Comments (2)
Yes @juliendenize, thank you very much. I have already been able to use the pre-trained network.
Thank you very much and congratulations on your results.
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Hello @edugzlez, thanks for taking an interest in our work.
I think the model loaded properly, it might have been a better idea on my part to remove from the state_dict
the transformation parameters as they are not pertinent and should be config-based. I think that you loaded it right.
Still, I would suggest that you filter the transform keys from the model state_dict if you do not wish to use any transforms. You can do something like this (I did not proofread it but I hope you get the idea):
config.model.train_transform=None # Remove train_transform initialization
config.model.val_transform=None # Remove val_transform initialization
config.model.test_transform=None # Remove test_transform initialization
model = ....
state_dict = torch.load(...)
filtered_state_dict = state_dict.copy()
for key in state_dict.keys():
if key.startswith(("train_transform","val_transform","test_transform")): # Remove transforms from state_dict
del filtered_state_dict[key]
model.load_state_dict(filtered_state_dict) # Only load model parameters
However, keep in mind you would have to resize and normalize the input data before passing them to the model.
If I am right the model._orig_mod.trunk layer directly extracts the features every 2 frames.
This is right indeed you should obtain 64 tokens at the output if you pass 128 frames.
Did I answer correctly to your questions?
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