Learning Invariance from Generated Variance for Unsupervised Person Re-identification

This is the official PyTorch implementation of the TPAMI paper Learning Invariance from Generated Variance for Unsupervised Person Re-identification.

The paper is an extened version of Joint Generative and Contrastive Learning for Unsupervised Person Re-identification.

Installation

Install GCL

Requirements

• Python 3.6
• Pytorch 1.2.0

git clone https://github.com/chenhao2345/GCL-extended
cd GCL-extended
python setup.py develop

Prepare Datasets

cd examples && mkdir data

Download the raw datasets DukeMTMC-reID, Market-1501, MSMT17, and then unzip them under the directory like

GCL/examples/data
├── dukemtmc-reid
│   └── DukeMTMC-reID
├── market1501
└── msmt17
    └── MSMT17_V1(or MSMT17_V2)

Install HMR for Mesh Estimation

Download our extracted meshes from Google Drive. Unzip them under the directory like

GCL/examples/mesh
├── dukeMTMC
├── market
└── msmt17

Or refer to HMR ro get meshes for ReID datasets.

Train GCL

Only support 1 GPU (GPU memory > 20GB) training for the moment.

Stage 1: Warm up identity encoder

Train a ResNet50 with an unsupervised method, for example, JVTC(or download our trained models from Google Drive) and MLC.

GCL/examples/logs
└── JVTC
    └── market
        └── resnet50_market075_epoch00045.pth
    └── duke
        └── resnet50_duke075_epoch00040.pth

Stage 2: Warm up structure encoder and discriminator

Adjust path for dataset, mesh, pre-trained identity encoder.

sh train_stage2_market.sh

Stage 3: Joint training

sh train_stage3_market.sh

TensorBoard Visualization

Stage 2:

For example,

tensorboard --logdir logs/market_init_JVTC_unsupervised/

Stage 3:

For example,

tensorboard --logdir logs/market_init_JVTC_unsupervised/stage3/

Citation

@ARTICLE{9970293,
  author={Chen, Hao and Wang, Yaohui and Lagadec, Benoit and Dantcheva, Antitza and Bremond, Francois},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Learning Invariance from Generated Variance for Unsupervised Person Re-identification}, 
  year={2022},
  volume={},
  number={},
  pages={1-15},
  doi={10.1109/TPAMI.2022.3226866}}