diff --git a/alignment/coordinate_reg/image_infer.py b/alignment/coordinate_reg/image_infer.py
index a336221e8..3665b1222 100644
--- a/alignment/coordinate_reg/image_infer.py
+++ b/alignment/coordinate_reg/image_infer.py
@@ -15,7 +15,7 @@
     color = (200, 160, 75)
     for face in faces:
         lmk = face.landmark_2d_106
-        lmk = np.round(lmk).astype(np.int)
+        lmk = np.round(lmk).astype(np.int32)
         for i in range(lmk.shape[0]):
             p = tuple(lmk[i])
             cv2.circle(tim, p, 1, color, 1, cv2.LINE_AA)
diff --git a/alignment/heatmap/metric.py b/alignment/heatmap/metric.py
index 2ddc96c9f..f2431aa08 100644
--- a/alignment/heatmap/metric.py
+++ b/alignment/heatmap/metric.py
@@ -51,7 +51,7 @@ def cal_nme(self, label, pred_label):
                     ind_gt = np.array(ind_gt)
                 else:
                     ind_gt = label[b][p]
-                    #ind_gt = ind_gt.astype(np.int)
+                    #ind_gt = ind_gt.astype(np.int32)
                     #print(ind_gt)
                 heatmap_pred = pred_label[b][p]
                 heatmap_pred = cv2.resize(
diff --git a/alignment/synthetics/datasets/augs.py b/alignment/synthetics/datasets/augs.py
index 08f47fccd..1ac0a047b 100644
--- a/alignment/synthetics/datasets/augs.py
+++ b/alignment/synthetics/datasets/augs.py
@@ -24,7 +24,7 @@ def apply(self, image, border_size_limit, **params):
         border_size[2] *= image.shape[1]
         border_size[1] *= image.shape[0]
         border_size[3] *= image.shape[0]
-        border_size = border_size.astype(np.int)
+        border_size = border_size.astype(np.int32)
         image[:,:border_size[0],:] = self.fill_value
         image[:border_size[1],:,:] = self.fill_value
         image[:,-border_size[2]:,:] = self.fill_value
diff --git a/alignment/synthetics/test_synthetics.py b/alignment/synthetics/test_synthetics.py
index e1a46f810..abdfb40ba 100644
--- a/alignment/synthetics/test_synthetics.py
+++ b/alignment/synthetics/test_synthetics.py
@@ -85,10 +85,10 @@
     outf.write(' ')
     outf.write(' '.join(["%.5f"%x for x in kps.flatten()]))
     outf.write("\n")
-    box = bbox.astype(np.int)
+    box = bbox.astype(np.int32)
     color = (0, 0, 255)
     cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2)
-    kps = kps.astype(np.int)
+    kps = kps.astype(np.int32)
     #print(landmark.shape)
     for l in range(kps.shape[0]):
         color = (0, 0, 255)
diff --git a/alignment/synthetics/tools/prepare_synthetics.py b/alignment/synthetics/tools/prepare_synthetics.py
index 5e977d611..8dcafa101 100644
--- a/alignment/synthetics/tools/prepare_synthetics.py
+++ b/alignment/synthetics/tools/prepare_synthetics.py
@@ -48,11 +48,11 @@
     _scale = output_size  / (max(w, h)*1.5)
     aimg, M = face_align.transform(dimg, center, output_size, _scale, rotate)
     pred = face_align.trans_points(pred, M)
-    #box = bbox.astype(np.int)
+    #box = bbox.astype(np.int32)
     #color = (0, 0, 255)
     #cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2)
 
-    #kps = pred.astype(np.int)
+    #kps = pred.astype(np.int32)
     #for l in range(kps.shape[0]):
     #    color = (0, 0, 255)
     #    cv2.circle(aimg, (kps[l][0], kps[l][1]), 1, color, 2)
diff --git a/detection/retinaface/rcnn/PY_OP/cascade_refine.py b/detection/retinaface/rcnn/PY_OP/cascade_refine.py
index e3c6556fa..a073963a6 100644
--- a/detection/retinaface/rcnn/PY_OP/cascade_refine.py
+++ b/detection/retinaface/rcnn/PY_OP/cascade_refine.py
@@ -127,8 +127,8 @@ def assign_anchor_fpn(self,
         if gt_boxes.size > 0:
             # overlap between the anchors and the gt boxes
             # overlaps (ex, gt)
-            overlaps = bbox_overlaps(anchors.astype(np.float),
-                                     gt_boxes.astype(np.float))
+            overlaps = bbox_overlaps(anchors.astype(np.float32),
+                                     gt_boxes.astype(np.float32))
             argmax_overlaps = overlaps.argmax(axis=1)
             #print('AAA', argmax_overlaps.shape)
             max_overlaps = overlaps[np.arange(num_anchors), argmax_overlaps]
@@ -344,13 +344,13 @@ def forward(self, is_train, req, in_data, out_data, aux):
                 assert anchors_t1.shape[0] == self.ori_anchors.shape[0]
 
                 #for i in range(_gt_boxes.shape[0]):
-                #  box = _gt_boxes[i].astype(np.int)
+                #  box = _gt_boxes[i].astype(np.int32)
                 #  print('%d: gt%d'%(self.nbatch, i), box)
                 #  #color = (0,0,255)
                 #  #cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
                 #for i in range(anchors_t1.shape[0]):
-                #  box1 = self.ori_anchors[i].astype(np.int)
-                #  box2 = anchors_t1[i].astype(np.int)
+                #  box1 = self.ori_anchors[i].astype(np.int32)
+                #  box2 = anchors_t1[i].astype(np.int32)
                 #  print('%d %d: anchorscompare %d'%(self.nbatch, self.stride, i), box1, box2)
                 #color = (255,255,0)
                 #cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
diff --git a/detection/retinaface/rcnn/core/loader.py b/detection/retinaface/rcnn/core/loader.py
index 1d34d2eb3..7b655c456 100644
--- a/detection/retinaface/rcnn/core/loader.py
+++ b/detection/retinaface/rcnn/core/loader.py
@@ -247,7 +247,7 @@ def get_batch(self):
                 print('DEBUG SHAPE', data['data'].shape,
                       label['gt_boxes'].shape)
 
-                box = label['gt_boxes'].copy()[0][0:4].astype(np.int)
+                box = label['gt_boxes'].copy()[0][0:4].astype(np.int32)
                 cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]),
                               (0, 255, 0), 2)
                 filename = './debugout/%d.png' % (self._debug_id)
diff --git a/detection/retinaface/rcnn/core/tester.py b/detection/retinaface/rcnn/core/tester.py
index d9d5d32b1..39eb5fa79 100644
--- a/detection/retinaface/rcnn/core/tester.py
+++ b/detection/retinaface/rcnn/core/tester.py
@@ -238,8 +238,8 @@ def test_proposals(predictor, test_data, imdb, roidb, vis=False):
                                                             gt_boxes[:, 1] + 1)
         num_pos += gt_boxes.shape[0]
 
-        overlaps = bbox_overlaps(boxes.astype(np.float),
-                                 gt_boxes.astype(np.float))
+        overlaps = bbox_overlaps(boxes.astype(np.float32),
+                                 gt_boxes.astype(np.float32))
         #print(im_info, gt_boxes.shape, boxes.shape, overlaps.shape, file=sys.stderr)
 
         _gt_overlaps = np.zeros((gt_boxes.shape[0]))
diff --git a/detection/retinaface/rcnn/dataset/ds_utils.py b/detection/retinaface/rcnn/dataset/ds_utils.py
index 9432515ee..9f88b6c5e 100644
--- a/detection/retinaface/rcnn/dataset/ds_utils.py
+++ b/detection/retinaface/rcnn/dataset/ds_utils.py
@@ -4,7 +4,7 @@
 def unique_boxes(boxes, scale=1.0):
     """ return indices of unique boxes """
     v = np.array([1, 1e3, 1e6, 1e9])
-    hashes = np.round(boxes * scale).dot(v).astype(np.int)
+    hashes = np.round(boxes * scale).dot(v).astype(np.int32)
     _, index = np.unique(hashes, return_index=True)
     return np.sort(index)
 
diff --git a/detection/retinaface/rcnn/dataset/imdb.py b/detection/retinaface/rcnn/dataset/imdb.py
index 3c19817a8..cb64fb036 100644
--- a/detection/retinaface/rcnn/dataset/imdb.py
+++ b/detection/retinaface/rcnn/dataset/imdb.py
@@ -133,8 +133,8 @@ def create_roidb_from_box_list(self, box_list, gt_roidb):
                 gt_boxes = gt_roidb[i]['boxes']
                 gt_classes = gt_roidb[i]['gt_classes']
                 # n boxes and k gt_boxes => n * k overlap
-                gt_overlaps = bbox_overlaps(boxes.astype(np.float),
-                                            gt_boxes.astype(np.float))
+                gt_overlaps = bbox_overlaps(boxes.astype(np.float32),
+                                            gt_boxes.astype(np.float32))
                 # for each box in n boxes, select only maximum overlap (must be greater than zero)
                 argmaxes = gt_overlaps.argmax(axis=1)
                 maxes = gt_overlaps.max(axis=1)
@@ -285,8 +285,8 @@ def evaluate_recall(self, roidb, candidate_boxes=None, thresholds=None):
                 if boxes.shape[0] == 0:
                     continue
 
-                overlaps = bbox_overlaps(boxes.astype(np.float),
-                                         gt_boxes.astype(np.float))
+                overlaps = bbox_overlaps(boxes.astype(np.float32),
+                                         gt_boxes.astype(np.float32))
 
                 _gt_overlaps = np.zeros((gt_boxes.shape[0]))
                 # choose whatever is smaller to iterate
diff --git a/detection/retinaface/rcnn/io/image.py b/detection/retinaface/rcnn/io/image.py
index 0296fb4de..05eb3bcb2 100644
--- a/detection/retinaface/rcnn/io/image.py
+++ b/detection/retinaface/rcnn/io/image.py
@@ -95,7 +95,7 @@ def get_image(roidb, scale=False):
         if 'boxes_mask' in roi_rec:
             im = im.astype(np.float32)
             boxes_mask = roi_rec['boxes_mask'].copy() * im_scale
-            boxes_mask = boxes_mask.astype(np.int)
+            boxes_mask = boxes_mask.astype(np.int32)
             for j in range(boxes_mask.shape[0]):
                 m = boxes_mask[j]
                 im_tensor[:, :, m[1]:m[3], m[0]:m[2]] = 0.0
@@ -156,7 +156,7 @@ def __get_crop_image(roidb):
         if 'boxes_mask' in roi_rec:
             #im = im.astype(np.float32)
             boxes_mask = roi_rec['boxes_mask'].copy()
-            boxes_mask = boxes_mask.astype(np.int)
+            boxes_mask = boxes_mask.astype(np.int32)
             for j in range(boxes_mask.shape[0]):
                 m = boxes_mask[j]
                 im[m[1]:m[3], m[0]:m[2], :] = 0
@@ -197,7 +197,7 @@ def __get_crop_image(roidb):
                         fy=im_scale,
                         interpolation=cv2.INTER_LINEAR)
         new_rec['boxes'] = roi_rec['boxes'].copy() * im_scale
-        box_scale = new_rec['boxes'][box_ind].copy().astype(np.int)
+        box_scale = new_rec['boxes'][box_ind].copy().astype(np.int32)
         ul_min = box_scale[2:4] - SIZE
         ul_max = box_scale[0:2]
         assert ul_min[0] <= ul_max[0]
@@ -223,7 +223,7 @@ def __get_crop_image(roidb):
         new_rec['boxes'][:, 2] -= left
         new_rec['boxes'][:, 1] -= up
         new_rec['boxes'][:, 3] -= up
-        box_trans = new_rec['boxes'][box_ind].copy().astype(np.int)
+        box_trans = new_rec['boxes'][box_ind].copy().astype(np.int32)
         #print('sel box', im_scale, box, box_scale, box_trans, file=sys.stderr)
         #print('before', new_rec['boxes'].shape[0])
         boxes_new = []
@@ -249,7 +249,7 @@ def __get_crop_image(roidb):
             if TMP_ID < 10:
                 tim = im.copy()
                 for i in range(new_rec['boxes'].shape[0]):
-                    box = new_rec['boxes'][i].copy().astype(np.int)
+                    box = new_rec['boxes'][i].copy().astype(np.int32)
                     cv2.rectangle(tim, (box[0], box[1]), (box[2], box[3]),
                                   (255, 0, 0), 1)
                 filename = './trainimages/train%d.png' % TMP_ID
@@ -320,7 +320,7 @@ def get_crop_image1(roidb):
         if 'boxes_mask' in roi_rec:
             #im = im.astype(np.float32)
             boxes_mask = roi_rec['boxes_mask'].copy()
-            boxes_mask = boxes_mask.astype(np.int)
+            boxes_mask = boxes_mask.astype(np.int32)
             for j in range(boxes_mask.shape[0]):
                 m = boxes_mask[j]
                 im[m[1]:m[3], m[0]:m[2], :] = 127
@@ -437,7 +437,7 @@ def get_crop_image1(roidb):
         if TMP_ID >= 0 and TMP_ID < 10:
             tim = im.copy().astype(np.uint8)
             for i in range(new_rec['boxes'].shape[0]):
-                box = new_rec['boxes'][i].copy().astype(np.int)
+                box = new_rec['boxes'][i].copy().astype(np.int32)
                 cv2.rectangle(tim, (box[0], box[1]), (box[2], box[3]),
                               (255, 0, 0), 1)
                 print('draw box:', box)
@@ -447,7 +447,7 @@ def get_crop_image1(roidb):
                     if landmark[0][2] < 0:
                         print('zero', landmark)
                         continue
-                    landmark = landmark.astype(np.int)
+                    landmark = landmark.astype(np.int32)
                     print('draw landmark', landmark)
                     for k in range(5):
                         color = (0, 0, 255)
@@ -498,7 +498,7 @@ def get_crop_image2(roidb):
         if 'boxes_mask' in roi_rec:
             #im = im.astype(np.float32)
             boxes_mask = roi_rec['boxes_mask'].copy()
-            boxes_mask = boxes_mask.astype(np.int)
+            boxes_mask = boxes_mask.astype(np.int32)
             for j in range(boxes_mask.shape[0]):
                 m = boxes_mask[j]
                 im[m[1]:m[3], m[0]:m[2], :] = 0
@@ -697,7 +697,7 @@ def get_crop_image2(roidb):
         if TMP_ID >= 0 and TMP_ID < 10:
             tim = im.copy().astype(np.uint8)
             for i in range(new_rec['boxes'].shape[0]):
-                box = new_rec['boxes'][i].copy().astype(np.int)
+                box = new_rec['boxes'][i].copy().astype(np.int32)
                 cv2.rectangle(tim, (box[0], box[1]), (box[2], box[3]),
                               (255, 0, 0), 1)
                 print('draw box:', box)
@@ -707,7 +707,7 @@ def get_crop_image2(roidb):
                     if landmark[10] == 0.0:
                         print('zero', landmark)
                         continue
-                    landmark = landmark.astype(np.int)
+                    landmark = landmark.astype(np.int32)
                     print('draw landmark', landmark)
                     for k in range(5):
                         color = (0, 0, 255)
diff --git a/detection/retinaface/rcnn/io/rcnn.py b/detection/retinaface/rcnn/io/rcnn.py
index 1b3a443f6..3c56184e8 100644
--- a/detection/retinaface/rcnn/io/rcnn.py
+++ b/detection/retinaface/rcnn/io/rcnn.py
@@ -128,8 +128,8 @@ def sample_rois(rois,
     :return: (labels, rois, bbox_targets, bbox_weights)
     """
     if labels is None:
-        overlaps = bbox_overlaps(rois[:, 1:].astype(np.float),
-                                 gt_boxes[:, :4].astype(np.float))
+        overlaps = bbox_overlaps(rois[:, 1:].astype(np.float32),
+                                 gt_boxes[:, :4].astype(np.float32))
         gt_assignment = overlaps.argmax(axis=1)
         overlaps = overlaps.max(axis=1)
         labels = gt_boxes[gt_assignment, 4]
@@ -345,8 +345,8 @@ def sample_rois(rois,
             overlaps = np.zeros((len(rois), ))
             labels = np.zeros((len(rois), ))
         else:
-            overlaps = bbox_overlaps(rois[:, 1:].astype(np.float),
-                                     gt_boxes[:, :4].astype(np.float))
+            overlaps = bbox_overlaps(rois[:, 1:].astype(np.float32),
+                                     gt_boxes[:, :4].astype(np.float32))
             gt_assignment = overlaps.argmax(axis=1)
             overlaps = overlaps.max(axis=1)
             labels = gt_boxes[gt_assignment, 4]
@@ -465,8 +465,8 @@ def sample_rois_fpn(rois,
             overlaps = np.zeros((len(rois), ))
             labels = np.zeros((len(rois), ))
         else:
-            overlaps = bbox_overlaps(rois[:, 1:].astype(np.float),
-                                     gt_boxes[:, :4].astype(np.float))
+            overlaps = bbox_overlaps(rois[:, 1:].astype(np.float32),
+                                     gt_boxes[:, :4].astype(np.float32))
             gt_assignment = overlaps.argmax(axis=1)
             overlaps = overlaps.max(axis=1)
             labels = gt_boxes[gt_assignment, 4]
@@ -617,8 +617,8 @@ def get_rois(rois,
     if labels is None:
         if len(gt_boxes) == 0:
             gt_boxes = np.array([[1, 1, 1, 1, 0]])
-        overlaps = bbox_overlaps(rois[:, 1:].astype(np.float),
-                                 gt_boxes[:, :4].astype(np.float))
+        overlaps = bbox_overlaps(rois[:, 1:].astype(np.float32),
+                                 gt_boxes[:, :4].astype(np.float32))
         gt_assignment = overlaps.argmax(axis=1)
         overlaps = overlaps.max(axis=1)
         labels = gt_boxes[gt_assignment, 4]
diff --git a/detection/retinaface/rcnn/io/rpn.py b/detection/retinaface/rcnn/io/rpn.py
index 998fad8c8..1de0dc3f6 100644
--- a/detection/retinaface/rcnn/io/rpn.py
+++ b/detection/retinaface/rcnn/io/rpn.py
@@ -245,8 +245,8 @@ def _unmap(data, count, inds, fill=0):
     if gt_boxes.size > 0:
         # overlap between the anchors and the gt boxes
         # overlaps (ex, gt)
-        overlaps = bbox_overlaps(anchors.astype(np.float),
-                                 gt_boxes.astype(np.float))
+        overlaps = bbox_overlaps(anchors.astype(np.float32),
+                                 gt_boxes.astype(np.float32))
         argmax_overlaps = overlaps.argmax(axis=1)
         #print('AAA', argmax_overlaps.shape)
         max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
@@ -618,8 +618,8 @@ def assign_anchor_fpn(self,
         if gt_boxes.size > 0:
             # overlap between the anchors and the gt boxes
             # overlaps (ex, gt)
-            overlaps = bbox_overlaps(anchors.astype(np.float),
-                                     gt_boxes.astype(np.float))
+            overlaps = bbox_overlaps(anchors.astype(np.float32),
+                                     gt_boxes.astype(np.float32))
             argmax_overlaps = overlaps.argmax(axis=1)
             #print('AAA', argmax_overlaps.shape)
             max_overlaps = overlaps[np.arange(len(inds_inside)),
diff --git a/detection/retinaface/test.py b/detection/retinaface/test.py
index b88c82ba6..2fec9eb15 100644
--- a/detection/retinaface/test.py
+++ b/detection/retinaface/test.py
@@ -44,12 +44,12 @@
     print('find', faces.shape[0], 'faces')
     for i in range(faces.shape[0]):
         #print('score', faces[i][4])
-        box = faces[i].astype(np.int)
+        box = faces[i].astype(np.int32)
         #color = (255,0,0)
         color = (0, 0, 255)
         cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
         if landmarks is not None:
-            landmark5 = landmarks[i].astype(np.int)
+            landmark5 = landmarks[i].astype(np.int32)
             #print(landmark.shape)
             for l in range(landmark5.shape[0]):
                 color = (0, 0, 255)
diff --git a/detection/retinaface/test_widerface.py b/detection/retinaface/test_widerface.py
index 78c2f83d9..572491aa7 100644
--- a/detection/retinaface/test_widerface.py
+++ b/detection/retinaface/test_widerface.py
@@ -128,7 +128,7 @@ def get_boxes(roi, pyramid):
         font = cv2.FONT_HERSHEY_SIMPLEX
         for i in range(boxes.shape[0]):
             box = boxes[i]
-            ibox = box[0:4].copy().astype(np.int)
+            ibox = box[0:4].copy().astype(np.int32)
             cv2.rectangle(im, (ibox[0], ibox[1]), (ibox[2], ibox[3]),
                           (255, 0, 0), 2)
             #print('box', ibox)
@@ -191,8 +191,8 @@ def test(args):
                         1) * (gt_boxes[:, 3] - gt_boxes[:, 1] + 1)
             num_pos += gt_boxes.shape[0]
 
-            overlaps = bbox_overlaps(boxes.astype(np.float),
-                                     gt_boxes.astype(np.float))
+            overlaps = bbox_overlaps(boxes.astype(np.float32),
+                                     gt_boxes.astype(np.float32))
             #print(im_info, gt_boxes.shape, boxes.shape, overlaps.shape, file=sys.stderr)
 
             _gt_overlaps = np.zeros((gt_boxes.shape[0]))
diff --git a/detection/retinaface_anticov/test.py b/detection/retinaface_anticov/test.py
index f7c282b6d..8fb116296 100644
--- a/detection/retinaface_anticov/test.py
+++ b/detection/retinaface_anticov/test.py
@@ -46,7 +46,7 @@
     for i in range(faces.shape[0]):
         #print('score', faces[i][4])
         face = faces[i]
-        box = face[0:4].astype(np.int)
+        box = face[0:4].astype(np.int32)
         mask = face[5]
         print(i, box, mask)
         #color = (255,0,0)
@@ -55,7 +55,7 @@
         else:
             color = (0, 255, 0)
         cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
-        landmark5 = landmarks[i].astype(np.int)
+        landmark5 = landmarks[i].astype(np.int32)
         #print(landmark.shape)
         for l in range(landmark5.shape[0]):
             color = (255, 0, 0)
diff --git a/detection/scrfd/mmdet/datasets/custom.py b/detection/scrfd/mmdet/datasets/custom.py
index 1f78a1f5a..24b73219c 100755
--- a/detection/scrfd/mmdet/datasets/custom.py
+++ b/detection/scrfd/mmdet/datasets/custom.py
@@ -139,7 +139,7 @@ def get_cat_ids(self, idx):
             list[int]: All categories in the image of specified index.
         """
 
-        return self.data_infos[idx]['ann']['labels'].astype(np.int).tolist()
+        return self.data_infos[idx]['ann']['labels'].astype(np.int32).tolist()
 
     def pre_pipeline(self, results):
         """Prepare results dict for pipeline."""
diff --git a/detection/scrfd/mmdet/models/roi_heads/bbox_heads/bbox_head.py b/detection/scrfd/mmdet/models/roi_heads/bbox_heads/bbox_head.py
index e0931e176..2653429d2 100755
--- a/detection/scrfd/mmdet/models/roi_heads/bbox_heads/bbox_head.py
+++ b/detection/scrfd/mmdet/models/roi_heads/bbox_heads/bbox_head.py
@@ -262,7 +262,7 @@ def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
             >>> labels = torch.randint(0, 2, (n_roi,)).long()
             >>> bbox_preds = random_boxes(n_roi, scale=scale, rng=rng)
             >>> # For each image, pretend random positive boxes are gts
-            >>> is_label_pos = (labels.numpy() > 0).astype(np.int)
+            >>> is_label_pos = (labels.numpy() > 0).astype(np.int32)
             >>> lbl_per_img = kwarray.group_items(is_label_pos,
             ...                                   img_ids.numpy())
             >>> pos_per_img = [sum(lbl_per_img.get(gid, []))
diff --git a/detection/scrfd/tools/scrfd.py b/detection/scrfd/tools/scrfd.py
index 176d90e9d..d8d825e45 100644
--- a/detection/scrfd/tools/scrfd.py
+++ b/detection/scrfd/tools/scrfd.py
@@ -324,12 +324,12 @@ def scrfd_2p5gkps(**kwargs):
             print(kpss.shape)
         for i in range(bboxes.shape[0]):
             bbox = bboxes[i]
-            x1,y1,x2,y2,score = bbox.astype(np.int)
+            x1,y1,x2,y2,score = bbox.astype(np.int32)
             cv2.rectangle(img, (x1,y1)  , (x2,y2) , (255,0,0) , 2)
             if kpss is not None:
                 kps = kpss[i]
                 for kp in kps:
-                    kp = kp.astype(np.int)
+                    kp = kp.astype(np.int32)
                     cv2.circle(img, tuple(kp) , 1, (0,0,255) , 2)
         filename = img_path.split('/')[-1]
         print('output:', filename)
diff --git a/examples/person_detection/scrfd_person.py b/examples/person_detection/scrfd_person.py
index e2422b0a1..c6f7d2d3c 100644
--- a/examples/person_detection/scrfd_person.py
+++ b/examples/person_detection/scrfd_person.py
@@ -11,8 +11,8 @@
 
 def detect_person(img, detector):
     bboxes, kpss = detector.detect(img)
-    bboxes = np.round(bboxes[:,:4]).astype(np.int)
-    kpss = np.round(kpss).astype(np.int)
+    bboxes = np.round(bboxes[:,:4]).astype(np.int32)
+    kpss = np.round(kpss).astype(np.int32)
     kpss[:,:,0] = np.clip(kpss[:,:,0], 0, img.shape[1])
     kpss[:,:,1] = np.clip(kpss[:,:,1], 0, img.shape[0])
     vbboxes = bboxes.copy()
diff --git a/parsing/dml_csr/loss/criterion.py b/parsing/dml_csr/loss/criterion.py
index 9fa1bdc65..5790c1a21 100644
--- a/parsing/dml_csr/loss/criterion.py
+++ b/parsing/dml_csr/loss/criterion.py
@@ -49,8 +49,8 @@ def forward(self, preds, target, cycle_n=None):
         
         # binary edge
         input_binary_labels = target[1].data.cpu().numpy().astype(np.int64)
-        binary_pos_num = np.sum(input_binary_labels==1).astype(np.float)
-        binary_neg_num = np.sum(input_binary_labels==0).astype(np.float)
+        binary_pos_num = np.sum(input_binary_labels==1).astype(np.float32)
+        binary_neg_num = np.sum(input_binary_labels==0).astype(np.float32)
 
         binary_weight_pos = binary_neg_num/(binary_pos_num + binary_neg_num)
         binary_weight_neg = binary_pos_num/(binary_pos_num + binary_neg_num)
@@ -67,11 +67,11 @@ def forward(self, preds, target, cycle_n=None):
         # semantic edge
         input_semantic_labels = target[2].data.cpu().numpy().astype(np.int64)
         semantic_weights = []
-        semantic_pos_num = np.sum(input_semantic_labels>0).astype(np.float)
-        semantic_neg_num = np.sum(input_semantic_labels==0).astype(np.float)
+        semantic_pos_num = np.sum(input_semantic_labels>0).astype(np.float32)
+        semantic_neg_num = np.sum(input_semantic_labels==0).astype(np.float32)
 
         for lbl in range(self.num_classes):
-            lbl_num = np.sum(input_semantic_labels==lbl).astype(np.float)
+            lbl_num = np.sum(input_semantic_labels==lbl).astype(np.float32)
             weight_lbl = lbl_num/(semantic_pos_num + semantic_neg_num)
             semantic_weights.append(weight_lbl)
         semantic_weights = torch.from_numpy(np.array(semantic_weights)).float().cuda()
diff --git a/python-package/insightface/model_zoo/scrfd.py b/python-package/insightface/model_zoo/scrfd.py
index 674db4bba..541c31e2f 100644
--- a/python-package/insightface/model_zoo/scrfd.py
+++ b/python-package/insightface/model_zoo/scrfd.py
@@ -335,12 +335,12 @@ def scrfd_2p5gkps(**kwargs):
             print(kpss.shape)
         for i in range(bboxes.shape[0]):
             bbox = bboxes[i]
-            x1,y1,x2,y2,score = bbox.astype(np.int)
+            x1,y1,x2,y2,score = bbox.astype(np.int32)
             cv2.rectangle(img, (x1,y1)  , (x2,y2) , (255,0,0) , 2)
             if kpss is not None:
                 kps = kpss[i]
                 for kp in kps:
-                    kp = kp.astype(np.int)
+                    kp = kp.astype(np.int32)
                     cv2.circle(img, tuple(kp) , 1, (0,0,255) , 2)
         filename = img_path.split('/')[-1]
         print('output:', filename)
diff --git a/recognition/_evaluation_/ijb/ijb_11.py b/recognition/_evaluation_/ijb/ijb_11.py
index 53b332697..988bf95c5 100644
--- a/recognition/_evaluation_/ijb/ijb_11.py
+++ b/recognition/_evaluation_/ijb/ijb_11.py
@@ -51,8 +51,8 @@
 def read_template_media_list(path):
     #ijb_meta = np.loadtxt(path, dtype=str)
     ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int32)
+    medias = ijb_meta[:, 2].astype(np.int32)
     return templates, medias
 
 
@@ -63,10 +63,10 @@ def read_template_pair_list(path):
     #pairs = np.loadtxt(path, dtype=str)
     pairs = pd.read_csv(path, sep=' ', header=None).values
     #print(pairs.shape)
-    #print(pairs[:, 0].astype(np.int))
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    #print(pairs[:, 0].astype(np.int32))
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/_evaluation_/ijb/ijb_1n.py b/recognition/_evaluation_/ijb/ijb_1n.py
index 85ea4051f..fcc39b018 100644
--- a/recognition/_evaluation_/ijb/ijb_1n.py
+++ b/recognition/_evaluation_/ijb/ijb_1n.py
@@ -22,23 +22,23 @@
 
 def read_template_subject_id_list(path):
     ijb_meta = np.loadtxt(path, dtype=str, skiprows=1, delimiter=',')
-    templates = ijb_meta[:, 0].astype(np.int)
-    subject_ids = ijb_meta[:, 1].astype(np.int)
+    templates = ijb_meta[:, 0].astype(np.int32)
+    subject_ids = ijb_meta[:, 1].astype(np.int32)
     return templates, subject_ids
 
 
 def read_template_media_list(path):
     ijb_meta = np.loadtxt(path, dtype=str)
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int32)
+    medias = ijb_meta[:, 2].astype(np.int32)
     return templates, medias
 
 
 def read_template_pair_list(path):
     pairs = np.loadtxt(path, dtype=str)
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/_evaluation_/ijb/ijb_onnx.py b/recognition/_evaluation_/ijb/ijb_onnx.py
index eb2edbe83..b09f6d3a0 100644
--- a/recognition/_evaluation_/ijb/ijb_onnx.py
+++ b/recognition/_evaluation_/ijb/ijb_onnx.py
@@ -77,16 +77,16 @@ def batchify_fn(data):
 
 def read_template_media_list(path):
     ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int32)
+    medias = ijb_meta[:, 2].astype(np.int32)
     return templates, medias
 
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/arcface_oneflow/eval/onnx_ijbc.py b/recognition/arcface_oneflow/eval/onnx_ijbc.py
index 0d07d8185..a14e31fa1 100644
--- a/recognition/arcface_oneflow/eval/onnx_ijbc.py
+++ b/recognition/arcface_oneflow/eval/onnx_ijbc.py
@@ -87,16 +87,16 @@ def batchify_fn(data):
 
 def read_template_media_list(path):
     ijb_meta = pd.read_csv(path, sep=" ", header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int32)
+    medias = ijb_meta[:, 2].astype(np.int32)
     return templates, medias
 
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=" ", header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/arcface_torch/eval_ijbc.py b/recognition/arcface_torch/eval_ijbc.py
index 9c5a650d4..2921ea765 100644
--- a/recognition/arcface_torch/eval_ijbc.py
+++ b/recognition/arcface_torch/eval_ijbc.py
@@ -120,8 +120,8 @@ def divideIntoNstrand(listTemp, n):
 def read_template_media_list(path):
     # ijb_meta = np.loadtxt(path, dtype=str)
     ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int32)
+    medias = ijb_meta[:, 2].astype(np.int32)
     return templates, medias
 
 
@@ -132,10 +132,10 @@ def read_template_pair_list(path):
     # pairs = np.loadtxt(path, dtype=str)
     pairs = pd.read_csv(path, sep=' ', header=None).values
     # print(pairs.shape)
-    # print(pairs[:, 0].astype(np.int))
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    # print(pairs[:, 0].astype(np.int32))
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/arcface_torch/onnx_ijbc.py b/recognition/arcface_torch/onnx_ijbc.py
index 31c491b1b..e228fdbdf 100644
--- a/recognition/arcface_torch/onnx_ijbc.py
+++ b/recognition/arcface_torch/onnx_ijbc.py
@@ -78,16 +78,16 @@ def collate_fn(data):
 
 def read_template_media_list(path):
     ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int32)
+    medias = ijb_meta[:, 2].astype(np.int32)
     return templates, medias
 
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/arcface_torch/torch2onnx.py b/recognition/arcface_torch/torch2onnx.py
index f6055d1fe..f51dfabfe 100644
--- a/recognition/arcface_torch/torch2onnx.py
+++ b/recognition/arcface_torch/torch2onnx.py
@@ -6,7 +6,7 @@
 def convert_onnx(net, path_module, output, opset=11, simplify=False):
     assert isinstance(net, torch.nn.Module)
     img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32)
-    img = img.astype(np.float)
+    img = img.astype(np.float32)
     img = (img / 255. - 0.5) / 0.5  # torch style norm
     img = img.transpose((2, 0, 1))
     img = torch.from_numpy(img).unsqueeze(0).float()
diff --git a/recognition/arcface_torch/utils/plot.py b/recognition/arcface_torch/utils/plot.py
index 7f1d39da6..c1ad0f469 100644
--- a/recognition/arcface_torch/utils/plot.py
+++ b/recognition/arcface_torch/utils/plot.py
@@ -17,9 +17,9 @@
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/partial_fc/mxnet/evaluation/ijb.py b/recognition/partial_fc/mxnet/evaluation/ijb.py
index ffcabd69e..4603edeec 100644
--- a/recognition/partial_fc/mxnet/evaluation/ijb.py
+++ b/recognition/partial_fc/mxnet/evaluation/ijb.py
@@ -162,16 +162,16 @@ def divideIntoNstrand(listTemp, n):
 
 def read_template_media_list(path):
     ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int32)
+    medias = ijb_meta[:, 2].astype(np.int32)
     return templates, medias
 
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/vpl/eval_ijbc.py b/recognition/vpl/eval_ijbc.py
index c144e4e8e..2561c37ec 100644
--- a/recognition/vpl/eval_ijbc.py
+++ b/recognition/vpl/eval_ijbc.py
@@ -120,8 +120,8 @@ def divideIntoNstrand(listTemp, n):
 def read_template_media_list(path):
     # ijb_meta = np.loadtxt(path, dtype=str)
     ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int32)
+    medias = ijb_meta[:, 2].astype(np.int32)
     return templates, medias
 
 
@@ -132,10 +132,10 @@ def read_template_pair_list(path):
     # pairs = np.loadtxt(path, dtype=str)
     pairs = pd.read_csv(path, sep=' ', header=None).values
     # print(pairs.shape)
-    # print(pairs[:, 0].astype(np.int))
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    # print(pairs[:, 0].astype(np.int32))
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/vpl/onnx_ijbc.py b/recognition/vpl/onnx_ijbc.py
index 05b50bfad..e6301855e 100644
--- a/recognition/vpl/onnx_ijbc.py
+++ b/recognition/vpl/onnx_ijbc.py
@@ -77,16 +77,16 @@ def batchify_fn(data):
 
 def read_template_media_list(path):
     ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int32)
+    medias = ijb_meta[:, 2].astype(np.int32)
     return templates, medias
 
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/recognition/vpl/torch2onnx.py b/recognition/vpl/torch2onnx.py
index 4e7a68e68..f1703021e 100644
--- a/recognition/vpl/torch2onnx.py
+++ b/recognition/vpl/torch2onnx.py
@@ -6,7 +6,7 @@
 def convert_onnx(net, path_module, output, opset=11, simplify=False):
     assert isinstance(net, torch.nn.Module)
     img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32)
-    img = img.astype(np.float)
+    img = img.astype(np.float32)
     img = (img / 255. - 0.5) / 0.5  # torch style norm
     img = img.transpose((2, 0, 1))
     img = torch.from_numpy(img).unsqueeze(0).float()
diff --git a/recognition/vpl/utils/plot.py b/recognition/vpl/utils/plot.py
index ccc588e5c..022060d3e 100644
--- a/recognition/vpl/utils/plot.py
+++ b/recognition/vpl/utils/plot.py
@@ -18,9 +18,9 @@
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/reconstruction/gaze/datasets/augs.py b/reconstruction/gaze/datasets/augs.py
index 1e701d9fa..2551d1edf 100644
--- a/reconstruction/gaze/datasets/augs.py
+++ b/reconstruction/gaze/datasets/augs.py
@@ -24,7 +24,7 @@ def apply(self, image, border_size_limit, **params):
         border_size[2] *= image.shape[1]
         border_size[1] *= image.shape[0]
         border_size[3] *= image.shape[0]
-        border_size = border_size.astype(np.int)
+        border_size = border_size.astype(np.int32)
         image[:,:border_size[0],:] = self.fill_value
         image[:border_size[1],:,:] = self.fill_value
         image[:,border_size[2]:,:] = self.fill_value
diff --git a/reconstruction/gaze/test_gaze.py b/reconstruction/gaze/test_gaze.py
index a4ebc9fa3..857510e53 100644
--- a/reconstruction/gaze/test_gaze.py
+++ b/reconstruction/gaze/test_gaze.py
@@ -87,7 +87,7 @@ def draw_item(self, eimg, item):
         #trf_crp.pseudoinverse().apply(PointCloud(eye_r[iris_idx_481]).with_dims([0, 1])).view(1)
 
         for _eye in [eye_l, eye_r]:
-            _kps = _eye[self.iris_idx_481,:].astype(np.int)
+            _kps = _eye[self.iris_idx_481,:].astype(np.int32)
             for l in range(_kps.shape[0]):
                 color = (0, 255, 0)
                 cv2.circle(eimg, (_kps[l][1], _kps[l][0]), 4, color, 4)
@@ -97,8 +97,8 @@ def draw_item(self, eimg, item):
                 for k in range(3):
                     ix = _tri[k]
                     iy = _tri[(k+1)%3]
-                    x = _eye[ix,:2].astype(np.int)[::-1]
-                    y = _eye[iy,:2].astype(np.int)[::-1]
+                    x = _eye[ix,:2].astype(np.int32)[::-1]
+                    y = _eye[iy,:2].astype(np.int32)[::-1]
                     cv2.line(eimg, x, y, color, 1)
 
         theta_x_l, theta_y_l, vec_l = angles_and_vec_from_eye(eye_l, self.iris_idx_481)
@@ -126,8 +126,8 @@ def draw_item(self, eimg, item):
         y = x.copy()
         y[0] += dx
         y[1] += dy
-        x = x.astype(np.int)
-        y = y.astype(np.int)
+        x = x.astype(np.int32)
+        y = y.astype(np.int32)
         color = (0,255,255)
         cv2.line(eimg, x, y, color, 2)
 
@@ -138,8 +138,8 @@ def draw_item(self, eimg, item):
         y = x.copy()
         y[0] += dx
         y[1] += dy
-        x = x.astype(np.int)
-        y = y.astype(np.int)
+        x = x.astype(np.int32)
+        y = y.astype(np.int32)
         color = (0,255,255)
         cv2.line(eimg, x, y, color, 2)
         return eimg
diff --git a/reconstruction/jmlr/dataset.py b/reconstruction/jmlr/dataset.py
index 5f82855f3..40a617a18 100644
--- a/reconstruction/jmlr/dataset.py
+++ b/reconstruction/jmlr/dataset.py
@@ -681,7 +681,7 @@ def test_dataset1(cfg):
         #img_local = (img_local+1.0) * 128.0
         #draw = img_local.astype(np.uint8).transpose( (1,2,0) )[:,:,::-1].copy()
         #for i in range(points2d.shape[0]):
-        #    pt = points2d[i].astype(np.int)
+        #    pt = points2d[i].astype(np.int32)
         #    cv2.circle(draw, pt, 2, (255,0,0), 2)
         ##output_path = "outputs/%d_%.3f_%.3f_%.3f.jpg"%(idx, label_6dof[0], label_6dof[1], label_6dof[2])
         #output_path = "outputs/%06d.jpg"%(idx)
@@ -787,10 +787,10 @@ def test_dataset2(cfg):
         img_local = (img_local+1.0) * 128.0
         draw = img_local.astype(np.uint8).transpose( (1,2,0) )[:,:,::-1].copy()
         for i in range(points2d.shape[0]):
-            pt = points2d[i].astype(np.int)
+            pt = points2d[i].astype(np.int32)
             cv2.circle(draw, pt, 2, (255,0,0), 2)
         for i in range(eye_points.shape[0]):
-            pt = eye_points[i].astype(np.int)
+            pt = eye_points[i].astype(np.int32)
             cv2.circle(draw, pt, 2, (0,255,0), 2)
         ##output_path = "outputs/%d_%.3f_%.3f_%.3f.jpg"%(idx, label_6dof[0], label_6dof[1], label_6dof[2])
         output_path = "outputs/%06d.jpg"%(idx)
diff --git a/reconstruction/jmlr/utils/plot.py b/reconstruction/jmlr/utils/plot.py
index ccc588e5c..022060d3e 100644
--- a/reconstruction/jmlr/utils/plot.py
+++ b/reconstruction/jmlr/utils/plot.py
@@ -18,9 +18,9 @@
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int32)
+    t2 = pairs[:, 1].astype(np.int32)
+    label = pairs[:, 2].astype(np.int32)
     return t1, t2, label
 
 
diff --git a/reconstruction/ostec/core/operator.py b/reconstruction/ostec/core/operator.py
index 7e988f84a..f4c160f91 100644
--- a/reconstruction/ostec/core/operator.py
+++ b/reconstruction/ostec/core/operator.py
@@ -161,7 +161,7 @@ def create_syn(self, face, trg_angle=[0, 0, 0], include_mask=None):
         if include_mask is not None:
             fill_mask = fill_mask | include_mask.astype(np.bool)
         if face.exclude_mask is not None:
-            tcoord_sampling = np.round(self.tcoords.points[:,::-1] * face.exclude_mask.shape).astype(np.int)
+            tcoord_sampling = np.round(self.tcoords.points[:,::-1] * face.exclude_mask.shape).astype(np.int32)
             fill_mask[self.mask] =  fill_mask[self.mask] & ~face.exclude_mask[face.exclude_mask.shape[0] - tcoord_sampling[:, 0], tcoord_sampling[:, 1]]
 
         mask_mesh = ColouredTriMesh(face.tmesh.points, trilist=face.tmesh.trilist, colours=np.tile(fill_mask, [3, 1]).T)
@@ -362,7 +362,7 @@ def run(self, im, reconstruction_dict, face_mask=None):
         angle_uv_list = [np.clip(angle_uv_src.pixels * face.coef_dict()['src'],-1,1)]
 
         view_angle_src_full = self.camera_tri_angle_src(face.tmesh_rotated)
-        tcoord_sampling = np.round(self.tcoords.points*angle_uv_src.shape).astype(np.int)
+        tcoord_sampling = np.round(self.tcoords.points*angle_uv_src.shape).astype(np.int32)
         view_angle_src_full[self.mask] = angle_uv_src.pixels[0, angle_uv_src.shape[0] - tcoord_sampling[:, 1], tcoord_sampling[:, 0]]
         view_angle_src_full[~self.tight_mask] = -1  # Only take tight crop from the original image
 
diff --git a/reconstruction/ostec/external/landmark_detector/utils.py b/reconstruction/ostec/external/landmark_detector/utils.py
index 93a3e9c5b..229fa0f29 100644
--- a/reconstruction/ostec/external/landmark_detector/utils.py
+++ b/reconstruction/ostec/external/landmark_detector/utils.py
@@ -411,7 +411,7 @@ def crop_image(img, center, scale, res, base=384):
     # Upper left point
     ul = np.floor(t.apply([0,0]))
     # Bottom right point
-    br = np.ceil(t.apply(res).astype(np.int))
+    br = np.ceil(t.apply(res).astype(np.int32))
 
     # crop and rescale
 
diff --git a/reconstruction/ostec/utils/utils.py b/reconstruction/ostec/utils/utils.py
index 6eb00eb33..36d049ac9 100644
--- a/reconstruction/ostec/utils/utils.py
+++ b/reconstruction/ostec/utils/utils.py
@@ -109,7 +109,7 @@ def uv_stiching(img_uv_list, angle_uv_list, smoothing_sigma = 10, seamless_clone
 
             im_clone = cv.seamlessClone(dst, mixed, mask_inv, (512,512), cv.NORMAL_CLONE)
             dst = (im_clone * (1-mask_g/255) +  dst* (mask_g/255)).astype(np.uint8)
-        final_uv = Image(np.transpose(dst.astype(np.float)/255,[2,0,1]))
+        final_uv = Image(np.transpose(dst.astype(np.float32)/255,[2,0,1]))
     else:
         final_uv = Image(np.sum(all_uvs * max_ind_one_hot_g, axis=0))
 
@@ -120,7 +120,7 @@ def im_menpo2PIL(menpo_im):
     return PIL.Image.fromarray((menpo_im.pixels_with_channels_at_back() * 255).astype(np.uint8))
 
 def im_PIL2menpo(pil_im):
-    return Image.init_from_channels_at_back(np.array(pil_im).astype(np.float) / 255)
+    return Image.init_from_channels_at_back(np.array(pil_im).astype(np.float32) / 255)
 
 def fill_UV(UV):
     mask = np.sum(UV.pixels, 0) == 0