Revert head norm changes to ConvNeXt as it broke some downstream use, alternate workaround for fcmae weights

timm/models/convnext.py

@@ -301,11 +301,10 @@ class ConvNeXt(nn.Module):
 
         # if head_norm_first == true, norm -> global pool -> fc ordering, like most other nets
         # otherwise pool -> norm -> fc, the default ConvNeXt ordering (pretrained FB weights)
-        self.head_norm_first = head_norm_first
         self.norm_pre = norm_layer(self.num_features) if head_norm_first else nn.Identity()
         self.head = nn.Sequential(OrderedDict([
                 ('global_pool', SelectAdaptivePool2d(pool_type=global_pool)),
-                ('norm', nn.Identity() if head_norm_first or num_classes == 0 else norm_layer(self.num_features)),
+                ('norm', nn.Identity() if head_norm_first else norm_layer(self.num_features)),
                 ('flatten', nn.Flatten(1) if global_pool else nn.Identity()),
                 ('drop', nn.Dropout(self.drop_rate)),
                 ('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())]))
@@ -336,14 +335,7 @@ class ConvNeXt(nn.Module):
         if global_pool is not None:
             self.head.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
             self.head.flatten = nn.Flatten(1) if global_pool else nn.Identity()
-        if num_classes == 0:
-            self.head.norm = nn.Identity()
-            self.head.fc = nn.Identity()
-        else:
-            if not self.head_norm_first:
-                norm_layer = type(self.stem[-1])  # obtain type from stem norm
-                self.head.norm = norm_layer(self.num_features)
-            self.head.fc = nn.Linear(self.num_features, num_classes)
+        self.head.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
     def forward_features(self, x):
         x = self.stem(x)
@@ -407,6 +399,11 @@ def checkpoint_filter_fn(state_dict, model):
 
 
 def _create_convnext(variant, pretrained=False, **kwargs):
+    if kwargs.get('pretrained_cfg', '') == 'fcmae':
+        # NOTE fcmae pretrained weights have no classifier or final norm-layer (`head.norm`)
+        # This is workaround loading with num_classes=0 w/o removing norm-layer.
+        kwargs.setdefault('pretrained_strict', False)
+
     model = build_model_with_cfg(
         ConvNeXt, variant, pretrained,
         pretrained_filter_fn=checkpoint_filter_fn,