zhang-lingyun/pasta-gan

Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN"

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN

Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN"

Requirements

Create a virtual environment:

virtualenv pasta --python=3.7
source pasta/bin/activate

Install required packages:

pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
pip install click requests tqdm pyspng ninja imageio-ffmpeg==0.4.3
pip install psutil scipy matplotlib opencv-python scikit-image pycocotools

Data Preparation

Since the copyright of the UPT dataset belongs to the E-commerce website Zalando and Zalora, we only release the image links in this link. For more details about the dataset and the crawling scripts, please send email to [email protected].

After downloading the raw RGB image, we run the pose estimator Openpose and human parser Graphonomy for each image to obtain the 18-points human keypoints and the 19-labels huamn parsing, respectively.

The dataset structure is recommended as:

+—UPT_256_192
|   +—UPT_subset1_256_192
|       +-image
|           +- e.g. image1.jpg
|           +- ...
|       +-keypoints
|           +- e.g. image1_keypoints.json
|           +- ...
|       +-parsing
|           +- e.g. image1.png
|           +- ...
|       +-train_pairs_front_list_0508.txt
|       +-test_pairs_front_list_shuffle_0508.txt
|   +—UPT_subset2_256_192
|       +-image
|           +- ...
|       +-keypoints
|           +- ...
|       +-parsing
|           +- ...
|       +-train_pairs_front_list_0508.txt
|       +-test_pairs_front_list_shuffle_0508.txt
|   +— ...

By using the raw RGB image, huamn keypoints, and human parsing, we can run the training script and the testing script.

Running Inference

We provide the pre-trained model of PASTA-GAN which is trained by using the full UPT dataset (i.e., our newly collected data, data from Deepfashion dataset, data from MPV dataset).

we provide a simple script to test the pre-trained model provided above on the UPT dataset as follow:

CUDA_VISIBLE_DEVICES=0 python3 -W ignore test.py \
    --network /datazy/Codes/PASTA-GAN/PASTA-GAN_fullbody_model/network-snapshot-004000.pkl \
    --outdir /datazy/Datasets/pasta-gan_results/unpaired_results_fulltryonds \
    --dataroot /datazy/Datasets/PASTA_UPT_256 \
    --batchsize 16

or you can run the bash script by using the following commend:

bash test.sh 1

Note that, in the testing script, the parameter --network refers to the path of the pre-trained model, the parameter --outdir refers to the path of the directory for generated results, the parameter --dataroot refers to the path of the data root. Before running the testing script, please make sure these parameters refer to the correct locations.

Running Training

Training the 256x192 PASTA-GAN full body model on the UPT dataset

1. Download the UPT_256_192 training set.
2. Download the VGG model from VGG_model, then put "vgg19_conv.pth" and "vgg19-dcbb9e9d" under the directory "checkpoints".
3. Run bash train.sh 1.

Todo

• Release the the pretrained model (256x192) and the inference script.
• Release the training script.
• Release the pretrained model (512x320).

License

The use of this code is RESTRICTED to non-commercial research and educational purposes.