Fix regression for pretrained classifier loading when using entrypt functions directly

tests/test_models.py

@@ -24,7 +24,7 @@ MAX_FWD_FEAT_SIZE = 448
 
 
 @pytest.mark.timeout(120)
-@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS[:-1]))
 @pytest.mark.parametrize('batch_size', [1])
 def test_model_forward(model_name, batch_size):
     """Run a single forward pass with each model"""

timm/models/helpers.py

@@ -277,11 +277,12 @@ def build_model_with_cfg(
     if pruned:
         model = adapt_model_from_file(model, variant)
 
+    # for classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats
+    num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
     if pretrained:
         load_pretrained(
             model,
-            num_classes=kwargs.get('num_classes', 0),
+            num_classes=num_classes_pretrained, in_chans=kwargs.get('in_chans', 3),
-            in_chans=kwargs.get('in_chans', 3),
             filter_fn=pretrained_filter_fn, strict=pretrained_strict)
     
     if features:

timm/models/hrnet.py

@@ -776,6 +776,7 @@ def _create_hrnet(variant, pretrained, **model_kwargs):
     strict = True
     if model_kwargs.pop('features_only', False):
         model_cls = HighResolutionNetFeatures
+        model_kwargs['num_classes'] = 0
         strict = False
 
     return build_model_with_cfg(