Add get and reset classifier method

timm/models/swin_transformer_v2_cr.py

@@ -831,9 +831,11 @@ class SwinTransformerV2CR(nn.Module):
         # Call super constructor
         super(SwinTransformerV2CR, self).__init__()
         # Save parameters
+        self.num_classes: int = num_classes
         self.patch_size: int = patch_size
         self.input_resolution: Tuple[int, int] = img_size
         self.window_size: int = window_size
+        self.num_features: int = int(embed_dim * (2 ** len(depths) - 1))
         # Init patch embedding
         self.patch_embedding: nn.Module = PatchEmbedding(in_channels=in_chans, out_channels=embed_dim,
                                                          patch_size=patch_size)
@@ -863,7 +865,7 @@ class SwinTransformerV2CR(nn.Module):
                 ))
         # Init final adaptive average pooling, and classification head
         self.average_pool: nn.Module = nn.AdaptiveAvgPool2d(1)
-        self.head: nn.Module = nn.Linear(in_features=embed_dim * (2 ** len(depths) - 1),
+        self.head: nn.Module = nn.Linear(in_features=self.num_features,
                                          out_features=num_classes)
 
     def update_resolution(self,
@@ -889,6 +891,25 @@ class SwinTransformerV2CR(nn.Module):
                                     new_input_resolution=(new_patch_resolution[0] // (2 ** max(index - 1, 0)),
                                                           new_patch_resolution[1] // (2 ** max(index - 1, 0))))
 
+    def get_classifier(self) -> nn.Module:
+        """ Method returns the classification head of the model.
+        Returns:
+            head (nn.Module): Current classification head
+        """
+        head: nn.Module = self.head
+        return head
+
+    def reset_classifier(self, num_classes: int, global_pool: str = '') -> None:
+        """ Method results the classification head
+
+        Args:
+            num_classes (int): Number of classes to be predicted
+            global_pool (str): Unused
+        """
+        self.num_classes: int = num_classes
+        self.head: nn.Module = nn.Linear(in_features=self.num_features, out_features=num_classes) \
+            if num_classes > 0 else nn.Identity()
+
     def forward_features(self,
                          input: torch.Tensor) -> List[torch.Tensor]:
         """ Forward pass to extract feature maps of each stage.