Add AnimeGANv2 model (#102)

* add animeganv2 network and dataset
* animegan:refine code,add License
Co-authored-by: Nqingqing01 <dangqingqing@baidu.com>

applications/tools/animeganv2.py

+import paddle
+import os
+import sys
+sys.path.insert(0, os.getcwd())
+from ppgan.apps import AnimeGANPredictor
+import argparse
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_image", type=str, help="path to source image")
+
+    parser.add_argument("--output_path",
+                        type=str,
+                        default='output_dir',
+                        help="path to output image dir")
+
+    parser.add_argument("--weight_path",
+                        type=str,
+                        default=None,
+                        help="path to model checkpoint path")
+
+    parser.add_argument("--use_adjust_brightness",
+                        action="store_false",
+                        help="adjust brightness mode.")
+
+    parser.add_argument("--cpu",
+                        dest="cpu",
+                        action="store_true",
+                        help="cpu mode.")
+
+    args = parser.parse_args()
+
+    if args.cpu:
+        paddle.set_device('cpu')
+
+    predictor = AnimeGANPredictor(args.output_path, args.weight_path,
+                                  args.use_adjust_brightness)
+    predictor.run(args.input_image)

configs/animeganv2.yaml

+epochs: 30
+output_dir: output_dir
+pretrain_ckpt: output_dir/AnimeGANV2PreTrainModel-2020-11-29-17-02/epoch_2_checkpoint.pdparams
+g_adv_weight: 300.
+d_adv_weight: 300.
+con_weight: 1.5
+sty_weight: 2.5
+color_weight: 10.
+tv_weight: 1.
+
+model:
+  name: AnimeGANV2Model
+  generator:
+    name: AnimeGenerator
+  discriminator:
+    name: AnimeDiscriminator
+  gan_mode: lsgan
+
+dataset:
+  train:
+    name: AnimeGANV2Dataset
+    num_workers: 4
+    batch_size: 4
+    dataroot: data/animedataset
+    style: Hayao
+    phase: train
+    direction: AtoB
+    transform_real:
+      - name: Transpose
+      - name: Normalize
+        mean: [127.5, 127.5, 127.5]
+        std: [127.5, 127.5, 127.5]
+    transform_anime:
+      - name: Add
+        value: [-4.4346957, -8.665916, 13.100612]
+      - name: Transpose
+      - name: Normalize
+        mean: [127.5, 127.5, 127.5]
+        std: [127.5, 127.5, 127.5]
+    transform_gray:
+      - name: Grayscale
+        num_output_channels: 3
+      - name: Transpose
+      - name: Normalize
+        mean: [127.5, 127.5, 127.5]
+        std: [127.5, 127.5, 127.5]
+  test:
+    name: SingleDataset
+    dataroot: data/animedataset/test/HR_photo
+    max_dataset_size: inf
+    direction: BtoA
+    input_nc: 3
+    output_nc: 3
+    serial_batches: False
+    pool_size: 50
+    transforms:
+      - name: ResizeToScale
+        size: [256, 256]
+        scale: 32
+        interpolation: bilinear
+      - name: Transpose
+      - name: Normalize
+        mean: [127.5, 127.5, 127.5]
+        std: [127.5, 127.5, 127.5]
+
+optimizer:
+  name: Adam
+  beta1: 0.5
+
+lr_scheduler:
+  name: linear
+  learning_rate: 0.00002
+  start_epoch: 100
+  decay_epochs: 100
+
+log_config:
+  interval: 100
+  visiual_interval: 100
+
+snapshot_config:
+  interval: 5

configs/animeganv2_pretrain.yaml

+epochs: 2
+output_dir: output_dir
+con_weight: 1
+pretrain_ckpt: null
+
+model:
+  name: AnimeGANV2PreTrainModel
+  generator:
+    name: AnimeGenerator
+  discriminator:
+    name: AnimeDiscriminator
+  gan_mode: lsgan
+
+dataset:
+  train:
+    name: AnimeGANV2Dataset
+    num_workers: 4
+    batch_size: 4
+    dataroot: data/animedataset
+    style: Hayao
+    phase: train
+    direction: AtoB
+    transform_real:
+      - name: Transpose
+      - name: Normalize
+        mean: [127.5, 127.5, 127.5]
+        std: [127.5, 127.5, 127.5]
+    transform_anime:
+      - name: Add
+        value: [-4.4346957, -8.665916, 13.100612]
+      - name: Transpose
+      - name: Normalize
+        mean: [127.5, 127.5, 127.5]
+        std: [127.5, 127.5, 127.5]
+    transform_gray:
+      - name: Grayscale
+        num_output_channels: 3
+      - name: Transpose
+      - name: Normalize
+        mean: [127.5, 127.5, 127.5]
+        std: [127.5, 127.5, 127.5]
+  test:
+    name: SingleDataset
+    dataroot: data/animedataset/test/test_photo
+    max_dataset_size: inf
+    direction: BtoA
+    input_nc: 3
+    output_nc: 3
+    serial_batches: False
+    pool_size: 50
+    transforms:
+      - name: Resize
+        size: [256, 256]
+        interpolation: "bicubic" #cv2.INTER_CUBIC
+      - name: Transpose
+      - name: Normalize
+        mean: [127.5, 127.5, 127.5]
+        std: [127.5, 127.5, 127.5]
+
+optimizer:
+  name: Adam
+  beta1: 0.5
+
+lr_scheduler:
+  name: linear
+  learning_rate: 0.0002
+  start_epoch: 100
+  decay_epochs: 100
+
+log_config:
+  interval: 100
+  visiual_interval: 100
+
+snapshot_config:
+  interval: 5

docs/en_US/tutorials/animegan.md

+# 1 AnimeGANv2
+
+## 1.1 Introduction
+
+[AnimeGAN](https://github.com/TachibanaYoshino/AnimeGANv2) improved the [CVPR paper CartoonGAN](https://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf), mainly to solve the over-stylized and color artifact area. For the details, you can refer to the [Zhihu article](https://zhuanlan.zhihu.com/p/76574388?from_voters_page=true) writes by the paper author.Based on the AnimeGAN, the AnimeGANv2 add the `total variation loss` in the generator loss.
+
+
+## 1.2 How to use
+
+### 1.2.1 Quick start
+
+After installing PaddleGAN, you can run python code as follows to generate the stylized image. Where the `PATH_OF_IMAGE` is your source image path.
+
+```python
+from ppgan.apps import AnimeGANPredictor
+predictor = AnimeGANPredictor()
+predictor.run(PATH_OF_IMAGE)
+```
+
+Or run such a command to get the same result:
+
+```sh
+python applications/tools/animeganv2.py --input_image ${PATH_OF_IMAGE}
+```
+
+### 1.2.1 Prepare dataset
+
+We download the dataset provided by the author from [here](https://github.com/TachibanaYoshino/AnimeGAN/releases/tag/dataset-1).Then unzip to the `data` directory.
+
+```sh
+wget https://github.com/TachibanaYoshino/AnimeGAN/releases/download/dataset-1/dataset.zip
+cd PaddleGAN
+unzip YOUR_DATASET_DIR/dataset.zip -d data/animedataset
+```
+
+For example, the structure of `animedataset` is as following:
+
+```sh
+animedataset
+├── Hayao
+│   ├── smooth
+│   └── style
+├── Paprika
+│   ├── smooth
+│   └── style
+├── Shinkai
+│   ├── smooth
+│   └── style
+├── SummerWar
+│   ├── smooth
+│   └── style
+├── test
+│   ├── HR_photo
+│   ├── label_map
+│   ├── real
+│   ├── test_photo
+│   └── test_photo256
+├── train_photo
+└── val
+```
+
+### 1.2.2 Training
+
+  An example is training to Hayao stylize.
+
+  1.  To ensure the generator can generate the original image, we need to warmup the model.:
+  ```sh
+  python tools/main.py --config-file configs/animeganv2_pretrain.yaml
+  ```
+
+  2.  After the warmup, we strat to training GAN.:
+  **NOTE：** you must modify the `configs/animeganv2.yaml > pretrain_ckpt ` parameter first! ensure the GAN can reuse the warmup generator model.
+  Set the `batch size=4` and the `learning rate=0.00002`. Train 30 epochs on a GTX2060S GPU to reproduce the result. For other hyperparameters, please refer to `configs/animeganv2.yaml`.
+  ```sh
+  python tools/main.py --config-file configs/animeganv2.yaml
+  ```
+
+  3.  Change target style
+  Modify `style` parameter in the `configs/animeganv2.yaml`, now support choice from `Hayao, Paprika, Shinkai, SummerWar`. If you want to use your own dataset, you can modify it to be your own in the configuration file.
+
+  **NOTE :** After modifying the target style, calculate the mean value of the target style dataset at first, and the `transform_anime->Add->value` parameter in `configs/animeganv2.yaml` must be modified.
+
+  The following example shows how to obtain the  mean value of the `Hayao` style:
+  ```sh
+  python tools/animegan_picmean.py --dataset data/animedataset/Hayao/style
+  image_num: 1792
+  100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1792/1792 [00:04<00:00, 444.95it/s]
+  RGB mean diff
+  [-4.4346957 -8.665916  13.100612 ]
+  ```
+
+
+### 1.2.3 Test
+
+test model on `data/animedataset/test/HR_photo`
+```sh
+python tools/main.py --config-file configs/animeganv2.yaml --evaluate-only --load ${PATH_OF_WEIGHT}
+```
+
+## 1.3 Results
+| original image                      | style image                        |
+| ----------------------------------- | ---------------------------------- |
+| ![](../../imgs/animeganv2_test.jpg) | ![](../../imgs/animeganv2_res.jpg) |

docs/zh_CN/apis/apps.md

@@ -351,3 +351,24 @@ ppgan.apps.FaceParsePredictor(output_path='output')
 > ```
 > **返回值:**
 > > - mask(numpy.ndarray): 返回解析完成的人脸成分mask矩阵, 数据类型为numpy.ndarray
+
+## ppgan.apps.AnimeGANPredictor
+
+```pyhton
+ppgan.apps.AnimeGANPredictor(output_path='output_dir',weight_path=None,use_adjust_brightness=True)
+```
+> 利用animeganv2来对景物图像进行动漫风格化。论文是 AnimeGAN: A Novel Lightweight GAN for Photo Animation, 论文链接: https://link.springer.com/chapter/10.1007/978-981-15-5577-0_18.
+
+> **参数:**
+>
+> > - input_image: 输入待解析的图片文件路径
+
+> **示例:**
+>
+> ```
+> from ppgan.apps import AnimeGANPredictor
+> predictor = AnimeGANPredictor()
+> predictor.run('docs/imgs/animeganv2_test.jpg')
+> ```
+> **返回值:**
+> > - anime_image(numpy.ndarray): 返回风格化后的景色图像

docs/zh_CN/tutorials/animegan.md

+# 1 AnimeGANv2
+
+## 1.1 原理介绍
+
+[AnimeGAN](https://github.com/TachibanaYoshino/AnimeGANv2)基于2018年[CVPR论文CartoonGAN](https://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf)基础上对其进行了一些改进，主要消除了过度风格化以及颜色伪影区域的问题。对于具体原理可以参见作者[知乎文章](https://zhuanlan.zhihu.com/p/76574388?from_voters_page=true)。AnimeGANv2是作者在AnimeGAN的基础上添加了`total variation loss`的新模型。
+
+
+## 1.2 如何使用
+
+### 1.2.1 快速体验
+
+安装`PaddleGAN`之后运行如下代码即生成风格化后的图像`output_dir/anime.png`，其中`PATH_OF_IMAGE`为你需要转换的图像路径。
+
+```python
+from ppgan.apps import AnimeGANPredictor
+predictor = AnimeGANPredictor()
+predictor.run(PATH_OF_IMAGE)
+```
+
+或者在终端中运行如下命令，也可获得相同结果：
+
+```sh
+python applications/tools/animeganv2.py --input_image ${PATH_OF_IMAGE}
+```
+
+### 1.2.1 数据准备
+
+我们下载作者提供的训练数据，训练数据可以从[这里](https://github.com/TachibanaYoshino/AnimeGAN/releases/tag/dataset-1)下载。
+下载后解压到data目录下：
+
+```sh
+wget https://github.com/TachibanaYoshino/AnimeGAN/releases/download/dataset-1/dataset.zip
+cd PaddleGAN
+unzip YOUR_DATASET_DIR/dataset.zip -d data/animedataset
+```
+
+解压完成后数据分布如下所示：
+
+```sh
+animedataset
+├── Hayao
+│   ├── smooth
+│   └── style
+├── Paprika
+│   ├── smooth
+│   └── style
+├── Shinkai
+│   ├── smooth
+│   └── style
+├── SummerWar
+│   ├── smooth
+│   └── style
+├── test
+│   ├── HR_photo
+│   ├── label_map
+│   ├── real
+│   ├── test_photo
+│   └── test_photo256
+├── train_photo
+└── val
+```
+
+### 1.2.2 训练
+  示例以训练Hayao风格的数据为例。
+
+  1.  为了保证模型具备生成原图的效果，需要预热模型:
+  ```sh
+  python tools/main.py --config-file configs/animeganv2_pretrain.yaml
+  ```
+
+  1.  预热模型完成后，训练风格迁移模型:
+  **注意：** 必须先修改在`configs/animeganv2.yaml`中的`pretrain_ckpt`参数，确保指向正确的 **预热模型权重路径**
+  设置`batch size=4`，`learning rate=0.00002`，在一个  GTX2060S GPU上训练30个epoch即可获得较好的效果，其他超参数请参考`configs/animeganv2.yaml`。
+
+  ```sh
+  python tools/main.py --config-file configs/animeganv2.yaml
+  ```
+
+  1.  改变目标图像的风格
+  修改`configs/animeganv2.yaml`中的`style`参数即可改变风格(目前可选择`Hayao,Paprika,Shinkai,SummerWar`)。如果您想使用自己的数据集，可以在配置文件中修改数据集为您自己的数据集。
+
+  **注意：** 修改目标风格后，必须计算目标风格数据集的像素均值，并修改`configs/animeganv2.yaml`中的`transform_anime->Add->value`参数。
+
+  如下例子展示了如何获得`Hayao`风格图像的像素均值：
+  ```sh
+  python tools/animegan_picmean.py --dataset data/animedataset/Hayao/style
+  image_num: 1792
+  100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1792/1792 [00:04<00:00, 444.95it/s]
+  RGB mean diff
+  [-4.4346957 -8.665916  13.100612 ]
+  ```
+
+### 1.2.3 测试
+
+测试模型：
+```sh
+python tools/main.py --config-file configs/animeganv2.yaml --evaluate-only --load ${PATH_OF_WEIGHT}
+```
+
+## 1.3 结果展示
+| 原始图像                            | 风格化后图像                       |
+| ----------------------------------- | ---------------------------------- |
+| ![](../../imgs/animeganv2_test.jpg) | ![](../../imgs/animeganv2_res.jpg) |

ppgan/apps/__init__.py

@@ -19,3 +19,4 @@ from .realsr_predictor import RealSRPredictor
 from .edvr_predictor import EDVRPredictor
 from .first_order_predictor import FirstOrderPredictor
 from .face_parse_predictor import FaceParsePredictor
+from .animegan_predictor import AnimeGANPredictor

ppgan/apps/animegan_predictor.py

+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import os
+import numpy as np
+import cv2
+
+import paddle
+from .base_predictor import BasePredictor
+from ppgan.datasets.transforms import ResizeToScale
+import paddle.vision.transforms as T
+from ppgan.models.generators import AnimeGenerator
+from ppgan.utils.download import get_path_from_url
+
+
+class AnimeGANPredictor(BasePredictor):
+    def __init__(self,
+                 output_path='output_dir',
+                 weight_path=None,
+                 use_adjust_brightness=True):
+        self.output_path = output_path
+        self.input_size = (256, 256)
+        self.use_adjust_brightness = use_adjust_brightness
+        if weight_path is None:
+            vox_cpk_weight_url = 'https://paddlegan.bj.bcebos.com/models/animeganv2_hayao.pdparams'
+            weight_path = get_path_from_url(vox_cpk_weight_url)
+        self.weight_path = weight_path
+        self.generator = self.load_checkpoints()
+        self.transform = T.Compose([
+            ResizeToScale((256, 256), 32),
+            T.Transpose(),
+            T.Normalize([127.5, 127.5, 127.5], [127.5, 127.5, 127.5])
+        ])
+
+    def load_checkpoints(self):
+        generator = AnimeGenerator()
+        checkpoint = paddle.load(self.weight_path)
+        generator.set_state_dict(checkpoint['netG'])
+        generator.eval()
+        return generator
+
+    @staticmethod
+    def calc_avg_brightness(img):
+        R = img[..., 0].mean()
+        G = img[..., 1].mean()
+        B = img[..., 2].mean()
+
+        brightness = 0.299 * R + 0.587 * G + 0.114 * B
+        return brightness, B, G, R
+
+    @staticmethod
+    def adjust_brightness(dst, src):
+        brightness1, B1, G1, R1 = AnimeGANPredictor.calc_avg_brightness(src)
+        brightness2, B2, G2, R2 = AnimeGANPredictor.calc_avg_brightness(dst)
+        brightness_difference = brightness1 / brightness2
+        dstf = dst * brightness_difference
+        dstf = np.clip(dstf, 0, 255)
+        dstf = np.uint8(dstf)
+        return dstf
+
+    def run(self, image):
+        image = cv2.cvtColor(cv2.imread(image, flags=cv2.IMREAD_COLOR),
+                             cv2.COLOR_BGR2RGB)
+        transformed_image = self.transform(image)
+        anime = (self.generator(paddle.to_tensor(transformed_image[None, ...]))
+                 * 0.5 + 0.5)[0].numpy() * 255
+        anime = anime.transpose((1, 2, 0))
+        if anime.shape[:2] != image.shape[:2]:
+            # to original size
+            anime = T.resize(anime, image.shape[:2])
+        if self.use_adjust_brightness:
+            anime = self.adjust_brightness(anime, image)
+        else:
+            anime = anime.astype('uint8')
+        if not os.path.exists(self.output_path):
+            os.makedirs(self.output_path)
+        save_path = os.path.join(self.output_path, 'anime.png')
+        cv2.imwrite(save_path, cv2.cvtColor(anime, cv2.COLOR_RGB2BGR))
+        return image

ppgan/datasets/__init__.py

@@ -17,3 +17,4 @@ from .single_dataset import SingleDataset
 from .paired_dataset import PairedDataset
 from .sr_image_dataset import SRImageDataset
 from .makeup_dataset import MakeupDataset
+from .animeganv2_dataset import AnimeGANV2Dataset

ppgan/datasets/animeganv2_dataset.py

+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import cv2
+import numpy as np
+import os.path
+from .base_dataset import BaseDataset
+from .image_folder import ImageFolder
+
+from .builder import DATASETS
+from .transforms.builder import build_transforms
+
+
+@DATASETS.register()
+class AnimeGANV2Dataset(BaseDataset):
+    """
+    """
+    def __init__(self, cfg):
+        """Initialize this dataset class.
+
+        Args:
+            cfg (dict) -- stores all the experiment flags
+        """
+        BaseDataset.__init__(self, cfg)
+        self.style = cfg.style
+
+        self.transform_real = build_transforms(self.cfg.transform_real)
+        self.transform_anime = build_transforms(self.cfg.transform_anime)
+        self.transform_gray = build_transforms(self.cfg.transform_gray)
+
+        self.real_root = os.path.join(self.root, 'train_photo')
+        self.anime_root = os.path.join(self.root, f'{self.style}', 'style')
+        self.smooth_root = os.path.join(self.root, f'{self.style}', 'smooth')
+
+        self.real = ImageFolder(self.real_root,
+                                transform=self.transform_real,
+                                loader=self.loader)
+        self.anime = ImageFolder(self.anime_root,
+                                 transform=self.transform_anime,
+                                 loader=self.loader)
+        self.anime_gray = ImageFolder(self.anime_root,
+                                      transform=self.transform_gray,
+                                      loader=self.loader)
+        self.smooth_gray = ImageFolder(self.smooth_root,
+                                       transform=self.transform_gray,
+                                       loader=self.loader)
+        self.sizes = [
+            len(fold) for fold in [self.real, self.anime, self.smooth_gray]
+        ]
+        self.size = max(self.sizes)
+        self.reshuffle()
+
+    @staticmethod
+    def loader(path):
+        return cv2.cvtColor(cv2.imread(path, flags=cv2.IMREAD_COLOR),
+                            cv2.COLOR_BGR2RGB)
+
+    def reshuffle(self):
+        indexs = []
+        for cur_size in self.sizes:
+            x = np.arange(0, cur_size)
+            np.random.shuffle(x)
+            if cur_size != self.size:
+                pad_num = self.size - cur_size
+                pad = np.random.choice(cur_size, pad_num, replace=True)
+                x = np.concatenate((x, pad))
+                np.random.shuffle(x)
+            indexs.append(x.tolist())
+        self.indexs = list(zip(*indexs))
+
+    def __getitem__(self, index):
+        try:
+            index = self.indexs.pop()
+        except IndexError as e:
+            self.reshuffle()
+            index = self.indexs.pop()
+
+        real_idx, anime_idx, smooth_idx = index
+
+        return {
+            'real': self.real[real_idx],
+            'anime': self.anime[anime_idx],
+            'anime_gray': self.anime_gray[anime_idx],
+            'smooth_gray': self.smooth_gray[smooth_idx]
+        }
+
+    def __len__(self):
+        return self.size

ppgan/datasets/transforms/__init__.py

@@ -12,4 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .transforms import PairedRandomCrop, PairedRandomHorizontalFlip
+from .transforms import PairedRandomCrop, PairedRandomHorizontalFlip, Add, ResizeToScale

ppgan/datasets/transforms/functional.py

+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+from __future__ import division
+
+from . import functional_cv2 as F_cv2
+from paddle.vision.transforms.functional import _is_numpy_image, _is_pil_image
+
+__all__ = ['add']
+
+
+def add(pic, value):
+    if not (_is_pil_image(pic) or _is_numpy_image(pic)):
+        raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(
+            type(pic)))
+
+    if _is_pil_image(pic):
+        raise NotImplementedError('add not support pil image')
+    else:
+        return F_cv2.add(pic, value)

ppgan/datasets/transforms/functional_cv2.py

+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+from __future__ import division
+import numpy as np
+
+
+def add(image, value):
+    return np.clip(image + value, 0, 255).astype('uint8')

ppgan/datasets/transforms/transforms.py

@@ -19,6 +19,7 @@ import collections
 import numpy as np
 
 import paddle.vision.transforms as T
+import ppgan.datasets.transforms.functional as custom_F
 import paddle.vision.transforms.functional as F
 
 from .builder import TRANSFORMS
@@ -35,6 +36,7 @@ TRANSFORMS.register(T.RandomCrop)
 TRANSFORMS.register(T.RandomHorizontalFlip)
 TRANSFORMS.register(T.Normalize)
 TRANSFORMS.register(T.Transpose)
+TRANSFORMS.register(T.Grayscale)
 
 
 @TRANSFORMS.register()
@@ -72,3 +74,73 @@ class PairedRandomHorizontalFlip(T.RandomHorizontalFlip):
         if self.params['flip']:
             return F.hflip(image)
         return image
+
+
+@TRANSFORMS.register()
+class Add(T.BaseTransform):
+    def __init__(self, value, keys=None):
+        """Initialize Add Transform
+
+        Parameters:
+            value (List[int]) -- the [r,g,b] value will add to image by pixel wise.
+        """
+        super().__init__(keys=keys)
+        self.value = value
+
+    def _get_params(self, inputs):
+        params = {}
+        params['value'] = self.value
+        return params
+
+    def _apply_image(self, image):
+        return custom_F.add(image, self.params['value'])
+
+
+@TRANSFORMS.register()
+class ResizeToScale(T.BaseTransform):
+    def __init__(self,
+                 size: int,
+                 scale: int,
+                 interpolation='bilinear',
+                 keys=None):
+        """Initialize ResizeToScale Transform
+
+        Parameters:
+            size (List[int]) -- the minimum target size
+            scale (List[int]) -- the stride scale
+            interpolation (Optional[str]) -- interpolation method
+        """
+        super().__init__(keys=keys)
+        if isinstance(size, int):
+            self.size = (size, size)
+        else:
+            self.size = size
+        self.scale = scale
+        self.interpolation = interpolation
+
+    def _get_params(self, inputs):
+        image = inputs[self.keys.index('image')]
+        hw = image.shape[:2]
+        params = {}
+        params['taget_size'] = self.reduce_to_scale(hw, self.size[::-1],
+                                                    self.scale)
+        return params
+
+    @staticmethod
+    def reduce_to_scale(img_hw, min_hw, scale):
+        im_h, im_w = img_hw
+        if im_h <= min_hw[0]:
+            im_h = min_hw[0]
+        else:
+            x = im_h % scale
+            im_h = im_h - x
+
+        if im_w < min_hw[1]:
+            im_w = min_hw[1]
+        else:
+            y = im_w % scale
+            im_w = im_w - y
+        return (im_h, im_w)
+
+    def _apply_image(self, image):
+        return F.resize(image, self.params['taget_size'], self.interpolation)

ppgan/models/__init__.py

@@ -20,3 +20,4 @@ from .sr_model import SRModel
 from .makeup_model import MakeupModel
 from .ugatit_model import UGATITModel
 from .dc_gan_model import DCGANModel
+from .animeganv2_model import AnimeGANV2Model, AnimeGANV2PreTrainModel

ppgan/models/animeganv2_model.py

+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import paddle
+from paddle import nn
+
+from .base_model import BaseModel
+from .builder import MODELS
+from .generators.builder import build_generator
+from .discriminators.builder import build_discriminator
+from .losses import GANLoss
+from ..modules.caffevgg import CaffeVGG19
+from ..solver import build_optimizer
+from ..modules.init import init_weights
+from ..utils.filesystem import load
+
+
+@MODELS.register()
+class AnimeGANV2Model(BaseModel):
+    def __init__(self, cfg):
+        """Initialize the AnimeGANV2 class.
+
+        Parameters:
+            opt (config dict)-- stores all the experiment flags; needs to be a subclass of Dict
+        """
+        super(AnimeGANV2Model, self).__init__(cfg)
+        # define networks (both generator and discriminator)
+        self.nets['netG'] = build_generator(cfg.model.generator)
+        init_weights(self.nets['netG'])
+
+        # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
+        if self.is_train:
+            self.nets['netD'] = build_discriminator(cfg.model.discriminator)
+            init_weights(self.nets['netD'])
+
+            self.pretrained = CaffeVGG19()
+
+            self.losses = {}
+            # define loss functions
+            self.criterionGAN = GANLoss(cfg.model.gan_mode)
+            self.criterionL1 = nn.L1Loss()
+            self.criterionHub = nn.SmoothL1Loss()
+
+            # build optimizers
+            self.build_lr_scheduler()
+            self.optimizers['optimizer_G'] = build_optimizer(
+                cfg.optimizer,
+                self.lr_scheduler,
+                parameter_list=self.nets['netG'].parameters())
+            self.optimizers['optimizer_D'] = build_optimizer(
+                cfg.optimizer,
+                self.lr_scheduler,
+                parameter_list=self.nets['netD'].parameters())
+
+            if self.cfg.pretrain_ckpt:
+                state_dicts = load(self.cfg.pretrain_ckpt)
+                self.nets['netG'].set_state_dict(state_dicts['netG'])
+                print('Load pretrained generator from', self.cfg.pretrain_ckpt)
+
+    def set_input(self, input):
+        """Unpack input data from the dataloader and perform necessary pre-processing steps.
+
+        """
+        if self.is_train:
+            self.real = paddle.to_tensor(input['real'])
+            self.anime = paddle.to_tensor(input['anime'])
+            self.anime_gray = paddle.to_tensor(input['anime_gray'])
+            self.smooth_gray = paddle.to_tensor(input['smooth_gray'])
+        else:
+            self.real = paddle.to_tensor(input['A'])
+            self.image_paths = input['A_paths']
+
+    def forward(self):
+        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
+        self.fake = self.nets['netG'](self.real)  # G(A)
+
+        # put items to visual dict
+        self.visual_items['real'] = self.real
+        self.visual_items['fake'] = self.fake
+
+    def test(self):
+        self.fake = self.nets['netG'](self.real)  # G(A)
+
+        # put items to visual dict
+        self.visual_items['real'] = self.real
+        self.visual_items['fake'] = self.fake
+
+    @staticmethod
+    def gram(x):
+        b, c, h, w = x.shape
+        x_tmp = x.reshape((b, c, (h * w)))
+        gram = paddle.matmul(x_tmp, x_tmp, transpose_y=True)
+        return gram / (c * h * w)
+
+    def style_loss(self, style, fake):
+        return self.criterionL1(self.gram(style), self.gram(fake))
+
+    def con_sty_loss(self, real, anime, fake):
+        real_feature_map = self.pretrained(real)
+        fake_feature_map = self.pretrained(fake)
+        anime_feature_map = self.pretrained(anime)
+
+        c_loss = self.criterionL1(real_feature_map, fake_feature_map)
+        s_loss = self.style_loss(anime_feature_map, fake_feature_map)
+
+        return c_loss, s_loss
+
+    @staticmethod
+    def rgb2yuv(rgb):
+        kernel = paddle.to_tensor([[0.299, -0.14714119, 0.61497538],
+                                   [0.587, -0.28886916, -0.51496512],
+                                   [0.114, 0.43601035, -0.10001026]],
+                                  dtype='float32')
+        rgb = paddle.transpose(rgb, (0, 2, 3, 1))
+        yuv = paddle.matmul(rgb, kernel)
+        return yuv
+
+    @staticmethod
+    def denormalize(image):
+        return image * 0.5 + 0.5
+
+    def color_loss(self, con, fake):
+        con = self.rgb2yuv(self.denormalize(con))
+        fake = self.rgb2yuv(self.denormalize(fake))
+        return (self.criterionL1(con[:, :, :, 0], fake[:, :, :, 0]) +
+                self.criterionHub(con[:, :, :, 1], fake[:, :, :, 1]) +
+                self.criterionHub(con[:, :, :, 2], fake[:, :, :, 2]))
+
+    @staticmethod
+    def variation_loss(image, ksize=1):
+        dh = image[:, :, :-ksize, :] - image[:, :, ksize:, :]
+        dw = image[:, :, :, :-ksize] - image[:, :, :, ksize:]
+        return (paddle.mean(paddle.abs(dh)) + paddle.mean(paddle.abs(dw)))
+
+    def backward_D(self):
+        """Calculate GAN loss for the discriminator"""
+        # Fake; stop backprop to the generator by detaching fake_B
+        # use conditional GANs; we need to feed both input and output to the discriminator
+        real_logit = self.nets['netD'](self.anime)
+        gray_logit = self.nets['netD'](self.anime_gray)
+        fake_logit = self.nets['netD'](self.fake.detach())
+        smooth_logit = self.nets['netD'](self.smooth_gray)
+
+        d_real_loss = (self.cfg.d_adv_weight * 1.2 *
+                       self.criterionGAN(real_logit, True))
+        d_gray_loss = (self.cfg.d_adv_weight * 1.2 *
+                       self.criterionGAN(gray_logit, False))
+        d_fake_loss = (self.cfg.d_adv_weight * 1.2 *
+                       self.criterionGAN(fake_logit, False))
+        d_blur_loss = (self.cfg.d_adv_weight * 0.8 *
+                       self.criterionGAN(smooth_logit, False))
+
+        self.loss_D = d_real_loss + d_gray_loss + d_fake_loss + d_blur_loss
+
+        self.loss_D.backward()
+
+        self.losses['d_loss'] = self.loss_D
+        self.losses['d_real_loss'] = d_real_loss
+        self.losses['d_fake_loss'] = d_fake_loss
+        self.losses['d_gray_loss'] = d_gray_loss
+        self.losses['d_blur_loss'] = d_blur_loss
+
+    def backward_G(self):
+        fake_logit = self.nets['netD'](self.fake)
+        c_loss, s_loss = self.con_sty_loss(self.real, self.anime_gray,
+                                           self.fake)
+        c_loss = self.cfg.con_weight * c_loss
+        s_loss = self.cfg.sty_weight * s_loss
+        tv_loss = self.cfg.tv_weight * self.variation_loss(self.fake)
+        col_loss = self.cfg.color_weight * self.color_loss(self.real, self.fake)
+        g_loss = (self.cfg.g_adv_weight * self.criterionGAN(fake_logit, True))
+
+        self.loss_G = c_loss + s_loss + col_loss + g_loss + tv_loss
+
+        self.loss_G.backward()
+
+        self.losses['g_loss'] = self.loss_G
+        self.losses['c_loss'] = c_loss
+        self.losses['s_loss'] = s_loss
+        self.losses['col_loss'] = col_loss
+        self.losses['tv_loss'] = tv_loss
+
+    def optimize_parameters(self):
+        # compute fake images: G(A)
+        self.forward()
+
+        # update D
+        self.optimizers['optimizer_D'].clear_grad()
+        self.backward_D()
+        self.optimizers['optimizer_D'].step()
+
+        # update G
+        self.optimizers['optimizer_G'].clear_grad()
+        self.backward_G()
+        self.optimizers['optimizer_G'].step()
+
+
+@MODELS.register()
+class AnimeGANV2PreTrainModel(AnimeGANV2Model):
+    def backward_G(self):
+        real_feature_map = self.pretrained(self.real)
+        fake_feature_map = self.pretrained(self.fake)
+        init_c_loss = self.criterionL1(real_feature_map, fake_feature_map)
+        loss = self.cfg.con_weight * init_c_loss
+        loss.backward()
+        self.losses['init_c_loss'] = init_c_loss
+
+    def optimize_parameters(self):
+        self.forward()
+        # update G
+        self.optimizers['optimizer_G'].clear_grad()
+        self.backward_G()
+        self.optimizers['optimizer_G'].step()

ppgan/models/discriminators/__init__.py

@@ -15,3 +15,4 @@
 from .nlayers import NLayerDiscriminator
 from .discriminator_ugatit import UGATITDiscriminator
 from .dcdiscriminator import DCDiscriminator
+from .discriminator_animegan import AnimeDiscriminator

ppgan/models/discriminators/discriminator_animegan.py

+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+from .builder import DISCRIMINATORS
+from ...modules.utils import spectral_norm
+
+
+@DISCRIMINATORS.register()
+class AnimeDiscriminator(nn.Layer):
+    def __init__(self, channel: int = 64, nblocks: int = 3) -> None:
+        super().__init__()
+        channel = channel // 2
+        last_channel = channel
+        f = [
+            spectral_norm(
+                nn.Conv2D(3, channel, 3, stride=1, padding=1, bias_attr=False)),
+            nn.LeakyReLU(0.2)
+        ]
+        in_h = 256
+        for i in range(1, nblocks):
+            f.extend([
+                spectral_norm(
+                    nn.Conv2D(last_channel,
+                              channel * 2,
+                              3,
+                              stride=2,
+                              padding=1,
+                              bias_attr=False)),
+                nn.LeakyReLU(0.2),
+                spectral_norm(
+                    nn.Conv2D(channel * 2,
+                              channel * 4,
+                              3,
+                              stride=1,
+                              padding=1,
+                              bias_attr=False)),
+                nn.GroupNorm(1, channel * 4),
+                nn.LeakyReLU(0.2)
+            ])
+            last_channel = channel * 4
+            channel = channel * 2
+            in_h = in_h // 2
+
+        self.body = nn.Sequential(*f)
+
+        self.head = nn.Sequential(*[
+            spectral_norm(
+                nn.Conv2D(last_channel,
+                          channel * 2,
+                          3,
+                          stride=1,
+                          padding=1,
+                          bias_attr=False)),
+            nn.GroupNorm(1, channel * 2),
+            nn.LeakyReLU(0.2),
+            spectral_norm(
+                nn.Conv2D(
+                    channel * 2, 1, 3, stride=1, padding=1, bias_attr=False))
+        ])
+
+    def forward(self, x):
+        x = self.body(x)
+        x = self.head(x)
+        return x

ppgan/models/generators/__init__.py

@@ -18,4 +18,5 @@ from .rrdb_net import RRDBNet
 from .makeup import GeneratorPSGANAttention
 from .resnet_ugatit import ResnetUGATITGenerator
 from .dcgenerator import DCGenerator
+from .generater_animegan import AnimeGenerator, AnimeGeneratorLite
 from .wav2lip import Wav2Lip
\ No newline at end of file

ppgan/models/generators/generater_animegan.py

+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+from .builder import GENERATORS
+
+
+class Conv2DNormLReLU(nn.Layer):
+    def __init__(self,
+                 in_channels: int,
+                 out_channels: int,
+                 kernel_size: int = 3,
+                 stride: int = 1,
+                 padding: int = 1,
+                 bias_attr=False) -> None:
+        super().__init__()
+        self.conv = nn.Conv2D(in_channels,
+                              out_channels,
+                              kernel_size,
+                              stride,
+                              padding,
+                              bias_attr=bias_attr)
+        # NOTE layer norm is crucial for animegan!
+        self.norm = nn.GroupNorm(1, out_channels)
+        self.lrelu = nn.LeakyReLU(0.2)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        x = self.lrelu(x)
+        return x
+
+
+class ResBlock(nn.Layer):
+    def __init__(self, in_channels: int, out_channels: int) -> None:
+        super().__init__()
+        self.body = nn.Sequential(
+            Conv2DNormLReLU(in_channels, out_channels, 1, padding=0),
+            Conv2DNormLReLU(out_channels, out_channels, 3),
+            nn.Conv2D(out_channels, out_channels // 2, 1, bias_attr=False))
+
+    def forward(self, x0):
+        x = self.body(x0)
+        return x0 + x
+
+
+class InvertedresBlock(nn.Layer):
+    def __init__(self,
+                 in_channels: int,
+                 expansion: float,
+                 out_channels: int,
+                 bias_attr=False):
+        super().__init__()
+        self.in_channels = in_channels
+        self.expansion = expansion
+        self.out_channels = out_channels
+        self.bottle_channels = round(self.expansion * self.in_channels)
+        self.body = nn.Sequential(
+            # pw
+            Conv2DNormLReLU(self.in_channels,
+                            self.bottle_channels,
+                            kernel_size=1,
+                            bias_attr=bias_attr),
+            # dw
+            nn.Conv2D(self.bottle_channels,
+                      self.bottle_channels,
+                      kernel_size=3,
+                      stride=1,
+                      padding=0,
+                      groups=self.bottle_channels,
+                      bias_attr=True),
+            nn.GroupNorm(1, self.bottle_channels),
+            nn.LeakyReLU(0.2),
+            # pw & linear
+            nn.Conv2D(self.bottle_channels,
+                      self.out_channels,
+                      kernel_size=1,
+                      padding=0,
+                      bias_attr=False),
+            nn.GroupNorm(1, self.out_channels),
+        )
+
+    def forward(self, x0):
+        x = self.body(x0)
+        if self.in_channels == self.out_channels:
+            out = paddle.add(x0, x)
+        else:
+            out = x
+        return x
+
+
+@GENERATORS.register()
+class AnimeGeneratorLite(nn.Layer):
+    def __init__(self) -> None:
+        super().__init__()
+        self.A = nn.Sequential(Conv2DNormLReLU(3, 32, 7, padding=3),
+                               Conv2DNormLReLU(32, 32, stride=2),
+                               Conv2DNormLReLU(32, 32))
+
+        self.B = nn.Sequential(Conv2DNormLReLU(32, 64, stride=2),
+                               Conv2DNormLReLU(64, 64), Conv2DNormLReLU(64, 64))
+
+        self.C = nn.Sequential(ResBlock(64, 128), ResBlock(64, 128),
+                               ResBlock(64, 128), ResBlock(64, 128))
+
+        self.D = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear'),
+                               Conv2DNormLReLU(64, 64), Conv2DNormLReLU(64, 64),
+                               Conv2DNormLReLU(64, 64))
+
+        self.E = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear'),
+                               Conv2DNormLReLU(64, 32), Conv2DNormLReLU(32, 32),
+                               Conv2DNormLReLU(32, 32, 7, padding=3))
+
+        self.out = nn.Sequential(nn.Conv2D(32, 3, 1, bias_attr=False),
+                                 nn.Tanh())
+
+    def forward(self, x):
+        x = self.A(x)
+        x = self.B(x)
+        x = self.C(x)
+        x = self.D(x)
+        x = self.E(x)
+        x = self.out(x)
+        return x
+
+
+@GENERATORS.register()
+class AnimeGenerator(nn.Layer):
+    def __init__(self) -> None:
+        super().__init__()
+        self.A = nn.Sequential(Conv2DNormLReLU(3, 32, 7, padding=3),
+                               Conv2DNormLReLU(32, 64, stride=2),
+                               Conv2DNormLReLU(64, 64))
+
+        self.B = nn.Sequential(Conv2DNormLReLU(64, 128, stride=2),
+                               Conv2DNormLReLU(128, 128),
+                               Conv2DNormLReLU(128, 128))
+
+        self.C = nn.Sequential(InvertedresBlock(128, 2, 256),
+                               InvertedresBlock(256, 2, 256),
+                               InvertedresBlock(256, 2, 256),
+                               InvertedresBlock(256, 2, 256),
+                               Conv2DNormLReLU(256, 128))
+
+        self.D = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear'),
+                               Conv2DNormLReLU(128, 128),
+                               Conv2DNormLReLU(128, 128))
+
+        self.E = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear'),
+                               Conv2DNormLReLU(128, 64),
+                               Conv2DNormLReLU(64, 64),
+                               Conv2DNormLReLU(64, 32, 7, padding=3))
+
+        self.out = nn.Sequential(nn.Conv2D(32, 3, 1, bias_attr=False),
+                                 nn.Tanh())
+
+    def forward(self, x):
+        x = self.A(x)
+        x = self.B(x)
+        x = self.C(x)
+        x = self.D(x)
+        x = self.E(x)
+        x = self.out(x)
+        return x

ppgan/modules/caffevgg.py

+import paddle
+import paddle.nn as nn
+import numpy as np
+from ppgan.utils.download import get_path_from_url
+model_urls = {
+    'caffevgg19': ('https://paddlegan.bj.bcebos.com/models/vgg19_no_fc.npy',
+                   '8ea1ef2374f8684b6cea9f300849be81')
+}
+
+
+class CaffeVGG19(nn.Layer):
+    cfg = [
+        64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512,
+        'M', 512, 512, 512, 512, 'M'
+    ]
+
+    def __init__(self, output_index: int = 26) -> None:
+        super().__init__()
+        arch = 'caffevgg19'
+        weights_path = get_path_from_url(model_urls[arch][0],
+                                         model_urls[arch][1])
+        data_dict: dict = np.load(weights_path,
+                                  encoding='latin1',
+                                  allow_pickle=True).item()
+        self.features = self.make_layers(self.cfg, data_dict)
+        del data_dict
+        self.features = nn.Sequential(*self.features.sublayers()[:output_index])
+        mean = paddle.to_tensor([103.939, 116.779, 123.68])
+        self.mean = mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+
+    def _process(self, x):
+        rgb = (x * 0.5 + 0.5) * 255  # value to 255
+        bgr = paddle.stack((rgb[:, 2, :, :], rgb[:, 1, :, :], rgb[:, 0, :, :]),
+                           1)  # rgb to bgr
+        return bgr - self.mean  # vgg norm
+
+    def _forward_impl(self, x):
+        x = self._process(x)
+        # NOTE get output with out relu activation
+        x = self.features(x)
+        return x
+
+    def forward(self, x):
+        return self._forward_impl(x)
+
+    @staticmethod
+    def get_conv_filter(data_dict, name):
+        return data_dict[name][0]
+
+    @staticmethod
+    def get_bias(data_dict, name):
+        return data_dict[name][1]
+
+    @staticmethod
+    def get_fc_weight(data_dict, name):
+        return data_dict[name][0]
+
+    def make_layers(self, cfg, data_dict, batch_norm=False) -> nn.Sequential:
+        layers = []
+        in_channels = 3
+        block = 1
+        number = 1
+        for v in cfg:
+            if v == 'M':
+                layers += [nn.MaxPool2D(kernel_size=2, stride=2)]
+                block += 1
+                number = 1
+            else:
+                conv2d = nn.Conv2D(in_channels, v, kernel_size=3, padding=1)
+                """ set value """
+                weight = paddle.to_tensor(
+                    self.get_conv_filter(data_dict, f'conv{block}_{number}'))
+                weight = weight.transpose((3, 2, 0, 1))
+                bias = paddle.to_tensor(
+                    self.get_bias(data_dict, f'conv{block}_{number}'))
+                conv2d.weight.set_value(weight)
+                conv2d.bias.set_value(bias)
+                number += 1
+                if batch_norm:
+                    layers += [conv2d, nn.BatchNorm2D(v), nn.ReLU()]
+                else:
+                    layers += [conv2d, nn.ReLU()]
+                in_channels = v
+
+        return nn.Sequential(*layers)

tools/animegan_picmean.py

+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import os
+import cv2
+import numpy as np
+from glob import glob
+from tqdm import tqdm
+import argparse
+
+
+def read_img(image_path):
+    img = cv2.imread(image_path)
+    assert len(img.shape) == 3
+    B = img[..., 0].mean()
+    G = img[..., 1].mean()
+    R = img[..., 2].mean()
+    return B, G, R
+
+
+def main(dataset):
+    file_list = glob(os.path.join(dataset, '*.jpg'))
+    image_num = len(file_list)
+    print('image_num:', image_num)
+
+    B_total = 0
+    G_total = 0
+    R_total = 0
+    for f in tqdm(file_list):
+        bgr = read_img(f)
+        B_total += bgr[0]
+        G_total += bgr[1]
+        R_total += bgr[2]
+
+    B_mean, G_mean, R_mean = B_total / image_num, G_total / image_num, R_total / image_num
+    mean = (B_mean + G_mean + R_mean) / 3
+
+    print('RGB mean diff')
+    print(
+        np.asfarray((mean - R_mean, mean - G_mean, mean - B_mean),
+                    dtype='float32'))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset',
+                        help="get the mean values of rgb from dataset",
+                        type=str,
+                        default='')
+
+    args = parser.parse_args()
+
+    main(args.dataset)