer l = {0}; l.type = SHORTCUT; l.batch = batch; @@ -36,32 +38,53 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int return l; } -void forward_shortcut_layer(const layer l, network_state state) +void resize_shortcut_layer(layer *l, int w, int h) +{ + assert(l->w == l->out_w); + assert(l->h == l->out_h); + l->w = l->out_w = w; + l->h = l->out_h = h; + l->outputs = w*h*l->out_c; + l->inputs = l->outputs; + l->delta = realloc(l->delta, l->outputs*l->batch*sizeof(float)); + l->output = realloc(l->output, l->outputs*l->batch*sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); +#endif + +} + + +void forward_shortcut_layer(const layer l, network net) { - copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); - shortcut_cpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.output); + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + shortcut_cpu(l.batch, l.w, l.h, l.c, net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.alpha, l.beta, l.output); activate_array(l.output, l.outputs*l.batch, l.activation); } -void backward_shortcut_layer(const layer l, network_state state) +void backward_shortcut_layer(const layer l, network net) { gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); - axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1); - shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); + axpy_cpu(l.outputs*l.batch, l.alpha, l.delta, 1, net.delta, 1); + shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, 1, l.beta, net.layers[l.index].delta); } #ifdef GPU -void forward_shortcut_layer_gpu(const layer l, network_state state) +void forward_shortcut_layer_gpu(const layer l, network net) { - copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - shortcut_gpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + shortcut_gpu(l.batch, l.w, l.h, l.c, net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.alpha, l.beta, l.output_gpu); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); } -void backward_shortcut_layer_gpu(const layer l, network_state state) +void backward_shortcut_layer_gpu(const layer l, network net) { - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - axpy_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1, state.delta, 1); - shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, state.net.layers[l.index].delta_gpu); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + axpy_gpu(l.outputs*l.batch, l.alpha, l.delta_gpu, 1, net.delta_gpu, 1); + shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, 1, l.beta, net.layers[l.index].delta_gpu); } #endif diff --git a/image.darknet/inst/include/darknet/src/shortcut_layer.h b/image.darknet/inst/include/darknet/src/shortcut_layer.h index c09a809..5f684fc 100644 --- a/image.darknet/inst/include/darknet/src/shortcut_layer.h +++ b/image.darknet/inst/include/darknet/src/shortcut_layer.h @@ -5,12 +5,13 @@ #include "network.h" layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2); -void forward_shortcut_layer(const layer l, network_state state); -void backward_shortcut_layer(const layer l, network_state state); +void forward_shortcut_layer(const layer l, network net); +void backward_shortcut_layer(const layer l, network net); +void resize_shortcut_layer(layer *l, int w, int h); #ifdef GPU -void forward_shortcut_layer_gpu(const layer l, network_state state); -void backward_shortcut_layer_gpu(const layer l, network_state state); +void forward_shortcut_layer_gpu(const layer l, network net); +void backward_shortcut_layer_gpu(const layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/softmax_layer.c b/image.darknet/inst/include/darknet/src/softmax_layer.c index 5d15314..9cbc6be 100644 --- a/image.darknet/inst/include/darknet/src/softmax_layer.c +++ b/image.darknet/inst/include/darknet/src/softmax_layer.c @@ -1,6 +1,7 @@ #include "softmax_layer.h" #include "blas.h" #include "cuda.h" + #include #include #include @@ -17,8 +18,10 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups) l.groups = groups; l.inputs = inputs; l.outputs = inputs; + l.loss = calloc(inputs*batch, sizeof(float)); l.output = calloc(inputs*batch, sizeof(float)); l.delta = calloc(inputs*batch, sizeof(float)); + l.cost = calloc(1, sizeof(float)); l.forward = forward_softmax_layer; l.backward = backward_softmax_layer; @@ -27,45 +30,35 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups) l.backward_gpu = backward_softmax_layer_gpu; l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.loss_gpu = cuda_make_array(l.loss, inputs*batch); l.delta_gpu = cuda_make_array(l.delta, inputs*batch); #endif return l; } -void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output) +void forward_softmax_layer(const softmax_layer l, network net) { - int b; - for(b = 0; b < batch; ++b){ + if(l.softmax_tree){ int i; int count = 0; - for(i = 0; i < hierarchy->groups; ++i){ - int group_size = hierarchy->group_size[i]; - softmax(input+b*inputs + count, group_size, temp, output+b*inputs + count); + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_cpu(net.input + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output + count); count += group_size; } + } else { + softmax_cpu(net.input, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output); } -} -void forward_softmax_layer(const softmax_layer l, network_state state) -{ - int b; - int inputs = l.inputs / l.groups; - int batch = l.batch * l.groups; - if(l.softmax_tree){ - softmax_tree(state.input, batch, inputs, l.temperature, l.softmax_tree, l.output); - } else { - for(b = 0; b < batch; ++b){ - softmax(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs); - } + if(net.truth && !l.noloss){ + softmax_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); } } -void backward_softmax_layer(const softmax_layer l, network_state state) +void backward_softmax_layer(const softmax_layer l, network net) { - int i; - for(i = 0; i < l.inputs*l.batch; ++i){ - state.delta[i] += l.delta[i]; - } + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); } #ifdef GPU @@ -75,26 +68,40 @@ void pull_softmax_layer_output(const softmax_layer layer) cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch); } -void forward_softmax_layer_gpu(const softmax_layer l, network_state state) +void forward_softmax_layer_gpu(const softmax_layer l, network net) { - int inputs = l.inputs / l.groups; - int batch = l.batch * l.groups; if(l.softmax_tree){ + softmax_tree(net.input_gpu, 1, l.batch, l.inputs, l.temperature, l.output_gpu, *l.softmax_tree); + /* int i; int count = 0; for (i = 0; i < l.softmax_tree->groups; ++i) { int group_size = l.softmax_tree->group_size[i]; - softmax_gpu(state.input+count, group_size, inputs, batch, l.temperature, l.output_gpu + count); + softmax_gpu(net.input_gpu + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output_gpu + count); count += group_size; } + */ } else { - softmax_gpu(state.input, inputs, inputs, batch, l.temperature, l.output_gpu); + if(l.spatial){ + softmax_gpu(net.input_gpu, l.c, l.batch*l.c, l.inputs/l.c, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu); + }else{ + softmax_gpu(net.input_gpu, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output_gpu); + } + } + if(net.truth && !l.noloss){ + softmax_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth_gpu, l.delta_gpu, l.loss_gpu); + if(l.softmax_tree){ + mask_gpu(l.batch*l.inputs, l.delta_gpu, SECRET_NUM, net.truth_gpu, 0); + mask_gpu(l.batch*l.inputs, l.loss_gpu, SECRET_NUM, net.truth_gpu, 0); + } + cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); } } -void backward_softmax_layer_gpu(const softmax_layer layer, network_state state) +void backward_softmax_layer_gpu(const softmax_layer layer, network net) { - axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1); + axpy_gpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, net.delta_gpu, 1); } #endif diff --git a/image.darknet/inst/include/darknet/src/softmax_layer.h b/image.darknet/inst/include/darknet/src/softmax_layer.h index 821a8dd..2e3ffe0 100644 --- a/image.darknet/inst/include/darknet/src/softmax_layer.h +++ b/image.darknet/inst/include/darknet/src/softmax_layer.h @@ -7,13 +7,13 @@ typedef layer softmax_layer; void softmax_array(float *input, int n, float temp, float *output); softmax_layer make_softmax_layer(int batch, int inputs, int groups); -void forward_softmax_layer(const softmax_layer l, network_state state); -void backward_softmax_layer(const softmax_layer l, network_state state); +void forward_softmax_layer(const softmax_layer l, network net); +void backward_softmax_layer(const softmax_layer l, network net); #ifdef GPU void pull_softmax_layer_output(const softmax_layer l); -void forward_softmax_layer_gpu(const softmax_layer l, network_state state); -void backward_softmax_layer_gpu(const softmax_layer l, network_state state); +void forward_softmax_layer_gpu(const softmax_layer l, network net); +void backward_softmax_layer_gpu(const softmax_layer l, network net); #endif #endif diff --git a/image.darknet/inst/include/darknet/src/stb_image.h b/image.darknet/inst/include/darknet/src/stb_image.h index d0fa9c2..d9c21bc 100644 --- a/image.darknet/inst/include/darknet/src/stb_image.h +++ b/image.darknet/inst/include/darknet/src/stb_image.h @@ -1,5 +1,5 @@ -/* stb_image - v2.06 - public domain image loader - http://nothings.org/stb_image.h - no warranty implied; use at your own risk +/* stb_image - v2.19 - public domain image loader - http://nothings.org/stb + no warranty implied; use at your own risk Do this: #define STB_IMAGE_IMPLEMENTATION @@ -21,17 +21,20 @@ avoid problematic images and only need the trivial interface JPEG baseline & progressive (12 bpc/arithmetic not supported, same as stock IJG lib) - PNG 1/2/4/8-bit-per-channel (16 bpc not supported) + PNG 1/2/4/8/16-bit-per-channel TGA (not sure what subset, if a subset) BMP non-1bpp, non-RLE - PSD (composited view only, no extra channels) + PSD (composited view only, no extra channels, 8/16 bit-per-channel) GIF (*comp always reports as 4-channel) HDR (radiance rgbE format) PIC (Softimage PIC) PNM (PPM and PGM binary only) + Animated GIF still needs a proper API, but here's one way to do it: + http://gist.github.com/urraka/685d9a6340b26b830d49 + - decode from memory or through FILE (define STBI_NO_STDIO to remove code) - decode from arbitrary I/O callbacks - SIMD acceleration on x86/x64 (SSE2) and ARM (NEON) @@ -39,176 +42,65 @@ Full documentation under "DOCUMENTATION" below. - Revision 2.00 release notes: - - - Progressive JPEG is now supported. - - - PPM and PGM binary formats are now supported, thanks to Ken Miller. - - - x86 platforms now make use of SSE2 SIMD instructions for - JPEG decoding, and ARM platforms can use NEON SIMD if requested. - This work was done by Fabian "ryg" Giesen. SSE2 is used by - default, but NEON must be enabled explicitly; see docs. - - With other JPEG optimizations included in this version, we see - 2x speedup on a JPEG on an x86 machine, and a 1.5x speedup - on a JPEG on an ARM machine, relative to previous versions of this - library. The same results will not obtain for all JPGs and for all - x86/ARM machines. (Note that progressive JPEGs are significantly - slower to decode than regular JPEGs.) This doesn't mean that this - is the fastest JPEG decoder in the land; rather, it brings it - closer to parity with standard libraries. If you want the fastest - decode, look elsewhere. (See "Philosophy" section of docs below.) - - See final bullet items below for more info on SIMD. - - - Added STBI_MALLOC, STBI_REALLOC, and STBI_FREE macros for replacing - the memory allocator. Unlike other STBI libraries, these macros don't - support a context parameter, so if you need to pass a context in to - the allocator, you'll have to store it in a global or a thread-local - variable. - - - Split existing STBI_NO_HDR flag into two flags, STBI_NO_HDR and - STBI_NO_LINEAR. - STBI_NO_HDR: suppress implementation of .hdr reader format - STBI_NO_LINEAR: suppress high-dynamic-range light-linear float API - - - You can suppress implementation of any of the decoders to reduce - your code footprint by #defining one or more of the following - symbols before creating the implementation. - - STBI_NO_JPEG - STBI_NO_PNG - STBI_NO_BMP - STBI_NO_PSD - STBI_NO_TGA - STBI_NO_GIF - STBI_NO_HDR - STBI_NO_PIC - STBI_NO_PNM (.ppm and .pgm) - - - You can request *only* certain decoders and suppress all other ones - (this will be more forward-compatible, as addition of new decoders - doesn't require you to disable them explicitly): - - STBI_ONLY_JPEG - STBI_ONLY_PNG - STBI_ONLY_BMP - STBI_ONLY_PSD - STBI_ONLY_TGA - STBI_ONLY_GIF - STBI_ONLY_HDR - STBI_ONLY_PIC - STBI_ONLY_PNM (.ppm and .pgm) - - Note that you can define multiples of these, and you will get all - of them ("only x" and "only y" is interpreted to mean "only x&y"). - - - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still - want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB - - - Compilation of all SIMD code can be suppressed with - #define STBI_NO_SIMD - It should not be necessary to disable SIMD unless you have issues - compiling (e.g. using an x86 compiler which doesn't support SSE - intrinsics or that doesn't support the method used to detect - SSE2 support at run-time), and even those can be reported as - bugs so I can refine the built-in compile-time checking to be - smarter. - - - The old STBI_SIMD system which allowed installing a user-defined - IDCT etc. has been removed. If you need this, don't upgrade. My - assumption is that almost nobody was doing this, and those who - were will find the built-in SIMD more satisfactory anyway. - - - RGB values computed for JPEG images are slightly different from - previous versions of stb_image. (This is due to using less - integer precision in SIMD.) The C code has been adjusted so - that the same RGB values will be computed regardless of whether - SIMD support is available, so your app should always produce - consistent results. But these results are slightly different from - previous versions. (Specifically, about 3% of available YCbCr values - will compute different RGB results from pre-1.49 versions by +-1; - most of the deviating values are one smaller in the G channel.) - - - If you must produce consistent results with previous versions of - stb_image, #define STBI_JPEG_OLD and you will get the same results - you used to; however, you will not get the SIMD speedups for - the YCbCr-to-RGB conversion step (although you should still see - significant JPEG speedup from the other changes). - - Please note that STBI_JPEG_OLD is a temporary feature; it will be - removed in future versions of the library. It is only intended for - near-term back-compatibility use. - - - Latest revision history: - 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value - 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning - 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit - 2.03 (2015-04-12) additional corruption checking - stbi_set_flip_vertically_on_load - fix NEON support; fix mingw support - 2.02 (2015-01-19) fix incorrect assert, fix warning - 2.01 (2015-01-17) fix various warnings - 2.00b (2014-12-25) fix STBI_MALLOC in progressive JPEG - 2.00 (2014-12-25) optimize JPEG, including x86 SSE2 & ARM NEON SIMD - progressive JPEG - PGM/PPM support - STBI_MALLOC,STBI_REALLOC,STBI_FREE - STBI_NO_*, STBI_ONLY_* - GIF bugfix - 1.48 (2014-12-14) fix incorrectly-named assert() - 1.47 (2014-12-14) 1/2/4-bit PNG support (both grayscale and paletted) - optimize PNG - fix bug in interlaced PNG with user-specified channel count +LICENSE + + See end of file for license information. + +RECENT REVISION HISTORY: + + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings + 2.16 (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes + 2.15 (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-12-04) experimental 16-bit API, only for PNG so far; fixes + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) 16-bit PNGS; enable SSE2 in non-gcc x64 + RGB-format JPEG; remove white matting in PSD; + allocate large structures on the stack; + correct channel count for PNG & BMP + 2.10 (2016-01-22) avoid warning introduced in 2.09 + 2.09 (2016-01-16) 16-bit TGA; comments in PNM files; STBI_REALLOC_SIZED See end of file for full revision history. ============================ Contributors ========================= - Image formats Bug fixes & warning fixes - Sean Barrett (jpeg, png, bmp) Marc LeBlanc - Nicolas Schulz (hdr, psd) Christpher Lloyd - Jonathan Dummer (tga) Dave Moore - Jean-Marc Lienher (gif) Won Chun - Tom Seddon (pic) the Horde3D community - Thatcher Ulrich (psd) Janez Zemva - Ken Miller (pgm, ppm) Jonathan Blow - Laurent Gomila - Aruelien Pocheville - Extensions, features Ryamond Barbiero - Jetro Lauha (stbi_info) David Woo - Martin "SpartanJ" Golini (stbi_info) Martin Golini - James "moose2000" Brown (iPhone PNG) Roy Eltham - Ben "Disch" Wenger (io callbacks) Luke Graham - Omar Cornut (1/2/4-bit PNG) Thomas Ruf - Nicolas Guillemot (vertical flip) John Bartholomew - Ken Hamada - Optimizations & bugfixes Cort Stratton - Fabian "ryg" Giesen Blazej Dariusz Roszkowski - Arseny Kapoulkine Thibault Reuille - Paul Du Bois - Guillaume George - If your name should be here but Jerry Jansson - isn't, let Sean know. Hayaki Saito - Johan Duparc - Ronny Chevalier - Michal Cichon - Tero Hanninen - Sergio Gonzalez - Cass Everitt - Engin Manap - Martins Mozeiko - Joseph Thomson - Phil Jordan - -License: - This software is in the public domain. Where that dedication is not - recognized, you are granted a perpetual, irrevocable license to copy - and modify this file however you want. - + Image formats Extensions, features + Sean Barrett (jpeg, png, bmp) Jetro Lauha (stbi_info) + Nicolas Schulz (hdr, psd) Martin "SpartanJ" Golini (stbi_info) + Jonathan Dummer (tga) James "moose2000" Brown (iPhone PNG) + Jean-Marc Lienher (gif) Ben "Disch" Wenger (io callbacks) + Tom Seddon (pic) Omar Cornut (1/2/4-bit PNG) + Thatcher Ulrich (psd) Nicolas Guillemot (vertical flip) + Ken Miller (pgm, ppm) Richard Mitton (16-bit PSD) + github:urraka (animated gif) Junggon Kim (PNM comments) + Christopher Forseth (animated gif) Daniel Gibson (16-bit TGA) + socks-the-fox (16-bit PNG) + Jeremy Sawicki (handle all ImageNet JPGs) + Optimizations & bugfixes Mikhail Morozov (1-bit BMP) + Fabian "ryg" Giesen Anael Seghezzi (is-16-bit query) + Arseny Kapoulkine + John-Mark Allen + + Bug & warning fixes + Marc LeBlanc David Woo Guillaume George Martins Mozeiko + Christpher Lloyd Jerry Jansson Joseph Thomson Phil Jordan + Dave Moore Roy Eltham Hayaki Saito Nathan Reed + Won Chun Luke Graham Johan Duparc Nick Verigakis + the Horde3D community Thomas Ruf Ronny Chevalier github:rlyeh + Janez Zemva John Bartholomew Michal Cichon github:romigrou + Jonathan Blow Ken Hamada Tero Hanninen github:svdijk + Laurent Gomila Cort Stratton Sergio Gonzalez github:snagar + Aruelien Pocheville Thibault Reuille Cass Everitt github:Zelex + Ryamond Barbiero Paul Du Bois Engin Manap github:grim210 + Aldo Culquicondor Philipp Wiesemann Dale Weiler github:sammyhw + Oriol Ferrer Mesia Josh Tobin Matthew Gregan github:phprus + Julian Raschke Gregory Mullen Baldur Karlsson github:poppolopoppo + Christian Floisand Kevin Schmidt github:darealshinji + Blazej Dariusz Roszkowski github:Michaelangel007 */ #ifndef STBI_INCLUDE_STB_IMAGE_H @@ -217,10 +109,8 @@ // DOCUMENTATION // // Limitations: -// - no 16-bit-per-channel PNG // - no 12-bit-per-channel JPEG // - no JPEGs with arithmetic coding -// - no 1-bit BMP // - GIF always returns *comp=4 // // Basic usage (see HDR discussion below for HDR usage): @@ -233,10 +123,10 @@ // stbi_image_free(data) // // Standard parameters: -// int *x -- outputs image width in pixels -// int *y -- outputs image height in pixels -// int *comp -- outputs # of image components in image file -// int req_comp -- if non-zero, # of image components requested in result +// int *x -- outputs image width in pixels +// int *y -- outputs image height in pixels +// int *channels_in_file -- outputs # of image components in image file +// int desired_channels -- if non-zero, # of image components requested in result // // The return value from an image loader is an 'unsigned char *' which points // to the pixel data, or NULL on an allocation failure or if the image is @@ -244,11 +134,12 @@ // with each pixel consisting of N interleaved 8-bit components; the first // pixel pointed to is top-left-most in the image. There is no padding between // image scanlines or between pixels, regardless of format. The number of -// components N is 'req_comp' if req_comp is non-zero, or *comp otherwise. -// If req_comp is non-zero, *comp has the number of components that _would_ -// have been output otherwise. E.g. if you set req_comp to 4, you will always -// get RGBA output, but you can check *comp to see if it's trivially opaque -// because e.g. there were only 3 channels in the source image. +// components N is 'desired_channels' if desired_channels is non-zero, or +// *channels_in_file otherwise. If desired_channels is non-zero, +// *channels_in_file has the number of components that _would_ have been +// output otherwise. E.g. if you set desired_channels to 4, you will always +// get RGBA output, but you can check *channels_in_file to see if it's trivially +// opaque because e.g. there were only 3 channels in the source image. // // An output image with N components has the following components interleaved // in this order in each pixel: @@ -260,10 +151,10 @@ // 4 red, green, blue, alpha // // If image loading fails for any reason, the return value will be NULL, -// and *x, *y, *comp will be unchanged. The function stbi_failure_reason() -// can be queried for an extremely brief, end-user unfriendly explanation -// of why the load failed. Define STBI_NO_FAILURE_STRINGS to avoid -// compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly +// and *x, *y, *channels_in_file will be unchanged. The function +// stbi_failure_reason() can be queried for an extremely brief, end-user +// unfriendly explanation of why the load failed. Define STBI_NO_FAILURE_STRINGS +// to avoid compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly // more user-friendly ones. // // Paletted PNG, BMP, GIF, and PIC images are automatically depalettized. @@ -282,13 +173,13 @@ // and for best performance I may provide less-easy-to-use APIs that give higher // performance, in addition to the easy to use ones. Nevertheless, it's important // to keep in mind that from the standpoint of you, a client of this library, -// all you care about is #1 and #3, and stb libraries do not emphasize #3 above all. +// all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all. // // Some secondary priorities arise directly from the first two, some of which // make more explicit reasons why performance can't be emphasized. // // - Portable ("ease of use") -// - Small footprint ("easy to maintain") +// - Small source code footprint ("easy to maintain") // - No dependencies ("ease of use") // // =========================================================================== @@ -320,13 +211,6 @@ // (at least this is true for iOS and Android). Therefore, the NEON support is // toggled by a build flag: define STBI_NEON to get NEON loops. // -// The output of the JPEG decoder is slightly different from versions where -// SIMD support was introduced (that is, for versions before 1.49). The -// difference is only +-1 in the 8-bit RGB channels, and only on a small -// fraction of pixels. You can force the pre-1.49 behavior by defining -// STBI_JPEG_OLD, but this will disable some of the SIMD decoding path -// and hence cost some performance. -// // If for some reason you do not want to use any of SIMD code, or if // you have issues compiling it, you can disable it entirely by // defining STBI_NO_SIMD. @@ -382,6 +266,41 @@ // says there's premultiplied data (currently only happens in iPhone images, // and only if iPhone convert-to-rgb processing is on). // +// =========================================================================== +// +// ADDITIONAL CONFIGURATION +// +// - You can suppress implementation of any of the decoders to reduce +// your code footprint by #defining one or more of the following +// symbols before creating the implementation. +// +// STBI_NO_JPEG +// STBI_NO_PNG +// STBI_NO_BMP +// STBI_NO_PSD +// STBI_NO_TGA +// STBI_NO_GIF +// STBI_NO_HDR +// STBI_NO_PIC +// STBI_NO_PNM (.ppm and .pgm) +// +// - You can request *only* certain decoders and suppress all other ones +// (this will be more forward-compatible, as addition of new decoders +// doesn't require you to disable them explicitly): +// +// STBI_ONLY_JPEG +// STBI_ONLY_PNG +// STBI_ONLY_BMP +// STBI_ONLY_PSD +// STBI_ONLY_TGA +// STBI_ONLY_GIF +// STBI_ONLY_HDR +// STBI_ONLY_PIC +// STBI_ONLY_PNM (.ppm and .pgm) +// +// - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still +// want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB +// #ifndef STBI_NO_STDIO @@ -392,7 +311,7 @@ enum { - STBI_default = 0, // only used for req_comp + STBI_default = 0, // only used for desired_channels STBI_grey = 1, STBI_grey_alpha = 2, @@ -401,6 +320,7 @@ enum }; typedef unsigned char stbi_uc; +typedef unsigned short stbi_us; #ifdef __cplusplus extern "C" { @@ -428,34 +348,60 @@ typedef struct int (*eof) (void *user); // returns nonzero if we are at end of file/data } stbi_io_callbacks; -STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *comp, int req_comp); -STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *comp, int req_comp); -STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *comp, int req_comp); +//////////////////////////////////// +// +// 8-bits-per-channel interface +// + +STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *channels_in_file, int desired_channels); +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +#endif + #ifndef STBI_NO_STDIO -STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *comp, int req_comp); +STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); // for stbi_load_from_file, file pointer is left pointing immediately after image #endif +//////////////////////////////////// +// +// 16-bits-per-channel interface +// + +STBIDEF stbi_us *stbi_load_16_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO +STBIDEF stbi_us *stbi_load_16 (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_from_file_16(FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +#endif + +//////////////////////////////////// +// +// float-per-channel interface +// #ifndef STBI_NO_LINEAR - STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *comp, int req_comp); - STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp); - STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp); + STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); #ifndef STBI_NO_STDIO - STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *comp, int req_comp); + STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); #endif #endif #ifndef STBI_NO_HDR STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); STBIDEF void stbi_hdr_to_ldr_scale(float scale); -#endif +#endif // STBI_NO_HDR #ifndef STBI_NO_LINEAR STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); STBIDEF void stbi_ldr_to_hdr_scale(float scale); -#endif // STBI_NO_HDR +#endif // STBI_NO_LINEAR // stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user); @@ -476,11 +422,14 @@ STBIDEF void stbi_image_free (void *retval_from_stbi_load); // get image dimensions & components without fully decoding STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp); STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len); +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *clbk, void *user); #ifndef STBI_NO_STDIO -STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); -STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); - +STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit (char const *filename); +STBIDEF int stbi_is_16_bit_from_file(FILE *f); #endif @@ -561,9 +510,10 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const ch #include // ptrdiff_t on osx #include #include +#include #if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) -#include // ldexp +#include // ldexp, pow #endif #ifndef STBI_NO_STDIO @@ -619,18 +569,22 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (32 - (y)))) #endif -#if defined(STBI_MALLOC) && defined(STBI_FREE) && defined(STBI_REALLOC) +#if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED)) // ok -#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) +#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) && !defined(STBI_REALLOC_SIZED) // ok #else -#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC." +#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC (or STBI_REALLOC_SIZED)." #endif #ifndef STBI_MALLOC -#define STBI_MALLOC(sz) malloc(sz) -#define STBI_REALLOC(p,sz) realloc(p,sz) -#define STBI_FREE(p) free(p) +#define STBI_MALLOC(sz) malloc(sz) +#define STBI_REALLOC(p,newsz) realloc(p,newsz) +#define STBI_FREE(p) free(p) +#endif + +#ifndef STBI_REALLOC_SIZED +#define STBI_REALLOC_SIZED(p,oldsz,newsz) STBI_REALLOC(p,newsz) #endif // x86/x64 detection @@ -640,12 +594,14 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #define STBI__X86_TARGET #endif -#if defined(__GNUC__) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) -// NOTE: not clear do we actually need this for the 64-bit path? +#if defined(__GNUC__) && defined(STBI__X86_TARGET) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) // gcc doesn't support sse2 intrinsics unless you compile with -msse2, -// (but compiling with -msse2 allows the compiler to use SSE2 everywhere; -// this is just broken and gcc are jerks for not fixing it properly -// http://www.virtualdub.org/blog/pivot/entry.php?id=363 ) +// which in turn means it gets to use SSE2 everywhere. This is unfortunate, +// but previous attempts to provide the SSE2 functions with runtime +// detection caused numerous issues. The way architecture extensions are +// exposed in GCC/Clang is, sadly, not really suited for one-file libs. +// New behavior: if compiled with -msse2, we use SSE2 without any +// detection; if not, we don't use it at all. #define STBI_NO_SIMD #endif @@ -664,7 +620,7 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #define STBI_NO_SIMD #endif -#if !defined(STBI_NO_SIMD) && defined(STBI__X86_TARGET) +#if !defined(STBI_NO_SIMD) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) #define STBI_SSE2 #include @@ -693,7 +649,7 @@ static int stbi__cpuid3(void) #define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name -static int stbi__sse2_available() +static int stbi__sse2_available(void) { int info3 = stbi__cpuid3(); return ((info3 >> 26) & 1) != 0; @@ -701,16 +657,12 @@ static int stbi__sse2_available() #else // assume GCC-style if not VC++ #define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) -static int stbi__sse2_available() +static int stbi__sse2_available(void) { -#if defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__) >= 408 // GCC 4.8 or later - // GCC 4.8+ has a nice way to do this - return __builtin_cpu_supports("sse2"); -#else - // portable way to do this, preferably without using GCC inline ASM? - // just bail for now. - return 0; -#endif + // If we're even attempting to compile this on GCC/Clang, that means + // -msse2 is on, which means the compiler is allowed to use SSE2 + // instructions at will, and so are we. + return 1; } #endif #endif @@ -749,7 +701,7 @@ typedef struct stbi_uc buffer_start[128]; stbi_uc *img_buffer, *img_buffer_end; - stbi_uc *img_buffer_original; + stbi_uc *img_buffer_original, *img_buffer_original_end; } stbi__context; @@ -761,7 +713,7 @@ static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len) s->io.read = NULL; s->read_from_callbacks = 0; s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer; - s->img_buffer_end = (stbi_uc *) buffer+len; + s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *) buffer+len; } // initialize a callback-based context @@ -773,6 +725,7 @@ static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void * s->read_from_callbacks = 1; s->img_buffer_original = s->buffer_start; stbi__refill_buffer(s); + s->img_buffer_original_end = s->img_buffer_end; } #ifndef STBI_NO_STDIO @@ -814,59 +767,76 @@ static void stbi__rewind(stbi__context *s) // but we just rewind to the beginning of the initial buffer, because // we only use it after doing 'test', which only ever looks at at most 92 bytes s->img_buffer = s->img_buffer_original; + s->img_buffer_end = s->img_buffer_original_end; } +enum +{ + STBI_ORDER_RGB, + STBI_ORDER_BGR +}; + +typedef struct +{ + int bits_per_channel; + int num_channels; + int channel_order; +} stbi__result_info; + #ifndef STBI_NO_JPEG static int stbi__jpeg_test(stbi__context *s); -static stbi_uc *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PNG static int stbi__png_test(stbi__context *s); -static stbi_uc *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__png_is16(stbi__context *s); #endif #ifndef STBI_NO_BMP static int stbi__bmp_test(stbi__context *s); -static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_TGA static int stbi__tga_test(stbi__context *s); -static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PSD static int stbi__psd_test(stbi__context *s); -static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc); static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__psd_is16(stbi__context *s); #endif #ifndef STBI_NO_HDR static int stbi__hdr_test(stbi__context *s); -static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PIC static int stbi__pic_test(stbi__context *s); -static stbi_uc *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_GIF static int stbi__gif_test(stbi__context *s); -static stbi_uc *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp); static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PNM static int stbi__pnm_test(stbi__context *s); -static stbi_uc *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); #endif @@ -889,6 +859,81 @@ static void *stbi__malloc(size_t size) return STBI_MALLOC(size); } +// stb_image uses ints pervasively, including for offset calculations. +// therefore the largest decoded image size we can support with the +// current code, even on 64-bit targets, is INT_MAX. this is not a +// significant limitation for the intended use case. +// +// we do, however, need to make sure our size calculations don't +// overflow. hence a few helper functions for size calculations that +// multiply integers together, making sure that they're non-negative +// and no overflow occurs. + +// return 1 if the sum is valid, 0 on overflow. +// negative terms are considered invalid. +static int stbi__addsizes_valid(int a, int b) +{ + if (b < 0) return 0; + // now 0 <= b <= INT_MAX, hence also + // 0 <= INT_MAX - b <= INTMAX. + // And "a + b <= INT_MAX" (which might overflow) is the + // same as a <= INT_MAX - b (no overflow) + return a <= INT_MAX - b; +} + +// returns 1 if the product is valid, 0 on overflow. +// negative factors are considered invalid. +static int stbi__mul2sizes_valid(int a, int b) +{ + if (a < 0 || b < 0) return 0; + if (b == 0) return 1; // mul-by-0 is always safe + // portable way to check for no overflows in a*b + return a <= INT_MAX/b; +} + +// returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow +static int stbi__mad2sizes_valid(int a, int b, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a*b, add); +} + +// returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow +static int stbi__mad3sizes_valid(int a, int b, int c, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__addsizes_valid(a*b*c, add); +} + +// returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__mul2sizes_valid(a*b*c, d) && stbi__addsizes_valid(a*b*c*d, add); +} +#endif + +// mallocs with size overflow checking +static void *stbi__malloc_mad2(int a, int b, int add) +{ + if (!stbi__mad2sizes_valid(a, b, add)) return NULL; + return stbi__malloc(a*b + add); +} + +static void *stbi__malloc_mad3(int a, int b, int c, int add) +{ + if (!stbi__mad3sizes_valid(a, b, c, add)) return NULL; + return stbi__malloc(a*b*c + add); +} + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) +{ + if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; + return stbi__malloc(a*b*c*d + add); +} +#endif + // stbi__err - error // stbi__errpf - error returning pointer to float // stbi__errpuc - error returning pointer to unsigned char @@ -901,8 +946,8 @@ static void *stbi__malloc(size_t size) #define stbi__err(x,y) stbi__err(x) #endif -#define stbi__errpf(x,y) ((float *) (stbi__err(x,y)?NULL:NULL)) -#define stbi__errpuc(x,y) ((unsigned char *) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpf(x,y) ((float *)(size_t) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpuc(x,y) ((unsigned char *)(size_t) (stbi__err(x,y)?NULL:NULL)) STBIDEF void stbi_image_free(void *retval_from_stbi_load) { @@ -924,33 +969,38 @@ STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) stbi__vertically_flip_on_load = flag_true_if_should_flip; } -static unsigned char *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) { + memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields + ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed + ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order + ri->num_channels = 0; + #ifndef STBI_NO_JPEG - if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp); + if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_PNG - if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp); + if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_BMP - if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp); + if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_GIF - if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp); + if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_PSD - if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp); + if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp, ri, bpc); #endif #ifndef STBI_NO_PIC - if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp); + if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_PNM - if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp); + if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp, ri); #endif #ifndef STBI_NO_HDR if (stbi__hdr_test(s)) { - float *hdr = stbi__hdr_load(s, x,y,comp,req_comp); + float *hdr = stbi__hdr_load(s, x,y,comp,req_comp, ri); return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); } #endif @@ -958,58 +1008,138 @@ static unsigned char *stbi__load_main(stbi__context *s, int *x, int *y, int *com #ifndef STBI_NO_TGA // test tga last because it's a crappy test! if (stbi__tga_test(s)) - return stbi__tga_load(s,x,y,comp,req_comp); + return stbi__tga_load(s,x,y,comp,req_comp, ri); #endif return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); } -static unsigned char *stbi__load_flip(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static stbi_uc *stbi__convert_16_to_8(stbi__uint16 *orig, int w, int h, int channels) { - unsigned char *result = stbi__load_main(s, x, y, comp, req_comp); + int i; + int img_len = w * h * channels; + stbi_uc *reduced; - if (stbi__vertically_flip_on_load && result != NULL) { - int w = *x, h = *y; - int depth = req_comp ? req_comp : *comp; - int row,col,z; - stbi_uc temp; - - // @OPTIMIZE: use a bigger temp buffer and memcpy multiple pixels at once - for (row = 0; row < (h>>1); row++) { - for (col = 0; col < w; col++) { - for (z = 0; z < depth; z++) { - temp = result[(row * w + col) * depth + z]; - result[(row * w + col) * depth + z] = result[((h - row - 1) * w + col) * depth + z]; - result[((h - row - 1) * w + col) * depth + z] = temp; - } - } + reduced = (stbi_uc *) stbi__malloc(img_len); + if (reduced == NULL) return stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling + + STBI_FREE(orig); + return reduced; +} + +static stbi__uint16 *stbi__convert_8_to_16(stbi_uc *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi__uint16 *enlarged; + + enlarged = (stbi__uint16 *) stbi__malloc(img_len*2); + if (enlarged == NULL) return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff + + STBI_FREE(orig); + return enlarged; +} + +static void stbi__vertical_flip(void *image, int w, int h, int bytes_per_pixel) +{ + int row; + size_t bytes_per_row = (size_t)w * bytes_per_pixel; + stbi_uc temp[2048]; + stbi_uc *bytes = (stbi_uc *)image; + + for (row = 0; row < (h>>1); row++) { + stbi_uc *row0 = bytes + row*bytes_per_row; + stbi_uc *row1 = bytes + (h - row - 1)*bytes_per_row; + // swap row0 with row1 + size_t bytes_left = bytes_per_row; + while (bytes_left) { + size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp); + memcpy(temp, row0, bytes_copy); + memcpy(row0, row1, bytes_copy); + memcpy(row1, temp, bytes_copy); + row0 += bytes_copy; + row1 += bytes_copy; + bytes_left -= bytes_copy; } } +} - return result; +static void stbi__vertical_flip_slices(void *image, int w, int h, int z, int bytes_per_pixel) +{ + int slice; + int slice_size = w * h * bytes_per_pixel; + + stbi_uc *bytes = (stbi_uc *)image; + for (slice = 0; slice < z; ++slice) { + stbi__vertical_flip(bytes, w, h, bytes_per_pixel); + bytes += slice_size; + } +} + +static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 8) { + STBI_ASSERT(ri.bits_per_channel == 16); + result = stbi__convert_16_to_8((stbi__uint16 *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 8; + } + + // @TODO: move stbi__convert_format to here + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc)); + } + + return (unsigned char *) result; } +static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 16) { + STBI_ASSERT(ri.bits_per_channel == 8); + result = stbi__convert_8_to_16((stbi_uc *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 16; + } + + // @TODO: move stbi__convert_format16 to here + // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16)); + } + + return (stbi__uint16 *) result; +} + +#if !defined(STBI_NO_HDR) || !defined(STBI_NO_LINEAR) static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp) { if (stbi__vertically_flip_on_load && result != NULL) { - int w = *x, h = *y; - int depth = req_comp ? req_comp : *comp; - int row,col,z; - float temp; - - // @OPTIMIZE: use a bigger temp buffer and memcpy multiple pixels at once - for (row = 0; row < (h>>1); row++) { - for (col = 0; col < w; col++) { - for (z = 0; z < depth; z++) { - temp = result[(row * w + col) * depth + z]; - result[(row * w + col) * depth + z] = result[((h - row - 1) * w + col) * depth + z]; - result[((h - row - 1) * w + col) * depth + z] = temp; - } - } - } + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(float)); } } - +#endif #ifndef STBI_NO_STDIO @@ -1041,28 +1171,83 @@ STBIDEF stbi_uc *stbi_load_from_file(FILE *f, int *x, int *y, int *comp, int req unsigned char *result; stbi__context s; stbi__start_file(&s,f); - result = stbi__load_flip(&s,x,y,comp,req_comp); + result = stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); if (result) { // need to 'unget' all the characters in the IO buffer fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); } return result; } + +STBIDEF stbi__uint16 *stbi_load_from_file_16(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__uint16 *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_16bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi_us *stbi_load_16(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + stbi__uint16 *result; + if (!f) return (stbi_us *) stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file_16(f,x,y,comp,req_comp); + fclose(f); + return result; +} + + #endif //!STBI_NO_STDIO +STBIDEF stbi_us *stbi_load_16_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + STBIDEF stbi_uc *stbi_load_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) { stbi__context s; stbi__start_mem(&s,buffer,len); - return stbi__load_flip(&s,x,y,comp,req_comp); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); } STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) { stbi__context s; stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); - return stbi__load_flip(&s,x,y,comp,req_comp); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_mem(&s,buffer,len); + + result = (unsigned char*) stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); + if (stbi__vertically_flip_on_load) { + stbi__vertical_flip_slices( result, *x, *y, *z, *comp ); + } + + return result; } +#endif #ifndef STBI_NO_LINEAR static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) @@ -1070,13 +1255,14 @@ static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int unsigned char *data; #ifndef STBI_NO_HDR if (stbi__hdr_test(s)) { - float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp); + stbi__result_info ri; + float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp, &ri); if (hdr_data) stbi__float_postprocess(hdr_data,x,y,comp,req_comp); return hdr_data; } #endif - data = stbi__load_flip(s, x, y, comp, req_comp); + data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp); if (data) return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); @@ -1146,13 +1332,18 @@ STBIDEF int stbi_is_hdr (char const *filename) return result; } -STBIDEF int stbi_is_hdr_from_file(FILE *f) +STBIDEF int stbi_is_hdr_from_file(FILE *f) { #ifndef STBI_NO_HDR + long pos = ftell(f); + int res; stbi__context s; stbi__start_file(&s,f); - return stbi__hdr_test(&s); + res = stbi__hdr_test(&s); + fseek(f, pos, SEEK_SET); + return res; #else + STBI_NOTUSED(f); return 0; #endif } @@ -1165,18 +1356,21 @@ STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); return stbi__hdr_test(&s); #else + STBI_NOTUSED(clbk); + STBI_NOTUSED(user); return 0; #endif } -static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; +#ifndef STBI_NO_LINEAR static float stbi__l2h_gamma=2.2f, stbi__l2h_scale=1.0f; -#ifndef STBI_NO_LINEAR STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } #endif +static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; + STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1/gamma; } STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1/scale; } @@ -1285,17 +1479,23 @@ static stbi__uint32 stbi__get32be(stbi__context *s) return (z << 16) + stbi__get16be(s); } +#if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) +// nothing +#else static int stbi__get16le(stbi__context *s) { int z = stbi__get8(s); return z + (stbi__get8(s) << 8); } +#endif +#ifndef STBI_NO_BMP static stbi__uint32 stbi__get32le(stbi__context *s) { stbi__uint32 z = stbi__get16le(s); return z + (stbi__get16le(s) << 16); } +#endif #define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings @@ -1324,7 +1524,7 @@ static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int r if (req_comp == img_n) return data; STBI_ASSERT(req_comp >= 1 && req_comp <= 4); - good = (unsigned char *) stbi__malloc(req_comp * x * y); + good = (unsigned char *) stbi__malloc_mad3(req_comp, x, y, 0); if (good == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); @@ -1334,26 +1534,75 @@ static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int r unsigned char *src = data + j * x * img_n ; unsigned char *dest = good + j * x * req_comp; - #define COMBO(a,b) ((a)*8+(b)) - #define CASE(a,b) case COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0], dest[1]=255; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=255; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=255; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = 255; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +static stbi__uint16 stbi__compute_y_16(int r, int g, int b) +{ + return (stbi__uint16) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + stbi__uint16 *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (stbi__uint16 *) stbi__malloc(req_comp * x * y * 2); + if (good == NULL) { + STBI_FREE(data); + return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + stbi__uint16 *src = data + j * x * img_n ; + stbi__uint16 *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) // convert source image with img_n components to one with req_comp components; // avoid switch per pixel, so use switch per scanline and massive macros - switch (COMBO(img_n, req_comp)) { - CASE(1,2) dest[0]=src[0], dest[1]=255; break; - CASE(1,3) dest[0]=dest[1]=dest[2]=src[0]; break; - CASE(1,4) dest[0]=dest[1]=dest[2]=src[0], dest[3]=255; break; - CASE(2,1) dest[0]=src[0]; break; - CASE(2,3) dest[0]=dest[1]=dest[2]=src[0]; break; - CASE(2,4) dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; break; - CASE(3,4) dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=255; break; - CASE(3,1) dest[0]=stbi__compute_y(src[0],src[1],src[2]); break; - CASE(3,2) dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = 255; break; - CASE(4,1) dest[0]=stbi__compute_y(src[0],src[1],src[2]); break; - CASE(4,2) dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = src[3]; break; - CASE(4,3) dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; break; + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0], dest[1]=0xffff; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=0xffff; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=0xffff; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = 0xffff; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; } break; default: STBI_ASSERT(0); } - #undef CASE + #undef STBI__CASE } STBI_FREE(data); @@ -1364,7 +1613,9 @@ static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int r static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) { int i,k,n; - float *output = (float *) stbi__malloc(x * y * comp * sizeof(float)); + float *output; + if (!data) return NULL; + output = (float *) stbi__malloc_mad4(x, y, comp, sizeof(float), 0); if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); } // compute number of non-alpha components if (comp & 1) n = comp; else n = comp-1; @@ -1384,7 +1635,9 @@ static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp) { int i,k,n; - stbi_uc *output = (stbi_uc *) stbi__malloc(x * y * comp); + stbi_uc *output; + if (!data) return NULL; + output = (stbi_uc *) stbi__malloc_mad3(x, y, comp, 0); if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); } // compute number of non-alpha components if (comp & 1) n = comp; else n = comp-1; @@ -1449,7 +1702,7 @@ typedef struct stbi__context *s; stbi__huffman huff_dc[4]; stbi__huffman huff_ac[4]; - stbi_uc dequant[4][64]; + stbi__uint16 dequant[4][64]; stbi__int16 fast_ac[4][1 << FAST_BITS]; // sizes for components, interleaved MCUs @@ -1485,6 +1738,9 @@ typedef struct int succ_high; int succ_low; int eob_run; + int jfif; + int app14_color_transform; // Adobe APP14 tag + int rgb; int scan_n, order[4]; int restart_interval, todo; @@ -1497,7 +1753,8 @@ typedef struct static int stbi__build_huffman(stbi__huffman *h, int *count) { - int i,j,k=0,code; + int i,j,k=0; + unsigned int code; // build size list for each symbol (from JPEG spec) for (i=0; i < 16; ++i) for (j=0; j < count[i]; ++j) @@ -1513,7 +1770,7 @@ static int stbi__build_huffman(stbi__huffman *h, int *count) if (h->size[k] == j) { while (h->size[k] == j) h->code[k++] = (stbi__uint16) (code++); - if (code-1 >= (1 << j)) return stbi__err("bad code lengths","Corrupt JPEG"); + if (code-1 >= (1u << j)) return stbi__err("bad code lengths","Corrupt JPEG"); } // compute largest code + 1 for this size, preshifted as needed later h->maxcode[j] = code << (16-j); @@ -1554,10 +1811,10 @@ static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) // magnitude code followed by receive_extend code int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); int m = 1 << (magbits - 1); - if (k < m) k += (-1 << magbits) + 1; + if (k < m) k += (~0U << magbits) + 1; // if the result is small enough, we can fit it in fast_ac table if (k >= -128 && k <= 127) - fast_ac[i] = (stbi__int16) ((k << 8) + (run << 4) + (len + magbits)); + fast_ac[i] = (stbi__int16) ((k * 256) + (run * 16) + (len + magbits)); } } } @@ -1566,9 +1823,10 @@ static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) static void stbi__grow_buffer_unsafe(stbi__jpeg *j) { do { - int b = j->nomore ? 0 : stbi__get8(j->s); + unsigned int b = j->nomore ? 0 : stbi__get8(j->s); if (b == 0xff) { int c = stbi__get8(j->s); + while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes if (c != 0) { j->marker = (unsigned char) c; j->nomore = 1; @@ -1581,7 +1839,7 @@ static void stbi__grow_buffer_unsafe(stbi__jpeg *j) } // (1 << n) - 1 -static stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; +static const stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; // decode a jpeg huffman value from the bitstream stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) @@ -1634,7 +1892,7 @@ stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) } // bias[n] = (-1<s); if (x != 0xff) return STBI__MARKER_none; while (x == 0xff) - x = stbi__get8(j->s); + x = stbi__get8(j->s); // consume repeated 0xff fill bytes return x; } @@ -2417,7 +2675,7 @@ static void stbi__jpeg_reset(stbi__jpeg *j) j->code_bits = 0; j->code_buffer = 0; j->nomore = 0; - j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = 0; + j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0; j->marker = STBI__MARKER_none; j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; j->eob_run = 0; @@ -2549,7 +2807,7 @@ static int stbi__parse_entropy_coded_data(stbi__jpeg *z) } } -static void stbi__jpeg_dequantize(short *data, stbi_uc *dequant) +static void stbi__jpeg_dequantize(short *data, stbi__uint16 *dequant) { int i; for (i=0; i < 64; ++i) @@ -2591,13 +2849,14 @@ static int stbi__process_marker(stbi__jpeg *z, int m) L = stbi__get16be(z->s)-2; while (L > 0) { int q = stbi__get8(z->s); - int p = q >> 4; + int p = q >> 4, sixteen = (p != 0); int t = q & 15,i; - if (p != 0) return stbi__err("bad DQT type","Corrupt JPEG"); + if (p != 0 && p != 1) return stbi__err("bad DQT type","Corrupt JPEG"); if (t > 3) return stbi__err("bad DQT table","Corrupt JPEG"); + for (i=0; i < 64; ++i) - z->dequant[t][stbi__jpeg_dezigzag[i]] = stbi__get8(z->s); - L -= 65; + z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s)); + L -= (sixteen ? 129 : 65); } return L==0; @@ -2630,12 +2889,50 @@ static int stbi__process_marker(stbi__jpeg *z, int m) } return L==0; } + // check for comment block or APP blocks if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { - stbi__skip(z->s, stbi__get16be(z->s)-2); + L = stbi__get16be(z->s); + if (L < 2) { + if (m == 0xFE) + return stbi__err("bad COM len","Corrupt JPEG"); + else + return stbi__err("bad APP len","Corrupt JPEG"); + } + L -= 2; + + if (m == 0xE0 && L >= 5) { // JFIF APP0 segment + static const unsigned char tag[5] = {'J','F','I','F','\0'}; + int ok = 1; + int i; + for (i=0; i < 5; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 5; + if (ok) + z->jfif = 1; + } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment + static const unsigned char tag[6] = {'A','d','o','b','e','\0'}; + int ok = 1; + int i; + for (i=0; i < 6; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 6; + if (ok) { + stbi__get8(z->s); // version + stbi__get16be(z->s); // flags0 + stbi__get16be(z->s); // flags1 + z->app14_color_transform = stbi__get8(z->s); // color transform + L -= 6; + } + } + + stbi__skip(z->s, L); return 1; } - return 0; + + return stbi__err("unknown marker","Corrupt JPEG"); } // after we see SOS @@ -2678,6 +2975,28 @@ static int stbi__process_scan_header(stbi__jpeg *z) return 1; } +static int stbi__free_jpeg_components(stbi__jpeg *z, int ncomp, int why) +{ + int i; + for (i=0; i < ncomp; ++i) { + if (z->img_comp[i].raw_data) { + STBI_FREE(z->img_comp[i].raw_data); + z->img_comp[i].raw_data = NULL; + z->img_comp[i].data = NULL; + } + if (z->img_comp[i].raw_coeff) { + STBI_FREE(z->img_comp[i].raw_coeff); + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].coeff = 0; + } + if (z->img_comp[i].linebuf) { + STBI_FREE(z->img_comp[i].linebuf); + z->img_comp[i].linebuf = NULL; + } + } + return why; +} + static int stbi__process_frame_header(stbi__jpeg *z, int scan) { stbi__context *s = z->s; @@ -2687,7 +3006,7 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires c = stbi__get8(s); - if (c != 3 && c != 1) return stbi__err("bad component count","Corrupt JPEG"); // JFIF requires + if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count","Corrupt JPEG"); s->img_n = c; for (i=0; i < c; ++i) { z->img_comp[i].data = NULL; @@ -2696,11 +3015,12 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) if (Lf != 8+3*s->img_n) return stbi__err("bad SOF len","Corrupt JPEG"); + z->rgb = 0; for (i=0; i < s->img_n; ++i) { + static const unsigned char rgb[3] = { 'R', 'G', 'B' }; z->img_comp[i].id = stbi__get8(s); - if (z->img_comp[i].id != i+1) // JFIF requires - if (z->img_comp[i].id != i) // some version of jpegtran outputs non-JFIF-compliant files! - return stbi__err("bad component ID","Corrupt JPEG"); + if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) + ++z->rgb; q = stbi__get8(s); z->img_comp[i].h = (q >> 4); if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H","Corrupt JPEG"); z->img_comp[i].v = q & 15; if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V","Corrupt JPEG"); @@ -2709,7 +3029,7 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) if (scan != STBI__SCAN_load) return 1; - if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); + if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) return stbi__err("too large", "Image too large to decode"); for (i=0; i < s->img_n; ++i) { if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h; @@ -2721,6 +3041,7 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) z->img_v_max = v_max; z->img_mcu_w = h_max * 8; z->img_mcu_h = v_max * 8; + // these sizes can't be more than 17 bits z->img_mcu_x = (s->img_x + z->img_mcu_w-1) / z->img_mcu_w; z->img_mcu_y = (s->img_y + z->img_mcu_h-1) / z->img_mcu_h; @@ -2732,28 +3053,27 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) // the bogus oversized data from using interleaved MCUs and their // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't // discard the extra data until colorspace conversion + // + // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier) + // so these muls can't overflow with 32-bit ints (which we require) z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; - z->img_comp[i].raw_data = stbi__malloc(z->img_comp[i].w2 * z->img_comp[i].h2+15); - - if (z->img_comp[i].raw_data == NULL) { - for(--i; i >= 0; --i) { - STBI_FREE(z->img_comp[i].raw_data); - z->img_comp[i].data = NULL; - } - return stbi__err("outofmem", "Out of memory"); - } + z->img_comp[i].coeff = 0; + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].linebuf = NULL; + z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15); + if (z->img_comp[i].raw_data == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); // align blocks for idct using mmx/sse z->img_comp[i].data = (stbi_uc*) (((size_t) z->img_comp[i].raw_data + 15) & ~15); - z->img_comp[i].linebuf = NULL; if (z->progressive) { - z->img_comp[i].coeff_w = (z->img_comp[i].w2 + 7) >> 3; - z->img_comp[i].coeff_h = (z->img_comp[i].h2 + 7) >> 3; - z->img_comp[i].raw_coeff = STBI_MALLOC(z->img_comp[i].coeff_w * z->img_comp[i].coeff_h * 64 * sizeof(short) + 15); + // w2, h2 are multiples of 8 (see above) + z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8; + z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8; + z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15); + if (z->img_comp[i].raw_coeff == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); z->img_comp[i].coeff = (short*) (((size_t) z->img_comp[i].raw_coeff + 15) & ~15); - } else { - z->img_comp[i].coeff = 0; - z->img_comp[i].raw_coeff = 0; } } @@ -2772,6 +3092,8 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) { int m; + z->jfif = 0; + z->app14_color_transform = -1; // valid values are 0,1,2 z->marker = STBI__MARKER_none; // initialize cached marker to empty m = stbi__get_marker(z); if (!stbi__SOI(m)) return stbi__err("no SOI","Corrupt JPEG"); @@ -2813,12 +3135,15 @@ static int stbi__decode_jpeg_image(stbi__jpeg *j) if (x == 255) { j->marker = stbi__get8(j->s); break; - } else if (x != 0) { - return stbi__err("junk before marker", "Corrupt JPEG"); } } // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 } + } else if (stbi__DNL(m)) { + int Ld = stbi__get16be(j->s); + stbi__uint32 NL = stbi__get16be(j->s); + if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); + if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); } else { if (!stbi__process_marker(j, m)) return 0; } @@ -3037,38 +3362,9 @@ static stbi_uc *stbi__resample_row_generic(stbi_uc *out, stbi_uc *in_near, stbi_ return out; } -#ifdef STBI_JPEG_OLD -// this is the same YCbCr-to-RGB calculation that stb_image has used -// historically before the algorithm changes in 1.49 -#define float2fixed(x) ((int) ((x) * 65536 + 0.5)) -static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) -{ - int i; - for (i=0; i < count; ++i) { - int y_fixed = (y[i] << 16) + 32768; // rounding - int r,g,b; - int cr = pcr[i] - 128; - int cb = pcb[i] - 128; - r = y_fixed + cr*float2fixed(1.40200f); - g = y_fixed - cr*float2fixed(0.71414f) - cb*float2fixed(0.34414f); - b = y_fixed + cb*float2fixed(1.77200f); - r >>= 16; - g >>= 16; - b >>= 16; - if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } - if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } - if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } - out[0] = (stbi_uc)r; - out[1] = (stbi_uc)g; - out[2] = (stbi_uc)b; - out[3] = 255; - out += step; - } -} -#else // this is a reduced-precision calculation of YCbCr-to-RGB introduced // to make sure the code produces the same results in both SIMD and scalar -#define float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) +#define stbi__float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) { int i; @@ -3077,9 +3373,9 @@ static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc int r,g,b; int cr = pcr[i] - 128; int cb = pcb[i] - 128; - r = y_fixed + cr* float2fixed(1.40200f); - g = y_fixed + (cr*-float2fixed(0.71414f)) + ((cb*-float2fixed(0.34414f)) & 0xffff0000); - b = y_fixed + cb* float2fixed(1.77200f); + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + (cr*-stbi__float2fixed(0.71414f)) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); r >>= 20; g >>= 20; b >>= 20; @@ -3093,7 +3389,6 @@ static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc out += step; } } -#endif #if defined(STBI_SSE2) || defined(STBI_NEON) static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc const *pcb, stbi_uc const *pcr, int count, int step) @@ -3212,9 +3507,9 @@ static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc cons int r,g,b; int cr = pcr[i] - 128; int cb = pcb[i] - 128; - r = y_fixed + cr* float2fixed(1.40200f); - g = y_fixed + cr*-float2fixed(0.71414f) + ((cb*-float2fixed(0.34414f)) & 0xffff0000); - b = y_fixed + cb* float2fixed(1.77200f); + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + cr*-stbi__float2fixed(0.71414f) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); r >>= 20; g >>= 20; b >>= 20; @@ -3240,18 +3535,14 @@ static void stbi__setup_jpeg(stbi__jpeg *j) #ifdef STBI_SSE2 if (stbi__sse2_available()) { j->idct_block_kernel = stbi__idct_simd; - #ifndef STBI_JPEG_OLD j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; - #endif j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; } #endif #ifdef STBI_NEON j->idct_block_kernel = stbi__idct_simd; - #ifndef STBI_JPEG_OLD j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; - #endif j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; #endif } @@ -3259,23 +3550,7 @@ static void stbi__setup_jpeg(stbi__jpeg *j) // clean up the temporary component buffers static void stbi__cleanup_jpeg(stbi__jpeg *j) { - int i; - for (i=0; i < j->s->img_n; ++i) { - if (j->img_comp[i].raw_data) { - STBI_FREE(j->img_comp[i].raw_data); - j->img_comp[i].raw_data = NULL; - j->img_comp[i].data = NULL; - } - if (j->img_comp[i].raw_coeff) { - STBI_FREE(j->img_comp[i].raw_coeff); - j->img_comp[i].raw_coeff = 0; - j->img_comp[i].coeff = 0; - } - if (j->img_comp[i].linebuf) { - STBI_FREE(j->img_comp[i].linebuf); - j->img_comp[i].linebuf = NULL; - } - } + stbi__free_jpeg_components(j, j->s->img_n, 0); } typedef struct @@ -3288,9 +3563,16 @@ typedef struct int ypos; // which pre-expansion row we're on } stbi__resample; +// fast 0..255 * 0..255 => 0..255 rounded multiplication +static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) +{ + unsigned int t = x*y + 128; + return (stbi_uc) ((t + (t >>8)) >> 8); +} + static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp, int req_comp) { - int n, decode_n; + int n, decode_n, is_rgb; z->s->img_n = 0; // make stbi__cleanup_jpeg safe // validate req_comp @@ -3300,9 +3582,11 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; } // determine actual number of components to generate - n = req_comp ? req_comp : z->s->img_n; + n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1; + + is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif)); - if (z->s->img_n == 3 && n < 3) + if (z->s->img_n == 3 && n < 3 && !is_rgb) decode_n = 1; else decode_n = z->s->img_n; @@ -3339,7 +3623,7 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp } // can't error after this so, this is safe - output = (stbi_uc *) stbi__malloc(n * z->s->img_x * z->s->img_y + 1); + output = (stbi_uc *) stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1); if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } // now go ahead and resample @@ -3362,7 +3646,39 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp if (n >= 3) { stbi_uc *y = coutput[0]; if (z->s->img_n == 3) { - z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + if (is_rgb) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = y[i]; + out[1] = coutput[1][i]; + out[2] = coutput[2][i]; + out[3] = 255; + out += n; + } + } else { + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else if (z->s->img_n == 4) { + if (z->app14_color_transform == 0) { // CMYK + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(coutput[0][i], m); + out[1] = stbi__blinn_8x8(coutput[1][i], m); + out[2] = stbi__blinn_8x8(coutput[2][i], m); + out[3] = 255; + out += n; + } + } else if (z->app14_color_transform == 2) { // YCCK + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(255 - out[0], m); + out[1] = stbi__blinn_8x8(255 - out[1], m); + out[2] = stbi__blinn_8x8(255 - out[2], m); + out += n; + } + } else { // YCbCr + alpha? Ignore the fourth channel for now + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } } else for (i=0; i < z->s->img_x; ++i) { out[0] = out[1] = out[2] = y[i]; @@ -3370,37 +3686,70 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp out += n; } } else { - stbi_uc *y = coutput[0]; - if (n == 1) - for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; - else - for (i=0; i < z->s->img_x; ++i) *out++ = y[i], *out++ = 255; + if (is_rgb) { + if (n == 1) + for (i=0; i < z->s->img_x; ++i) + *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + else { + for (i=0; i < z->s->img_x; ++i, out += 2) { + out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + out[1] = 255; + } + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 0) { + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + stbi_uc r = stbi__blinn_8x8(coutput[0][i], m); + stbi_uc g = stbi__blinn_8x8(coutput[1][i], m); + stbi_uc b = stbi__blinn_8x8(coutput[2][i], m); + out[0] = stbi__compute_y(r, g, b); + out[1] = 255; + out += n; + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 2) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]); + out[1] = 255; + out += n; + } + } else { + stbi_uc *y = coutput[0]; + if (n == 1) + for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; + else + for (i=0; i < z->s->img_x; ++i) *out++ = y[i], *out++ = 255; + } } } stbi__cleanup_jpeg(z); *out_x = z->s->img_x; *out_y = z->s->img_y; - if (comp) *comp = z->s->img_n; // report original components, not output + if (comp) *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output return output; } } -static unsigned char *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { - stbi__jpeg j; - j.s = s; - stbi__setup_jpeg(&j); - return load_jpeg_image(&j, x,y,comp,req_comp); + unsigned char* result; + stbi__jpeg* j = (stbi__jpeg*) stbi__malloc(sizeof(stbi__jpeg)); + STBI_NOTUSED(ri); + j->s = s; + stbi__setup_jpeg(j); + result = load_jpeg_image(j, x,y,comp,req_comp); + STBI_FREE(j); + return result; } static int stbi__jpeg_test(stbi__context *s) { int r; - stbi__jpeg j; - j.s = s; - stbi__setup_jpeg(&j); - r = stbi__decode_jpeg_header(&j, STBI__SCAN_type); + stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + j->s = s; + stbi__setup_jpeg(j); + r = stbi__decode_jpeg_header(j, STBI__SCAN_type); stbi__rewind(s); + STBI_FREE(j); return r; } @@ -3412,15 +3761,18 @@ static int stbi__jpeg_info_raw(stbi__jpeg *j, int *x, int *y, int *comp) } if (x) *x = j->s->img_x; if (y) *y = j->s->img_y; - if (comp) *comp = j->s->img_n; + if (comp) *comp = j->s->img_n >= 3 ? 3 : 1; return 1; } static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) { - stbi__jpeg j; - j.s = s; - return stbi__jpeg_info_raw(&j, x, y, comp); + int result; + stbi__jpeg* j = (stbi__jpeg*) (stbi__malloc(sizeof(stbi__jpeg))); + j->s = s; + result = stbi__jpeg_info_raw(j, x, y, comp); + STBI_FREE(j); + return result; } #endif @@ -3466,7 +3818,7 @@ stbi_inline static int stbi__bit_reverse(int v, int bits) return stbi__bitreverse16(v) >> (16-bits); } -static int stbi__zbuild_huffman(stbi__zhuffman *z, stbi_uc *sizelist, int num) +static int stbi__zbuild_huffman(stbi__zhuffman *z, const stbi_uc *sizelist, int num) { int i,k=0; int code, next_code[16], sizes[17]; @@ -3501,10 +3853,10 @@ static int stbi__zbuild_huffman(stbi__zhuffman *z, stbi_uc *sizelist, int num) z->size [c] = (stbi_uc ) s; z->value[c] = (stbi__uint16) i; if (s <= STBI__ZFAST_BITS) { - int k = stbi__bit_reverse(next_code[s],s); - while (k < (1 << STBI__ZFAST_BITS)) { - z->fast[k] = fastv; - k += (1 << s); + int j = stbi__bit_reverse(next_code[s],s); + while (j < (1 << STBI__ZFAST_BITS)) { + z->fast[j] = fastv; + j += (1 << s); } } ++next_code[s]; @@ -3543,7 +3895,7 @@ static void stbi__fill_bits(stbi__zbuf *z) { do { STBI_ASSERT(z->code_buffer < (1U << z->num_bits)); - z->code_buffer |= stbi__zget8(z) << z->num_bits; + z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits; z->num_bits += 8; } while (z->num_bits <= 24); } @@ -3593,14 +3945,15 @@ stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room for n bytes { char *q; - int cur, limit; + int cur, limit, old_limit; z->zout = zout; if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG"); cur = (int) (z->zout - z->zout_start); - limit = (int) (z->zout_end - z->zout_start); + limit = old_limit = (int) (z->zout_end - z->zout_start); while (cur + n > limit) limit *= 2; - q = (char *) STBI_REALLOC(z->zout_start, limit); + q = (char *) STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); + STBI_NOTUSED(old_limit); if (q == NULL) return stbi__err("outofmem", "Out of memory"); z->zout_start = q; z->zout = q + cur; @@ -3608,18 +3961,18 @@ static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room return 1; } -static int stbi__zlength_base[31] = { +static const int stbi__zlength_base[31] = { 3,4,5,6,7,8,9,10,11,13, 15,17,19,23,27,31,35,43,51,59, 67,83,99,115,131,163,195,227,258,0,0 }; -static int stbi__zlength_extra[31]= +static const int stbi__zlength_extra[31]= { 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; -static int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, +static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, 257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; -static int stbi__zdist_extra[32] = +static const int stbi__zdist_extra[32] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; static int stbi__parse_huffman_block(stbi__zbuf *a) @@ -3666,7 +4019,7 @@ static int stbi__parse_huffman_block(stbi__zbuf *a) static int stbi__compute_huffman_codes(stbi__zbuf *a) { - static stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; + static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; stbi__zhuffman z_codelength; stbi_uc lencodes[286+32+137];//padding for maximum single op stbi_uc codelength_sizes[19]; @@ -3675,6 +4028,7 @@ static int stbi__compute_huffman_codes(stbi__zbuf *a) int hlit = stbi__zreceive(a,5) + 257; int hdist = stbi__zreceive(a,5) + 1; int hclen = stbi__zreceive(a,4) + 4; + int ntot = hlit + hdist; memset(codelength_sizes, 0, sizeof(codelength_sizes)); for (i=0; i < hclen; ++i) { @@ -3684,33 +4038,35 @@ static int stbi__compute_huffman_codes(stbi__zbuf *a) if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0; n = 0; - while (n < hlit + hdist) { + while (n < ntot) { int c = stbi__zhuffman_decode(a, &z_codelength); if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG"); if (c < 16) lencodes[n++] = (stbi_uc) c; - else if (c == 16) { - c = stbi__zreceive(a,2)+3; - memset(lencodes+n, lencodes[n-1], c); - n += c; - } else if (c == 17) { - c = stbi__zreceive(a,3)+3; - memset(lencodes+n, 0, c); - n += c; - } else { - STBI_ASSERT(c == 18); - c = stbi__zreceive(a,7)+11; - memset(lencodes+n, 0, c); + else { + stbi_uc fill = 0; + if (c == 16) { + c = stbi__zreceive(a,2)+3; + if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG"); + fill = lencodes[n-1]; + } else if (c == 17) + c = stbi__zreceive(a,3)+3; + else { + STBI_ASSERT(c == 18); + c = stbi__zreceive(a,7)+11; + } + if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG"); + memset(lencodes+n, fill, c); n += c; } } - if (n != hlit+hdist) return stbi__err("bad codelengths","Corrupt PNG"); + if (n != ntot) return stbi__err("bad codelengths","Corrupt PNG"); if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0; if (!stbi__zbuild_huffman(&a->z_distance, lencodes+hlit, hdist)) return 0; return 1; } -static int stbi__parse_uncomperssed_block(stbi__zbuf *a) +static int stbi__parse_uncompressed_block(stbi__zbuf *a) { stbi_uc header[4]; int len,nlen,k; @@ -3752,9 +4108,24 @@ static int stbi__parse_zlib_header(stbi__zbuf *a) return 1; } -// @TODO: should statically initialize these for optimal thread safety -static stbi_uc stbi__zdefault_length[288], stbi__zdefault_distance[32]; -static void stbi__init_zdefaults(void) +static const stbi_uc stbi__zdefault_length[288] = +{ + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8 +}; +static const stbi_uc stbi__zdefault_distance[32] = +{ + 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 +}; +/* +Init algorithm: { int i; // use <= to match clearly with spec for (i=0; i <= 143; ++i) stbi__zdefault_length[i] = 8; @@ -3764,6 +4135,7 @@ static void stbi__init_zdefaults(void) for (i=0; i <= 31; ++i) stbi__zdefault_distance[i] = 5; } +*/ static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) { @@ -3776,13 +4148,12 @@ static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) final = stbi__zreceive(a,1); type = stbi__zreceive(a,2); if (type == 0) { - if (!stbi__parse_uncomperssed_block(a)) return 0; + if (!stbi__parse_uncompressed_block(a)) return 0; } else if (type == 3) { return 0; } else { if (type == 1) { // use fixed code lengths - if (!stbi__zdefault_distance[31]) stbi__init_zdefaults(); if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , 288)) return 0; if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; } else { @@ -3907,7 +4278,7 @@ static stbi__pngchunk stbi__get_chunk_header(stbi__context *s) static int stbi__check_png_header(stbi__context *s) { - static stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; + static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; int i; for (i=0; i < 8; ++i) if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG"); @@ -3918,6 +4289,7 @@ typedef struct { stbi__context *s; stbi_uc *idata, *expanded, *out; + int depth; } stbi__png; @@ -3952,35 +4324,40 @@ static int stbi__paeth(int a, int b, int c) return c; } -static stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; +static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; // create the png data from post-deflated data static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) { + int bytes = (depth == 16? 2 : 1); stbi__context *s = a->s; - stbi__uint32 i,j,stride = x*out_n; + stbi__uint32 i,j,stride = x*out_n*bytes; stbi__uint32 img_len, img_width_bytes; int k; int img_n = s->img_n; // copy it into a local for later + int output_bytes = out_n*bytes; + int filter_bytes = img_n*bytes; + int width = x; + STBI_ASSERT(out_n == s->img_n || out_n == s->img_n+1); - a->out = (stbi_uc *) stbi__malloc(x * y * out_n); // extra bytes to write off the end into + a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into if (!a->out) return stbi__err("outofmem", "Out of memory"); + if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); img_width_bytes = (((img_n * x * depth) + 7) >> 3); img_len = (img_width_bytes + 1) * y; - if (s->img_x == x && s->img_y == y) { - if (raw_len != img_len) return stbi__err("not enough pixels","Corrupt PNG"); - } else { // interlaced: - if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); - } + + // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, + // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), + // so just check for raw_len < img_len always. + if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); for (j=0; j < y; ++j) { stbi_uc *cur = a->out + stride*j; - stbi_uc *prior = cur - stride; + stbi_uc *prior; int filter = *raw++; - int filter_bytes = img_n; - int width = x; + if (filter > 4) return stbi__err("invalid filter","Corrupt PNG"); @@ -3990,6 +4367,7 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r filter_bytes = 1; width = img_width_bytes; } + prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above // if first row, use special filter that doesn't sample previous row if (j == 0) filter = first_row_filter[filter]; @@ -4013,6 +4391,14 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r raw += img_n; cur += out_n; prior += out_n; + } else if (depth == 16) { + if (img_n != out_n) { + cur[filter_bytes] = 255; // first pixel top byte + cur[filter_bytes+1] = 255; // first pixel bottom byte + } + raw += filter_bytes; + cur += output_bytes; + prior += output_bytes; } else { raw += 1; cur += 1; @@ -4021,38 +4407,47 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r // this is a little gross, so that we don't switch per-pixel or per-component if (depth < 8 || img_n == out_n) { - int nk = (width - 1)*img_n; - #define CASE(f) \ + int nk = (width - 1)*filter_bytes; + #define STBI__CASE(f) \ case f: \ for (k=0; k < nk; ++k) switch (filter) { // "none" filter turns into a memcpy here; make that explicit. case STBI__F_none: memcpy(cur, raw, nk); break; - CASE(STBI__F_sub) cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); break; - CASE(STBI__F_up) cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; - CASE(STBI__F_avg) cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); break; - CASE(STBI__F_paeth) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); break; - CASE(STBI__F_avg_first) cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); break; - CASE(STBI__F_paeth_first) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); } break; } - #undef CASE + #undef STBI__CASE raw += nk; } else { STBI_ASSERT(img_n+1 == out_n); - #define CASE(f) \ + #define STBI__CASE(f) \ case f: \ - for (i=x-1; i >= 1; --i, cur[img_n]=255,raw+=img_n,cur+=out_n,prior+=out_n) \ - for (k=0; k < img_n; ++k) + for (i=x-1; i >= 1; --i, cur[filter_bytes]=255,raw+=filter_bytes,cur+=output_bytes,prior+=output_bytes) \ + for (k=0; k < filter_bytes; ++k) switch (filter) { - CASE(STBI__F_none) cur[k] = raw[k]; break; - CASE(STBI__F_sub) cur[k] = STBI__BYTECAST(raw[k] + cur[k-out_n]); break; - CASE(STBI__F_up) cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; - CASE(STBI__F_avg) cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-out_n])>>1)); break; - CASE(STBI__F_paeth) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-out_n],prior[k],prior[k-out_n])); break; - CASE(STBI__F_avg_first) cur[k] = STBI__BYTECAST(raw[k] + (cur[k-out_n] >> 1)); break; - CASE(STBI__F_paeth_first) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-out_n],0,0)); break; + STBI__CASE(STBI__F_none) { cur[k] = raw[k]; } break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k- output_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k- output_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],prior[k],prior[k- output_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k- output_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],0,0)); } break; + } + #undef STBI__CASE + + // the loop above sets the high byte of the pixels' alpha, but for + // 16 bit png files we also need the low byte set. we'll do that here. + if (depth == 16) { + cur = a->out + stride*j; // start at the beginning of the row again + for (i=0; i < x; ++i,cur+=output_bytes) { + cur[filter_bytes+1] = 255; + } } - #undef CASE } } @@ -4109,25 +4504,36 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01); } if (img_n != out_n) { + int q; // insert alpha = 255 - stbi_uc *cur = a->out + stride*j; - int i; + cur = a->out + stride*j; if (img_n == 1) { - for (i=x-1; i >= 0; --i) { - cur[i*2+1] = 255; - cur[i*2+0] = cur[i]; + for (q=x-1; q >= 0; --q) { + cur[q*2+1] = 255; + cur[q*2+0] = cur[q]; } } else { STBI_ASSERT(img_n == 3); - for (i=x-1; i >= 0; --i) { - cur[i*4+3] = 255; - cur[i*4+2] = cur[i*3+2]; - cur[i*4+1] = cur[i*3+1]; - cur[i*4+0] = cur[i*3+0]; + for (q=x-1; q >= 0; --q) { + cur[q*4+3] = 255; + cur[q*4+2] = cur[q*3+2]; + cur[q*4+1] = cur[q*3+1]; + cur[q*4+0] = cur[q*3+0]; } } } } + } else if (depth == 16) { + // force the image data from big-endian to platform-native. + // this is done in a separate pass due to the decoding relying + // on the data being untouched, but could probably be done + // per-line during decode if care is taken. + stbi_uc *cur = a->out; + stbi__uint16 *cur16 = (stbi__uint16*)cur; + + for(i=0; i < x*y*out_n; ++i,cur16++,cur+=2) { + *cur16 = (cur[0] << 8) | cur[1]; + } } return 1; @@ -4135,13 +4541,15 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced) { + int bytes = (depth == 16 ? 2 : 1); + int out_bytes = out_n * bytes; stbi_uc *final; int p; if (!interlaced) return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); // de-interlacing - final = (stbi_uc *) stbi__malloc(a->s->img_x * a->s->img_y * out_n); + final = (stbi_uc *) stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); for (p=0; p < 7; ++p) { int xorig[] = { 0,4,0,2,0,1,0 }; int yorig[] = { 0,0,4,0,2,0,1 }; @@ -4161,8 +4569,8 @@ static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint3 for (i=0; i < x; ++i) { int out_y = j*yspc[p]+yorig[p]; int out_x = i*xspc[p]+xorig[p]; - memcpy(final + out_y*a->s->img_x*out_n + out_x*out_n, - a->out + (j*x+i)*out_n, out_n); + memcpy(final + out_y*a->s->img_x*out_bytes + out_x*out_bytes, + a->out + (j*x+i)*out_bytes, out_bytes); } } STBI_FREE(a->out); @@ -4200,12 +4608,37 @@ static int stbi__compute_transparency(stbi__png *z, stbi_uc tc[3], int out_n) return 1; } +static int stbi__compute_transparency16(stbi__png *z, stbi__uint16 tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi__uint16 *p = (stbi__uint16*) z->out; + + // compute color-based transparency, assuming we've + // already got 65535 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i = 0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 65535); + p += 2; + } + } else { + for (i = 0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int pal_img_n) { stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; stbi_uc *p, *temp_out, *orig = a->out; - p = (stbi_uc *) stbi__malloc(pixel_count * pal_img_n); + p = (stbi_uc *) stbi__malloc_mad2(pixel_count, pal_img_n, 0); if (p == NULL) return stbi__err("outofmem", "Out of memory"); // between here and free(out) below, exitting would leak @@ -4271,9 +4704,10 @@ static void stbi__de_iphone(stbi__png *z) stbi_uc a = p[3]; stbi_uc t = p[0]; if (a) { - p[0] = p[2] * 255 / a; - p[1] = p[1] * 255 / a; - p[2] = t * 255 / a; + stbi_uc half = a / 2; + p[0] = (p[2] * 255 + half) / a; + p[1] = (p[1] * 255 + half) / a; + p[2] = ( t * 255 + half) / a; } else { p[0] = p[2]; p[2] = t; @@ -4292,14 +4726,15 @@ static void stbi__de_iphone(stbi__png *z) } } -#define STBI__PNG_TYPE(a,b,c,d) (((a) << 24) + ((b) << 16) + ((c) << 8) + (d)) +#define STBI__PNG_TYPE(a,b,c,d) (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d)) static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) { stbi_uc palette[1024], pal_img_n=0; stbi_uc has_trans=0, tc[3]; + stbi__uint16 tc16[3]; stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0; - int first=1,k,interlace=0, color=0, depth=0, is_iphone=0; + int first=1,k,interlace=0, color=0, is_iphone=0; stbi__context *s = z->s; z->expanded = NULL; @@ -4324,8 +4759,9 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG"); s->img_x = stbi__get32be(s); if (s->img_x > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); s->img_y = stbi__get32be(s); if (s->img_y > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); - depth = stbi__get8(s); if (depth != 1 && depth != 2 && depth != 4 && depth != 8) return stbi__err("1/2/4/8-bit only","PNG not supported: 1/2/4/8-bit only"); + z->depth = stbi__get8(s); if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) return stbi__err("1/2/4/8/16-bit only","PNG not supported: 1/2/4/8/16-bit only"); color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3 && z->depth == 16) return stbi__err("bad ctype","Corrupt PNG"); if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype","Corrupt PNG"); comp = stbi__get8(s); if (comp) return stbi__err("bad comp method","Corrupt PNG"); filter= stbi__get8(s); if (filter) return stbi__err("bad filter method","Corrupt PNG"); @@ -4373,8 +4809,11 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG"); if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG"); has_trans = 1; - for (k=0; k < s->img_n; ++k) - tc[k] = (stbi_uc) (stbi__get16be(s) & 255) * stbi__depth_scale_table[depth]; // non 8-bit images will be larger + if (z->depth == 16) { + for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is + } else { + for (k = 0; k < s->img_n; ++k) tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger + } } break; } @@ -4385,11 +4824,13 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (scan == STBI__SCAN_header) { s->img_n = pal_img_n; return 1; } if ((int)(ioff + c.length) < (int)ioff) return 0; if (ioff + c.length > idata_limit) { + stbi__uint32 idata_limit_old = idata_limit; stbi_uc *p; if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096; while (ioff + c.length > idata_limit) idata_limit *= 2; - p = (stbi_uc *) STBI_REALLOC(z->idata, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); + STBI_NOTUSED(idata_limit_old); + p = (stbi_uc *) STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); z->idata = p; } if (!stbi__getn(s, z->idata+ioff,c.length)) return stbi__err("outofdata","Corrupt PNG"); @@ -4403,7 +4844,7 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (scan != STBI__SCAN_load) return 1; if (z->idata == NULL) return stbi__err("no IDAT","Corrupt PNG"); // initial guess for decoded data size to avoid unnecessary reallocs - bpl = (s->img_x * depth + 7) / 8; // bytes per line, per component + bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */; z->expanded = (stbi_uc *) stbi_zlib_decode_malloc_guesssize_headerflag((char *) z->idata, ioff, raw_len, (int *) &raw_len, !is_iphone); if (z->expanded == NULL) return 0; // zlib should set error @@ -4412,9 +4853,14 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) s->img_out_n = s->img_n+1; else s->img_out_n = s->img_n; - if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, depth, color, interlace)) return 0; - if (has_trans) - if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) return 0; + if (has_trans) { + if (z->depth == 16) { + if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) return 0; + } else { + if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + } + } if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2) stbi__de_iphone(z); if (pal_img_n) { @@ -4424,6 +4870,9 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (req_comp >= 3) s->img_out_n = req_comp; if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) return 0; + } else if (has_trans) { + // non-paletted image with tRNS -> source image has (constant) alpha + ++s->img_n; } STBI_FREE(z->expanded); z->expanded = NULL; return 1; @@ -4451,21 +4900,28 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) } } -static unsigned char *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp) +static void *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp, stbi__result_info *ri) { - unsigned char *result=NULL; + void *result=NULL; if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { + if (p->depth < 8) + ri->bits_per_channel = 8; + else + ri->bits_per_channel = p->depth; result = p->out; p->out = NULL; if (req_comp && req_comp != p->s->img_out_n) { - result = stbi__convert_format(result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + if (ri->bits_per_channel == 8) + result = stbi__convert_format((unsigned char *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + else + result = stbi__convert_format16((stbi__uint16 *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); p->s->img_out_n = req_comp; if (result == NULL) return result; } *x = p->s->img_x; *y = p->s->img_y; - if (n) *n = p->s->img_out_n; + if (n) *n = p->s->img_n; } STBI_FREE(p->out); p->out = NULL; STBI_FREE(p->expanded); p->expanded = NULL; @@ -4474,11 +4930,11 @@ static unsigned char *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req return result; } -static unsigned char *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { stbi__png p; p.s = s; - return stbi__do_png(&p, x,y,comp,req_comp); + return stbi__do_png(&p, x,y,comp,req_comp, ri); } static int stbi__png_test(stbi__context *s) @@ -4507,6 +4963,19 @@ static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp) p.s = s; return stbi__png_info_raw(&p, x, y, comp); } + +static int stbi__png_is16(stbi__context *s) +{ + stbi__png p; + p.s = s; + if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) + return 0; + if (p.depth != 16) { + stbi__rewind(p.s); + return 0; + } + return 1; +} #endif // Microsoft/Windows BMP image @@ -4558,36 +5027,46 @@ static int stbi__bitcount(unsigned int a) return a & 0xff; } +// extract an arbitrarily-aligned N-bit value (N=bits) +// from v, and then make it 8-bits long and fractionally +// extend it to full full range. static int stbi__shiftsigned(int v, int shift, int bits) { - int result; - int z=0; - - if (shift < 0) v <<= -shift; - else v >>= shift; - result = v; - - z = bits; - while (z < 8) { - result += v >> z; - z += bits; - } - return result; + static unsigned int mul_table[9] = { + 0, + 0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/, + 0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/, + }; + static unsigned int shift_table[9] = { + 0, 0,0,1,0,2,4,6,0, + }; + if (shift < 0) + v <<= -shift; + else + v >>= shift; + STBI_ASSERT(v >= 0 && v < 256); + v >>= (8-bits); + STBI_ASSERT(bits >= 0 && bits <= 8); + return (int) ((unsigned) v * mul_table[bits]) >> shift_table[bits]; } -static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +typedef struct { - stbi_uc *out; - unsigned int mr=0,mg=0,mb=0,ma=0, fake_a=0; - stbi_uc pal[256][4]; - int psize=0,i,j,compress=0,width; - int bpp, flip_vertically, pad, target, offset, hsz; + int bpp, offset, hsz; + unsigned int mr,mg,mb,ma, all_a; +} stbi__bmp_data; + +static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) +{ + int hsz; if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP"); stbi__get32le(s); // discard filesize stbi__get16le(s); // discard reserved stbi__get16le(s); // discard reserved - offset = stbi__get32le(s); - hsz = stbi__get32le(s); + info->offset = stbi__get32le(s); + info->hsz = hsz = stbi__get32le(s); + info->mr = info->mg = info->mb = info->ma = 0; + if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); if (hsz == 12) { s->img_x = stbi__get16le(s); @@ -4597,15 +5076,9 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int s->img_y = stbi__get32le(s); } if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); - bpp = stbi__get16le(s); - if (bpp == 1) return stbi__errpuc("monochrome", "BMP type not supported: 1-bit"); - flip_vertically = ((int) s->img_y) > 0; - s->img_y = abs((int) s->img_y); - if (hsz == 12) { - if (bpp < 24) - psize = (offset - 14 - 24) / 3; - } else { - compress = stbi__get32le(s); + info->bpp = stbi__get16le(s); + if (hsz != 12) { + int compress = stbi__get32le(s); if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); stbi__get32le(s); // discard sizeof stbi__get32le(s); // discard hres @@ -4619,27 +5092,25 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int stbi__get32le(s); stbi__get32le(s); } - if (bpp == 16 || bpp == 32) { - mr = mg = mb = 0; + if (info->bpp == 16 || info->bpp == 32) { if (compress == 0) { - if (bpp == 32) { - mr = 0xffu << 16; - mg = 0xffu << 8; - mb = 0xffu << 0; - ma = 0xffu << 24; - fake_a = 1; // @TODO: check for cases like alpha value is all 0 and switch it to 255 - STBI_NOTUSED(fake_a); + if (info->bpp == 32) { + info->mr = 0xffu << 16; + info->mg = 0xffu << 8; + info->mb = 0xffu << 0; + info->ma = 0xffu << 24; + info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 } else { - mr = 31u << 10; - mg = 31u << 5; - mb = 31u << 0; + info->mr = 31u << 10; + info->mg = 31u << 5; + info->mb = 31u << 0; } } else if (compress == 3) { - mr = stbi__get32le(s); - mg = stbi__get32le(s); - mb = stbi__get32le(s); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); // not documented, but generated by photoshop and handled by mspaint - if (mr == mg && mg == mb) { + if (info->mr == info->mg && info->mg == info->mb) { // ?!?!? return stbi__errpuc("bad BMP", "bad BMP"); } @@ -4647,11 +5118,13 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int return stbi__errpuc("bad BMP", "bad BMP"); } } else { - STBI_ASSERT(hsz == 108 || hsz == 124); - mr = stbi__get32le(s); - mg = stbi__get32le(s); - mb = stbi__get32le(s); - ma = stbi__get32le(s); + int i; + if (hsz != 108 && hsz != 124) + return stbi__errpuc("bad BMP", "bad BMP"); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->ma = stbi__get32le(s); stbi__get32le(s); // discard color space for (i=0; i < 12; ++i) stbi__get32le(s); // discard color space parameters @@ -4662,63 +5135,119 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int stbi__get32le(s); // discard reserved } } - if (bpp < 16) - psize = (offset - 14 - hsz) >> 2; } + return (void *) 1; +} + + +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + unsigned int mr=0,mg=0,mb=0,ma=0, all_a; + stbi_uc pal[256][4]; + int psize=0,i,j,width; + int flip_vertically, pad, target; + stbi__bmp_data info; + STBI_NOTUSED(ri); + + info.all_a = 255; + if (stbi__bmp_parse_header(s, &info) == NULL) + return NULL; // error code already set + + flip_vertically = ((int) s->img_y) > 0; + s->img_y = abs((int) s->img_y); + + mr = info.mr; + mg = info.mg; + mb = info.mb; + ma = info.ma; + all_a = info.all_a; + + if (info.hsz == 12) { + if (info.bpp < 24) + psize = (info.offset - 14 - 24) / 3; + } else { + if (info.bpp < 16) + psize = (info.offset - 14 - info.hsz) >> 2; + } + s->img_n = ma ? 4 : 3; if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 target = req_comp; else target = s->img_n; // if they want monochrome, we'll post-convert - out = (stbi_uc *) stbi__malloc(target * s->img_x * s->img_y); + + // sanity-check size + if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "Corrupt BMP"); + + out = (stbi_uc *) stbi__malloc_mad3(target, s->img_x, s->img_y, 0); if (!out) return stbi__errpuc("outofmem", "Out of memory"); - if (bpp < 16) { + if (info.bpp < 16) { int z=0; if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); } for (i=0; i < psize; ++i) { pal[i][2] = stbi__get8(s); pal[i][1] = stbi__get8(s); pal[i][0] = stbi__get8(s); - if (hsz != 12) stbi__get8(s); + if (info.hsz != 12) stbi__get8(s); pal[i][3] = 255; } - stbi__skip(s, offset - 14 - hsz - psize * (hsz == 12 ? 3 : 4)); - if (bpp == 4) width = (s->img_x + 1) >> 1; - else if (bpp == 8) width = s->img_x; + stbi__skip(s, info.offset - 14 - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); + if (info.bpp == 1) width = (s->img_x + 7) >> 3; + else if (info.bpp == 4) width = (s->img_x + 1) >> 1; + else if (info.bpp == 8) width = s->img_x; else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } pad = (-width)&3; - for (j=0; j < (int) s->img_y; ++j) { - for (i=0; i < (int) s->img_x; i += 2) { - int v=stbi__get8(s),v2=0; - if (bpp == 4) { - v2 = v & 15; - v >>= 4; + if (info.bpp == 1) { + for (j=0; j < (int) s->img_y; ++j) { + int bit_offset = 7, v = stbi__get8(s); + for (i=0; i < (int) s->img_x; ++i) { + int color = (v>>bit_offset)&0x1; + out[z++] = pal[color][0]; + out[z++] = pal[color][1]; + out[z++] = pal[color][2]; + if((--bit_offset) < 0) { + bit_offset = 7; + v = stbi__get8(s); + } } - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) out[z++] = 255; - if (i+1 == (int) s->img_x) break; - v = (bpp == 8) ? stbi__get8(s) : v2; - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) out[z++] = 255; + stbi__skip(s, pad); + } + } else { + for (j=0; j < (int) s->img_y; ++j) { + for (i=0; i < (int) s->img_x; i += 2) { + int v=stbi__get8(s),v2=0; + if (info.bpp == 4) { + v2 = v & 15; + v >>= 4; + } + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + v = (info.bpp == 8) ? stbi__get8(s) : v2; + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + } + stbi__skip(s, pad); } - stbi__skip(s, pad); } } else { int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0; int z = 0; int easy=0; - stbi__skip(s, offset - 14 - hsz); - if (bpp == 24) width = 3 * s->img_x; - else if (bpp == 16) width = 2*s->img_x; + stbi__skip(s, info.offset - 14 - info.hsz); + if (info.bpp == 24) width = 3 * s->img_x; + else if (info.bpp == 16) width = 2*s->img_x; else /* bpp = 32 and pad = 0 */ width=0; pad = (-width) & 3; - if (bpp == 24) { + if (info.bpp == 24) { easy = 1; - } else if (bpp == 32) { + } else if (info.bpp == 32) { if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) easy = 2; } @@ -4739,22 +5268,31 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int out[z+0] = stbi__get8(s); z += 3; a = (easy == 2 ? stbi__get8(s) : 255); + all_a |= a; if (target == 4) out[z++] = a; } } else { + int bpp = info.bpp; for (i=0; i < (int) s->img_x; ++i) { stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s)); - int a; + unsigned int a; out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); + all_a |= a; if (target == 4) out[z++] = STBI__BYTECAST(a); } } stbi__skip(s, pad); } } + + // if alpha channel is all 0s, replace with all 255s + if (target == 4 && all_a == 0) + for (i=4*s->img_x*s->img_y-1; i >= 0; i -= 4) + out[i] = 255; + if (flip_vertically) { stbi_uc t; for (j=0; j < (int) s->img_y>>1; ++j) { @@ -4781,20 +5319,55 @@ static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int // Targa Truevision - TGA // by Jonathan Dummer #ifndef STBI_NO_TGA +// returns STBI_rgb or whatever, 0 on error +static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16) +{ + // only RGB or RGBA (incl. 16bit) or grey allowed + if (is_rgb16) *is_rgb16 = 0; + switch(bits_per_pixel) { + case 8: return STBI_grey; + case 16: if(is_grey) return STBI_grey_alpha; + // fallthrough + case 15: if(is_rgb16) *is_rgb16 = 1; + return STBI_rgb; + case 24: // fallthrough + case 32: return bits_per_pixel/8; + default: return 0; + } +} + static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) { - int tga_w, tga_h, tga_comp; - int sz; + int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp; + int sz, tga_colormap_type; stbi__get8(s); // discard Offset - sz = stbi__get8(s); // color type - if( sz > 1 ) { + tga_colormap_type = stbi__get8(s); // colormap type + if( tga_colormap_type > 1 ) { stbi__rewind(s); return 0; // only RGB or indexed allowed } - sz = stbi__get8(s); // image type - // only RGB or grey allowed, +/- RLE - if ((sz != 1) && (sz != 2) && (sz != 3) && (sz != 9) && (sz != 10) && (sz != 11)) return 0; - stbi__skip(s,9); + tga_image_type = stbi__get8(s); // image type + if ( tga_colormap_type == 1 ) { // colormapped (paletted) image + if (tga_image_type != 1 && tga_image_type != 9) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip image x and y origin + tga_colormap_bpp = sz; + } else { // "normal" image w/o colormap - only RGB or grey allowed, +/- RLE + if ( (tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11) ) { + stbi__rewind(s); + return 0; // only RGB or grey allowed, +/- RLE + } + stbi__skip(s,9); // skip colormap specification and image x/y origin + tga_colormap_bpp = 0; + } tga_w = stbi__get16le(s); if( tga_w < 1 ) { stbi__rewind(s); @@ -4805,45 +5378,81 @@ static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) stbi__rewind(s); return 0; // test height } - sz = stbi__get8(s); // bits per pixel - // only RGB or RGBA or grey allowed - if ((sz != 8) && (sz != 16) && (sz != 24) && (sz != 32)) { - stbi__rewind(s); - return 0; + tga_bits_per_pixel = stbi__get8(s); // bits per pixel + stbi__get8(s); // ignore alpha bits + if (tga_colormap_bpp != 0) { + if((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) { + // when using a colormap, tga_bits_per_pixel is the size of the indexes + // I don't think anything but 8 or 16bit indexes makes sense + stbi__rewind(s); + return 0; + } + tga_comp = stbi__tga_get_comp(tga_colormap_bpp, 0, NULL); + } else { + tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL); + } + if(!tga_comp) { + stbi__rewind(s); + return 0; } - tga_comp = sz; if (x) *x = tga_w; if (y) *y = tga_h; - if (comp) *comp = tga_comp / 8; + if (comp) *comp = tga_comp; return 1; // seems to have passed everything } static int stbi__tga_test(stbi__context *s) { - int res; - int sz; + int res = 0; + int sz, tga_color_type; stbi__get8(s); // discard Offset - sz = stbi__get8(s); // color type - if ( sz > 1 ) return 0; // only RGB or indexed allowed + tga_color_type = stbi__get8(s); // color type + if ( tga_color_type > 1 ) goto errorEnd; // only RGB or indexed allowed sz = stbi__get8(s); // image type - if ( (sz != 1) && (sz != 2) && (sz != 3) && (sz != 9) && (sz != 10) && (sz != 11) ) return 0; // only RGB or grey allowed, +/- RLE - stbi__get16be(s); // discard palette start - stbi__get16be(s); // discard palette length - stbi__get8(s); // discard bits per palette color entry - stbi__get16be(s); // discard x origin - stbi__get16be(s); // discard y origin - if ( stbi__get16be(s) < 1 ) return 0; // test width - if ( stbi__get16be(s) < 1 ) return 0; // test height + if ( tga_color_type == 1 ) { // colormapped (paletted) image + if (sz != 1 && sz != 9) goto errorEnd; // colortype 1 demands image type 1 or 9 + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + stbi__skip(s,4); // skip image x and y origin + } else { // "normal" image w/o colormap + if ( (sz != 2) && (sz != 3) && (sz != 10) && (sz != 11) ) goto errorEnd; // only RGB or grey allowed, +/- RLE + stbi__skip(s,9); // skip colormap specification and image x/y origin + } + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test width + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test height sz = stbi__get8(s); // bits per pixel - if ( (sz != 8) && (sz != 16) && (sz != 24) && (sz != 32) ) - res = 0; - else - res = 1; + if ( (tga_color_type == 1) && (sz != 8) && (sz != 16) ) goto errorEnd; // for colormapped images, bpp is size of an index + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + + res = 1; // if we got this far, everything's good and we can return 1 instead of 0 + +errorEnd: stbi__rewind(s); return res; } -static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +// read 16bit value and convert to 24bit RGB +static void stbi__tga_read_rgb16(stbi__context *s, stbi_uc* out) +{ + stbi__uint16 px = (stbi__uint16)stbi__get16le(s); + stbi__uint16 fiveBitMask = 31; + // we have 3 channels with 5bits each + int r = (px >> 10) & fiveBitMask; + int g = (px >> 5) & fiveBitMask; + int b = px & fiveBitMask; + // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later + out[0] = (stbi_uc)((r * 255)/31); + out[1] = (stbi_uc)((g * 255)/31); + out[2] = (stbi_uc)((b * 255)/31); + + // some people claim that the most significant bit might be used for alpha + // (possibly if an alpha-bit is set in the "image descriptor byte") + // but that only made 16bit test images completely translucent.. + // so let's treat all 15 and 16bit TGAs as RGB with no alpha. +} + +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { // read in the TGA header stuff int tga_offset = stbi__get8(s); @@ -4858,16 +5467,18 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int int tga_width = stbi__get16le(s); int tga_height = stbi__get16le(s); int tga_bits_per_pixel = stbi__get8(s); - int tga_comp = tga_bits_per_pixel / 8; + int tga_comp, tga_rgb16=0; int tga_inverted = stbi__get8(s); + // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?) // image data unsigned char *tga_data; unsigned char *tga_palette = NULL; int i, j; - unsigned char raw_data[4]; + unsigned char raw_data[4] = {0}; int RLE_count = 0; int RLE_repeating = 0; int read_next_pixel = 1; + STBI_NOTUSED(ri); // do a tiny bit of precessing if ( tga_image_type >= 8 ) @@ -4875,41 +5486,33 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int tga_image_type -= 8; tga_is_RLE = 1; } - /* int tga_alpha_bits = tga_inverted & 15; */ tga_inverted = 1 - ((tga_inverted >> 5) & 1); - // error check - if ( //(tga_indexed) || - (tga_width < 1) || (tga_height < 1) || - (tga_image_type < 1) || (tga_image_type > 3) || - ((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16) && - (tga_bits_per_pixel != 24) && (tga_bits_per_pixel != 32)) - ) - { - return NULL; // we don't report this as a bad TGA because we don't even know if it's TGA - } - // If I'm paletted, then I'll use the number of bits from the palette - if ( tga_indexed ) - { - tga_comp = tga_palette_bits / 8; - } + if ( tga_indexed ) tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16); + else tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16); + + if(!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency + return stbi__errpuc("bad format", "Can't find out TGA pixelformat"); // tga info *x = tga_width; *y = tga_height; if (comp) *comp = tga_comp; - tga_data = (unsigned char*)stbi__malloc( (size_t)tga_width * tga_height * tga_comp ); + if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0)) + return stbi__errpuc("too large", "Corrupt TGA"); + + tga_data = (unsigned char*)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0); if (!tga_data) return stbi__errpuc("outofmem", "Out of memory"); // skip to the data's starting position (offset usually = 0) stbi__skip(s, tga_offset ); - if ( !tga_indexed && !tga_is_RLE) { + if ( !tga_indexed && !tga_is_RLE && !tga_rgb16 ) { for (i=0; i < tga_height; ++i) { - int y = tga_inverted ? tga_height -i - 1 : i; - stbi_uc *tga_row = tga_data + y*tga_width*tga_comp; + int row = tga_inverted ? tga_height -i - 1 : i; + stbi_uc *tga_row = tga_data + row*tga_width*tga_comp; stbi__getn(s, tga_row, tga_width * tga_comp); } } else { @@ -4919,15 +5522,22 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int // any data to skip? (offset usually = 0) stbi__skip(s, tga_palette_start ); // load the palette - tga_palette = (unsigned char*)stbi__malloc( tga_palette_len * tga_palette_bits / 8 ); + tga_palette = (unsigned char*)stbi__malloc_mad2(tga_palette_len, tga_comp, 0); if (!tga_palette) { STBI_FREE(tga_data); return stbi__errpuc("outofmem", "Out of memory"); } - if (!stbi__getn(s, tga_palette, tga_palette_len * tga_palette_bits / 8 )) { - STBI_FREE(tga_data); - STBI_FREE(tga_palette); - return stbi__errpuc("bad palette", "Corrupt TGA"); + if (tga_rgb16) { + stbi_uc *pal_entry = tga_palette; + STBI_ASSERT(tga_comp == STBI_rgb); + for (i=0; i < tga_palette_len; ++i) { + stbi__tga_read_rgb16(s, pal_entry); + pal_entry += tga_comp; + } + } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) { + STBI_FREE(tga_data); + STBI_FREE(tga_palette); + return stbi__errpuc("bad palette", "Corrupt TGA"); } } // load the data @@ -4957,23 +5567,22 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int // load however much data we did have if ( tga_indexed ) { - // read in 1 byte, then perform the lookup - int pal_idx = stbi__get8(s); - if ( pal_idx >= tga_palette_len ) - { - // invalid index + // read in index, then perform the lookup + int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s); + if ( pal_idx >= tga_palette_len ) { + // invalid index pal_idx = 0; } - pal_idx *= tga_bits_per_pixel / 8; - for (j = 0; j*8 < tga_bits_per_pixel; ++j) - { + pal_idx *= tga_comp; + for (j = 0; j < tga_comp; ++j) { raw_data[j] = tga_palette[pal_idx+j]; } - } else - { + } else if(tga_rgb16) { + STBI_ASSERT(tga_comp == STBI_rgb); + stbi__tga_read_rgb16(s, raw_data); + } else { // read in the data raw - for (j = 0; j*8 < tga_bits_per_pixel; ++j) - { + for (j = 0; j < tga_comp; ++j) { raw_data[j] = stbi__get8(s); } } @@ -5012,8 +5621,8 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int } } - // swap RGB - if (tga_comp >= 3) + // swap RGB - if the source data was RGB16, it already is in the right order + if (tga_comp >= 3 && !tga_rgb16) { unsigned char* tga_pixel = tga_data; for (i=0; i < tga_width * tga_height; ++i) @@ -5049,13 +5658,53 @@ static int stbi__psd_test(stbi__context *s) return r; } -static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static int stbi__psd_decode_rle(stbi__context *s, stbi_uc *p, int pixelCount) { - int pixelCount; + int count, nleft, len; + + count = 0; + while ((nleft = pixelCount - count) > 0) { + len = stbi__get8(s); + if (len == 128) { + // No-op. + } else if (len < 128) { + // Copy next len+1 bytes literally. + len++; + if (len > nleft) return 0; // corrupt data + count += len; + while (len) { + *p = stbi__get8(s); + p += 4; + len--; + } + } else if (len > 128) { + stbi_uc val; + // Next -len+1 bytes in the dest are replicated from next source byte. + // (Interpret len as a negative 8-bit int.) + len = 257 - len; + if (len > nleft) return 0; // corrupt data + val = stbi__get8(s); + count += len; + while (len) { + *p = val; + p += 4; + len--; + } + } + } + + return 1; +} + +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + int pixelCount; int channelCount, compression; - int channel, i, count, len; + int channel, i; + int bitdepth; int w,h; stbi_uc *out; + STBI_NOTUSED(ri); // Check identifier if (stbi__get32be(s) != 0x38425053) // "8BPS" @@ -5078,8 +5727,9 @@ static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int w = stbi__get32be(s); // Make sure the depth is 8 bits. - if (stbi__get16be(s) != 8) - return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 bit"); + bitdepth = stbi__get16be(s); + if (bitdepth != 8 && bitdepth != 16) + return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit"); // Make sure the color mode is RGB. // Valid options are: @@ -5111,8 +5761,18 @@ static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int if (compression > 1) return stbi__errpuc("bad compression", "PSD has an unknown compression format"); + // Check size + if (!stbi__mad3sizes_valid(4, w, h, 0)) + return stbi__errpuc("too large", "Corrupt PSD"); + // Create the destination image. - out = (stbi_uc *) stbi__malloc(4 * w*h); + + if (!compression && bitdepth == 16 && bpc == 16) { + out = (stbi_uc *) stbi__malloc_mad3(8, w, h, 0); + ri->bits_per_channel = 16; + } else + out = (stbi_uc *) stbi__malloc(4 * w*h); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); pixelCount = w*h; @@ -5144,61 +5804,86 @@ static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int *p = (channel == 3 ? 255 : 0); } else { // Read the RLE data. - count = 0; - while (count < pixelCount) { - len = stbi__get8(s); - if (len == 128) { - // No-op. - } else if (len < 128) { - // Copy next len+1 bytes literally. - len++; - count += len; - while (len) { - *p = stbi__get8(s); - p += 4; - len--; - } - } else if (len > 128) { - stbi_uc val; - // Next -len+1 bytes in the dest are replicated from next source byte. - // (Interpret len as a negative 8-bit int.) - len ^= 0x0FF; - len += 2; - val = stbi__get8(s); - count += len; - while (len) { - *p = val; - p += 4; - len--; - } - } + if (!stbi__psd_decode_rle(s, p, pixelCount)) { + STBI_FREE(out); + return stbi__errpuc("corrupt", "bad RLE data"); } } } } else { // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) - // where each channel consists of an 8-bit value for each pixel in the image. + // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image. // Read the data by channel. for (channel = 0; channel < 4; channel++) { - stbi_uc *p; - - p = out + channel; - if (channel > channelCount) { + if (channel >= channelCount) { // Fill this channel with default data. - for (i = 0; i < pixelCount; i++, p += 4) - *p = channel == 3 ? 255 : 0; + if (bitdepth == 16 && bpc == 16) { + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + stbi__uint16 val = channel == 3 ? 65535 : 0; + for (i = 0; i < pixelCount; i++, q += 4) + *q = val; + } else { + stbi_uc *p = out+channel; + stbi_uc val = channel == 3 ? 255 : 0; + for (i = 0; i < pixelCount; i++, p += 4) + *p = val; + } } else { - // Read the data. - for (i = 0; i < pixelCount; i++, p += 4) - *p = stbi__get8(s); + if (ri->bits_per_channel == 16) { // output bpc + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + for (i = 0; i < pixelCount; i++, q += 4) + *q = (stbi__uint16) stbi__get16be(s); + } else { + stbi_uc *p = out+channel; + if (bitdepth == 16) { // input bpc + for (i = 0; i < pixelCount; i++, p += 4) + *p = (stbi_uc) (stbi__get16be(s) >> 8); + } else { + for (i = 0; i < pixelCount; i++, p += 4) + *p = stbi__get8(s); + } + } + } + } + } + + // remove weird white matte from PSD + if (channelCount >= 4) { + if (ri->bits_per_channel == 16) { + for (i=0; i < w*h; ++i) { + stbi__uint16 *pixel = (stbi__uint16 *) out + 4*i; + if (pixel[3] != 0 && pixel[3] != 65535) { + float a = pixel[3] / 65535.0f; + float ra = 1.0f / a; + float inv_a = 65535.0f * (1 - ra); + pixel[0] = (stbi__uint16) (pixel[0]*ra + inv_a); + pixel[1] = (stbi__uint16) (pixel[1]*ra + inv_a); + pixel[2] = (stbi__uint16) (pixel[2]*ra + inv_a); + } + } + } else { + for (i=0; i < w*h; ++i) { + unsigned char *pixel = out + 4*i; + if (pixel[3] != 0 && pixel[3] != 255) { + float a = pixel[3] / 255.0f; + float ra = 1.0f / a; + float inv_a = 255.0f * (1 - ra); + pixel[0] = (unsigned char) (pixel[0]*ra + inv_a); + pixel[1] = (unsigned char) (pixel[1]*ra + inv_a); + pixel[2] = (unsigned char) (pixel[2]*ra + inv_a); + } } } } + // convert to desired output format if (req_comp && req_comp != 4) { - out = stbi__convert_format(out, 4, req_comp, w, h); + if (ri->bits_per_channel == 16) + out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, 4, req_comp, w, h); + else + out = stbi__convert_format(out, 4, req_comp, w, h); if (out == NULL) return out; // stbi__convert_format frees input on failure } @@ -5350,7 +6035,6 @@ static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *c if (count >= 128) { // Repeated stbi_uc value[4]; - int i; if (count==128) count = stbi__get16be(s); @@ -5383,10 +6067,13 @@ static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *c return result; } -static stbi_uc *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp) +static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp, stbi__result_info *ri) { stbi_uc *result; - int i, x,y; + int i, x,y, internal_comp; + STBI_NOTUSED(ri); + + if (!comp) comp = &internal_comp; for (i=0; i<92; ++i) stbi__get8(s); @@ -5394,14 +6081,14 @@ static stbi_uc *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int re x = stbi__get16be(s); y = stbi__get16be(s); if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pic header)"); - if ((1 << 28) / x < y) return stbi__errpuc("too large", "Image too large to decode"); + if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode"); stbi__get32be(s); //skip `ratio' stbi__get16be(s); //skip `fields' stbi__get16be(s); //skip `pad' // intermediate buffer is RGBA - result = (stbi_uc *) stbi__malloc(x*y*4); + result = (stbi_uc *) stbi__malloc_mad3(x, y, 4, 0); memset(result, 0xff, x*y*4); if (!stbi__pic_load_core(s,x,y,comp, result)) { @@ -5439,10 +6126,12 @@ typedef struct { int w,h; stbi_uc *out; // output buffer (always 4 components) + stbi_uc *background; // The current "background" as far as a gif is concerned + stbi_uc *history; int flags, bgindex, ratio, transparent, eflags; stbi_uc pal[256][4]; stbi_uc lpal[256][4]; - stbi__gif_lzw codes[4096]; + stbi__gif_lzw codes[8192]; stbi_uc *color_table; int parse, step; int lflags; @@ -5450,6 +6139,7 @@ typedef struct int max_x, max_y; int cur_x, cur_y; int line_size; + int delay; } stbi__gif; static int stbi__gif_test_raw(stbi__context *s) @@ -5510,19 +6200,22 @@ static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_in static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) { - stbi__gif g; - if (!stbi__gif_header(s, &g, comp, 1)) { + stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif)); + if (!stbi__gif_header(s, g, comp, 1)) { + STBI_FREE(g); stbi__rewind( s ); return 0; } - if (x) *x = g.w; - if (y) *y = g.h; + if (x) *x = g->w; + if (y) *y = g->h; + STBI_FREE(g); return 1; } static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) { stbi_uc *p, *c; + int idx; // recurse to decode the prefixes, since the linked-list is backwards, // and working backwards through an interleaved image would be nasty @@ -5531,10 +6224,12 @@ static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) if (g->cur_y >= g->max_y) return; - p = &g->out[g->cur_x + g->cur_y]; - c = &g->color_table[g->codes[code].suffix * 4]; + idx = g->cur_x + g->cur_y; + p = &g->out[idx]; + g->history[idx / 4] = 1; - if (c[3] >= 128) { + c = &g->color_table[g->codes[code].suffix * 4]; + if (c[3] > 128) { // don't render transparent pixels; p[0] = c[2]; p[1] = c[1]; p[2] = c[0]; @@ -5557,7 +6252,7 @@ static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) { stbi_uc lzw_cs; - stbi__int32 len, code; + stbi__int32 len, init_code; stbi__uint32 first; stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; stbi__gif_lzw *p; @@ -5570,10 +6265,10 @@ static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) codemask = (1 << codesize) - 1; bits = 0; valid_bits = 0; - for (code = 0; code < clear; code++) { - g->codes[code].prefix = -1; - g->codes[code].first = (stbi_uc) code; - g->codes[code].suffix = (stbi_uc) code; + for (init_code = 0; init_code < clear; init_code++) { + g->codes[init_code].prefix = -1; + g->codes[init_code].first = (stbi_uc) init_code; + g->codes[init_code].suffix = (stbi_uc) init_code; } // support no starting clear code @@ -5608,11 +6303,16 @@ static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) stbi__skip(s,len); return g->out; } else if (code <= avail) { - if (first) return stbi__errpuc("no clear code", "Corrupt GIF"); + if (first) { + return stbi__errpuc("no clear code", "Corrupt GIF"); + } if (oldcode >= 0) { p = &g->codes[avail++]; - if (avail > 4096) return stbi__errpuc("too many codes", "Corrupt GIF"); + if (avail > 8192) { + return stbi__errpuc("too many codes", "Corrupt GIF"); + } + p->prefix = (stbi__int16) oldcode; p->first = g->codes[oldcode].first; p->suffix = (code == avail) ? p->first : g->codes[code].first; @@ -5634,43 +6334,70 @@ static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) } } -static void stbi__fill_gif_background(stbi__gif *g) -{ - int i; - stbi_uc *c = g->pal[g->bgindex]; - // @OPTIMIZE: write a dword at a time - for (i = 0; i < g->w * g->h * 4; i += 4) { - stbi_uc *p = &g->out[i]; - p[0] = c[2]; - p[1] = c[1]; - p[2] = c[0]; - p[3] = c[3]; - } -} - // this function is designed to support animated gifs, although stb_image doesn't support it -static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp) +// two back is the image from two frames ago, used for a very specific disposal format +static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp, stbi_uc *two_back) { - int i; - stbi_uc *old_out = 0; + int dispose; + int first_frame; + int pi; + int pcount; + // on first frame, any non-written pixels get the background colour (non-transparent) + first_frame = 0; if (g->out == 0) { if (!stbi__gif_header(s, g, comp,0)) return 0; // stbi__g_failure_reason set by stbi__gif_header g->out = (stbi_uc *) stbi__malloc(4 * g->w * g->h); + g->background = (stbi_uc *) stbi__malloc(4 * g->w * g->h); + g->history = (stbi_uc *) stbi__malloc(g->w * g->h); if (g->out == 0) return stbi__errpuc("outofmem", "Out of memory"); - stbi__fill_gif_background(g); + + // image is treated as "tranparent" at the start - ie, nothing overwrites the current background; + // background colour is only used for pixels that are not rendered first frame, after that "background" + // color refers to teh color that was there the previous frame. + memset( g->out, 0x00, 4 * g->w * g->h ); + memset( g->background, 0x00, 4 * g->w * g->h ); // state of the background (starts transparent) + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + first_frame = 1; } else { - // animated-gif-only path - if (((g->eflags & 0x1C) >> 2) == 3) { - old_out = g->out; - g->out = (stbi_uc *) stbi__malloc(4 * g->w * g->h); - if (g->out == 0) return stbi__errpuc("outofmem", "Out of memory"); - memcpy(g->out, old_out, g->w*g->h*4); + // second frame - how do we dispoase of the previous one? + dispose = (g->eflags & 0x1C) >> 2; + pcount = g->w * g->h; + + if ((dispose == 3) && (two_back == 0)) { + dispose = 2; // if I don't have an image to revert back to, default to the old background } + + if (dispose == 3) { // use previous graphic + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &two_back[pi * 4], 4 ); + } + } + } else if (dispose == 2) { + // restore what was changed last frame to background before that frame; + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &g->background[pi * 4], 4 ); + } + } + } else { + // This is a non-disposal case eithe way, so just + // leave the pixels as is, and they will become the new background + // 1: do not dispose + // 0: not specified. + } + + // background is what out is after the undoing of the previou frame; + memcpy( g->background, g->out, 4 * g->w * g->h ); } + // clear my history; + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + for (;;) { - switch (stbi__get8(s)) { + int tag = stbi__get8(s); + switch (tag) { case 0x2C: /* Image Descriptor */ { stbi__int32 x, y, w, h; @@ -5705,38 +6432,60 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); g->color_table = (stbi_uc *) g->lpal; } else if (g->flags & 0x80) { - for (i=0; i < 256; ++i) // @OPTIMIZE: stbi__jpeg_reset only the previous transparent - g->pal[i][3] = 255; - if (g->transparent >= 0 && (g->eflags & 0x01)) - g->pal[g->transparent][3] = 0; g->color_table = (stbi_uc *) g->pal; } else - return stbi__errpuc("missing color table", "Corrupt GIF"); - + return stbi__errpuc("missing color table", "Corrupt GIF"); + o = stbi__process_gif_raster(s, g); if (o == NULL) return NULL; - if (req_comp && req_comp != 4) - o = stbi__convert_format(o, 4, req_comp, g->w, g->h); + // if this was the first frame, + pcount = g->w * g->h; + if (first_frame && (g->bgindex > 0)) { + // if first frame, any pixel not drawn to gets the background color + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi] == 0) { + g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; + memcpy( &g->out[pi * 4], &g->pal[g->bgindex], 4 ); + } + } + } + return o; } case 0x21: // Comment Extension. { int len; - if (stbi__get8(s) == 0xF9) { // Graphic Control Extension. + int ext = stbi__get8(s); + if (ext == 0xF9) { // Graphic Control Extension. len = stbi__get8(s); if (len == 4) { g->eflags = stbi__get8(s); - stbi__get16le(s); // delay - g->transparent = stbi__get8(s); + g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. + + // unset old transparent + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 255; + } + if (g->eflags & 0x01) { + g->transparent = stbi__get8(s); + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 0; + } + } else { + // don't need transparent + stbi__skip(s, 1); + g->transparent = -1; + } } else { stbi__skip(s, len); break; } - } - while ((len = stbi__get8(s)) != 0) + } + while ((len = stbi__get8(s)) != 0) { stbi__skip(s, len); + } break; } @@ -5749,19 +6498,90 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i } } -static stbi_uc *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + if (stbi__gif_test(s)) { + int layers = 0; + stbi_uc *u = 0; + stbi_uc *out = 0; + stbi_uc *two_back = 0; + stbi__gif g; + int stride; + memset(&g, 0, sizeof(g)); + if (delays) { + *delays = 0; + } + + do { + u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + + if (u) { + *x = g.w; + *y = g.h; + ++layers; + stride = g.w * g.h * 4; + + if (out) { + out = (stbi_uc*) STBI_REALLOC( out, layers * stride ); + if (delays) { + *delays = (int*) STBI_REALLOC( *delays, sizeof(int) * layers ); + } + } else { + out = (stbi_uc*)stbi__malloc( layers * stride ); + if (delays) { + *delays = (int*) stbi__malloc( layers * sizeof(int) ); + } + } + memcpy( out + ((layers - 1) * stride), u, stride ); + if (layers >= 2) { + two_back = out - 2 * stride; + } + + if (delays) { + (*delays)[layers - 1U] = g.delay; + } + } + } while (u != 0); + + // free temp buffer; + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); + + // do the final conversion after loading everything; + if (req_comp && req_comp != 4) + out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + + *z = layers; + return out; + } else { + return stbi__errpuc("not GIF", "Image was not as a gif type."); + } +} + +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { stbi_uc *u = 0; stbi__gif g; memset(&g, 0, sizeof(g)); - u = stbi__gif_load_next(s, &g, comp, req_comp); + u = stbi__gif_load_next(s, &g, comp, req_comp, 0); if (u == (stbi_uc *) s) u = 0; // end of animated gif marker if (u) { *x = g.w; *y = g.h; + + // moved conversion to after successful load so that the same + // can be done for multiple frames. + if (req_comp && req_comp != 4) + u = stbi__convert_format(u, 4, req_comp, g.w, g.h); } + // free buffers needed for multiple frame loading; + STBI_FREE(g.history); + STBI_FREE(g.background); + return u; } @@ -5775,20 +6595,24 @@ static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp) // Radiance RGBE HDR loader // originally by Nicolas Schulz #ifndef STBI_NO_HDR -static int stbi__hdr_test_core(stbi__context *s) +static int stbi__hdr_test_core(stbi__context *s, const char *signature) { - const char *signature = "#?RADIANCE\n"; int i; for (i=0; signature[i]; ++i) if (stbi__get8(s) != signature[i]) - return 0; + return 0; + stbi__rewind(s); return 1; } static int stbi__hdr_test(stbi__context* s) { - int r = stbi__hdr_test_core(s); + int r = stbi__hdr_test_core(s, "#?RADIANCE\n"); stbi__rewind(s); + if(!r) { + r = stbi__hdr_test_core(s, "#?RGBE\n"); + stbi__rewind(s); + } return r; } @@ -5842,7 +6666,7 @@ static void stbi__hdr_convert(float *output, stbi_uc *input, int req_comp) } } -static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { char buffer[STBI__HDR_BUFLEN]; char *token; @@ -5853,10 +6677,12 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re int len; unsigned char count, value; int i, j, k, c1,c2, z; - + const char *headerToken; + STBI_NOTUSED(ri); // Check identifier - if (strcmp(stbi__hdr_gettoken(s,buffer), "#?RADIANCE") != 0) + headerToken = stbi__hdr_gettoken(s,buffer); + if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0) return stbi__errpf("not HDR", "Corrupt HDR image"); // Parse header @@ -5885,8 +6711,13 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re if (comp) *comp = 3; if (req_comp == 0) req_comp = 3; + if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0)) + return stbi__errpf("too large", "HDR image is too large"); + // Read data - hdr_data = (float *) stbi__malloc(height * width * req_comp * sizeof(float)); + hdr_data = (float *) stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0); + if (!hdr_data) + return stbi__errpf("outofmem", "Out of memory"); // Load image data // image data is stored as some number of sca @@ -5925,20 +6756,29 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re len <<= 8; len |= stbi__get8(s); if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); } - if (scanline == NULL) scanline = (stbi_uc *) stbi__malloc(width * 4); + if (scanline == NULL) { + scanline = (stbi_uc *) stbi__malloc_mad2(width, 4, 0); + if (!scanline) { + STBI_FREE(hdr_data); + return stbi__errpf("outofmem", "Out of memory"); + } + } for (k = 0; k < 4; ++k) { + int nleft; i = 0; - while (i < width) { + while ((nleft = width - i) > 0) { count = stbi__get8(s); if (count > 128) { // Run value = stbi__get8(s); count -= 128; + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } for (z = 0; z < count; ++z) scanline[i++ * 4 + k] = value; } else { // Dump + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } for (z = 0; z < count; ++z) scanline[i++ * 4 + k] = stbi__get8(s); } @@ -5947,7 +6787,8 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re for (i=0; i < width; ++i) stbi__hdr_convert(hdr_data+(j*width + i)*req_comp, scanline + i*4, req_comp); } - STBI_FREE(scanline); + if (scanline) + STBI_FREE(scanline); } return hdr_data; @@ -5958,8 +6799,13 @@ static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) char buffer[STBI__HDR_BUFLEN]; char *token; int valid = 0; + int dummy; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; - if (strcmp(stbi__hdr_gettoken(s,buffer), "#?RADIANCE") != 0) { + if (stbi__hdr_test(s) == 0) { stbi__rewind( s ); return 0; } @@ -5996,29 +6842,17 @@ static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) #ifndef STBI_NO_BMP static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) { - int hsz; - if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') { - stbi__rewind( s ); - return 0; - } - stbi__skip(s,12); - hsz = stbi__get32le(s); - if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) { - stbi__rewind( s ); - return 0; - } - if (hsz == 12) { - *x = stbi__get16le(s); - *y = stbi__get16le(s); - } else { - *x = stbi__get32le(s); - *y = stbi__get32le(s); - } - if (stbi__get16le(s) != 1) { - stbi__rewind( s ); - return 0; - } - *comp = stbi__get16le(s) / 8; + void *p; + stbi__bmp_data info; + + info.all_a = 255; + p = stbi__bmp_parse_header(s, &info); + stbi__rewind( s ); + if (p == NULL) + return 0; + if (x) *x = s->img_x; + if (y) *y = s->img_y; + if (comp) *comp = info.ma ? 4 : 3; return 1; } #endif @@ -6026,7 +6860,10 @@ static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) #ifndef STBI_NO_PSD static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) { - int channelCount; + int channelCount, dummy, depth; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; if (stbi__get32be(s) != 0x38425053) { stbi__rewind( s ); return 0; @@ -6043,7 +6880,8 @@ static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) } *y = stbi__get32be(s); *x = stbi__get32be(s); - if (stbi__get16be(s) != 8) { + depth = stbi__get16be(s); + if (depth != 8 && depth != 16) { stbi__rewind( s ); return 0; } @@ -6054,22 +6892,61 @@ static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) *comp = 4; return 1; } + +static int stbi__psd_is16(stbi__context *s) +{ + int channelCount, depth; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + (void) stbi__get32be(s); + (void) stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 16) { + stbi__rewind( s ); + return 0; + } + return 1; +} #endif #ifndef STBI_NO_PIC static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) { - int act_comp=0,num_packets=0,chained; + int act_comp=0,num_packets=0,chained,dummy; stbi__pic_packet packets[10]; - stbi__skip(s, 92); + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) { + stbi__rewind(s); + return 0; + } + + stbi__skip(s, 88); *x = stbi__get16be(s); *y = stbi__get16be(s); - if (stbi__at_eof(s)) return 0; + if (stbi__at_eof(s)) { + stbi__rewind( s); + return 0; + } if ( (*x) != 0 && (1 << 28) / (*x) < (*y)) { - stbi__rewind( s ); - return 0; + stbi__rewind( s ); + return 0; } stbi__skip(s, 8); @@ -6129,16 +7006,22 @@ static int stbi__pnm_test(stbi__context *s) return 1; } -static stbi_uc *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { stbi_uc *out; + STBI_NOTUSED(ri); + if (!stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n)) return 0; + *x = s->img_x; *y = s->img_y; - *comp = s->img_n; + if (comp) *comp = s->img_n; + + if (!stbi__mad3sizes_valid(s->img_n, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "PNM too large"); - out = (stbi_uc *) stbi__malloc(s->img_n * s->img_x * s->img_y); + out = (stbi_uc *) stbi__malloc_mad3(s->img_n, s->img_x, s->img_y, 0); if (!out) return stbi__errpuc("outofmem", "Out of memory"); stbi__getn(s, out, s->img_n * s->img_x * s->img_y); @@ -6156,8 +7039,16 @@ static int stbi__pnm_isspace(char c) static void stbi__pnm_skip_whitespace(stbi__context *s, char *c) { - while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) - *c = (char) stbi__get8(s); + for (;;) { + while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) + *c = (char) stbi__get8(s); + + if (stbi__at_eof(s) || *c != '#') + break; + + while (!stbi__at_eof(s) && *c != '\n' && *c != '\r' ) + *c = (char) stbi__get8(s); + } } static int stbi__pnm_isdigit(char c) @@ -6179,16 +7070,20 @@ static int stbi__pnm_getinteger(stbi__context *s, char *c) static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp) { - int maxv; + int maxv, dummy; char c, p, t; - stbi__rewind( s ); + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + stbi__rewind(s); // Get identifier p = (char) stbi__get8(s); t = (char) stbi__get8(s); if (p != 'P' || (t != '5' && t != '6')) { - stbi__rewind( s ); + stbi__rewind(s); return 0; } @@ -6254,6 +7149,19 @@ static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp) return stbi__err("unknown image type", "Image not of any known type, or corrupt"); } +static int stbi__is_16_main(stbi__context *s) +{ + #ifndef STBI_NO_PNG + if (stbi__png_is16(s)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_is16(s)) return 1; + #endif + + return 0; +} + #ifndef STBI_NO_STDIO STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) { @@ -6275,6 +7183,27 @@ STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp) fseek(f,pos,SEEK_SET); return r; } + +STBIDEF int stbi_is_16_bit(char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_is_16_bit_from_file(f); + fclose(f); + return result; +} + +STBIDEF int stbi_is_16_bit_from_file(FILE *f) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__is_16_main(&s); + fseek(f,pos,SEEK_SET); + return r; +} #endif // !STBI_NO_STDIO STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp) @@ -6291,10 +7220,63 @@ STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int return stbi__info_main(&s,x,y,comp); } +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__is_16_main(&s); +} + +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *c, void *user) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__is_16_main(&s); +} + #endif // STB_IMAGE_IMPLEMENTATION /* revision history: + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug + 1-bit BMP + *_is_16_bit api + avoid warnings + 2.16 (2017-07-23) all functions have 16-bit variants; + STBI_NO_STDIO works again; + compilation fixes; + fix rounding in unpremultiply; + optimize vertical flip; + disable raw_len validation; + documentation fixes + 2.15 (2017-03-18) fix png-1,2,4 bug; now all Imagenet JPGs decode; + warning fixes; disable run-time SSE detection on gcc; + uniform handling of optional "return" values; + thread-safe initialization of zlib tables + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-11-29) add 16-bit API, only supported for PNG right now + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) allocate large structures on the stack + remove white matting for transparent PSD + fix reported channel count for PNG & BMP + re-enable SSE2 in non-gcc 64-bit + support RGB-formatted JPEG + read 16-bit PNGs (only as 8-bit) + 2.10 (2016-01-22) avoid warning introduced in 2.09 by STBI_REALLOC_SIZED + 2.09 (2016-01-16) allow comments in PNM files + 16-bit-per-pixel TGA (not bit-per-component) + info() for TGA could break due to .hdr handling + info() for BMP to shares code instead of sloppy parse + can use STBI_REALLOC_SIZED if allocator doesn't support realloc + code cleanup + 2.08 (2015-09-13) fix to 2.07 cleanup, reading RGB PSD as RGBA + 2.07 (2015-09-13) fix compiler warnings + partial animated GIF support + limited 16-bpc PSD support + #ifdef unused functions + bug with < 92 byte PIC,PNM,HDR,TGA 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit @@ -6435,3 +7417,46 @@ STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int 0.50 (2006-11-19) first released version */ + + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/image.darknet/inst/include/darknet/src/stb_image_write.h b/image.darknet/inst/include/darknet/src/stb_image_write.h index f5250b3..c05e958 100644 --- a/image.darknet/inst/include/darknet/src/stb_image_write.h +++ b/image.darknet/inst/include/darknet/src/stb_image_write.h @@ -1,7 +1,6 @@ -/* stb_image_write - v0.98 - public domain - http://nothings.org/stb/stb_image_write.h - writes out PNG/BMP/TGA images to C stdio - Sean Barrett 2010 - no warranty implied; use at your own risk - +/* stb_image_write - v1.09 - public domain - http://nothings.org/stb/stb_image_write.h + writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015 + no warranty implied; use at your own risk Before #including, @@ -11,31 +10,67 @@ Will probably not work correctly with strict-aliasing optimizations. + If using a modern Microsoft Compiler, non-safe versions of CRT calls may cause + compilation warnings or even errors. To avoid this, also before #including, + + #define STBI_MSC_SECURE_CRT + ABOUT: This header file is a library for writing images to C stdio. It could be adapted to write to memory or a general streaming interface; let me know. The PNG output is not optimal; it is 20-50% larger than the file - written by a decent optimizing implementation. This library is designed - for source code compactness and simplicitly, not optimal image file size - or run-time performance. + written by a decent optimizing implementation; though providing a custom + zlib compress function (see STBIW_ZLIB_COMPRESS) can mitigate that. + This library is designed for source code compactness and simplicity, + not optimal image file size or run-time performance. BUILDING: You can #define STBIW_ASSERT(x) before the #include to avoid using assert.h. You can #define STBIW_MALLOC(), STBIW_REALLOC(), and STBIW_FREE() to replace malloc,realloc,free. - You can define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_ZLIB_COMPRESS to use a custom zlib-style compress function + for PNG compression (instead of the builtin one), it must have the following signature: + unsigned char * my_compress(unsigned char *data, int data_len, int *out_len, int quality); + The returned data will be freed with STBIW_FREE() (free() by default), + so it must be heap allocated with STBIW_MALLOC() (malloc() by default), USAGE: - There are four functions, one for each image file format: + There are five functions, one for each image file format: int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); - int stbi_write_hdr(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_jpg(char const *filename, int w, int h, int comp, const void *data, int quality); + int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); + + void stbi_flip_vertically_on_write(int flag); // flag is non-zero to flip data vertically + + There are also five equivalent functions that use an arbitrary write function. You are + expected to open/close your file-equivalent before and after calling these: + + int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); + int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + + where the callback is: + void stbi_write_func(void *context, void *data, int size); + + You can configure it with these global variables: + int stbi_write_tga_with_rle; // defaults to true; set to 0 to disable RLE + int stbi_write_png_compression_level; // defaults to 8; set to higher for more compression + int stbi_write_force_png_filter; // defaults to -1; set to 0..5 to force a filter mode + + + You can define STBI_WRITE_NO_STDIO to disable the file variant of these + functions, so the library will not use stdio.h at all. However, this will + also disable HDR writing, because it requires stdio for formatted output. Each function returns 0 on failure and non-0 on success. @@ -59,63 +94,138 @@ writer, both because it is in BGR order and because it may have padding at the end of the line.) + PNG allows you to set the deflate compression level by setting the global + variable 'stbi_write_png_compression_level' (it defaults to 8). + HDR expects linear float data. Since the format is always 32-bit rgb(e) data, alpha (if provided) is discarded, and for monochrome data it is replicated across all three channels. + TGA supports RLE or non-RLE compressed data. To use non-RLE-compressed + data, set the global variable 'stbi_write_tga_with_rle' to 0. + + JPEG does ignore alpha channels in input data; quality is between 1 and 100. + Higher quality looks better but results in a bigger image. + JPEG baseline (no JPEG progressive). + CREDITS: - PNG/BMP/TGA - Sean Barrett - HDR - Baldur Karlsson - TGA monochrome: - Jean-Sebastien Guay - misc enhancements: - Tim Kelsey + + Sean Barrett - PNG/BMP/TGA + Baldur Karlsson - HDR + Jean-Sebastien Guay - TGA monochrome + Tim Kelsey - misc enhancements + Alan Hickman - TGA RLE + Emmanuel Julien - initial file IO callback implementation + Jon Olick - original jo_jpeg.cpp code + Daniel Gibson - integrate JPEG, allow external zlib + Aarni Koskela - allow choosing PNG filter + bugfixes: github:Chribba + Guillaume Chereau + github:jry2 + github:romigrou + Sergio Gonzalez + Jonas Karlsson + Filip Wasil + Thatcher Ulrich + github:poppolopoppo + Patrick Boettcher + github:xeekworx + Cap Petschulat + Simon Rodriguez + Ivan Tikhonov + github:ignotion + Adam Schackart + +LICENSE + + See end of file for license information. + */ #ifndef INCLUDE_STB_IMAGE_WRITE_H #define INCLUDE_STB_IMAGE_WRITE_H +// if STB_IMAGE_WRITE_STATIC causes problems, try defining STBIWDEF to 'inline' or 'static inline' +#ifndef STBIWDEF +#ifdef STB_IMAGE_WRITE_STATIC +#define STBIWDEF static +#else #ifdef __cplusplus -extern "C" { +#define STBIWDEF extern "C" +#else +#define STBIWDEF extern +#endif +#endif #endif -extern int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); -extern int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); -extern int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); -extern int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); +#ifndef STB_IMAGE_WRITE_STATIC // C++ forbids static forward declarations +extern int stbi_write_tga_with_rle; +extern int stbi_write_png_compression_level; +extern int stbi_write_force_png_filter; +#endif -#ifdef __cplusplus -} +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); #endif +typedef void stbi_write_func(void *context, void *data, int size); + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + +STBIWDEF void stbi_flip_vertically_on_write(int flip_boolean); + #endif//INCLUDE_STB_IMAGE_WRITE_H #ifdef STB_IMAGE_WRITE_IMPLEMENTATION +#ifdef _WIN32 + #ifndef _CRT_SECURE_NO_WARNINGS + #define _CRT_SECURE_NO_WARNINGS + #endif + #ifndef _CRT_NONSTDC_NO_DEPRECATE + #define _CRT_NONSTDC_NO_DEPRECATE + #endif +#endif + +#ifndef STBI_WRITE_NO_STDIO +#include +#endif // STBI_WRITE_NO_STDIO + #include #include -#include #include #include -#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && defined(STBIW_REALLOC) +#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && (defined(STBIW_REALLOC) || defined(STBIW_REALLOC_SIZED)) // ok -#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) +#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) && !defined(STBIW_REALLOC_SIZED) // ok #else -#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC." +#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC (or STBIW_REALLOC_SIZED)." #endif #ifndef STBIW_MALLOC -#define STBIW_MALLOC(sz) malloc(sz) -#define STBIW_REALLOC(p,sz) realloc(p,sz) -#define STBIW_FREE(p) free(p) +#define STBIW_MALLOC(sz) malloc(sz) +#define STBIW_REALLOC(p,newsz) realloc(p,newsz) +#define STBIW_FREE(p) free(p) +#endif + +#ifndef STBIW_REALLOC_SIZED +#define STBIW_REALLOC_SIZED(p,oldsz,newsz) STBIW_REALLOC(p,newsz) #endif + + #ifndef STBIW_MEMMOVE #define STBIW_MEMMOVE(a,b,sz) memmove(a,b,sz) #endif @@ -126,22 +236,90 @@ extern int stbi_write_hdr(char const *filename, int w, int h, int comp, const fl #define STBIW_ASSERT(x) assert(x) #endif +#define STBIW_UCHAR(x) (unsigned char) ((x) & 0xff) + +#ifdef STB_IMAGE_WRITE_STATIC +static int stbi__flip_vertically_on_write=0; +static int stbi_write_png_compression_level = 8; +static int stbi_write_tga_with_rle = 1; +static int stbi_write_force_png_filter = -1; +#else +int stbi_write_png_compression_level = 8; +int stbi__flip_vertically_on_write=0; +int stbi_write_tga_with_rle = 1; +int stbi_write_force_png_filter = -1; +#endif + +STBIWDEF void stbi_flip_vertically_on_write(int flag) +{ + stbi__flip_vertically_on_write = flag; +} + +typedef struct +{ + stbi_write_func *func; + void *context; +} stbi__write_context; + +// initialize a callback-based context +static void stbi__start_write_callbacks(stbi__write_context *s, stbi_write_func *c, void *context) +{ + s->func = c; + s->context = context; +} + +#ifndef STBI_WRITE_NO_STDIO + +static void stbi__stdio_write(void *context, void *data, int size) +{ + fwrite(data,1,size,(FILE*) context); +} + +static int stbi__start_write_file(stbi__write_context *s, const char *filename) +{ + FILE *f; +#ifdef STBI_MSC_SECURE_CRT + if (fopen_s(&f, filename, "wb")) + f = NULL; +#else + f = fopen(filename, "wb"); +#endif + stbi__start_write_callbacks(s, stbi__stdio_write, (void *) f); + return f != NULL; +} + +static void stbi__end_write_file(stbi__write_context *s) +{ + fclose((FILE *)s->context); +} + +#endif // !STBI_WRITE_NO_STDIO + typedef unsigned int stbiw_uint32; typedef int stb_image_write_test[sizeof(stbiw_uint32)==4 ? 1 : -1]; -static void writefv(FILE *f, const char *fmt, va_list v) +static void stbiw__writefv(stbi__write_context *s, const char *fmt, va_list v) { while (*fmt) { switch (*fmt++) { case ' ': break; - case '1': { unsigned char x = (unsigned char) va_arg(v, int); fputc(x,f); break; } - case '2': { int x = va_arg(v,int); unsigned char b[2]; - b[0] = (unsigned char) x; b[1] = (unsigned char) (x>>8); - fwrite(b,2,1,f); break; } - case '4': { stbiw_uint32 x = va_arg(v,int); unsigned char b[4]; - b[0]=(unsigned char)x; b[1]=(unsigned char)(x>>8); - b[2]=(unsigned char)(x>>16); b[3]=(unsigned char)(x>>24); - fwrite(b,4,1,f); break; } + case '1': { unsigned char x = STBIW_UCHAR(va_arg(v, int)); + s->func(s->context,&x,1); + break; } + case '2': { int x = va_arg(v,int); + unsigned char b[2]; + b[0] = STBIW_UCHAR(x); + b[1] = STBIW_UCHAR(x>>8); + s->func(s->context,b,2); + break; } + case '4': { stbiw_uint32 x = va_arg(v,int); + unsigned char b[4]; + b[0]=STBIW_UCHAR(x); + b[1]=STBIW_UCHAR(x>>8); + b[2]=STBIW_UCHAR(x>>16); + b[3]=STBIW_UCHAR(x>>24); + s->func(s->context,b,4); + break; } default: STBIW_ASSERT(0); return; @@ -149,22 +327,70 @@ static void writefv(FILE *f, const char *fmt, va_list v) } } -static void write3(FILE *f, unsigned char a, unsigned char b, unsigned char c) +static void stbiw__writef(stbi__write_context *s, const char *fmt, ...) +{ + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); +} + +static void stbiw__putc(stbi__write_context *s, unsigned char c) +{ + s->func(s->context, &c, 1); +} + +static void stbiw__write3(stbi__write_context *s, unsigned char a, unsigned char b, unsigned char c) { unsigned char arr[3]; arr[0] = a, arr[1] = b, arr[2] = c; - fwrite(arr, 3, 1, f); + s->func(s->context, arr, 3); } -static void write_pixels(FILE *f, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) +static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, int write_alpha, int expand_mono, unsigned char *d) { unsigned char bg[3] = { 255, 0, 255}, px[3]; + int k; + + if (write_alpha < 0) + s->func(s->context, &d[comp - 1], 1); + + switch (comp) { + case 2: // 2 pixels = mono + alpha, alpha is written separately, so same as 1-channel case + case 1: + if (expand_mono) + stbiw__write3(s, d[0], d[0], d[0]); // monochrome bmp + else + s->func(s->context, d, 1); // monochrome TGA + break; + case 4: + if (!write_alpha) { + // composite against pink background + for (k = 0; k < 3; ++k) + px[k] = bg[k] + ((d[k] - bg[k]) * d[3]) / 255; + stbiw__write3(s, px[1 - rgb_dir], px[1], px[1 + rgb_dir]); + break; + } + /* FALLTHROUGH */ + case 3: + stbiw__write3(s, d[1 - rgb_dir], d[1], d[1 + rgb_dir]); + break; + } + if (write_alpha > 0) + s->func(s->context, &d[comp - 1], 1); +} + +static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) +{ stbiw_uint32 zero = 0; - int i,j,k, j_end; + int i,j, j_end; if (y <= 0) return; + if (stbi__flip_vertically_on_write) + vdir *= -1; + if (vdir < 0) j_end = -1, j = y-1; else @@ -173,73 +399,157 @@ static void write_pixels(FILE *f, int rgb_dir, int vdir, int x, int y, int comp, for (; j != j_end; j += vdir) { for (i=0; i < x; ++i) { unsigned char *d = (unsigned char *) data + (j*x+i)*comp; - if (write_alpha < 0) - fwrite(&d[comp-1], 1, 1, f); - switch (comp) { - case 1: fwrite(d, 1, 1, f); - break; - case 2: if (expand_mono) - write3(f, d[0],d[0],d[0]); // monochrome bmp - else - fwrite(d, 1, 1, f); // monochrome TGA - break; - case 4: - if (!write_alpha) { - // composite against pink background - for (k=0; k < 3; ++k) - px[k] = bg[k] + ((d[k] - bg[k]) * d[3])/255; - write3(f, px[1-rgb_dir],px[1],px[1+rgb_dir]); - break; - } - /* FALLTHROUGH */ - case 3: - write3(f, d[1-rgb_dir],d[1],d[1+rgb_dir]); - break; - } - if (write_alpha > 0) - fwrite(&d[comp-1], 1, 1, f); + stbiw__write_pixel(s, rgb_dir, comp, write_alpha, expand_mono, d); } - fwrite(&zero,scanline_pad,1,f); + s->func(s->context, &zero, scanline_pad); } } -static int outfile(char const *filename, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...) +static int stbiw__outfile(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...) { - FILE *f; - if (y < 0 || x < 0) return 0; - f = fopen(filename, "wb"); - if (f) { + if (y < 0 || x < 0) { + return 0; + } else { va_list v; va_start(v, fmt); - writefv(f, fmt, v); + stbiw__writefv(s, fmt, v); va_end(v); - write_pixels(f,rgb_dir,vdir,x,y,comp,data,alpha,pad,expand_mono); - fclose(f); + stbiw__write_pixels(s,rgb_dir,vdir,x,y,comp,data,alpha,pad, expand_mono); + return 1; } - return f != NULL; } -int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) +static int stbi_write_bmp_core(stbi__write_context *s, int x, int y, int comp, const void *data) { int pad = (-x*3) & 3; - return outfile(filename,-1,-1,x,y,comp,1,(void *) data,0,pad, + return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *) data,0,pad, "11 4 22 4" "4 44 22 444444", 'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40, // file header 40, x,y, 1,24, 0,0,0,0,0,0); // bitmap header } -int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_bmp_core(&s, x, y, comp, data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_bmp_core(&s, x, y, comp, data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif //!STBI_WRITE_NO_STDIO + +static int stbi_write_tga_core(stbi__write_context *s, int x, int y, int comp, void *data) { int has_alpha = (comp == 2 || comp == 4); int colorbytes = has_alpha ? comp-1 : comp; int format = colorbytes < 2 ? 3 : 2; // 3 color channels (RGB/RGBA) = 2, 1 color channel (Y/YA) = 3 - return outfile(filename, -1,-1, x, y, comp, 0, (void *) data, has_alpha, 0, - "111 221 2222 11", 0,0,format, 0,0,0, 0,0,x,y, (colorbytes+has_alpha)*8, has_alpha*8); + + if (y < 0 || x < 0) + return 0; + + if (!stbi_write_tga_with_rle) { + return stbiw__outfile(s, -1, -1, x, y, comp, 0, (void *) data, has_alpha, 0, + "111 221 2222 11", 0, 0, format, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8); + } else { + int i,j,k; + int jend, jdir; + + stbiw__writef(s, "111 221 2222 11", 0,0,format+8, 0,0,0, 0,0,x,y, (colorbytes + has_alpha) * 8, has_alpha * 8); + + if (stbi__flip_vertically_on_write) { + j = 0; + jend = y; + jdir = 1; + } else { + j = y-1; + jend = -1; + jdir = -1; + } + for (; j != jend; j += jdir) { + unsigned char *row = (unsigned char *) data + j * x * comp; + int len; + + for (i = 0; i < x; i += len) { + unsigned char *begin = row + i * comp; + int diff = 1; + len = 1; + + if (i < x - 1) { + ++len; + diff = memcmp(begin, row + (i + 1) * comp, comp); + if (diff) { + const unsigned char *prev = begin; + for (k = i + 2; k < x && len < 128; ++k) { + if (memcmp(prev, row + k * comp, comp)) { + prev += comp; + ++len; + } else { + --len; + break; + } + } + } else { + for (k = i + 2; k < x && len < 128; ++k) { + if (!memcmp(begin, row + k * comp, comp)) { + ++len; + } else { + break; + } + } + } + } + + if (diff) { + unsigned char header = STBIW_UCHAR(len - 1); + s->func(s->context, &header, 1); + for (k = 0; k < len; ++k) { + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin + k * comp); + } + } else { + unsigned char header = STBIW_UCHAR(len - 129); + s->func(s->context, &header, 1); + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin); + } + } + } + } + return 1; +} + +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_tga_core(&s, x, y, comp, (void *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_tga_core(&s, x, y, comp, (void *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; } +#endif // ************************************************************************************************* // Radiance RGBE HDR writer // by Baldur Karlsson + #define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) @@ -247,7 +557,7 @@ void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) int exponent; float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2])); - if (maxcomp < 1e-32) { + if (maxcomp < 1e-32f) { rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0; } else { float normalize = (float) frexp(maxcomp, &exponent) * 256.0f/maxcomp; @@ -259,27 +569,27 @@ void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) } } -void stbiw__write_run_data(FILE *f, int length, unsigned char databyte) +void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char databyte) { - unsigned char lengthbyte = (unsigned char) (length+128); + unsigned char lengthbyte = STBIW_UCHAR(length+128); STBIW_ASSERT(length+128 <= 255); - fwrite(&lengthbyte, 1, 1, f); - fwrite(&databyte, 1, 1, f); + s->func(s->context, &lengthbyte, 1); + s->func(s->context, &databyte, 1); } -void stbiw__write_dump_data(FILE *f, int length, unsigned char *data) +void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *data) { - unsigned char lengthbyte = (unsigned char )(length & 0xff); + unsigned char lengthbyte = STBIW_UCHAR(length); STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code - fwrite(&lengthbyte, 1, 1, f); - fwrite(data, length, 1, f); + s->func(s->context, &lengthbyte, 1); + s->func(s->context, data, length); } -void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scratch, const float *scanline) +void stbiw__write_hdr_scanline(stbi__write_context *s, int width, int ncomp, unsigned char *scratch, float *scanline) { unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; unsigned char rgbe[4]; - float linear[3] = {0}; + float linear[3]; int x; scanlineheader[2] = (width&0xff00)>>8; @@ -288,31 +598,31 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra /* skip RLE for images too small or large */ if (width < 8 || width >= 32768) { for (x=0; x < width; x++) { - switch (comp) { + switch (ncomp) { case 4: /* fallthrough */ - case 3: linear[2] = scanline[x*comp + 2]; - linear[1] = scanline[x*comp + 1]; - linear[0] = scanline[x*comp + 0]; + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; break; - case 2: /* fallthrough */ - case 1: linear[0] = linear[1] = linear[2] = scanline[x*comp + 0]; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; break; } stbiw__linear_to_rgbe(rgbe, linear); - fwrite(rgbe, 4, 1, f); + s->func(s->context, rgbe, 4); } } else { int c,r; /* encode into scratch buffer */ for (x=0; x < width; x++) { - switch(comp) { + switch(ncomp) { case 4: /* fallthrough */ - case 3: linear[2] = scanline[x*comp + 2]; - linear[1] = scanline[x*comp + 1]; - linear[0] = scanline[x*comp + 0]; + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; break; - case 2: /* fallthrough */ - case 1: linear[0] = linear[1] = linear[2] = scanline[x*comp + 0]; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; break; } stbiw__linear_to_rgbe(rgbe, linear); @@ -322,7 +632,7 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra scratch[x + width*3] = rgbe[3]; } - fwrite(scanlineheader, 4, 1, f); + s->func(s->context, scanlineheader, 4); /* RLE each component separately */ for (c=0; c < 4; c++) { @@ -343,7 +653,7 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra while (x < r) { int len = r-x; if (len > 128) len = 128; - stbiw__write_dump_data(f, len, &comp[x]); + stbiw__write_dump_data(s, len, &comp[x]); x += len; } // if there's a run, output it @@ -355,7 +665,7 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra while (x < r) { int len = r-x; if (len > 127) len = 127; - stbiw__write_run_data(f, len, comp[x]); + stbiw__write_run_data(s, len, comp[x]); x += len; } } @@ -364,28 +674,59 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra } } -int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) +static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, float *data) { - int i; - FILE *f; - if (y <= 0 || x <= 0 || data == NULL) return 0; - f = fopen(filename, "wb"); - if (f) { - /* Each component is stored separately. Allocate scratch space for full output scanline. */ + if (y <= 0 || x <= 0 || data == NULL) + return 0; + else { + // Each component is stored separately. Allocate scratch space for full output scanline. unsigned char *scratch = (unsigned char *) STBIW_MALLOC(x*4); - fprintf(f, "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n" ); - fprintf(f, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n" , y, x); + int i, len; + char buffer[128]; + char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n"; + s->func(s->context, header, sizeof(header)-1); + +#ifdef STBI_MSC_SECURE_CRT + len = sprintf_s(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#else + len = sprintf(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#endif + s->func(s->context, buffer, len); + for(i=0; i < y; i++) - stbiw__write_hdr_scanline(f, x, comp, scratch, data + comp*i*x); + stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp*x*(stbi__flip_vertically_on_write ? y-1-i : i)*x); STBIW_FREE(scratch); - fclose(f); + return 1; } - return f != NULL; } -///////////////////////////////////////////////////////// -// PNG +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_hdr_core(&s, x, y, comp, (float *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_hdr_core(&s, x, y, comp, (float *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif // STBI_WRITE_NO_STDIO + +////////////////////////////////////////////////////////////////////////////// +// +// PNG writer +// + +#ifndef STBIW_ZLIB_COMPRESS // stretchy buffer; stbiw__sbpush() == vector<>::push_back() -- stbiw__sbcount() == vector<>::size() #define stbiw__sbraw(a) ((int *) (a) - 2) #define stbiw__sbm(a) stbiw__sbraw(a)[0] @@ -402,7 +743,7 @@ int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *da static void *stbiw__sbgrowf(void **arr, int increment, int itemsize) { int m = *arr ? 2*stbiw__sbm(*arr)+increment : increment+1; - void *p = STBIW_REALLOC(*arr ? stbiw__sbraw(*arr) : 0, itemsize * m + sizeof(int)*2); + void *p = STBIW_REALLOC_SIZED(*arr ? stbiw__sbraw(*arr) : 0, *arr ? (stbiw__sbm(*arr)*itemsize + sizeof(int)*2) : 0, itemsize * m + sizeof(int)*2); STBIW_ASSERT(p); if (p) { if (!*arr) ((int *) p)[1] = 0; @@ -415,7 +756,7 @@ static void *stbiw__sbgrowf(void **arr, int increment, int itemsize) static unsigned char *stbiw__zlib_flushf(unsigned char *data, unsigned int *bitbuffer, int *bitcount) { while (*bitcount >= 8) { - stbiw__sbpush(data, (unsigned char) *bitbuffer); + stbiw__sbpush(data, STBIW_UCHAR(*bitbuffer)); *bitbuffer >>= 8; *bitcount -= 8; } @@ -466,8 +807,14 @@ static unsigned int stbiw__zhash(unsigned char *data) #define stbiw__ZHASH 16384 +#endif // STBIW_ZLIB_COMPRESS + unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality) { +#ifdef STBIW_ZLIB_COMPRESS + // user provided a zlib compress implementation, use that + return STBIW_ZLIB_COMPRESS(data, data_len, out_len, quality); +#else // use builtin static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 }; static unsigned char lengtheb[]= { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0 }; static unsigned short distc[] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 }; @@ -475,7 +822,9 @@ unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_l unsigned int bitbuf=0; int i,j, bitcount=0; unsigned char *out = NULL; - unsigned char **hash_table[stbiw__ZHASH]; // 64KB on the stack! + unsigned char ***hash_table = (unsigned char***) STBIW_MALLOC(stbiw__ZHASH * sizeof(char**)); + if (hash_table == NULL) + return NULL; if (quality < 5) quality = 5; stbiw__sbpush(out, 0x78); // DEFLATE 32K window @@ -547,43 +896,77 @@ unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_l for (i=0; i < stbiw__ZHASH; ++i) (void) stbiw__sbfree(hash_table[i]); + STBIW_FREE(hash_table); { // compute adler32 on input - unsigned int i=0, s1=1, s2=0, blocklen = data_len % 5552; - int j=0; + unsigned int s1=1, s2=0; + int blocklen = (int) (data_len % 5552); + j=0; while (j < data_len) { for (i=0; i < blocklen; ++i) s1 += data[j+i], s2 += s1; s1 %= 65521, s2 %= 65521; j += blocklen; blocklen = 5552; } - stbiw__sbpush(out, (unsigned char) (s2 >> 8)); - stbiw__sbpush(out, (unsigned char) s2); - stbiw__sbpush(out, (unsigned char) (s1 >> 8)); - stbiw__sbpush(out, (unsigned char) s1); + stbiw__sbpush(out, STBIW_UCHAR(s2 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s2)); + stbiw__sbpush(out, STBIW_UCHAR(s1 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s1)); } *out_len = stbiw__sbn(out); // make returned pointer freeable STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len); return (unsigned char *) stbiw__sbraw(out); +#endif // STBIW_ZLIB_COMPRESS } -unsigned int stbiw__crc32(unsigned char *buffer, int len) +static unsigned int stbiw__crc32(unsigned char *buffer, int len) { - static unsigned int crc_table[256]; + static unsigned int crc_table[256] = + { + 0x00000000, 0x77073096, 0xEE0E612C, 0x990951BA, 0x076DC419, 0x706AF48F, 0xE963A535, 0x9E6495A3, + 0x0eDB8832, 0x79DCB8A4, 0xE0D5E91E, 0x97D2D988, 0x09B64C2B, 0x7EB17CBD, 0xE7B82D07, 0x90BF1D91, + 0x1DB71064, 0x6AB020F2, 0xF3B97148, 0x84BE41DE, 0x1ADAD47D, 0x6DDDE4EB, 0xF4D4B551, 0x83D385C7, + 0x136C9856, 0x646BA8C0, 0xFD62F97A, 0x8A65C9EC, 0x14015C4F, 0x63066CD9, 0xFA0F3D63, 0x8D080DF5, + 0x3B6E20C8, 0x4C69105E, 0xD56041E4, 0xA2677172, 0x3C03E4D1, 0x4B04D447, 0xD20D85FD, 0xA50AB56B, + 0x35B5A8FA, 0x42B2986C, 0xDBBBC9D6, 0xACBCF940, 0x32D86CE3, 0x45DF5C75, 0xDCD60DCF, 0xABD13D59, + 0x26D930AC, 0x51DE003A, 0xC8D75180, 0xBFD06116, 0x21B4F4B5, 0x56B3C423, 0xCFBA9599, 0xB8BDA50F, + 0x2802B89E, 0x5F058808, 0xC60CD9B2, 0xB10BE924, 0x2F6F7C87, 0x58684C11, 0xC1611DAB, 0xB6662D3D, + 0x76DC4190, 0x01DB7106, 0x98D220BC, 0xEFD5102A, 0x71B18589, 0x06B6B51F, 0x9FBFE4A5, 0xE8B8D433, + 0x7807C9A2, 0x0F00F934, 0x9609A88E, 0xE10E9818, 0x7F6A0DBB, 0x086D3D2D, 0x91646C97, 0xE6635C01, + 0x6B6B51F4, 0x1C6C6162, 0x856530D8, 0xF262004E, 0x6C0695ED, 0x1B01A57B, 0x8208F4C1, 0xF50FC457, + 0x65B0D9C6, 0x12B7E950, 0x8BBEB8EA, 0xFCB9887C, 0x62DD1DDF, 0x15DA2D49, 0x8CD37CF3, 0xFBD44C65, + 0x4DB26158, 0x3AB551CE, 0xA3BC0074, 0xD4BB30E2, 0x4ADFA541, 0x3DD895D7, 0xA4D1C46D, 0xD3D6F4FB, + 0x4369E96A, 0x346ED9FC, 0xAD678846, 0xDA60B8D0, 0x44042D73, 0x33031DE5, 0xAA0A4C5F, 0xDD0D7CC9, + 0x5005713C, 0x270241AA, 0xBE0B1010, 0xC90C2086, 0x5768B525, 0x206F85B3, 0xB966D409, 0xCE61E49F, + 0x5EDEF90E, 0x29D9C998, 0xB0D09822, 0xC7D7A8B4, 0x59B33D17, 0x2EB40D81, 0xB7BD5C3B, 0xC0BA6CAD, + 0xEDB88320, 0x9ABFB3B6, 0x03B6E20C, 0x74B1D29A, 0xEAD54739, 0x9DD277AF, 0x04DB2615, 0x73DC1683, + 0xE3630B12, 0x94643B84, 0x0D6D6A3E, 0x7A6A5AA8, 0xE40ECF0B, 0x9309FF9D, 0x0A00AE27, 0x7D079EB1, + 0xF00F9344, 0x8708A3D2, 0x1E01F268, 0x6906C2FE, 0xF762575D, 0x806567CB, 0x196C3671, 0x6E6B06E7, + 0xFED41B76, 0x89D32BE0, 0x10DA7A5A, 0x67DD4ACC, 0xF9B9DF6F, 0x8EBEEFF9, 0x17B7BE43, 0x60B08ED5, + 0xD6D6A3E8, 0xA1D1937E, 0x38D8C2C4, 0x4FDFF252, 0xD1BB67F1, 0xA6BC5767, 0x3FB506DD, 0x48B2364B, + 0xD80D2BDA, 0xAF0A1B4C, 0x36034AF6, 0x41047A60, 0xDF60EFC3, 0xA867DF55, 0x316E8EEF, 0x4669BE79, + 0xCB61B38C, 0xBC66831A, 0x256FD2A0, 0x5268E236, 0xCC0C7795, 0xBB0B4703, 0x220216B9, 0x5505262F, + 0xC5BA3BBE, 0xB2BD0B28, 0x2BB45A92, 0x5CB36A04, 0xC2D7FFA7, 0xB5D0CF31, 0x2CD99E8B, 0x5BDEAE1D, + 0x9B64C2B0, 0xEC63F226, 0x756AA39C, 0x026D930A, 0x9C0906A9, 0xEB0E363F, 0x72076785, 0x05005713, + 0x95BF4A82, 0xE2B87A14, 0x7BB12BAE, 0x0CB61B38, 0x92D28E9B, 0xE5D5BE0D, 0x7CDCEFB7, 0x0BDBDF21, + 0x86D3D2D4, 0xF1D4E242, 0x68DDB3F8, 0x1FDA836E, 0x81BE16CD, 0xF6B9265B, 0x6FB077E1, 0x18B74777, + 0x88085AE6, 0xFF0F6A70, 0x66063BCA, 0x11010B5C, 0x8F659EFF, 0xF862AE69, 0x616BFFD3, 0x166CCF45, + 0xA00AE278, 0xD70DD2EE, 0x4E048354, 0x3903B3C2, 0xA7672661, 0xD06016F7, 0x4969474D, 0x3E6E77DB, + 0xAED16A4A, 0xD9D65ADC, 0x40DF0B66, 0x37D83BF0, 0xA9BCAE53, 0xDEBB9EC5, 0x47B2CF7F, 0x30B5FFE9, + 0xBDBDF21C, 0xCABAC28A, 0x53B39330, 0x24B4A3A6, 0xBAD03605, 0xCDD70693, 0x54DE5729, 0x23D967BF, + 0xB3667A2E, 0xC4614AB8, 0x5D681B02, 0x2A6F2B94, 0xB40BBE37, 0xC30C8EA1, 0x5A05DF1B, 0x2D02EF8D + }; + unsigned int crc = ~0u; - int i,j; - if (crc_table[1] == 0) - for(i=0; i < 256; i++) - for (crc_table[i]=i, j=0; j < 8; ++j) - crc_table[i] = (crc_table[i] >> 1) ^ (crc_table[i] & 1 ? 0xedb88320 : 0); + int i; for (i=0; i < len; ++i) crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)]; return ~crc; } -#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=(unsigned char)(a),(o)[1]=(unsigned char)(b),(o)[2]=(unsigned char)(c),(o)[3]=(unsigned char)(d),(o)+=4) +#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=STBIW_UCHAR(a),(o)[1]=STBIW_UCHAR(b),(o)[2]=STBIW_UCHAR(c),(o)[3]=STBIW_UCHAR(d),(o)+=4) #define stbiw__wp32(data,v) stbiw__wpng4(data, (v)>>24,(v)>>16,(v)>>8,(v)); #define stbiw__wptag(data,s) stbiw__wpng4(data, s[0],s[1],s[2],s[3]) @@ -596,66 +979,94 @@ static void stbiw__wpcrc(unsigned char **data, int len) static unsigned char stbiw__paeth(int a, int b, int c) { int p = a + b - c, pa = abs(p-a), pb = abs(p-b), pc = abs(p-c); - if (pa <= pb && pa <= pc) return (unsigned char) a; - if (pb <= pc) return (unsigned char) b; - return (unsigned char) c; + if (pa <= pb && pa <= pc) return STBIW_UCHAR(a); + if (pb <= pc) return STBIW_UCHAR(b); + return STBIW_UCHAR(c); +} + +// @OPTIMIZE: provide an option that always forces left-predict or paeth predict +static void stbiw__encode_png_line(unsigned char *pixels, int stride_bytes, int width, int height, int y, int n, int filter_type, signed char *line_buffer) +{ + static int mapping[] = { 0,1,2,3,4 }; + static int firstmap[] = { 0,1,0,5,6 }; + int *mymap = (y != 0) ? mapping : firstmap; + int i; + int type = mymap[filter_type]; + unsigned char *z = pixels + stride_bytes * (stbi__flip_vertically_on_write ? height-1-y : y); + int signed_stride = stbi__flip_vertically_on_write ? -stride_bytes : stride_bytes; + for (i = 0; i < n; ++i) { + switch (type) { + case 0: line_buffer[i] = z[i]; break; + case 1: line_buffer[i] = z[i]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - (z[i-signed_stride]>>1); break; + case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-signed_stride],0)); break; + case 5: line_buffer[i] = z[i]; break; + case 6: line_buffer[i] = z[i]; break; + } + } + for (i=n; i < width*n; ++i) { + switch (type) { + case 0: line_buffer[i] = z[i]; break; + case 1: line_buffer[i] = z[i] - z[i-n]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - ((z[i-n] + z[i-signed_stride])>>1); break; + case 4: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-signed_stride], z[i-signed_stride-n]); break; + case 5: line_buffer[i] = z[i] - (z[i-n]>>1); break; + case 6: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; + } + } } unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len) { + int force_filter = stbi_write_force_png_filter; int ctype[5] = { -1, 0, 4, 2, 6 }; unsigned char sig[8] = { 137,80,78,71,13,10,26,10 }; unsigned char *out,*o, *filt, *zlib; signed char *line_buffer; - int i,j,k,p,zlen; + int j,zlen; if (stride_bytes == 0) stride_bytes = x * n; + if (force_filter >= 5) { + force_filter = -1; + } + filt = (unsigned char *) STBIW_MALLOC((x*n+1) * y); if (!filt) return 0; line_buffer = (signed char *) STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; } for (j=0; j < y; ++j) { - static int mapping[] = { 0,1,2,3,4 }; - static int firstmap[] = { 0,1,0,5,6 }; - int *mymap = j ? mapping : firstmap; - int best = 0, bestval = 0x7fffffff; - for (p=0; p < 2; ++p) { - for (k= p?best:0; k < 5; ++k) { - int type = mymap[k],est=0; - unsigned char *z = pixels + stride_bytes*j; - for (i=0; i < n; ++i) - switch (type) { - case 0: line_buffer[i] = z[i]; break; - case 1: line_buffer[i] = z[i]; break; - case 2: line_buffer[i] = z[i] - z[i-stride_bytes]; break; - case 3: line_buffer[i] = z[i] - (z[i-stride_bytes]>>1); break; - case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-stride_bytes],0)); break; - case 5: line_buffer[i] = z[i]; break; - case 6: line_buffer[i] = z[i]; break; - } - for (i=n; i < x*n; ++i) { - switch (type) { - case 0: line_buffer[i] = z[i]; break; - case 1: line_buffer[i] = z[i] - z[i-n]; break; - case 2: line_buffer[i] = z[i] - z[i-stride_bytes]; break; - case 3: line_buffer[i] = z[i] - ((z[i-n] + z[i-stride_bytes])>>1); break; - case 4: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-stride_bytes], z[i-stride_bytes-n]); break; - case 5: line_buffer[i] = z[i] - (z[i-n]>>1); break; - case 6: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; - } - } - if (p) break; - for (i=0; i < x*n; ++i) + int filter_type; + if (force_filter > -1) { + filter_type = force_filter; + stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, force_filter, line_buffer); + } else { // Estimate the best filter by running through all of them: + int best_filter = 0, best_filter_val = 0x7fffffff, est, i; + for (filter_type = 0; filter_type < 5; filter_type++) { + stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, filter_type, line_buffer); + + // Estimate the entropy of the line using this filter; the less, the better. + est = 0; + for (i = 0; i < x*n; ++i) { est += abs((signed char) line_buffer[i]); - if (est < bestval) { bestval = est; best = k; } + } + if (est < best_filter_val) { + best_filter_val = est; + best_filter = filter_type; + } + } + if (filter_type != best_filter) { // If the last iteration already got us the best filter, don't redo it + stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, best_filter, line_buffer); + filter_type = best_filter; } } - // when we get here, best contains the filter type, and line_buffer contains the data - filt[j*(x*n+1)] = (unsigned char) best; + // when we get here, filter_type contains the filter type, and line_buffer contains the data + filt[j*(x*n+1)] = (unsigned char) filter_type; STBIW_MEMMOVE(filt+j*(x*n+1)+1, line_buffer, x*n); } STBIW_FREE(line_buffer); - zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, 8); // increase 8 to get smaller but use more memory + zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, stbi_write_png_compression_level); STBIW_FREE(filt); if (!zlib) return 0; @@ -671,7 +1082,7 @@ unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, in stbiw__wp32(o, x); stbiw__wp32(o, y); *o++ = 8; - *o++ = (unsigned char) ctype[n]; + *o++ = STBIW_UCHAR(ctype[n]); *o++ = 0; *o++ = 0; *o++ = 0; @@ -693,22 +1104,407 @@ unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, in return out; } -int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes) +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes) { FILE *f; int len; unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len); - if (!png) return 0; + if (png == NULL) return 0; +#ifdef STBI_MSC_SECURE_CRT + if (fopen_s(&f, filename, "wb")) + f = NULL; +#else f = fopen(filename, "wb"); +#endif if (!f) { STBIW_FREE(png); return 0; } fwrite(png, 1, len, f); fclose(f); STBIW_FREE(png); return 1; } +#endif + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int stride_bytes) +{ + int len; + unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + func(context, png, len); + STBIW_FREE(png); + return 1; +} + + +/* *************************************************************************** + * + * JPEG writer + * + * This is based on Jon Olick's jo_jpeg.cpp: + * public domain Simple, Minimalistic JPEG writer - http://www.jonolick.com/code.html + */ + +static const unsigned char stbiw__jpg_ZigZag[] = { 0,1,5,6,14,15,27,28,2,4,7,13,16,26,29,42,3,8,12,17,25,30,41,43,9,11,18, + 24,31,40,44,53,10,19,23,32,39,45,52,54,20,22,33,38,46,51,55,60,21,34,37,47,50,56,59,61,35,36,48,49,57,58,62,63 }; + +static void stbiw__jpg_writeBits(stbi__write_context *s, int *bitBufP, int *bitCntP, const unsigned short *bs) { + int bitBuf = *bitBufP, bitCnt = *bitCntP; + bitCnt += bs[1]; + bitBuf |= bs[0] << (24 - bitCnt); + while(bitCnt >= 8) { + unsigned char c = (bitBuf >> 16) & 255; + stbiw__putc(s, c); + if(c == 255) { + stbiw__putc(s, 0); + } + bitBuf <<= 8; + bitCnt -= 8; + } + *bitBufP = bitBuf; + *bitCntP = bitCnt; +} + +static void stbiw__jpg_DCT(float *d0p, float *d1p, float *d2p, float *d3p, float *d4p, float *d5p, float *d6p, float *d7p) { + float d0 = *d0p, d1 = *d1p, d2 = *d2p, d3 = *d3p, d4 = *d4p, d5 = *d5p, d6 = *d6p, d7 = *d7p; + float z1, z2, z3, z4, z5, z11, z13; + + float tmp0 = d0 + d7; + float tmp7 = d0 - d7; + float tmp1 = d1 + d6; + float tmp6 = d1 - d6; + float tmp2 = d2 + d5; + float tmp5 = d2 - d5; + float tmp3 = d3 + d4; + float tmp4 = d3 - d4; + + // Even part + float tmp10 = tmp0 + tmp3; // phase 2 + float tmp13 = tmp0 - tmp3; + float tmp11 = tmp1 + tmp2; + float tmp12 = tmp1 - tmp2; + + d0 = tmp10 + tmp11; // phase 3 + d4 = tmp10 - tmp11; + + z1 = (tmp12 + tmp13) * 0.707106781f; // c4 + d2 = tmp13 + z1; // phase 5 + d6 = tmp13 - z1; + + // Odd part + tmp10 = tmp4 + tmp5; // phase 2 + tmp11 = tmp5 + tmp6; + tmp12 = tmp6 + tmp7; + + // The rotator is modified from fig 4-8 to avoid extra negations. + z5 = (tmp10 - tmp12) * 0.382683433f; // c6 + z2 = tmp10 * 0.541196100f + z5; // c2-c6 + z4 = tmp12 * 1.306562965f + z5; // c2+c6 + z3 = tmp11 * 0.707106781f; // c4 + + z11 = tmp7 + z3; // phase 5 + z13 = tmp7 - z3; + + *d5p = z13 + z2; // phase 6 + *d3p = z13 - z2; + *d1p = z11 + z4; + *d7p = z11 - z4; + + *d0p = d0; *d2p = d2; *d4p = d4; *d6p = d6; +} + +static void stbiw__jpg_calcBits(int val, unsigned short bits[2]) { + int tmp1 = val < 0 ? -val : val; + val = val < 0 ? val-1 : val; + bits[1] = 1; + while(tmp1 >>= 1) { + ++bits[1]; + } + bits[0] = val & ((1<0)&&(DU[end0pos]==0); --end0pos) { + } + // end0pos = first element in reverse order !=0 + if(end0pos == 0) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + return DU[0]; + } + for(i = 1; i <= end0pos; ++i) { + int startpos = i; + int nrzeroes; + unsigned short bits[2]; + for (; DU[i]==0 && i<=end0pos; ++i) { + } + nrzeroes = i-startpos; + if ( nrzeroes >= 16 ) { + int lng = nrzeroes>>4; + int nrmarker; + for (nrmarker=1; nrmarker <= lng; ++nrmarker) + stbiw__jpg_writeBits(s, bitBuf, bitCnt, M16zeroes); + nrzeroes &= 15; + } + stbiw__jpg_calcBits(DU[i], bits); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTAC[(nrzeroes<<4)+bits[1]]); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits); + } + if(end0pos != 63) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + } + return DU[0]; +} + +static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality) { + // Constants that don't pollute global namespace + static const unsigned char std_dc_luminance_nrcodes[] = {0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0}; + static const unsigned char std_dc_luminance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_luminance_nrcodes[] = {0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d}; + static const unsigned char std_ac_luminance_values[] = { + 0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08, + 0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28, + 0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59, + 0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89, + 0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6, + 0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2, + 0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + static const unsigned char std_dc_chrominance_nrcodes[] = {0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0}; + static const unsigned char std_dc_chrominance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_chrominance_nrcodes[] = {0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77}; + static const unsigned char std_ac_chrominance_values[] = { + 0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91, + 0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26, + 0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58, + 0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87, + 0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4, + 0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda, + 0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + // Huffman tables + static const unsigned short YDC_HT[256][2] = { {0,2},{2,3},{3,3},{4,3},{5,3},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9}}; + static const unsigned short UVDC_HT[256][2] = { {0,2},{1,2},{2,2},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9},{1022,10},{2046,11}}; + static const unsigned short YAC_HT[256][2] = { + {10,4},{0,2},{1,2},{4,3},{11,4},{26,5},{120,7},{248,8},{1014,10},{65410,16},{65411,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {12,4},{27,5},{121,7},{502,9},{2038,11},{65412,16},{65413,16},{65414,16},{65415,16},{65416,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {28,5},{249,8},{1015,10},{4084,12},{65417,16},{65418,16},{65419,16},{65420,16},{65421,16},{65422,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{503,9},{4085,12},{65423,16},{65424,16},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1016,10},{65430,16},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2039,11},{65438,16},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {123,7},{4086,12},{65446,16},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {250,8},{4087,12},{65454,16},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{32704,15},{65462,16},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65470,16},{65471,16},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65479,16},{65480,16},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1017,10},{65488,16},{65489,16},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{65497,16},{65498,16},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2040,11},{65506,16},{65507,16},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {65515,16},{65516,16},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65525,16},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const unsigned short UVAC_HT[256][2] = { + {0,2},{1,2},{4,3},{10,4},{24,5},{25,5},{56,6},{120,7},{500,9},{1014,10},{4084,12},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {11,4},{57,6},{246,8},{501,9},{2038,11},{4085,12},{65416,16},{65417,16},{65418,16},{65419,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {26,5},{247,8},{1015,10},{4086,12},{32706,15},{65420,16},{65421,16},{65422,16},{65423,16},{65424,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {27,5},{248,8},{1016,10},{4087,12},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{65430,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{502,9},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{65438,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1017,10},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{65446,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {121,7},{2039,11},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{65454,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2040,11},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{65462,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {249,8},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{65470,16},{65471,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {503,9},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{65479,16},{65480,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{65488,16},{65489,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{65497,16},{65498,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{65506,16},{65507,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{65515,16},{65516,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {16352,14},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{65525,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{32707,15},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const int YQT[] = {16,11,10,16,24,40,51,61,12,12,14,19,26,58,60,55,14,13,16,24,40,57,69,56,14,17,22,29,51,87,80,62,18,22, + 37,56,68,109,103,77,24,35,55,64,81,104,113,92,49,64,78,87,103,121,120,101,72,92,95,98,112,100,103,99}; + static const int UVQT[] = {17,18,24,47,99,99,99,99,18,21,26,66,99,99,99,99,24,26,56,99,99,99,99,99,47,66,99,99,99,99,99,99, + 99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99}; + static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, + 1.0f * 2.828427125f, 0.785694958f * 2.828427125f, 0.541196100f * 2.828427125f, 0.275899379f * 2.828427125f }; + + int row, col, i, k; + float fdtbl_Y[64], fdtbl_UV[64]; + unsigned char YTable[64], UVTable[64]; + + if(!data || !width || !height || comp > 4 || comp < 1) { + return 0; + } + + quality = quality ? quality : 90; + quality = quality < 1 ? 1 : quality > 100 ? 100 : quality; + quality = quality < 50 ? 5000 / quality : 200 - quality * 2; + + for(i = 0; i < 64; ++i) { + int uvti, yti = (YQT[i]*quality+50)/100; + YTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (yti < 1 ? 1 : yti > 255 ? 255 : yti); + uvti = (UVQT[i]*quality+50)/100; + UVTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (uvti < 1 ? 1 : uvti > 255 ? 255 : uvti); + } + + for(row = 0, k = 0; row < 8; ++row) { + for(col = 0; col < 8; ++col, ++k) { + fdtbl_Y[k] = 1 / (YTable [stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + fdtbl_UV[k] = 1 / (UVTable[stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + } + } + + // Write Headers + { + static const unsigned char head0[] = { 0xFF,0xD8,0xFF,0xE0,0,0x10,'J','F','I','F',0,1,1,0,0,1,0,1,0,0,0xFF,0xDB,0,0x84,0 }; + static const unsigned char head2[] = { 0xFF,0xDA,0,0xC,3,1,0,2,0x11,3,0x11,0,0x3F,0 }; + const unsigned char head1[] = { 0xFF,0xC0,0,0x11,8,(unsigned char)(height>>8),STBIW_UCHAR(height),(unsigned char)(width>>8),STBIW_UCHAR(width), + 3,1,0x11,0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 }; + s->func(s->context, (void*)head0, sizeof(head0)); + s->func(s->context, (void*)YTable, sizeof(YTable)); + stbiw__putc(s, 1); + s->func(s->context, UVTable, sizeof(UVTable)); + s->func(s->context, (void*)head1, sizeof(head1)); + s->func(s->context, (void*)(std_dc_luminance_nrcodes+1), sizeof(std_dc_luminance_nrcodes)-1); + s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values)); + stbiw__putc(s, 0x10); // HTYACinfo + s->func(s->context, (void*)(std_ac_luminance_nrcodes+1), sizeof(std_ac_luminance_nrcodes)-1); + s->func(s->context, (void*)std_ac_luminance_values, sizeof(std_ac_luminance_values)); + stbiw__putc(s, 1); // HTUDCinfo + s->func(s->context, (void*)(std_dc_chrominance_nrcodes+1), sizeof(std_dc_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_dc_chrominance_values, sizeof(std_dc_chrominance_values)); + stbiw__putc(s, 0x11); // HTUACinfo + s->func(s->context, (void*)(std_ac_chrominance_nrcodes+1), sizeof(std_ac_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_ac_chrominance_values, sizeof(std_ac_chrominance_values)); + s->func(s->context, (void*)head2, sizeof(head2)); + } + + // Encode 8x8 macroblocks + { + static const unsigned short fillBits[] = {0x7F, 7}; + const unsigned char *imageData = (const unsigned char *)data; + int DCY=0, DCU=0, DCV=0; + int bitBuf=0, bitCnt=0; + // comp == 2 is grey+alpha (alpha is ignored) + int ofsG = comp > 2 ? 1 : 0, ofsB = comp > 2 ? 2 : 0; + int x, y, pos; + for(y = 0; y < height; y += 8) { + for(x = 0; x < width; x += 8) { + float YDU[64], UDU[64], VDU[64]; + for(row = y, pos = 0; row < y+8; ++row) { + for(col = x; col < x+8; ++col, ++pos) { + int p = (stbi__flip_vertically_on_write ? height-1-row : row)*width*comp + col*comp; + float r, g, b; + if(row >= height) { + p -= width*comp*(row+1 - height); + } + if(col >= width) { + p -= comp*(col+1 - width); + } + + r = imageData[p+0]; + g = imageData[p+ofsG]; + b = imageData[p+ofsB]; + YDU[pos]=+0.29900f*r+0.58700f*g+0.11400f*b-128; + UDU[pos]=-0.16874f*r-0.33126f*g+0.50000f*b; + VDU[pos]=+0.50000f*r-0.41869f*g-0.08131f*b; + } + } + + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, YDU, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, UDU, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, VDU, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + } + } + + // Do the bit alignment of the EOI marker + stbiw__jpg_writeBits(s, &bitBuf, &bitCnt, fillBits); + } + + // EOI + stbiw__putc(s, 0xFF); + stbiw__putc(s, 0xD9); + + return 1; +} + +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality); +} + + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_jpg_core(&s, x, y, comp, data, quality); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + #endif // STB_IMAGE_WRITE_IMPLEMENTATION /* Revision history + 1.09 (2018-02-11) + fix typo in zlib quality API, improve STB_I_W_STATIC in C++ + 1.08 (2018-01-29) + add stbi__flip_vertically_on_write, external zlib, zlib quality, choose PNG filter + 1.07 (2017-07-24) + doc fix + 1.06 (2017-07-23) + writing JPEG (using Jon Olick's code) + 1.05 ??? + 1.04 (2017-03-03) + monochrome BMP expansion + 1.03 ??? + 1.02 (2016-04-02) + avoid allocating large structures on the stack + 1.01 (2016-01-16) + STBIW_REALLOC_SIZED: support allocators with no realloc support + avoid race-condition in crc initialization + minor compile issues + 1.00 (2015-09-14) + installable file IO function + 0.99 (2015-09-13) + warning fixes; TGA rle support 0.98 (2015-04-08) added STBIW_MALLOC, STBIW_ASSERT etc 0.97 (2015-01-18) @@ -728,3 +1524,45 @@ int stbi_write_png(char const *filename, int x, int y, int comp, const void *dat first public release 0.90 first internal release */ + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/image.darknet/inst/include/darknet/src/tree.c b/image.darknet/inst/include/darknet/src/tree.c index dd44515..67b6d43 100644 --- a/image.darknet/inst/include/darknet/src/tree.c +++ b/image.darknet/inst/include/darknet/src/tree.c @@ -24,33 +24,33 @@ void change_leaves(tree *t, char *leaf_list) fprintf(stderr, "Found %d leaves.\n", found); } -float get_hierarchy_probability(float *x, tree *hier, int c) +float get_hierarchy_probability(float *x, tree *hier, int c, int stride) { float p = 1; while(c >= 0){ - p = p * x[c]; + p = p * x[c*stride]; c = hier->parent[c]; } return p; } -void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves) +void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride) { int j; for(j = 0; j < n; ++j){ int parent = hier->parent[j]; if(parent >= 0){ - predictions[j] *= predictions[parent]; + predictions[j*stride] *= predictions[parent*stride]; } } if(only_leaves){ for(j = 0; j < n; ++j){ - if(!hier->leaf[j]) predictions[j] = 0; + if(!hier->leaf[j]) predictions[j*stride] = 0; } } } -int hierarchy_top_prediction(float *predictions, tree *hier, float thresh) +int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride) { float p = 1; int group = 0; @@ -61,7 +61,7 @@ int hierarchy_top_prediction(float *predictions, tree *hier, float thresh) for(i = 0; i < hier->group_size[group]; ++i){ int index = i + hier->group_offset[group]; - float val = predictions[i + hier->group_offset[group]]; + float val = predictions[(i + hier->group_offset[group])*stride]; if(val > max){ max_i = index; max = val; @@ -71,6 +71,8 @@ int hierarchy_top_prediction(float *predictions, tree *hier, float thresh) p = p*max; group = hier->child[max_i]; if(hier->child[max_i] < 0) return max_i; + } else if (group == 0){ + return max_i; } else { return hier->parent[hier->group_offset[group]]; } diff --git a/image.darknet/inst/include/darknet/src/tree.h b/image.darknet/inst/include/darknet/src/tree.h index dbd4c39..3802b8e 100644 --- a/image.darknet/inst/include/darknet/src/tree.h +++ b/image.darknet/inst/include/darknet/src/tree.h @@ -1,23 +1,8 @@ #ifndef TREE_H #define TREE_H +#include "darknet.h" -typedef struct{ - int *leaf; - int n; - int *parent; - int *child; - int *group; - char **name; - - int groups; - int *group_size; - int *group_offset; -} tree; - -tree *read_tree(char *filename); -void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves); -void change_leaves(tree *t, char *leaf_list); -int hierarchy_top_prediction(float *predictions, tree *hier, float thresh); -float get_hierarchy_probability(float *x, tree *hier, int c); +int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride); +float get_hierarchy_probability(float *x, tree *hier, int c, int stride); #endif diff --git a/image.darknet/inst/include/darknet/src/upsample_layer.c b/image.darknet/inst/include/darknet/src/upsample_layer.c new file mode 100644 index 0000000..605f21f --- /dev/null +++ b/image.darknet/inst/include/darknet/src/upsample_layer.c @@ -0,0 +1,106 @@ +#include "upsample_layer.h" +#include "cuda.h" +#include "blas.h" + +#include + +layer make_upsample_layer(int batch, int w, int h, int c, int stride) +{ + layer l = {0}; + l.type = UPSAMPLE; + l.batch = batch; + l.w = w; + l.h = h; + l.c = c; + l.out_w = w*stride; + l.out_h = h*stride; + l.out_c = c; + if(stride < 0){ + stride = -stride; + l.reverse=1; + l.out_w = w/stride; + l.out_h = h/stride; + } + l.stride = stride; + l.outputs = l.out_w*l.out_h*l.out_c; + l.inputs = l.w*l.h*l.c; + l.delta = calloc(l.outputs*batch, sizeof(float)); + l.output = calloc(l.outputs*batch, sizeof(float));; + + l.forward = forward_upsample_layer; + l.backward = backward_upsample_layer; + #ifdef GPU + l.forward_gpu = forward_upsample_layer_gpu; + l.backward_gpu = backward_upsample_layer_gpu; + + l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); + l.output_gpu = cuda_make_array(l.output, l.outputs*batch); + #endif + if(l.reverse) fprintf(stderr, "downsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + else fprintf(stderr, "upsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + return l; +} + +void resize_upsample_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + l->out_w = w*l->stride; + l->out_h = h*l->stride; + if(l->reverse){ + l->out_w = w/l->stride; + l->out_h = h/l->stride; + } + l->outputs = l->out_w*l->out_h*l->out_c; + l->inputs = l->h*l->w*l->c; + l->delta = realloc(l->delta, l->outputs*l->batch*sizeof(float)); + l->output = realloc(l->output, l->outputs*l->batch*sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); +#endif + +} + +void forward_upsample_layer(const layer l, network net) +{ + fill_cpu(l.outputs*l.batch, 0, l.output, 1); + if(l.reverse){ + upsample_cpu(l.output, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input); + }else{ + upsample_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output); + } +} + +void backward_upsample_layer(const layer l, network net) +{ + if(l.reverse){ + upsample_cpu(l.delta, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, net.delta); + }else{ + upsample_cpu(net.delta, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta); + } +} + +#ifdef GPU +void forward_upsample_layer_gpu(const layer l, network net) +{ + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); + if(l.reverse){ + upsample_gpu(l.output_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input_gpu); + }else{ + upsample_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output_gpu); + } +} + +void backward_upsample_layer_gpu(const layer l, network net) +{ + if(l.reverse){ + upsample_gpu(l.delta_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, net.delta_gpu); + }else{ + upsample_gpu(net.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta_gpu); + } +} +#endif diff --git a/image.darknet/inst/include/darknet/src/upsample_layer.h b/image.darknet/inst/include/darknet/src/upsample_layer.h new file mode 100644 index 0000000..86790d1 --- /dev/null +++ b/image.darknet/inst/include/darknet/src/upsample_layer.h @@ -0,0 +1,15 @@ +#ifndef UPSAMPLE_LAYER_H +#define UPSAMPLE_LAYER_H +#include "darknet.h" + +layer make_upsample_layer(int batch, int w, int h, int c, int stride); +void forward_upsample_layer(const layer l, network net); +void backward_upsample_layer(const layer l, network net); +void resize_upsample_layer(layer *l, int w, int h); + +#ifdef GPU +void forward_upsample_layer_gpu(const layer l, network net); +void backward_upsample_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/image.darknet/inst/include/darknet/src/utils.c b/image.darknet/inst/include/darknet/src/utils.c index b5181d7..626b467 100644 --- a/image.darknet/inst/include/darknet/src/utils.c +++ b/image.darknet/inst/include/darknet/src/utils.c @@ -6,9 +6,56 @@ #include #include #include +#include +#include #include "utils.h" + +/* +// old timing. is it better? who knows!! +double get_wall_time() +{ + struct timeval time; + if (gettimeofday(&time,NULL)){ + return 0; + } + return (double)time.tv_sec + (double)time.tv_usec * .000001; +} +*/ + +double what_time_is_it_now() +{ + struct timeval time; + if (gettimeofday(&time,NULL)){ + return 0; + } + return (double)time.tv_sec + (double)time.tv_usec * .000001; +} + +int *read_intlist(char *gpu_list, int *ngpus, int d) +{ + int *gpus = 0; + if(gpu_list){ + int len = strlen(gpu_list); + *ngpus = 1; + int i; + for(i = 0; i < len; ++i){ + if (gpu_list[i] == ',') ++*ngpus; + } + gpus = calloc(*ngpus, sizeof(int)); + for(i = 0; i < *ngpus; ++i){ + gpus[i] = atoi(gpu_list); + gpu_list = strchr(gpu_list, ',')+1; + } + } else { + gpus = calloc(1, sizeof(float)); + *gpus = d; + *ngpus = 1; + } + return gpus; +} + int *read_map(char *filename) { int n = 0; @@ -47,6 +94,22 @@ void shuffle(void *arr, size_t n, size_t size) } } +int *random_index_order(int min, int max) +{ + int *inds = calloc(max-min, sizeof(int)); + int i; + for(i = min; i < max; ++i){ + inds[i] = i; + } + for(i = min; i < max-1; ++i){ + int swap = inds[i]; + int index = i + rand()%(max-i); + inds[i] = inds[index]; + inds[index] = swap; + } + return inds; +} + void del_arg(int argc, char **argv, int index) { int i; @@ -194,6 +257,21 @@ void error(const char *s) exit(-1); } +unsigned char *read_file(char *filename) +{ + FILE *fp = fopen(filename, "rb"); + size_t size; + + fseek(fp, 0, SEEK_END); + size = ftell(fp); + fseek(fp, 0, SEEK_SET); + + unsigned char *text = calloc(size+1, sizeof(char)); + fread(text, 1, size, fp); + fclose(fp); + return text; +} + void malloc_error() { fprintf(stderr, "Malloc error\n"); @@ -524,6 +602,20 @@ int sample_array(float *a, int n) return n-1; } +int max_int_index(int *a, int n) +{ + if(n <= 0) return -1; + int i, max_i = 0; + int max = a[0]; + for(i = 1; i < n; ++i){ + if(a[i] > max){ + max = a[i]; + max_i = i; + } + } + return max_i; +} + int max_index(float *a, int n) { if(n <= 0) return -1; @@ -538,6 +630,15 @@ int max_index(float *a, int n) return max_i; } +int int_index(int *a, int val, int n) +{ + int i; + for(i = 0; i < n; ++i){ + if(a[i] == val) return i; + } + return -1; +} + int rand_int(int min, int max) { if (max < min){ @@ -585,13 +686,13 @@ float rand_normal() size_t rand_size_t() { return ((size_t)(rand()&0xff) << 56) | - ((size_t)(rand()&0xff) << 48) | - ((size_t)(rand()&0xff) << 40) | - ((size_t)(rand()&0xff) << 32) | - ((size_t)(rand()&0xff) << 24) | - ((size_t)(rand()&0xff) << 16) | - ((size_t)(rand()&0xff) << 8) | - ((size_t)(rand()&0xff) << 0); + ((size_t)(rand()&0xff) << 48) | + ((size_t)(rand()&0xff) << 40) | + ((size_t)(rand()&0xff) << 32) | + ((size_t)(rand()&0xff) << 24) | + ((size_t)(rand()&0xff) << 16) | + ((size_t)(rand()&0xff) << 8) | + ((size_t)(rand()&0xff) << 0); } float rand_uniform(float min, float max) diff --git a/image.darknet/inst/include/darknet/src/utils.h b/image.darknet/inst/include/darknet/src/utils.h index bbc6765..ef24da7 100644 --- a/image.darknet/inst/include/darknet/src/utils.h +++ b/image.darknet/inst/include/darknet/src/utils.h @@ -2,16 +2,22 @@ #define UTILS_H #include #include +#include "darknet.h" #include "list.h" -#define SECRET_NUM -1234 -#define TWO_PI 6.2831853071795864769252866 +#define TIME(a) \ + do { \ + double start = what_time_is_it_now(); \ + a; \ + printf("%s took: %f seconds\n", #a, what_time_is_it_now() - start); \ + } while (0) -int *read_map(char *filename); +#define TWO_PI 6.2831853071795864769252866f + +double what_time_is_it_now(); void shuffle(void *arr, size_t n, size_t size); void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections); void free_ptrs(void **ptrs, int n); -char *basecfg(char *cfgfile); int alphanum_to_int(char c); char int_to_alphanum(int i); int read_int(int fd); @@ -21,44 +27,27 @@ void write_all(int fd, char *buffer, size_t bytes); int read_all_fail(int fd, char *buffer, size_t bytes); int write_all_fail(int fd, char *buffer, size_t bytes); void find_replace(char *str, char *orig, char *rep, char *output); -void error(const char *s); void malloc_error(); void file_error(char *s); void strip(char *s); void strip_char(char *s, char bad); -void top_k(float *a, int n, int k, int *index); list *split_str(char *s, char delim); char *fgetl(FILE *fp); list *parse_csv_line(char *line); char *copy_string(char *s); int count_fields(char *line); float *parse_fields(char *line, int n); -void normalize_array(float *a, int n); -void scale_array(float *a, int n, float s); void translate_array(float *a, int n, float s); -int max_index(float *a, int n); float constrain(float min, float max, float a); int constrain_int(int a, int min, int max); -float mse_array(float *a, int n); -float rand_normal(); -size_t rand_size_t(); -float rand_uniform(float min, float max); float rand_scale(float s); int rand_int(int min, int max); -float sum_array(float *a, int n); -float mean_array(float *a, int n); void mean_arrays(float **a, int n, int els, float *avg); -float variance_array(float *a, int n); -float mag_array(float *a, int n); float dist_array(float *a, float *b, int n, int sub); float **one_hot_encode(float *a, int n, int k); float sec(clock_t clocks); -int find_int_arg(int argc, char **argv, char *arg, int def); -float find_float_arg(int argc, char **argv, char *arg, float def); -int find_arg(int argc, char* argv[], char *arg); -char *find_char_arg(int argc, char **argv, char *arg, char *def); -int sample_array(float *a, int n); void print_statistics(float *a, int n); +int int_index(int *a, int val, int n); #endif diff --git a/image.darknet/inst/include/darknet/src/yolo_layer.c b/image.darknet/inst/include/darknet/src/yolo_layer.c new file mode 100644 index 0000000..c338036 --- /dev/null +++ b/image.darknet/inst/include/darknet/src/yolo_layer.c @@ -0,0 +1,374 @@ +#include "yolo_layer.h" +#include "activations.h" +#include "blas.h" +#include "box.h" +#include "cuda.h" +#include "utils.h" + +#include +#include +#include +#include + +layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes) +{ + int i; + layer l = {0}; + l.type = YOLO; + + l.n = n; + l.total = total; + l.batch = batch; + l.h = h; + l.w = w; + l.c = n*(classes + 4 + 1); + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; + l.classes = classes; + l.cost = calloc(1, sizeof(float)); + l.biases = calloc(total*2, sizeof(float)); + if(mask) l.mask = mask; + else{ + l.mask = calloc(n, sizeof(int)); + for(i = 0; i < n; ++i){ + l.mask[i] = i; + } + } + l.bias_updates = calloc(n*2, sizeof(float)); + l.outputs = h*w*n*(classes + 4 + 1); + l.inputs = l.outputs; + l.truths = 90*(4 + 1); + l.delta = calloc(batch*l.outputs, sizeof(float)); + l.output = calloc(batch*l.outputs, sizeof(float)); + for(i = 0; i < total*2; ++i){ + l.biases[i] = .5; + } + + l.forward = forward_yolo_layer; + l.backward = backward_yolo_layer; +#ifdef GPU + l.forward_gpu = forward_yolo_layer_gpu; + l.backward_gpu = backward_yolo_layer_gpu; + l.output_gpu = cuda_make_array(l.output, batch*l.outputs); + l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); +#endif + + fprintf(stderr, "yolo\n"); + srand(0); + + return l; +} + +void resize_yolo_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + + l->outputs = h*w*l->n*(l->classes + 4 + 1); + l->inputs = l->outputs; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); +#endif +} + +box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) +{ + box b; + b.x = (i + x[index + 0*stride]) / lw; + b.y = (j + x[index + 1*stride]) / lh; + b.w = exp(x[index + 2*stride]) * biases[2*n] / w; + b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; + return b; +} + +float delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride) +{ + box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); + float iou = box_iou(pred, truth); + + float tx = (truth.x*lw - i); + float ty = (truth.y*lh - j); + float tw = log(truth.w*w / biases[2*n]); + float th = log(truth.h*h / biases[2*n + 1]); + + delta[index + 0*stride] = scale * (tx - x[index + 0*stride]); + delta[index + 1*stride] = scale * (ty - x[index + 1*stride]); + delta[index + 2*stride] = scale * (tw - x[index + 2*stride]); + delta[index + 3*stride] = scale * (th - x[index + 3*stride]); + return iou; +} + + +void delta_yolo_class(float *output, float *delta, int index, int class, int classes, int stride, float *avg_cat) +{ + int n; + if (delta[index]){ + delta[index + stride*class] = 1 - output[index + stride*class]; + if(avg_cat) *avg_cat += output[index + stride*class]; + return; + } + for(n = 0; n < classes; ++n){ + delta[index + stride*n] = ((n == class)?1 : 0) - output[index + stride*n]; + if(n == class && avg_cat) *avg_cat += output[index + stride*n]; + } +} + +static int entry_index(layer l, int batch, int location, int entry) +{ + int n = location / (l.w*l.h); + int loc = location % (l.w*l.h); + return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc; +} + +void forward_yolo_layer(const layer l, network net) +{ + int i,j,b,t,n; + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + +#ifndef GPU + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC); + } + } +#endif + + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if(!net.train) return; + float avg_iou = 0; + float recall = 0; + float recall75 = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; + int class_count = 0; + *(l.cost) = 0; + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.w*l.h); + float best_iou = 0; + int best_t = 0; + for(t = 0; t < l.max_boxes; ++t){ + box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1); + if(!truth.x) break; + float iou = box_iou(pred, truth); + if (iou > best_iou) { + best_iou = iou; + best_t = t; + } + } + int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); + avg_anyobj += l.output[obj_index]; + l.delta[obj_index] = 0 - l.output[obj_index]; + if (best_iou > l.ignore_thresh) { + l.delta[obj_index] = 0; + } + if (best_iou > l.truth_thresh) { + l.delta[obj_index] = 1 - l.output[obj_index]; + + int class = net.truth[best_t*(4 + 1) + b*l.truths + 4]; + if (l.map) class = l.map[class]; + int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0); + box truth = float_to_box(net.truth + best_t*(4 + 1) + b*l.truths, 1); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + } + } + } + } + for(t = 0; t < l.max_boxes; ++t){ + box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1); + + if(!truth.x) break; + float best_iou = 0; + int best_n = 0; + i = (truth.x * l.w); + j = (truth.y * l.h); + box truth_shift = truth; + truth_shift.x = truth_shift.y = 0; + for(n = 0; n < l.total; ++n){ + box pred = {0}; + pred.w = l.biases[2*n]/net.w; + pred.h = l.biases[2*n+1]/net.h; + float iou = box_iou(pred, truth_shift); + if (iou > best_iou){ + best_iou = iou; + best_n = n; + } + } + + int mask_n = int_index(l.mask, best_n, l.n); + if(mask_n >= 0){ + int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + float iou = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + + int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); + avg_obj += l.output[obj_index]; + l.delta[obj_index] = 1 - l.output[obj_index]; + + int class = net.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class = l.map[class]; + int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat); + + ++count; + ++class_count; + if(iou > .5) recall += 1; + if(iou > .75) recall75 += 1; + avg_iou += iou; + } + } + } + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", net.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count); +} + +void backward_yolo_layer(const layer l, network net) +{ + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); +} + +void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +{ + int i; + int new_w=0; + int new_h=0; + if (((float)netw/w) < ((float)neth/h)) { + new_w = netw; + new_h = (h * netw)/w; + } else { + new_h = neth; + new_w = (w * neth)/h; + } + for (i = 0; i < n; ++i){ + box b = dets[i].bbox; + b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); + b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); + b.w *= (float)netw/new_w; + b.h *= (float)neth/new_h; + if(!relative){ + b.x *= w; + b.w *= w; + b.y *= h; + b.h *= h; + } + dets[i].bbox = b; + } +} + +int yolo_num_detections(layer l, float thresh) +{ + int i, n; + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4); + if(l.output[obj_index] > thresh){ + ++count; + } + } + } + return count; +} + +void avg_flipped_yolo(layer l) +{ + int i,j,n,z; + float *flip = l.output + l.outputs; + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w/2; ++i) { + for (n = 0; n < l.n; ++n) { + for(z = 0; z < l.classes + 4 + 1; ++z){ + int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; + int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); + float swap = flip[i1]; + flip[i1] = flip[i2]; + flip[i2] = swap; + if(z == 0){ + flip[i1] = -flip[i1]; + flip[i2] = -flip[i2]; + } + } + } + } + } + for(i = 0; i < l.outputs; ++i){ + l.output[i] = (l.output[i] + flip[i])/2.; + } +} + +int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets) +{ + int i,j,n; + float *predictions = l.output; + if (l.batch == 2) avg_flipped_yolo(l); + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + int row = i / l.w; + int col = i % l.w; + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4); + float objectness = predictions[obj_index]; + if(objectness <= thresh) continue; + int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); + dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].objectness = objectness; + dets[count].classes = l.classes; + for(j = 0; j < l.classes; ++j){ + int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j); + float prob = objectness*predictions[class_index]; + dets[count].prob[j] = (prob > thresh) ? prob : 0; + } + ++count; + } + } + correct_yolo_boxes(dets, count, w, h, netw, neth, relative); + return count; +} + +#ifdef GPU + +void forward_yolo_layer_gpu(const layer l, network net) +{ + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + int b, n; + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array_gpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); + } + } + if(!net.train || l.onlyforward){ + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + return; + } + + cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); + forward_yolo_layer(l, net); + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); +} + +void backward_yolo_layer_gpu(const layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif + diff --git a/image.darknet/inst/include/darknet/src/yolo_layer.h b/image.darknet/inst/include/darknet/src/yolo_layer.h new file mode 100644 index 0000000..d2a0243 --- /dev/null +++ b/image.darknet/inst/include/darknet/src/yolo_layer.h @@ -0,0 +1,19 @@ +#ifndef YOLO_LAYER_H +#define YOLO_LAYER_H + +#include "darknet.h" +#include "layer.h" +#include "network.h" + +layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes); +void forward_yolo_layer(const layer l, network net); +void backward_yolo_layer(const layer l, network net); +void resize_yolo_layer(layer *l, int w, int h); +int yolo_num_detections(layer l, float thresh); + +#ifdef GPU +void forward_yolo_layer_gpu(const layer l, network net); +void backward_yolo_layer_gpu(layer l, network net); +#endif + +#endif From 8a49e6c9f1c10b4eb83b3971828da9de564f28da Mon Sep 17 00:00:00 2001 From: Jeroen Staab Date: Mon, 24 Dec 2018 16:26:23 +0100 Subject: [PATCH 4/8] Copied file from YOLO repository ./include/darknet.h --- image.darknet/src/darknet.h | 805 ++++++++++++++++++++++++++++++++++++ 1 file changed, 805 insertions(+) create mode 100644 image.darknet/src/darknet.h diff --git a/image.darknet/src/darknet.h b/image.darknet/src/darknet.h new file mode 100644 index 0000000..4390c61 --- /dev/null +++ b/image.darknet/src/darknet.h @@ -0,0 +1,805 @@ +#ifndef DARKNET_API +#define DARKNET_API +#include +#include +#include +#include + +#ifdef GPU + #define BLOCK 512 + + #include "cuda_runtime.h" + #include "curand.h" + #include "cublas_v2.h" + + #ifdef CUDNN + #include "cudnn.h" + #endif +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +#define SECRET_NUM -1234 +extern int gpu_index; + +typedef struct{ + int classes; + char **names; +} metadata; + +metadata get_metadata(char *file); + +typedef struct{ + int *leaf; + int n; + int *parent; + int *child; + int *group; + char **name; + + int groups; + int *group_size; + int *group_offset; +} tree; +tree *read_tree(char *filename); + +typedef enum{ + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU +} ACTIVATION; + +typedef enum{ + PNG, BMP, TGA, JPG +} IMTYPE; + +typedef enum{ + MULT, ADD, SUB, DIV +} BINARY_ACTIVATION; + +typedef enum { + CONVOLUTIONAL, + DECONVOLUTIONAL, + CONNECTED, + MAXPOOL, + SOFTMAX, + DETECTION, + DROPOUT, + CROP, + ROUTE, + COST, + NORMALIZATION, + AVGPOOL, + LOCAL, + SHORTCUT, + ACTIVE, + RNN, + GRU, + LSTM, + CRNN, + BATCHNORM, + NETWORK, + XNOR, + REGION, + YOLO, + ISEG, + REORG, + UPSAMPLE, + LOGXENT, + L2NORM, + BLANK +} LAYER_TYPE; + +typedef enum{ + SSE, MASKED, L1, SEG, SMOOTH,WGAN +} COST_TYPE; + +typedef struct{ + int batch; + float learning_rate; + float momentum; + float decay; + int adam; + float B1; + float B2; + float eps; + int t; +} update_args; + +struct network; +typedef struct network network; + +struct layer; +typedef struct layer layer; + +struct layer{ + LAYER_TYPE type; + ACTIVATION activation; + COST_TYPE cost_type; + void (*forward) (struct layer, struct network); + void (*backward) (struct layer, struct network); + void (*update) (struct layer, update_args); + void (*forward_gpu) (struct layer, struct network); + void (*backward_gpu) (struct layer, struct network); + void (*update_gpu) (struct layer, update_args); + int batch_normalize; + int shortcut; + int batch; + int forced; + int flipped; + int inputs; + int outputs; + int nweights; + int nbiases; + int extra; + int truths; + int h,w,c; + int out_h, out_w, out_c; + int n; + int max_boxes; + int groups; + int size; + int side; + int stride; + int reverse; + int flatten; + int spatial; + int pad; + int sqrt; + int flip; + int index; + int binary; + int xnor; + int steps; + int hidden; + int truth; + float smooth; + float dot; + float angle; + float jitter; + float saturation; + float exposure; + float shift; + float ratio; + float learning_rate_scale; + float clip; + int noloss; + int softmax; + int classes; + int coords; + int background; + int rescore; + int objectness; + int joint; + int noadjust; + int reorg; + int log; + int tanh; + int *mask; + int total; + + float alpha; + float beta; + float kappa; + + float coord_scale; + float object_scale; + float noobject_scale; + float mask_scale; + float class_scale; + int bias_match; + int random; + float ignore_thresh; + float truth_thresh; + float thresh; + float focus; + int classfix; + int absolute; + + int onlyforward; + int stopbackward; + int dontload; + int dontsave; + int dontloadscales; + int numload; + + float temperature; + float probability; + float scale; + + char * cweights; + int * indexes; + int * input_layers; + int * input_sizes; + int * map; + int * counts; + float ** sums; + float * rand; + float * cost; + float * state; + float * prev_state; + float * forgot_state; + float * forgot_delta; + float * state_delta; + float * combine_cpu; + float * combine_delta_cpu; + + float * concat; + float * concat_delta; + + float * binary_weights; + + float * biases; + float * bias_updates; + + float * scales; + float * scale_updates; + + float * weights; + float * weight_updates; + + float * delta; + float * output; + float * loss; + float * squared; + float * norms; + + float * spatial_mean; + float * mean; + float * variance; + + float * mean_delta; + float * variance_delta; + + float * rolling_mean; + float * rolling_variance; + + float * x; + float * x_norm; + + float * m; + float * v; + + float * bias_m; + float * bias_v; + float * scale_m; + float * scale_v; + + + float *z_cpu; + float *r_cpu; + float *h_cpu; + float * prev_state_cpu; + + float *temp_cpu; + float *temp2_cpu; + float *temp3_cpu; + + float *dh_cpu; + float *hh_cpu; + float *prev_cell_cpu; + float *cell_cpu; + float *f_cpu; + float *i_cpu; + float *g_cpu; + float *o_cpu; + float *c_cpu; + float *dc_cpu; + + float * binary_input; + + struct layer *input_layer; + struct layer *self_layer; + struct layer *output_layer; + + struct layer *reset_layer; + struct layer *update_layer; + struct layer *state_layer; + + struct layer *input_gate_layer; + struct layer *state_gate_layer; + struct layer *input_save_layer; + struct layer *state_save_layer; + struct layer *input_state_layer; + struct layer *state_state_layer; + + struct layer *input_z_layer; + struct layer *state_z_layer; + + struct layer *input_r_layer; + struct layer *state_r_layer; + + struct layer *input_h_layer; + struct layer *state_h_layer; + + struct layer *wz; + struct layer *uz; + struct layer *wr; + struct layer *ur; + struct layer *wh; + struct layer *uh; + struct layer *uo; + struct layer *wo; + struct layer *uf; + struct layer *wf; + struct layer *ui; + struct layer *wi; + struct layer *ug; + struct layer *wg; + + tree *softmax_tree; + + size_t workspace_size; + +#ifdef GPU + int *indexes_gpu; + + float *z_gpu; + float *r_gpu; + float *h_gpu; + + float *temp_gpu; + float *temp2_gpu; + float *temp3_gpu; + + float *dh_gpu; + float *hh_gpu; + float *prev_cell_gpu; + float *cell_gpu; + float *f_gpu; + float *i_gpu; + float *g_gpu; + float *o_gpu; + float *c_gpu; + float *dc_gpu; + + float *m_gpu; + float *v_gpu; + float *bias_m_gpu; + float *scale_m_gpu; + float *bias_v_gpu; + float *scale_v_gpu; + + float * combine_gpu; + float * combine_delta_gpu; + + float * prev_state_gpu; + float * forgot_state_gpu; + float * forgot_delta_gpu; + float * state_gpu; + float * state_delta_gpu; + float * gate_gpu; + float * gate_delta_gpu; + float * save_gpu; + float * save_delta_gpu; + float * concat_gpu; + float * concat_delta_gpu; + + float * binary_input_gpu; + float * binary_weights_gpu; + + float * mean_gpu; + float * variance_gpu; + + float * rolling_mean_gpu; + float * rolling_variance_gpu; + + float * variance_delta_gpu; + float * mean_delta_gpu; + + float * x_gpu; + float * x_norm_gpu; + float * weights_gpu; + float * weight_updates_gpu; + float * weight_change_gpu; + + float * biases_gpu; + float * bias_updates_gpu; + float * bias_change_gpu; + + float * scales_gpu; + float * scale_updates_gpu; + float * scale_change_gpu; + + float * output_gpu; + float * loss_gpu; + float * delta_gpu; + float * rand_gpu; + float * squared_gpu; + float * norms_gpu; +#ifdef CUDNN + cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; + cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; + cudnnTensorDescriptor_t normTensorDesc; + cudnnFilterDescriptor_t weightDesc; + cudnnFilterDescriptor_t dweightDesc; + cudnnConvolutionDescriptor_t convDesc; + cudnnConvolutionFwdAlgo_t fw_algo; + cudnnConvolutionBwdDataAlgo_t bd_algo; + cudnnConvolutionBwdFilterAlgo_t bf_algo; +#endif +#endif +}; + +void free_layer(layer); + +typedef enum { + CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM +} learning_rate_policy; + +typedef struct network{ + int n; + int batch; + size_t *seen; + int *t; + float epoch; + int subdivisions; + layer *layers; + float *output; + learning_rate_policy policy; + + float learning_rate; + float momentum; + float decay; + float gamma; + float scale; + float power; + int time_steps; + int step; + int max_batches; + float *scales; + int *steps; + int num_steps; + int burn_in; + + int adam; + float B1; + float B2; + float eps; + + int inputs; + int outputs; + int truths; + int notruth; + int h, w, c; + int max_crop; + int min_crop; + float max_ratio; + float min_ratio; + int center; + float angle; + float aspect; + float exposure; + float saturation; + float hue; + int random; + + int gpu_index; + tree *hierarchy; + + float *input; + float *truth; + float *delta; + float *workspace; + int train; + int index; + float *cost; + float clip; + +#ifdef GPU + float *input_gpu; + float *truth_gpu; + float *delta_gpu; + float *output_gpu; +#endif + +} network; + +typedef struct { + int w; + int h; + float scale; + float rad; + float dx; + float dy; + float aspect; +} augment_args; + +typedef struct { + int w; + int h; + int c; + float *data; +} image; + +typedef struct{ + float x, y, w, h; +} box; + +typedef struct detection{ + box bbox; + int classes; + float *prob; + float *mask; + float objectness; + int sort_class; +} detection; + +typedef struct matrix{ + int rows, cols; + float **vals; +} matrix; + + +typedef struct{ + int w, h; + matrix X; + matrix y; + int shallow; + int *num_boxes; + box **boxes; +} data; + +typedef enum { + CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA, ISEG_DATA +} data_type; + +typedef struct load_args{ + int threads; + char **paths; + char *path; + int n; + int m; + char **labels; + int h; + int w; + int out_w; + int out_h; + int nh; + int nw; + int num_boxes; + int min, max, size; + int classes; + int background; + int scale; + int center; + int coords; + float jitter; + float angle; + float aspect; + float saturation; + float exposure; + float hue; + data *d; + image *im; + image *resized; + data_type type; + tree *hierarchy; +} load_args; + +typedef struct{ + int id; + float x,y,w,h; + float left, right, top, bottom; +} box_label; + + +network *load_network(char *cfg, char *weights, int clear); +load_args get_base_args(network *net); + +void free_data(data d); + +typedef struct node{ + void *val; + struct node *next; + struct node *prev; +} node; + +typedef struct list{ + int size; + node *front; + node *back; +} list; + +pthread_t load_data(load_args args); +list *read_data_cfg(char *filename); +list *read_cfg(char *filename); +unsigned char *read_file(char *filename); +data resize_data(data orig, int w, int h); +data *tile_data(data orig, int divs, int size); +data select_data(data *orig, int *inds); + +void forward_network(network *net); +void backward_network(network *net); +void update_network(network *net); + + +float dot_cpu(int N, float *X, int INCX, float *Y, int INCY); +void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); +void copy_cpu(int N, float *X, int INCX, float *Y, int INCY); +void scal_cpu(int N, float ALPHA, float *X, int INCX); +void fill_cpu(int N, float ALPHA, float * X, int INCX); +void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial); +void softmax(float *input, int n, float temp, int stride, float *output); + +int best_3d_shift_r(image a, image b, int min, int max); +#ifdef GPU +void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); +void fill_gpu(int N, float ALPHA, float * X, int INCX); +void scal_gpu(int N, float ALPHA, float * X, int INCX); +void copy_gpu(int N, float * X, int INCX, float * Y, int INCY); + +void cuda_set_device(int n); +void cuda_free(float *x_gpu); +float *cuda_make_array(float *x, size_t n); +void cuda_pull_array(float *x_gpu, float *x, size_t n); +float cuda_mag_array(float *x_gpu, size_t n); +void cuda_push_array(float *x_gpu, float *x, size_t n); + +void forward_network_gpu(network *net); +void backward_network_gpu(network *net); +void update_network_gpu(network *net); + +float train_networks(network **nets, int n, data d, int interval); +void sync_nets(network **nets, int n, int interval); +void harmless_update_network_gpu(network *net); +#endif +image get_label(image **characters, char *string, int size); +void draw_label(image a, int r, int c, image label, const float *rgb); +void save_image(image im, const char *name); +void save_image_options(image im, const char *name, IMTYPE f, int quality); +void get_next_batch(data d, int n, int offset, float *X, float *y); +void grayscale_image_3c(image im); +void normalize_image(image p); +void matrix_to_csv(matrix m); +float train_network_sgd(network *net, data d, int n); +void rgbgr_image(image im); +data copy_data(data d); +data concat_data(data d1, data d2); +data load_cifar10_data(char *filename); +float matrix_topk_accuracy(matrix truth, matrix guess, int k); +void matrix_add_matrix(matrix from, matrix to); +void scale_matrix(matrix m, float scale); +matrix csv_to_matrix(char *filename); +float *network_accuracies(network *net, data d, int n); +float train_network_datum(network *net); +image make_random_image(int w, int h, int c); + +void denormalize_connected_layer(layer l); +void denormalize_convolutional_layer(layer l); +void statistics_connected_layer(layer l); +void rescale_weights(layer l, float scale, float trans); +void rgbgr_weights(layer l); +image *get_weights(layer l); + +void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, int avg, float hier_thresh, int w, int h, int fps, int fullscreen); +void get_detection_detections(layer l, int w, int h, float thresh, detection *dets); + +char *option_find_str(list *l, char *key, char *def); +int option_find_int(list *l, char *key, int def); +int option_find_int_quiet(list *l, char *key, int def); + +network *parse_network_cfg(char *filename); +void save_weights(network *net, char *filename); +void load_weights(network *net, char *filename); +void save_weights_upto(network *net, char *filename, int cutoff); +void load_weights_upto(network *net, char *filename, int start, int cutoff); + +void zero_objectness(layer l); +void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets); +int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets); +void free_network(network *net); +void set_batch_network(network *net, int b); +void set_temp_network(network *net, float t); +image load_image(char *filename, int w, int h, int c); +image load_image_color(char *filename, int w, int h); +image make_image(int w, int h, int c); +image resize_image(image im, int w, int h); +void censor_image(image im, int dx, int dy, int w, int h); +image letterbox_image(image im, int w, int h); +image crop_image(image im, int dx, int dy, int w, int h); +image center_crop_image(image im, int w, int h); +image resize_min(image im, int min); +image resize_max(image im, int max); +image threshold_image(image im, float thresh); +image mask_to_rgb(image mask); +int resize_network(network *net, int w, int h); +void free_matrix(matrix m); +void test_resize(char *filename); +int show_image(image p, const char *name, int ms); +image copy_image(image p); +void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b); +float get_current_rate(network *net); +void composite_3d(char *f1, char *f2, char *out, int delta); +data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); +size_t get_current_batch(network *net); +void constrain_image(image im); +image get_network_image_layer(network *net, int i); +layer get_network_output_layer(network *net); +void top_predictions(network *net, int n, int *index); +void flip_image(image a); +image float_to_image(int w, int h, int c, float *data); +void ghost_image(image source, image dest, int dx, int dy); +float network_accuracy(network *net, data d); +void random_distort_image(image im, float hue, float saturation, float exposure); +void fill_image(image m, float s); +image grayscale_image(image im); +void rotate_image_cw(image im, int times); +double what_time_is_it_now(); +image rotate_image(image m, float rad); +void visualize_network(network *net); +float box_iou(box a, box b); +data load_all_cifar10(); +box_label *read_boxes(char *filename, int *n); +box float_to_box(float *f, int stride); +void draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes); + +matrix network_predict_data(network *net, data test); +image **load_alphabet(); +image get_network_image(network *net); +float *network_predict(network *net, float *input); + +int network_width(network *net); +int network_height(network *net); +float *network_predict_image(network *net, image im); +void network_detect(network *net, image im, float thresh, float hier_thresh, float nms, detection *dets); +detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num); +void free_detections(detection *dets, int n); + +void reset_network_state(network *net, int b); + +char **get_labels(char *filename); +void do_nms_obj(detection *dets, int total, int classes, float thresh); +void do_nms_sort(detection *dets, int total, int classes, float thresh); + +matrix make_matrix(int rows, int cols); + +#ifdef OPENCV +void *open_video_stream(const char *f, int c, int w, int h, int fps); +image get_image_from_stream(void *p); +void make_window(char *name, int w, int h, int fullscreen); +#endif + +void free_image(image m); +float train_network(network *net, data d); +pthread_t load_data_in_thread(load_args args); +void load_data_blocking(load_args args); +list *get_paths(char *filename); +void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride); +void change_leaves(tree *t, char *leaf_list); + +int find_int_arg(int argc, char **argv, char *arg, int def); +float find_float_arg(int argc, char **argv, char *arg, float def); +int find_arg(int argc, char* argv[], char *arg); +char *find_char_arg(int argc, char **argv, char *arg, char *def); +char *basecfg(char *cfgfile); +void find_replace(char *str, char *orig, char *rep, char *output); +void free_ptrs(void **ptrs, int n); +char *fgetl(FILE *fp); +void strip(char *s); +float sec(clock_t clocks); +void **list_to_array(list *l); +void top_k(float *a, int n, int k, int *index); +int *read_map(char *filename); +void error(const char *s); +int max_index(float *a, int n); +int max_int_index(int *a, int n); +int sample_array(float *a, int n); +int *random_index_order(int min, int max); +void free_list(list *l); +float mse_array(float *a, int n); +float variance_array(float *a, int n); +float mag_array(float *a, int n); +void scale_array(float *a, int n, float s); +float mean_array(float *a, int n); +float sum_array(float *a, int n); +void normalize_array(float *a, int n); +int *read_intlist(char *s, int *n, int d); +size_t rand_size_t(); +float rand_normal(); +float rand_uniform(float min, float max); + +#ifdef __cplusplus +} +#endif +#endif From ba658f9af6d9e393b2136fde194e69df34982fd5 Mon Sep 17 00:00:00 2001 From: Jeroen Staab Date: Mon, 24 Dec 2018 16:26:47 +0100 Subject: [PATCH 5/8] Debugged using code lines from detector.c --- image.darknet/src/__R_API_detector.c | 32 +++++++---------- image.darknet/src/compare.c | 54 +++++++++++----------------- 2 files changed, 34 insertions(+), 52 deletions(-) diff --git a/image.darknet/src/__R_API_detector.c b/image.darknet/src/__R_API_detector.c index 905e582..b0b0f1b 100644 --- a/image.darknet/src/__R_API_detector.c +++ b/image.darknet/src/__R_API_detector.c @@ -32,11 +32,9 @@ image **load_alphabet_pkg(char *path) int darknet_test_detector(char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char **names, char *path) { image **alphabet = load_alphabet_pkg(path); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + + set_batch_network(net, 1); srand(2222222); clock_t time; char buff[256]; @@ -47,8 +45,9 @@ int darknet_test_detector(char *cfgfile, char *weightfile, char *filename, float while(1){ strncpy(input, filename, 256); image im = load_image_color(input,0,0); - image sized = resize_image(im, net.w, net.h); - layer l = net.layers[net.n-1]; + + image sized = letterbox_image(im, net->w, net->h); + layer l = net->layers[net->n-1]; box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); @@ -58,20 +57,15 @@ int darknet_test_detector(char *cfgfile, char *weightfile, char *filename, float time=clock(); network_predict(net, X); printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0, hier_thresh); - if (l.softmax_tree && nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms); - else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms); - for(int i = 0; i < l.w*l.h*l.n; ++i){ - int class = max_index(probs[i], l.classes); - float prob = probs[i][class]; - if(prob > thresh){ - boxes_abovethreshold = boxes_abovethreshold + 1; - } - } + int nboxes = 0; + detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes); + //printf("%d\n", nboxes); + //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes); + free_detections(dets, nboxes); - printf("Boxes: %d of which %d above the threshold.\n", l.w*l.h*l.n, boxes_abovethreshold); - draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes); save_image(im, "predictions"); free_image(im); diff --git a/image.darknet/src/compare.c b/image.darknet/src/compare.c index d2d2b3b..ef1de6c 100644 --- a/image.darknet/src/compare.c +++ b/image.darknet/src/compare.c @@ -14,10 +14,8 @@ void train_compare(char *cfgfile, char *weightfile) char *base = basecfg(cfgfile); char *backup_directory = "/home/pjreddie/backup/"; printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } + network net = *load_network(cfgfile, weightfile, 0); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); int imgs = 1024; list *plist = get_paths("data/compare.train.list"); @@ -51,7 +49,7 @@ void train_compare(char *cfgfile, char *weightfile) load_thread = load_data_in_thread(args); printf("Loaded: %lf seconds\n", sec(clock()-time)); time=clock(); - float loss = train_network(net, train); + float loss = train_network(&net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); @@ -59,32 +57,29 @@ void train_compare(char *cfgfile, char *weightfile) if(i%100 == 0){ char buff[256]; sprintf(buff, "%s/%s_%d_minor_%d.weights",backup_directory,base, epoch, i); - save_weights(net, buff); + save_weights(&net, buff); } if(*net.seen/N > epoch){ epoch = *net.seen/N; i = 0; char buff[256]; sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); + save_weights(&net, buff); if(epoch%22 == 0) net.learning_rate *= .1; } } pthread_join(load_thread, 0); free_data(buffer); - free_network(net); + free_network(&net); free_ptrs((void**)paths, plist->size); free_list(plist); free(base); } -void validate_compare(char *filename, char *weightfile) +void validate_compare(char *cfgfile, char *weightfile) { int i = 0; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } + network net = *load_network(cfgfile, weightfile, 0); srand(time(0)); list *plist = get_paths("data/compare.val.list"); @@ -127,7 +122,7 @@ void validate_compare(char *filename, char *weightfile) printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time)); time=clock(); - matrix pred = network_predict_data(net, val); + matrix pred = network_predict_data(&net, val); int j,k; for(j = 0; j < val.y.rows; ++j){ for(k = 0; k < 20; ++k){ @@ -179,7 +174,7 @@ int bbox_comparator(const void *a, const void *b) float *X = calloc(net.w*net.h*net.c, sizeof(float)); memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float)); memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float)); - float *predictions = network_predict(net, X); + float *predictions = network_predict(&net, X); free_image(im1); free_image(im2); @@ -208,7 +203,7 @@ void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, in float *X = calloc(net.w*net.h*net.c, sizeof(float)); memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float)); memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float)); - float *predictions = network_predict(net, X); + float *predictions = network_predict(&net, X); ++total_compares; int i; @@ -224,15 +219,12 @@ void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, in free(X); } -void SortMaster3000(char *filename, char *weightfile) +void SortMaster3000(char *cfgfile, char *weightfile) { int i = 0; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, 0); srand(time(0)); - set_batch_network(&net, 1); + set_batch_network(net, 1); list *plist = get_paths("data/compare.sort.list"); //list *plist = get_paths("data/compare.val.old"); @@ -243,7 +235,7 @@ void SortMaster3000(char *filename, char *weightfile) printf("Sorting %d boxes...\n", N); for(i = 0; i < N; ++i){ boxes[i].filename = paths[i]; - boxes[i].net = net; + boxes[i].net = *net; boxes[i].class = 7; boxes[i].elo = 1500; } @@ -255,17 +247,13 @@ void SortMaster3000(char *filename, char *weightfile) printf("Sorted in %d compares, %f secs\n", total_compares, sec(clock()-time)); } -void BattleRoyaleWithCheese(char *filename, char *weightfile) +void BattleRoyaleWithCheese(char *cfgfile, char *weightfile) { int classes = 20; int i,j; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); - } + network *net = load_network(cfgfile, weightfile, 0); srand(time(0)); - set_batch_network(&net, 1); - + set_batch_network(net, 1); list *plist = get_paths("data/compare.sort.list"); //list *plist = get_paths("data/compare.small.list"); //list *plist = get_paths("data/compare.cat.list"); @@ -278,7 +266,7 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile) printf("Battling %d boxes...\n", N); for(i = 0; i < N; ++i){ boxes[i].filename = paths[i]; - boxes[i].net = net; + boxes[i].net = *net; boxes[i].classes = classes; boxes[i].elos = calloc(classes, sizeof(float));; for(j = 0; j < classes; ++j){ @@ -292,7 +280,7 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile) printf("Round: %d\n", round); shuffle(boxes, N, sizeof(sortable_bbox)); for(i = 0; i < N/2; ++i){ - bbox_fight(net, boxes+i*2, boxes+i*2+1, classes, -1); + bbox_fight(*net, boxes+i*2, boxes+i*2+1, classes, -1); } printf("Round: %f secs, %d remaining\n", sec(clock()-round_time), N); } @@ -312,7 +300,7 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile) sorta_shuffle(boxes, N, sizeof(sortable_bbox), 10); for(i = 0; i < N/2; ++i){ - bbox_fight(net, boxes+i*2, boxes+i*2+1, classes, class); + bbox_fight(*net, boxes+i*2, boxes+i*2+1, classes, class); } qsort(boxes, N, sizeof(sortable_bbox), elo_comparator); if(round <= 20) N = (N*9/10)/2*2; From 2ca7f7379615c11387fa1030dad0e17e2682b2ad Mon Sep 17 00:00:00 2001 From: Jeroen Staab Date: Mon, 24 Dec 2018 16:55:23 +0100 Subject: [PATCH 6/8] Successfully updated package to work with yolov3 --- image.darknet/R/yolo_detect.R | 4 ++-- image.darknet/man/image_darknet_detect.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/image.darknet/R/yolo_detect.R b/image.darknet/R/yolo_detect.R index 98ea2eb..42ce291 100644 --- a/image.darknet/R/yolo_detect.R +++ b/image.darknet/R/yolo_detect.R @@ -32,8 +32,8 @@ #' weights <- file.path(system.file(package="image.darknet", "models"), "yolo.weights") #' download.file(url = "http://pjreddie.com/media/files/yolo.weights", destfile = weights) #' yolo_coco <- image_darknet_model(type = 'detect', -#' model = "yolo.cfg", -#' weights = system.file(package="image.darknet", "models", "yolo.weights"), +#' model = "yolov3.cfg", +#' weights = system.file(package="image.darknet", "models", "yolov3.weights"), #' labels = system.file(package="image.darknet", "include", "darknet", "data", "coco.names")) #' yolo_coco #' diff --git a/image.darknet/man/image_darknet_detect.Rd b/image.darknet/man/image_darknet_detect.Rd index 91b0cd3..52f770b 100644 --- a/image.darknet/man/image_darknet_detect.Rd +++ b/image.darknet/man/image_darknet_detect.Rd @@ -45,8 +45,8 @@ x <- image_darknet_detect(file = f, object = yolo_tiny_voc) weights <- file.path(system.file(package="image.darknet", "models"), "yolo.weights") download.file(url = "http://pjreddie.com/media/files/yolo.weights", destfile = weights) yolo_coco <- image_darknet_model(type = 'detect', - model = "yolo.cfg", - weights = system.file(package="image.darknet", "models", "yolo.weights"), + model = "yolov3.cfg", + weights = system.file(package="image.darknet", "models", "yolov3.weights"), labels = system.file(package="image.darknet", "include", "darknet", "data", "coco.names")) yolo_coco From 4327b83d70e7e36b52023e0dca9704d98092a0e0 Mon Sep 17 00:00:00 2001 From: Jeroen Staab Date: Wed, 26 Dec 2018 04:16:15 +0100 Subject: [PATCH 7/8] Successfully updated package to work with yolov3 --- image.darknet/inst/models/.gitignore | 1 + image.darknet/src/__R_API_classifier.c | 15 ++++++--------- 2 files changed, 7 insertions(+), 9 deletions(-) create mode 100644 image.darknet/inst/models/.gitignore diff --git a/image.darknet/inst/models/.gitignore b/image.darknet/inst/models/.gitignore new file mode 100644 index 0000000..7e26728 --- /dev/null +++ b/image.darknet/inst/models/.gitignore @@ -0,0 +1 @@ +yolov3.weights diff --git a/image.darknet/src/__R_API_classifier.c b/image.darknet/src/__R_API_classifier.c index cfffa5a..4e88201 100644 --- a/image.darknet/src/__R_API_classifier.c +++ b/image.darknet/src/__R_API_classifier.c @@ -22,11 +22,8 @@ image get_image_from_stream(CvCapture *cap); void darknet_predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top, char **pred_lab, double *pred_score, char **names, int resize){ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); + network *net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); srand(2222222); /* list *options = read_data_cfg(datacfg); @@ -41,21 +38,21 @@ void darknet_predict_classifier(char *datacfg, char *cfgfile, char *weightfile, int *indexes = calloc(top, sizeof(int)); char buff[256]; char *input = buff; - int size = net.w; + int size = net->w; while(1){ strncpy(input, filename, 256); image im = load_image_color(input, 0, 0); image r = resize_min(im, size); if(resize > 0) { - resize_network(&net, r.w, r.h); + resize_network(net, r.w, r.h); } //printf("%d %d\n", r.w, r.h); float *X = r.data; time=clock(); float *predictions = network_predict(net, X); - if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0); - top_k(predictions, net.outputs, top, indexes); + if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 0, 1); + top_k(predictions, net->outputs, top, indexes); //printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); for(i = 0; i < top; ++i){ int index = indexes[i]; From f40bb4ecf4e7623d9146ebd5dbfc11b27e583eee Mon Sep 17 00:00:00 2001 From: Jee Roen Date: Fri, 28 Dec 2018 20:21:02 +0100 Subject: [PATCH 8/8] Delete Makefile.bak --- .../inst/include/darknet/Makefile.bak | 82 ------------------- 1 file changed, 82 deletions(-) delete mode 100644 image.darknet/inst/include/darknet/Makefile.bak diff --git a/image.darknet/inst/include/darknet/Makefile.bak b/image.darknet/inst/include/darknet/Makefile.bak deleted file mode 100644 index 3d3d5e4..0000000 --- a/image.darknet/inst/include/darknet/Makefile.bak +++ /dev/null @@ -1,82 +0,0 @@ -GPU=0 -CUDNN=0 -OPENCV=0 -DEBUG=0 - -ARCH= -gencode arch=compute_20,code=[sm_20,sm_21] \ - -gencode arch=compute_30,code=sm_30 \ - -gencode arch=compute_35,code=sm_35 \ - -gencode arch=compute_50,code=[sm_50,compute_50] \ - -gencode arch=compute_52,code=[sm_52,compute_52] - -# This is what I use, uncomment if you know your arch and want to specify -# ARCH= -gencode arch=compute_52,code=compute_52 - -VPATH=./src/ -EXEC=darknet -OBJDIR=./obj/ - -CC=gcc -NVCC=nvcc -OPTS=-Ofast -LDFLAGS= -lm -pthread -COMMON= -CFLAGS=-Wall -Wfatal-errors - -ifeq ($(DEBUG), 1) -OPTS=-O0 -g -endif - -CFLAGS+=$(OPTS) - -ifeq ($(OPENCV), 1) -COMMON+= -DOPENCV -CFLAGS+= -DOPENCV -LDFLAGS+= `pkg-config --libs opencv` -COMMON+= `pkg-config --cflags opencv` -endif - -ifeq ($(GPU), 1) -COMMON+= -DGPU -I/usr/local/cuda/include/ -CFLAGS+= -DGPU -LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -endif - -ifeq ($(CUDNN), 1) -COMMON+= -DCUDNN -CFLAGS+= -DCUDNN -LDFLAGS+= -lcudnn -endif - -OBJ=gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o tree.o -ifeq ($(GPU), 1) -LDFLAGS+= -lstdc++ -OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o -endif - -OBJS = $(addprefix $(OBJDIR), $(OBJ)) -DEPS = $(wildcard src/*.h) Makefile - -all: obj backup results $(EXEC) - -$(EXEC): $(OBJS) - $(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) - -$(OBJDIR)%.o: %.c $(DEPS) - $(CC) $(COMMON) $(CFLAGS) -c $< -o $@ - -$(OBJDIR)%.o: %.cu $(DEPS) - $(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@ - -obj: - mkdir -p obj -backup: - mkdir -p backup -results: - mkdir -p results - -.PHONY: clean - -clean: - rm -rf $(OBJS) $(EXEC) -