Update metaformers.py

timm/models/metaformers.py

@@ -20,12 +20,20 @@ Some implementations are modified from timm (https://github.com/rwightman/pytorc
 from functools import partial
 import torch
 import torch.nn as nn
-from timm.layers import trunc_normal_, DropPath
-from ._registry import register_model
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from timm.layers import trunc_normal_, DropPath
 from timm.layers.helpers import to_2tuple
+from ._builder import build_model_with_cfg
+from ._features import FeatureInfo
+from ._features_fx import register_notrace_function
+from ._manipulate import checkpoint_seq
+from ._pretrained import generate_default_cfgs
+from ._registry import register_model
+
 
 
+__all__ = ['MetaFormer']
+
 def _cfg(url='', **kwargs):
     return {
         'url': url,
@@ -187,6 +195,7 @@ default_cfgs = {
         num_classes=21841),
 }
 
+cfgs_v2 = generate_default_cfgs(default_cfgs)
 
 class Downsampling(nn.Module):
     """
@@ -592,16 +601,17 @@ class MetaFormer(nn.Module):
         cur = 0
         for i in range(num_stage):
             stage = nn.Sequential(
-                *[MetaFormerBlock(dim=dims[i],
-                token_mixer=token_mixers[i],
-                mlp=mlps[i],
-                norm_layer=norm_layers[i],
-                drop_path=dp_rates[cur + j],
-                layer_scale_init_value=layer_scale_init_values[i],
-                res_scale_init_value=res_scale_init_values[i],
+                downsample_layers[i],
+                *[MetaFormerBlock(
+                    dim=dims[i],
+                    token_mixer=token_mixers[i],
+                    mlp=mlps[i],
+                    norm_layer=norm_layers[i],
+                    drop_path=dp_rates[cur + j],
+                    layer_scale_init_value=layer_scale_init_values[i],
+                    res_scale_init_value=res_scale_init_values[i],
                 ) for j in range(depths[i])]
             )
-            stages.append(downsample_layers[i])
             stages.append(stage)
             cur += depths[i]
         
@@ -639,8 +649,20 @@ class MetaFormer(nn.Module):
         x = self.head(x)
         return x
 
-
-
+def _create_metaformer(variant, pretrained=False, **kwargs):
+    default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (2, 2, 6, 2))))
+    out_indices = kwargs.pop('out_indices', default_out_indices)
+    
+    model = build_model_with_cfg(
+        MetaFormer,
+        variant,
+        pretrained,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        feature_cfg=dict(flatten_sequential=True, out_indices = out_indices),
+        **kwargs)
+        
+    return model
+'''
 @register_model
 def identityformer_s12(pretrained=False, **kwargs):
     model = MetaFormer(
@@ -656,6 +678,17 @@ def identityformer_s12(pretrained=False, **kwargs):
         model.load_state_dict(state_dict)
     return model
 
+'''
+
+@register_model
+def identityformer_s12(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        depths=[2, 2, 6, 2],
+        dims=[64, 128, 320, 512],
+        token_mixers=nn.Identity,
+        norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
+        **kwargs)
+    return _create_metaformer('identityformer_s12', pretrained=pretrained, **model_kwargs)
 
 @register_model
 def identityformer_s24(pretrained=False, **kwargs):