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ptgan_test_progressive's Introduction

PTGAN for test

We add GAN into this ReID model (AICITY2021_Track2_DMT),and our model is vehicle pose transform by generative adversarial network for ReID. AICITY2021_Track2_DMT is the 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.

Get Started

  1. cd to folder where you want to download this repo
├── AIC21
│   ├── AIC21_Track2_ReID
│   │   ├── cropped_aic_test
│   │   ├── cropped_train
│   │   ├── image_query
│   │   ├── image_test
│   │   ├── image_train
│   │   ├── tools
│   │   └── training_part_seg
│   ├── AIC21_Track2_ReID_Simulation
│   │   ├── sys_image_train
│   │   └── sys_image_train_tr
│   └── veri_pose
│       ├── image_query
│       ├── image_test
│       ├── image_train
│       ├── query
│       ├── test
│       └── train
└── PTGAN_test
    ├── config
    │   ├── stage1
    │   └── stage2
    ├── gallery_features
    ├── gan
    │   └── weights
    ├── logs
    │   └── stage2
    ├── pretrained
    ├── reid_model
    │   ├── backbones
    │   ├── checkpoints
    │   └── layers
    ├── utils
    └── ...

  1. Run git clone

  2. Install dependencies: pip install requirements.txt

    We use cuda 11.0/python 3.7/torch 1.6.0/torchvision 0.7.0 for training and testing.

單張圖片ReID模型使用方式

GPU: 1080ti

We use cuda 11.0/python 3.7/torch 1.6.0/torchvision 0.7.0 for training and testing.

Install dependencies: pip install requirements.txt

  1. 執行 PTGAN_test_for_CCK.py (會有8個模型需要測試，但僅先測 transreid 就可以)
python PTGAN_test_for_one_CCK.py --config_file
config/stage2/transreid_256_veri_gan.yml
MODEL.DEVICE_ID
"('0')"
TEST.WEIGHT
'./reid_model/checkpoints/stage2/transreid_256/v1/transformer_2.pth'
OUTPUT_DIR
'./logs/stage2/transreid_256/v1/veri_for_test'

Detailed code process_for_test_CCK.py line: 217

# this place will return progressive distance matrix and gallery img name
distmats = compute_distmat(feat, query_data, gen_gallery, ori_gallery, evaluator, cfg, gen_P, gen_neg_vec, P, neg_vec)
  1. 執行之後會出現 sililiar_img_distmat.csv ，裡面存了圖片路徑和相對應圖片的距離 (值越大越不像，越小越像)

column: query image path

row: gallery image path

目前使用的圖片檔皆從 veri_pose 資料夾中取得 我們寫的dataset只會載入一張 query 圖片進行 ReID

query_data = get_one_img('../AIC21/veri_pose/query/filename.jpg', transform=transform)

Detailed code process_for_test_CCK.py line: 217

# this place will return progressive distance matrix and gallery img name
distmats = compute_distmat(feat, query_data, gen_gallery, ori_gallery, evaluator, cfg, gen_P, gen_neg_vec, P, neg_vec)

GitHub Introduction (可以不用看)

ReID Model Backbone AICITY2021_Track2_DMT: Detailed information of NVIDIA AI City Challenge 2021 can be found here.

The code is modified from AICITY2020_DMT_VehicleReID, TransReID, reid_strong baseline,and AICITY2021_Track2_DMT.

Get Started

  1. cd to folder where you want to download this repo

  2. Run git clone

  3. Install dependencies: pip install requirements.txt

    We use cuda 11.0/python 3.7/torch 1.6.0/torchvision 0.7.0 for training and testing.

  4. Prepare Datasets Download Original dataset, Cropped_dataset, SPGAN_dataset, Veri-776,and veri_pose.

    The name of veri_pose folder is xxx_y_z (ex:000_0_3).

    • xxx: identity
    • y: color
    • z: type

    and the numbers of 1~8 are the pose of vehicles.

├── AIC21/
│   ├── AIC21_Track2_ReID/
│   	├── image_train/
│   	├── image_test/
│   	├── image_query/
│   	├── train_label.xml
│   	├── ...
│   	├── training_part_seg/
│   	    ├── cropped_patch/
│   	├── cropped_aic_test
│   	    ├── image_test/
│   	    ├── image_query/		
│   ├── AIC21_Track2_ReID_Simulation/
│   	├── sys_image_train/
│   	├── sys_image_train_tr/
│   ├── veri_pose/
│   	├── train/
│   	    ├── 000_0_3/
│   	    	├── 0/
│   	    	├── 3/
│   	    	├── 4/
│   	    	├── ...
│   	    ├── ...
│   	├── query/
│   	    ├── 0002_c002_00030600_0.jpg
│   	    ├── ...
│   	├── test/
│   	    ├── 000_0_3/
│   	    	├── 1/
│   	    	├── 3/
│   	    	├── 4/
│   	    	├── ...
│   	    ├── ...
│   	├── train_label.xml
│   	├── query_label.xml
│   	├── test_label.xml
│   	├── ...
  1. Put pre-trained models into ./pretrained/
    • resnet101_ibn_a-59ea0ac6.pth, densenet169_ibn_a-9f32c161.pth, resnext101_ibn_a-6ace051d.pth and se_resnet101_ibn_a-fabed4e2.pth can be downloaded from IBN-Net
    • resnest101-22405ba7.pth can be downloaded from ResNest
    • jx_vit_base_p16_224-80ecf9dd.pth can be downloaded from here

GAN Model Training

We utilize 1 GPU 1080ti(11GB) for training.

  1. cd to folder where you want to download this repo.

  2. Run git clone , we use the code to train GAN models.

  3. Prepare Datasets Download Original dataset, Veri-776,and veri_pose.

├── veri_pose/
│   	├── train/
│   	    ├── 000_0_3/
│   	    	├── 0/
│   	    	├── 3/
│   	    	├── 4/
│   	    	├── ...
│   	    ├── ...
│   	├── query/
│   	    ├── 0002_c002_00030600_0.jpg
│   	    ├── ...
│   	├── test/
│   	    ├── 000_0_3/
│   	    	├── 1/
│   	    	├── 3/
│   	    	├── 4/
│   	    	├── ...
│   	    ├── ...
│   	├── train_label.xml
│   	├── query_label.xml
│   	├── test_label.xml
│   	├── list_color.txt
│   	├── list_type.txt
│   	├── ...
  1. Start training

Open Visdom to monitor training process

python -m visdom.server -port [xxxx]

Training Orthogonal Encoder

python orthogonal_encoder.py

Training GAN model

python train.py --stage 1 --dataset train --dataroot [path]
python train.py --stage 2 --dataset train --dataroot [path]
  1. Put trained models into
    1. ./code_GAN/gan/weights/
      • model_130.pth
    2. ./code_GAN/weights/GAN_stage_2
      • 17_net_Di.pth
      • 17_net_Dp.pth
      • 17_net_E.pth
      • 17_net_G.pth
├── PTGAN/
│   ├── code_GAN/
│   	├── gan/
│   	    ├── weights/
│   	        ├── model_130.pth
│   	├── weights/
│   	    ├── GAN_stage_2/
│   	        ├── 17_net_Di.pth
│   	        ├── 17_net_Dp.pth
│   	        ├── 17_net_E.pth
│   	        ├── 17_net_G.pth
│   	        ├── 17_net_Dp.pth
│   ├── ...

ReID Model Training

We utilize 8 GPU 1080ti(11GB) for training. You can train one backbone as follow.

# ResNext101-IBN-a
python train.py --config_file configs/stage1/resnext101a_384.yml MODEL.DEVICE_ID "('0')"
python train_stage2_v1.py --config_file configs/stage2/resnext101a_384.yml MODEL.DEVICE_ID "('0')" OUTPUT_DIR './logs/stage2/resnext101a_384/v1'
python train_stage2_v2.py --config_file configs/stage2/resnext101a_384.yml MODEL.DEVICE_ID "('0')" OUTPUT_DIR './logs/stage2/resnext101a_384/v2'

You should train camera and viewpoint models before the inference stage. You also can directly use our trained results (track_cam_rk.npy and track_view_rk.npy):

python train_cam.py --config_file configs/camera_view/camera_101a.yml
python train_view.py --config_file configs/camera_view/view_101a.yml

You can train all eight backbones by checking run.sh. Then, you can ensemble all results:

python ensemble.py

All ReID trained models can be downloaded from here

Test

After Training all the above models, you can test one backbone as follow.

python PTGAN.py --config_file configs/stage2/resnext101a_384_veri_gan.yml MODEL.DEVICE_ID "('0')" TEST.WEIGHT './logs/stage2/resnext101a_384/v1/resnext101_ibn_a_2.pth' OUTPUT_DIR './logs/stage2/resnext101a_384/veri_gan_v1'
python PTGAN.py --config_file configs/stage2/resnext101a_384_veri_gan.yml MODEL.DEVICE_ID "('0')" TEST.WEIGHT './logs/stage2/resnext101a_384/v2/resnext101_ibn_a_2.pth' OUTPUT_DIR './logs/stage2/resnext101a_384/veri_gan_v2'

VeRi-776 Result

We train the model on AICITY2021 dataset, and test on VeRi-776 by stage2/v1 ReID model. (will be updated other results in the future)

Baseline

Backbones mAP R-1 R-5 R-10
ResNet101-IBN-a 48.8 86.1 86.4 91.5
ResNet101-IBN-a+ 48.7 87.0 87.3 91.8
ResNet101-IBN-a ++ 48.6 87.1 87.4 92.3
ResNext101-IBN-a 48.1 86.1 86.4 91.1
ResNest101 48.2 85.9 86.3 91.0
SeResNet101-IBN 46.5 85.0 85.2 90.2
DenseNet169-IBN 46.5 84.8 85.2 90.2
TransReID 48.9 87.0 87.4 91.3

Our Model Transform to gallery pose

Backbones mAP R-1 R-5 R-10
ResNet101-IBN-a 47.4 85.7 88.9 93.1
ResNet101-IBN-a+ 47.2 86.2 90.0 93.7
ResNet101-IBN-a ++ 47.5 86.7 90.6 94.0
ResNext101-IBN-a 46.9 85.5 89.3 92.6
ResNest101 46.3 84.9 90.2 94.3
SeResNet101-IBN 47.0 84.9 90.2 91.7
DenseNet169-IBN 45.1 84.2 87.9 92.1
TransReID 47.0 85.6 91.1 94.0

Transform all photo

Backbones mAP R-1 R-5 R-10
ResNet101-IBN-a 47.1 86.1 88.9 92.3
ResNet101-IBN-a+ 46.9 86.2 89.7 92.8
ResNet101-IBN-a ++ 47.1 86.5 89.6 93.6
ResNext101-IBN-a 46.5 85.8 88.9 92.3
ResNest101 46.7 85.1 88.3 92.3
SeResNet101-IBN 46.6 85.0 87.6 91.7
DenseNet169-IBN 44.8 84.6 87.8 92.1
TransReID 50.3 87.7 90.9 93.6

Citation

If you find this work useful in your research, please consider citing:

@inproceedings{luo2021empirical,
 title={An Empirical Study of Vehicle Re-Identification on the AI City Challenge},
 author={Luo, Hao and Chen, Weihua and Xu Xianzhe and Gu Jianyang and Zhang, Yuqi and Chong Liu and Jiang Qiyi and He, Shuting and Wang, Fan and Li, Hao},
 booktitle={Proc. CVPR Workshops},
 year={2021}
}

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