Update metaformers.py

timm/models/metaformers.py

@@ -197,7 +197,7 @@ default_cfgs = {
 }
 
 cfgs_v2 = generate_default_cfgs(default_cfgs)
-'''
+
 class Downsampling(nn.Module):
     """
     Downsampling implemented by a layer of convolution.
@@ -213,13 +213,13 @@ class Downsampling(nn.Module):
         self.post_norm = post_norm(out_channels) if post_norm else nn.Identity()
 
     def forward(self, x):
-        x = self.pre_norm(x)
         if self.pre_permute:
             # if take [B, H, W, C] as input, permute it to [B, C, H, W]
             x = x.permute(0, 3, 1, 2)
+        x = self.pre_norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        
         x = self.conv(x)
-        x = x.permute(0, 2, 3, 1) # [B, C, H, W] -> [B, H, W, C]
+        x = self.post_norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
-        x = self.post_norm(x)
         return x
 '''
 class Downsampling(nn.Module):
@@ -244,7 +244,7 @@ class Downsampling(nn.Module):
         x = self.post_norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
         print(x.shape)
         return x
-
+'''
 class Scale(nn.Module):
     """
     Scale vector by element multiplications.