Norton (NOise Robust Temporal Optimal traNsport) is a contrastive model for long-term video learning that enjoys zero-shot transfer to retrieval/QA/sequence labeling style tasks, especially for long videos.
Yijie Lin, Jie Zhang, Zhenyu Huang, Jia Liu, Zujie Wen, Xi Peng, Multi-granularity Correspondence Learning from Long-term Noisy Videos, ICLR 2024 (oral). [paper]
Existing video-language studies mainly focus on learning short video clips, leaving long-term temporal dependencies rarely explored due to over-high computational cost of modeling long videos. To address this issue, one feasible solution is learning the correspondence between video clips and captions, which inevitably encounters the multi-granularity noisy correspondence (MNC) problem as shown in Fig. 1. To be specific, MNC refers to the clip-caption misalignment (coarse-grained) and frame-word misalignment (fine-grained), hindering temporal learning and video understanding. In this paper, we propose NOise Robust Temporal Optimal traNsport (Norton) that addresses MNC in a unified optimal transport (OT) framework.
We perform video-paragraph contrastive learning to capture long-term temporal correlations from a fine-to-coarse perspective. Specifically, we first utilize the log-sum-exp operator on the frame-word similarity matrix to obtain fine-grained similarity between clip and caption. Additionally, we append an alignable prompt bucket on the clip-caption similarity matrix to filter out the irrelevant clips or captions. By applying Sinkhorn iterations on the clip-caption similarity matrix, we effectively tackle the asynchronous problem and obtain the optimal transport distance as the video-paragraph similarity.
- [2023-4-16] We provide the feature for pre-training, see DATASET.md.
- [2023-4-14] We are pleased to provide the feature for downstream tasks, see endtask.md.
- [2023-1-16] Norton is accepted to ICLR 2024 as oral presentation.
- Release Norton checkpoint.
- Release pre-training data (30 fps S3D of Howto100M).
- Release downstream data.
The core components and contribution of Norton are placed in mmpt/losses/nce.py, including video-paragraph contrastive loss and clip-caption contrastive loss.
├── data
│ place the data here
│ └── how2
│ place Howto100M data feature and annotation
├── projects
│ the config files for training/evaluation pipeline
├── mmpt
│ the core code of Norton
│ ├── losses
│ │ the loss functions
│ │ └── nce.py
│ │ the core components and contribution of Norton, including video-paragraph contrastive loss and clip-caption contrastive loss
│ ├── models
│ │ backbone models, the same with VideoCLIP
│ ├── modules
│ │ hard negative searching code, the same with VideoCLIP
│ ├── evaluators
│ │ └── metric.py
│ │ the evaluation metrics like long/short video retrieval, QA, sequence labeling
│ ├── processors
│ │ the data processors, including the data sampling of Howto100M and downstream tasks
│ │ ├── how2retriprocessor.py
│ │ │ the data loading of Howto100M
│ │ └── dsprocessor.py
│ │ the data loading of downstream tasks
├── mmpt_cli
│ the job starting code of training/evaluation pipeline
│ ├── localjob.py
│ │ the job start code of training pipeline
│ └── predict.py
│ the job start code of evaluation pipeline
├── scripts
│ the scripts for extracting video and text features, see DATASET.md for details
├── locallaunch.py
│ entry code, launching jobs
└── run.sh
demo script for training and evaluating Norton
We use fairseq as the main trainer (no models/datasets dependency on fairseq). Simply using
pip install fairseq or
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install -e . # also optionally follow fairseq README for apex installation for fp16 training.
export MKL_THREADING_LAYER=GNU # fairseq may need this for numpy.
The code is developed under Python=3.8.13, Pytorch=1.11.0, cuda=11.3 with fairseq=0.12.2.
Most models require transformers==3.4 for API compatibility pip install transformers==3.4.
In addition, some downstream tasks may need conda install pandas.
We use pre-trained S3D for video feature extraction. Please place the models as pretrained_models/s3d_dict.npy and pretrained_models/s3d_howto100m.pth.
Download Norton checkpoint https://drive.google.com/file/d/1ovUBCb-XSoD7bAFKAVa5w13yUqXmCpiS/view?usp=share_link to runs/retri/norton.
Download VideoCLIP checkpoint https://dl.fbaipublicfiles.com/MMPT/retri/videoclip/checkpoint_best.pt to runs/retri/videoclip (used for post-pretraining in our work).
import torch
from mmpt.models import MMPTModel
model, tokenizer, aligner = MMPTModel.from_pretrained(
"projects/retri/norton/how2_pretrain.yaml")
model.eval()
# B, T, FPS, H, W, C (Norton is trained on 30 fps of s3d)
video_frames = torch.randn(1, 2, 30, 224, 224, 3)
caps, cmasks = aligner._build_text_seq(
tokenizer("some text", add_special_tokens=False)["input_ids"]
)
caps, cmasks = caps[None, :], cmasks[None, :] # bsz=1
with torch.no_grad():
output = model(video_frames, caps, cmasks, return_score=True)
print(output["score"]) # dot-productSee dataset for each dataset.
We organize the config file for training/testing pipeline under projects projects/retri/norton.
Either training or evaluation process is configed by a concrete config file (we save all complex arguments into the concrete config file for reproducibility, including fairseq args). For example, pretraining of Norton in projects/retri/norton/how2_pretrain.yaml and zero-shot on vtt is in projects/retri/norton/test_vtt_zs.yaml.
We wrap all cmds into locallaunch.py and mmpt_cli/localjob.py. You can check concrete cmds by --dryrun and then drop it for actual run.
For example, run zero-shot evaluation on MSRVTT,
python locallaunch.py projects/retri/norton/test_vtt_zs.yaml --jobtype local_predict # zero-shot evaluation.
python locallaunch.py projects/retri/norton/vttqa_ft.yaml --jobtype local_single --dryrun # fine-tuning: use --dryrun to check cmds and drop it to make an actual run; local_single will run on one gpu.
python locallaunch.py projects/retri/norton/test_vttqa_ft.yaml --jobtype local_predict # testing on fine-tuned model.
Pretraining can be run as:
python locallaunch.py projects/retri/norton/how2_pretrain.yaml --jobtype local_single --dryrun # check then drop dryrun; paper is ran on local_small as 2 gpus.
You may need to change --jobtype, check/extend LocalJob in mmpt_cli/localjob.py for multi-gpu/multi-node pre-training.
For debuging, you could simply using train.py and predict.py to start the tasks. The following instructions are generated by locallaunch.py in Line 133.
python -m torch.distributed.launch --nproc_per_node=2 train.py projects/retri/norton/how2-pretrain.yaml --user-dir mmpt --task mmtask --arch mmarch --criterion mmloss --distributed-world-size 2 --log-interval 1000 --fp16 --num-workers 4 --batch-size 1 --lr 1e-05 --clip-norm 2.0 --optimizer adam --adam-betas '(0.9, 0.98)' --lr-scheduler polynomial_decay --total-num-update 1000000 --warmup-updates 1000 --weight-decay 0.0 --ddp-backend no_c10d --max-epoch 6 --restore-file runs/retri/videoclip/checkpoint_best.pt --reset-optimizer --reset-dataloader --reset-meters --save-dir runs/retri/norton/ --save-interval-updates 1024 --keep-interval-updates 2 --keep-last-epochs 50
python mmpt_cli/predict.py projects/retri/norton/test_vttqa_zs.yaml
The detailed instructions of pretraining and fine-tuning can be found at pretraining instruction and finetuning instruction.
Multi-modal research introduces the complexity on modality alignment from different input sources to losses.
This toolkit leverages mmpt/processors to handle various needs of data preprocessing and loading, alleviating the needs of multiple torch.data.utils.Dataset (that can be tricky for ablation study).
Processors can also be decoupled from torch.data.utils.Dataset for offline preprocessing instead of on-the-fly data preprocessing.
The dataset mmpt.MMDataset is decoupled as 3 types of processors: MetaProcessor, VideoProcessor, TextProcessor and Aligner. They can be configed in dataset field of a config file (e.g., see projects/retri/norton/how2_pretrain.yaml).
MetaProcessor is used to load the meta data about a dataset, aka, all video_ids of how2 dataset.
VideoProcessor is used to load the video features about a dataset. For example, S3D features for each second of a video.
TextProcessor is used to load the text (feature). For example, BERT pre-tokenized text clips for how2 dataset (with starts, ends of timestamps and cap for token_ids).
Aligner is the core class for different baselines that prepares the training data. For example, sampling a clip, masking tokens for MLM, etc.
To speed up pre-training, this toolkit uses sharded features stored in mmaped numpy, backed by ShardedTensor in mmpt/utils/shardedtensor.py (adopted from MARGE paper). This reduces the loads of IO for multi-GPU training without loading all features for a video into the memory each time and ShardedTensor ensure features are stored in continuous disk space for near random access. This is used for both How2 video features and texts in mmpt/processors/how2processor.py.
If this codebase is useful for your work, please cite the following papers:
@inproceedings{lin2024norton,
title={Multi-granularity Correspondence Learning from Long-term Noisy Videos},
author={Lin, Yijie and Zhang, Jie and Huang, Zhenyu and Liu, Jia and Wen, Zujie and Peng, Xi},
booktitle={Proceedings of the International Conference on Learning Representations},
month={May},
year={2024}
}This repo is built upon the framework of VideoCLIP, thanks for their excellent work.
