# First stage

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port=11323 --use_env main.py --window_size 100 --batch_size 32 --stage 1 --num_queries 32 --point_prob_normalize

# Second stage for relaxation mechanism

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port=11324 --use_env main.py --window_size 100 --batch_size 32 --lr 1e-5 --stage 2 --epochs 10 --lr_drop 5 --num_queries 32 --point_prob_normalize --load outputs/checkpoint_best_sum_ar.pth

# Third stage for completeness head

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port=11325 --use_env main.py --window_size 100 --batch_size 32 --lr 1e-4 --stage 3 --epochs 20 --num_queries 32 --point_prob_normalize --load outputs/checkpoint_best_sum_ar.pth

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port=11325 --use_env main.py --window_size 100 --batch_size 32 --lr 1e-4 --stage 3 --epochs 20 --num_queries 32 --point_prob_normalize --eval --resume outputs/checkpoint_best_sum_ar.pth

@InProceedings{Tan_2021_RTD,
    author    = {Tan, Jing and Tang, Jiaqi and Wang, Limin and Wu, Gangshan},
    title     = {Relaxed Transformer Decoders for Direct Action Proposal Generation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {13526-13535}
}

Jing Tan: [email protected]
Jiaqi Tang: [email protected]