V0.2.2.6

add unittest for model and utils
add unittest for dataset and archs
2021-12-09 17:41:19 +08:00 · 2021-11-28 19:54:19 +08:00 · 2021-11-28 15:59:14 +08:00 · 2021-11-24 00:14:05 +08:00 · 2021-11-23 00:52:00 +08:00 · 2021-11-01 19:16:48 +08:00
51 changed files with 1218 additions and 174 deletions
--- a/.github/workflows/no-response.yml
+++ b/.github/workflows/no-response.yml
@@ -1,12 +1,11 @@
 name: No Response
 # TODO: it seems not to work
 # Modified from: https://raw.githubusercontent.com/github/docs/main/.github/workflows/no-response.yaml
-# **What it does**: Closes issues that don't have enough information to be
+# **What it does**: Closes issues that don't have enough information to be actionable.
-#                   actionable.
+# **Why we have it**: To remove the need for maintainers to remember to check back on issues periodically
-# **Why we have it**: To remove the need for maintainers to remember to check
+#                     to see if contributors have responded.
 #                     back on issues periodically to see if contributors have
 #                     responded.
 # **Who does it impact**: Everyone that works on docs or docs-internal.
 on:
--- a/.github/workflows/pylint.yml
+++ b/.github/workflows/pylint.yml
@@ -20,11 +20,12 @@ jobs:
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
-        pip install flake8 yapf isort
+        pip install codespell flake8 isort yapf
    # modify the folders accordingly
    - name: Lint
      run: |
        codespell
        flake8 .
        isort --check-only --diff realesrgan/ scripts/ inference_realesrgan.py setup.py
        yapf -r -d realesrgan/ scripts/ inference_realesrgan.py setup.py
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -24,6 +24,12 @@ repos:
    hooks:
      - id: yapf
  # codespell
  - repo: https://github.com/codespell-project/codespell
    rev: v2.1.0
    hooks:
      - id: codespell
  # pre-commit-hooks
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v3.2.0
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -39,7 +39,3 @@ Here are some TODOs:
 - [ ] support controllable restoration strength
 :one: There are also [several issues](https://github.com/xinntao/Real-ESRGAN/issues) that require helpers to improve. If you can help, please let me know :smile:
 ## Contributors
 - [AK391](https://github.com/AK391): Integrate RealESRGAN to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Real-ESRGAN).
--- a/README.md
+++ b/README.md
@@ -8,17 +8,24 @@
 [![python lint](https://github.com/xinntao/Real-ESRGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/pylint.yml)
 [![Publish-pip](https://github.com/xinntao/Real-ESRGAN/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/publish-pip.yml)
 [English](README.md) **|** [简体中文](README_CN.md)
 1. [Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) for Real-ESRGAN <a href="https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>.
-2. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. You can find more information [here](#Portable-executable-files).
+2. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. You can find more information [here](#Portable-executable-files). The ncnn implementation is in [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
 Thanks for your interests and use:-) There are still many problems about the anime/illustration model, mainly including: 1. It cannot deal with videos; 2. It cannot be aware of depth/depth-of-field; 3. It is not adjustable; 4. May change the original style. Thanks for your valuable feedbacks/suggestions. All the feedbacks are updated in [feedback.md](feedback.md). Hopefully, a new model will be available soon.
 感谢大家的关注和使用:-) 关于动漫插画的模型，目前还有很多问题，主要有: 1. 视频处理不了; 2. 景深虚化有问题; 3. 不可调节, 效果过了; 4. 改变原来的风格。大家提供了很好的反馈。这些反馈会逐步更新在 [这个文档](feedback.md)。希望不久之后，有新模型可以使用.
 Real-ESRGAN aims at developing **Practical Algorithms for General Image Restoration**.<br>
 We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
-:art: Real-ESRGAN needs your contributions. Any contributions are welcome, such as new features/models/typo fixes/suggestions/maintenance, *etc*. See [CONTRIBUTING.md](CONTRIBUTING.md). All contributors are list [here](CONTRIBUTING.md#Contributors).
+:art: Real-ESRGAN needs your contributions. Any contributions are welcome, such as new features/models/typo fixes/suggestions/maintenance, *etc*. See [CONTRIBUTING.md](CONTRIBUTING.md). All contributors are list [here](README.md#hugs-acknowledgement).
-:question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md).
+:question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md) (Well, it is still empty there =-=||).
 :triangular_flag_on_post: **Updates**
 - :white_check_mark: Add the ncnn implementation [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
 - :white_check_mark: Add [*RealESRGAN_x4plus_anime_6B.pth*](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth), which is optimized for **anime** images with much smaller model size. More details and comparisons with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) are in [**anime_model.md**](docs/anime_model.md)
 - :white_check_mark: Support finetuning on your own data or paired data (*i.e.*, finetuning ESRGAN). See [here](Training.md#Finetune-Real-ESRGAN-on-your-own-dataset)
 - :white_check_mark: Integrate [GFPGAN](https://github.com/TencentARC/GFPGAN) to support **face enhancement**.
@@ -32,7 +39,7 @@ We extend the powerful ESRGAN to a practical restoration application (namely, Re
 If Real-ESRGAN is helpful in your photos/projects, please help to :star: this repo or recommend it to your friends. Thanks:blush: <br>
 Other recommended projects:<br>
 :arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN): A practical algorithm for real-world face restoration <br>
-:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): An ppen-source image and video restoration toolbox<br>
+:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): An open-source image and video restoration toolbox<br>
 :arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions.<br>
 :arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison. <br>
@@ -40,10 +47,9 @@ Other recommended projects:<br>
 ### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
-> [[Paper](https://arxiv.org/abs/2107.10833)] &emsp; [Project Page] &emsp; [Demo] <br>
+> [[Paper](https://arxiv.org/abs/2107.10833)] &emsp; [Project Page] &emsp; [[YouTube Video](https://www.youtube.com/watch?v=fxHWoDSSvSc)] &emsp; [[B站讲解](https://www.bilibili.com/video/BV1H34y1m7sS/)] &emsp; [[Poster](https://xinntao.github.io/projects/RealESRGAN_src/RealESRGAN_poster.pdf)] &emsp; [[PPT slides](https://docs.google.com/presentation/d/1QtW6Iy8rm8rGLsJ0Ldti6kP-7Qyzy6XL/edit?usp=sharing&ouid=109799856763657548160&rtpof=true&sd=true)]<br>
 > [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
-> Applied Research Center (ARC), Tencent PCG<br>
+> Tencent ARC Lab; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
 > Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
 <p align="center">
  <img src="assets/teaser.jpg">
@@ -92,6 +98,28 @@ We have provided three models:
 You can use the `-n` argument for other models, for example, `./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrnet-x4plus`
 ### Usage of executable files
 1. Please refer to [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan#computer-usages) for more details.
 1. Note that it does not support all the functions (such as `outscale`) as the python script `inference_realesrgan.py`.
 ```console
 Usage: realesrgan-ncnn-vulkan.exe -i infile -o outfile [options]...
  -h                   show this help
  -v                   verbose output
  -i input-path        input image path (jpg/png/webp) or directory
  -o output-path       output image path (jpg/png/webp) or directory
  -s scale             upscale ratio (4, default=4)
  -t tile-size         tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu
  -m model-path        folder path to pre-trained models(default=models)
  -n model-name        model name (default=realesrgan-x4plus, can be realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)
  -g gpu-id            gpu device to use (default=0) can be 0,1,2 for multi-gpu
  -j load:proc:save    thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu
  -x                   enable tta mode
  -f format            output image format (jpg/png/webp, default=ext/png)
 ```
 Note that it may introduce block inconsistency (and also generate slightly different results from the PyTorch implementation), because this executable file first crops the input image into several tiles, and then processes them separately, finally stitches together.
 This executable file is based on the wonderful [Tencent/ncnn](https://github.com/Tencent/ncnn) and [realsr-ncnn-vulkan](https://github.com/nihui/realsr-ncnn-vulkan) by [nihui](https://github.com/nihui).
@@ -161,16 +189,42 @@ python inference_realesrgan.py --model_path experiments/pretrained_models/RealES
 Results are in the `results` folder
 ### Usage of python script
 1. You can use X4 model for **arbitrary output size** with the argument `outscale`. The program will further perform cheap resize operation after the Real-ESRGAN output.
 ```console
 Usage: python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input infile --output outfile [options]...
 A common command: python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input infile --netscale 4 --outscale 3.5 --half --face_enhance
  -h                   show this help
  --input              Input image or folder. Default: inputs
  --output             Output folder. Default: results
  --model_path         Path to the pre-trained model. Default: experiments/pretrained_models/RealESRGAN_x4plus.pth
  --netscale           Upsample scale factor of the network. Default: 4
  --outscale           The final upsampling scale of the image. Default: 4
  --suffix             Suffix of the restored image. Default: out
  --tile               Tile size, 0 for no tile during testing. Default: 0
  --face_enhance       Whether to use GFPGAN to enhance face. Default: False
  --half               Whether to use half precision during inference. Default: False
  --ext                Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto
 ```
 ## :european_castle: Model Zoo
- [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)
+- [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth): X4 model for general images
 - [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth): Optimized for anime images; 6 RRDB blocks (slightly smaller network)
 - [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth): X2 model for general images
 - [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth): X4 model with MSE loss (over-smooth effects)
 - [official ESRGAN_x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth): official ESRGAN model (X4)
 The following models are **discriminators**, which are usually used for fine-tuning.
 - [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth)
 - [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)
 - [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth)
 - [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth)
 - [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth)
 - [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth)
- [official ESRGAN_x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth)
+- [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth)
 ## :computer: Training and Finetuning on your own dataset
@@ -178,13 +232,21 @@ A detailed guide can be found in [Training.md](Training.md).
 ## BibTeX
-    @Article{wang2021realesrgan,
+    @InProceedings{wang2021realesrgan,
-        title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
+        author    = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
-        author={Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
+        title     = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
-        journal={arXiv:2107.10833},
+        booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
-        year={2021}
+        date      = {2021}
    }
 ## :e-mail: Contact
 If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
 ## :hugs: Acknowledgement
 Thanks for all the contributors.
 - [AK391](https://github.com/AK391): Integrate RealESRGAN to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Real-ESRGAN).
 - [Asiimoviet](https://github.com/Asiimoviet): Translate the README.md to Chinese (中文).
 - [2ji3150](https://github.com/2ji3150): Thanks for the [detailed and valuable feedbacks/suggestions](https://github.com/xinntao/Real-ESRGAN/issues/131).
--- a/README_CN.md
+++ b/README_CN.md
@@ -0,0 +1,248 @@
 # Real-ESRGAN
 [![download](https://img.shields.io/github/downloads/xinntao/Real-ESRGAN/total.svg)](https://github.com/xinntao/Real-ESRGAN/releases)
 [![PyPI](https://img.shields.io/pypi/v/realesrgan)](https://pypi.org/project/realesrgan/)
 [![Open issue](https://img.shields.io/github/issues/xinntao/Real-ESRGAN)](https://github.com/xinntao/Real-ESRGAN/issues)
 [![Closed issue](https://img.shields.io/github/issues-closed/xinntao/Real-ESRGAN)](https://github.com/xinntao/Real-ESRGAN/issues)
 [![LICENSE](https://img.shields.io/github/license/xinntao/Real-ESRGAN.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE)
 [![python lint](https://github.com/xinntao/Real-ESRGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/pylint.yml)
 [![Publish-pip](https://github.com/xinntao/Real-ESRGAN/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/publish-pip.yml)
 [English](README.md) **|** [简体中文](README_CN.md)
 1. Real-ESRGAN的[Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) <a href="https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>.
 2. **支持Intel/AMD/Nvidia显卡**的绿色版exe文件： [Windows版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-macos.zip)，详情请移步[这里](#便携版（绿色版）可执行文件)。NCNN的实现在 [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan)。
 感谢大家的关注和使用:-) 关于动漫插画的模型，目前还有很多问题，主要有: 1. 视频处理不了; 2. 景深虚化有问题; 3. 不可调节, 效果过了; 4. 改变原来的风格。大家提供了很好的反馈。这些反馈会逐步更新在 [这个文档](feedback.md)。希望不久之后，有新模型可以使用.
 Real-ESRGAN 的目标是开发出**实用的图像修复算法**。<br>
 我们在 ESRGAN 的基础上使用纯合成的数据来进行训练，以使其能被应用于实际的图片修复的场景（顾名思义：Real-ESRGAN）。
 :art: Real-ESRGAN 需要，也很欢迎你的贡献，如新功能、模型、bug修复、建议、维护等等。详情可以查看[CONTRIBUTING.md](CONTRIBUTING.md)，所有的贡献者都会被列在[此处](README_CN.md#hugs-感谢)。
 :question: 常见的问题可以在[FAQ.md](FAQ.md)中找到答案。（好吧，现在还是空白的=-=||）
 :triangular_flag_on_post: **更新**
 - :white_check_mark: 添加了ncnn 实现：[Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
 - :white_check_mark: 添加了 [*RealESRGAN_x4plus_anime_6B.pth*](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)，对二次元图片进行了优化，并减少了model的大小。详情 以及 与[waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan)的对比请查看[**anime_model.md**](docs/anime_model.md)
 - :white_check_mark: 支持用户在自己的数据上进行微调 (finetune)：[详情](Training.md#Finetune-Real-ESRGAN-on-your-own-dataset)
 - :white_check_mark: 支持使用[GFPGAN](https://github.com/TencentARC/GFPGAN)**增强人脸**
 - :white_check_mark: 通过[Gradio](https://github.com/gradio-app/gradio)添加到了[Huggingface Spaces](https://huggingface.co/spaces)（一个机器学习应用的在线平台）：[Gradio在线版](https://huggingface.co/spaces/akhaliq/Real-ESRGAN)。感谢[@AK391](https://github.com/AK391)
 - :white_check_mark: 支持任意比例的缩放：`--outscale`（实际上使用`LANCZOS4`来更进一步调整输出图像的尺寸）。添加了*RealESRGAN_x2plus.pth*模型
 - :white_check_mark: [推断脚本](inference_realesrgan.py)支持: 1) 分块处理**tile**; 2) 带**alpha通道**的图像; 3) **灰色**图像; 4) **16-bit**图像.
 - :white_check_mark: 训练代码已经发布，具体做法可查看：[Training.md](Training.md)。
 ---
 如果 Real-ESRGAN 对你有帮助，可以给本项目一个 Star :star: ，或者推荐给你的朋友们，谢谢！:blush: <br/>
 其他推荐的项目：<br/>
 :arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN): 实用的人脸复原算法 <br>
 :arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): 开源的图像和视频工具箱<br>
 :arrow_forward: [facexlib](https://github.com/xinntao/facexlib): 提供与人脸相关的工具箱<br>
 :arrow_forward: [HandyView](https://github.com/xinntao/HandyView): 基于PyQt5的图片查看器，方便查看以及比较 <br>
 ---
 ### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
 > [[论文](https://arxiv.org/abs/2107.10833)] &emsp; [项目主页] &emsp; [[YouTube 视频](https://www.youtube.com/watch?v=fxHWoDSSvSc)] &emsp; [[B站视频](https://www.bilibili.com/video/BV1H34y1m7sS/)] &emsp; [[Poster](https://xinntao.github.io/projects/RealESRGAN_src/RealESRGAN_poster.pdf)] &emsp; [[PPT](https://docs.google.com/presentation/d/1QtW6Iy8rm8rGLsJ0Ldti6kP-7Qyzy6XL/edit?usp=sharing&ouid=109799856763657548160&rtpof=true&sd=true)]<br>
 > [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
 > Tencent ARC Lab; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
 <p align="center">
  <img src="assets/teaser.jpg">
 </p>
 ---
 我们提供了一套训练好的模型（*RealESRGAN_x4plus.pth*)，可以进行4倍的超分辨率。<br>
 **现在的 Real-ESRGAN 还是有几率失败的，因为现实生活的降质过程比较复杂。**<br>
 而且，本项目对**人脸以及文字之类**的效果还不是太好，但是我们会持续进行优化的。<br>
 Real-ESRGAN 将会被长期支持，我会在空闲的时间中持续维护更新。
 这些是未来计划的几个新功能：
 - [ ] 优化人脸
 - [ ] 优化文字
 - [x] 优化动画图像
 - [ ] 支持更多的超分辨率比例
 - [ ] 可调节的复原
 如果你有好主意或需求，欢迎在 issue 或 discussion 中提出。<br/>
 如果你有一些 Real-ESRGAN 中有问题的照片，你也可以在 issue 或者 discussion 中发出来。我会留意（但是不一定能解决:stuck_out_tongue:）。如果有必要的话，我还会专门开一页来记录那些有待解决的图像。
 ---
 ### 便携版（绿色版）可执行文件
 你可以下载**支持Intel/AMD/Nvidia显卡**的绿色版exe文件： [Windows版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-macos.zip)。
 绿色版指的是这些exe你可以直接运行（放U盘里拷走都没问题），因为里面已经有所需的文件和模型了。它不需要 CUDA 或者 PyTorch运行环境。<br>
 你可以通过下面这个命令来运行（Windows版本的例子，更多信息请查看对应版本的README.md）：
 ```bash
 ./realesrgan-ncnn-vulkan.exe -i 输入图像.jpg -o 输出图像.png
 ```
 我们提供了三种模型：
 1. realesrgan-x4plus（默认）
 2. reaesrnet-x4plus
 3. realesrgan-x4plus-anime（针对动漫插画图像优化，有更小的体积）
 你可以通过`-n`参数来使用其他模型，例如`./realesrgan-ncnn-vulkan.exe -i 二次元图片.jpg -o 二刺螈图片.png -n realesrgan-x4plus-anime`
 ### 可执行文件的用法
 1. 更多细节可以参考 [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan#computer-usages).
 2. 注意：可执行文件并没有支持 python 脚本 `inference_realesrgan.py` 中所有的功能，比如 `outscale` 选项) .
 ```console
 Usage: realesrgan-ncnn-vulkan.exe -i infile -o outfile [options]...
  -h                   show this help
  -v                   verbose output
  -i input-path        input image path (jpg/png/webp) or directory
  -o output-path       output image path (jpg/png/webp) or directory
  -s scale             upscale ratio (4, default=4)
  -t tile-size         tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu
  -m model-path        folder path to pre-trained models(default=models)
  -n model-name        model name (default=realesrgan-x4plus, can be realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)
  -g gpu-id            gpu device to use (default=0) can be 0,1,2 for multi-gpu
  -j load:proc:save    thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu
  -x                   enable tta mode
  -f format            output image format (jpg/png/webp, default=ext/png)
 ```
 由于这些exe文件会把图像分成几个板块，然后来分别进行处理，再合成导出，输出的图像可能会有一点割裂感（而且可能跟PyTorch的输出不太一样）
 这些exe文件均基于[Tencent/ncnn](https://github.com/Tencent/ncnn)以及[nihui](https://github.com/nihui)的[realsr-ncnn-vulkan](https://github.com/nihui/realsr-ncnn-vulkan)，感谢！
 ---
 ## :wrench: 依赖以及安装
 - Python >= 3.7 (推荐使用[Anaconda](https://www.anaconda.com/download/#linux)或[Miniconda](https://docs.conda.io/en/latest/miniconda.html))
 - [PyTorch >= 1.7](https://pytorch.org/)
 #### 安装
 1. 把项目克隆到本地
    ```bash
    git clone https://github.com/xinntao/Real-ESRGAN.git
    cd Real-ESRGAN
    ```
 2. 安装各种依赖
    ```bash
    # 安装 basicsr - https://github.com/xinntao/BasicSR
    # 我们使用BasicSR来训练以及推断
    pip install basicsr
    # facexlib和gfpgan是用来增强人脸的
    pip install facexlib
    pip install gfpgan
    pip install -r requirements.txt
    python setup.py develop
    ```
 ## :zap: 快速上手
 ### 普通图片
 下载我们训练好的模型: [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)
 ```bash
 wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
 ```
 推断!
 ```bash
 python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input inputs --face_enhance
 ```
 结果在`results`文件夹
 ### 动画图片
 <p align="center">
  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">
 </p>
 训练好的模型: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)<br>
 有关[waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan)的更多信息和对比在[**anime_model.md**](docs/anime_model.md)中。
 ```bash
 # 下载模型
 wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P experiments/pretrained_models
 # 推断
 python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth --input inputs
 ```
 结果在`results`文件夹
 ### Python 脚本的用法
 1. 虽然你实用了 X4 模型，但是你可以 **输出任意尺寸比例的图片**，只要实用了 `outscale` 参数. 程序会进一步对模型的输出图像进行缩放。
 ```console
 Usage: python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input infile --output outfile [options]...
 A common command: python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input infile --netscale 4 --outscale 3.5 --half --face_enhance
  -h                   show this help
  --input              Input image or folder. Default: inputs
  --output             Output folder. Default: results
  --model_path         Path to the pre-trained model. Default: experiments/pretrained_models/RealESRGAN_x4plus.pth
  --netscale           Upsample scale factor of the network. Default: 4
  --outscale           The final upsampling scale of the image. Default: 4
  --suffix             Suffix of the restored image. Default: out
  --tile               Tile size, 0 for no tile during testing. Default: 0
  --face_enhance       Whether to use GFPGAN to enhance face. Default: False
  --half               Whether to use half precision during inference. Default: False
  --ext                Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto
 ```
 ## :european_castle: 模型库
 - [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth): X4 model for general images
 - [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth): Optimized for anime images; 6 RRDB blocks (slightly smaller network)
 - [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth): X2 model for general images
 - [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth): X4 model with MSE loss (over-smooth effects)
 - [official ESRGAN_x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth): official ESRGAN model (X4)
 下面是 **判别器** 模型, 他们经常被用来微调（fine-tune）模型.
 - [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth)
 - [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth)
 - [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth)
 ## :computer: 训练，在你的数据上微调（Fine-tune）
 这里有一份详细的指南：[Training.md](Training.md).
 ## BibTeX 引用
    @Article{wang2021realesrgan,
        title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
        author={Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
        journal={arXiv:2107.10833},
        year={2021}
    }
 ## :e-mail: 联系我们
 如果你有任何问题，请通过 `xintao.wang@outlook.com` 或 `xintaowang@tencent.com` 联系我们。
 ## :hugs: 感谢
 感谢所有的贡献者大大们~
 - [AK391](https://github.com/AK391): 通过[Gradio](https://github.com/gradio-app/gradio)添加到了[Huggingface Spaces](https://huggingface.co/spaces)（一个机器学习应用的在线平台）：[Gradio在线版](https://huggingface.co/spaces/akhaliq/Real-ESRGAN)。
 - [Asiimoviet](https://github.com/Asiimoviet): 把 README.md 文档 翻译成了中文。
 - [2ji3150](https://github.com/2ji3150): 感谢详尽并且富有价值的[反馈、建议](https://github.com/xinntao/Real-ESRGAN/issues/131).
--- a/Training.md
+++ b/Training.md
@@ -7,7 +7,7 @@
  - [Train Real-ESRGAN](#Train-Real-ESRGAN)
 - [Finetune Real-ESRGAN on your own dataset](#Finetune-Real-ESRGAN-on-your-own-dataset)
  - [Generate degraded images on the fly](#Generate-degraded-images-on-the-fly)
-  - [Use paired training data](#Use-paired-training-data)
+  - [Use paired training data](#use-your-own-paired-data)
 ## Train Real-ESRGAN
@@ -32,7 +32,7 @@ Here are steps for data preparation.
 #### Step 1: [Optional] Generate multi-scale images
 For the DF2K dataset, we use a multi-scale strategy, *i.e.*, we downsample HR images to obtain several Ground-Truth images with different scales. <br>
-You can use the [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) script to geneate multi-scale images. <br>
+You can use the [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) script to generate multi-scale images. <br>
 Note that this step can be omitted if you just want to have a fast try.
 ```bash
@@ -114,12 +114,22 @@ You can merge several folders into one meta_info txt. Here is the example:
    CUDA_VISIBLE_DEVICES=0,1,2,3 \
    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --debug
    ```
    Train with **a single GPU** in the *debug* mode:
    ```bash
    python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --debug
    ```
 1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
    ```bash
    CUDA_VISIBLE_DEVICES=0,1,2,3 \
    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --auto_resume
    ```
    Train with **a single GPU**:
    ```bash
    python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --auto_resume
    ```
 ### Train Real-ESRGAN
 1. After the training of Real-ESRNet, you now have the file `experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/model/net_g_1000000.pth`. If you need to specify the pre-trained path to other files, modify the `pretrain_network_g` value in the option file `train_realesrgan_x4plus.yml`.
@@ -129,12 +139,22 @@ You can merge several folders into one meta_info txt. Here is the example:
    CUDA_VISIBLE_DEVICES=0,1,2,3 \
    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --debug
    ```
    Train with **a single GPU** in the *debug* mode:
    ```bash
    python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --debug
    ```
 1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
    ```bash
    CUDA_VISIBLE_DEVICES=0,1,2,3 \
    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --auto_resume
    ```
    Train with **a single GPU**:
    ```bash
    python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --auto_resume
    ```
 ## Finetune Real-ESRGAN on your own dataset
 You can finetune Real-ESRGAN on your own dataset. Typically, the fine-tuning process can be divided into two cases:
@@ -185,6 +205,11 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 \
 python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --launcher pytorch --auto_resume
 ```
 Finetune with **a single GPU**:
 ```bash
 python realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --auto_resume
 ```
 ### Use your own paired data
 You can also finetune RealESRGAN with your own paired data. It is more similar to fine-tuning ESRGAN.
@@ -237,3 +262,8 @@ We use four GPUs for training. We use the `--auto_resume` argument to automatica
 CUDA_VISIBLE_DEVICES=0,1,2,3 \
 python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --launcher pytorch --auto_resume
 ```
 Finetune with **a single GPU**:
 ```bash
 python realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --auto_resume
 ```
--- a/2
+++ b/2
@@ -1 +1 @@
-0.2.2.4
+0.2.2.6
--- a/docs/anime_model.md
+++ b/docs/anime_model.md
@@ -6,7 +6,7 @@
  - [PyTorch Inference](#PyTorch-Inference)
  - [ncnn Executable File](#ncnn-Executable-File)
 - [Comparisons with waifu2x](#Comparisons-with-waifu2x)
- [Comparions with Sliding Bars](#Comparions-with-Sliding-Bars)
+- [Comparisons with Sliding Bars](#Comparions-with-Sliding-Bars)
 <p align="center">
  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">
--- a/feedback.md
+++ b/feedback.md
@@ -0,0 +1,11 @@
 # Feedback 反馈
 ## 动漫插画模型
 1. 视频处理不了: 目前的模型，不是针对视频的，所以视频效果很很不好。我们在探究针对视频的模型了
 1. 景深虚化有问题: 现在的模型把一些景深 和 特意的虚化 都复原了，感觉不好。这个后面我们会考虑把这个信息结合进入。一个简单的做法是识别景深和虚化，然后作为条件告诉神经网络，哪些地方复原强一些，哪些地方复原要弱一些
 1. 不可以调节: 像 Waifu2X 可以调节。可以根据自己的喜好，做调整，但是 Real-ESRGAN-anime 并不可以。导致有些恢复效果过了
 1. 把原来的风格改变了: 不同的动漫插画都有自己的风格，现在的 Real-ESRGAN-anime 倾向于恢复成一种风格（这是受到训练数据集影响的）。风格是动漫很重要的一个要素，所以要尽可能保持
 1. 模型太大: 目前的模型处理太慢，能够更快。这个我们有相关的工作在探究，希望能够尽快有结果，并应用到 Real-ESRGAN 这一系列的模型上
 Thanks for the [detailed and valuable feedbacks/suggestions](https://github.com/xinntao/Real-ESRGAN/issues/131) by [2ji3150](https://github.com/2ji3150).
--- a/inference_realesrgan.py
+++ b/inference_realesrgan.py
@@ -8,6 +8,8 @@ from realesrgan import RealESRGANer
 def main():
    """Inference demo for Real-ESRGAN.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', type=str, default='inputs', help='Input image or folder')
    parser.add_argument(
@@ -53,7 +55,7 @@ def main():
        pre_pad=args.pre_pad,
        half=args.half)
-    if args.face_enhance:
+    if args.face_enhance:  # Use GFPGAN for face enhancement
        from gfpgan import GFPGANer
        face_enhancer = GFPGANer(
            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
@@ -78,20 +80,21 @@ def main():
        else:
            img_mode = None
        # give warnings for too large/small images
        h, w = img.shape[0:2]
        if max(h, w) > 1000 and args.netscale == 4:
            import warnings
-            warnings.warn('The input image is large, try X2 model for better performace.')
+            warnings.warn('The input image is large, try X2 model for better performance.')
        if max(h, w) < 500 and args.netscale == 2:
            import warnings
-            warnings.warn('The input image is small, try X4 model for better performace.')
+            warnings.warn('The input image is small, try X4 model for better performance.')
        try:
            if args.face_enhance:
                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
            else:
                output, _ = upsampler.enhance(img, outscale=args.outscale)
-        except Exception as error:
+        except RuntimeError as error:
            print('Error', error)
            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
        else:
--- a/options/finetune_realesrgan_x4plus.yml
+++ b/options/finetune_realesrgan_x4plus.yml
@@ -90,7 +90,6 @@ network_g:
  num_block: 23
  num_grow_ch: 32
 network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
@@ -169,7 +168,7 @@ train:
 #   save_img: True
 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/finetune_realesrgan_x4plus_pairdata.yml
+++ b/options/finetune_realesrgan_x4plus_pairdata.yml
@@ -52,7 +52,6 @@ network_g:
  num_block: 23
  num_grow_ch: 32
 network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
@@ -131,7 +130,7 @@ train:
 #   save_img: True
 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/train_realesrgan_x2plus.yml
+++ b/options/train_realesrgan_x2plus.yml
@@ -2,7 +2,7 @@
 name: train_RealESRGANx2plus_400k_B12G4
 model_type: RealESRGANModel
 scale: 2
-num_gpu: 4
+num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
 manual_seed: 0
 # ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
@@ -91,7 +91,6 @@ network_g:
  num_grow_ch: 32
  scale: 2
 network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
@@ -167,7 +166,7 @@ train:
 #   save_img: True
 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/train_realesrgan_x4plus.yml
+++ b/options/train_realesrgan_x4plus.yml
@@ -2,7 +2,7 @@
 name: train_RealESRGANx4plus_400k_B12G4
 model_type: RealESRGANModel
 scale: 4
-num_gpu: 4
+num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
 manual_seed: 0
 # ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
@@ -90,7 +90,6 @@ network_g:
  num_block: 23
  num_grow_ch: 32
 network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
@@ -166,7 +165,7 @@ train:
 #   save_img: True
 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/train_realesrnet_x2plus.yml
+++ b/options/train_realesrnet_x2plus.yml
@@ -2,7 +2,7 @@
 name: train_RealESRNetx2plus_1000k_B12G4
 model_type: RealESRNetModel
 scale: 2
-num_gpu: 4
+num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
 manual_seed: 0
 # ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
@@ -125,7 +125,7 @@ train:
 #   save_img: True
 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/train_realesrnet_x4plus.yml
+++ b/options/train_realesrnet_x4plus.yml
@@ -2,7 +2,7 @@
 name: train_RealESRNetx4plus_1000k_B12G4
 model_type: RealESRNetModel
 scale: 4
-num_gpu: 4
+num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
 manual_seed: 0
 # ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
@@ -124,7 +124,7 @@ train:
 #   save_img: True
 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/realesrgan/init.py
+++ b/realesrgan/init.py
@@ -3,4 +3,4 @@ from .archs import *
 from .data import *
 from .models import *
 from .utils import *
-from .version import __gitsha__, __version__
+from .version import __version__
--- a/realesrgan/archs/discriminator_arch.py
+++ b/realesrgan/archs/discriminator_arch.py
@@ -6,15 +6,23 @@ from torch.nn.utils import spectral_norm
@ARCH_REGISTRY.register()
 class UNetDiscriminatorSN(nn.Module):
-    """Defines a U-Net discriminator with spectral normalization (SN)"""
+    """Defines a U-Net discriminator with spectral normalization (SN)
    It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
    Arg:
        num_in_ch (int): Channel number of inputs. Default: 3.
        num_feat (int): Channel number of base intermediate features. Default: 64.
        skip_connection (bool): Whether to use skip connections between U-Net. Default: True.
    """
    def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
        super(UNetDiscriminatorSN, self).__init__()
        self.skip_connection = skip_connection
        norm = spectral_norm
-
+        # the first convolution
        self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)
-
+        # downsample
        self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False))
        self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False))
        self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False))
@@ -22,14 +30,13 @@ class UNetDiscriminatorSN(nn.Module):
        self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False))
        self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False))
        self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False))
-
+        # extra convolutions
        # extra
        self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
        self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
        self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1)
    def forward(self, x):
        # downsample
        x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True)
        x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True)
        x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True)
@@ -52,7 +59,7 @@ class UNetDiscriminatorSN(nn.Module):
        if self.skip_connection:
            x6 = x6 + x0
-        # extra
+        # extra convolutions
        out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True)
        out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True)
        out = self.conv9(out)
--- a/realesrgan/data/realesrgan_dataset.py
+++ b/realesrgan/data/realesrgan_dataset.py
@@ -15,18 +15,31 @@ from torch.utils import data as data
@DATASET_REGISTRY.register()
 class RealESRGANDataset(data.Dataset):
-    """
+    """Dataset used for Real-ESRGAN model:
-    Dataset used for Real-ESRGAN model.
+    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
    It loads gt (Ground-Truth) images, and augments them.
    It also generates blur kernels and sinc kernels for generating low-quality images.
    Note that the low-quality images are processed in tensors on GPUS for faster processing.
    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
            Please see more options in the codes.
    """
    def __init__(self, opt):
        super(RealESRGANDataset, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.gt_folder = opt['dataroot_gt']
        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
@@ -35,18 +48,20 @@ class RealESRGANDataset(data.Dataset):
            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
                self.paths = [line.split('.')[0] for line in fin]
        else:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
-                paths = [line.strip() for line in fin]
+                paths = [line.strip().split(' ')[0] for line in fin]
                self.paths = [os.path.join(self.gt_folder, v) for v in paths]
        # blur settings for the first degradation
        self.blur_kernel_size = opt['blur_kernel_size']
        self.kernel_list = opt['kernel_list']
-        self.kernel_prob = opt['kernel_prob']
+        self.kernel_prob = opt['kernel_prob']  # a list for each kernel probability
        self.blur_sigma = opt['blur_sigma']
-        self.betag_range = opt['betag_range']
+        self.betag_range = opt['betag_range']  # betag used in generalized Gaussian blur kernels
-        self.betap_range = opt['betap_range']
+        self.betap_range = opt['betap_range']  # betap used in plateau blur kernels
-        self.sinc_prob = opt['sinc_prob']
+        self.sinc_prob = opt['sinc_prob']  # the probability for sinc filters
        # blur settings for the second degradation
        self.blur_kernel_size2 = opt['blur_kernel_size2']
@@ -61,6 +76,7 @@ class RealESRGANDataset(data.Dataset):
        self.final_sinc_prob = opt['final_sinc_prob']
        self.kernel_range = [2 * v + 1 for v in range(3, 11)]  # kernel size ranges from 7 to 21
        # TODO: kernel range is now hard-coded, should be in the configure file
        self.pulse_tensor = torch.zeros(21, 21).float()  # convolving with pulse tensor brings no blurry effect
        self.pulse_tensor[10, 10] = 1
@@ -76,7 +92,7 @@ class RealESRGANDataset(data.Dataset):
        while retry > 0:
            try:
                img_bytes = self.file_client.get(gt_path, 'gt')
-            except Exception as e:
+            except (IOError, OSError) as e:
                logger = get_root_logger()
                logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
                # change another file to read
@@ -89,10 +105,11 @@ class RealESRGANDataset(data.Dataset):
                retry -= 1
        img_gt = imfrombytes(img_bytes, float32=True)
-        # -------------------- augmentation for training: flip, rotation -------------------- #
+        # -------------------- Do augmentation for training: flip, rotation -------------------- #
        img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])
-        # crop or pad to 400: 400 is hard-coded. You may change it accordingly
+        # crop or pad to 400
        # TODO: 400 is hard-coded. You may change it accordingly
        h, w = img_gt.shape[0:2]
        crop_pad_size = 400
        # pad
@@ -154,7 +171,7 @@ class RealESRGANDataset(data.Dataset):
        pad_size = (21 - kernel_size) // 2
        kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))
-        # ------------------------------------- sinc kernel ------------------------------------- #
+        # ------------------------------------- the final sinc kernel ------------------------------------- #
        if np.random.uniform() < self.opt['final_sinc_prob']:
            kernel_size = random.choice(self.kernel_range)
            omega_c = np.random.uniform(np.pi / 3, np.pi)
--- a/realesrgan/data/realesrgan_paired_dataset.py
+++ b/realesrgan/data/realesrgan_paired_dataset.py
@@ -11,8 +11,7 @@ from torchvision.transforms.functional import normalize
 class RealESRGANPairedDataset(data.Dataset):
    """Paired image dataset for image restoration.
-    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
+    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.
    GT image pairs.
    There are three modes:
    1. 'lmdb': Use lmdb files.
@@ -28,8 +27,8 @@ class RealESRGANPairedDataset(data.Dataset):
            dataroot_lq (str): Data root path for lq.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
-            filename_tmpl (str): Template for each filename. Note that the
+            filename_tmpl (str): Template for each filename. Note that the template excludes the file extension.
-                template excludes the file extension. Default: '{}'.
+                Default: '{}'.
            gt_size (int): Cropped patched size for gt patches.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
@@ -42,23 +41,23 @@ class RealESRGANPairedDataset(data.Dataset):
    def __init__(self, opt):
        super(RealESRGANPairedDataset, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        # mean and std for normalizing the input images
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
-        if 'filename_tmpl' in opt:
+        self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'
            self.filename_tmpl = opt['filename_tmpl']
        else:
            self.filename_tmpl = '{}'
        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
            self.io_backend_opt['client_keys'] = ['lq', 'gt']
            self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
        elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip() for line in fin]
            self.paths = []
@@ -68,6 +67,9 @@ class RealESRGANPairedDataset(data.Dataset):
                lq_path = os.path.join(self.lq_folder, lq_path)
                self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
        else:
            # disk backend
            # it will scan the whole folder to get meta info
            # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
            self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
    def __getitem__(self, index):
--- a/realesrgan/models/realesrgan_model.py
+++ b/realesrgan/models/realesrgan_model.py
@@ -13,35 +13,45 @@ from torch.nn import functional as F
@MODEL_REGISTRY.register()
 class RealESRGANModel(SRGANModel):
-    """RealESRGAN Model"""
+    """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """
    def __init__(self, opt):
        super(RealESRGANModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()
+        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
-        self.usm_sharpener = USMSharp().cuda()
+        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
        self.queue_size = opt.get('queue_size', 180)
    @torch.no_grad()
    def _dequeue_and_enqueue(self):
-        # training pair pool
+        """It is the training pair pool for increasing the diversity in a batch.
        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
        to increase the degradation diversity in a batch.
        """
        # initialize
        b, c, h, w = self.lq.size()
        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, 'queue size should be divisible by batch size'
+            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
            _, c, h, w = self.gt.size()
            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # full
+        if self.queue_ptr == self.queue_size:  # the pool is full
            # do dequeue and enqueue
            # shuffle
            idx = torch.randperm(self.queue_size)
            self.queue_lr = self.queue_lr[idx]
            self.queue_gt = self.queue_gt[idx]
-            # get
+            # get first b samples
            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update
+            # update the queue
            self.queue_lr[0:b, :, :, :] = self.lq.clone()
            self.queue_gt[0:b, :, :, :] = self.gt.clone()
@@ -55,6 +65,8 @@ class RealESRGANModel(SRGANModel):
    @torch.no_grad()
    def feed_data(self, data):
        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
        """
        if self.is_train and self.opt.get('high_order_degradation', True):
            # training data synthesis
            self.gt = data['gt'].to(self.device)
@@ -79,7 +91,7 @@ class RealESRGANModel(SRGANModel):
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # noise
+            # add noise
            gray_noise_prob = self.opt['gray_noise_prob']
            if np.random.uniform() < self.opt['gaussian_noise_prob']:
                out = random_add_gaussian_noise_pt(
@@ -93,7 +105,7 @@ class RealESRGANModel(SRGANModel):
                    rounds=False)
            # JPEG compression
            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)
+            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
            out = self.jpeger(out, quality=jpeg_p)
            # ----------------------- The second degradation process ----------------------- #
@@ -111,7 +123,7 @@ class RealESRGANModel(SRGANModel):
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(
                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # noise
+            # add noise
            gray_noise_prob = self.opt['gray_noise_prob2']
            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
                out = random_add_gaussian_noise_pt(
@@ -162,7 +174,9 @@ class RealESRGANModel(SRGANModel):
            self._dequeue_and_enqueue()
            # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
            self.gt_usm = self.usm_sharpener(self.gt)
            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
        else:
            # for paired training or validation
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
@@ -175,6 +189,7 @@ class RealESRGANModel(SRGANModel):
        self.is_train = True
    def optimize_parameters(self, current_iter):
        # usm sharpening
        l1_gt = self.gt_usm
        percep_gt = self.gt_usm
        gan_gt = self.gt_usm
--- a/realesrgan/models/realesrnet_model.py
+++ b/realesrgan/models/realesrnet_model.py
@@ -12,35 +12,46 @@ from torch.nn import functional as F
@MODEL_REGISTRY.register()
 class RealESRNetModel(SRModel):
-    """RealESRNet Model"""
+    """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
    It is trained without GAN losses.
    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """
    def __init__(self, opt):
        super(RealESRNetModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()
+        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
-        self.usm_sharpener = USMSharp().cuda()
+        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
        self.queue_size = opt.get('queue_size', 180)
    @torch.no_grad()
    def _dequeue_and_enqueue(self):
-        # training pair pool
+        """It is the training pair pool for increasing the diversity in a batch.
        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
        to increase the degradation diversity in a batch.
        """
        # initialize
        b, c, h, w = self.lq.size()
        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, 'queue size should be divisible by batch size'
+            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
            _, c, h, w = self.gt.size()
            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # full
+        if self.queue_ptr == self.queue_size:  # the pool is full
            # do dequeue and enqueue
            # shuffle
            idx = torch.randperm(self.queue_size)
            self.queue_lr = self.queue_lr[idx]
            self.queue_gt = self.queue_gt[idx]
-            # get
+            # get first b samples
            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update
+            # update the queue
            self.queue_lr[0:b, :, :, :] = self.lq.clone()
            self.queue_gt[0:b, :, :, :] = self.gt.clone()
@@ -54,10 +65,12 @@ class RealESRNetModel(SRModel):
    @torch.no_grad()
    def feed_data(self, data):
        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
        """
        if self.is_train and self.opt.get('high_order_degradation', True):
            # training data synthesis
            self.gt = data['gt'].to(self.device)
-            # USM the GT images
+            # USM sharpen the GT images
            if self.opt['gt_usm'] is True:
                self.gt = self.usm_sharpener(self.gt)
@@ -80,7 +93,7 @@ class RealESRNetModel(SRModel):
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # noise
+            # add noise
            gray_noise_prob = self.opt['gray_noise_prob']
            if np.random.uniform() < self.opt['gaussian_noise_prob']:
                out = random_add_gaussian_noise_pt(
@@ -94,7 +107,7 @@ class RealESRNetModel(SRModel):
                    rounds=False)
            # JPEG compression
            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)
+            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
            out = self.jpeger(out, quality=jpeg_p)
            # ----------------------- The second degradation process ----------------------- #
@@ -112,7 +125,7 @@ class RealESRNetModel(SRModel):
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(
                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # noise
+            # add noise
            gray_noise_prob = self.opt['gray_noise_prob2']
            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
                out = random_add_gaussian_noise_pt(
@@ -160,7 +173,9 @@ class RealESRNetModel(SRModel):
            # training pair pool
            self._dequeue_and_enqueue()
            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
        else:
            # for paired training or validation
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
--- a/realesrgan/utils.py
+++ b/realesrgan/utils.py
@@ -4,14 +4,26 @@ import numpy as np
 import os
 import torch
 from basicsr.archs.rrdbnet_arch import RRDBNet
-from torch.hub import download_url_to_file, get_dir
+from basicsr.utils.download_util import load_file_from_url
 from torch.nn import functional as F
 from urllib.parse import urlparse
 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 class RealESRGANer():
    """A helper class for upsampling images with RealESRGAN.
    Args:
        scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
        model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
        model (nn.Module): The defined network. If None, the model will be constructed here. Default: None.
        tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
            input images into tiles, and then process each of them. Finally, they will be merged into one image.
            0 denotes for do not use tile. Default: 0.
        tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
        pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
        half (float): Whether to use half precision during inference. Default: False.
    """
    def __init__(self, scale, model_path, model=None, tile=0, tile_pad=10, pre_pad=10, half=False):
        self.scale = scale
@@ -26,10 +38,12 @@ class RealESRGANer():
        if model is None:
            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
        # if the model_path starts with https, it will first download models to the folder: realesrgan/weights
        if model_path.startswith('https://'):
            model_path = load_file_from_url(
-                url=model_path, model_dir='realesrgan/weights', progress=True, file_name=None)
+                url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None)
        loadnet = torch.load(model_path)
        # prefer to use params_ema
        if 'params_ema' in loadnet:
            keyname = 'params_ema'
        else:
@@ -41,6 +55,8 @@ class RealESRGANer():
            self.model = self.model.half()
    def pre_process(self, img):
        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
        """
        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
        self.img = img.unsqueeze(0).to(self.device)
        if self.half:
@@ -49,7 +65,7 @@ class RealESRGANer():
        # pre_pad
        if self.pre_pad != 0:
            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
-        # mod pad
+        # mod pad for divisible borders
        if self.scale == 2:
            self.mod_scale = 2
        elif self.scale == 1:
@@ -64,10 +80,14 @@ class RealESRGANer():
            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
    def process(self):
        # model inference
        self.output = self.model(self.img)
    def tile_process(self):
-        """Modified from: https://github.com/ata4/esrgan-launcher
+        """It will first crop input images to tiles, and then process each tile.
        Finally, all the processed tiles are merged into one images.
        Modified from: https://github.com/ata4/esrgan-launcher
        """
        batch, channel, height, width = self.img.shape
        output_height = height * self.scale
@@ -107,7 +127,7 @@ class RealESRGANer():
                try:
                    with torch.no_grad():
                        output_tile = self.model(input_tile)
-                except Exception as error:
+                except RuntimeError as error:
                    print('Error', error)
                print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
@@ -188,7 +208,7 @@ class RealESRGANer():
                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
-            else:
+            else:  # use the cv2 resize for alpha channel
                h, w = alpha.shape[0:2]
                output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)
@@ -210,23 +230,3 @@ class RealESRGANer():
                ), interpolation=cv2.INTER_LANCZOS4)
        return output, img_mode
 def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
    """
    if model_dir is None:
        hub_dir = get_dir()
        model_dir = os.path.join(hub_dir, 'checkpoints')
    os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
    parts = urlparse(url)
    filename = os.path.basename(parts.path)
    if file_name is not None:
        filename = file_name
    cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
    if not os.path.exists(cached_file):
        print(f'Downloading: "{url}" to {cached_file}\n')
        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
    return cached_file
--- a/scripts/extract_subimages.py
+++ b/scripts/extract_subimages.py
@@ -14,34 +14,24 @@ def main(args):
    opt (dict): Configuration dict. It contains:
        n_thread (int): Thread number.
-        compression_level (int):  CV_IMWRITE_PNG_COMPRESSION from 0 to 9.
+        compression_level (int):  CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size
-            A higher value means a smaller size and longer compression time.
+            and longer compression time. Use 0 for faster CPU decompression. Default: 3, same in cv2.
            Use 0 for faster CPU decompression. Default: 3, same in cv2.
        input_folder (str): Path to the input folder.
        save_folder (str): Path to save folder.
        crop_size (int): Crop size.
        step (int): Step for overlapped sliding window.
-        thresh_size (int): Threshold size. Patches whose size is lower
+        thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.
            than thresh_size will be dropped.
    Usage:
        For each folder, run this script.
-        Typically, there are four folders to be processed for DIV2K dataset.
+        Typically, there are GT folder and LQ folder to be processed for DIV2K dataset.
-            DIV2K_train_HR
+        After process, each sub_folder should have the same number of subimages.
            DIV2K_train_LR_bicubic/X2
            DIV2K_train_LR_bicubic/X3
            DIV2K_train_LR_bicubic/X4
        After process, each sub_folder should have the same number of
        subimages.
        Remember to modify opt configurations according to your settings.
    """
    opt = {}
    opt['n_thread'] = args.n_thread
    opt['compression_level'] = args.compression_level
    # HR images
    opt['input_folder'] = args.input
    opt['save_folder'] = args.output
    opt['crop_size'] = args.crop_size
@@ -68,6 +58,7 @@ def extract_subimages(opt):
        print(f'Folder {save_folder} already exists. Exit.')
        sys.exit(1)
    # scan all images
    img_list = list(scandir(input_folder, full_path=True))
    pbar = tqdm(total=len(img_list), unit='image', desc='Extract')
@@ -88,8 +79,7 @@ def worker(path, opt):
        opt (dict): Configuration dict. It contains:
            crop_size (int): Crop size.
            step (int): Step for overlapped sliding window.
-            thresh_size (int): Threshold size. Patches whose size is lower
+            thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.
                than thresh_size will be dropped.
            save_folder (str): Path to save folder.
            compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.
@@ -106,13 +96,7 @@ def worker(path, opt):
    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-    if img.ndim == 2:
+    h, w = img.shape[0:2]
        h, w = img.shape
    elif img.ndim == 3:
        h, w, c = img.shape
    else:
        raise ValueError(f'Image ndim should be 2 or 3, but got {img.ndim}')
    h_space = np.arange(0, h - crop_size + 1, step)
    if h - (h_space[-1] + crop_size) > thresh_size:
        h_space = np.append(h_space, h - crop_size)
--- a/scripts/generate_meta_info.py
+++ b/scripts/generate_meta_info.py
@@ -11,15 +11,17 @@ def main(args):
        for img_path in img_paths:
            status = True
            if args.check:
                # read the image once for check, as some images may have errors
                try:
                    img = cv2.imread(img_path)
-                except Exception as error:
+                except (IOError, OSError) as error:
                    print(f'Read {img_path} error: {error}')
                    status = False
                if img is None:
                    status = False
                    print(f'Img is None: {img_path}')
            if status:
                # get the relative path
                img_name = os.path.relpath(img_path, root)
                print(img_name)
                txt_file.write(f'{img_name}\n')
--- a/scripts/generate_meta_info_pairdata.py
+++ b/scripts/generate_meta_info_pairdata.py
@@ -5,6 +5,7 @@ import os
 def main(args):
    txt_file = open(args.meta_info, 'w')
    # sca images
    img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
    img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))
@@ -12,6 +13,7 @@ def main(args):
                                                    f'{len(img_paths_gt)} and {len(img_paths_lq)}.')
    for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
        # get the relative paths
        img_name_gt = os.path.relpath(img_path_gt, args.root[0])
        img_name_lq = os.path.relpath(img_path_lq, args.root[1])
        print(f'{img_name_gt}, {img_name_lq}')
@@ -19,7 +21,7 @@ def main(args):
 if __name__ == '__main__':
-    """Generate meta info (txt file) for paired images.
+    """This script is used to generate meta info (txt file) for paired images.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
--- a/scripts/generate_multiscale_DF2K.py
+++ b/scripts/generate_multiscale_DF2K.py
@@ -5,7 +5,6 @@ from PIL import Image
 def main(args):
    # For DF2K, we consider the following three scales,
    # and the smallest image whose shortest edge is 400
    scale_list = [0.75, 0.5, 1 / 3]
@@ -37,6 +36,9 @@ def main(args):
 if __name__ == '__main__':
    """Generate multi-scale versions for GT images with LANCZOS resampling.
    It is now used for DF2K dataset (DIV2K + Flickr 2K)
    """
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
    parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
--- a/scripts/pytorch2onnx.py
+++ b/scripts/pytorch2onnx.py
@@ -1,17 +1,36 @@
 import argparse
 import torch
 import torch.onnx
 from basicsr.archs.rrdbnet_arch import RRDBNet
 # An instance of your model
 model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
 model.load_state_dict(torch.load('experiments/pretrained_models/RealESRGAN_x4plus.pth')['params_ema'])
 # set the train mode to false since we will only run the forward pass.
 model.train(False)
 model.cpu().eval()
-# An example input you would normally provide to your model's forward() method
+def main(args):
-x = torch.rand(1, 3, 64, 64)
+    # An instance of the model
    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
    if args.params:
        keyname = 'params'
    else:
        keyname = 'params_ema'
    model.load_state_dict(torch.load(args.input)[keyname])
    # set the train mode to false since we will only run the forward pass.
    model.train(False)
    model.cpu().eval()
-# Export the model
+    # An example input
-with torch.no_grad():
+    x = torch.rand(1, 3, 64, 64)
-    torch_out = torch.onnx._export(model, x, 'realesrgan-x4.onnx', opset_version=11, export_params=True)
+    # Export the model
    with torch.no_grad():
        torch_out = torch.onnx._export(model, x, args.output, opset_version=11, export_params=True)
    print(torch_out.shape)
 if __name__ == '__main__':
    """Convert pytorch model to onnx models"""
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input', type=str, default='experiments/pretrained_models/RealESRGAN_x4plus.pth', help='Input model path')
    parser.add_argument('--output', type=str, default='realesrgan-x4.onnx', help='Output onnx path')
    parser.add_argument('--params', action='store_false', help='Use params instead of params_ema')
    args = parser.parse_args()
    main(args)
--- a/setup.cfg
+++ b/setup.cfg
@@ -17,6 +17,17 @@ line_length = 120
 multi_line_output = 0
 known_standard_library = pkg_resources,setuptools
 known_first_party = realesrgan
-known_third_party = PIL,basicsr,cv2,numpy,torch,torchvision,tqdm
+known_third_party = PIL,basicsr,cv2,numpy,pytest,torch,torchvision,tqdm,yaml
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
 [codespell]
 skip = .git,./docs/build
 count =
 quiet-level = 3
 [aliases]
 test=pytest
 [tool:pytest]
 addopts=tests/
--- a/setup.py
+++ b/setup.py
@@ -43,12 +43,6 @@ def get_git_hash():
 def get_hash():
    if os.path.exists('.git'):
        sha = get_git_hash()[:7]
    elif os.path.exists(version_file):
        try:
            from realesrgan.version import __version__
            sha = __version__.split('+')[-1]
        except ImportError:
            raise ImportError('Unable to get git version')
    else:
        sha = 'unknown'
--- a/tests/data/gt.lmdb/data.mdb
+++ b/tests/data/gt.lmdb/data.mdb
--- a/tests/data/gt.lmdb/lock.mdb
+++ b/tests/data/gt.lmdb/lock.mdb
--- a/tests/data/gt.lmdb/meta_info.txt
+++ b/tests/data/gt.lmdb/meta_info.txt
@@ -0,0 +1,2 @@
 baboon.png (480,500,3) 1
 comic.png (360,240,3) 1
--- a/tests/data/gt/baboon.png
+++ b/tests/data/gt/baboon.png
--- a/tests/data/gt/comic.png
+++ b/tests/data/gt/comic.png
--- a/tests/data/lq.lmdb/data.mdb
+++ b/tests/data/lq.lmdb/data.mdb
--- a/tests/data/lq.lmdb/lock.mdb
+++ b/tests/data/lq.lmdb/lock.mdb
--- a/tests/data/lq.lmdb/meta_info.txt
+++ b/tests/data/lq.lmdb/meta_info.txt
@@ -0,0 +1,2 @@
 baboon.png (120,125,3) 1
 comic.png (80,60,3) 1
--- a/tests/data/lq/baboon.png
+++ b/tests/data/lq/baboon.png
--- a/tests/data/lq/comic.png
+++ b/tests/data/lq/comic.png
--- a/tests/data/meta_info_gt.txt
+++ b/tests/data/meta_info_gt.txt
@@ -0,0 +1,2 @@
 baboon.png
 comic.png
--- a/tests/data/meta_info_pair.txt
+++ b/tests/data/meta_info_pair.txt
@@ -0,0 +1,2 @@
 gt/baboon.png, lq/baboon.png
 gt/comic.png, lq/comic.png
--- a/tests/data/test_realesrgan_dataset.yml
+++ b/tests/data/test_realesrgan_dataset.yml
@@ -0,0 +1,28 @@
 name: Demo
 type: RealESRGANDataset
 dataroot_gt: tests/data/gt
 meta_info: tests/data/meta_info_gt.txt
 io_backend:
  type: disk
 blur_kernel_size: 21
 kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
 kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
 sinc_prob: 1
 blur_sigma: [0.2, 3]
 betag_range: [0.5, 4]
 betap_range: [1, 2]
 blur_kernel_size2: 21
 kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
 kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
 sinc_prob2: 1
 blur_sigma2: [0.2, 1.5]
 betag_range2: [0.5, 4]
 betap_range2: [1, 2]
 final_sinc_prob: 1
 gt_size: 128
 use_hflip: True
 use_rot: False
--- a/tests/data/test_realesrgan_model.yml
+++ b/tests/data/test_realesrgan_model.yml
@@ -0,0 +1,115 @@
 scale: 4
 num_gpu: 1
 manual_seed: 0
 is_train: True
 dist: False
 # ----------------- options for synthesizing training data ----------------- #
 # USM the ground-truth
 l1_gt_usm: True
 percep_gt_usm: True
 gan_gt_usm: False
 # the first degradation process
 resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
 resize_range: [0.15, 1.5]
 gaussian_noise_prob: 1
 noise_range: [1, 30]
 poisson_scale_range: [0.05, 3]
 gray_noise_prob: 1
 jpeg_range: [30, 95]
 # the second degradation process
 second_blur_prob: 1
 resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
 resize_range2: [0.3, 1.2]
 gaussian_noise_prob2: 1
 noise_range2: [1, 25]
 poisson_scale_range2: [0.05, 2.5]
 gray_noise_prob2: 1
 jpeg_range2: [30, 95]
 gt_size: 32
 queue_size: 1
 # network structures
 network_g:
  type: RRDBNet
  num_in_ch: 3
  num_out_ch: 3
  num_feat: 4
  num_block: 1
  num_grow_ch: 2
 network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
  num_feat: 2
  skip_connection: True
 # path
 path:
  pretrain_network_g: ~
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~
 # training settings
 train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.99]
  optim_d:
    type: Adam
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.99]
  scheduler:
    type: MultiStepLR
    milestones: [400000]
    gamma: 0.5
  total_iter: 400000
  warmup_iter: -1  # no warm up
  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean
  # perceptual loss (content and style losses)
  perceptual_opt:
    type: PerceptualLoss
    layer_weights:
      # before relu
      'conv1_2': 0.1
      'conv2_2': 0.1
      'conv3_4': 1
      'conv4_4': 1
      'conv5_4': 1
    vgg_type: vgg19
    use_input_norm: true
    perceptual_weight: !!float 1.0
    style_weight: 0
    range_norm: false
    criterion: l1
  # gan loss
  gan_opt:
    type: GANLoss
    gan_type: vanilla
    real_label_val: 1.0
    fake_label_val: 0.0
    loss_weight: !!float 1e-1
  net_d_iters: 1
  net_d_init_iters: 0
 # validation settings
 val:
  val_freq: !!float 5e3
  save_img: False
--- a/tests/data/test_realesrgan_paired_dataset.yml
+++ b/tests/data/test_realesrgan_paired_dataset.yml
@@ -0,0 +1,13 @@
 name: Demo
 type: RealESRGANPairedDataset
 scale: 4
 dataroot_gt: tests/data
 dataroot_lq: tests/data
 meta_info: tests/data/meta_info_pair.txt
 io_backend:
  type: disk
 phase: train
 gt_size: 128
 use_hflip: True
 use_rot: False
--- a/tests/data/test_realesrnet_model.yml
+++ b/tests/data/test_realesrnet_model.yml
@@ -0,0 +1,75 @@
 scale: 4
 num_gpu: 1
 manual_seed: 0
 is_train: True
 dist: False
 # ----------------- options for synthesizing training data ----------------- #
 gt_usm: True  # USM the ground-truth
 # the first degradation process
 resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
 resize_range: [0.15, 1.5]
 gaussian_noise_prob: 1
 noise_range: [1, 30]
 poisson_scale_range: [0.05, 3]
 gray_noise_prob: 1
 jpeg_range: [30, 95]
 # the second degradation process
 second_blur_prob: 1
 resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
 resize_range2: [0.3, 1.2]
 gaussian_noise_prob2: 1
 noise_range2: [1, 25]
 poisson_scale_range2: [0.05, 2.5]
 gray_noise_prob2: 1
 jpeg_range2: [30, 95]
 gt_size: 32
 queue_size: 1
 # network structures
 network_g:
  type: RRDBNet
  num_in_ch: 3
  num_out_ch: 3
  num_feat: 4
  num_block: 1
  num_grow_ch: 2
 # path
 path:
  pretrain_network_g: ~
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~
 # training settings
 train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]
  scheduler:
    type: MultiStepLR
    milestones: [1000000]
    gamma: 0.5
  total_iter: 1000000
  warmup_iter: -1  # no warm up
  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean
 # validation settings
 val:
  val_freq: !!float 5e3
  save_img: False
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -0,0 +1,151 @@
 import pytest
 import yaml
 from realesrgan.data.realesrgan_dataset import RealESRGANDataset
 from realesrgan.data.realesrgan_paired_dataset import RealESRGANPairedDataset
 def test_realesrgan_dataset():
    with open('tests/data/test_realesrgan_dataset.yml', mode='r') as f:
        opt = yaml.load(f, Loader=yaml.FullLoader)
    dataset = RealESRGANDataset(opt)
    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
    assert len(dataset) == 2  # whether to read correct meta info
    assert dataset.kernel_list == [
        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
    ]  # correct initialization the degradation configurations
    assert dataset.betag_range2 == [0.5, 4]
    # test __getitem__
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 400, 400)
    assert result['kernel1'].shape == (21, 21)
    assert result['kernel2'].shape == (21, 21)
    assert result['sinc_kernel'].shape == (21, 21)
    assert result['gt_path'] == 'tests/data/gt/baboon.png'
    # ------------------ test lmdb backend -------------------- #
    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
    opt['io_backend']['type'] = 'lmdb'
    dataset = RealESRGANDataset(opt)
    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
    assert len(dataset.paths) == 2  # whether to read correct meta info
    assert dataset.kernel_list == [
        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
    ]  # correct initialization the degradation configurations
    assert dataset.betag_range2 == [0.5, 4]
    # test __getitem__
    result = dataset.__getitem__(1)
    # check returned keys
    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 400, 400)
    assert result['kernel1'].shape == (21, 21)
    assert result['kernel2'].shape == (21, 21)
    assert result['sinc_kernel'].shape == (21, 21)
    assert result['gt_path'] == 'comic'
    # ------------------ test with sinc_prob = 0 -------------------- #
    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
    opt['io_backend']['type'] = 'lmdb'
    opt['sinc_prob'] = 0
    opt['sinc_prob2'] = 0
    opt['final_sinc_prob'] = 0
    dataset = RealESRGANDataset(opt)
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 400, 400)
    assert result['kernel1'].shape == (21, 21)
    assert result['kernel2'].shape == (21, 21)
    assert result['sinc_kernel'].shape == (21, 21)
    assert result['gt_path'] == 'baboon'
    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
    with pytest.raises(ValueError):
        opt['dataroot_gt'] = 'tests/data/gt'
        opt['io_backend']['type'] = 'lmdb'
        dataset = RealESRGANDataset(opt)
 def test_realesrgan_paired_dataset():
    with open('tests/data/test_realesrgan_paired_dataset.yml', mode='r') as f:
        opt = yaml.load(f, Loader=yaml.FullLoader)
    dataset = RealESRGANPairedDataset(opt)
    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
    assert len(dataset) == 2  # whether to read correct meta info
    # test __getitem__
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 128, 128)
    assert result['lq'].shape == (3, 32, 32)
    assert result['gt_path'] == 'tests/data/gt/baboon.png'
    assert result['lq_path'] == 'tests/data/lq/baboon.png'
    # ------------------ test lmdb backend -------------------- #
    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
    opt['dataroot_lq'] = 'tests/data/lq.lmdb'
    opt['io_backend']['type'] = 'lmdb'
    dataset = RealESRGANPairedDataset(opt)
    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
    assert len(dataset) == 2  # whether to read correct meta info
    # test __getitem__
    result = dataset.__getitem__(1)
    # check returned keys
    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 128, 128)
    assert result['lq'].shape == (3, 32, 32)
    assert result['gt_path'] == 'comic'
    assert result['lq_path'] == 'comic'
    # ------------------ test paired_paths_from_folder -------------------- #
    opt['dataroot_gt'] = 'tests/data/gt'
    opt['dataroot_lq'] = 'tests/data/lq'
    opt['io_backend'] = dict(type='disk')
    opt['meta_info'] = None
    dataset = RealESRGANPairedDataset(opt)
    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
    assert len(dataset) == 2  # whether to read correct meta info
    # test __getitem__
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 128, 128)
    assert result['lq'].shape == (3, 32, 32)
    # ------------------ test normalization -------------------- #
    dataset.mean = [0.5, 0.5, 0.5]
    dataset.std = [0.5, 0.5, 0.5]
    # test __getitem__
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 128, 128)
    assert result['lq'].shape == (3, 32, 32)
--- a/tests/test_discriminator_arch.py
+++ b/tests/test_discriminator_arch.py
@@ -0,0 +1,19 @@
 import torch
 from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
 def test_unetdiscriminatorsn():
    """Test arch: UNetDiscriminatorSN."""
    # model init and forward (cpu)
    net = UNetDiscriminatorSN(num_in_ch=3, num_feat=4, skip_connection=True)
    img = torch.rand((1, 3, 32, 32), dtype=torch.float32)
    output = net(img)
    assert output.shape == (1, 1, 32, 32)
    # model init and forward (gpu)
    if torch.cuda.is_available():
        net.cuda()
        output = net(img.cuda())
        assert output.shape == (1, 1, 32, 32)
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -0,0 +1,126 @@
 import torch
 import yaml
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from basicsr.data.paired_image_dataset import PairedImageDataset
 from basicsr.losses.losses import GANLoss, L1Loss, PerceptualLoss
 from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
 from realesrgan.models.realesrgan_model import RealESRGANModel
 from realesrgan.models.realesrnet_model import RealESRNetModel
 def test_realesrnet_model():
    with open('tests/data/test_realesrnet_model.yml', mode='r') as f:
        opt = yaml.load(f, Loader=yaml.FullLoader)
    # build model
    model = RealESRNetModel(opt)
    # test attributes
    assert model.__class__.__name__ == 'RealESRNetModel'
    assert isinstance(model.net_g, RRDBNet)
    assert isinstance(model.cri_pix, L1Loss)
    assert isinstance(model.optimizers[0], torch.optim.Adam)
    # prepare data
    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
    model.feed_data(data)
    # check dequeue
    model.feed_data(data)
    # check data shape
    assert model.lq.shape == (1, 3, 8, 8)
    assert model.gt.shape == (1, 3, 32, 32)
    # change probability to test if-else
    model.opt['gaussian_noise_prob'] = 0
    model.opt['gray_noise_prob'] = 0
    model.opt['second_blur_prob'] = 0
    model.opt['gaussian_noise_prob2'] = 0
    model.opt['gray_noise_prob2'] = 0
    model.feed_data(data)
    # check data shape
    assert model.lq.shape == (1, 3, 8, 8)
    assert model.gt.shape == (1, 3, 32, 32)
    # ----------------- test nondist_validation -------------------- #
    # construct dataloader
    dataset_opt = dict(
        name='Demo',
        dataroot_gt='tests/data/gt',
        dataroot_lq='tests/data/lq',
        io_backend=dict(type='disk'),
        scale=4,
        phase='val')
    dataset = PairedImageDataset(dataset_opt)
    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
    assert model.is_train is True
    model.nondist_validation(dataloader, 1, None, False)
    assert model.is_train is True
 def test_realesrgan_model():
    with open('tests/data/test_realesrgan_model.yml', mode='r') as f:
        opt = yaml.load(f, Loader=yaml.FullLoader)
    # build model
    model = RealESRGANModel(opt)
    # test attributes
    assert model.__class__.__name__ == 'RealESRGANModel'
    assert isinstance(model.net_g, RRDBNet)  # generator
    assert isinstance(model.net_d, UNetDiscriminatorSN)  # discriminator
    assert isinstance(model.cri_pix, L1Loss)
    assert isinstance(model.cri_perceptual, PerceptualLoss)
    assert isinstance(model.cri_gan, GANLoss)
    assert isinstance(model.optimizers[0], torch.optim.Adam)
    assert isinstance(model.optimizers[1], torch.optim.Adam)
    # prepare data
    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
    model.feed_data(data)
    # check dequeue
    model.feed_data(data)
    # check data shape
    assert model.lq.shape == (1, 3, 8, 8)
    assert model.gt.shape == (1, 3, 32, 32)
    # change probability to test if-else
    model.opt['gaussian_noise_prob'] = 0
    model.opt['gray_noise_prob'] = 0
    model.opt['second_blur_prob'] = 0
    model.opt['gaussian_noise_prob2'] = 0
    model.opt['gray_noise_prob2'] = 0
    model.feed_data(data)
    # check data shape
    assert model.lq.shape == (1, 3, 8, 8)
    assert model.gt.shape == (1, 3, 32, 32)
    # ----------------- test nondist_validation -------------------- #
    # construct dataloader
    dataset_opt = dict(
        name='Demo',
        dataroot_gt='tests/data/gt',
        dataroot_lq='tests/data/lq',
        io_backend=dict(type='disk'),
        scale=4,
        phase='val')
    dataset = PairedImageDataset(dataset_opt)
    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
    assert model.is_train is True
    model.nondist_validation(dataloader, 1, None, False)
    assert model.is_train is True
    # ----------------- test optimize_parameters -------------------- #
    model.feed_data(data)
    model.optimize_parameters(1)
    assert model.output.shape == (1, 3, 32, 32)
    assert isinstance(model.log_dict, dict)
    # check returned keys
    expected_keys = ['l_g_pix', 'l_g_percep', 'l_g_gan', 'l_d_real', 'out_d_real', 'l_d_fake', 'out_d_fake']
    assert set(expected_keys).issubset(set(model.log_dict.keys()))
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -0,0 +1,87 @@
 import numpy as np
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from realesrgan.utils import RealESRGANer
 def test_realesrganer():
    # initialize with default model
    restorer = RealESRGANer(
        scale=4,
        model_path='experiments/pretrained_models/RealESRGAN_x4plus.pth',
        model=None,
        tile=10,
        tile_pad=10,
        pre_pad=2,
        half=False)
    assert isinstance(restorer.model, RRDBNet)
    assert restorer.half is False
    # initialize with user-defined model
    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
    restorer = RealESRGANer(
        scale=4,
        model_path='experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth',
        model=model,
        tile=10,
        tile_pad=10,
        pre_pad=2,
        half=True)
    # test attribute
    assert isinstance(restorer.model, RRDBNet)
    assert restorer.half is True
    # ------------------ test pre_process ---------------- #
    img = np.random.random((12, 12, 3)).astype(np.float32)
    restorer.pre_process(img)
    assert restorer.img.shape == (1, 3, 14, 14)
    # with modcrop
    restorer.scale = 1
    restorer.pre_process(img)
    assert restorer.img.shape == (1, 3, 16, 16)
    # ------------------ test process ---------------- #
    restorer.process()
    assert restorer.output.shape == (1, 3, 64, 64)
    # ------------------ test post_process ---------------- #
    restorer.mod_scale = 4
    output = restorer.post_process()
    assert output.shape == (1, 3, 60, 60)
    # ------------------ test tile_process ---------------- #
    restorer.scale = 4
    img = np.random.random((12, 12, 3)).astype(np.float32)
    restorer.pre_process(img)
    restorer.tile_process()
    assert restorer.output.shape == (1, 3, 64, 64)
    # ------------------ test enhance ---------------- #
    img = np.random.random((12, 12, 3)).astype(np.float32)
    result = restorer.enhance(img, outscale=2)
    assert result[0].shape == (24, 24, 3)
    assert result[1] == 'RGB'
    # ------------------ test enhance with 16-bit image---------------- #
    img = np.random.random((4, 4, 3)).astype(np.uint16) + 512
    result = restorer.enhance(img, outscale=2)
    assert result[0].shape == (8, 8, 3)
    assert result[1] == 'RGB'
    # ------------------ test enhance with gray image---------------- #
    img = np.random.random((4, 4)).astype(np.float32)
    result = restorer.enhance(img, outscale=2)
    assert result[0].shape == (8, 8)
    assert result[1] == 'L'
    # ------------------ test enhance with RGBA---------------- #
    img = np.random.random((4, 4, 4)).astype(np.float32)
    result = restorer.enhance(img, outscale=2)
    assert result[0].shape == (8, 8, 4)
    assert result[1] == 'RGBA'
    # ------------------ test enhance with RGBA, alpha_upsampler---------------- #
    restorer.tile_size = 0
    img = np.random.random((4, 4, 4)).astype(np.float32)
    result = restorer.enhance(img, outscale=2, alpha_upsampler=None)
    assert result[0].shape == (8, 8, 4)
    assert result[1] == 'RGBA'
Author	SHA1	Message	Date
Xintao	3e0085aeda	V0.2.2.6	2021-12-09 17:41:19 +08:00
Xintao	42110857ef	add unittest for model and utils	2021-11-28 19:54:19 +08:00
Xintao	1d180efaf3	add unittest for dataset and archs	2021-11-28 15:59:14 +08:00
Xintao	7dd860a881	catch more specific errors	2021-11-24 00:14:05 +08:00
Xintao	35ee6f781e	improve codes comments	2021-11-23 00:52:00 +08:00
Xintao	c9023b3d7a	Update README_CN.md (#142 ) * update contribution * updte readme * updte readme * update readme-cn * update readme-cn * update readme-cn * update readme-cn * update readme-cn	2021-11-01 19:16:48 +08:00
Asiimoviet	fb79d65ff3	Added Chinese README (#126 ) * Added Chinese README * Update README_CN.md * Create README_CN.md	2021-11-01 17:00:06 +08:00
Xintao	3338b31f48	update setup.py, V0.2.2.5	2021-10-22 17:16:43 +08:00
Xintao	501efe3da6	update ReadMe	2021-10-17 01:33:45 +08:00
Xintao	8beb7ed17d	add feedback of anime models	2021-10-17 01:27:04 +08:00
Xintao	e2d30f9ea4	update readme: add usage guidance	2021-10-17 01:03:55 +08:00
Xintao	d715e3d26a	update readme	2021-10-16 23:30:57 +08:00
Xintao	772923e207	add codespell to pre-commit hook	2021-09-27 15:35:37 +08:00
Christian Clauss	14247a89d9	Fix typos discovered by codespell (#95 ) * Improve performance * !fixup Fix typo discovered by codespell * fixup! Fix typo discovered by codespell * fixup! Add codespell to lint process	2021-09-27 14:53:03 +08:00
Xintao	aa584e05bc	minor updates on Training.md	2021-09-17 10:30:52 +08:00
Xintao	b525d1793b	add trainining with one gpu	2021-09-17 10:13:25 +08:00
Xintao	0ad2e9c61e	set num_gpu to auto in options	2021-09-17 10:07:09 +08:00
Xintao	90ddf13b5e	Merge branch 'master' of github.com:xinntao/Real-ESRGAN	2021-09-07 21:28:03 +08:00
Xintao	8675208bc9	update: format and standard	2021-09-07 21:27:45 +08:00
Pratik Goyal	8f8536b6d1	Minor spelling correction (#67 )	2021-09-03 14:38:03 +08:00
		`@@ -0,0 +1,2 @@`
							`baboon.png (480,500,3) 1`
							`comic.png (360,240,3) 1`
		`@@ -0,0 +1,2 @@`
							`baboon.png (120,125,3) 1`
							`comic.png (80,60,3) 1`
		`@@ -0,0 +1,2 @@`
							`gt/baboon.png, lq/baboon.png`
							`gt/comic.png, lq/comic.png`