From 10de3c641bbb14b57458ef45ee8eb82f98190a6d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=83=91=E5=90=AF=E8=88=AA?= <597323109@qq.com> Date: Sat, 5 Dec 2020 22:38:14 +0800 Subject: [PATCH] Add AnimeGANv2 model (#102) * add animeganv2 network and dataset * animegan:refine code,add License Co-authored-by: qingqing01 --- applications/tools/animeganv2.py | 38 +++ configs/animeganv2.yaml | 81 +++++++ configs/animeganv2_pretrain.yaml | 75 ++++++ docs/en_US/tutorials/animegan.md | 103 ++++++++ docs/imgs/animeganv2_res.jpg | Bin 0 -> 124798 bytes docs/imgs/animeganv2_test.jpg | Bin 0 -> 201386 bytes docs/zh_CN/apis/apps.md | 21 ++ docs/zh_CN/tutorials/animegan.md | 103 ++++++++ ppgan/apps/__init__.py | 1 + ppgan/apps/animegan_predictor.py | 90 +++++++ ppgan/datasets/__init__.py | 1 + ppgan/datasets/animeganv2_dataset.py | 99 ++++++++ ppgan/datasets/transforms/__init__.py | 2 +- ppgan/datasets/transforms/functional.py | 30 +++ ppgan/datasets/transforms/functional_cv2.py | 19 ++ ppgan/datasets/transforms/transforms.py | 72 ++++++ ppgan/models/__init__.py | 1 + ppgan/models/animeganv2_model.py | 224 ++++++++++++++++++ ppgan/models/discriminators/__init__.py | 1 + .../discriminators/discriminator_animegan.py | 78 ++++++ ppgan/models/generators/__init__.py | 3 +- ppgan/models/generators/generater_animegan.py | 178 ++++++++++++++ ppgan/modules/caffevgg.py | 85 +++++++ tools/animegan_picmean.py | 64 +++++ 24 files changed, 1367 insertions(+), 2 deletions(-) create mode 100644 applications/tools/animeganv2.py create mode 100644 configs/animeganv2.yaml create mode 100644 configs/animeganv2_pretrain.yaml create mode 100644 docs/en_US/tutorials/animegan.md create mode 100644 docs/imgs/animeganv2_res.jpg create mode 100644 docs/imgs/animeganv2_test.jpg create mode 100644 docs/zh_CN/tutorials/animegan.md create mode 100644 ppgan/apps/animegan_predictor.py create mode 100644 ppgan/datasets/animeganv2_dataset.py create mode 100644 ppgan/datasets/transforms/functional.py create mode 100644 ppgan/datasets/transforms/functional_cv2.py create mode 100644 ppgan/models/animeganv2_model.py create mode 100644 ppgan/models/discriminators/discriminator_animegan.py create mode 100644 ppgan/models/generators/generater_animegan.py create mode 100644 ppgan/modules/caffevgg.py create mode 100644 tools/animegan_picmean.py diff --git a/applications/tools/animeganv2.py b/applications/tools/animeganv2.py new file mode 100644 index 0000000..7503c54 --- /dev/null +++ b/applications/tools/animeganv2.py @@ -0,0 +1,38 @@ +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) diff --git a/configs/animeganv2.yaml b/configs/animeganv2.yaml new file mode 100644 index 0000000..f09ea13 --- /dev/null +++ b/configs/animeganv2.yaml @@ -0,0 +1,81 @@ +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 diff --git a/configs/animeganv2_pretrain.yaml b/configs/animeganv2_pretrain.yaml new file mode 100644 index 0000000..e481b7c --- /dev/null +++ b/configs/animeganv2_pretrain.yaml @@ -0,0 +1,75 @@ +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 diff --git a/docs/en_US/tutorials/animegan.md b/docs/en_US/tutorials/animegan.md new file mode 100644 index 0000000..38aaaf3 --- /dev/null +++ b/docs/en_US/tutorials/animegan.md @@ -0,0 +1,103 @@ +# 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 | +| ----------------------------------- | 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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 diff --git a/docs/zh_CN/tutorials/animegan.md b/docs/zh_CN/tutorials/animegan.md new file mode 100644 index 0000000..bc3d5aa --- /dev/null +++ b/docs/zh_CN/tutorials/animegan.md @@ -0,0 +1,103 @@ +# 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) | diff --git a/ppgan/apps/__init__.py b/ppgan/apps/__init__.py index 19e2cf8..5115d05 100644 --- a/ppgan/apps/__init__.py +++ b/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 diff --git a/ppgan/apps/animegan_predictor.py b/ppgan/apps/animegan_predictor.py new file mode 100644 index 0000000..b3579b5 --- /dev/null +++ b/ppgan/apps/animegan_predictor.py @@ -0,0 +1,90 @@ +# 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 diff --git a/ppgan/datasets/__init__.py b/ppgan/datasets/__init__.py index ff93aed..a1e5418 100644 --- a/ppgan/datasets/__init__.py +++ b/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 diff --git a/ppgan/datasets/animeganv2_dataset.py b/ppgan/datasets/animeganv2_dataset.py new file mode 100644 index 0000000..6ceb6d5 --- /dev/null +++ b/ppgan/datasets/animeganv2_dataset.py @@ -0,0 +1,99 @@ +# 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 diff --git a/ppgan/datasets/transforms/__init__.py b/ppgan/datasets/transforms/__init__.py index 9cfb01a..3590ede 100644 --- a/ppgan/datasets/transforms/__init__.py +++ b/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 diff --git a/ppgan/datasets/transforms/functional.py b/ppgan/datasets/transforms/functional.py new file mode 100644 index 0000000..83350f5 --- /dev/null +++ b/ppgan/datasets/transforms/functional.py @@ -0,0 +1,30 @@ +# 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) diff --git a/ppgan/datasets/transforms/functional_cv2.py b/ppgan/datasets/transforms/functional_cv2.py new file mode 100644 index 0000000..e688a97 --- /dev/null +++ b/ppgan/datasets/transforms/functional_cv2.py @@ -0,0 +1,19 @@ +# 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') diff --git a/ppgan/datasets/transforms/transforms.py b/ppgan/datasets/transforms/transforms.py index 9d0c83a..f553964 100644 --- a/ppgan/datasets/transforms/transforms.py +++ b/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) diff --git a/ppgan/models/__init__.py b/ppgan/models/__init__.py index 5fea006..62839bc 100644 --- a/ppgan/models/__init__.py +++ b/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 diff --git a/ppgan/models/animeganv2_model.py b/ppgan/models/animeganv2_model.py new file mode 100644 index 0000000..819a069 --- /dev/null +++ b/ppgan/models/animeganv2_model.py @@ -0,0 +1,224 @@ +# 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 and .""" + 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() diff --git a/ppgan/models/discriminators/__init__.py b/ppgan/models/discriminators/__init__.py index 9d8653f..9157dd5 100644 --- a/ppgan/models/discriminators/__init__.py +++ b/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 diff --git a/ppgan/models/discriminators/discriminator_animegan.py b/ppgan/models/discriminators/discriminator_animegan.py new file mode 100644 index 0000000..d0c7bad --- /dev/null +++ b/ppgan/models/discriminators/discriminator_animegan.py @@ -0,0 +1,78 @@ +# 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 diff --git a/ppgan/models/generators/__init__.py b/ppgan/models/generators/__init__.py index 9c23977..0c6fd94 100644 --- a/ppgan/models/generators/__init__.py +++ b/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 .wav2lip import Wav2Lip +from .generater_animegan import AnimeGenerator, AnimeGeneratorLite +from .wav2lip import Wav2Lip \ No newline at end of file diff --git a/ppgan/models/generators/generater_animegan.py b/ppgan/models/generators/generater_animegan.py new file mode 100644 index 0000000..a2b09fe --- /dev/null +++ b/ppgan/models/generators/generater_animegan.py @@ -0,0 +1,178 @@ +# 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 diff --git a/ppgan/modules/caffevgg.py b/ppgan/modules/caffevgg.py new file mode 100644 index 0000000..bf40f5b --- /dev/null +++ b/ppgan/modules/caffevgg.py @@ -0,0 +1,85 @@ +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) diff --git a/tools/animegan_picmean.py b/tools/animegan_picmean.py new file mode 100644 index 0000000..3e934a4 --- /dev/null +++ b/tools/animegan_picmean.py @@ -0,0 +1,64 @@ +# 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) -- GitLab