Bug in last mod for features_only default_cfg

Author: rwightman

timm/models/efficientnet.py

@@ -453,19 +453,19 @@ def forward(self, x) -> List[torch.Tensor]:
 
 
 def _create_effnet(model_kwargs, variant, pretrained=False):
+    features_only = False
+    model_cls = EfficientNet
     if model_kwargs.pop('features_only', False):
-        load_strict = False
+        features_only = True
         model_kwargs.pop('num_classes', 0)
         model_kwargs.pop('num_features', 0)
         model_kwargs.pop('head_conv', None)
         model_cls = EfficientNetFeatures
-    else:
-        load_strict = True
-        model_cls = EfficientNet
     model = build_model_with_cfg(
         model_cls, variant, pretrained, default_cfg=default_cfgs[variant],
-        pretrained_strict=load_strict, **model_kwargs)
-    model.default_cfg = default_cfg_for_features(model.default_cfg)
+        pretrained_strict=not features_only, **model_kwargs)
+    if features_only:
+        model.default_cfg = default_cfg_for_features(model.default_cfg)
     return model
 
 

timm/models/hrnet.py

@@ -773,16 +773,16 @@ def forward(self, x) -> List[torch.tensor]:
 
 def _create_hrnet(variant, pretrained, **model_kwargs):
     model_cls = HighResolutionNet
-    strict = True
+    features_only = False
     if model_kwargs.pop('features_only', False):
         model_cls = HighResolutionNetFeatures
         model_kwargs['num_classes'] = 0
-        strict = False
-
+        features_only = True
     model = build_model_with_cfg(
         model_cls, variant, pretrained, default_cfg=default_cfgs[variant],
-        model_cfg=cfg_cls[variant], pretrained_strict=strict, **model_kwargs)
-    model.default_cfg = default_cfg_for_features(model.default_cfg)
+        model_cfg=cfg_cls[variant], pretrained_strict=not features_only, **model_kwargs)
+    if features_only:
+        model.default_cfg = default_cfg_for_features(model.default_cfg)
     return model
 
 

timm/models/mobilenetv3.py

@@ -201,20 +201,20 @@ def forward(self, x) -> List[torch.Tensor]:
 
 
 def _create_mnv3(model_kwargs, variant, pretrained=False):
+    features_only = False
+    model_cls = MobileNetV3
     if model_kwargs.pop('features_only', False):
-        load_strict = False
+        features_only = True
         model_kwargs.pop('num_classes', 0)
         model_kwargs.pop('num_features', 0)
         model_kwargs.pop('head_conv', None)
         model_kwargs.pop('head_bias', None)
         model_cls = MobileNetV3Features
-    else:
-        load_strict = True
-        model_cls = MobileNetV3
     model = build_model_with_cfg(
         model_cls, variant, pretrained, default_cfg=default_cfgs[variant],
-        pretrained_strict=load_strict, **model_kwargs)
-    model.default_cfg = default_cfg_for_features(model.default_cfg)
+        pretrained_strict=not features_only, **model_kwargs)
+    if features_only:
+        model.default_cfg = default_cfg_for_features(model.default_cfg)
     return model