Add default_cfg back to models wrapped in feature extraction module as per discussion in #294.

timm/models/efficientnet.py

@@ -34,7 +34,7 @@ from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCE
 from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
 from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
 from .features import FeatureInfo, FeatureHooks
-from .helpers import build_model_with_cfg
+from .helpers import build_model_with_cfg, default_cfg_for_features
 from .layers import create_conv2d, create_classifier
 from .registry import register_model
 
@@ -462,9 +462,11 @@ def _create_effnet(model_kwargs, variant, pretrained=False):
     else:
         load_strict = True
         model_cls = EfficientNet
-    return build_model_with_cfg(
+    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)
+    return model
 
 
 def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):

timm/models/helpers.py

@@ -251,6 +251,15 @@ def adapt_model_from_file(parent_module, model_variant):
         return adapt_model_from_string(parent_module, f.read().strip())
 
 
+def default_cfg_for_features(default_cfg):
+    default_cfg = deepcopy(default_cfg)
+    # remove default pretrained cfg fields that don't have much relevance for feature backbone
+    to_remove = ('num_classes', 'crop_pct', 'classifier')  # add default final pool size?
+    for tr in to_remove:
+        default_cfg.pop(tr, None)
+    return default_cfg
+
+
 def build_model_with_cfg(
         model_cls: Callable,
         variant: str,
@@ -296,5 +305,6 @@ def build_model_with_cfg(
                 else:
                     assert False, f'Unknown feature class {feature_cls}'
         model = feature_cls(model, **feature_cfg)
+        model.default_cfg = default_cfg_for_features(default_cfg)  # add back default_cfg
     
     return model

timm/models/hrnet.py

@@ -17,7 +17,7 @@ import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .features import FeatureInfo
-from .helpers import build_model_with_cfg
+from .helpers import build_model_with_cfg, default_cfg_for_features
 from .layers import create_classifier
 from .registry import register_model
 from .resnet import BasicBlock, Bottleneck  # leveraging ResNet blocks w/ additional features like SE
@@ -779,9 +779,11 @@ def _create_hrnet(variant, pretrained, **model_kwargs):
         model_kwargs['num_classes'] = 0
         strict = False
 
-    return build_model_with_cfg(
+    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)
+    return model
 
 
 @register_model

timm/models/mobilenetv3.py

@@ -17,7 +17,7 @@ from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCE
 from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
 from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
 from .features import FeatureInfo, FeatureHooks
-from .helpers import build_model_with_cfg
+from .helpers import build_model_with_cfg, default_cfg_for_features
 from .layers import SelectAdaptivePool2d, Linear, create_conv2d, get_act_fn, hard_sigmoid
 from .registry import register_model
 
@@ -211,9 +211,11 @@ def _create_mnv3(model_kwargs, variant, pretrained=False):
     else:
         load_strict = True
         model_cls = MobileNetV3
-    return build_model_with_cfg(
+    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)
+    return model
 
 
 def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kwargs):