hrnet features_only pretrained weight loading issue. Fix #232.

Author: rwightman

tests/test_models.py

@@ -120,6 +120,12 @@ def test_model_load_pretrained(model_name, batch_size):
         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)
 
+    @pytest.mark.timeout(120)
+    @pytest.mark.parametrize('model_name', list_models(pretrained=True))
+    @pytest.mark.parametrize('batch_size', [1])
+    def test_model_features_pretrained(model_name, batch_size):
+        """Create that pretrained weights load when features_only==True."""
+        create_model(model_name, pretrained=True, features_only=True)
 
 EXCLUDE_JIT_FILTERS = [
     '*iabn*', 'tresnet*',  # models using inplace abn unlikely to ever be scriptable

timm/models/hrnet.py

@@ -773,12 +773,14 @@ def forward(self, x) -> List[torch.tensor]:
 
 def _create_hrnet(variant, pretrained, **model_kwargs):
     model_cls = HighResolutionNet
+    strict = True
     if model_kwargs.pop('features_only', False):
         model_cls = HighResolutionNetFeatures
+        strict = False
 
     return build_model_with_cfg(
         model_cls, variant, pretrained, default_cfg=default_cfgs[variant],
-        model_cfg=cfg_cls[variant], **model_kwargs)
+        model_cfg=cfg_cls[variant], pretrained_strict=strict, **model_kwargs)
 
 
 @register_model