diff --git a/README.md b/README.md index e35ed861c85b883c75888c750e5d3985a5d4556e..91c8cea30fc0a06a80e43a2d5cae87785db1c7c0 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ GAN-Generative Adversarial Network, was praised by "the Father of Convolutional ## 🚀 Recent Updates -- 🔥 **Latest Release: [PP-MSVSR](./docs/en_US/tutorials/video_super_resolution.md)** 🔥 +- 🔥 **Latest Release: [PP-MSVSR](./docs/en_US/tutorials/video_super_resolution.md)** 🔥 - **Video Super Resolution SOTA models**
@@ -106,6 +106,7 @@ GAN-Generative Adversarial Network, was praised by "the Father of Convolutional * [Video Super Resolution(VSR)](./docs/en_US/tutorials/video_super_resolution.md) * [StyleGAN2](./docs/en_US/tutorials/styleganv2.md) * [Pixel2Style2Pixel](./docs/en_US/tutorials/pixel2style2pixel.md) +* [StarGANv2](docs/en_US/tutorials/starganv2.md) ## Composite Application diff --git a/README_cn.md b/README_cn.md index 3632b1b86e4daa3f30caa5cbf42ded2279956f65..4c332b4003843a192109a2468b42d3a518b28451 100644 --- a/README_cn.md +++ b/README_cn.md @@ -121,6 +121,7 @@ GAN--生成对抗网络，被“卷积网络之父”**Yann LeCun（杨立昆） * 照片动漫化：[AnimeGANv2](./docs/zh_CN/tutorials/animegan.md) * 人像动漫化：[U-GAT-IT](./docs/zh_CN/tutorials/ugatit.md) * 人脸卡通化：[Photo2Cartoon](docs/zh_CN/tutorials/photo2cartoon.md) + * 多种风格迁移：[StarGANv2](docs/zh_CN/tutorials/starganv2.md) * 动作迁移 * 人脸表情迁移：[First Order Motion Model](./docs/zh_CN/tutorials/motion_driving.md) * 唇形合成：[Wav2Lip](docs/zh_CN/tutorials/wav2lip.md) diff --git a/configs/starganv2_afhq.yaml b/configs/starganv2_afhq.yaml index 6a7ec539b09e8b6b8a004ad0f87d3812ae480afb..ca8d54b32af9afc2509a6a4d0499d2bb419a3eef 100644 --- a/configs/starganv2_afhq.yaml +++ b/configs/starganv2_afhq.yaml @@ -49,11 +49,11 @@ dataset: size: [*IMAGE_SIZE, *IMAGE_SIZE] scale: [0.8, 1.0] ratio: [0.9, 1.1] - interpolation: 'bilinear' + interpolation: 'bilinear' keys: [image, image, image] - name: Resize size: [*IMAGE_SIZE, *IMAGE_SIZE] - interpolation: 'bilinear' + interpolation: 'bilinear' keys: [image, image, image] - name: RandomHorizontalFlip prob: 0.5 @@ -130,12 +130,12 @@ optimizer: weight_decay: 0.0001 validate: - interval: 5000 + interval: 3000 save_img: false log_config: - interval: 5 - visiual_interval: 100 + interval: 100 + visiual_interval: 3000 snapshot_config: interval: 5 diff --git a/configs/starganv2_celeba_hq.yaml b/configs/starganv2_celeba_hq.yaml index 8ea231c3c7279b0569e6a58d1fdf9cb096d029f4..8519d2cb216f2db4a02a4b982b79b8f6ee79c24c 100644 --- a/configs/starganv2_celeba_hq.yaml +++ b/configs/starganv2_celeba_hq.yaml @@ -52,11 +52,11 @@ dataset: size: [*IMAGE_SIZE, *IMAGE_SIZE] scale: [0.8, 1.0] ratio: [0.9, 1.1] - interpolation: 'bilinear' + interpolation: 'bilinear' keys: [image, image, image] - name: Resize size: [*IMAGE_SIZE, *IMAGE_SIZE] - interpolation: 'bilinear' + interpolation: 'bilinear' keys: [image, image, image] - name: RandomHorizontalFlip prob: 0.5 @@ -133,12 +133,12 @@ optimizer: weight_decay: 0.0001 validate: - interval: 5000 + interval: 3000 save_img: false log_config: - interval: 5 - visiual_interval: 100 + interval: 100 + visiual_interval: 3000 snapshot_config: interval: 5 diff --git a/docs/en_US/tutorials/starganv2.md b/docs/en_US/tutorials/starganv2.md new file mode 100644 index 0000000000000000000000000000000000000000..5c1866f9dd5e89433f8b3a48e35ab29fb3607794 --- /dev/null +++ b/docs/en_US/tutorials/starganv2.md @@ -0,0 +1,74 @@ +# StarGAN V2 + +## 1 Introduction + + [StarGAN V2](https://arxiv.org/pdf/1912.01865.pdf)is an image-to-image translation model published on CVPR2020. + A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. StarGAN v2 is a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate superiority of StarGAN v2 in terms of visual quality, diversity, and scalability. + +## 2 How to use + +### 2.1 Prepare dataset + + The CelebAHQ dataset used by StarGAN V2 can be downloaded from [here](https://www.dropbox.com/s/f7pvjij2xlpff59/celeba_hq.zip?dl=0), and the AFHQ dataset can be downloaded from [here](https://www.dropbox.com/s/t9l9o3vsx2jai3z/afhq.zip?dl=0). Then unzip dataset to the ``PaddleGAN/data`` directory. + + The structure of dataset is as following: + + ``` + ├── data + ├── afhq + | ├── train + | | ├── cat + | | ├── dog + | | └── wild + | └── val + | ├── cat + | ├── dog + | └── wild + └── celeba_hq + ├── train + | ├── female + | └── male + └── val + ├── female + └── male + + ``` + +### 2.2 Train/Test + + The example uses the AFHQ dataset as an example. If you want to use the CelebAHQ dataset, you can change the config file. + + train model: + ``` + python -u tools/main.py --config-file configs/starganv2_afhq.yaml + ``` + + test model: + ``` + python tools/main.py --config-file configs/starganv2_afhq.yaml --evaluate-only --load ${PATH_OF_WEIGHT} + ``` + +## 3 Results + +![](https://user-images.githubusercontent.com/79366697/146308440-65259d70-d056-43d4-8cf5-a82530993910.jpg) + +## 4 Model Download +| 模型 | 数据集 | 下载地址 | +|---|---|---| +| starganv2_afhq | AFHQ | [starganv2_afhq](https://paddlegan.bj.bcebos.com/models/starganv2_afhq.pdparams) + + + + +# References + +- 1. [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://arxiv.org/abs/1912.01865) + + ``` + @inproceedings{choi2020starganv2, + title={StarGAN v2: Diverse Image Synthesis for Multiple Domains}, + author={Yunjey Choi and Youngjung Uh and Jaejun Yoo and Jung-Woo Ha}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + year={2020} + } + ``` diff --git a/docs/zh_CN/tutorials/starganv2.md b/docs/zh_CN/tutorials/starganv2.md new file mode 100644 index 0000000000000000000000000000000000000000..7960fb92fd8903ba4111f03a125cd854c5d8e26e --- /dev/null +++ b/docs/zh_CN/tutorials/starganv2.md @@ -0,0 +1,74 @@ +# StarGAN V2 + +## 1 原理介绍 + + [StarGAN V2](https://arxiv.org/pdf/1912.01865.pdf)是发布在CVPR2020上的一个图像转换模型。 + 一个好的图像到图像转换模型应该学习不同视觉域之间的映射，同时满足以下属性：1）生成图像的多样性和 2）多个域的可扩展性。 现有方法只解决了其中一个问题，领域的多样性有限或对所有领域用多个模型。 StarGAN V2是一个单一的框架，可以同时解决这两个问题，并在基线上显示出显着改善的结果。 CelebAHQ 和新的动物面孔数据集 (AFHQ) 上的实验验证了StarGAN V2在视觉质量、多样性和可扩展性方面的优势。 + +## 2 如何使用 + +### 2.1 数据准备 + + StarGAN V2使用的CelebAHQ数据集可以从[这里](https://www.dropbox.com/s/f7pvjij2xlpff59/celeba_hq.zip?dl=0)下载，使用的AFHQ数据集可以从[这里](https://www.dropbox.com/s/t9l9o3vsx2jai3z/afhq.zip?dl=0)下载。将数据集下载解压后放到``PaddleGAN/data``文件夹下 。 + + 数据的组成形式为： + + ``` + ├── data + ├── afhq + | ├── train + | | ├── cat + | | ├── dog + | | └── wild + | └── val + | ├── cat + | ├── dog + | └── wild + └── celeba_hq + ├── train + | ├── female + | └── male + └── val + ├── female + └── male + + ``` + +### 2.2 训练/测试 + + 示例以AFHQ数据集为例。如果您想使用CelebAHQ数据集，可以在换一下配置文件。 + + 训练模型: + ``` + python -u tools/main.py --config-file configs/starganv2_afhq.yaml + ``` + + 测试模型: + ``` + python tools/main.py --config-file configs/starganv2_afhq.yaml --evaluate-only --load ${PATH_OF_WEIGHT} + ``` + +## 3 结果展示 + +![](https://user-images.githubusercontent.com/79366697/146308440-65259d70-d056-43d4-8cf5-a82530993910.jpg) + +## 4 模型下载 +| 模型 | 数据集 | 下载地址 | +|---|---|---| +| starganv2_afhq | AFHQ | [starganv2_afhq](https://paddlegan.bj.bcebos.com/models/starganv2_afhq.pdparams) + + + + +# 参考文献 + +- 1. [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://arxiv.org/abs/1912.01865) + + ``` + @inproceedings{choi2020starganv2, + title={StarGAN v2: Diverse Image Synthesis for Multiple Domains}, + author={Yunjey Choi and Youngjung Uh and Jaejun Yoo and Jung-Woo Ha}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + year={2020} + } + ``` diff --git a/ppgan/models/generators/generator_starganv2.py b/ppgan/models/generators/generator_starganv2.py index a8cef90cf88fcd811d4422b5b6a43b6b439ca8d3..e5bbce0f980df64699b426503b9940edf35ede15 100755 --- a/ppgan/models/generators/generator_starganv2.py +++ b/ppgan/models/generators/generator_starganv2.py @@ -16,22 +16,6 @@ from ppgan.utils.download import get_path_from_url FAN_WEIGHT_URL = "https://paddlegan.bj.bcebos.com/models/wing.pdparams" -class AvgPool2D(nn.Layer): - """ - AvgPool2D - Peplace avg_pool2d because paddle.grad will cause avg_pool2d to report an error when training. - In the future Paddle framework will supports avg_pool2d and remove this class. - """ - def __init__(self): - super(AvgPool2D, self).__init__() - self.filter = paddle.to_tensor([[1, 1], [1, 1]], dtype='float32') - - def forward(self, x): - filter = self.filter.unsqueeze(0).unsqueeze(1).tile( - [x.shape[1], 1, 1, 1]) - return F.conv2d(x, filter, stride=2, padding=0, groups=x.shape[1]) / 4 - - class ResBlk(nn.Layer): def __init__(self, dim_in, @@ -45,6 +29,7 @@ class ResBlk(nn.Layer): self.downsample = downsample self.learned_sc = dim_in != dim_out self._build_weights(dim_in, dim_out) + self.maxpool = nn.AvgPool2D(kernel_size=2) def _build_weights(self, dim_in, dim_out): self.conv1 = nn.Conv2D(dim_in, dim_in, 3, 1, 1) @@ -63,7 +48,7 @@ class ResBlk(nn.Layer): if self.learned_sc: x = self.conv1x1(x) if self.downsample: - x = AvgPool2D()(x) + x = self.maxpool(x) return x def _residual(self, x): @@ -72,7 +57,7 @@ class ResBlk(nn.Layer): x = self.actv(x) x = self.conv1(x) if self.downsample: - x = AvgPool2D()(x) + x = self.maxpool(x) if self.normalize: x = self.norm2(x) x = self.actv(x)