tar -xvf lvvit_s-26M-384-84-4.pth.tar tar: This does not look like a tar archive tar: Skipping to next header tar: Exiting with failure status due to previous errors

Hello! I use ILSVRC2012_img_train and ILSVRC2012_img_val, and use the provided label_top5_train_nfnet from Google Drive. I train lv-vit-s with batch_size 64 without apex for one epoch. Thanks for your advice.

Hi,

Thanks for sharing your work. Could you also provide the pre-trained weights for the LV-ViT-T model variant, the one that achieves 79.1% top1-acc. as mentioned in Table 1 of your paper?

All the best, Marc

Hi, thanks for you great work. When I train script, some error occurs: AttributeError: 'tuple' object has no attribute 'log_softmax'

with amp_autocast():   
            output = model(input)  
            loss = loss_fn(output, target)  # error occurs

and loss function is train_loss_fn = LabelSmoothingCrossEntropy(smoothing=0.0).cuda()

by the way: Could you please tell me why we need to specify smoothing=0.0?

I am a green hand of DL. When I run the code of volo with tlt in a single or multi GPU, I get an error as follows: /pytorch/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:312: operator(): block: [0,0,0], thread: [25,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. Traceback (most recent call last): File "main.py", line 949, in main() File "main.py", line 664, in main optimizers=optimizers) File "main.py", line 773, in train_one_epoch label_size=args.token_label_size) File "/opt/conda/lib/python3.6/site-packages/tlt/data/mixup.py", line 90, in mixup_target y1 = get_labelmaps_with_coords(target, num_classes, on_value=on_value, off_value=off_value, device=device, label_size=label_size) File "/opt/conda/lib/python3.6/site-packages/tlt/data/mixup.py", line 64, in get_labelmaps_with_coords num_classes=num_classes,device=device) File "/opt/conda/lib/python3.6/site-packages/tlt/data/mixup.py", line 16, in get_featuremaps _label_topk[1][:, :, :].long(), RuntimeError: CUDA error: device-side assert triggered.

I can't fix this problem right now.

Hello,

Thank you for sharing your work. I am currently trying to generate token label to a custom dataset for model lvvit_s, but I keep getting the loss close to 7 and the Accuracy 0 (not pre-trained and using 1 GPU in Google Colab). I also tried using the pre-trained model with --transfer but got 0 in both Loss and Acc . What option should I use for a custom dataset?

Hi,

When I tried to run the label generation script for the model lvvit_s it returned an error "RuntimeError: Unknown model".

Solution: It worked when I added the line "import tlt.models" in the file generate_label.py.

Greetings,

Thank you for this incredible research.

I would like to know if it is possible to use Token Labeling to achieve scores higher than that of the annotator model, I believe this was the case with VOLO D5 model where it achieved higher score than NFNet, model used for annotation.

Hi Thanks so much for the nice work! I am curious if you could share the insight on processing of the label_map. If I understand it correctly, after we load image and the corresponding, we shall do the same cropping/ flip/ resize, but in https://github.com/zihangJiang/TokenLabeling/blob/aa438eff9b9fc2daa8c8b4cc6bfaa6e3721f995e/tlt/data/label_transforms_factory.py#L58-L73 Seems only image was cropped, but the label map does not do the same cropping, which make the label map not match with the image?

Shall we do

        return torchvision_F.resized_crop(
                img, i, j, h, w, self.size, interpolation
        ), torchvision_F.resized_crop(
                label_map, i / ratio, j / ratio, h / ratio, w / ratio, self.size, interpolation
        )

Thanks

Test: [ 0/1] Time: 11.293 (11.293) Loss: 0.7043 (0.7043) [email protected]: 42.1875 (42.1875) [email protected]: 100.0000 (100.0000) Test: [ 1/1] Time: 0.108 (5.701) Loss: 0.5847 (0.6689) [email protected]: 89.8148 (56.3187) [email protected]: 100.0000 (100.0000) free(): invalid pointer free(): invalid pointer Traceback (most recent call last): File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 303, in <module> main() File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 294, in main raise subprocess.CalledProcessError(returncode=process.returncode, subprocess.CalledProcessError: Command '['/opt/conda/bin/python3.8', '-u', 'main.py', '--local_rank=1', './dataset/c/c', '--model', 'lvvit_s', '-b', '128', '--apex-amp', '--img-size', '224', '--drop-path', '0.1', '--token-label', '--token-label-size', '14', '--dense-weight', '0.0', '--num-classes', '2', '--finetune', './pretrained/lvvit_s-26M-384-84-4.pth.tar']' died with <Signals.SIGABRT: 6>. [email protected]:/puxin_libochao/TokenLabeling# CUDA_VISIBLE_DEVICES=0,1 bash ./distributed_train.sh 2 ./dataset/c/c --model lvvit_s -b 128 --apex-amp --img-size 224 --drop-path 0.1 --token-label --token-label-size 14 --dense-weight 0.0 --num-classes 2 --finetune ./pretrained/lvvit_s-26M-384-84-4.pth.tar

Hi, thanks for your wonderful work. I have a question that whether training techniques mentioned in the LV-Vit can be used in other downstream task like object detection? In your paper, I see that many of this techniques are used in ImageNet. Thanks!

Hello ~ I'm interested in your token labeling technique, So I want to apply this technique in CNN based model because ViT is very heavy to train.

can I get the your code with CNN token labeling? if you're not give me some detail for implementing

thank you.

I am interested if there is any LV-ViT- model setup you have tested for Cifar10. I would like to know the proper setup of all blocks in none pretrained weights settings.