Comments (6)
Hello @zhi-xuan-chen, thank you very much for your interest in our work. Are you using our zero-shot inference code to load checkpoints? Also, could you please check your torch version, which should be 2.0.1? It's unexpected because it works in my case. Discr blocks are not used in CT-CLIP anyway, as the encoder is from another work of ours that require discr blocks. Therefore, it would be better if I remove them from the checkpoints and the model code (as it takes some unnecessary GPU memory). I will send the PR for edited codes and update the weight files in hugging face today after ensuring they do not break anything.
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Hello @zhi-xuan-chen, I have updated model weights and the transformer_maskgit code. Discr blocks should be removed now and it should work in your case as well. Please let me know if you can run the model.
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OK! Only the transformer_maskgit code was changed, so I only need to update the code of this part?
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I have noticed only ctvit.py was modified. I will try your new code immediately!
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Yes, I only modified ctvit.py to remove weights that are not used in the forward run of CT-Clip. Let me know if you have any further inquiries on this!
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The model checkpoint can be successfully loaded. Thank you very much!
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Related Issues (14)
- I applied to access and use the CT-RATE dataset, but after waiting for a few days, I still couldn't pass. HOT 2
- Dataset storage size HOT 1
- Issue with loading the dataset with huggingface HOT 1
- Error while reproducing the project HOT 3
- How to resume training CT-CLIP HOT 1
- Long Preprocessing Times HOT 5
- Training command with multi-GPU for CT-Clip Zero-shot model HOT 3
- CPU Memory size for loading total training data HOT 2
- Saved model is incomplete when use --use_fsdp HOT 10
- Mixed precision does not work HOT 1
- Question about validation set HOT 1
- Labels for supervised training HOT 5
- Shape Mis-Match HOT 2
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