Merge branch 'metaformer_baselines_for_vision' of https://github.com/fffffgggg54/pytorch-image-models into metaformer_baselines_for_vision

timm/models/metaformers.py

@@ -437,8 +437,9 @@ class MetaFormerBlock(nn.Module):
             if res_scale_init_value else nn.Identity()
         
     def forward(self, x):
-        B, C, H, W = x.shape
+        #B, C, H, W = x.shape
-        x = x.view(B, H, W, C)
+        #x = x.view(B, H, W, C)
+        x = x.permute(0, 2, 3, 1)
         x = self.res_scale1(x) + \
             self.layer_scale1(
                 self.drop_path1(
@@ -451,7 +452,8 @@ class MetaFormerBlock(nn.Module):
                     self.mlp(self.norm2(x))
                 )
             )
-        x = x.view(B, C, H, W)
+        #x = x.view(B, C, H, W)
+        x = x.permute(0, 3, 1, 2)
         return x
 
 class MetaFormer(nn.Module):
@@ -630,11 +632,12 @@ class MetaFormer(nn.Module):
         if pre_logits:
             return x
         
-        x = self.global_pool(x)
+        #x = self.global_pool(x)
-        x = x.squeeze()
+        #x = x.squeeze()
-        x = self.norm(x)
+        #x = self.norm(x)
         # (B, H, W, C) -> (B, C)
-        x = self.head(x)
+        #x = self.head(x)
+        x=self.head(self.norm(x.mean([2, 3])))
         return x
         
     def forward_features(self, x):
@@ -655,6 +658,7 @@ def checkpoint_filter_fn(state_dict, model):
     import re
     out_dict = {}
     for k, v in state_dict.items():
+        '''
         k = k.replace('proj', 'conv')
         k = re.sub(r'layer_scale_([0-9]+)', r'layer_scale\1.scale', k)
         k = k.replace('network.1', 'downsample_layers.1')
@@ -664,6 +668,7 @@ def checkpoint_filter_fn(state_dict, model):
         k = k.replace('network.4', 'network.2')
         k = k.replace('network.6', 'network.3')
         k = k.replace('network', 'stages')
+        '''
         k = re.sub(r'downsample_layers.([0-9]+)', r'stages.\1.downsample', k)
         k = re.sub(r'([0-9]+).([0-9]+)', r'\1.blocks.\2', k)
         k = k.replace('stages.0.downsample', 'patch_embed')