diff --git a/applications/tools/animeganv2.py b/applications/tools/animeganv2.py index 7503c547da157e856901be7d94180ea0c2f3a420..74588559b81fb82ae7e52f3bc8d65feb253c75b1 100644 --- a/applications/tools/animeganv2.py +++ b/applications/tools/animeganv2.py @@ -1,6 +1,21 @@ +# Copyright (c) 2021 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 os import sys + sys.path.insert(0, os.getcwd()) from ppgan.apps import AnimeGANPredictor import argparse diff --git a/applications/tools/face_parse.py b/applications/tools/face_parse.py index 5eded74084dd96731b3b98ce4d736ea5192694fb..6cd907ebb394d44730a2bdc4402f134a84de818f 100644 --- a/applications/tools/face_parse.py +++ b/applications/tools/face_parse.py @@ -1,3 +1,17 @@ +# Copyright (c) 2021 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 argparse import paddle diff --git a/applications/tools/image_restoration.py b/applications/tools/image_restoration.py index bc409c67fea0331b6db426767ad49a0be5c7e1ef..d64a376d0604e3c5aa7584021b4663511e110abf 100644 --- a/applications/tools/image_restoration.py +++ b/applications/tools/image_restoration.py @@ -1,6 +1,21 @@ +# Copyright (c) 2021 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 os import sys + sys.path.insert(0, os.getcwd()) from ppgan.apps import MPRPredictor import argparse @@ -22,18 +37,18 @@ if __name__ == "__main__": default=None, help="sample random seed for model's image generation") - parser.add_argument('--images_path', - default=None, + parser.add_argument('--images_path', + default=None, required=True, - type=str, + type=str, help='Single image or images directory.') - parser.add_argument('--task', - required=True, - type=str, - help='Task to run', + parser.add_argument('--task', + required=True, + type=str, + help='Task to run', choices=['Deblurring', 'Denoising', 'Deraining']) - + parser.add_argument("--cpu", dest="cpu", action="store_true", @@ -44,11 +59,9 @@ if __name__ == "__main__": if args.cpu: paddle.set_device('cpu') - predictor = MPRPredictor( - images_path=args.images_path, - output_path=args.output_path, - weight_path=args.weight_path, - seed=args.seed, - task=args.task - ) + predictor = MPRPredictor(images_path=args.images_path, + output_path=args.output_path, + weight_path=args.weight_path, + seed=args.seed, + task=args.task) predictor.run() diff --git a/applications/tools/lapstyle.py b/applications/tools/lapstyle.py index f44f4e3b4b2328720720e326f6ee2517e797d2d5..2b1f6c80896bdecfbd4c578f6000c1a304468b7b 100644 --- a/applications/tools/lapstyle.py +++ b/applications/tools/lapstyle.py @@ -1,3 +1,17 @@ +# Copyright (c) 2021 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 os import sys diff --git a/applications/tools/mprnet.py b/applications/tools/mprnet.py new file mode 100644 index 0000000000000000000000000000000000000000..8cb63925058d8015719b9c5b8fc036d422086690 --- /dev/null +++ b/applications/tools/mprnet.py @@ -0,0 +1,56 @@ +# Copyright (c) 2021 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 os +import sys + +sys.path.insert(0, os.getcwd()) +from ppgan.apps import MPRPredictor +import argparse + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--input_image", type=str, help="path to 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 weight path") + + parser.add_argument( + "--task", + type=str, + default='Deblurring', + help="task can be chosen in 'Deblurring', 'Denoising', 'Deraining'") + + parser.add_argument("--cpu", + dest="cpu", + action="store_true", + help="cpu mode.") + + args = parser.parse_args() + + if args.cpu: + paddle.set_device('cpu') + + predictor = MPRPredictor(output_path=args.output_path, + task=args.task, + weight_path=args.weight_path) + predictor.run(args.input_image) diff --git a/applications/tools/photopen.py b/applications/tools/photopen.py index 8aed9f813287b1982bb500e6b6f9104c5be7d0b4..8ab5af218b4b73d45eae475b1971b1144f2c3d95 100644 --- a/applications/tools/photopen.py +++ b/applications/tools/photopen.py @@ -1,3 +1,17 @@ +# Copyright (c) 2021 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 os import sys @@ -39,10 +53,9 @@ if __name__ == "__main__": if args.cpu: paddle.set_device('cpu') - + cfg = get_config(args.config_file) predictor = PhotoPenPredictor(output_path=args.output_path, - weight_path=args.weight_path, - gen_cfg=cfg.predict) + weight_path=args.weight_path, + gen_cfg=cfg.predict) predictor.run(semantic_label_path=args.semantic_label_path) - \ No newline at end of file diff --git a/applications/tools/wav2lip.py b/applications/tools/wav2lip.py index ac655a68fd4ade5f96b3d214b4ab08c293f26103..684fd8a261db2c3c6fda3fc389616bb1383b76da 100644 --- a/applications/tools/wav2lip.py +++ b/applications/tools/wav2lip.py @@ -1,3 +1,17 @@ +# Copyright (c) 2021 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 argparse import paddle @@ -103,11 +117,10 @@ parser.add_argument( type=str, default='sfd', help="face detector to be used, can choose s3fd or blazeface") -parser.add_argument( - "--face_enhancement", - dest="face_enhancement", - action="store_true", - help="use face enhance for face") +parser.add_argument("--face_enhancement", + dest="face_enhancement", + action="store_true", + help="use face enhance for face") parser.set_defaults(face_enhancement=False) if __name__ == "__main__": @@ -115,17 +128,17 @@ if __name__ == "__main__": if args.cpu: paddle.set_device('cpu') - predictor = Wav2LipPredictor(checkpoint_path = args.checkpoint_path, - static = args.static, - fps = args.fps, - pads = args.pads, - face_det_batch_size = args.face_det_batch_size, - wav2lip_batch_size = args.wav2lip_batch_size, - resize_factor = args.resize_factor, - crop = args.crop, - box = args.box, - rotate = args.rotate, - nosmooth = args.nosmooth, - face_detector = args.face_detector, - face_enhancement = args.face_enhancement) + predictor = Wav2LipPredictor(checkpoint_path=args.checkpoint_path, + static=args.static, + fps=args.fps, + pads=args.pads, + face_det_batch_size=args.face_det_batch_size, + wav2lip_batch_size=args.wav2lip_batch_size, + resize_factor=args.resize_factor, + crop=args.crop, + box=args.box, + rotate=args.rotate, + nosmooth=args.nosmooth, + face_detector=args.face_detector, + face_enhancement=args.face_enhancement) predictor.run(args.face, args.audio, args.outfile) diff --git a/configs/mprnet_deblurring.yaml b/configs/mprnet_deblurring.yaml index 4082a6aa0158b2c74a786d0e8098fc9203722430..64775364a8156866a2e4f7fe397ba74cfad5765e 100644 --- a/configs/mprnet_deblurring.yaml +++ b/configs/mprnet_deblurring.yaml @@ -6,6 +6,9 @@ model: name: MPRModel generator: name: MPRNet + n_feat: 96 + scale_unetfeats: 48 + scale_orsnetfeats: 32 char_criterion: name: CharbonnierLoss @@ -15,16 +18,16 @@ model: dataset: train: name: MPRTrain - rgb_dir: 'data/GoPro/train' + rgb_dir: data/GoPro/train num_workers: 4 - batch_size: 2 + batch_size: 2 # 8GPUs img_options: patch_size: 256 test: name: MPRVal - rgb_dir: 'data/GoPro/test' - num_workers: 4 - batch_size: 2 + rgb_dir: data/GoPro/test + num_workers: 1 + batch_size: 1 img_options: patch_size: 256 diff --git a/configs/mprnet_denoising.yaml b/configs/mprnet_denoising.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a6160972dafb32a85fdd4d6c7976347d932bd1c2 --- /dev/null +++ b/configs/mprnet_denoising.yaml @@ -0,0 +1,69 @@ +total_iters: 100000 +output_dir: output_dir + +model: + name: MPRModel + generator: + name: MPRNet + n_feat: 80 + scale_unetfeats: 48 + scale_orsnetfeats: 32 + + char_criterion: + name: CharbonnierLoss + edge_criterion: + name: EdgeLoss + +dataset: + train: + name: MPRTrain + rgb_dir: data/SIDD/train + num_workers: 16 + batch_size: 4 # 4GPUs + img_options: + patch_size: 256 + test: + name: MPRTrain + rgb_dir: data/SIDD/val + num_workers: 1 + batch_size: 1 + img_options: + patch_size: 256 + +lr_scheduler: + name: CosineAnnealingRestartLR + learning_rate: !!float 2e-4 + periods: [25000, 25000, 25000, 25000] + restart_weights: [1, 1, 1, 1] + eta_min: !!float 1e-6 + +validate: + interval: 5000 + save_img: false + + metrics: + psnr: # metric name, can be arbitrary + name: PSNR + crop_border: 4 + test_y_channel: True + ssim: + name: SSIM + crop_border: 4 + test_y_channel: True + +optimizer: + name: Adam + # add parameters of net_name to optim + # name should in self.nets + net_names: + - generator + beta1: 0.9 + beta2: 0.999 + epsilon: 1e-8 + +log_config: + interval: 10 + visiual_interval: 5000 + +snapshot_config: + interval: 5000 diff --git a/configs/mprnet_deraining.yaml b/configs/mprnet_deraining.yaml new file mode 100644 index 0000000000000000000000000000000000000000..94de4dfbe01aac2b2f2daff72cd2b01032ad766b --- /dev/null +++ b/configs/mprnet_deraining.yaml @@ -0,0 +1,69 @@ +total_iters: 100000 +output_dir: output_dir + +model: + name: MPRModel + generator: + name: MPRNet + n_feat: 40 + scale_unetfeats: 20 + scale_orsnetfeats: 16 + + char_criterion: + name: CharbonnierLoss + edge_criterion: + name: EdgeLoss + +dataset: + train: + name: MPRTrain + rgb_dir: data/Synthetic_Rain_Datasets/train + num_workers: 16 + batch_size: 4 # 4GPUs + img_options: + patch_size: 256 + test: + name: MPRTrain + rgb_dir: data/Synthetic_Rain_Datasets/test/Rain100L + num_workers: 1 + batch_size: 1 + img_options: + patch_size: 256 + +lr_scheduler: + name: CosineAnnealingRestartLR + learning_rate: !!float 2e-4 + periods: [25000, 25000, 25000, 25000] + restart_weights: [1, 1, 1, 1] + eta_min: !!float 1e-6 + +validate: + interval: 5000 + save_img: false + + metrics: + psnr: # metric name, can be arbitrary + name: PSNR + crop_border: 4 + test_y_channel: True + ssim: + name: SSIM + crop_border: 4 + test_y_channel: True + +optimizer: + name: Adam + # add parameters of net_name to optim + # name should in self.nets + net_names: + - generator + beta1: 0.9 + beta2: 0.999 + epsilon: 1e-8 + +log_config: + interval: 10 + visiual_interval: 5000 + +snapshot_config: + interval: 5000 diff --git a/docs/en_US/tutorials/mpr_net.md b/docs/en_US/tutorials/mpr_net.md new file mode 100644 index 0000000000000000000000000000000000000000..40d096f8c38e7e809cb63f053bccdf24e5d73f0c --- /dev/null +++ b/docs/en_US/tutorials/mpr_net.md @@ -0,0 +1,124 @@ +# MPR_Net + +## 1 Introduction + +[MPR_Net](https://arxiv.org/abs/2102.02808) is an image restoration method published in CVPR2021. Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. MPR_Net propose a novel synergistic design that can optimally balance these competing goals. The main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, the model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, MPR_Net introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features. A key ingredient in such a multi-stage architecture is the information exchange between different stages. To this end, MPR_Net propose a two-faceted approach where the information is not only exchanged sequentially from early to late stages, but lateral connections between feature processing blocks also exist to avoid any loss of information. The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets across a range of tasks including image deraining, deblurring, and denoising. + +## 2 How to use + +### 2.1 Quick start + +After installing PaddleGAN, you can run python code as follows to generate the restorated image. Where the `task` is the type of restoration method, you can chose in `Deblurring`、`Denoising` and `Deraining`, and `PATH_OF_IMAGE`is your image path. + +```python +from ppgan.apps import MPRPredictor +predictor = MPRPredictor(task='Deblurring') +predictor.run(PATH_OF_IMAGE) +``` + +Or run such a command to get the same result: + +```sh +python applications/tools/mprnet.py --input_image ${PATH_OF_IMAGE} --task Deblurring +``` +Where the `task` is the type of restoration method, you can chose in `Deblurring`、`Denoising` and `Deraining`, and `PATH_OF_IMAGE`is your image path. + +### 2.1 Prepare dataset + +The Deblurring training datasets is GoPro. The GoPro datasets used for deblurring consists of 3214 blurred images with a size of 1,280×720. These images are divided into 2103 training images and 1111 test images. It can be downloaded from [here](https://drive.google.com/file/d/1H0PIXvJH4c40pk7ou6nAwoxuR4Qh_Sa2/view?usp=sharing). +After downloading, decompress it to the data directory. After decompression, the structure of `GoProdataset` is as following: + +```sh +GoPro +├── train +│ ├── input +│ └── target +└── test + ├── input + └── target + +``` + +The Denoising training datasets is SIDD, an image denoising datasets, containing 30,000 noisy images from 10 different lighting conditions, which can be downloaded from [training datasets](https://www.eecs.yorku.ca/~kamel/sidd/dataset.php) and [Test datasets](https://drive.google.com/drive/folders/1S44fHXaVxAYW3KLNxK41NYCnyX9S79su). +After downloading, decompress it to the data directory. After decompression, the structure of `SIDDdataset` is as following: + +```sh +SIDD +├── train +│ ├── input +│ └── target +└── val + ├── input + └── target + +``` + +Deraining training datasets is Synthetic Rain Datasets, which consists of 13,712 clean rain image pairs collected from multiple datasets (Rain14000, Rain1800, Rain800, Rain12), which can be downloaded from [training datasets](https://drive.google.com/drive/folders/1Hnnlc5kI0v9_BtfMytC2LR5VpLAFZtVe) and [Test datasets](https://drive.google.com/drive/folders/1PDWggNh8ylevFmrjo-JEvlmqsDlWWvZs). +After downloading, decompress it to the data directory. After decompression, the structure of `Synthetic_Rain_Datasets` is as following: + +```sh +Synthetic_Rain_Datasets +├── train +│ ├── input +│ └── target +└── test + ├── Test100 + ├── Rain100H + ├── Rain100L + ├── Test1200 + └── Test2800 + +``` + +### 2.2 Training + An example is training to deblur. If you want to train for other tasks, you can replace the config file. + + ```sh + python -u tools/main.py --config-file configs/mprnet_deblurring.yaml + ``` + +### 2.3 Test + +test model: +```sh +python tools/main.py --config-file configs/mprnet_deblurring.yaml --evaluate-only --load ${PATH_OF_WEIGHT} +``` + +## 3 Results +Deblurring +| model | dataset | PSNR/SSIM | +|---|---|---| +| MPRNet | GoPro | 33.4360/0.9410 | + +Denoising +| model | dataset | PSNR/SSIM | +|---|---|---| +| MPRNet | SIDD | 43.6100 / 0.9586 | + +Deraining +| model | dataset | PSNR/SSIM | +|---|---|---| +| MPRNet | Rain100L | 36.2848 / 0.9651 | + +## 4 Download + +| model | link | +|---|---| +| MPR_Deblurring | [MPR_Deblurring](https://paddlegan.bj.bcebos.com/models/MPR_Deblurring.pdparams) | +| MPR_Denoising | [MPR_Denoising](https://paddlegan.bj.bcebos.com/models/MPR_Denoising.pdparams) | +| MPR_Deraining | [MPR_Deraining](https://paddlegan.bj.bcebos.com/models/MPR_Deraining.pdparams) | + + + +# References + +- [Multi-Stage Progressive Image Restoration](https://arxiv.org/abs/2102.02808) + + ``` + @inproceedings{Kim2020U-GAT-IT:, + title={Multi-Stage Progressive Image Restoration}, + author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang and Ling Shao}, + booktitle={CVPR}, + year={2021} + } + ``` diff --git a/docs/en_US/tutorials/single_image_super_resolution.md b/docs/en_US/tutorials/single_image_super_resolution.md index 053fc2f7026cfd369dead2f57bead5e0351c5066..95b95ee2c2b6c76cf947ac62518d41dee9dc17a0 100644 --- a/docs/en_US/tutorials/single_image_super_resolution.md +++ b/docs/en_US/tutorials/single_image_super_resolution.md @@ -130,11 +130,6 @@ The metrics are PSNR / SSIM. | pan_x4 | 30.4574 / 0.8643 | 26.7204 / 0.7434 | 28.9187 / 0.8176 | | drns_x4 | 32.6684 / 0.8999 | 28.9037 / 0.7885 | - | -Deblur models zoo -| model | GoPro | Download Link | -|---|---|---| -| MPRNet | 33.4360 / 0.9410 | [link](https://paddlegan.bj.bcebos.com/models/MPR_Deblurring.pdparams) | - diff --git a/docs/zh_CN/tutorials/mpr_net.md b/docs/zh_CN/tutorials/mpr_net.md new file mode 100644 index 0000000000000000000000000000000000000000..ca57ca967a6fde5589709119f502a965b80dc287 --- /dev/null +++ b/docs/zh_CN/tutorials/mpr_net.md @@ -0,0 +1,125 @@ +# MPR_Net + +## 1 原理介绍 + +[MPR_Net](https://arxiv.org/abs/2102.02808)是发表在CVPR2021的一篇图像修复方法。图像修复任务需要在恢复图像时在空间细节和高级上下文信息之间实现复杂的平衡。MPR_Net提出了一种新颖的协同设计，可以最佳地平衡这些相互竞争的目标。其中主要提议是一个多阶段架构，它逐步学习退化输入的恢复函数，从而将整个恢复过程分解为更易于管理的步骤。具体来说，MPR_Net首先使用编码器-解码器架构学习上下文特征，然后将它们与保留本地信息的高分辨率分支相结合。在每个阶段引入了一种新颖的每像素自适应设计，利用原位监督注意力来重新加权局部特征。这种多阶段架构的一个关键要素是不同阶段之间的信息交换。为此，MPR_Net提出了一种双向方法，其中信息不仅从早期到后期按顺序交换，而且特征处理块之间也存在横向连接以避免任何信息丢失。由此产生的紧密互连的多阶段架构，称为MPRNet，在包括图像去雨、去模糊和去噪在内的一系列任务中，在十个数据集上提供了强大的性能提升。 + +## 2 如何使用 + +### 2.1 快速体验 + +安装`PaddleGAN`之后运行如下代码即生成修复后的图像`output_dir/Deblurring/image_name.png`，其中`task`为你想要修复的任务，可以在`Deblurring`、`Denoising`和`Deraining`中选择，`PATH_OF_IMAGE`为你需要转换的图像路径。 + +```python +from ppgan.apps import MPRPredictor +predictor = MPRPredictor(task='Deblurring') +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/mprnet.py --input_image ${PATH_OF_IMAGE} --task Deblurring +``` +其中`task`为你想要修复的任务，可以在`Deblurring`、`Denoising`和`Deraining`中选择，`PATH_OF_IMAGE`为你需要转换的图像路径。 + +### 2.1 数据准备 + +Deblurring训练数据是GoPro，用于去模糊的GoPro数据集由3214张1,280×720大小的模糊图像组成，这些图像分为2103张训练图像和1111张测试图像。可以从[这里](https://drive.google.com/file/d/1H0PIXvJH4c40pk7ou6nAwoxuR4Qh_Sa2/view?usp=sharing)下载。 +下载后解压到data目录下，解压完成后数据分布如下所示： + +```sh +GoPro +├── train +│ ├── input +│ └── target +└── test + ├── input + └── target + +``` + +Denoising训练数据是SIDD，一个图像去噪数据集，包含来自10个不同光照条件下的3万幅噪声图像，可以从[训练数据集下载](https://www.eecs.yorku.ca/~kamel/sidd/dataset.php)和[测试数据集下载](https://drive.google.com/drive/folders/1S44fHXaVxAYW3KLNxK41NYCnyX9S79su)下载。 +下载后解压到data目录下，解压完成后数据分布如下所示： + +```sh +SIDD +├── train +│ ├── input +│ └── target +└── val + ├── input + └── target + +``` + +Deraining训练数据是Synthetic Rain Datasets，由13712张从多个数据集(Rain14000, Rain1800, Rain800, Rain12)收集的干净雨图像对组成，可以从[训练数据集下载](https://drive.google.com/drive/folders/1Hnnlc5kI0v9_BtfMytC2LR5VpLAFZtVe)和[测试数据集下载](https://drive.google.com/drive/folders/1PDWggNh8ylevFmrjo-JEvlmqsDlWWvZs)下载。 +下载后解压到data目录下，解压完成后数据分布如下所示： + +```sh +Synthetic_Rain_Datasets +├── train +│ ├── input +│ └── target +└── test + ├── Test100 + ├── Rain100H + ├── Rain100L + ├── Test1200 + └── Test2800 + +``` + +### 2.2 训练 + 示例以训练Deblurring的数据为例。如果想训练其他任务可以通过替换配置文件。 + + ```sh + python -u tools/main.py --config-file configs/mprnet_deblurring.yaml + ``` + +### 2.3 测试 + +测试模型： +```sh +python tools/main.py --config-file configs/mprnet_deblurring.yaml --evaluate-only --load ${PATH_OF_WEIGHT} +``` + +## 3 结果展示 + +去模糊 +| 模型 | 数据集 | PSNR/SSIM | +|---|---|---| +| MPRNet | GoPro | 33.4360/0.9410 | + +去噪 +| 模型 | 数据集 | PSNR/SSIM | +|---|---|---| +| MPRNet | SIDD | 43.6100 / 0.9586 | + +去雨 +| 模型 | 数据集 | PSNR/SSIM | +|---|---|---| +| MPRNet | Rain100L | 36.2848 / 0.9651 | + + +## 4 模型下载 + +| 模型 | 下载地址 | +|---|---| +| MPR_Deblurring | [MPR_Deblurring](https://paddlegan.bj.bcebos.com/models/MPR_Deblurring.pdparams) | +| MPR_Denoising | [MPR_Denoising](https://paddlegan.bj.bcebos.com/models/MPR_Denoising.pdparams) | +| MPR_Deraining | [MPR_Deraining](https://paddlegan.bj.bcebos.com/models/MPR_Deraining.pdparams) | + + +# 参考文献 + +- [Multi-Stage Progressive Image Restoration](https://arxiv.org/abs/2102.02808) + + ``` + @inproceedings{Kim2020U-GAT-IT:, + title={Multi-Stage Progressive Image Restoration}, + author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang and Ling Shao}, + booktitle={CVPR}, + year={2021} + } + ``` diff --git a/docs/zh_CN/tutorials/single_image_super_resolution.md b/docs/zh_CN/tutorials/single_image_super_resolution.md index 63e5f2b5c8415d33804b5169bbffd7566bbf920b..b4e43acf675624cf5dac74e27eef949a205a5479 100644 --- a/docs/zh_CN/tutorials/single_image_super_resolution.md +++ b/docs/zh_CN/tutorials/single_image_super_resolution.md @@ -120,11 +120,6 @@ paddle模型使用DIV2K数据集训练，torch模型使用df2k和DIV2K数据集 | paddle | 30.4574 / 0.8643 | 26.7204 / 0.7434 | | torch | 30.2183 / 0.8643 | 26.8035 / 0.7445 | -去模糊模型 -| 模型 | GoPro | 下载地址 | -|---|---|---| -| MPRNet | 33.4360 / 0.9410 | [链接](https://paddlegan.bj.bcebos.com/models/MPR_Deblurring.pdparams) | - diff --git a/ppgan/apps/mpr_predictor.py b/ppgan/apps/mpr_predictor.py index e76cc47b63be4d9485824e3d394d303c5bf8f5a7..f9560e0c01b115d3f1a194b8bfd0bec996b15196 100644 --- a/ppgan/apps/mpr_predictor.py +++ b/ppgan/apps/mpr_predictor.py @@ -55,13 +55,11 @@ model_cfgs = { class MPRPredictor(BasePredictor): def __init__(self, - images_path=None, output_path='output_dir', weight_path=None, seed=None, task=None): self.output_path = output_path - self.images_path = images_path self.task = task self.max_size = 640 self.img_multiple_of = 8 @@ -108,7 +106,7 @@ class MPRPredictor(BasePredictor): img = img.resize((dw, dh)) return img - def run(self): + def run(self, images_path=None): os.makedirs(self.output_path, exist_ok=True) task_path = os.path.join(self.output_path, self.task) os.makedirs(task_path, exist_ok=True)