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Vamos: Versatile Action Models for Video Understanding

This repository contains the official implementation for the paper Vamos: Versatile Action Models for Video Understanding, including codes to train Vamos on Video QA tasks (NeXT-QA, IntentQA, and Perception Test) and EgoSchema zero-shot evaluation.

Setup

To install requirements, run:

git clone git@github.com:brown-palm/Vamos.git
cd Vamos
conda create -n vamos python=3.8
conda activate vamos
sh setup.sh

Video QA Dataset & LLaMA Preparation to Train Vamos

All the codes for training Vamos for video QA tasks are under the finetune folder.

cd finetune
mkdir pretrained
mkdir data

You can download our preprocessed datasets (NExT-QA, IntentQA, Perception Test) at here. Put them in data. Also, you can download original LLaMA at here, and put the checkpoint in pretrained.

finetune/pretrained
    |─ llama
    |  |─ 7B
    |  |   |─ consolidated.00.pth
    |  |   └─ params.json
    |  └─ tokenizer.model
    |   :
    |─ llama2
    |   :
    └─ llama3
        :

finetune/data
   |─ nextqa
   |   |─ train.csv
   |   |─ val.csv
   |   |─ test.csv
   |   |─ llava_v15_7b_n6.json
   |   |─ llava_v15_13b_n6.json
   |   └─ clipvitl14.pth
   |─ intentqa
   |   |─ train.csv
   |   |─ val.csv
   |   |─ test.csv
   |   |─ blip2_n6.json
   |   └─ clipvitl14.pth
   └─ ptest
       |─ train.csv
       |─ val.csv
       |─ llava_v15_7b_n6.json
       |─ llava_v15_13b_n6.json
       └─ clipvitl14.pth

Training Vamos

After preparing the datasets and LLaMA weights, excute the following codes under finetune folder. For pure visual representation, 40G A100/A6000 is recommended, for text and text + vision, 80G A100 is recommended. --batch_size and --accum_iter can be adjusted according to GPU number and memory size.

NExT-QA

# text representation
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 8B --max_seq_len 1200 --batch_size 2 --epochs 10 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 6 --dataset nextqa --blr 9e-2 --weight_decay 0.14 --accum_iter 8 --use_cap --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/nextqa_cap_ep10_llama3 --project_name nextqa

# visual representation
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 8B --max_seq_len 128 --batch_size 8 --epochs 5 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 6 --dataset nextqa --blr 9e-2 --weight_decay 0.14 --accum_iter 2 --use_vis --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/nextqa_vis_ep5_llama3 --project_name nextqa

# vision + text
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 8B --max_seq_len 1200 --batch_size 2 --epochs 10 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 6 --dataset nextqa --blr 9e-2 --weight_decay 0.14 --accum_iter 8 --use_cap --use_vis --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/nextqa_vis_cap_ep10_llama3 --project_name nextqa --alter_train

IntentQA

# text representation
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 8B --max_seq_len 512 --batch_size 8 --epochs 5 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 6 --dataset intentqa --blr 9e-2 --weight_decay 0.14 --accum_iter 2 --use_cap --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/intentqa_cap_ep5_llama3 --project_name intentqa

# visual representation
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 8B --max_seq_len 128 --batch_size 8 --epochs 5 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 6 --dataset intentqa --blr 9e-2 --weight_decay 0.14 --accum_iter 2 --use_vis --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/intentqa_vis_ep5_llama3 --project_name intentqa

# vision + text
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 8B --max_seq_len 1200 --batch_size 8 --epochs 10 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 6 --dataset intentqa --blr 9e-2 --weight_decay 0.14 --accum_iter 2 --use_cap --use_vis --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/intentqa_vis_cap_ep10_llama3 --project_name intentqa --alter_train

Perception Test

# text representation
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 8B --max_seq_len 1200 --batch_size 2 --epochs 10 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 10 --dataset ptest --blr 9e-2 --weight_decay 0.14 --accum_iter 8 --use_cap --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/ptest_cap_ep10_llama3 --project_name ptest

# visual representation
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 8B --max_seq_len 256 --batch_size 8 --epochs 5 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 10 --dataset ptest --blr 9e-2 --weight_decay 0.14 --accum_iter 2 --use_vis --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/ptest_vis_ep5_llama3 --project_name ptest

# vision + text
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 8B --max_seq_len 1200 --batch_size 2 --epochs 10 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 10 --dataset ptest --blr 9e-2 --weight_decay 0.14 --accum_iter 8 --use_cap --use_vis --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/ptest_vis_cap_ep10_llama3 --project_name ptest --alter_train

Pretrained checkpoints

Pretrained Vamos-LLaMA3 checkpoints are provided: NeXT-QA, IntentQA, and Perception Test.

Evaluation

From the training command, simply replace train.py to eval.py and add --resume your/checkpoint.pth, for example:

# Eval NeXT-QA with pure text representation
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 eval.py --model 8B --max_seq_len 1200 --batch_size 2 --epochs 10 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 10 --dataset nextqa --blr 9e-2 --weight_decay 0.14 --accum_iter 8 --use_cap --llama_model_path ./pretrained/llama3/ --output_dir ./checkpoint/nextqa_cap_ep10_llama3 --project_name nextqa --resume checkpoint/nextqa_cap_13b_llama3.pth

Zero-shot Video QA on EgoSchema

All the codes for are under the zero_shot_egoschema folder.

# zs inference with OpenAI GPTs (GPT-4o by default)
python egoschema_zs.py --output_name gpt4o_result.json --openai_key {your-openai-key}

# evaluate on the 500 subset
python eval_subset.py --pred_file gpt4o_result.json

# generate submission file to eval the full set
python gen_submission.py --pred_file gpt4o_result.json

Acknowledgements

This repo is built upon LLaMA-Adapter and Flipped-VQA.

Citations

@misc{wang2023vamos,
        title={Vamos: Versatile Action Models for Video Understanding}, 
        author={Shijie Wang and Qi Zhao and Minh Quan Do and Nakul Agarwal and Kwonjoon Lee and Chen Sun},
        year={2023},
        eprint={2311.13627},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }