Update metaformers.py

timm/models/metaformers.py

@@ -646,7 +646,10 @@ class MetaFormer(nn.Module):
             layer_scale_init_values = [layer_scale_init_values] * num_stage
         if not isinstance(res_scale_init_values, (list, tuple)):
             res_scale_init_values = [res_scale_init_values] * num_stage
-
+        
+        self.grad_checkpointing = False
+        self.feature_info = []
+        
         stages = nn.ModuleList() # each stage consists of multiple metaformer blocks
         cur = 0
         for i in range(num_stage):
@@ -665,6 +668,7 @@ class MetaFormer(nn.Module):
             )
             stages.append(stage)
             cur += depths[i]
+            self.feature_info += [dict(num_chs=dims[stage_id], reduction=2, module=f'stages.{stage_id}')]
         
         self.stages = nn.Sequential(*stages)
         self.norm = output_norm(dims[-1])
@@ -687,17 +691,26 @@ class MetaFormer(nn.Module):
     @torch.jit.ignore
     def no_weight_decay(self):
         return {'norm'}
-
-    def forward_features(self, x):
-        x = self.stages(x)
+    
+    def forward_head(self, x, pre_logits: bool = False):
+        if pre_logits:
+            return x
+        
         x = x.mean([1,2]) # TODO use adaptive pool instead of mean
         x = self.norm(x)
         # (B, H, W, C) -> (B, C)
+        x = self.head(x)
+        return x
+        
+    def forward_features(self, x):
+        x = self.stages(x)
+        
+        
         return x 
 
     def forward(self, x):
         x = self.forward_features(x)
-        x = self.head(x)
+        x = forward_head(x)
         return x
 
 def checkpoint_filter_fn(state_dict, model):