Update davit.py

timm/models/davit.py

@@ -792,34 +792,36 @@ class DaViT(nn.Module):
     def forward_network(self, x : Tensor):
         size: Tuple[int, int] = (x.size(2), x.size(3))
         features = [x]
-        sizes = [size]
+        #sizes = [size]
         
         for stage in self.stages:
-            features[-1], sizes[-1] = stage(features[-1], sizes[-1])
+            features[-1] = stage(features[-1])
             
             # don't append outputs of last stage, since they are already there
             if(len(features) < self.num_stages):
                 features.append(features[-1])
-                sizes.append(sizes[-1])
+                
             
 
         # non-normalized pyramid features + corresponding sizes
-        return features, sizes
+        return features
     
     def forward_pyramid_features(self, x) -> List[Tensor]:
-        x, sizes = self.forward_network(x)
+        x = self.forward_network(x)
+        '''
         outs = []
         for i, out in enumerate(x):
             H, W = sizes[i]
-            outs.append(out.view(-1, H, W, self.embed_dims[i]).permute(0, 3, 1, 2).contiguous())       
+            outs.append(out.view(-1, H, W, self.embed_dims[i]).permute(0, 3, 1, 2).contiguous())   
-        return outs
+        '''
+        return x
 
     def forward_features(self, x):
-        x, sizes = self.forward_network(x)
+        x = self.forward_network(x)
         # take final feature and norm
         x = self.norms(x[-1])
-        H, W = sizes[-1]
+        #H, W = sizes[-1]
-        x = x.view(-1, H, W, self.embed_dims[-1]).permute(0, 3, 1, 2).contiguous()
+        #x = x.view(-1, H, W, self.embed_dims[-1]).permute(0, 3, 1, 2).contiguous()
         return x
     
     def forward_head(self, x, pre_logits: bool = False):