CCA8290 / Attentive-WaveBlock

Pytorch implementation of "Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond"

Attentive WaveBlock (AWB)

The official implementation for the "Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond". Many thanks for the framework offered by MMT.

Performance

Requirement

• Python 3.6
• Pytorch 1.1.0
• sklearn 0.21.3
• PIL 4.0.0
• Numpy 1.16.0
• Torchvision 0.2.0

Reproduction Environment

• Test our models: 1 GTX 1080Ti GPU.
• Train new models: 4 GTX 1080Ti GPUs.

Installation

You can directly get the codes by:

  git clone https://github.com/WangWenhao0716/Attentive-WaveBlock.git

Note

In the following codes and statements, for simplicity, Pre-A denotes Pre-A (CBAM) and Post-A denotes Post-A (Non-local). You can find the details in our paper.

Preparation

1. Dataset

We evaluate our algorithm on DukeMTMC-reID, Market-1501 and MSMT17. For convenience, we have prepared data for you. You should download them and prepare the directory structure like this:

*DATA_PATH
      *data
         *dukemtmc
             *DukeMTMC-reID
                 *bounding_box_test
                 ...
          
         *market1501
             *Market-1501-v15.09.15
                 *bounding_box_test
                 ...
         *msmt17
             *MSMT17_V1
                 *test
                 *train
                 ...

2. Pretrained Models

We use ResNet-50 as backbone, and the pretrained models will be downloaded automatically.

3. Our Trained Models

We provide our trained Pre-A and Post-A models on Duke-to-Market, Market-to-Duke, Duke-to-MSMT, Market-to-MSMT, MSMT-to-Duke, and MSMT-to-Market domain adaptation tasks.

Duke-to-Market: Pre-A(78.8% mAP) Post-A(81.0% mAP)

Market-to-Duke: Pre-A(70.0% mAP) Post-A(70.9% mAP)

Duke-to-MSMT: Pre-A(29.5% mAP) Post-A(29.0% mAP)

Market-to-MSMT: Pre-A(27.3% mAP) Post-A(29.0% mAP)

MSMT-to-Duke: Pre-A(68.6% mAP) Post-A(69.6% mAP)

MSMT-to-Market: Pre-A(77.1% mAP) Post-A(79.4% mAP)

For example, to test our trained model on Duke-to-Market, you should save the models in ./logs/dukemtmcTOmarket1501/resnet50-train-pre-a-s2 and ./logs/dukemtmcTOmarket1501/resnet50-train-post-a-s2, respectively.

Train

We use Duke-to-Market as an example, other UDA tasks will follow similar pipelines.

Step 1: Pre-train models on source domain

First Network:

CUDA_VISIBLE_DEVICES=0,1,2,3 python source_pretrain.py -ds dukemtmc -dt market1501 -a resnet50 --seed 1 --margin 0.0 --num-instances 4 -b 64 -j 4 --warmup-step 10 --lr 0.00035 --milestones 40 70 --iters 200 --epochs 80 --eval-step 40 --logs-dir logs/dukemtmcTOmarket1501/resnet50-pretrain-1

Second Network:

CUDA_VISIBLE_DEVICES=0,1,2,3 python source_pretrain.py -ds dukemtmc -dt market1501 -a resnet50 --seed 2 --margin 0.0 --num-instances 4 -b 64 -j 4 --warmup-step 10 --lr 0.00035 --milestones 40 70 --iters 200 --epochs 80 --eval-step 40 --logs-dir logs/dukemtmcTOmarket1501/resnet50-pretrain-2

Step 2: Train our attention model on target domain (Stage 1)

Pre-A

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_pre_a_s1.py -dt market1501 -a resnet50 --num-clusters 500 --num-instances 4 --lr 0.00035 --iters 800 -b 64 --epochs 10 --soft-ce-weight 0.5 --soft-tri-weight 0.8 --dropout 0 --init-1 logs/dukemtmcTOmarket1501/resnet50-pretrain-1/model_best.pth.tar --init-2 logs/dukemtmcTOmarket1501/resnet50-pretrain-2/model_best.pth.tar --logs-dir logs/dukemtmcTOmarket1501/resnet50-train-pre-a-s1

Post-A

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_post_a_s1.py -dt market1501 -a resnet50 --num-clusters 500 --num-instances 4 --lr 0.00035 --iters 800 -b 64 --epochs 10 --soft-ce-weight 0.5 --soft-tri-weight 0.8 --dropout 0 --init-1 logs/dukemtmcTOmarket1501/resnet50-pretrain-1/model_best.pth.tar --init-2 logs/dukemtmcTOmarket1501/resnet50-pretrain-2/model_best.pth.tar --logs-dir logs/dukemtmcTOmarket1501/resnet50-train-post-a-s1

Step 3: Train on target domain (Stage 2)

Pre-A

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_pre_a_s2.py -dt market1501 -a resnet50 --num-clusters 500 --num-instances 4 --lr 0.00035 --iters 800 -b 64 --epochs 80 --soft-ce-weight 0.5 --soft-tri-weight 0.8 --dropout 0 --init-1 logs/dukemtmcTOmarket1501/resnet50-train-pre-a-s1/model1_checkpoint.pth.tar --init-2 logs/dukemtmcTOmarket1501/resnet50-train-pre-a-s1/model2_checkpoint.pth.tar --logs-dir logs/dukemtmcTOmarket1501/resnet50-train-pre-a-s2

Post-A

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_post_a_s2.py -dt market1501 -a resnet50 --num-clusters 500 --num-instances 4 --lr 0.00035 --iters 800 -b 64 --epochs 80 --soft-ce-weight 0.5 --soft-tri-weight 0.8 --dropout 0 --init-1 logs/dukemtmcTOmarket1501/resnet50-train-post-a-s1/model1_checkpoint.pth.tar --init-2 logs/dukemtmcTOmarket1501/resnet50-train-post-a-s1/model2_checkpoint.pth.tar --logs-dir logs/dukemtmcTOmarket1501/resnet50-train-post-a-s2

Test

We use Duke-to-Market as an example, other UDA tasks follow similar pipelines.

Pre-A

CUDA_VISIBLE_DEVICES=0 python test_pre_a.py -b 256 -j 8 --dataset-target market1501 -a resnet50 --resume logs/dukemtmcTOmarket1501/resnet50-train-pre-a-s2/model_best.pth.tar

Post-A

CUDA_VISIBLE_DEVICES=0 python test_post_a.py -b 256 -j 8 --dataset-target market1501 -a resnet50 --resume logs/dukemtmcTOmarket1501/resnet50-train-post-a-s2/model_best.pth.tar

Citation

If you think our work is useful in your research, please consider citing:

@misc{wang2020attentive,
      title={Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond}, 
      author={Wenhao Wang and Fang Zhao and Shengcai Liao and Ling Shao},
      year={2020},
      eprint={2006.06525},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}