Merge branch 'dev' into mask-debug

mmdet/core/losses/losses.py

@@ -36,7 +36,7 @@ def sigmoid_focal_loss(pred,
     weight = (alpha * target + (1 - alpha) * (1 - target)) * weight
     weight = weight * pt.pow(gamma)
     return F.binary_cross_entropy_with_logits(
-        pred, target, weight, size_average=reduction)
+        pred, target, weight, reduction=reduction)
 
 
 def weighted_sigmoid_focal_loss(pred,
@@ -61,16 +61,6 @@ def mask_cross_entropy(pred, target, label):
         pred_slice, target, reduction='elementwise_mean')[None]
 
 
-def weighted_mask_cross_entropy(pred, target, weight, label):
-    num_rois = pred.size()[0]
-    num_samples = torch.sum(weight > 0).float().item() + 1e-6
-    assert num_samples >= 1
-    inds = torch.arange(0, num_rois).long().cuda()
-    pred_slice = pred[inds, label].squeeze(1)
-    return F.binary_cross_entropy_with_logits(
-        pred_slice, target, weight, size_average=False)[None] / num_samples
-
-
 def smooth_l1_loss(pred, target, beta=1.0, reduction='elementwise_mean'):
     assert beta > 0
     assert pred.size() == target.size() and target.numel() > 0