python3 eval.py  --model  save/nsc-sat-2-from-nsc-seqkd/model-best.pth   --infos_path  save/nsc-sat-2-from-nsc-seqkd/infos_nsc-sat-2-from-nsc-seqkd-best.pkl    --batch_size  1   --beam_size   1   --id  nsc-sat-2-from-nsc-seqkd   

python3 eval_cocotest.py  --input_json  data/cocotest.json  --input_fc_dir data/cocotest/cocotest_bu_fc --input_att_dir  data/cocotest/cocotest_bu_att   --input_label_h5    data/cocotalk_label.h5  --num_images -1    --language_eval 0
--model  save/nsc-sat-4-from-nsc-seqkd/model-best.pth   --infos_path  save/nsc-sat-4-from-nsc-seqkd/infos_nsc-sat-4-from-nsc-seqkd-best.pkl    --batch_size  32   --beam_size   3   --id   captions_test2014_alg_results  

python3  train.py   --noamopt --noamopt_warmup 20000 --label_smoothing 0.0  --seq_per_img 5 --batch_size 10 --beam_size 1 --learning_rate 5e-4 --num_layers 6 --input_encoding_size 512 --rnn_size 2048 --learning_rate_decay_start 0 --scheduled_sampling_start 0  --save_checkpoint_every 3000 --language_eval 1 --val_images_use 5000 --max_epochs 15    --input_label_h5   data/cocotalk_seq-kd-from-nsc-transformer-baseline-b5_label.h5   --checkpoint_path   save/sat-2-from-nsc-seqkd   --id   sat-2-from-nsc-seqkd   --K  2

cd save
./copy_model.sh  sat-2-from-nsc-seqkd    nsc-sat-2-from-nsc-seqkd
cd ..

python3  train.py    --seq_per_img 5 --batch_size 10 --beam_size 1 --learning_rate 1e-5 --num_layers 6 --input_encoding_size 512 --rnn_size 2048  --save_checkpoint_every 3000 --language_eval 1 --val_images_use 5000 --self_critical_after 10  --max_epochs    40   --input_label_h5    data/cocotalk_label.h5   --start_from   save/nsc-sat-2-from-nsc-seqkd   --checkpoint_path   save/nsc-sat-2-from-nsc-seqkd  --id  nsc-sat-2-from-nsc-seqkd    --K 2

@article{zhou2021semi,
  title={Semi-Autoregressive Transformer for Image Captioning},
  author={Zhou, Yuanen and Zhang, Yong and Hu, Zhenzhen and Wang, Meng},
  journal={arXiv preprint arXiv:2106.09436},
  year={2021}
}