diff --git a/src/lib/model/losses.py b/src/lib/model/losses.py
index 47591b3..ba19c5f 100644
--- a/src/lib/model/losses.py
+++ b/src/lib/model/losses.py
@@ -155,9 +155,20 @@ def compute_res_loss(output, target):
     return F.smooth_l1_loss(output, target, reduction='mean')
 
 def compute_bin_loss(output, target, mask):
-    mask = mask.expand_as(output)
-    output = output * mask.float()
-    return F.cross_entropy(output, target, reduction='mean')
+    """
+    Compute loss from classifying if angle is in this bin or in the other bin with cross entropy.
+    Use the prediction if its in this bin (=1) AND if its in the other bin (=0) because bins overlap
+    and the angle can be in both bins.
+    Mask predictions by wether or not the annotations even have a gt angle labeled or not.
+    Don't learn from making a prediction when there is no target.
+    """
+    nonzero_idx = mask.nonzero()[:,0].long()
+    if nonzero_idx.shape[0] > 0: # if there are any annotations with a labeled angle
+      output_nz = output.index_select(0, nonzero_idx)
+      target_nz = target.index_select(0, nonzero_idx)
+      return F.cross_entropy(output_nz, target_nz, reduction='mean') 
+    else: # loss would be NaN if computed normally when no annotation is given 
+      return torch.tensor(0.0).cuda() # set to different grad_fn but not relevant since loss is zero
 
 def compute_rot_loss(output, target_bin, target_res, mask):
     # output: (B, 128, 8) [bin1_cls[0], bin1_cls[1], bin1_sin, bin1_cos, 
diff --git a/src/lib/utils/pointcloud.py b/src/lib/utils/pointcloud.py
index 9dc0ee8..0bfdfdd 100644
--- a/src/lib/utils/pointcloud.py
+++ b/src/lib/utils/pointcloud.py
@@ -6,7 +6,7 @@
 from nuscenes.utils.data_classes import RadarPointCloud
 from functools import reduce
 from typing import Tuple, Dict
-from model.utils import _topk, _tranpose_and_gather_feat
+from model.utils import _nms, _sigmoid, _topk, _tranpose_and_gather_feat
 import os.path as osp
 import torch
 import timeit
@@ -208,7 +208,8 @@ def get_dist_thresh(calib, ct, dim, alpha):
 def generate_pc_hm(output, pc_dep, calib, opt):
       K = opt.K
       # K = 100
-      heat = output['hm']
+      heat = _sigmoid(output['hm'])
+      heat = _nms(heat)
       wh = output['wh']
       pc_hm = torch.zeros_like(pc_dep)