Fix bug in classification head

timm/models/swin_transformer_v2_cr.py

@@ -918,7 +918,7 @@ class SwinTransformerV2CR(nn.Module):
         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))
+        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, norm_layer=norm_layer)
@@ -1038,7 +1038,7 @@ class SwinTransformerV2CR(nn.Module):
         for stage in self.stages:
             output: torch.Tensor = stage(output)
         # Perform average pooling
-        output: torch.Tensor = self.average_pool(output)
+        output: torch.Tensor = self.average_pool(output).flatten(start_dim=1)
         # Predict classification
         classification: torch.Tensor = self.head(output)
         return classification