Notebook 05_EfficientNet_and_Custom_Weights.ipynb fails on google colab on line
learn.summary()
with error
RuntimeError: running_mean should contain 3072 elements not 6144
I think if you have the latest versions of timm, fastai, and walkwithfastai this shouldnāt be a problem.
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Actually this is an active bug I think. This is different than the timm notebook (itās on my things to tackle next week)
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Hi @muellerzr ,
I think fastai has updated the create_head code. We donāt have to explicitly multiply nf with 2 when concat is True, create_head already does it.
So in create_timm_model function we donāt need to do this ā nf = num_features_model(nn.Sequential(*body.children())) * (2 if concat_pool else 1)
Because of this nf is multiplying 4 times instead of 2.
You are right! The timm tutorial has it the right way, but the efficientnet one does not! Will update today 
Timm tutorial: Utilizing the `timm` Library Inside of `fastai` (Intermediate) | walkwithfastai
@ne1s0n and @RadhikaBansal this has now been updated
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Thanks for the effort! Unfortunately, it still encounters problems. Iāve opened an issue on GitHub. Basically Google Colab defaults to newer library versions. In particular fastai now masks the internal _update_first_layer function. If I add the old definition the notebook runs.
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Thanks @ne1s0n! Iāve added in the import. Not sure if this was a miss on my part or what, but seems I forgot an import along the way!
For those wanting the direct, its: from fastai.vision.learner import _update_first_layer
Has anyone faced mismatched keys with the state_dict? Timm model state doct and fastai trained timm model have slightly different keys in their respective state_dicts. Iāve tested by retraining using current versions and am not sure if there is a simple workaround to this problem other than renaming the keys to match. All other model information appears to match perfectly. Thanks for any help
Dan