PC2-NoiseofWeb

This repo is the official Pytorch implementation of our paper:

PC2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval
Authors: Yue Duan, Zhangxuan Gu, Zhenzhe Ying, Lei Qi, Changhua Meng and Yinghuan Shi

• 🔗 Quick links: [PDF/Abs-arXiv | Dataset | 文章解读-知乎(Zhihu) | 视频解读-bilibili]

• 📰 Latest news:

  • We provide a video presentation (in chinese) of this work on bilibili.
  • We write a detailed explanation (in chinese) of this work on 知乎(Zhihu).
  • Our paper is accepted by ACM International Conference on Multimedia (ACM MM) 2024 🎉🎉. Thanks to users.

• 📑 More of my works:

  • 🆕 [LATEST] Interested in the SSL in fine-grained visual classification (SS-FGVC)? 👉 Check out our AAAI'24 paper SoC [PDF-arXiv | Code].
  • Interested in more scenarios of SSL with mismatched distributions? 👉 Check out our ICCV'23 paper PRG [PDF-arXiv | Code].
  • Interested in robust SSL in MNAR setting with mismatched distributions? 👉 Check out our ECCV'22 paper RDA [PDF-arXiv | Code].
  • Interested in the conventional SSL or more application of complementary label in SSL? 👉 Check out our TNNLS paper MutexMatch [PDF-arXiv | Code].

Dataset Contribution: Noise of Web (NoW)

Data Collection

We develop a new dataset named Noise of Web (NoW) for NCL. It contains 100K image-text pairs consisting of website pages and multilingual website meta-descriptions (98,000 pairs for training, 1,000 for validation, and 1,000 for testing). NoW has two main characteristics: without human annotations and the noisy pairs are naturally captured. The source image data of NoW is obtained by taking screenshots when accessing web pages on mobile user interface (MUI) with 720 $\times$ 1280 resolution, and we parse the meta-description field in the HTML source code as the captions. In NCR (predecessor of NCL), each image in all datasets were preprocessed using Faster-RCNN detector provided by Bottom-up Attention Model to generate 36 region proposals, and each proposal was encoded as a 2048-dimensional feature. Thus, following NCR, we release our the features instead of raw images for fair comparison. However, we can not just use detection methods like Faster-RCNN to extract image features since it is trained on real-world animals and objects on MS-COCO. To tackle this, we adapt APT as the detection model since it is trained on MUI data. Then, we capture the 768-dimensional features of top 36 objects for one image. Due to the automated and non-human curated data collection process, the noise in NoW is highly authentic and intrinsic. The estimated noise ratio of this dataset is nearly 70%.

Data Structure

|-- h5100k_precomp
|   |-- dev_caps_bpe.txt
|   |-- dev_caps_bert.txt
|   |-- dev_caps_jieba.txt
|   |-- dev_ids.txt
|   |-- dev_ims.npy
|   |-- test_caps_bpe.txt
|   |-- test_caps_bert.txt
|   |-- test_caps_jieba.txt
|   |-- test_ids.txt
|   |-- test_ims.npy
|   |-- train_caps_bpe.txt
|   |-- train_caps_bert.txt
|   |-- train_caps_jieba.txt
|   |-- train_ids.txt
|   |-- train_ims.npy
|-- vocab
|   |-- now100k_precomp_vocab_bert.json
|   |-- now100k_precomp_vocab_bpe.json
|   |-- now100k_precomp_vocab_jieba.json

Please note that since our raw data contains some sensitive business data, we only provide the encoded image features (*_ims.npy) and the token ids of the text tokenized. For tokenizer, we provide Tokenizers with BPE to produce *_caps_bpe.txt, BertTokenizer with bert-base-multilingual-cased pre-trained model to produce *_caps_bert.txt, and Jieba to produce *_caps_jieba.txt. Our vocabulary size of BPETokenizer is 10,000, while BertTokenizer and JiebaTokenizer have a vocabulary size of 32,702 and 56,271 respectively. (recorded in now100k_precomp_vocab_*.txt). *_ids.txt records the data indexs in the original 500k dataset. In the future, we may process and make the original dataset public.

Download Link

📎 Download NoW at https://huggingface.co/datasets/NJUyued/NoW/resolve/main/NoW.zip?download=true.

🤗 See HuggingFace's homepage https://huggingface.co/datasets/NJUyued/NoW for details.

Usage

# data_path: your dataset name and path
# data_split: {train,dev,test}
# tokenizer: {bpe,bert,jieba}
# vocabulary size of {bpe,bert,jieba} is {10,000,32702,56271} 

# captions
with open(os.path.join(data_path, "{}_caps_{}.txt".format(data_split, tokenizer))) as f:
    for line in f:
        captions.append(line.strip())
captions_token = []
for index in range(len(captions)):
  caption = captions[index]
  tokens = caption.split(',')
  caption = []
  caption.append(vocab("<start>"))
  caption.extend([int(token) for token in tokens if token])
  caption.append(vocab("<end>"))
  captions_token.append(caption)

# images
images = np.load(os.path.join(data_path, "%s_ims.npy" % data_split))

return captions_token, images

Additionally, you can search for code snippets containing the string now100k_precomp in co_train.py, data.py, evaluation.py, and run.py in this repo and refer to them to process the NoW dataset for use in your own code.

PC2

Introduction

In the realm of cross-modal retrieval, seamlessly integrating diverse modalities within multimedia remains a formidable challenge, especially given the complexities introduced by noisy correspondence learning (NCL). Such noise often stems from mismatched data pairs, a significant obstacle distinct from traditional noisy labels. This paper introduces Pseudo-Classification based Pseudo-Captioning ($\text{PC}^2$) framework to address this challenge.

Requirements

• matplotlib==3.4.2
• nltk==3.8.1
• numpy==1.22.3
• scikit_learn==0.24.2
• scipy==1.6.2
• torch==2.2.2

How to Train

Important Args

• --lambda_en: Entropy loss weight.
• --proj_dim: Dimensionality of the projection head. By default, --proj_dim 128 is set.
• --nb: Number of tracked bathches.
• --img_dim : Dimensionality of the image embedding. --img_dim 2048 is used for {coco,f30k,cc152k} and please set it to 768 for now100k.
• --warmup_epoch : Epochs of warm up stage.
• --warmup_epoch_2 : Epochs of training with clean data only.
• --po_dir : When --resume, use this path to load the PO data for resuming training.
• --model_path : Use this path to load the checkpoint for resuming training when --resume, or use this path to load the warmup checkpoint for resuming training without --resume.
• --data_name {coco,f30k,cc152k,now100k}_precomp and --data_path : Your dataset name and path.
• --tokenizer {bpe,bert,jieba}: The tokenizer used for NoW dataset.
• --noise_ratio: Noisy ratio for Flickr30K and MS-COCO.
• --noise_file: Noise file for the feproduction of noise correspondence.

Training with Single GPU

We recommend using a single NVIDIA Tesla A100 80G for training to better reproduce our results. Multi-GPU training is feasible, but our results are all obtained from single GPU training.

python ./PC2/run.py --world-size 1 --rank 0 --gpu [0/1/...] @@@other args@@@

Training with Multi-GPUs

• Using DistributedDataParallel with single node

python ./PC2/run.py --world-size 1 --rank 0 --multiprocessing-distributed @@@other args@@@

Please note that our code is based on the NCR implementation and the original training code can only run on a single GPU (see issue#4). In order to make it easier for you to use our code, we tried to provide a multi-GPU parallel training version based on DistributedDataParallel. Unfortunately, there seem to be some bugs that we have not yet solved. The following error may occur during training:

[rank0]:[E ProcessGroupNCCL.cpp:523] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=16349, OpType=ALLGATHER, NumelIn=1, NumelOut=2, Timeout(ms)=600000) ran for 600341 milliseconds before timing out.
[rank0]:[E ProcessGroupNCCL.cpp:537] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[rank0]:[E ProcessGroupNCCL.cpp:543] To avoid data inconsistency, we are taking the entire process down.
[rank0]:[E ProcessGroupNCCL.cpp:1182] [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=16349, OpType=ALLGATHER, NumelIn=1, NumelOut=2, Timeout(ms)=600000) ran for 600341 milliseconds before timing out.
Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:525 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f1e10143d87 in /home/dy/.local/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1e6 (0x7f1d990756e6 in /home/dy/.local/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #2: c10d::ProcessGroupNCCL::workCleanupLoop() + 0x19d (0x7f1d99078c3d in /home/dy/.local/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x119 (0x7f1d99079839 in /home/dy/.local/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #4: <unknown function> + 0xc9039 (0x7f1e1034f039 in /usr/local/miniconda3/envs/sharedEnv/bin/../lib/libstdc++.so.6)
frame #5: <unknown function> + 0x76db (0x7f1e14a626db in /lib/x86_64-linux-gnu/libpthread.so.0)
frame #6: clone + 0x3f (0x7f1e1478b61f in /lib/x86_64-linux-gnu/libc.so.6)

If any friends have insights on the occurrence of this problem, please contact us. At the same time, please rest assured that there will be no problem training with a single GPU (i.e., using --gpu to specify the GPU id).

Examples of Running

By default, the warmup checkpoint warmup_model_{}.pth.tar, best checkpoint checkpoint_best_test.pth.tar, best validattion checkpointcheckpoint_best_validattion.pth.tar and PO data (the pseudo-preditions of pseudo-classification) distri_bank_{}.pkl will be saved in ./output_dir.

NoW

python ./pc2/run.py --world-size 1 --rank 0 --gpu 0 --workers 8 --lr_update 30 --warmup_epoch 10 --warmup_epoch_2 25 --data_name h5100k_precomp --tokenizer bert --data_path ./data --vocab_path ./data/vocab --output_dir ./output --proj_dim 128 --lambda_en 10 --img_dim 768 

Flickr30k

python ./pc2/run.py --world-size 1 --rank 0 --gpu 0 --workers 8 --warmup_epoch 5 --warmup_epoch_2 25 --data_name f30k_precomp --data_path ./data --vocab_path ./data/vocab  --output_dir ./output --proj_dim 128 --lambda_en 10 --noise_ratio 0.4 --noise_file noise_index/f30k_precomp_0.4

MS-COCO

python ./pc2/run.py --world-size 1 --rank 0 --gpu 0 --workers 8 --warmup_epoch 5 --warmup_epoch_2 25 --data_name coco_precomp --data_path ./data --vocab_path ./data/vocab  --output_dir ./output --proj_dim 128 --lambda_en 10 --noise_ratio 0.4 --noise_file noise_index/coco_precomp_0.4

Resume Training and Evaluation

• If you restart the training from normal checkpoints, please use --resume --model_path @your_weight_path.

• If you restart the training from warmup checkpoints, please use --model_path @your_warmup_weight_path.

• For evaluation, run

  python ./PC2/evaluation.py --data_path @your_data_path --model_path @your_weight_path --gpu @your_gpu_id
  

  By default, your evaluation process will directly use the dataset name saved in your checkpoint.

Citation

Please cite our paper if you find $\text{PC}^2$ useful:

@article{duan2024pc,
  title={PC $\^{} 2$: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval},
  author={Duan, Yue and Gu, Zhangxuan and Ying, Zhenzhe and Qi, Lei and Meng, Changhua and Shi, Yinghuan},
  journal={arXiv preprint arXiv:2408.01349},
  year={2024}
}