Fix ResNetV2 pretrained classifier issue. Fixes #540

tests/test_models.py

@@ -132,7 +132,7 @@ if 'GITHUB_ACTIONS' not in os.environ:
     def test_model_load_pretrained(model_name, batch_size):
         """Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
         in_chans = 3 if 'pruned' in model_name else 1  # pruning not currently supported with in_chans change
-        create_model(model_name, pretrained=True, in_chans=in_chans)
+        create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
 
     @pytest.mark.timeout(120)
     @pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))

timm/models/resnetv2.py

@@ -365,6 +365,7 @@ class ResNetV2(nn.Module):
         return self.head.fc
 
     def reset_classifier(self, num_classes, global_pool='avg'):
+        self.num_classes = num_classes
         self.head = ClassifierHead(
             self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate, use_conv=True)
 
@@ -393,8 +394,9 @@ class ResNetV2(nn.Module):
                 self.stem.conv.weight.copy_(stem_conv_w)
             self.norm.weight.copy_(tf2th(weights[f'{prefix}group_norm/gamma']))
             self.norm.bias.copy_(tf2th(weights[f'{prefix}group_norm/beta']))
-            self.head.fc.weight.copy_(tf2th(weights[f'{prefix}head/conv2d/kernel']))
-            self.head.fc.bias.copy_(tf2th(weights[f'{prefix}head/conv2d/bias']))
+            if self.head.fc.weight.shape[0] == weights[f'{prefix}head/conv2d/kernel'].shape[-1]:
+                self.head.fc.weight.copy_(tf2th(weights[f'{prefix}head/conv2d/kernel']))
+                self.head.fc.bias.copy_(tf2th(weights[f'{prefix}head/conv2d/bias']))
             for i, (sname, stage) in enumerate(self.stages.named_children()):
                 for j, (bname, block) in enumerate(stage.blocks.named_children()):
                     convname = 'standardized_conv2d'