Fix features for resnetv2_50t

timm/models/resnetv2.py

@@ -291,6 +291,10 @@ class ResNetStage(nn.Module):
         return x
 
 
+def is_stem_deep(stem_type):
+    return any([s in stem_type for s in ('deep', 'tiered')])
+
+
 def create_resnetv2_stem(
         in_chs, out_chs=64, stem_type='', preact=True,
         conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32)):
@@ -298,7 +302,7 @@ def create_resnetv2_stem(
     assert stem_type in ('', 'fixed', 'same', 'deep', 'deep_fixed', 'deep_same', 'tiered')
 
     # NOTE conv padding mode can be changed by overriding the conv_layer def
-    if any([s in stem_type for s in ('deep', 'tiered')]):
+    if is_stem_deep(stem_type):
         # A 3 deep 3x3  conv stack as in ResNet V1D models
         if 'tiered' in stem_type:
             stem_chs = (3 * out_chs // 8, out_chs // 2)  # 'T' resnets in resnet.py
@@ -350,7 +354,7 @@ class ResNetV2(nn.Module):
         stem_chs = make_div(stem_chs * wf)
         self.stem = create_resnetv2_stem(
             in_chans, stem_chs, stem_type, preact, conv_layer=conv_layer, norm_layer=norm_layer)
-        stem_feat = ('stem.conv3' if 'deep' in stem_type else 'stem.conv') if preact else 'stem.norm'
+        stem_feat = ('stem.conv3' if is_stem_deep(stem_type) else 'stem.conv') if preact else 'stem.norm'
         self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module=stem_feat))
 
         prev_chs = stem_chs