How to reproduce the Rev-ViT validation accuracy

Hi, Could you provide the script to reproduce the Rev-ViT validation accuracy?

I use the following command and cannot get the right accuracy.

python tools/run_net.py --cfg configs/ImageNet/REV_VIT_S.yaml \
DATA.PATH_TO_DATA_DIR data/imagenet/ \
TRAIN.ENABLE False \
TEST.CHECKPOINT_FILE_PATH ckpt/REV_VIT_S.pyth

I can run the above command only when comment the following lines https://github.com/facebookresearch/SlowFast/blob/5b5d9ecb15a54188943af0cbf5f7c420d8409018/tools/test_net.py#L131-L135 and https://github.com/facebookresearch/SlowFast/blob/5b5d9ecb15a54188943af0cbf5f7c420d8409018/tools/test_net.py#L225-L229

Finally :

[10/21 01:48:30][WARNING] meters.py:  390: clip count Ids=tensor([[   0,    1,    2,  ..., 1663, 1664, 1665]]) = tensor([0, 0, 0,  ..., 0, 0, 0]) (should be 30)                                                                                                                                                        
[10/21 01:48:30][INFO] logging.py:   99: json_stats: {"split": "test_final", "top1_acc": "0.00", "top5_acc": "100.00"}                                      [10/21 01:48:30][INFO] test_net.py:  261: Finalized testing with 10 temporal clips and 3 spatial crops                                                      [10/21 01:48:30][INFO] test_net.py:  283: _p22.43_f4.58_10a0.00 Top5 Acc: 100.00 MEM: 1.52 f: 4.5842

Could you give some hints? Thanks @haooooooqi @karttikeya

[tonysy]

Great, many thanks.

[karttikeya]

Yes.

Additionally, here’s a few days old 80.4 REV ViT S model trained with configs/ImageNet/REV_VIT_S.yaml along with the training log, so it is easier to reproduce.

Model + Log: https://drive.google.com/drive/folders/1JYSpejA4ckOdEbbw3PFHrzT4YugiZTJU?usp=share_link