Merge f560849 into 3d18775

configs/_base_/models/fast_scnn.py

@@ -25,7 +25,7 @@
         norm_cfg=norm_cfg,
         align_corners=False,
         loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.)),
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
     auxiliary_head=[
         dict(
             type='FCNHead',
@@ -38,7 +38,7 @@
             concat_input=False,
             align_corners=False,
             loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
         dict(
             type='FCNHead',
             in_channels=64,
@@ -50,7 +50,7 @@
             concat_input=False,
             align_corners=False,
             loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
     ])
 
 # model training and testing settings

configs/fastscnn/fast_scnn_4x8_80k_lr0.12_cityscapes.py

@@ -4,7 +4,7 @@
 ]
 
 # Re-config the data sampler.
-data = dict(samples_per_gpu=8, workers_per_gpu=4)
+data = dict(samples_per_gpu=2, workers_per_gpu=4)
 
 # Re-config the optimizer.
 optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5)

mmseg/models/decode_heads/decode_head.py

@@ -35,7 +35,8 @@ class BaseDecodeHead(nn.Module, metaclass=ABCMeta):
             Default: None.
         loss_decode (dict): Config of decode loss.
             Default: dict(type='CrossEntropyLoss').
-        ignore_index (int): The label index to be ignored. Default: 255
+        ignore_index (int | None): The label index to be ignored. When using
+            masked BCE loss, ignore_index should be set to None. Default: 255
         sampler (dict|None): The config of segmentation map sampler.
             Default: None.
         align_corners (bool): align_corners argument of F.interpolate.

mmseg/models/losses/cross_entropy_loss.py

@@ -32,17 +32,25 @@ def cross_entropy(pred,
     return loss
 
 
-def _expand_onehot_labels(labels, label_weights, label_channels):
+def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index):
     """Expand onehot labels to match the size of prediction."""
-    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
-    inds = torch.nonzero(labels >= 1, as_tuple=False).squeeze()
-    if inds.numel() > 0:
-        bin_labels[inds, labels[inds] - 1] = 1
+    bin_labels = labels.new_zeros(target_shape)
+    valid_mask = (labels >= 0) & (labels != ignore_index)
+    inds = torch.nonzero(valid_mask, as_tuple=True)
+
+    if inds[0].numel() > 0:
+        if labels.dim() == 3:
+            bin_labels[inds[0], labels[valid_mask], inds[1], inds[2]] = 1
+        else:
+            bin_labels[inds[0], labels[valid_mask]] = 1
+
+    valid_mask = valid_mask.unsqueeze(1).expand(target_shape).float()
     if label_weights is None:
-        bin_label_weights = None
+        bin_label_weights = valid_mask
     else:
-        bin_label_weights = label_weights.view(-1, 1).expand(
-            label_weights.size(0), label_channels)
+        bin_label_weights = label_weights.unsqueeze(1).expand(target_shape)
+        bin_label_weights *= valid_mask
+
     return bin_labels, bin_label_weights
 
 
@@ -51,7 +59,8 @@ def binary_cross_entropy(pred,
                          weight=None,
                          reduction='mean',
                          avg_factor=None,
-                         class_weight=None):
+                         class_weight=None,
+                         ignore_index=255):
     """Calculate the binary CrossEntropy loss.
 
     Args:
@@ -63,18 +72,24 @@ def binary_cross_entropy(pred,
         avg_factor (int, optional): Average factor that is used to average
             the loss. Defaults to None.
         class_weight (list[float], optional): The weight for each class.
+        ignore_index (int | None): The label index to be ignored. Default: 255
 
     Returns:
         torch.Tensor: The calculated loss
     """
     if pred.dim() != label.dim():
-        label, weight = _expand_onehot_labels(label, weight, pred.size(-1))
+        assert (pred.dim() == 2 and label.dim() == 1) or (
+                pred.dim() == 4 and label.dim() == 3), \
+            'Only pred shape [N, C], label shape [N] or pred shape [N, C, ' \
+            'H, W], label shape [N, H, W] are supported'
+        label, weight = _expand_onehot_labels(label, weight, pred.shape,
+                                              ignore_index)
 
     # weighted element-wise losses
     if weight is not None:
         weight = weight.float()
     loss = F.binary_cross_entropy_with_logits(
-        pred, label.float(), weight=class_weight, reduction='none')
+        pred, label.float(), pos_weight=class_weight, reduction='none')
     # do the reduction for the weighted loss
     loss = weight_reduce_loss(
         loss, weight, reduction=reduction, avg_factor=avg_factor)
@@ -87,7 +102,8 @@ def mask_cross_entropy(pred,
                        label,
                        reduction='mean',
                        avg_factor=None,
-                       class_weight=None):
+                       class_weight=None,
+                       ignore_index=None):
     """Calculate the CrossEntropy loss for masks.
 
     Args:
@@ -103,10 +119,13 @@ def mask_cross_entropy(pred,
         avg_factor (int, optional): Average factor that is used to average
             the loss. Defaults to None.
         class_weight (list[float], optional): The weight for each class.
+        ignore_index (None): Placeholder, to be consistent with other loss.
+            Default: None.
 
     Returns:
         torch.Tensor: The calculated loss
     """
+    assert ignore_index is None, 'BCE loss does not support ignore_index'
     # TODO: handle these two reserved arguments
     assert reduction == 'mean' and avg_factor is None
     num_rois = pred.size()[0]

tests/test_models/test_losses.py

@@ -71,7 +71,17 @@ def test_ce_loss():
     loss_cls_cfg = dict(
         type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)
     loss_cls = build_loss(loss_cls_cfg)
-    assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(0.))
+    assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(100.))
+
+    fake_pred = torch.full(size=(2, 21, 8, 8), fill_value=0.5)
+    fake_label = torch.ones(2, 8, 8).long()
+    assert torch.allclose(
+        loss_cls(fake_pred, fake_label), torch.tensor(0.9503), atol=1e-4)
+    fake_label[:, 0, 0] = 255
+    assert torch.allclose(
+        loss_cls(fake_pred, fake_label, ignore_index=255),
+        torch.tensor(0.9354),
+        atol=1e-4)
 
     # TODO test use_mask