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AttGAN-Tensorflow

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[TIP'19] AttGAN: Facial Attribute Editing by Only Changing What You Want

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최종 버전 다운로드 (.zip)

- __init__.py
- basic.py
- dtype.py
- transform.py
- first_view.png
- sample_validation.jpg
- sample_validation_256x.jpg
- sample_validation_384x_hd.jpg
- sample_validation_40.jpg
- schema.jpg
- slide.png
- style.jpg
- __init__.py
- argument.py
- path.py
- processing.py
- serialization.py
- timer.py
- align.py
- cropper.py
- split_CelebA-HQ.py
- - __init__.py
  - dataset.py
- - __init__.py
  - filter.py
  - image.py
- - __init__.py
  - layers.py
  - layers_slim.py
- - __init__.py
  - losses.py
- - __init__.py
  - metrics.py
- - __init__.py
  - ops.py
- - __init__.py
  - collection.py
  - distribute.py
  - learning_rate.py
  - utils.py
- __init__.py
- - __init__.py
  - gradient_penalty.py
  - loss.py
- __init__.py
.gitignore
data.py
examples.md
LICENSE
module.py
README.md
results.md
test.py
test_multi.py
test_slide.py
to_pb.py
train.py
utils.py

// repository documentation

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<p align="center"> <img src="./pics/first_view.png" width="36%">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<img src="./pics/slide.png" width="54%"> </p>

<p align="center"> <img src="./pics/style.jpg" width="92%"> </p>

<h1 align="center">
    <a href="https://kdocs.cn/l/cpp7J2ZsFUW7">AttGAN</a>
</h1>

<a href="https://kdocs.cn/l/cpp7J2ZsFUW7"><img src="https://img.shields.io/badge/TIP 2019-3A98B9?label=%F0%9F%93%9D&labelColor=FFFDD0" style="height: 28px" /></a>
<a href="https://arxiv.org/pdf/1711.10678v1.pdf"><img src="https://img.shields.io/badge/arXiv%202017-B31B1B?label=%F0%9F%93%9D&labelColor=FFFDD0" style="height: 28px" /></a>

> **[AttGAN: Facial Attribute Editing by Only Changing What You Want](https://kdocs.cn/l/cpp7J2ZsFUW7)** \
> [Zhenliang He](https://lynnho.github.io)<sup>1,2</sup>, [Wangmeng Zuo](https://scholar.google.com/citations?user=rUOpCEYAAAAJ)<sup>4</sup>, [Meina Kan](https://scholar.google.com/citations?user=4AKCKKEAAAAJ)<sup>1</sup>, [Shiguang Shan](https://scholar.google.com/citations?user=Vkzd7MIAAAAJ)<sup>1,3</sup>, [Xilin Chen](https://scholar.google.com/citations?user=vVx2v20AAAAJ)<sup>1</sup> \
> <sup>1</sup>Key Lab of Intelligent Information Processing, Institute of Computing Technology, CAS, China \
> <sup>2</sup>University of Chinese Academy of Sciences, China \
> <sup>3</sup>CAS Center for Excellence in Brain Science and Intelligence Technology, China \
> <sup>4</sup>School of Computer Science and Technology, Harbin Institute of Technology, China

<p align="center"> <img src="./pics/schema.jpg" width="100%"> </p>

## Related

- Other implementations of AttGAN

- [AttGAN-PyTorch](https://github.com/elvisyjlin/AttGAN-PyTorch) by Yu-Jing Lin

- [AttGAN-PaddlePaddle](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleCV/gan) by ceci3 and zhumanyu (**AttGAN is one of the official reproduced models of [PaddlePaddle](https://github.com/PaddlePaddle?type=source)**)

- Closely related works

- **An excellent work built upon our code - [STGAN](https://github.com/csmliu/STGAN) (CVPR 2019) by Ming Liu**

- [Changing-the-Memorability](https://github.com/acecreamu/Changing-the-Memorability) (CVPR 2019 MBCCV Workshop) by acecreamu

- [Fashion-AttGAN](https://github.com/ChanningPing/Fashion_Attribute_Editing) (CVPR 2019 FSS-USAD Workshop) by Qing Ping

- An unofficial [demo video](https://www.youtube.com/watch?v=gnN4ZjEWe-8) of AttGAN by 王一凡

## Exemplar Results

- See [results.md](./results.md) for more results, we try **higher resolution** and **more attributes** (all **40** attributes!!!)

- Inverting 13 attributes respectively

from left to right: *Input, Reconstruction, Bald, Bangs, Black_Hair, Blond_Hair, Brown_Hair, Bushy_Eyebrows, Eyeglasses, Male, Mouth_Slightly_Open, Mustache, No_Beard, Pale_Skin, Young*

<img src="./pics/sample_validation.jpg" width="95%">

## Usage

- Environment

- Python 3.6

- TensorFlow 1.15

- OpenCV, scikit-image, tqdm, oyaml

- *we recommend [Anaconda](https://www.anaconda.com/distribution/#download-section) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html#linux-installers), then you can create the AttGAN environment with commands below*

```console
        conda create -n AttGAN python=3.6

source activate AttGAN

conda install opencv scikit-image tqdm tensorflow-gpu=1.15

conda install -c conda-forge oyaml
        ```

- *NOTICE: if you create a new conda environment, remember to activate it before any other command*

```console
        source activate AttGAN
        ```

- Data Preparation

- Option 1: [CelebA](http://openaccess.thecvf.com/content_iccv_2015/papers/Liu_Deep_Learning_Face_ICCV_2015_paper.pdf)-unaligned (higher quality than the aligned data, 10.2GB)

- download the dataset

- img_celeba.7z (move to **./data/img_celeba/img_celeba.7z**): [Google Drive](https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg?resourcekey=0-rJlzl934LzC-Xp28GeIBzQ) or [Baidu Netdisk](https://pan.baidu.com/s/1CRxxhoQ97A5qbsKO7iaAJg) (password rp0s)

- annotations.zip (move to **./data/img_celeba/annotations.zip**): [Google Drive](https://drive.google.com/file/d/1xd-d1WRnbt3yJnwh5ORGZI3g-YS-fKM9/view?usp=sharing)

- unzip and process the data

```console
            7z x ./data/img_celeba/img_celeba.7z/img_celeba.7z.001 -o./data/img_celeba/

unzip ./data/img_celeba/annotations.zip -d ./data/img_celeba/

python ./scripts/align.py
            ```

- Option 2: CelebA-HQ (we use the data from [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ), 3.2GB)

- CelebAMask-HQ.zip (move to **./data/CelebAMask-HQ.zip**): [Google Drive](https://drive.google.com/open?id=1badu11NqxGf6qM3PTTooQDJvQbejgbTv) or [Baidu Netdisk](https://pan.baidu.com/s/1wN1E-B1bJ7mE1mrn9loj5g)

- unzip and process the data

```console
            unzip ./data/CelebAMask-HQ.zip -d ./data/

python ./scripts/split_CelebA-HQ.py
            ```

- Run AttGAN

- training (see [examples.md](./examples.md) for more training commands)

```console
        \\ for CelebA
        CUDA_VISIBLE_DEVICES=0 \
        python train.py \
        --load_size 143 \
        --crop_size 128 \
        --model model_128 \
        --experiment_name AttGAN_128

\\ for CelebA-HQ
        CUDA_VISIBLE_DEVICES=0 \
        python train.py \
        --img_dir ./data/CelebAMask-HQ/CelebA-HQ-img \
        --train_label_path ./data/CelebAMask-HQ/train_label.txt \
        --val_label_path ./data/CelebAMask-HQ/val_label.txt \
        --load_size 128 \
        --crop_size 128 \
        --n_epochs 200 \
        --epoch_start_decay 100 \
        --model model_128 \
        --experiment_name AttGAN_128_CelebA-HQ
        ```

- testing

- **single** attribute editing (inversion)

```console
            \\ for CelebA
            CUDA_VISIBLE_DEVICES=0 \
            python test.py \
            --experiment_name AttGAN_128

\\ for CelebA-HQ
            CUDA_VISIBLE_DEVICES=0 \
            python test.py \
            --img_dir ./data/CelebAMask-HQ/CelebA-HQ-img \
            --test_label_path ./data/CelebAMask-HQ/test_label.txt \
            --experiment_name AttGAN_128_CelebA-HQ
            ```

- **multiple** attribute editing (inversion) example

```console
            \\ for CelebA
            CUDA_VISIBLE_DEVICES=0 \
            python test_multi.py \
            --test_att_names Bushy_Eyebrows Pale_Skin \
            --experiment_name AttGAN_128
            ```

- attribute sliding example

```console
            \\ for CelebA
            CUDA_VISIBLE_DEVICES=0 \
            python test_slide.py \
            --test_att_name Pale_Skin \
            --test_int_min -2 \
            --test_int_max 2 \
            --test_int_step 0.5 \
            --experiment_name AttGAN_128
            ```

- loss visualization

```console
        CUDA_VISIBLE_DEVICES='' \
        tensorboard \
        --logdir ./output/AttGAN_128/summaries \
        --port 6006
        ```

- convert trained model to .pb file

```console
        python to_pb.py --experiment_name AttGAN_128
        ```

- Using Trained Weights

- alternative trained weights (move to **./output/\*.zip**)

- [AttGAN_128.zip](https://drive.google.com/file/d/1Oy4F1xtYdxj4iyiLyaEd-dkGIJ0mwo41/view?usp=sharing) (987.5MB)

- *including G, D, and the state of the optimizer*

- [AttGAN_128_generator_only.zip](https://drive.google.com/file/d/1lcQ-ijNrGD4919eJ5Dv-7ja5rsx5p0Tp/view?usp=sharing) (161.5MB)

- *G only*

- [AttGAN_384_generator_only.zip](https://drive.google.com/open?id=1scaKWcWIpTfsV0yrWCI-wg_JDmDsKKm1) (91.1MB)

- unzip the file (AttGAN_128.zip for example)

```console
        unzip ./output/AttGAN_128.zip -d ./output/
        ```

- testing (see above)

- Example for Custom Dataset

- [AttGAN-Cartoon](https://github.com/LynnHo/AttGAN-Cartoon-Tensorflow)

## Citation

If you find [AttGAN](https://kdocs.cn/l/cpp7J2ZsFUW7) useful in your research work, please consider citing:

@ARTICLE{8718508,
    author={Z. {He} and W. {Zuo} and M. {Kan} and S. {Shan} and X. {Chen}},
    journal={IEEE Transactions on Image Processing},
    title={AttGAN: Facial Attribute Editing by Only Changing What You Want},
    year={2019},
    volume={28},
    number={11},
    pages={5464-5478},
    keywords={Face;Facial features;Task analysis;Decoding;Image reconstruction;Hair;Gallium nitride;Facial attribute editing;attribute style manipulation;adversarial learning},
    doi={10.1109/TIP.2019.2916751},
    ISSN={1057-7149},
    month={Nov},}