minor change to adapt to pytorch 0.4.0

lib/model/rpn/anchor_target_layer.py

@@ -154,8 +154,8 @@ def forward(self, input):
 
         if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:
             num_examples = torch.sum(labels[i] >= 0)
-            positive_weights = 1.0 / num_examples
-            negative_weights = 1.0 / num_examples
+            positive_weights = 1.0 / num_examples.item()
+            negative_weights = 1.0 / num_examples.item()
         else:
             assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &
                     (cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))

lib/model/rpn/proposal_target_layer_cascade.py

@@ -130,8 +130,7 @@ def _sample_rois_pytorch(self, all_rois, gt_boxes, fg_rois_per_image, rois_per_i
         offset = torch.arange(0, batch_size)*gt_boxes.size(1)
         offset = offset.view(-1, 1).type_as(gt_assignment) + gt_assignment
 
-        labels = gt_boxes[:,:,4].contiguous().view(-1).index(offset.view(-1))\
-                                                            .view(batch_size, -1)
+        labels = gt_boxes[:,:,4].contiguous().view(-1).index((offset.view(-1), )).view(batch_size, -1)
 
         labels_batch = labels.new(batch_size, rois_per_image).zero_()
         rois_batch  = all_rois.new(batch_size, rois_per_image, 5).zero_()
@@ -151,8 +150,8 @@ def _sample_rois_pytorch(self, all_rois, gt_boxes, fg_rois_per_image, rois_per_i
             if fg_num_rois > 0 and bg_num_rois > 0:
                 # sampling fg
                 fg_rois_per_this_image = min(fg_rois_per_image, fg_num_rois)
-                
-                # torch.randperm seems has a bug on multi-gpu setting that cause the segfault. 
+
+                # torch.randperm seems has a bug on multi-gpu setting that cause the segfault.
                 # See https://github.com/pytorch/pytorch/issues/1868 for more details.
                 # use numpy instead.
                 #rand_num = torch.randperm(fg_num_rois).long().cuda()
@@ -162,8 +161,8 @@ def _sample_rois_pytorch(self, all_rois, gt_boxes, fg_rois_per_image, rois_per_i
                 # sampling bg
                 bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
 
-                # Seems torch.rand has a bug, it will generate very large number and make an error. 
-                # We use numpy rand instead. 
+                # Seems torch.rand has a bug, it will generate very large number and make an error.
+                # We use numpy rand instead.
                 #rand_num = (torch.rand(bg_rois_per_this_image) * bg_num_rois).long().cuda()
                 rand_num = np.floor(np.random.rand(bg_rois_per_this_image) * bg_num_rois)
                 rand_num = torch.from_numpy(rand_num).type_as(gt_boxes).long()
@@ -188,7 +187,7 @@ def _sample_rois_pytorch(self, all_rois, gt_boxes, fg_rois_per_image, rois_per_i
                 fg_rois_per_this_image = 0
             else:
                 raise ValueError("bg_num_rois = 0 and fg_num_rois = 0, this should not happen!")
-                
+
             # The indices that we're selecting (both fg and bg)
             keep_inds = torch.cat([fg_inds, bg_inds], 0)