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