PyTorch implementation of InstaGAN: Instance-aware Image-to-Image Translation

Last update: Dec 29, 2022

Overview

InstaGAN: Instance-aware Image-to-Image Translation

Warning: This repo contains a model which has potential ethical concerns. Remark that the task of jeans<->skirt was a bad application and should not be used in future research. See the twitter thread for the discussion.

PyTorch implementation of "InstaGAN: Instance-aware Image-to-Image Translation" (ICLR 2019). The implementation is based on the official CycleGAN code. Our major contributions are in ./models/insta_gan_model.py and ./models/networks.py.

Getting Started

Installation

Clone this repository

git clone https://github.com/sangwoomo/instagan

Install PyTorch 0.4+ and torchvision from http://pytorch.org and other dependencies (e.g., visdom and dominate). You can install all the dependencies by

pip install -r requirements.txt

For Conda users, you can use a script ./scripts/conda_deps.sh to install PyTorch and other libraries.
Acknowledgment: Installation scripts are from the official CycleGAN code.

Download base datasets

Download clothing-co-parsing (CCP) dataset:

git clone https://github.com/bearpaw/clothing-co-parsing ./datasets/clothing-co-parsing

Download multi-human parsing (MHP) dataset:

# Download "LV-MHP-v1" from the link and locate in ./datasets

Download MS COCO dataset:

./datasets/download_coco.sh

Generate two-domain datasets

Generate two-domain dataset for experiments:

python ./datasets/generate_ccp_dataset.py --save_root ./datasets/jeans2skirt_ccp --cat1 jeans --cat2 skirt
python ./datasets/generate_mhp_dataset.py --save_root ./datasets/pants2skirt_mhp --cat1 pants --cat2 skirt
python ./datasets/generate_coco_dataset.py --save_root ./datasets/shp2gir_coco --cat1 sheep --cat2 giraffe

Note: Generated dataset contains images and corresponding masks, which are located in image folders (e.g., 'trainA') and mask folders (e.g., 'trainA_seg'), respectively. For each image (e.g., '0001.png'), corresponding masks for each instance (e.g., '0001_0.png', '0001_1.png', ...) are provided.

Run experiments

Train a model:

python train.py --dataroot ./datasets/jeans2skirt_ccp --model insta_gan --name jeans2skirt_ccp_instagan --loadSizeH 330 --loadSizeW 220 --fineSizeH 300 --fineSizeW 200 --niter 400 --niter_decay 200
python train.py --dataroot ./datasets/pants2skirt_mhp --model insta_gan --name pants2skirt_mhp_instagan --loadSizeH 270 --loadSizeW 180 --fineSizeH 240 --fineSizeW 160
python train.py --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 220 --loadSizeW 220 --fineSizeH 200 --fineSizeW 200

To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097. To see more intermediate results, check out ./checkpoints/experiment_name/web/index.html.
For faster experiment, increase batch size and use more gpus:

python train.py --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 220 --loadSizeW 220 --fineSizeH 200 --fineSizeW 200 --batch_size 4 --gpu_ids 0,1,2,3

Test the model:

python test.py --dataroot ./datasets/jeans2skirt_ccp --model insta_gan --name jeans2skirt_ccp_instagan --loadSizeH 300 --loadSizeW 200 --fineSizeH 300 --fineSizeW 200
python test.py --dataroot ./datasets/pants2skirt_mhp --model insta_gan --name pants2skirt_mhp_instagan --loadSizeH 240 --loadSizeW 160 --fineSizeH 240 --fineSizeW 160 --ins_per 2 --ins_max 20
python test.py --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 200 --loadSizeW 200 --fineSizeH 200 --fineSizeW 200 --ins_per 2 --ins_max 20

The test results will be saved to a html file here: ./results/experiment_name/latest_test/index.html.

Apply a pre-trained model

You can download a pre-trained model (pants->skirt and/or sheep->giraffe) from the following Google drive link. Save the pretrained model in ./checkpoints/ directory.
We provide samples of two datasets (pants->skirt and sheep->giraffe) in this repository. To test the model:

python test.py --dataroot ./datasets/pants2skirt_mhp --model insta_gan --name pants2skirt_mhp_instagan --loadSizeH 240 --loadSizeW 160 --fineSizeH 240 --fineSizeW 160 --ins_per 2 --ins_max 20 --phase sample --epoch 200
python test.py --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 200 --loadSizeW 200 --fineSizeH 200 --fineSizeW 200 --ins_per 2 --ins_max 20 --phase sample --epoch 200

Results

We provide some translation results of our model. See the link for more translation results.

1. Fashion dataset (pants->skirt)

2. COCO dataset (sheep->giraffe)

3. Results on Google-searched images (pants->skirt)

4. Results on YouTube-searched videos (pants->skirt)

Citation

If you use this code for your research, please cite our papers.

@inproceedings{
    mo2019instagan,
    title={InstaGAN: Instance-aware Image-to-Image Translation},
    author={Sangwoo Mo and Minsu Cho and Jinwoo Shin},
    booktitle={International Conference on Learning Representations},
    year={2019},
    url={https://openreview.net/forum?id=ryxwJhC9YX},
}

PyTorch implementation of InstaGAN: Instance-aware Image-to-Image Translation

Related tags

Overview

InstaGAN: Instance-aware Image-to-Image Translation

Getting Started

Installation

Download base datasets

Generate two-domain datasets

Run experiments

Apply a pre-trained model

Results

1. Fashion dataset (pants->skirt)

2. COCO dataset (sheep->giraffe)

3. Results on Google-searched images (pants->skirt)

4. Results on YouTube-searched videos (pants->skirt)

Citation

Owner

Sangwoo Mo

Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks

Proof-Of-Concept Piano-Drums Music AI Model/Implementation

Official tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”

Code for Talk-to-Edit (ICCV2021). Paper: Talk-to-Edit: Fine-Grained Facial Editing via Dialog.

Official PyTorch implementation of "The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation" (ICCV 21).

People log into different sites every day to get information and browse through these sites one by one

Facial expression detector

Grammar Induction using a Template Tree Approach

A Comparative Framework for Multimodal Recommender Systems

PSGAN running with ncnn⚡妆容迁移/仿妆⚡Imitation Makeup/Makeup Transfer⚡

TensorLight - A high-level framework for TensorFlow

A library for researching neural networks compression and acceleration methods.

Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing"

Experiment about Deep Person Re-identification with EfficientNet-v2

Supporting code for "Autoregressive neural-network wavefunctions for ab initio quantum chemistry".

A paper using optimal transport to solve the graph matching problem.

Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)

Customer Segmentation using RFM

Adaptable tools to make reinforcement learning and evolutionary computation algorithms.