[Feature] Support kenerl updation for some decoder heads. (#1299)

* [Feature] Add kenerl updation for some decoder heads.

* [Feature] Add kenerl updation for some decoder heads.

* directly use forward_feature && modify other 3 decoder heads

* remover kernel_update attr

* delete unnecessary variables in forward function

* delete kernel update function

* delete kernel update function

* delete unnecessary docstrings

* modify comments in self._forward_feature()

* modify docstrings in self._forward_feature()

* fix docstring

* modify uperhead

mmseg/models/decode_heads/aspp_head.py

@@ -91,8 +91,17 @@ class ASPPHead(BaseDecodeHead):
             norm_cfg=self.norm_cfg,
             act_cfg=self.act_cfg)
 
-    def forward(self, inputs):
+    def _forward_feature(self, inputs):
-        """Forward function."""
+        """Forward function for feature maps before classifying each pixel with
+        ``self.cls_seg`` fc.
+
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+
+        Returns:
+            feats (Tensor): A tensor of shape (batch_size, self.channels,
+                H, W) which is feature map for last layer of decoder head.
+        """
         x = self._transform_inputs(inputs)
         aspp_outs = [
             resize(
@@ -103,6 +112,11 @@ class ASPPHead(BaseDecodeHead):
         ]
         aspp_outs.extend(self.aspp_modules(x))
         aspp_outs = torch.cat(aspp_outs, dim=1)
-        output = self.bottleneck(aspp_outs)
+        feats = self.bottleneck(aspp_outs)
+        return feats
+
+    def forward(self, inputs):
+        """Forward function."""
+        output = self._forward_feature(inputs)
         output = self.cls_seg(output)
         return output

mmseg/models/decode_heads/fcn_head.py

@@ -72,11 +72,25 @@ class FCNHead(BaseDecodeHead):
                 norm_cfg=self.norm_cfg,
                 act_cfg=self.act_cfg)
 
+    def _forward_feature(self, inputs):
+        """Forward function for feature maps before classifying each pixel with
+        ``self.cls_seg`` fc.
+
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+
+        Returns:
+            feats (Tensor): A tensor of shape (batch_size, self.channels,
+                H, W) which is feature map for last layer of decoder head.
+        """
+        x = self._transform_inputs(inputs)
+        feats = self.convs(x)
+        if self.concat_input:
+            feats = self.conv_cat(torch.cat([x, feats], dim=1))
+        return feats
+
     def forward(self, inputs):
         """Forward function."""
-        x = self._transform_inputs(inputs)
+        output = self._forward_feature(inputs)
-        output = self.convs(x)
-        if self.concat_input:
-            output = self.conv_cat(torch.cat([x, output], dim=1))
         output = self.cls_seg(output)
         return output

mmseg/models/decode_heads/psp_head.py

@@ -92,12 +92,26 @@ class PSPHead(BaseDecodeHead):
             norm_cfg=self.norm_cfg,
             act_cfg=self.act_cfg)
 
-    def forward(self, inputs):
+    def _forward_feature(self, inputs):
-        """Forward function."""
+        """Forward function for feature maps before classifying each pixel with
+        ``self.cls_seg`` fc.
+
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+
+        Returns:
+            feats (Tensor): A tensor of shape (batch_size, self.channels,
+                H, W) which is feature map for last layer of decoder head.
+        """
         x = self._transform_inputs(inputs)
         psp_outs = [x]
         psp_outs.extend(self.psp_modules(x))
         psp_outs = torch.cat(psp_outs, dim=1)
-        output = self.bottleneck(psp_outs)
+        feats = self.bottleneck(psp_outs)
+        return feats
+
+    def forward(self, inputs):
+        """Forward function."""
+        output = self._forward_feature(inputs)
         output = self.cls_seg(output)
         return output

mmseg/models/decode_heads/uper_head.py

@@ -84,9 +84,17 @@ class UPerHead(BaseDecodeHead):
 
         return output
 
-    def forward(self, inputs):
+    def _forward_feature(self, inputs):
-        """Forward function."""
+        """Forward function for feature maps before classifying each pixel with
+        ``self.cls_seg`` fc.
 
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+
+        Returns:
+            feats (Tensor): A tensor of shape (batch_size, self.channels,
+                H, W) which is feature map for last layer of decoder head.
+        """
         inputs = self._transform_inputs(inputs)
 
         # build laterals
@@ -122,6 +130,11 @@ class UPerHead(BaseDecodeHead):
                 mode='bilinear',
                 align_corners=self.align_corners)
         fpn_outs = torch.cat(fpn_outs, dim=1)
-        output = self.fpn_bottleneck(fpn_outs)
+        feats = self.fpn_bottleneck(fpn_outs)
+        return feats
+
+    def forward(self, inputs):
+        """Forward function."""
+        output = self._forward_feature(inputs)
         output = self.cls_seg(output)
         return output