train error: AttributeError: 'tuple' object has no attribute 'log_softmax' about tokenlabeling HOT 5 CLOSED

Hi @lxy5513 ,

We've updated and tested support for transfer learning. The dataset folder structure should be the same as ImageNet structure https://github.com/zihangJiang/TokenLabeling#requirements (with train and val split). You can clone the main branch, try to specify --num-classes flag, and add --token-label --dense-weight 0 for fine-tuning.

Let me know if you have any further questions.

from tokenlabeling.

Hi,
Can you post your training script? The label smoothing is handled in Mixup & Cutmix here

and here

If you train without token labeling, We suggest you add --mixup 0.8 or --cutmix 1.0 as regularization, and use lvvit model. Model like lvvit_s returns all tokens by default which will cause error if you train without token labeling.
If you train with token labeling, please add --token-label flag.

from tokenlabeling.

@zihangJiang Thanks for your response.
My training script is here:
python main.py /home/liuxingyu/data/patrol/comfort_classification/nursery_v16 --model lvvit_m -b 64 --apex-amp --img-size 384 --drop-path 0.2 --token-label-size 24 --model-ema --finetune lvvit_m-56M-384-85.4.pth.tar --token-label-data ''

I train without token labeling, because I have not generate token labels for my custome dataset.

Model like lvvit_s returns all tokens by default which will cause error if you train without token labeling

Is this mean if I want to train without token labeling, I can't use pretrain model ?

from tokenlabeling.

We haven't tested on other datasets yet, but you can of course use the pre-trained model. I think you can add --token-label --dense-weight 0 flag. This can work without token label data.

However, some other bugs may occur. You can wait for our further update supporting transfer learning.

from tokenlabeling.

Well, thanks, expect for further update.

from tokenlabeling.