Merge pull request #2369 from brianhou0208/fix_reduction

Fix feature_info.reduction

timm/models/davit.py

@@ -556,19 +556,19 @@ class DaVit(nn.Module):
 
         dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
         stages = []
-        for stage_idx in range(num_stages):
-            out_chs = embed_dims[stage_idx]
+        for i in range(num_stages):
+            out_chs = embed_dims[i]
             stage = DaVitStage(
                 in_chs,
                 out_chs,
-                depth=depths[stage_idx],
-                downsample=stage_idx > 0,
+                depth=depths[i],
+                downsample=i > 0,
                 attn_types=attn_types,
-                num_heads=num_heads[stage_idx],
+                num_heads=num_heads[i],
                 window_size=window_size,
                 mlp_ratio=mlp_ratio,
                 qkv_bias=qkv_bias,
-                drop_path_rates=dpr[stage_idx],
+                drop_path_rates=dpr[i],
                 norm_layer=norm_layer,
                 norm_layer_cl=norm_layer_cl,
                 ffn=ffn,
@@ -579,7 +579,7 @@ class DaVit(nn.Module):
             )
             in_chs = out_chs
             stages.append(stage)
-            self.feature_info += [dict(num_chs=out_chs, reduction=2, module=f'stages.{stage_idx}')]
+            self.feature_info += [dict(num_chs=out_chs, reduction=2**(i+2), module=f'stages.{i}')]
 
         self.stages = nn.Sequential(*stages)
 

timm/models/efficientformer.py

@@ -407,7 +407,7 @@ class EfficientFormer(nn.Module):
             )
             prev_dim = embed_dims[i]
             stages.append(stage)
-            self.feature_info += [dict(num_chs=embed_dims[i], reduction=2**(1+i), module=f'stages.{i}')]
+            self.feature_info += [dict(num_chs=embed_dims[i], reduction=2**(i+2), module=f'stages.{i}')]
         self.stages = nn.Sequential(*stages)
 
         # Classifier head

timm/models/metaformer.py

@@ -541,7 +541,7 @@ class MetaFormer(nn.Module):
                 **kwargs,
             )]
             prev_dim = dims[i]
-            self.feature_info += [dict(num_chs=dims[i], reduction=2, module=f'stages.{i}')]
+            self.feature_info += [dict(num_chs=dims[i], reduction=2**(i+2), module=f'stages.{i}')]
 
         self.stages = nn.Sequential(*stages)