[BUG] EfficientNet class doesn't have cfg argument

[RealAntonVoronov]

Describe the bug
The bug happens when trying to specify local path to pretrained weights (in order to run model on devices with no access to the Internet). When passing a custom config as an argument to a function code falls when initializing an EfficientNet instance with the keyword argument 'cfg', which is not present in EfficientNet class init.

To Reproduce

from timm.models.efficientnet import _cfg, tf_efficientnet_b7_ns

config = _cfg(url='', file='../tf_efficientnet_b7_ns-1dbc32de.pth',
              input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949)
model = tf_efficientnet_b7_ns(pretrained=True, model_cfg=config, drop_path_rate=0.2)

---

TypeError Traceback (most recent call last)
/tmp/ipykernel_4061624/3684105051.py in
3 config = _cfg(url='', file='../tf_efficientnet_b7_ns-1dbc32de.pth',
4 input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949)
----> 5 model = tf_efficientnet_b7_ns(pretrained=True, model_cfg=config, drop_path_rate=0.2)

~/.imgenv-fakedetection-voronov-0/lib/python3.7/site-packages/timm/models/efficientnet.py in tf_efficientnet_b7_ns(pretrained, **kwargs)
1916 kwargs['pad_type'] = 'same'
1917 model = _gen_efficientnet(
-> 1918 'tf_efficientnet_b7_ns', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
1919 return model
1920

~/.imgenv-fakedetection-voronov-0/lib/python3.7/site-packages/timm/models/efficientnet.py in _gen_efficientnet(variant, channel_multiplier, depth_multiplier, pretrained, **kwargs)
842 **kwargs,
843 )
--> 844 model = _create_effnet(variant, pretrained, **model_kwargs)
845 return model
846

~/.imgenv-fakedetection-voronov-0/lib/python3.7/site-packages/timm/models/efficientnet.py in _create_effnet(variant, pretrained, **kwargs)
597 pretrained_strict=not features_only,
598 kwargs_filter=kwargs_filter,
--> 599 **kwargs)
600 if features_only:
601 model.default_cfg = default_cfg_for_features(model.default_cfg)

~/.imgenv-fakedetection-voronov-0/lib/python3.7/site-packages/timm/models/helpers.py in build_model_with_cfg(model_cls, variant, pretrained, default_cfg, model_cfg, feature_cfg, pretrained_strict, pretrained_filter_fn, pretrained_custom_load, kwargs_filter, **kwargs)
451
452 # Build the model
--> 453 model = model_cls(**kwargs) if model_cfg is None else model_cls(cfg=model_cfg, **kwargs)
454 model.default_cfg = default_cfg
455

TypeError: init() got an unexpected keyword argument 'cfg'

Expected behavior
I expect the model to be created with weights loaded from local file.

Desktop (please complete the following information):

• OS: Ubuntu 18.04
• timm==0.5.4
• PyTorch version: 1.9.0+cu111

[rwightman]

@RealAntonVoronov that's now how the cfg args are used (model_cfg is for architectures that support specifcying the full arch via a config without any intemediary), the support for 'file' in pretrained_cfg for local pretrained weights is a WIP (ie not fully complte & tested), that said, that config is to be passed via pretrained_cfg argument. For now it's best to continue as before and make sure the downloaded weight is in the cache dir

So there is a chance this will work...

pretrained_config = _cfg(url='', file='../tf_efficientnet_b7_ns-1dbc32de.pth',
              input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949)
model = tf_efficientnet_b7_ns(pretrained=True, pretrained_cfg=pretrained_config, drop_path_rate=0.2)

[RealAntonVoronov]

No, this does not work as well. Falls with the same error message. As far as I understand the problem is that model_cls is an instance of EfficientNet class and it tries to initialize it with passing the argument pretrained_cfg or in my original case model_cfg, but this class does not have any of this arguments. Thanks for the reply anyway, I got that this feature is not implemented yet, I'll workaround it some other way.

[rwightman]

@RealAntonVoronov I took a closer look at this since it's not supposed to be completely broken... it seems to work for me

>>> ptcfg = timm.models.registry.get_pretrained_cfg('tf_efficientnet_b7_ns')
>>> ptcfg.pop('url')
'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth'
>>> ptcfg['file']='./tf_efficientnet_b7_ns-1dbc32de.pth'
>>> ptcfg
{'num_classes': 1000, 'input_size': (3, 600, 600), 'pool_size': (19, 19), 'crop_pct': 0.949, 'interpolation': 'bicubic', 'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225), 'first_conv': 'conv_stem', 'classifier': 'classifier', 'file': './tf_efficientnet_b7_ns-1dbc32de.pth'}
>>> m = timm.models.tf_efficientnet_b7_ns(pretrained=True, pretrained_cfg=ptcfg, drop_path_rate=0.2)
>>> m.classifier.weight
Parameter containing:
tensor([[ 0.0050, -0.0276,  0.0390,  ..., -0.0076, -0.0338,  0.0537],
        [-0.0399,  0.0463,  0.0057,  ..., -0.0437, -0.0358,  0.0621],
        [-0.0185, -0.0606,  0.0494,  ..., -0.0119, -0.0221,  0.0491],
        ...,
        [-0.0137,  0.0394, -0.0450,  ...,  0.0036, -0.0685,  0.0440],
        [-0.0168,  0.0478, -0.0789,  ..., -0.0590, -0.0488, -0.0713],
        [-0.0700,  0.0158,  0.0114,  ..., -0.0023,  0.0537,  0.0528]],
       requires_grad=True)

Pretrained via defaults (url):

>>> m = timm.models.tf_efficientnet_b7_ns(pretrained=True, drop_path_rate=0.2)
>>> m.classifier.weight
Parameter containing:
tensor([[ 0.0050, -0.0276,  0.0390,  ..., -0.0076, -0.0338,  0.0537],
        [-0.0399,  0.0463,  0.0057,  ..., -0.0437, -0.0358,  0.0621],
        [-0.0185, -0.0606,  0.0494,  ..., -0.0119, -0.0221,  0.0491],
        ...,
        [-0.0137,  0.0394, -0.0450,  ...,  0.0036, -0.0685,  0.0440],
        [-0.0168,  0.0478, -0.0789,  ..., -0.0590, -0.0488, -0.0713],
        [-0.0700,  0.0158,  0.0114,  ..., -0.0023,  0.0537,  0.0528]],
       requires_grad=True)

Random init:

>>> m = timm.models.tf_efficientnet_b7_ns(pretrained=False, drop_path_rate=0.2)
>>> m.classifier.weight
Parameter containing:
tensor([[ 2.3909e-02, -2.5098e-02,  3.1557e-02,  ..., -4.3423e-04,
          2.6137e-03,  6.8434e-03],
        [-3.7444e-03, -1.7700e-03, -1.9717e-02,  ..., -8.9100e-03,
          9.8918e-03, -1.9784e-02],
        [-7.5210e-05, -2.5257e-02,  2.5059e-02,  ...,  1.6234e-02,
          5.3525e-03, -2.8231e-02],
        ...,
        [ 1.2436e-02, -7.4817e-03, -1.1816e-02,  ...,  2.0292e-02,
          1.9143e-02,  2.9750e-02],
        [ 2.5358e-02,  2.3163e-02,  1.0472e-03,  ..., -2.0183e-02,
         -1.3123e-02,  1.7490e-02],
        [ 1.1625e-02,  1.0379e-02,  3.1121e-02,  ...,  2.0877e-03,
          2.7863e-02, -2.6154e-02]], requires_grad=True)

[rwightman]

I'm going to move this to discussion as I feel some others might be interested... also related to #488 #967