The implementation of paper UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation.

UniVL is a video-language pretrain model. It is designed with four modules and five objectives for both video language understanding and generation tasks.It is also a flexible model for most of the multimodal downstream tasks considering both efficiency and effectiveness.

Preliminary

Excute below scripts in the main folder firstly.

mkdir modules/bert-base-uncased
cd modules/bert-base-uncased/
wget https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt
mv bert-base-uncased-vocab.txt vocab.txt
wget https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz
tar -xvf bert-base-uncased.tar.gz
rm bert-base-uncased.tar.gz
cd ../../

Requirements

• python==3.6.9
• torch==1.7.0+cu92
• tqdm
• boto3
• requests
• pandas
• nlg-eval (Install Java 1.8.0 (or higher) firstly)

conda create -n py_univl python=3.6.9 tqdm boto3 requests pandas
conda activate py_univl
pip install torch==1.7.1+cu92
pip install git+https://github.com/Maluuba/nlg-eval.git@master

Finetune on YoucookII

Retrieval

1. Run retrieval task on YoucookII

DATATYPE="youcook"
TRAIN_CSV="data/youcookii/youcookii_train.csv"
VAL_CSV="data/youcookii/youcookii_val.csv"
DATA_PATH="data/youcookii/youcookii_data.pickle"
FEATURES_PATH="data/youcookii/youcookii_videos_features.pickle"
INIT_MODEL="weight/univl.pretrained.bin"
OUTPUT_ROOT="ckpts"

python -m torch.distributed.launch --nproc_per_node=4 \
main_task_retrieval.py \
--do_train --num_thread_reader=16 \
--epochs=5 --batch_size=32 \
--n_display=100 \
--train_csv ${TRAIN_CSV} \
--val_csv ${VAL_CSV} \
--data_path ${DATA_PATH} \
--features_path ${FEATURES_PATH} \
--output_dir ${OUTPUT_ROOT}/ckpt_youcook_retrieval --bert_model bert-base-uncased \
--do_lower_case --lr 3e-5 --max_words 48 --max_frames 48 \
--batch_size_val 200 --visual_num_hidden_layers 6 \
--datatype ${DATATYPE} --init_model ${INIT_MODEL}

The results are close to R@1: 0.2269 - R@5: 0.5245 - R@10: 0.6586 - Median R: 5.0

2. Run retrieval task on MSRVTT

DATATYPE="msrvtt"
TRAIN_CSV="data/msrvtt/MSRVTT_train.9k.csv"
VAL_CSV="data/msrvtt/MSRVTT_JSFUSION_test.csv"
DATA_PATH="data/msrvtt/MSRVTT_data.json"
FEATURES_PATH="data/msrvtt/msrvtt_videos_features.pickle"
INIT_MODEL="weight/univl.pretrained.bin"
OUTPUT_ROOT="ckpts"

python -m torch.distributed.launch --nproc_per_node=4 \
main_task_retrieval.py \
--do_train --num_thread_reader=16 \
--epochs=5 --batch_size=128 \
--n_display=100 \
--train_csv ${TRAIN_CSV} \
--val_csv ${VAL_CSV} \
--data_path ${DATA_PATH} \
--features_path ${FEATURES_PATH} \
--output_dir ${OUTPUT_ROOT}/ckpt_msrvtt_retrieval --bert_model bert-base-uncased \
--do_lower_case --lr 5e-5 --max_words 48 --max_frames 48 \
--batch_size_val 200 --visual_num_hidden_layers 6 \
--datatype ${DATATYPE} --expand_msrvtt_sentences --init_model ${INIT_MODEL}

The results are close to R@1: 0.2720 - R@5: 0.5570 - R@10: 0.6870 - Median R: 4.0

Caption

Run caption task on YoucookII

TRAIN_CSV="data/youcookii/youcookii_train.csv"
VAL_CSV="data/youcookii/youcookii_val.csv"
DATA_PATH="data/youcookii/youcookii_data.pickle"
FEATURES_PATH="data/youcookii/youcookii_videos_features.pickle"
INIT_MODEL="weight/univl.pretrained.bin"
OUTPUT_ROOT="ckpts"

python -m torch.distributed.launch --nproc_per_node=4 \
main_task_caption.py \
--do_train --num_thread_reader=4 \
--epochs=5 --batch_size=16 \
--n_display=100 \
--train_csv ${TRAIN_CSV} \
--val_csv ${VAL_CSV} \
--data_path ${DATA_PATH} \
--features_path ${FEATURES_PATH} \
--output_dir ${OUTPUT_ROOT}/ckpt_youcook_caption --bert_model bert-base-uncased \
--do_lower_case --lr 3e-5 --max_words 128 --max_frames 96 \
--batch_size_val 64 --visual_num_hidden_layers 6 \
--decoder_num_hidden_layers 3 --stage_two \
--init_model ${INIT_MODEL}

The results are close to

BLEU_1: 0.4746, BLEU_2: 0.3355, BLEU_3: 0.2423, BLEU_4: 0.1779
METEOR: 0.2261, ROUGE_L: 0.4697, CIDEr: 1.8631

Run caption task on MSRVTT

DATATYPE="msrvtt"
TRAIN_CSV="data/msrvtt/MSRVTT_train.9k.csv"
VAL_CSV="data/msrvtt/MSRVTT_JSFUSION_test.csv"
DATA_PATH="data/msrvtt/MSRVTT_data.json"
FEATURES_PATH="data/msrvtt/msrvtt_videos_features.pickle"
INIT_MODEL="weight/univl.pretrained.bin"
OUTPUT_ROOT="ckpts"

python -m torch.distributed.launch --nproc_per_node=4 \
main_task_caption.py \
--do_train --num_thread_reader=4 \
--epochs=5 --batch_size=128 \
--n_display=100 \
--train_csv ${TRAIN_CSV} \
--val_csv ${VAL_CSV} \
--data_path ${DATA_PATH} \
--features_path ${FEATURES_PATH} \
--output_dir ${OUTPUT_ROOT}/ckpt_msrvtt_caption --bert_model bert-base-uncased \
--do_lower_case --lr 3e-5 --max_words 48 --max_frames 48 \
--batch_size_val 32 --visual_num_hidden_layers 6 \
--decoder_num_hidden_layers 3 --datatype ${DATATYPE} --stage_two \
--init_model ${INIT_MODEL}

The results are close to

BLEU_1: 0.8051, BLEU_2: 0.6672, BLEU_3: 0.5342, BLEU_4: 0.4179
METEOR: 0.2894, ROUGE_L: 0.6078, CIDEr: 0.5004

Pretrain on HowTo100M

Format of csv

video_id,feature_file
Z8xhli297v8,Z8xhli297v8.npy
...

Stage I

ROOT_PATH=.
DATA_PATH=${ROOT_PATH}/data
SAVE_PATH=${ROOT_PATH}/models
MODEL_PATH=${ROOT_PATH}/UniVL
python -m torch.distributed.launch --nproc_per_node=8 \
${MODEL_PATH}/main_pretrain.py \
 --do_pretrain --num_thread_reader=0 --epochs=50 \
--batch_size=1920 --n_pair=3 --n_display=100 \
--bert_model bert-base-uncased --do_lower_case --lr 1e-4 \
--max_words 48 --max_frames 64 --batch_size_val 344 \
--output_dir ${SAVE_PATH}/pre_trained/L48_V6_D3_Phase1 \
--features_path ${DATA_PATH}/features \
--train_csv ${DATA_PATH}/HowTo100M.csv \
--data_path ${DATA_PATH}/caption.pickle \
--visual_num_hidden_layers 6 --gradient_accumulation_steps 16 \
--sampled_use_mil --load_checkpoint

Stage II

ROOT_PATH=.
DATA_PATH=${ROOT_PATH}/data
SAVE_PATH=${ROOT_PATH}/models
MODEL_PATH=${ROOT_PATH}/UniVL
INIT_MODEL=<from first stage>
python -m torch.distributed.launch --nproc_per_node=8 \
${MODEL_PATH}/main_pretrain.py \
--do_pretrain --num_thread_reader=0 --epochs=50 \
--batch_size=960 --n_pair=3 --n_display=100 \
--bert_model bert-base-uncased --do_lower_case --lr 1e-4 \
--max_words 48 --max_frames 64 --batch_size_val 344 \
--output_dir ${SAVE_PATH}/pre_trained/L48_V6_D3_Phase2 \
--features_path ${DATA_PATH}/features \
--train_csv ${DATA_PATH}/HowTo100M.csv \
--data_path ${DATA_PATH}/caption.pickle \
--visual_num_hidden_layers 6 --decoder_num_hidden_layers 3 \
--gradient_accumulation_steps 60 \
--stage_two --pretrain_with_joint_sim --sampled_use_mil \
--pretrain_enhance_vmodal \
--load_checkpoint --init_model ${INIT_MODEL}

Citation

If you find UniVL useful in your work, you can cite the following paper:

@Article{Luo2020UniVL,
  author  = {Huaishao Luo and Lei Ji and Botian Shi and Haoyang Huang and Nan Duan and Tianrui Li and Jason Li and Taroon Bharti and Ming Zhou},
  title   = {UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation},
  journal = {arXiv preprint arXiv:2002.06353},
  year    = {2020},
}

License

This project is licensed under the license found in the LICENSE file in the root directory of this source tree.

Microsoft Open Source Code of Conduct

Acknowledgments

Our code is based on pytorch-transformers v0.4.0. We thank the authors for their wonderful open-source efforts.