rename

mmdet/core/bbox/samplers/ohem_sampler.py

@@ -21,8 +21,12 @@ class OHEMSampler(BaseSampler):
         self.bbox_roi_extractor = bbox_roi_extractor
         self.bbox_head = bbox_head
 
-    def _sample_pos(self, assign_result, num_expected, bboxes=None,
-                    feats=None, **kwargs):
+    def _sample_pos(self,
+                    assign_result,
+                    num_expected,
+                    bboxes=None,
+                    feats=None,
+                    **kwargs):
         """Hard sample some positive samples."""
         pos_inds = torch.nonzero(assign_result.gt_inds > 0)
         if pos_inds.numel() != 0:
@@ -42,12 +46,16 @@ class OHEMSampler(BaseSampler):
                     label_weights=cls_score.new_ones(cls_score.size(0)),
                     bbox_targets=None,
                     bbox_weights=None,
-                    reduction=False)['loss_cls']
+                    reduce=False)['loss_cls']
                 _, topk_loss_pos_inds = loss_pos.topk(num_expected)
             return pos_inds[topk_loss_pos_inds]
 
-    def _sample_neg(self, assign_result, num_expected, bboxes=None,
-                    feats=None, **kwargs):
+    def _sample_neg(self,
+                    assign_result,
+                    num_expected,
+                    bboxes=None,
+                    feats=None,
+                    **kwargs):
         """Hard sample some negative samples."""
         neg_inds = torch.nonzero(assign_result.gt_inds == 0)
         if neg_inds.numel() != 0:
@@ -67,6 +75,6 @@ class OHEMSampler(BaseSampler):
                     label_weights=cls_score.new_ones(cls_score.size(0)),
                     bbox_targets=None,
                     bbox_weights=None,
-                    reduction=False)['loss_cls']
+                    reduce=False)['loss_cls']
                 _, topk_loss_neg_inds = loss_neg.topk(num_expected)
             return neg_inds[topk_loss_neg_inds]

mmdet/core/loss/losses.py

@@ -11,11 +11,11 @@ def weighted_nll_loss(pred, label, weight, avg_factor=None):
 
 
 def weighted_cross_entropy(pred, label, weight, avg_factor=None,
-                           reduction=True):
+                           reduce=True):
     if avg_factor is None:
         avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
     raw = F.cross_entropy(pred, label, reduction='none')
-    if reduction:
+    if reduce:
         return torch.sum(raw * weight)[None] / avg_factor
     else:
         return raw * weight / avg_factor

mmdet/models/bbox_heads/bbox_head.py

@@ -79,11 +79,11 @@ class BBoxHead(nn.Module):
         return cls_reg_targets
 
     def loss(self, cls_score, bbox_pred, labels, label_weights, bbox_targets,
-             bbox_weights, reduction=True):
+             bbox_weights, reduce=True):
         losses = dict()
         if cls_score is not None:
             losses['loss_cls'] = weighted_cross_entropy(
-                cls_score, labels, label_weights, reduction=reduction)
+                cls_score, labels, label_weights, reduce=reduce)
             losses['acc'] = accuracy(cls_score, labels)
         if bbox_pred is not None:
             losses['loss_reg'] = weighted_smoothl1(

mmdet/models/detectors/two_stage.py

@@ -119,7 +119,7 @@ class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
                     proposal_list[i],
                     gt_bboxes[i],
                     gt_labels[i],
-                    feats=[xx[i][None] for xx in x])
+                    feats=[lvl_feat[i][None] for lvl_feat in x])
                 assign_results.append(assign_result)
                 sampling_results.append(sampling_result)