rename reduction of loss

mmdet/core/bbox/samplers/ohem_sampler.py

@@ -42,7 +42,7 @@ class OHEMSampler(BaseSampler):
                     label_weights=cls_score.new_ones(cls_score.size(0)),
                     bbox_targets=None,
                     bbox_weights=None,
-                    reduction='none')['loss_cls']
+                    reduction=False)['loss_cls']
                 _, topk_loss_pos_inds = loss_pos.topk(num_expected)
             return pos_inds[topk_loss_pos_inds]
 
@@ -67,6 +67,6 @@ class OHEMSampler(BaseSampler):
                     label_weights=cls_score.new_ones(cls_score.size(0)),
                     bbox_targets=None,
                     bbox_weights=None,
-                    reduction='none')['loss_cls']
+                    reduction=False)['loss_cls']
                 _, topk_loss_neg_inds = loss_neg.topk(num_expected)
             return neg_inds[topk_loss_neg_inds]

mmdet/core/loss/losses.py

@@ -11,13 +11,13 @@ def weighted_nll_loss(pred, label, weight, avg_factor=None):
 
 
 def weighted_cross_entropy(pred, label, weight, avg_factor=None,
-                           reduction='elementwise_sum'):
+                           reduction=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 == 'elementwise_sum':
+    if reduction:
         return torch.sum(raw * weight)[None] / avg_factor
-    elif reduction == 'none':
+    else:
         return raw * weight / avg_factor
 
 

mmdet/models/bbox_heads/bbox_head.py

@@ -79,7 +79,7 @@ class BBoxHead(nn.Module):
         return cls_reg_targets
 
     def loss(self, cls_score, bbox_pred, labels, label_weights, bbox_targets,
-             bbox_weights, reduction='elementwise_sum'):
+             bbox_weights, reduction=True):
         losses = dict()
         if cls_score is not None:
             losses['loss_cls'] = weighted_cross_entropy(