Symbiotic Adversarial Learning for Attribute-based Person Search

PyTorch implementation of Symbiotic Adversarial Learning for Attribute-based Person Search (ECCV2020).

Update

01/08/21: Updated requirements.txt. Upgrading Pillow to version 8.2.0 or later due to some issues in Pillow.

28/02/21: Updated train.py and scripts. Uploaded trained models.

Problem setting

Proposed model

Requirements

Python 3

h5py>=2.8.0

scipy>=1.1.0

requests>=2.20.1

torchvision=>0.2.1

Cython>=0.29.12

numpy>=1.13.3

pytorch>=0.4.1

Pillow>=7.2.0

Installation

1. Run git clone https://github.com/ycao5602/SAL.git.
2. Install dependencies by pip install -r requirements.txt (if necessary).
3. To install the cython-based evaluation toolbox, cd to torchreid/eval_cylib and do make. As a result, eval_metrics_cy.so is generated under the same folder. Run python test_cython.py to test if the toolbox is installed successfully (credit to luzai).

Datasets

Image-reid datasets:

• Market1501 Market-1501 attribute (market1501) (both should be downloaded for training)
• PETA (peta)

After downloading Market1501 and the attribute annotations, the directory should be organized as,

├── root
│   ├── Market-1501
│       ├── bounding_box_test
│       ├── bounding_box_train
│       ├── query
│       ├── attribute
│           ├── market_attribute.mat

After downloading PETA, the directory should be organized as,

├── root
│   ├── PETA
│       ├── 00001.png
│       ...
│   ├── PETA.mat

Part of the data preparation code credits to Kevin.h.

Backbone Model

• ResNet

Tutorial

Train

Training methods are implemented in train.py

Input arguments for the above training scripts are unified in args.py.

Training scripts in scripts. sh peta.sh or sh market.sh (sh market_alt.sh for better results on market1501) to run.

Test

Evaluation mode

Use --evaluate to switch to the evaluation mode. Doing so, no model training is performed.

To resume an image branch pretrained model from checkpoints, the example script is

python train.py \
-s market1501 \ 
--test-batch-size 100 \
--evaluate \
-a resnet50 \
--resume-i1 dir/checkpoint_ep_al_I1.pth.tar \
--resume-i2 dir/checkpoint_ep_al_I2.pth.tar \
--resume-i2sem dir/checkpoint_ep_al_I2sem.pth.tar \
--resume-x dir/checkpoint_ep_al_x.pth.tar \
--save-dir log/eval \
--gpu-devices 0 

To resume a jointly pretrained model from checkpoints, the example script is

python train.py \
-s market1501 \ 
--test-batch-size 100 \
--evaluate \
-a resnet50 \
--resume-i1 dir/checkpoint_ep_al_I1.pth.tar \
--resume-g dir/checkpoint_ep_al_G.pth.tar \
--resume-i2 dir/checkpoint_ep_al_I2.pth.tar \
--resume-i2sem dir/checkpoint_ep_al_I2sem.pth.tar \
--resume-a dir/checkpoint_ep_al_a.pth.tar \
--resume-x dir/checkpoint_ep_al_x.pth.tar \
--save-dir log/eval \
--gpu-devices 0 

To resume a full SAL model from checkpoints, , the example script is

python train.py \
-s market1501 \ 
--test-batch-size 100 \
--evaluate \
-a resnet50 \
--resume-i1 dir/checkpoint_ep_al_I1.pth.tar \
--resume-g dir/checkpoint_ep_al_G.pth.tar \
--resume-i2 dir/checkpoint_ep_al_I2.pth.tar \
--resume-i2sem dir/checkpoint_ep_al_I2sem.pth.tar \
--resume-gx dir/checkpoint_ep_al_GX.pth.tar \
--resume-ga dir/checkpoint_ep_al_GA.pth.tar \
--resume-dc dir/checkpoint_ep_al_DC.pth.tar \
--resume-a dir/checkpoint_ep_al_a.pth.tar \
--resume-x dir/checkpoint_ep_al_x.pth.tar \
--resume-ds dir/checkpoint_ep_al_DS.pth.tar \
--save-dir log/eval \
--gpu-devices 0 

Evaluation frequency

Use --eval-freq to control the evaluation frequency and --start-eval to indicate when to start counting the evaluation frequency. This is useful when you want to test the model for every --eval-freq epochs to diagnose the training.

Visualize ranked results

Ranked results can be visualized via --visualize-ranks, which works along with --evaluate. Ranked images will be saved in save_dir/ranked_results/dataset_name where save_dir is the directory you specify with --save-dir. This function is implemented in torchreid/utils/reidtools.py.

Trained models

Pretrained and state-of-the-art models can be downloaded from url.

Citation

If you find this code useful to your research, please cite the following paper

@inproceedings{cao2020symbiotic,
title={Symbiotic Adversarial Learning for Attribute-based Person Search},
author={Cao, Yu-Tong and Wang, Jingya and Tao, Dacheng},
booktitle={European Conference on Computer Vision},
pages={230--247},
year={2020},
organization={Springer}
}

Related

The project is developed based on deep-person-reid.

@article{torchreid,
  title={Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch},
  author={Zhou, Kaiyang and Xiang, Tao},
  journal={arXiv preprint arXiv:1910.10093},
  year={2019}
}

@inproceedings{zhou2019osnet,
  title={Omni-Scale Feature Learning for Person Re-Identification},
  author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
  booktitle={ICCV},
  year={2019}
}

@article{zhou2019learning,
  title={Learning Generalisable Omni-Scale Representations for Person Re-Identification},
  author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
  journal={arXiv preprint arXiv:1910.06827},
  year={2019}
}

License

This project is under the MIT License.