[Refactor] Support passing arguments to loss from head.

mmcls/models/heads/cls_head.py

@@ -39,11 +39,12 @@ def __init__(self,
         self.compute_accuracy = Accuracy(topk=self.topk)
         self.cal_acc = cal_acc
 
-    def loss(self, cls_score, gt_label):
+    def loss(self, cls_score, gt_label, **kwargs):
         num_samples = len(cls_score)
         losses = dict()
         # compute loss
-        loss = self.compute_loss(cls_score, gt_label, avg_factor=num_samples)
+        loss = self.compute_loss(
+            cls_score, gt_label, avg_factor=num_samples, **kwargs)
         if self.cal_acc:
             # compute accuracy
             acc = self.compute_accuracy(cls_score, gt_label)
@@ -55,10 +56,10 @@ def loss(self, cls_score, gt_label):
         losses['loss'] = loss
         return losses
 
-    def forward_train(self, cls_score, gt_label):
+    def forward_train(self, cls_score, gt_label, **kwargs):
         if isinstance(cls_score, tuple):
             cls_score = cls_score[-1]
-        losses = self.loss(cls_score, gt_label)
+        losses = self.loss(cls_score, gt_label, **kwargs)
         return losses
 
     def simple_test(self, cls_score):

mmcls/models/heads/linear_head.py

@@ -46,9 +46,9 @@ def simple_test(self, x):
 
         return self.post_process(pred)
 
-    def forward_train(self, x, gt_label):
+    def forward_train(self, x, gt_label, **kwargs):
         if isinstance(x, tuple):
             x = x[-1]
         cls_score = self.fc(x)
-        losses = self.loss(cls_score, gt_label)
+        losses = self.loss(cls_score, gt_label, **kwargs)
         return losses

mmcls/models/heads/multi_label_head.py

@@ -40,11 +40,11 @@ def loss(self, cls_score, gt_label):
         losses['loss'] = loss
         return losses
 
-    def forward_train(self, cls_score, gt_label):
+    def forward_train(self, cls_score, gt_label, **kwargs):
         if isinstance(cls_score, tuple):
             cls_score = cls_score[-1]
         gt_label = gt_label.type_as(cls_score)
-        losses = self.loss(cls_score, gt_label)
+        losses = self.loss(cls_score, gt_label, **kwargs)
         return losses
 
     def simple_test(self, x):

mmcls/models/heads/multi_label_linear_head.py

@@ -39,12 +39,12 @@ def __init__(self,
 
         self.fc = nn.Linear(self.in_channels, self.num_classes)
 
-    def forward_train(self, x, gt_label):
+    def forward_train(self, x, gt_label, **kwargs):
         if isinstance(x, tuple):
             x = x[-1]
         gt_label = gt_label.type_as(x)
         cls_score = self.fc(x)
-        losses = self.loss(cls_score, gt_label)
+        losses = self.loss(cls_score, gt_label, **kwargs)
         return losses
 
     def simple_test(self, x):

mmcls/models/heads/stacked_head.py

@@ -127,11 +127,11 @@ def simple_test(self, x):
 
         return self.post_process(pred)
 
-    def forward_train(self, x, gt_label):
+    def forward_train(self, x, gt_label, **kwargs):
         if isinstance(x, tuple):
             x = x[-1]
         cls_score = x
         for layer in self.layers:
             cls_score = layer(cls_score)
-        losses = self.loss(cls_score, gt_label)
+        losses = self.loss(cls_score, gt_label, **kwargs)
         return losses

mmcls/models/heads/vision_transformer_head.py

@@ -79,9 +79,9 @@ def simple_test(self, x):
 
         return self.post_process(pred)
 
-    def forward_train(self, x, gt_label):
+    def forward_train(self, x, gt_label, **kwargs):
         x = x[-1]
         _, cls_token = x
         cls_score = self.layers(cls_token)
-        losses = self.loss(cls_score, gt_label)
+        losses = self.loss(cls_score, gt_label, **kwargs)
         return losses

tests/test_models/test_heads.py

@@ -27,6 +27,13 @@ def test_cls_head(feat):
     losses = head.forward_train(feat, fake_gt_label)
     assert losses['loss'].item() > 0
 
+    # test ClsHead with weight
+    weight = torch.tensor([0.5, 0.5, 0.5, 0.5])
+
+    losses_ = head.forward_train(feat, fake_gt_label)
+    losses = head.forward_train(feat, fake_gt_label, weight=weight)
+    assert losses['loss'].item() == losses_['loss'].item() * 0.5
+
 
 @pytest.mark.parametrize('feat', [torch.rand(4, 3), (torch.rand(4, 3), )])
 def test_linear_head(feat):