• Pose Guided Person Image Generation (NIPS2017)
• Disentangled Person Image Generation (CVPR2018 Spotlight)
• Progressive Pose Attention Transfer for Person Image Generation (CVPR2019 Oral)

• pytorch 1.0+
• ignite
• torchvision
• numpy
• scipy
• scikit-image
• pandas
• tqdm

batch_size=16, Memory-Usage: <5GB batch_size=32, Memory-Usage: <10GB

For fair comparison, all implementation use 263,632 training pairs and 12,000 testing pairs from Market-1501 as in PATN

copy&rename above pair and annotation file to ./data

Finally, your ./data folder looks like:

data
├── market
│   ├── annotation-test.csv
│   ├── annotation-train.csv
│   ├── pairs-test.csv
│   ├── pairs-train.csv
│   ├── attribute
│   │   ├── evaluate_market_attribute.m
│   │   ├── gallery_market.mat
│   │   ├── market_attribute.mat
│   │   ├── README.md
│   │   └── sample_image.jpg
│   ├── test # WILL BE GENERATED IN NEXT STEP
│   │   ├── pose_map_image
│   │   └── pose_mask_image
│   └── train # WILL BE GENERATED IN NEXT STEP
│       ├── pose_map_image
│       └── pose_mask_image

1. python3 tool/generate_pose_map_add_mask.py --type train
2. python3 tool/generate_pose_map_add_mask.py --type test

All implementation use the same train.py.

1. use python3 train.py -h to see supported train process.
2. use python3 train.py --name <TrainName> -h to see train option for TrainName.
3. use python3 train.py --name <TrainName> to train.

You can observe the generated results during the train: cd output_dir && python3 -m http.server

I just create the index.html in output_dir, so feel free to use anything(like devd) to see mid product

All implementation use the same test.py.

1. use python3 test.py -h to see support test process.
2. use python3 test.py --name <TestName> -h to see test option for TestName.
3. use python3 test.py --name <TestName> to test.

First, please use test.py --name PG2-Generate to generate 12000 test images.

For fair comparisons, I just copy&use the same evaluation codes in previous works Deform, PG2 and PATN which

I recommend using docker to evaluate the result because evaluation codes use some outdated frameworks, like Tensorflow 1.4.1

So, next:

1. build docker image with ./evaluate/Dockerfile
2. run evaluate script

$ cd evaluate
$ docker build -t hpt_evaluate . 
$  # For user in China, you can build docker image like this:
$ docker build -t hpt_evaluate . --build-arg PIP_PYPI="https://pypi.tuna.tsinghua.edu.cn/simple"
$ cd ..
$ docker run -v $(pwd):/tmp -e NVIDIA_VISIBLE_DEVICES=0 -w /tmp --runtime=nvidia -it --rm hpt_evaluate:latest python evaluate/getMetrics_market.py

Or use image tensorflow/tensorflow:1.4.1-gpu-py3 to evaluate in docker bash:

docker run -v $(pwd):/tmp -w /tmp --runtime=nvidia -it --rm tensorflow/tensorflow:1.4.1-gpu-py3 bash
# now in docker:
$ pip install scikit-image tqdm 
$ python evaluate/getMetrics_market.py

# stage 1
python3 train.py --name PG2-1 \
    --gpu_id 0 \
    --epochs 2 \
    --output_dir "./checkpoints/PG2-1" \
    --train_pair_path "./data/market/pairs-train.csv" \
    --test_pair_path "./data/market/pairs-test.csv" \
    --market1501 "/root/data/Market-1501-v15.09.15"

# stage 2
MARKET1501="/root/data/Market-1501-v15.09.15/"
G1PATH="./data/market/models/PG2/G1.pth"
python3 train.py --name PG2-2 \
    --epochs 2 \
    --gpu_id 1 \
    --batch_size 32 \
    --output_dir "checkpoints/PG2-2" \
    --market1501  ${MARKET1501} \
    --G1_path  ${G1PATH} \
    --save_interval 200 \
    --print_freq 200 \
    --gan_loss BCELoss \
    --discriminator DCGAN-improve

# generate images
GENERATED_IMAGE_PATH="./generated"
LIMIT=-1
GPU_ID=1
G1_MODEL_PATH="./data/market/models/PG2/G1.pth"
G2_MODEL_PATH="./checkpoints/patchgan/lr0.00008/models/networks_G2_13400.pth"
MARKET1501="/root/data/Market-1501-v15.09.15/"

python3 test.py --name PG2-Generate
    --market1501 ${MARKET1501}
    --gpu_id ${GPU_ID}
    --G1_path ${G1_MODEL_PATH}
    --G2_path ${G2_MODEL_PATH}
    --output_dir ${GENERATED_IMAGE_PATH}
    --limit ${LIMIT}

Liqian Ma - PG2's Tensorflow implementation Thanks for his patience. (￣▽￣)"

@tengteng95 - Pose-Transfer for clear code structure and his great paper.