Introduction

The code for our paper EANet: Enhancing Alignment for Cross-Domain Person Re-identification. A Chinese version of introduction for this paper can be found here.

This repository provides

Almost all experiments, with trained models
Used data
Easy-to-extend framework

Installation

Python 2.7 (Recommend to use Anaconda for easy package management.)
Pytorch 1.0.0
Torchvision 0.2.1

The other packages and versions are listed in requirements.txt. You can install them by pip install -r requirements.txt.

Dataset Structure

Prepare datasets to have following structure:

${project_dir}/dataset
    market1501
        Market-1501-v15.09.15                   # Extracted from Market-1501-v15.09.15.zip, https://www.liangzheng.org/Project/project_reid.html
        Market-1501-v15.09.15_ps_label
        bounding_box_train_duke_style
        im_path_to_kpt.pkl
    cuhk03_np_detected_jpg
        cuhk03-np                               # Extracted from cuhk03-np.zip, https://pan.baidu.com/s/1RNvebTccjmmj1ig-LVjw7A
        cuhk03-np-jpg_ps_label
        im_path_to_kpt.pkl
    duke
        DukeMTMC-reID                           # Extracted from DukeMTMC-reID.zip, https://github.com/layumi/DukeMTMC-reID_evaluation
        DukeMTMC-reID_ps_label
        bounding_box_train_market1501_style
        im_path_to_kpt.pkl
    msmt17
        MSMT17_V1                               # Extracted from MSMT17_V1.tar.gz, https://www.pkuvmc.com/publications/msmt17.html
        MSMT17_V1_ps_label
        im_path_to_kpt.pkl
    partial_reid
        Partial-REID_Dataset                    # Extracted from Partial-REID_Dataset.rar, https://isee.sysu.edu.cn/files/resource/Partial-REID_Dataset.rar
    partial_ilids
        Partial_iLIDS                           # Provided by https://github.com/lingxiao-he/Partial-Person-ReID
    coco
        images
        masks_7_parts
        im_name_to_kpt.pkl
        im_name_to_h_w.pkl

Keypoints and part segmentation labels: Baidu Cloud or Google Drive. Our keypoint model is currently not public, while this repo can be an alternative. For part segmentation, we release our code at this repo.

Keypoint Format

The following example shows the keypoint format.

from __future__ import print_function
import cPickle
res = cPickle.load(open('dataset/market1501/im_path_to_kpt.pkl'))
# 'Market-1501-v15.09.15/bounding_box_train/0742_c1s4_014906_01.jpg' ...
print(res.keys()[:5])
# ['kpt', 'im_h_w']
print(res.values()[0].keys())
# A numpy array with shape [17, 3], for 17 keypoints. Each row is (x, y, confidence); we treat those with confidence > 0.1 as visible.
print(res.values()[0]['kpt'])
# [h, w] of the image
print(res.values()[0]['im_h_w'])

For COCO, there is a bit difference.

from __future__ import print_function
import cPickle
res = cPickle.load(open('dataset/coco/im_name_to_kpt.pkl'))
# Currently only contain train set.
# 'COCO_train2014_000000426663_185693.jpg' ...
print(res.keys()[:5])
# A numpy array with shape [17, 3], each row is (x, y, visibility), visibility is one of [0, 1, 2], refer to COCO dataset for detail
print(res.values()[0])

# image size
res = cPickle.load(open('dataset/coco/im_name_to_h_w.pkl'))
print(res.keys()[0])
print(res.values()[0])

Part Segmentation Label Format

Part segmentation label for each image is a single-channel PNG file, with same resolution as the image. Label mapping is as follows

{
    'background': 0,
    'head': 1,
    'torso': 2,
    'upper_arm': 3,
    'lower_arm': 4,
    'upper_leg': 5,
    'lower_leg': 6,
    'foot': 7,
}

Train/Test/Inference Example

Our trained models: Baidu Cloud or Google Drive.

Train model GlobalPool on market1501

cd ${project_dir}
CUDA_VISIBLE_DEVICES=0 python -m package.optim.eanet_trainer --exp_dir exp/eanet/GlobalPool/market1501 --cfg_file package/config/default.py --ow_file paper_configs/GlobalPool.txt --ow_str "cfg.dataset.train.name = 'market1501'"

Test the GlobalPool model that was trained on market1501. Make sure directory exp_dir exists and a ckpt.pth is inside it.

cd ${project_dir}
CUDA_VISIBLE_DEVICES=0 python -m package.optim.eanet_trainer --exp_dir exp/eanet/GlobalPool/market1501 --cfg_file package/config/default.py --ow_file paper_configs/GlobalPool.txt --ow_str "cfg.dataset.train.name = 'market1501'; cfg.only_test = True"

Users can also use a trained model for inference on their own images, with or without identity labels. script/exp/visualize_rank_list.py takes a query image directory and a gallery directory, and then visualize the retrieving results. E.g. use PCB trained on Market1501 to perform retrieving on Market1501 query and gallery images. Make sure directory exp_dir exists and the ckpt.pth is inside it.
```
CUDA_VISIBLE_DEVICES=0 \
python script/exp/visualize_rank_list.py \
--exp_dir exp/vis_rank_list_PCB_M_to_M_id_aware \
--cfg_file package/config/default.py \
--ow_file paper_configs/PCB.txt \
--ow_str "cfg.only_infer = True" \
--q_im_dir dataset/market1501/Market-1501-v15.09.15/query \
--g_im_dir dataset/market1501/Market-1501-v15.09.15/bounding_box_test \
--save_dir exp/vis_rank_list_PCB_M_to_M_id_aware/result \
--id_aware true
```
The result is shown in misc/PCB_rank_list_M_to_M.
(Almost) All experiments of the paper is in script/exp/train_all.sh. Look at it for details.

To test (almost) all models of the paper. Download and place the trained models in the following structure

${project_dir}/exp/eanet/test_paper_models
├── GlobalPool
│   ├── cuhk03_np_detected_jpg
│   │   └── ckpt.pth
│   ├── duke
│   │   └── ckpt.pth
│   └── market1501
│       └── ckpt.pth
├── PAP
│   ├── cuhk03_np_detected_jpg
│   │   └── ckpt.pth
│   ├── duke
│   │   └── ckpt.pth
│   └── market1501
│       └── ckpt.pth
├── PAP_6P
│   ├── cuhk03_np_detected_jpg
│   │   └── ckpt.pth
│   ├── duke
│   │   └── ckpt.pth
│   └── market1501
│       └── ckpt.pth
├── PAP_ST_PS
│   ├── cuhk03_np_detected_jpg_to_duke
│   │   └── ckpt.pth
│   ├── cuhk03_np_detected_jpg_to_market1501
│   │   └── ckpt.pth
│   ├── duke_to_cuhk03_np_detected_jpg
│   │   └── ckpt.pth
│   ├── duke_to_market1501
│   │   └── ckpt.pth
│   ├── market1501_to_cuhk03_np_detected_jpg
│   │   └── ckpt.pth
│   └── market1501_to_duke
│       └── ckpt.pth
├── PAP_ST_PS_SPGAN
│   ├── duke_to_market1501
│   │   └── ckpt.pth
│   └── market1501_to_duke
│       └── ckpt.pth
├── PAP_ST_PS_SPGAN_CFT
│   ├── duke_to_market1501
│   │   └── ckpt.pth
│   └── market1501_to_duke
│       └── ckpt.pth
├── PAP_S_PS
│   ├── cuhk03_np_detected_jpg
│   │   └── ckpt.pth
│   ├── duke
│   │   └── ckpt.pth
│   └── market1501
│       └── ckpt.pth
├── PAP_StC_PS
│   ├── cuhk03_np_detected_jpg
│   │   └── ckpt.pth
│   ├── duke
│   │   └── ckpt.pth
│   └── market1501
│       └── ckpt.pth
└── PCB
    ├── cuhk03_np_detected_jpg
    │   └── ckpt.pth
    ├── duke
    │   └── ckpt.pth
    └── market1501
        └── ckpt.pth

Then, run

cd ${project_dir}
bash script/exp/test_all.sh

You should get the following testing scores (Refer to the paper for table format). Note: The original models for PAP-ST-PS and PAP_ST_PS_SPGAN_CFT are missing, so we train the models again and their scores are not identical to the paper's.

	M->M	C->C	D->D	M->C	M->D	C->M	C->D	D->M	D->C
GlobalPool	88.2 (71.3)	42.4 (39.6)	79.2 (61.9)	10.7 ( 9.3)	38.7 (21.5)	45.7 (21.8)	32.5 (15.7)	47.9 (21.6)	9.1 ( 7.7)
PCB	93.2 (81.1)	65.2 (60.0)	86.3 (72.7)	8.9 ( 7.8)	42.9 (23.8)	52.1 (26.5)	29.2 (15.2)	56.5 (27.7)	8.4 ( 6.9)
PAP-6P	94.4 (84.2)	68.1 (62.4)	85.6 (72.4)	11.6 ( 9.9)	47.6 (28.3)	54.6 (29.3)	33.9 (18.1)	59.7 (31.4)	9.2 ( 8.2)
PAP	94.4 (84.5)	72.0 (66.2)	86.1 (73.3)	11.4 ( 9.9)	46.4 (27.9)	55.5 (30.0)	34.0 (17.9)	59.5 (30.6)	9.7 ( 8.0)
PAP-S-PS	94.6 (85.6)	72.5 (66.7)	87.5 (74.6)	14.2 (12.8)	51.4 (31.7)	59.4 (33.3)	39.3 (22.0)	61.7 (32.8)	11.4 ( 9.6)
PAP-StC-PS	94.7 (84.9)	70.1 (64.4)	87.0 (73.4)	19.1 (16.4)	56.3 (35.1)	65.5 (38.6)	45.2 (26.1)	65.2 (35.7)	12.2 (10.5)
PAP-ST-PS	-	-	-	22.4 (19.5)	55.3 (35.7)	67.8 (40.7)	43.9 (25.9)	66.5 (36.6)	15.1 (13.4)
PAP-ST-PS-SPGAN	-	-	-	-	61.4 (39.4)	-	-	69.6 (39.3)	-
PAP-ST-PS-SPGAN-CFT	-	-	-	-	67.0 (47.0)	-	-	76.4 (50.3)	-

Code Explanation

Training

The training logic is defined in package/optim/eanet_trainer.py and package/optim/cft_trainer.py, the latter for Clustering and Finetuning. A training command looks like

cd ${project_dir}
CUDA_VISIBLE_DEVICES=0 python -m package.optim.${trainer} --exp_dir ${exp_dir} --cfg_file ${cfg_file} [--ow_file ${ow_file}] [--ow_str ${ow_str}]

${trainer} is one of eanet_trainer or cft_trainer.
${exp_dir} is the directory for experiment output.
${cfg_file} is a file defining configurations. Look at package/config/default.py for an example.
[Optional] ${ow_file} is a text file defining configurations to overwrite your ${cfg_file}.
[Optional] ${ow_str} is a string defining configurations to overwrite your ${cfg_file}.
${ow_file} is mainly for storing the configurations used in the paper. Look at paper_configs/*.txt for details.
${ow_str} is a handy way to modify some settings in command line without manually modify the config file. This is convenient, e.g. when running the same model on many datasets.
The code will copy ${cfg_file} into ${exp_dir}, and then overwrite the items specified in ${ow_file} (if provided) and then those in ${ow_str} (if provided).

Testing

Test sets and testing interval can be set in config file, and the training script will test the model during training. If you want to test a trained model, create a exp_dir and place the ckpt.pth inside it, then set cfg.only_test = True in ${config_file} and run package/optim/eanet_trainer.py. In this case, the code only performs testing.

Inference

To use a trained model for inference (extracting feature), the overall logic is

Create a exp_dir and place the ckpt.pth inside it
Set cfg.only_infer = True
trainer = EANetTrainer(), then try trainer.infer_one_im or trainer.infer_im_list or trainer.infer_dataloader, which is flexible.

NOTE: Our model inference (feature extractor) can be used as an API, not just run in command line. For example, if you want to use PCB feature extractor trained on Market1501 in another project, you can add our package path to $PYTHONPATH. Then in your project, do something like this

from package.optim.eanet_trainer import EANetTrainer
from package.eval.np_distance import compute_dist
from easydict import EasyDict

args = EasyDict()
args.exp_dir = 'exp/try_pcb_trained_on_market1501_for_reid_feature'  # There should be the corresponding `ckpt.pth` in it
args.cfg_file = '${EANet_PROJECT_DIR}/package/config/default.py'  # Set this `${EANet_PROJECT_DIR}`
args.ow_file = '${EANet_PROJECT_DIR}/paper_configs/PCB.txt'  # Set this `${EANet_PROJECT_DIR}`
args.ow_str = "cfg.only_infer = True"

eanet_trainer = EANetTrainer(args=args)
feature_dict1 = eanet_trainer.infer_one_im(im_path='YOUR/IMAGE/PATH/1.jpg', squeeze=False)  # You can also provide PIL Image instead of image path
feature_dict2 = eanet_trainer.infer_one_im(im_path='YOUR/IMAGE/PATH/2.jpg', squeeze=False)  # You can also provide PIL Image instead of image path
cosine_distance = compute_dist(feature_dict1['feat'], feature_dict2['feat'])[0][0]

NOTE: For your own images, if you want to perform Part Aligned Pooling for inference, you have to provide keypoint generated pap_mask. Though, as alternatives you can use GlobalPool or PCB, which also achieve satisfactory performance in our implementation.

For details of inference, refer to package/optim/reid_trainer.py.

Extension: Dataset

Current datasets

Market1501
CUHK03-NP detected Subset
DukeMTMC-reID
MSMT17
Partial-REID
Partial-iLIDs

You can create new dataset class in package/data/datasets/ and then register it in package/data/create_dataset.py.

Extension: Model

Current backbone is ResNet. You can implement new backbones and then register them in package/model/backbone.py. Or you can re-define the whole model package/model/model.py, as long as you implement the methods declared in package/model/base_model.py.

Note: If you re-implement the whole model, you may have to modify the model.forward() logic in following files as well

package/optim/eanet_trainer.py
package/optim/cft_trainer.py
package/eval/extract_feat.py

This model calling can be abstracted away in the future.

Extension: Training

package/optim/reid_trainer.py covers the common logic for ReID training, with some abstraction to be implemented by sub classes. package/optim/eanet_trainer.py and package/optim/cft_trainer.py are concrete implementations, which also demonstrate the usage of hybrid batches and multi losses, etc.

Design Logic

We heavily use dict for passing data / configuration arguments when calling functions. This simplifies function headers and reduces the amount of code to modify when changing experiment settings.

TODO

More datasets
More backbones
Run TripletLoss
Run warmup
Test TensorBoard
Implement forward_type == 'ps_reid_serial'
Write commit num to log

Debug why it prints

Loaded pickle file dataset/market1501/im_path_to_kpt.pkl
Loaded pickle file dataset/market1501/im_path_to_kpt.pkl

at the beginning of every epoch?

Misc

Tricky! EasyDict.__setattr__ will transform tuple into list! So don't rely on it to store tuples! You have to transform them into tuples wherever tuple is needed.
If you meet error ImportError: /lib64/libstdc++.so.6: version CXXABI_1.3.9 not found (required by ${Anaconda}/lib/python2.7/site-packages/scipy/sparse/_sparsetools.so), try conda install libgcc; export LD_LIBRARY_PATH=${YOUR_ANACONDA_HOME}/lib:${LD_LIBRARY_PATH}
The CUHK03 dataset provides image data in .mat format. open-reid transforms it to JPG images, while CUHK03-NP provides PNG images. Throughout the paper, we use JPG version of CUHK03-NP, due to some historical reasons. After the paper, we find that CUHK03-NP PNG has better performance than JPG on the GlobalPool baseline; We did not perform further experiments on other model architectures, due to time limitation.

Citation

If you find our work useful, please kindly cite our paper:

@article{huang2018eanet,
  title={EANet: Enhancing Alignment for Cross-Domain Person Re-identification},
  author={Huang, Houjing and Yang, Wenjie and Chen, Xiaotang and Zhao, Xin and Huang, Kaiqi and Lin, Jinbin and Huang, Guan and Du, Dalong},
  journal={arXiv preprint arXiv:1812.11369},
  year={2018}
}

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README.md

README.md

Introduction

Installation

Dataset Structure

Keypoint Format

Part Segmentation Label Format

Train/Test/Inference Example

Code Explanation

Training

Testing

Inference

Extension: Dataset

Extension: Model

Extension: Training

Design Logic

TODO

Misc

Citation

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README.md

File metadata and controls

Introduction

Installation

Dataset Structure

Keypoint Format

Part Segmentation Label Format

Train/Test/Inference Example

Code Explanation

Training

Testing

Inference

Extension: Dataset

Extension: Model

Extension: Training

Design Logic

TODO

Misc

Citation