Update LayerNorm

segmentation_models_pytorch/encoders/mix_transformer.py

@@ -21,14 +21,10 @@ def forward(self, x):
         if x.ndim == 4:
             B, C, H, W = x.shape
             x = x.view(B, C, -1).transpose(1, 2)
-            x = nn.functional.layer_norm(
-                x, self.normalized_shape, self.weight, self.bias, self.eps
-            )
-            x = x.transpose(1, 2).view(B, -1, H, W).contiguous()
+            x = super().forward(x)
+            x = x.transpose(1, 2).view(B, C, H, W)
         else:
-            x = nn.functional.layer_norm(
-                x, self.normalized_shape, self.weight, self.bias, self.eps
-            )
+            x = super().forward(x)
         return x
 
 
@@ -472,25 +468,25 @@ def forward_features(self, x):
         # stage 1
         x = self.patch_embed1(x)
         x = self.block1(x)
-        x = self.norm1(x)
+        x = self.norm1(x).contiguous()
         outs.append(x)
 
         # stage 2
         x = self.patch_embed2(x)
         x = self.block2(x)
-        x = self.norm2(x)
+        x = self.norm2(x).contiguous()
         outs.append(x)
 
         # stage 3
         x = self.patch_embed3(x)
         x = self.block3(x)
-        x = self.norm3(x)
+        x = self.norm3(x).contiguous()
         outs.append(x)
 
         # stage 4
         x = self.patch_embed4(x)
         x = self.block4(x)
-        x = self.norm4(x)
+        x = self.norm4(x).contiguous()
         outs.append(x)
 
         return outs
@@ -552,7 +548,7 @@ def forward(self, x):
             if i == 1:
                 features.append(dummy)
             else:
-                x = stages[i](x)
+                x = stages[i](x).contiguous()
                 features.append(x)
         return features