Repository: xinntao/Real-ESRGAN
Branch: master
Commit: a4abfb2979a7
Files: 69
Total size: 232.9 KB
Directory structure:
gitextract_mbg1yq50/
├── .github/
│ └── workflows/
│ ├── publish-pip.yml
│ ├── pylint.yml
│ └── release.yml
├── .gitignore
├── .pre-commit-config.yaml
├── .vscode/
│ └── settings.json
├── CODE_OF_CONDUCT.md
├── LICENSE
├── MANIFEST.in
├── README.md
├── README_CN.md
├── VERSION
├── cog.yaml
├── cog_predict.py
├── docs/
│ ├── CONTRIBUTING.md
│ ├── FAQ.md
│ ├── Training.md
│ ├── Training_CN.md
│ ├── anime_comparisons.md
│ ├── anime_comparisons_CN.md
│ ├── anime_model.md
│ ├── anime_video_model.md
│ ├── feedback.md
│ ├── model_zoo.md
│ └── ncnn_conversion.md
├── inference_realesrgan.py
├── inference_realesrgan_video.py
├── options/
│ ├── finetune_realesrgan_x4plus.yml
│ ├── finetune_realesrgan_x4plus_pairdata.yml
│ ├── train_realesrgan_x2plus.yml
│ ├── train_realesrgan_x4plus.yml
│ ├── train_realesrnet_x2plus.yml
│ └── train_realesrnet_x4plus.yml
├── realesrgan/
│ ├── __init__.py
│ ├── archs/
│ │ ├── __init__.py
│ │ ├── discriminator_arch.py
│ │ └── srvgg_arch.py
│ ├── data/
│ │ ├── __init__.py
│ │ ├── realesrgan_dataset.py
│ │ └── realesrgan_paired_dataset.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── realesrgan_model.py
│ │ └── realesrnet_model.py
│ ├── train.py
│ └── utils.py
├── requirements.txt
├── scripts/
│ ├── extract_subimages.py
│ ├── generate_meta_info.py
│ ├── generate_meta_info_pairdata.py
│ ├── generate_multiscale_DF2K.py
│ └── pytorch2onnx.py
├── setup.cfg
├── setup.py
└── tests/
├── data/
│ ├── gt.lmdb/
│ │ ├── data.mdb
│ │ ├── lock.mdb
│ │ └── meta_info.txt
│ ├── lq.lmdb/
│ │ ├── data.mdb
│ │ ├── lock.mdb
│ │ └── meta_info.txt
│ ├── meta_info_gt.txt
│ ├── meta_info_pair.txt
│ ├── test_realesrgan_dataset.yml
│ ├── test_realesrgan_model.yml
│ ├── test_realesrgan_paired_dataset.yml
│ └── test_realesrnet_model.yml
├── test_dataset.py
├── test_discriminator_arch.py
├── test_model.py
└── test_utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/publish-pip.yml
================================================
name: PyPI Publish
on: push
jobs:
build-n-publish:
runs-on: ubuntu-latest
if: startsWith(github.event.ref, 'refs/tags')
steps:
- uses: actions/checkout@v2
- name: Set up Python 3.8
uses: actions/setup-python@v1
with:
python-version: 3.8
- name: Upgrade pip
run: pip install pip --upgrade
- name: Install PyTorch (cpu)
run: pip install torch==1.7.0+cpu torchvision==0.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
- name: Install dependencies
run: |
pip install basicsr
pip install facexlib
pip install gfpgan
pip install -r requirements.txt
- name: Build and install
run: rm -rf .eggs && pip install -e .
- name: Build for distribution
run: python setup.py sdist bdist_wheel
- name: Publish distribution to PyPI
uses: pypa/gh-action-pypi-publish@master
with:
password: ${{ secrets.PYPI_API_TOKEN }}
================================================
FILE: .github/workflows/pylint.yml
================================================
name: PyLint
on: [push, pull_request]
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: [3.8]
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
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
================================================
FILE: .github/workflows/release.yml
================================================
name: release
on:
push:
tags:
- '*'
jobs:
build:
permissions: write-all
name: Create Release
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Create Release
id: create_release
uses: actions/create-release@v1
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
tag_name: ${{ github.ref }}
release_name: Real-ESRGAN ${{ github.ref }} Release Note
body: |
🚀 See you again 😸
🚀Have a nice day 😸 and happy everyday 😃
🚀 Long time no see ☄️
✨ **Highlights**
✅ [Features] Support ...
🐛 **Bug Fixes**
🌴 **Improvements**
📢📢📢
draft: true
prerelease: false
================================================
FILE: .gitignore
================================================
# ignored folders
datasets/*
experiments/*
results/*
tb_logger/*
wandb/*
tmp/*
weights/*
version.py
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
================================================
FILE: .pre-commit-config.yaml
================================================
repos:
# flake8
- repo: https://github.com/PyCQA/flake8
rev: 3.8.3
hooks:
- id: flake8
args: ["--config=setup.cfg", "--ignore=W504, W503"]
# modify known_third_party
- repo: https://github.com/asottile/seed-isort-config
rev: v2.2.0
hooks:
- id: seed-isort-config
# isort
- repo: https://github.com/timothycrosley/isort
rev: 5.2.2
hooks:
- id: isort
# yapf
- repo: https://github.com/pre-commit/mirrors-yapf
rev: v0.30.0
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
hooks:
- id: trailing-whitespace # Trim trailing whitespace
- id: check-yaml # Attempt to load all yaml files to verify syntax
- id: check-merge-conflict # Check for files that contain merge conflict strings
- id: double-quote-string-fixer # Replace double quoted strings with single quoted strings
- id: end-of-file-fixer # Make sure files end in a newline and only a newline
- id: requirements-txt-fixer # Sort entries in requirements.txt and remove incorrect entry for pkg-resources==0.0.0
- id: fix-encoding-pragma # Remove the coding pragma: # -*- coding: utf-8 -*-
args: ["--remove"]
- id: mixed-line-ending # Replace or check mixed line ending
args: ["--fix=lf"]
================================================
FILE: .vscode/settings.json
================================================
{
"files.trimTrailingWhitespace": true,
"editor.wordWrap": "on",
"editor.rulers": [
80,
120
],
"editor.renderWhitespace": "all",
"editor.renderControlCharacters": true,
"python.formatting.provider": "yapf",
"python.formatting.yapfArgs": [
"--style",
"{BASED_ON_STYLE = pep8, BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true, SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true, COLUMN_LIMIT = 120}"
],
"python.linting.flake8Enabled": true,
"python.linting.flake8Args": [
"max-line-length=120"
],
}
================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
xintao.wang@outlook.com or xintaowang@tencent.com.
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series
of actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within
the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.
================================================
FILE: LICENSE
================================================
BSD 3-Clause License
Copyright (c) 2021, Xintao Wang
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
================================================
FILE: MANIFEST.in
================================================
include assets/*
include inputs/*
include scripts/*.py
include inference_realesrgan.py
include VERSION
include LICENSE
include requirements.txt
include weights/README.md
================================================
FILE: README.md
================================================
##
👀[**Demos**](#-demos-videos) **|** 🚩[**Updates**](#-updates) **|** ⚡[**Usage**](#-quick-inference) **|** 🏰[**Model Zoo**](docs/model_zoo.md) **|** 🔧[Install](#-dependencies-and-installation) **|** 💻[Train](docs/Training.md) **|** ❓[FAQ](docs/FAQ.md) **|** 🎨[Contribution](docs/CONTRIBUTING.md)
[](https://github.com/xinntao/Real-ESRGAN/releases)
[](https://pypi.org/project/realesrgan/)
[](https://github.com/xinntao/Real-ESRGAN/issues)
[](https://github.com/xinntao/Real-ESRGAN/issues)
[](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE)
[](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/pylint.yml)
[](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/publish-pip.yml)
🔥 **AnimeVideo-v3 model (动漫视频小模型)**. Please see [[*anime video models*](docs/anime_video_model.md)] and [[*comparisons*](docs/anime_comparisons.md)]
🔥 **RealESRGAN_x4plus_anime_6B** for anime images **(动漫插图模型)**. Please see [[*anime_model*](docs/anime_model.md)]
1. :boom: **Update** online Replicate demo: [](https://replicate.com/xinntao/realesrgan)
1. Online Colab demo for Real-ESRGAN: [](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) **|** Online Colab demo for for Real-ESRGAN (**anime videos**): [](https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing)
1. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. You can find more information [here](#portable-executable-files-ncnn). The ncnn implementation is in [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan)
Real-ESRGAN aims at developing **Practical Algorithms for General Image/Video Restoration**.
We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
🌌 Thanks for your valuable feedbacks/suggestions. All the feedbacks are updated in [feedback.md](docs/feedback.md).
---
If Real-ESRGAN is helpful, please help to ⭐ this repo or recommend it to your friends 😊
Other recommended projects:
▶️ [GFPGAN](https://github.com/TencentARC/GFPGAN): A practical algorithm for real-world face restoration
▶️ [BasicSR](https://github.com/xinntao/BasicSR): An open-source image and video restoration toolbox
▶️ [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions.
▶️ [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison
▶️ [HandyFigure](https://github.com/xinntao/HandyFigure): Open source of paper figures
---
### 📖 Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
> [[Paper](https://arxiv.org/abs/2107.10833)] [[YouTube Video](https://www.youtube.com/watch?v=fxHWoDSSvSc)] [[B站讲解](https://www.bilibili.com/video/BV1H34y1m7sS/)] [[Poster](https://xinntao.github.io/projects/RealESRGAN_src/RealESRGAN_poster.pdf)] [[PPT slides](https://docs.google.com/presentation/d/1QtW6Iy8rm8rGLsJ0Ldti6kP-7Qyzy6XL/edit?usp=sharing&ouid=109799856763657548160&rtpof=true&sd=true)]
> [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)
> [Tencent ARC Lab](https://arc.tencent.com/en/ai-demos/imgRestore); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
---
## 🚩 Updates
- ✅ Add the **realesr-general-x4v3** model - a tiny small model for general scenes. It also supports the **-dn** option to balance the noise (avoiding over-smooth results). **-dn** is short for denoising strength.
- ✅ Update the **RealESRGAN AnimeVideo-v3** model. Please see [anime video models](docs/anime_video_model.md) and [comparisons](docs/anime_comparisons.md) for more details.
- ✅ Add small models for anime videos. More details are in [anime video models](docs/anime_video_model.md).
- ✅ Add the ncnn implementation [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
- ✅ 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)
- ✅ Support finetuning on your own data or paired data (*i.e.*, finetuning ESRGAN). See [here](docs/Training.md#Finetune-Real-ESRGAN-on-your-own-dataset)
- ✅ Integrate [GFPGAN](https://github.com/TencentARC/GFPGAN) to support **face enhancement**.
- ✅ Integrated 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). Thanks [@AK391](https://github.com/AK391)
- ✅ Support arbitrary scale with `--outscale` (It actually further resizes outputs with `LANCZOS4`). Add *RealESRGAN_x2plus.pth* model.
- ✅ [The inference code](inference_realesrgan.py) supports: 1) **tile** options; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images.
- ✅ The training codes have been released. A detailed guide can be found in [Training.md](docs/Training.md).
---
## 👀 Demos Videos
#### Bilibili
- [大闹天宫片段](https://www.bilibili.com/video/BV1ja41117zb)
- [Anime dance cut 动漫魔性舞蹈](https://www.bilibili.com/video/BV1wY4y1L7hT/)
- [海贼王片段](https://www.bilibili.com/video/BV1i3411L7Gy/)
#### YouTube
## 🔧 Dependencies and Installation
- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
- [PyTorch >= 1.7](https://pytorch.org/)
### Installation
1. Clone repo
```bash
git clone https://github.com/xinntao/Real-ESRGAN.git
cd Real-ESRGAN
```
1. Install dependent packages
```bash
# Install basicsr - https://github.com/xinntao/BasicSR
# We use BasicSR for both training and inference
pip install basicsr
# facexlib and gfpgan are for face enhancement
pip install facexlib
pip install gfpgan
pip install -r requirements.txt
python setup.py develop
```
---
## ⚡ Quick Inference
There are usually three ways to inference Real-ESRGAN.
1. [Online inference](#online-inference)
1. [Portable executable files (NCNN)](#portable-executable-files-ncnn)
1. [Python script](#python-script)
### Online inference
1. You can try in our website: [ARC Demo](https://arc.tencent.com/en/ai-demos/imgRestore) (now only support RealESRGAN_x4plus_anime_6B)
1. [Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) for Real-ESRGAN **|** [Colab Demo](https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing) for Real-ESRGAN (**anime videos**).
### Portable executable files (NCNN)
You can download [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**.
This executable file is **portable** and includes all the binaries and models required. No CUDA or PyTorch environment is needed.
You can simply run the following command (the Windows example, more information is in the README.md of each executable files):
```bash
./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n model_name
```
We have provided five models:
1. realesrgan-x4plus (default)
2. realesrnet-x4plus
3. realesrgan-x4plus-anime (optimized for anime images, small model size)
4. realesr-animevideov3 (animation video)
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 portable 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
-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 (can be 2, 3, 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 the pre-trained models. default=models
-n model-name model name (default=realesr-animevideov3, can be realesr-animevideov3 | realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)
-g gpu-id gpu device to use (default=auto) 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)
-v verbose output
```
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.
### Python script
#### 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 -n RealESRGAN_x4plus -i infile -o outfile [options]...
A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile --outscale 3.5 --face_enhance
-h show this help
-i --input Input image or folder. Default: inputs
-o --output Output folder. Default: results
-n --model_name Model name. Default: RealESRGAN_x4plus
-s, --outscale The final upsampling scale of the image. Default: 4
--suffix Suffix of the restored image. Default: out
-t, --tile Tile size, 0 for no tile during testing. Default: 0
--face_enhance Whether to use GFPGAN to enhance face. Default: False
--fp32 Use fp32 precision during inference. Default: fp16 (half precision).
--ext Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto
```
#### Inference general images
Download pre-trained models: [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 weights
```
Inference!
```bash
python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs --face_enhance
```
Results are in the `results` folder
#### Inference anime images
Pre-trained models: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)
More details and comparisons with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) are in [**anime_model.md**](docs/anime_model.md)
```bash
# download model
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P weights
# inference
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs
```
Results are in the `results` folder
---
## BibTeX
@InProceedings{wang2021realesrgan,
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},
booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
date = {2021}
}
## 📧 Contact
If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
## 🧩 Projects that use Real-ESRGAN
If you develop/use Real-ESRGAN in your projects, welcome to let me know.
- NCNN-Android: [RealSR-NCNN-Android](https://github.com/tumuyan/RealSR-NCNN-Android) by [tumuyan](https://github.com/tumuyan)
- VapourSynth: [vs-realesrgan](https://github.com/HolyWu/vs-realesrgan) by [HolyWu](https://github.com/HolyWu)
- NCNN: [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan)
**GUI**
- [Waifu2x-Extension-GUI](https://github.com/AaronFeng753/Waifu2x-Extension-GUI) by [AaronFeng753](https://github.com/AaronFeng753)
- [Squirrel-RIFE](https://github.com/Justin62628/Squirrel-RIFE) by [Justin62628](https://github.com/Justin62628)
- [Real-GUI](https://github.com/scifx/Real-GUI) by [scifx](https://github.com/scifx)
- [Real-ESRGAN_GUI](https://github.com/net2cn/Real-ESRGAN_GUI) by [net2cn](https://github.com/net2cn)
- [Real-ESRGAN-EGUI](https://github.com/WGzeyu/Real-ESRGAN-EGUI) by [WGzeyu](https://github.com/WGzeyu)
- [anime_upscaler](https://github.com/shangar21/anime_upscaler) by [shangar21](https://github.com/shangar21)
- [Upscayl](https://github.com/upscayl/upscayl) by [Nayam Amarshe](https://github.com/NayamAmarshe) and [TGS963](https://github.com/TGS963)
## 🤗 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).
- [Jared-02](https://github.com/Jared-02): Translate the Training.md to Chinese (中文).
================================================
FILE: README_CN.md
================================================
##
[](https://github.com/xinntao/Real-ESRGAN/releases)
[](https://pypi.org/project/realesrgan/)
[](https://github.com/xinntao/Real-ESRGAN/issues)
[](https://github.com/xinntao/Real-ESRGAN/issues)
[](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE)
[](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/pylint.yml)
[](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/publish-pip.yml)
:fire: 更新动漫视频的小模型 **RealESRGAN AnimeVideo-v3**. 更多信息在 [[动漫视频模型介绍](docs/anime_video_model.md)] 和 [[比较](docs/anime_comparisons_CN.md)] 中.
1. Real-ESRGAN的[Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) | Real-ESRGAN**动漫视频** 的[Colab Demo](https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing)
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/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-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)。
Real-ESRGAN 的目标是开发出**实用的图像/视频修复算法**。
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](docs/CONTRIBUTING.md)，所有的贡献者都会被列在[此处](README_CN.md#hugs-感谢)。
:milky_way: 感谢大家提供了很好的反馈。这些反馈会逐步更新在 [这个文档](docs/feedback.md)。
:question: 常见的问题可以在[FAQ.md](docs/FAQ.md)中找到答案。（好吧，现在还是空白的=-=||）
---
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:
其他推荐的项目：
:arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN): 实用的人脸复原算法
:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): 开源的图像和视频工具箱
:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): 提供与人脸相关的工具箱
: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
---
🚩更新
- ✅ 更新动漫视频的小模型 **RealESRGAN AnimeVideo-v3**. 更多信息在 [anime video models](docs/anime_video_model.md) 和 [comparisons](docs/anime_comparisons.md)中.
- ✅ 添加了针对动漫视频的小模型, 更多信息在 [anime video models](docs/anime_video_model.md) 中.
- ✅ 添加了ncnn 实现：[Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
- ✅ 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)
- ✅支持用户在自己的数据上进行微调 (finetune)：[详情](docs/Training.md#Finetune-Real-ESRGAN-on-your-own-dataset)
- ✅ 支持使用[GFPGAN](https://github.com/TencentARC/GFPGAN)**增强人脸**
- ✅ 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)
- ✅ 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
- ✅ [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.
- ✅ 训练代码已经发布，具体做法可查看：[Training.md](docs/Training.md)。
🧩使用Real-ESRGAN的项目
👋 如果你开发/使用/集成了Real-ESRGAN, 欢迎联系我添加
- NCNN-Android: [RealSR-NCNN-Android](https://github.com/tumuyan/RealSR-NCNN-Android) by [tumuyan](https://github.com/tumuyan)
- VapourSynth: [vs-realesrgan](https://github.com/HolyWu/vs-realesrgan) by [HolyWu](https://github.com/HolyWu)
- NCNN: [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan)
**易用的图形界面**
- [Waifu2x-Extension-GUI](https://github.com/AaronFeng753/Waifu2x-Extension-GUI) by [AaronFeng753](https://github.com/AaronFeng753)
- [Squirrel-RIFE](https://github.com/Justin62628/Squirrel-RIFE) by [Justin62628](https://github.com/Justin62628)
- [Real-GUI](https://github.com/scifx/Real-GUI) by [scifx](https://github.com/scifx)
- [Real-ESRGAN_GUI](https://github.com/net2cn/Real-ESRGAN_GUI) by [net2cn](https://github.com/net2cn)
- [Real-ESRGAN-EGUI](https://github.com/WGzeyu/Real-ESRGAN-EGUI) by [WGzeyu](https://github.com/WGzeyu)
- [anime_upscaler](https://github.com/shangar21/anime_upscaler) by [shangar21](https://github.com/shangar21)
- [RealESRGAN-GUI](https://github.com/Baiyuetribe/paper2gui/blob/main/Video%20Super%20Resolution/RealESRGAN-GUI.md) by [Baiyuetribe](https://github.com/Baiyuetribe)
👀Demo视频（B站）
- [大闹天宫片段](https://www.bilibili.com/video/BV1ja41117zb)
### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
> [[论文](https://arxiv.org/abs/2107.10833)] [项目主页] [[YouTube 视频](https://www.youtube.com/watch?v=fxHWoDSSvSc)] [[B站视频](https://www.bilibili.com/video/BV1H34y1m7sS/)] [[Poster](https://xinntao.github.io/projects/RealESRGAN_src/RealESRGAN_poster.pdf)] [[PPT](https://docs.google.com/presentation/d/1QtW6Iy8rm8rGLsJ0Ldti6kP-7Qyzy6XL/edit?usp=sharing&ouid=109799856763657548160&rtpof=true&sd=true)]
> [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)
> Tencent ARC Lab; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
---
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。
**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。**
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。
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。
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/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-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。
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 -n 模型名字
```
我们提供了五种模型：
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）
4. realesr-animevideov3 (针对动漫视频)
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
-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 (can be 2, 3, 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 the pre-trained models. default=models
-n model-name model name (default=realesr-animevideov3, can be realesr-animevideov3 | realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)
-g gpu-id gpu device to use (default=auto) 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)
-v verbose output
```
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）
---
## :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 weights
```
推断!
```bash
python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs --face_enhance
```
结果在`results`文件夹
### 动画图片
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)
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 weights
# 推断
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs
```
结果在`results`文件夹
### Python 脚本的用法
1. 虽然你使用了 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 -n RealESRGAN_x4plus -i infile -o outfile [options]...
A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile --outscale 3.5 --face_enhance
-h show this help
-i --input Input image or folder. Default: inputs
-o --output Output folder. Default: results
-n --model_name Model name. Default: RealESRGAN_x4plus
-s, --outscale The final upsampling scale of the image. Default: 4
--suffix Suffix of the restored image. Default: out
-t, --tile Tile size, 0 for no tile during testing. Default: 0
--face_enhance Whether to use GFPGAN to enhance face. Default: False
--fp32 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
请参见 [docs/model_zoo.md](docs/model_zoo.md)
## :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](docs/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).
- [Jared-02](https://github.com/Jared-02): 把 Training.md 文档 翻译成了中文。
================================================
FILE: VERSION
================================================
0.3.0
================================================
FILE: cog.yaml
================================================
# This file is used for constructing replicate env
image: "r8.im/tencentarc/realesrgan"
build:
gpu: true
python_version: "3.8"
system_packages:
- "libgl1-mesa-glx"
- "libglib2.0-0"
python_packages:
- "torch==1.7.1"
- "torchvision==0.8.2"
- "numpy==1.21.1"
- "lmdb==1.2.1"
- "opencv-python==4.5.3.56"
- "PyYAML==5.4.1"
- "tqdm==4.62.2"
- "yapf==0.31.0"
- "basicsr==1.4.2"
- "facexlib==0.2.5"
predict: "cog_predict.py:Predictor"
================================================
FILE: cog_predict.py
================================================
# flake8: noqa
# This file is used for deploying replicate models
# running: cog predict -i img=@inputs/00017_gray.png -i version='General - v3' -i scale=2 -i face_enhance=True -i tile=0
# push: cog push r8.im/xinntao/realesrgan
import os
os.system('pip install gfpgan')
os.system('python setup.py develop')
import cv2
import shutil
import tempfile
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from realesrgan.utils import RealESRGANer
try:
from cog import BasePredictor, Input, Path
from gfpgan import GFPGANer
except Exception:
print('please install cog and realesrgan package')
class Predictor(BasePredictor):
def setup(self):
os.makedirs('output', exist_ok=True)
# download weights
if not os.path.exists('weights/realesr-general-x4v3.pth'):
os.system(
'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./weights'
)
if not os.path.exists('weights/GFPGANv1.4.pth'):
os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./weights')
if not os.path.exists('weights/RealESRGAN_x4plus.pth'):
os.system(
'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./weights'
)
if not os.path.exists('weights/RealESRGAN_x4plus_anime_6B.pth'):
os.system(
'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P ./weights'
)
if not os.path.exists('weights/realesr-animevideov3.pth'):
os.system(
'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P ./weights'
)
def choose_model(self, scale, version, tile=0):
half = True if torch.cuda.is_available() else False
if version == 'General - RealESRGANplus':
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
model_path = 'weights/RealESRGAN_x4plus.pth'
self.upsampler = RealESRGANer(
scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
elif version == 'General - v3':
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
model_path = 'weights/realesr-general-x4v3.pth'
self.upsampler = RealESRGANer(
scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
elif version == 'Anime - anime6B':
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
model_path = 'weights/RealESRGAN_x4plus_anime_6B.pth'
self.upsampler = RealESRGANer(
scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
elif version == 'AnimeVideo - v3':
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
model_path = 'weights/realesr-animevideov3.pth'
self.upsampler = RealESRGANer(
scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
self.face_enhancer = GFPGANer(
model_path='weights/GFPGANv1.4.pth',
upscale=scale,
arch='clean',
channel_multiplier=2,
bg_upsampler=self.upsampler)
def predict(
self,
img: Path = Input(description='Input'),
version: str = Input(
description='RealESRGAN version. Please see [Readme] below for more descriptions',
choices=['General - RealESRGANplus', 'General - v3', 'Anime - anime6B', 'AnimeVideo - v3'],
default='General - v3'),
scale: float = Input(description='Rescaling factor', default=2),
face_enhance: bool = Input(
description='Enhance faces with GFPGAN. Note that it does not work for anime images/vidoes', default=False),
tile: int = Input(
description=
'Tile size. Default is 0, that is no tile. When encountering the out-of-GPU-memory issue, please specify it, e.g., 400 or 200',
default=0)
) -> Path:
if tile <= 100 or tile is None:
tile = 0
print(f'img: {img}. version: {version}. scale: {scale}. face_enhance: {face_enhance}. tile: {tile}.')
try:
extension = os.path.splitext(os.path.basename(str(img)))[1]
img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED)
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
elif len(img.shape) == 2:
img_mode = None
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
else:
img_mode = None
h, w = img.shape[0:2]
if h < 300:
img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
self.choose_model(scale, version, tile)
try:
if face_enhance:
_, _, output = self.face_enhancer.enhance(
img, has_aligned=False, only_center_face=False, paste_back=True)
else:
output, _ = self.upsampler.enhance(img, outscale=scale)
except RuntimeError as error:
print('Error', error)
print('If you encounter CUDA out of memory, try to set "tile" to a smaller size, e.g., 400.')
if img_mode == 'RGBA': # RGBA images should be saved in png format
extension = 'png'
# save_path = f'output/out.{extension}'
# cv2.imwrite(save_path, output)
out_path = Path(tempfile.mkdtemp()) / f'out.{extension}'
cv2.imwrite(str(out_path), output)
except Exception as error:
print('global exception: ', error)
finally:
clean_folder('output')
return out_path
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print(f'Failed to delete {file_path}. Reason: {e}')
================================================
FILE: docs/CONTRIBUTING.md
================================================
# Contributing to Real-ESRGAN
:art: Real-ESRGAN needs your contributions. Any contributions are welcome, such as new features/models/typo fixes/suggestions/maintenance, *etc*. See [CONTRIBUTING.md](docs/CONTRIBUTING.md). All contributors are list [here](README.md#hugs-acknowledgement).
We like open-source and want to develop practical algorithms for general image restoration. However, individual strength is limited. So, any kinds of contributions are welcome, such as:
- New features
- New models (your fine-tuned models)
- Bug fixes
- Typo fixes
- Suggestions
- Maintenance
- Documents
- *etc*
## Workflow
1. Fork and pull the latest Real-ESRGAN repository
1. Checkout a new branch (do not use master branch for PRs)
1. Commit your changes
1. Create a PR
**Note**:
1. Please check the code style and linting
1. The style configuration is specified in [setup.cfg](setup.cfg)
1. If you use VSCode, the settings are configured in [.vscode/settings.json](.vscode/settings.json)
1. Strongly recommend using `pre-commit hook`. It will check your code style and linting before your commit.
1. In the root path of project folder, run `pre-commit install`
1. The pre-commit configuration is listed in [.pre-commit-config.yaml](.pre-commit-config.yaml)
1. Better to [open a discussion](https://github.com/xinntao/Real-ESRGAN/discussions) before large changes.
1. Welcome to discuss :sunglasses:. I will try my best to join the discussion.
## TODO List
:zero: The most straightforward way of improving model performance is to fine-tune on some specific datasets.
Here are some TODOs:
- [ ] optimize for human faces
- [ ] optimize for texts
- [ ] 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:
================================================
FILE: docs/FAQ.md
================================================
# FAQ
1. **Q: How to select models?**
A: Please refer to [docs/model_zoo.md](docs/model_zoo.md)
1. **Q: Can `face_enhance` be used for anime images/animation videos?**
A: No, it can only be used for real faces. It is recommended not to use this option for anime images/animation videos to save GPU memory.
1. **Q: Error "slow_conv2d_cpu" not implemented for 'Half'**
A: In order to save GPU memory consumption and speed up inference, Real-ESRGAN uses half precision (fp16) during inference by default. However, some operators for half inference are not implemented in CPU mode. You need to add **`--fp32` option** for the commands. For example, `python inference_realesrgan.py -n RealESRGAN_x4plus.pth -i inputs --fp32`.
================================================
FILE: docs/Training.md
================================================
# :computer: How to Train/Finetune Real-ESRGAN
- [Train Real-ESRGAN](#train-real-esrgan)
- [Overview](#overview)
- [Dataset Preparation](#dataset-preparation)
- [Train Real-ESRNet](#Train-Real-ESRNet)
- [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-your-own-paired-data)
[English](Training.md) **|** [简体中文](Training_CN.md)
## Train Real-ESRGAN
### Overview
The training has been divided into two stages. These two stages have the same data synthesis process and training pipeline, except for the loss functions. Specifically,
1. We first train Real-ESRNet with L1 loss from the pre-trained model ESRGAN.
1. We then use the trained Real-ESRNet model as an initialization of the generator, and train the Real-ESRGAN with a combination of L1 loss, perceptual loss and GAN loss.
### Dataset Preparation
We use DF2K (DIV2K and Flickr2K) + OST datasets for our training. Only HR images are required.
You can download from :
1. DIV2K: http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip
2. Flickr2K: https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
3. OST: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip
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.
You can use the [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) script to generate multi-scale images.
Note that this step can be omitted if you just want to have a fast try.
```bash
python scripts/generate_multiscale_DF2K.py --input datasets/DF2K/DF2K_HR --output datasets/DF2K/DF2K_multiscale
```
#### Step 2: [Optional] Crop to sub-images
We then crop DF2K images into sub-images for faster IO and processing.
This step is optional if your IO is enough or your disk space is limited.
You can use the [scripts/extract_subimages.py](scripts/extract_subimages.py) script. Here is the example:
```bash
python scripts/extract_subimages.py --input datasets/DF2K/DF2K_multiscale --output datasets/DF2K/DF2K_multiscale_sub --crop_size 400 --step 200
```
#### Step 3: Prepare a txt for meta information
You need to prepare a txt file containing the image paths. The following are some examples in `meta_info_DF2Kmultiscale+OST_sub.txt` (As different users may have different sub-images partitions, this file is not suitable for your purpose and you need to prepare your own txt file):
```txt
DF2K_HR_sub/000001_s001.png
DF2K_HR_sub/000001_s002.png
DF2K_HR_sub/000001_s003.png
...
```
You can use the [scripts/generate_meta_info.py](scripts/generate_meta_info.py) script to generate the txt file.
You can merge several folders into one meta_info txt. Here is the example:
```bash
python scripts/generate_meta_info.py --input datasets/DF2K/DF2K_HR datasets/DF2K/DF2K_multiscale --root datasets/DF2K datasets/DF2K --meta_info datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt
```
### Train Real-ESRNet
1. Download pre-trained model [ESRGAN](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth) into `experiments/pretrained_models`.
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth -P experiments/pretrained_models
```
1. Modify the content in the option file `options/train_realesrnet_x4plus.yml` accordingly:
```yml
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: datasets/DF2K # modify to the root path of your folder
meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt # modify to your own generate meta info txt
io_backend:
type: disk
```
1. If you want to perform validation during training, uncomment those lines and modify accordingly:
```yml
# Uncomment these for validation
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
...
# Uncomment these for validation
# validation settings
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # metric name, can be arbitrary
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
```
1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training:
```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 --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`.
1. Modify the option file `train_realesrgan_x4plus.yml` accordingly. Most modifications are similar to those listed above.
1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training:
```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 --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:
1. [Generate degraded images on the fly](#Generate-degraded-images-on-the-fly)
1. [Use your own **paired** data](#Use-paired-training-data)
### Generate degraded images on the fly
Only high-resolution images are required. The low-quality images are generated with the degradation process described in Real-ESRGAN during training.
**1. Prepare dataset**
See [this section](#dataset-preparation) for more details.
**2. Download pre-trained models**
Download pre-trained models into `experiments/pretrained_models`.
- *RealESRGAN_x4plus.pth*:
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
```
- *RealESRGAN_x4plus_netD.pth*:
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
```
**3. Finetune**
Modify [options/finetune_realesrgan_x4plus.yml](options/finetune_realesrgan_x4plus.yml) accordingly, especially the `datasets` part:
```yml
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: datasets/DF2K # modify to the root path of your folder
meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt # modify to your own generate meta info txt
io_backend:
type: disk
```
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/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.
**1. Prepare dataset**
Assume that you already have two folders:
- **gt folder** (Ground-truth, high-resolution images): *datasets/DF2K/DIV2K_train_HR_sub*
- **lq folder** (Low quality, low-resolution images): *datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub*
Then, you can prepare the meta_info txt file using the script [scripts/generate_meta_info_pairdata.py](scripts/generate_meta_info_pairdata.py):
```bash
python scripts/generate_meta_info_pairdata.py --input datasets/DF2K/DIV2K_train_HR_sub datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub --meta_info datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt
```
**2. Download pre-trained models**
Download pre-trained models into `experiments/pretrained_models`.
- *RealESRGAN_x4plus.pth*
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
```
- *RealESRGAN_x4plus_netD.pth*
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
```
**3. Finetune**
Modify [options/finetune_realesrgan_x4plus_pairdata.yml](options/finetune_realesrgan_x4plus_pairdata.yml) accordingly, especially the `datasets` part:
```yml
train:
name: DIV2K
type: RealESRGANPairedDataset
dataroot_gt: datasets/DF2K # modify to the root path of your folder
dataroot_lq: datasets/DF2K # modify to the root path of your folder
meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt # modify to your own generate meta info txt
io_backend:
type: disk
```
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/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
```
================================================
FILE: docs/Training_CN.md
================================================
# :computer: 如何训练/微调 Real-ESRGAN
- [训练 Real-ESRGAN](#训练-real-esrgan)
- [概述](#概述)
- [准备数据集](#准备数据集)
- [训练 Real-ESRNet 模型](#训练-real-esrnet-模型)
- [训练 Real-ESRGAN 模型](#训练-real-esrgan-模型)
- [用自己的数据集微调 Real-ESRGAN](#用自己的数据集微调-real-esrgan)
- [动态生成降级图像](#动态生成降级图像)
- [使用已配对的数据](#使用已配对的数据)
[English](Training.md) **|** [简体中文](Training_CN.md)
## 训练 Real-ESRGAN
### 概述
训练分为两个步骤。除了 loss 函数外，这两个步骤拥有相同数据合成以及训练的一条龙流程。具体点说：
1. 首先使用 L1 loss 训练 Real-ESRNet 模型，其中 L1 loss 来自预先训练的 ESRGAN 模型。
2. 然后我们将 Real-ESRNet 模型作为生成器初始化，结合L1 loss、感知 loss、GAN loss 三者的参数对 Real-ESRGAN 进行训练。
### 准备数据集
我们使用 DF2K ( DIV2K 和 Flickr2K ) + OST 数据集进行训练。只需要HR图像！
下面是网站链接:
1. DIV2K: http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip
2. Flickr2K: https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
3. OST: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip
以下是数据的准备步骤。
#### 第1步：【可选】生成多尺寸图片
针对 DF2K 数据集，我们使用多尺寸缩放策略，*换言之*，我们对 HR 图像进行下采样，就能获得多尺寸的标准参考（Ground-Truth）图像。
您可以使用这个 [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) 脚本快速生成多尺寸的图像。
注意：如果您只想简单试试，那么可以跳过此步骤。
```bash
python scripts/generate_multiscale_DF2K.py --input datasets/DF2K/DF2K_HR --output datasets/DF2K/DF2K_multiscale
```
#### 第2步：【可选】裁切为子图像
我们可以将 DF2K 图像裁切为子图像，以加快 IO 和处理速度。
如果你的 IO 够好或储存空间有限，那么此步骤是可选的。
您可以使用脚本 [scripts/extract_subimages.py](scripts/extract_subimages.py)。这是使用示例:
```bash
python scripts/extract_subimages.py --input datasets/DF2K/DF2K_multiscale --output datasets/DF2K/DF2K_multiscale_sub --crop_size 400 --step 200
```
#### 第3步：准备元信息 txt
您需要准备一个包含图像路径的 txt 文件。下面是 `meta_info_DF2Kmultiscale+OST_sub.txt` 中的部分展示（由于各个用户可能有截然不同的子图像划分，这个文件不适合你的需求，你得准备自己的 txt 文件)：
```txt
DF2K_HR_sub/000001_s001.png
DF2K_HR_sub/000001_s002.png
DF2K_HR_sub/000001_s003.png
...
```
你可以使用该脚本 [scripts/generate_meta_info.py](scripts/generate_meta_info.py) 生成包含图像路径的 txt 文件。
你还可以合并多个文件夹的图像路径到一个元信息（meta_info）txt。这是使用示例:
```bash
python scripts/generate_meta_info.py --input datasets/DF2K/DF2K_HR, datasets/DF2K/DF2K_multiscale --root datasets/DF2K, datasets/DF2K --meta_info datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt
```
### 训练 Real-ESRNet 模型
1. 下载预先训练的模型 [ESRGAN](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth)，放到 `experiments/pretrained_models`目录下。
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth -P experiments/pretrained_models
```
2. 相应地修改选项文件 `options/train_realesrnet_x4plus.yml` 中的内容：
```yml
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: datasets/DF2K # 修改为你的数据集文件夹根目录
meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt # 修改为你自己生成的元信息txt
io_backend:
type: disk
```
3. 如果你想在训练过程中执行验证，就取消注释这些内容并进行相应的修改：
```yml
# 取消注释这些以进行验证
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
...
# 取消注释这些以进行验证
# 验证设置
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # 指标名称，可以是任意的
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
```
4. 正式训练之前，你可以用 `--debug` 模式检查是否正常运行。我们用了4个GPU进行训练：
```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 --debug
```
用 **1个GPU** 训练的 debug 模式示例:
```bash
python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --debug
```
5. 正式训练开始。我们用了4个GPU进行训练。还可以使用参数 `--auto_resume` 在必要时自动恢复训练。
```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
```
用 **1个GPU** 训练：
```bash
python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --auto_resume
```
### 训练 Real-ESRGAN 模型
1. 训练 Real-ESRNet 模型后，您得到了这个 `experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/model/net_g_1000000.pth` 文件。如果需要指定预训练路径到其他文件，请修改选项文件 `train_realesrgan_x4plus.yml` 中 `pretrain_network_g` 的值。
1. 修改选项文件 `train_realesrgan_x4plus.yml` 的内容。大多数修改与上节提到的类似。
1. 正式训练之前，你可以以 `--debug` 模式检查是否正常运行。我们使用了4个GPU进行训练：
```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 --debug
```
用 **1个GPU** 训练的 debug 模式示例:
```bash
python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --debug
```
1. 正式训练开始。我们使用4个GPU进行训练。还可以使用参数 `--auto_resume` 在必要时自动恢复训练。
```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
```
用 **1个GPU** 训练：
```bash
python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --auto_resume
```
## 用自己的数据集微调 Real-ESRGAN
你可以用自己的数据集微调 Real-ESRGAN。一般地，微调（Fine-Tune）程序可以分为两种类型:
1. [动态生成降级图像](#动态生成降级图像)
2. [使用**已配对**的数据](#使用已配对的数据)
### 动态生成降级图像
只需要高分辨率图像。在训练过程中，使用 Real-ESRGAN 描述的降级模型生成低质量图像。
**1. 准备数据集**
完整信息请参见[本节](#准备数据集)。
**2. 下载预训练模型**
下载预先训练的模型到 `experiments/pretrained_models` 目录下。
- *RealESRGAN_x4plus.pth*:
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
```
- *RealESRGAN_x4plus_netD.pth*:
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
```
**3. 微调**
修改选项文件 [options/finetune_realesrgan_x4plus.yml](options/finetune_realesrgan_x4plus.yml) ，特别是 `datasets` 部分：
```yml
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: datasets/DF2K # 修改为你的数据集文件夹根目录
meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt # 修改为你自己生成的元信息txt
io_backend:
type: disk
```
我们使用4个GPU进行训练。还可以使用参数 `--auto_resume` 在必要时自动恢复训练。
```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/finetune_realesrgan_x4plus.yml --launcher pytorch --auto_resume
```
用 **1个GPU** 训练：
```bash
python realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --auto_resume
```
### 使用已配对的数据
你还可以用自己已经配对的数据微调 RealESRGAN。这个过程更类似于微调 ESRGAN。
**1. 准备数据集**
假设你已经有两个文件夹（folder）:
- **gt folder**（标准参考，高分辨率图像）：*datasets/DF2K/DIV2K_train_HR_sub*
- **lq folder**（低质量，低分辨率图像）：*datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub*
然后，您可以使用脚本 [scripts/generate_meta_info_pairdata.py](scripts/generate_meta_info_pairdata.py) 生成元信息（meta_info）txt 文件。
```bash
python scripts/generate_meta_info_pairdata.py --input datasets/DF2K/DIV2K_train_HR_sub datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub --meta_info datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt
```
**2. 下载预训练模型**
下载预先训练的模型到 `experiments/pretrained_models` 目录下。
- *RealESRGAN_x4plus.pth*:
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
```
- *RealESRGAN_x4plus_netD.pth*:
```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
```
**3. 微调**
修改选项文件 [options/finetune_realesrgan_x4plus_pairdata.yml](options/finetune_realesrgan_x4plus_pairdata.yml) ，特别是 `datasets` 部分：
```yml
train:
name: DIV2K
type: RealESRGANPairedDataset
dataroot_gt: datasets/DF2K # 修改为你的 gt folder 文件夹根目录
dataroot_lq: datasets/DF2K # 修改为你的 lq folder 文件夹根目录
meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt # 修改为你自己生成的元信息txt
io_backend:
type: disk
```
我们使用4个GPU进行训练。还可以使用参数 `--auto_resume` 在必要时自动恢复训练。
```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/finetune_realesrgan_x4plus_pairdata.yml --launcher pytorch --auto_resume
```
用 **1个GPU** 训练：
```bash
python realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --auto_resume
```
================================================
FILE: docs/anime_comparisons.md
================================================
# Comparisons among different anime models
[English](anime_comparisons.md) **|** [简体中文](anime_comparisons_CN.md)
## Update News
- 2022/04/24: Release **AnimeVideo-v3**. We have made the following improvements:
- **better naturalness**
- **Fewer artifacts**
- **more faithful to the original colors**
- **better texture restoration**
- **better background restoration**
## Comparisons
We have compared our RealESRGAN-AnimeVideo-v3 with the following methods.
Our RealESRGAN-AnimeVideo-v3 can achieve better results with faster inference speed.
- [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) with the hyperparameters: `tile=0`, `noiselevel=2`
- [Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN): we use the [20220227](https://github.com/bilibili/ailab/releases/tag/Real-CUGAN-add-faster-low-memory-mode) version, the hyperparameters are: `cache_mode=0`, `tile=0`, `alpha=1`.
- our RealESRGAN-AnimeVideo-v3
## Results
You may need to **zoom in** for comparing details, or **click the image** to see in the full size. Please note that the images
in the table below are the resized and cropped patches from the original images, you can download the original inputs and outputs from [Google Drive](https://drive.google.com/drive/folders/1bc_Hje1Nqop9NDkUvci2VACSjL7HZMRp?usp=sharing) .
**More natural results, better background restoration**
| Input | waifu2x | Real-CUGAN | RealESRGAN
AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
| |  |  |  |
| |  |  |  |
| |  |  |  |
**Fewer artifacts, better detailed textures**
| Input | waifu2x | Real-CUGAN | RealESRGAN
AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
| |  |  |  |
| |  |  |  |
| |  |  |  |
| |  |  |  |
**Other better results**
| Input | waifu2x | Real-CUGAN | RealESRGAN
AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
| |  |  |  |
| |  |  |  |
|  |   |   |   |
| |  |  |  |
| |  |  |  |
## Inference Speed
### PyTorch
Note that we only report the **model** time, and ignore the IO time.
| GPU | Input Resolution | waifu2x | Real-CUGAN | RealESRGAN-AnimeVideo-v3
| :---: | :---: | :---: | :---: | :---: |
| V100 | 1921 x 1080 | - | 3.4 fps | **10.0** fps |
| V100 | 1280 x 720 | - | 7.2 fps | **22.6** fps |
| V100 | 640 x 480 | - | 24.4 fps | **65.9** fps |
### ncnn
- [ ] TODO
================================================
FILE: docs/anime_comparisons_CN.md
================================================
# 动漫视频模型比较
[English](anime_comparisons.md) **|** [简体中文](anime_comparisons_CN.md)
## 更新
- 2022/04/24: 发布 **AnimeVideo-v3**. 主要做了以下更新：
- **更自然**
- **更少瑕疵**
- **颜色保持得更好**
- **更好的纹理恢复**
- **虚化背景处理**
## 比较
我们将 RealESRGAN-AnimeVideo-v3 与以下方法进行了比较。我们的 RealESRGAN-AnimeVideo-v3 可以以更快的推理速度获得更好的结果。
- [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan). 超参数: `tile=0`, `noiselevel=2`
- [Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN): 我们使用了[20220227](https://github.com/bilibili/ailab/releases/tag/Real-CUGAN-add-faster-low-memory-mode)版本, 超参: `cache_mode=0`, `tile=0`, `alpha=1`.
- 我们的 RealESRGAN-AnimeVideo-v3
## 结果
您可能需要**放大**以比较详细信息, 或者**单击图像**以查看完整尺寸。 请注意下面表格的图片是从原图里裁剪patch并且resize后的结果，您可以从
[Google Drive](https://drive.google.com/drive/folders/1bc_Hje1Nqop9NDkUvci2VACSjL7HZMRp?usp=sharing) 里下载原始的输入和输出。
**更自然的结果，更好的虚化背景恢复**
| 输入 | waifu2x | Real-CUGAN | RealESRGAN
AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
| |  |  |  |
| |  |  |  |
| |  |  |  |
**更少瑕疵，更好的细节纹理**
| 输入 | waifu2x | Real-CUGAN | RealESRGAN
AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
| |  |  |  |
| |  |  |  |
| |  |  |  |
| |  |  |  |
**其他更好的结果**
| 输入 | waifu2x | Real-CUGAN | RealESRGAN
AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
| |  |  |  |
| |  |  |  |
|  |   |   |   |
| |  |  |  |
| |  |  |  |
## 推理速度比较
### PyTorch
请注意，我们只报告了**模型推理**的时间, 而忽略了读写硬盘的时间.
| GPU | 输入尺寸 | waifu2x | Real-CUGAN | RealESRGAN-AnimeVideo-v3
| :---: | :---: | :---: | :---: | :---: |
| V100 | 1921 x 1080 | - | 3.4 fps | **10.0** fps |
| V100 | 1280 x 720 | - | 7.2 fps | **22.6** fps |
| V100 | 640 x 480 | - | 24.4 fps | **65.9** fps |
### ncnn
- [ ] TODO
================================================
FILE: docs/anime_model.md
================================================
# Anime Model
:white_check_mark: We 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.
- [How to Use](#how-to-use)
- [PyTorch Inference](#pytorch-inference)
- [ncnn Executable File](#ncnn-executable-file)
- [Comparisons with waifu2x](#comparisons-with-waifu2x)
- [Comparisons with Sliding Bars](#comparisons-with-sliding-bars)
The following is a video comparison with sliding bar. You may need to use the full-screen mode for better visual quality, as the original image is large; otherwise, you may encounter aliasing issue.
## How to Use
### PyTorch Inference
Pre-trained models: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)
```bash
# download model
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P weights
# inference
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs
```
### ncnn Executable File
Download the latest portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**.
Taking the Windows as example, run:
```bash
./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrgan-x4plus-anime
```
## Comparisons with waifu2x
We compare Real-ESRGAN-anime with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan). We use the `-n 2 -s 4` for waifu2x.
## Comparisons with Sliding Bars
The following are video comparisons with sliding bar. You may need to use the full-screen mode for better visual quality, as the original image is large; otherwise, you may encounter aliasing issue.
================================================
FILE: docs/anime_video_model.md
================================================
# Anime Video Models
:white_check_mark: We add small models that are optimized for anime videos :-)
More comparisons can be found in [anime_comparisons.md](anime_comparisons.md)
- [How to Use](#how-to-use)
- [PyTorch Inference](#pytorch-inference)
- [ncnn Executable File](#ncnn-executable-file)
- [Step 1: Use ffmpeg to extract frames from video](#step-1-use-ffmpeg-to-extract-frames-from-video)
- [Step 2: Inference with Real-ESRGAN executable file](#step-2-inference-with-real-esrgan-executable-file)
- [Step 3: Merge the enhanced frames back into a video](#step-3-merge-the-enhanced-frames-back-into-a-video)
- [More Demos](#more-demos)
| Models | Scale | Description |
| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
| [realesr-animevideov3](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth) | X4 1 | Anime video model with XS size |
Note:
1 This model can also be used for X1, X2, X3.
---
The following are some demos (best view in the full screen mode).
## How to Use
### PyTorch Inference
```bash
# download model
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P weights
# single gpu and single process inference
CUDA_VISIBLE_DEVICES=0 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2
# single gpu and multi process inference (you can use multi-processing to improve GPU utilization)
CUDA_VISIBLE_DEVICES=0 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 --num_process_per_gpu 2
# multi gpu and multi process inference
CUDA_VISIBLE_DEVICES=0,1,2,3 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 --num_process_per_gpu 2
```
```console
Usage:
--num_process_per_gpu The total number of process is num_gpu * num_process_per_gpu. The bottleneck of
the program lies on the IO, so the GPUs are usually not fully utilized. To alleviate
this issue, you can use multi-processing by setting this parameter. As long as it
does not exceed the CUDA memory
--extract_frame_first If you encounter ffmpeg error when using multi-processing, you can turn this option on.
```
### NCNN Executable File
#### Step 1: Use ffmpeg to extract frames from video
```bash
ffmpeg -i onepiece_demo.mp4 -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 tmp_frames/frame%08d.png
```
- Remember to create the folder `tmp_frames` ahead
#### Step 2: Inference with Real-ESRGAN executable file
1. Download the latest portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**
1. Taking the Windows as example, run:
```bash
./realesrgan-ncnn-vulkan.exe -i tmp_frames -o out_frames -n realesr-animevideov3 -s 2 -f jpg
```
- Remember to create the folder `out_frames` ahead
#### Step 3: Merge the enhanced frames back into a video
1. First obtain fps from input videos by
```bash
ffmpeg -i onepiece_demo.mp4
```
```console
Usage:
-i input video path
```
You will get the output similar to the following screenshot.
2. Merge frames
```bash
ffmpeg -r 23.98 -i out_frames/frame%08d.jpg -c:v libx264 -r 23.98 -pix_fmt yuv420p output.mp4
```
```console
Usage:
-i input video path
-c:v video encoder (usually we use libx264)
-r fps, remember to modify it to meet your needs
-pix_fmt pixel format in video
```
If you also want to copy audio from the input videos, run:
```bash
ffmpeg -r 23.98 -i out_frames/frame%08d.jpg -i onepiece_demo.mp4 -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r 23.98 -pix_fmt yuv420p output_w_audio.mp4
```
```console
Usage:
-i input video path, here we use two input streams
-c:v video encoder (usually we use libx264)
-r fps, remember to modify it to meet your needs
-pix_fmt pixel format in video
```
## More Demos
- Input video for One Piece:
- Out video for One Piece
**More comparisons**
================================================
FILE: docs/feedback.md
================================================
# 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).
================================================
FILE: docs/model_zoo.md
================================================
# :european_castle: Model Zoo
- [For General Images](#for-general-images)
- [For Anime Images](#for-anime-images)
- [For Anime Videos](#for-anime-videos)
---
## For General Images
| Models | Scale | Description |
| ------------------------------------------------------------------------------------------------------------------------------- | :---- | :------------------------------------------- |
| [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth) | X4 | X4 model for general images |
| [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth) | X2 | X2 model for general images |
| [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth) | X4 | 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) | X4 | official ESRGAN model |
| [realesr-general-x4v3](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth) | X4 (can also be used for X1, X2, X3) | A tiny small model (consume much fewer GPU memory and time); not too strong deblur and denoise capacity |
The following models are **discriminators**, which are usually used for fine-tuning.
| Models | Corresponding model |
| ---------------------------------------------------------------------------------------------------------------------- | :------------------ |
| [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth) | RealESRGAN_x4plus |
| [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth) | RealESRGAN_x2plus |
## For Anime Images / Illustrations
| Models | Scale | Description |
| ------------------------------------------------------------------------------------------------------------------------------ | :---- | :---------------------------------------------------------- |
| [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth) | X4 | Optimized for anime images; 6 RRDB blocks (smaller network) |
The following models are **discriminators**, which are usually used for fine-tuning.
| Models | Corresponding model |
| ---------------------------------------------------------------------------------------------------------------------------------------- | :------------------------- |
| [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth) | RealESRGAN_x4plus_anime_6B |
## For Animation Videos
| Models | Scale | Description |
| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
| [realesr-animevideov3](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth) | X41 | Anime video model with XS size |
Note:
1 This model can also be used for X1, X2, X3.
The following models are **discriminators**, which are usually used for fine-tuning.
TODO
================================================
FILE: docs/ncnn_conversion.md
================================================
# Instructions on converting to NCNN models
1. Convert to onnx model with `scripts/pytorch2onnx.py`. Remember to modify codes accordingly
1. Convert onnx model to ncnn model
1. `cd ncnn-master\ncnn\build\tools\onnx`
1. `onnx2ncnn.exe realesrgan-x4.onnx realesrgan-x4-raw.param realesrgan-x4-raw.bin`
1. Optimize ncnn model
1. fp16 mode
1. `cd ncnn-master\ncnn\build\tools`
1. `ncnnoptimize.exe realesrgan-x4-raw.param realesrgan-x4-raw.bin realesrgan-x4.param realesrgan-x4.bin 1`
1. Modify the blob name in `realesrgan-x4.param`: `data` and `output`
================================================
FILE: inference_realesrgan.py
================================================
import argparse
import cv2
import glob
import os
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
def main():
"""Inference demo for Real-ESRGAN.
"""
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
parser.add_argument(
'-n',
'--model_name',
type=str,
default='RealESRGAN_x4plus',
help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | '
'realesr-animevideov3 | realesr-general-x4v3'))
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
parser.add_argument(
'-dn',
'--denoise_strength',
type=float,
default=0.5,
help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
'Only used for the realesr-general-x4v3 model'))
parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
parser.add_argument(
'--model_path', type=str, default=None, help='[Option] Model path. Usually, you do not need to specify it')
parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
parser.add_argument(
'--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
parser.add_argument(
'--alpha_upsampler',
type=str,
default='realesrgan',
help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
parser.add_argument(
'--ext',
type=str,
default='auto',
help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
parser.add_argument(
'-g', '--gpu-id', type=int, default=None, help='gpu device to use (default=None) can be 0,1,2 for multi-gpu')
args = parser.parse_args()
# determine models according to model names
args.model_name = args.model_name.split('.')[0]
if args.model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
elif args.model_name == 'RealESRNet_x4plus': # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
elif args.model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
elif args.model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
netscale = 2
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
elif args.model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
elif args.model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
netscale = 4
file_url = [
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
]
# determine model paths
if args.model_path is not None:
model_path = args.model_path
else:
model_path = os.path.join('weights', args.model_name + '.pth')
if not os.path.isfile(model_path):
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
for url in file_url:
# model_path will be updated
model_path = load_file_from_url(
url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
# use dni to control the denoise strength
dni_weight = None
if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
model_path = [model_path, wdn_model_path]
dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
# restorer
upsampler = RealESRGANer(
scale=netscale,
model_path=model_path,
dni_weight=dni_weight,
model=model,
tile=args.tile,
tile_pad=args.tile_pad,
pre_pad=args.pre_pad,
half=not args.fp32,
gpu_id=args.gpu_id)
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/v1.3.0/GFPGANv1.3.pth',
upscale=args.outscale,
arch='clean',
channel_multiplier=2,
bg_upsampler=upsampler)
os.makedirs(args.output, exist_ok=True)
if os.path.isfile(args.input):
paths = [args.input]
else:
paths = sorted(glob.glob(os.path.join(args.input, '*')))
for idx, path in enumerate(paths):
imgname, extension = os.path.splitext(os.path.basename(path))
print('Testing', idx, imgname)
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
else:
img_mode = None
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 RuntimeError as error:
print('Error', error)
print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
else:
if args.ext == 'auto':
extension = extension[1:]
else:
extension = args.ext
if img_mode == 'RGBA': # RGBA images should be saved in png format
extension = 'png'
if args.suffix == '':
save_path = os.path.join(args.output, f'{imgname}.{extension}')
else:
save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
cv2.imwrite(save_path, output)
if __name__ == '__main__':
main()
================================================
FILE: inference_realesrgan_video.py
================================================
import argparse
import cv2
import glob
import mimetypes
import numpy as np
import os
import shutil
import subprocess
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url
from os import path as osp
from tqdm import tqdm
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
try:
import ffmpeg
except ImportError:
import pip
pip.main(['install', '--user', 'ffmpeg-python'])
import ffmpeg
def get_video_meta_info(video_path):
ret = {}
probe = ffmpeg.probe(video_path)
video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
has_audio = any(stream['codec_type'] == 'audio' for stream in probe['streams'])
ret['width'] = video_streams[0]['width']
ret['height'] = video_streams[0]['height']
ret['fps'] = eval(video_streams[0]['avg_frame_rate'])
ret['audio'] = ffmpeg.input(video_path).audio if has_audio else None
ret['nb_frames'] = int(video_streams[0]['nb_frames'])
return ret
def get_sub_video(args, num_process, process_idx):
if num_process == 1:
return args.input
meta = get_video_meta_info(args.input)
duration = int(meta['nb_frames'] / meta['fps'])
part_time = duration // num_process
print(f'duration: {duration}, part_time: {part_time}')
os.makedirs(osp.join(args.output, f'{args.video_name}_inp_tmp_videos'), exist_ok=True)
out_path = osp.join(args.output, f'{args.video_name}_inp_tmp_videos', f'{process_idx:03d}.mp4')
cmd = [
args.ffmpeg_bin, f'-i {args.input}', '-ss', f'{part_time * process_idx}',
f'-to {part_time * (process_idx + 1)}' if process_idx != num_process - 1 else '', '-async 1', out_path, '-y'
]
print(' '.join(cmd))
subprocess.call(' '.join(cmd), shell=True)
return out_path
class Reader:
def __init__(self, args, total_workers=1, worker_idx=0):
self.args = args
input_type = mimetypes.guess_type(args.input)[0]
self.input_type = 'folder' if input_type is None else input_type
self.paths = [] # for image&folder type
self.audio = None
self.input_fps = None
if self.input_type.startswith('video'):
video_path = get_sub_video(args, total_workers, worker_idx)
self.stream_reader = (
ffmpeg.input(video_path).output('pipe:', format='rawvideo', pix_fmt='bgr24',
loglevel='error').run_async(
pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin))
meta = get_video_meta_info(video_path)
self.width = meta['width']
self.height = meta['height']
self.input_fps = meta['fps']
self.audio = meta['audio']
self.nb_frames = meta['nb_frames']
else:
if self.input_type.startswith('image'):
self.paths = [args.input]
else:
paths = sorted(glob.glob(os.path.join(args.input, '*')))
tot_frames = len(paths)
num_frame_per_worker = tot_frames // total_workers + (1 if tot_frames % total_workers else 0)
self.paths = paths[num_frame_per_worker * worker_idx:num_frame_per_worker * (worker_idx + 1)]
self.nb_frames = len(self.paths)
assert self.nb_frames > 0, 'empty folder'
from PIL import Image
tmp_img = Image.open(self.paths[0])
self.width, self.height = tmp_img.size
self.idx = 0
def get_resolution(self):
return self.height, self.width
def get_fps(self):
if self.args.fps is not None:
return self.args.fps
elif self.input_fps is not None:
return self.input_fps
return 24
def get_audio(self):
return self.audio
def __len__(self):
return self.nb_frames
def get_frame_from_stream(self):
img_bytes = self.stream_reader.stdout.read(self.width * self.height * 3) # 3 bytes for one pixel
if not img_bytes:
return None
img = np.frombuffer(img_bytes, np.uint8).reshape([self.height, self.width, 3])
return img
def get_frame_from_list(self):
if self.idx >= self.nb_frames:
return None
img = cv2.imread(self.paths[self.idx])
self.idx += 1
return img
def get_frame(self):
if self.input_type.startswith('video'):
return self.get_frame_from_stream()
else:
return self.get_frame_from_list()
def close(self):
if self.input_type.startswith('video'):
self.stream_reader.stdin.close()
self.stream_reader.wait()
class Writer:
def __init__(self, args, audio, height, width, video_save_path, fps):
out_width, out_height = int(width * args.outscale), int(height * args.outscale)
if out_height > 2160:
print('You are generating video that is larger than 4K, which will be very slow due to IO speed.',
'We highly recommend to decrease the outscale(aka, -s).')
if audio is not None:
self.stream_writer = (
ffmpeg.input('pipe:', format='rawvideo', pix_fmt='bgr24', s=f'{out_width}x{out_height}',
framerate=fps).output(
audio,
video_save_path,
pix_fmt='yuv420p',
vcodec='libx264',
loglevel='error',
acodec='copy').overwrite_output().run_async(
pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin))
else:
self.stream_writer = (
ffmpeg.input('pipe:', format='rawvideo', pix_fmt='bgr24', s=f'{out_width}x{out_height}',
framerate=fps).output(
video_save_path, pix_fmt='yuv420p', vcodec='libx264',
loglevel='error').overwrite_output().run_async(
pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin))
def write_frame(self, frame):
frame = frame.astype(np.uint8).tobytes()
self.stream_writer.stdin.write(frame)
def close(self):
self.stream_writer.stdin.close()
self.stream_writer.wait()
def inference_video(args, video_save_path, device=None, total_workers=1, worker_idx=0):
# ---------------------- determine models according to model names ---------------------- #
args.model_name = args.model_name.split('.pth')[0]
if args.model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
elif args.model_name == 'RealESRNet_x4plus': # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
elif args.model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
elif args.model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
netscale = 2
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
elif args.model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
elif args.model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
netscale = 4
file_url = [
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
]
# ---------------------- determine model paths ---------------------- #
model_path = os.path.join('weights', args.model_name + '.pth')
if not os.path.isfile(model_path):
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
for url in file_url:
# model_path will be updated
model_path = load_file_from_url(
url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
# use dni to control the denoise strength
dni_weight = None
if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
model_path = [model_path, wdn_model_path]
dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
# restorer
upsampler = RealESRGANer(
scale=netscale,
model_path=model_path,
dni_weight=dni_weight,
model=model,
tile=args.tile,
tile_pad=args.tile_pad,
pre_pad=args.pre_pad,
half=not args.fp32,
device=device,
)
if 'anime' in args.model_name and args.face_enhance:
print('face_enhance is not supported in anime models, we turned this option off for you. '
'if you insist on turning it on, please manually comment the relevant lines of code.')
args.face_enhance = False
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/v1.3.0/GFPGANv1.3.pth',
upscale=args.outscale,
arch='clean',
channel_multiplier=2,
bg_upsampler=upsampler) # TODO support custom device
else:
face_enhancer = None
reader = Reader(args, total_workers, worker_idx)
audio = reader.get_audio()
height, width = reader.get_resolution()
fps = reader.get_fps()
writer = Writer(args, audio, height, width, video_save_path, fps)
pbar = tqdm(total=len(reader), unit='frame', desc='inference')
while True:
img = reader.get_frame()
if img is None:
break
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 RuntimeError as error:
print('Error', error)
print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
else:
writer.write_frame(output)
torch.cuda.synchronize(device)
pbar.update(1)
reader.close()
writer.close()
def run(args):
args.video_name = osp.splitext(os.path.basename(args.input))[0]
video_save_path = osp.join(args.output, f'{args.video_name}_{args.suffix}.mp4')
if args.extract_frame_first:
tmp_frames_folder = osp.join(args.output, f'{args.video_name}_inp_tmp_frames')
os.makedirs(tmp_frames_folder, exist_ok=True)
os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 {tmp_frames_folder}/frame%08d.png')
args.input = tmp_frames_folder
num_gpus = torch.cuda.device_count()
num_process = num_gpus * args.num_process_per_gpu
if num_process == 1:
inference_video(args, video_save_path)
return
ctx = torch.multiprocessing.get_context('spawn')
pool = ctx.Pool(num_process)
os.makedirs(osp.join(args.output, f'{args.video_name}_out_tmp_videos'), exist_ok=True)
pbar = tqdm(total=num_process, unit='sub_video', desc='inference')
for i in range(num_process):
sub_video_save_path = osp.join(args.output, f'{args.video_name}_out_tmp_videos', f'{i:03d}.mp4')
pool.apply_async(
inference_video,
args=(args, sub_video_save_path, torch.device(i % num_gpus), num_process, i),
callback=lambda arg: pbar.update(1))
pool.close()
pool.join()
# combine sub videos
# prepare vidlist.txt
with open(f'{args.output}/{args.video_name}_vidlist.txt', 'w') as f:
for i in range(num_process):
f.write(f'file \'{args.video_name}_out_tmp_videos/{i:03d}.mp4\'\n')
cmd = [
args.ffmpeg_bin, '-f', 'concat', '-safe', '0', '-i', f'{args.output}/{args.video_name}_vidlist.txt', '-c',
'copy', f'{video_save_path}'
]
print(' '.join(cmd))
subprocess.call(cmd)
shutil.rmtree(osp.join(args.output, f'{args.video_name}_out_tmp_videos'))
if osp.exists(osp.join(args.output, f'{args.video_name}_inp_tmp_videos')):
shutil.rmtree(osp.join(args.output, f'{args.video_name}_inp_tmp_videos'))
os.remove(f'{args.output}/{args.video_name}_vidlist.txt')
def main():
"""Inference demo for Real-ESRGAN.
It mainly for restoring anime videos.
"""
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, default='inputs', help='Input video, image or folder')
parser.add_argument(
'-n',
'--model_name',
type=str,
default='realesr-animevideov3',
help=('Model names: realesr-animevideov3 | RealESRGAN_x4plus_anime_6B | RealESRGAN_x4plus | RealESRNet_x4plus |'
' RealESRGAN_x2plus | realesr-general-x4v3'
'Default:realesr-animevideov3'))
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
parser.add_argument(
'-dn',
'--denoise_strength',
type=float,
default=0.5,
help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
'Only used for the realesr-general-x4v3 model'))
parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored video')
parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
parser.add_argument(
'--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
parser.add_argument('--fps', type=float, default=None, help='FPS of the output video')
parser.add_argument('--ffmpeg_bin', type=str, default='ffmpeg', help='The path to ffmpeg')
parser.add_argument('--extract_frame_first', action='store_true')
parser.add_argument('--num_process_per_gpu', type=int, default=1)
parser.add_argument(
'--alpha_upsampler',
type=str,
default='realesrgan',
help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
parser.add_argument(
'--ext',
type=str,
default='auto',
help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
args = parser.parse_args()
args.input = args.input.rstrip('/').rstrip('\\')
os.makedirs(args.output, exist_ok=True)
if mimetypes.guess_type(args.input)[0] is not None and mimetypes.guess_type(args.input)[0].startswith('video'):
is_video = True
else:
is_video = False
if is_video and args.input.endswith('.flv'):
mp4_path = args.input.replace('.flv', '.mp4')
os.system(f'ffmpeg -i {args.input} -codec copy {mp4_path}')
args.input = mp4_path
if args.extract_frame_first and not is_video:
args.extract_frame_first = False
run(args)
if args.extract_frame_first:
tmp_frames_folder = osp.join(args.output, f'{args.video_name}_inp_tmp_frames')
shutil.rmtree(tmp_frames_folder)
if __name__ == '__main__':
main()
================================================
FILE: options/finetune_realesrgan_x4plus.yml
================================================
# general settings
name: finetune_RealESRGANx4plus_400k
model_type: RealESRGANModel
scale: 4
num_gpu: auto
manual_seed: 0
# ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
# 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: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]
# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3] # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]
gt_size: 256
queue_size: 180
# dataset and data loader settings
datasets:
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: datasets/DF2K
meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.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: 0.1
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: 0.1
blur_sigma2: [0.2, 1.5]
betag_range2: [0.5, 4]
betap_range2: [1, 2]
final_sinc_prob: 0.8
gt_size: 256
use_hflip: True
use_rot: False
# data loader
use_shuffle: true
num_worker_per_gpu: 5
batch_size_per_gpu: 12
dataset_enlarge_ratio: 1
prefetch_mode: ~
# Uncomment these for validation
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
# network structures
network_g:
type: RRDBNet
num_in_ch: 3
num_out_ch: 3
num_feat: 64
num_block: 23
num_grow_ch: 32
network_d:
type: UNetDiscriminatorSN
num_in_ch: 3
num_feat: 64
skip_connection: True
# path
path:
# use the pre-trained Real-ESRNet model
pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth
param_key_g: params_ema
strict_load_g: true
pretrain_network_d: experiments/pretrained_models/RealESRGAN_x4plus_netD.pth
param_key_d: params
strict_load_d: 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
# Uncomment these for validation
# validation settings
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # metric name
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
# logging settings
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500
================================================
FILE: options/finetune_realesrgan_x4plus_pairdata.yml
================================================
# general settings
name: finetune_RealESRGANx4plus_400k_pairdata
model_type: RealESRGANModel
scale: 4
num_gpu: auto
manual_seed: 0
# USM the ground-truth
l1_gt_usm: True
percep_gt_usm: True
gan_gt_usm: False
high_order_degradation: False # do not use the high-order degradation generation process
# dataset and data loader settings
datasets:
train:
name: DIV2K
type: RealESRGANPairedDataset
dataroot_gt: datasets/DF2K
dataroot_lq: datasets/DF2K
meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt
io_backend:
type: disk
gt_size: 256
use_hflip: True
use_rot: False
# data loader
use_shuffle: true
num_worker_per_gpu: 5
batch_size_per_gpu: 12
dataset_enlarge_ratio: 1
prefetch_mode: ~
# Uncomment these for validation
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
# network structures
network_g:
type: RRDBNet
num_in_ch: 3
num_out_ch: 3
num_feat: 64
num_block: 23
num_grow_ch: 32
network_d:
type: UNetDiscriminatorSN
num_in_ch: 3
num_feat: 64
skip_connection: True
# path
path:
# use the pre-trained Real-ESRNet model
pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth
param_key_g: params_ema
strict_load_g: true
pretrain_network_d: experiments/pretrained_models/RealESRGAN_x4plus_netD.pth
param_key_d: params
strict_load_d: 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
# Uncomment these for validation
# validation settings
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # metric name
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
# logging settings
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500
================================================
FILE: options/train_realesrgan_x2plus.yml
================================================
# general settings
name: train_RealESRGANx2plus_400k_B12G4
model_type: RealESRGANModel
scale: 2
num_gpu: auto # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0
# ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
# 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: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]
# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3] # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]
gt_size: 256
queue_size: 180
# dataset and data loader settings
datasets:
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: datasets/DF2K
meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.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: 0.1
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: 0.1
blur_sigma2: [0.2, 1.5]
betag_range2: [0.5, 4]
betap_range2: [1, 2]
final_sinc_prob: 0.8
gt_size: 256
use_hflip: True
use_rot: False
# data loader
use_shuffle: true
num_worker_per_gpu: 5
batch_size_per_gpu: 12
dataset_enlarge_ratio: 1
prefetch_mode: ~
# Uncomment these for validation
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
# network structures
network_g:
type: RRDBNet
num_in_ch: 3
num_out_ch: 3
num_feat: 64
num_block: 23
num_grow_ch: 32
scale: 2
network_d:
type: UNetDiscriminatorSN
num_in_ch: 3
num_feat: 64
skip_connection: True
# path
path:
# use the pre-trained Real-ESRNet model
pretrain_network_g: experiments/pretrained_models/RealESRNet_x2plus.pth
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
# Uncomment these for validation
# validation settings
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # metric name
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
# logging settings
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500
================================================
FILE: options/train_realesrgan_x4plus.yml
================================================
# general settings
name: train_RealESRGANx4plus_400k_B12G4
model_type: RealESRGANModel
scale: 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 ----------------- #
# 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: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]
# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3] # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]
gt_size: 256
queue_size: 180
# dataset and data loader settings
datasets:
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: datasets/DF2K
meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.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: 0.1
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: 0.1
blur_sigma2: [0.2, 1.5]
betag_range2: [0.5, 4]
betap_range2: [1, 2]
final_sinc_prob: 0.8
gt_size: 256
use_hflip: True
use_rot: False
# data loader
use_shuffle: true
num_worker_per_gpu: 5
batch_size_per_gpu: 12
dataset_enlarge_ratio: 1
prefetch_mode: ~
# Uncomment these for validation
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
# network structures
network_g:
type: RRDBNet
num_in_ch: 3
num_out_ch: 3
num_feat: 64
num_block: 23
num_grow_ch: 32
network_d:
type: UNetDiscriminatorSN
num_in_ch: 3
num_feat: 64
skip_connection: True
# path
path:
# use the pre-trained Real-ESRNet model
pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth
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
# Uncomment these for validation
# validation settings
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # metric name
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
# logging settings
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500
================================================
FILE: options/train_realesrnet_x2plus.yml
================================================
# general settings
name: train_RealESRNetx2plus_1000k_B12G4
model_type: RealESRNetModel
scale: 2
num_gpu: auto # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0
# ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
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: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]
# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3] # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]
gt_size: 256
queue_size: 180
# dataset and data loader settings
datasets:
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: datasets/DF2K
meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.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: 0.1
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: 0.1
blur_sigma2: [0.2, 1.5]
betag_range2: [0.5, 4]
betap_range2: [1, 2]
final_sinc_prob: 0.8
gt_size: 256
use_hflip: True
use_rot: False
# data loader
use_shuffle: true
num_worker_per_gpu: 5
batch_size_per_gpu: 12
dataset_enlarge_ratio: 1
prefetch_mode: ~
# Uncomment these for validation
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
# network structures
network_g:
type: RRDBNet
num_in_ch: 3
num_out_ch: 3
num_feat: 64
num_block: 23
num_grow_ch: 32
scale: 2
# path
path:
pretrain_network_g: experiments/pretrained_models/RealESRGAN_x4plus.pth
param_key_g: params_ema
strict_load_g: False
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
# Uncomment these for validation
# validation settings
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # metric name
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
# logging settings
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500
================================================
FILE: options/train_realesrnet_x4plus.yml
================================================
# general settings
name: train_RealESRNetx4plus_1000k_B12G4
model_type: RealESRNetModel
scale: 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 ----------------- #
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: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]
# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3] # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]
gt_size: 256
queue_size: 180
# dataset and data loader settings
datasets:
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: datasets/DF2K
meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.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: 0.1
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: 0.1
blur_sigma2: [0.2, 1.5]
betag_range2: [0.5, 4]
betap_range2: [1, 2]
final_sinc_prob: 0.8
gt_size: 256
use_hflip: True
use_rot: False
# data loader
use_shuffle: true
num_worker_per_gpu: 5
batch_size_per_gpu: 12
dataset_enlarge_ratio: 1
prefetch_mode: ~
# Uncomment these for validation
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
# network structures
network_g:
type: RRDBNet
num_in_ch: 3
num_out_ch: 3
num_feat: 64
num_block: 23
num_grow_ch: 32
# path
path:
pretrain_network_g: experiments/pretrained_models/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth
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
# Uncomment these for validation
# validation settings
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # metric name
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
# logging settings
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500
================================================
FILE: realesrgan/__init__.py
================================================
# flake8: noqa
from .archs import *
from .data import *
from .models import *
from .utils import *
from .version import *
================================================
FILE: realesrgan/archs/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp
# automatically scan and import arch modules for registry
# scan all the files that end with '_arch.py' under the archs folder
arch_folder = osp.dirname(osp.abspath(__file__))
arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
# import all the arch modules
_arch_modules = [importlib.import_module(f'realesrgan.archs.{file_name}') for file_name in arch_filenames]
================================================
FILE: realesrgan/archs/discriminator_arch.py
================================================
from basicsr.utils.registry import ARCH_REGISTRY
from torch import nn as nn
from torch.nn import functional as F
from torch.nn.utils import spectral_norm
@ARCH_REGISTRY.register()
class UNetDiscriminatorSN(nn.Module):
"""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))
# upsample
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
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)
x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True)
# upsample
x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False)
x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True)
if self.skip_connection:
x4 = x4 + x2
x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False)
x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True)
if self.skip_connection:
x5 = x5 + x1
x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False)
x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True)
if self.skip_connection:
x6 = x6 + x0
# 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)
return out
================================================
FILE: realesrgan/archs/srvgg_arch.py
================================================
from basicsr.utils.registry import ARCH_REGISTRY
from torch import nn as nn
from torch.nn import functional as F
@ARCH_REGISTRY.register()
class SRVGGNetCompact(nn.Module):
"""A compact VGG-style network structure for super-resolution.
It is a compact network structure, which performs upsampling in the last layer and no convolution is
conducted on the HR feature space.
Args:
num_in_ch (int): Channel number of inputs. Default: 3.
num_out_ch (int): Channel number of outputs. Default: 3.
num_feat (int): Channel number of intermediate features. Default: 64.
num_conv (int): Number of convolution layers in the body network. Default: 16.
upscale (int): Upsampling factor. Default: 4.
act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
"""
def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
super(SRVGGNetCompact, self).__init__()
self.num_in_ch = num_in_ch
self.num_out_ch = num_out_ch
self.num_feat = num_feat
self.num_conv = num_conv
self.upscale = upscale
self.act_type = act_type
self.body = nn.ModuleList()
# the first conv
self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
# the first activation
if act_type == 'relu':
activation = nn.ReLU(inplace=True)
elif act_type == 'prelu':
activation = nn.PReLU(num_parameters=num_feat)
elif act_type == 'leakyrelu':
activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.body.append(activation)
# the body structure
for _ in range(num_conv):
self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
# activation
if act_type == 'relu':
activation = nn.ReLU(inplace=True)
elif act_type == 'prelu':
activation = nn.PReLU(num_parameters=num_feat)
elif act_type == 'leakyrelu':
activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.body.append(activation)
# the last conv
self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
# upsample
self.upsampler = nn.PixelShuffle(upscale)
def forward(self, x):
out = x
for i in range(0, len(self.body)):
out = self.body[i](out)
out = self.upsampler(out)
# add the nearest upsampled image, so that the network learns the residual
base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
out += base
return out
================================================
FILE: realesrgan/data/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp
# automatically scan and import dataset modules for registry
# scan all the files that end with '_dataset.py' under the data folder
data_folder = osp.dirname(osp.abspath(__file__))
dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
# import all the dataset modules
_dataset_modules = [importlib.import_module(f'realesrgan.data.{file_name}') for file_name in dataset_filenames]
================================================
FILE: realesrgan/data/realesrgan_dataset.py
================================================
import cv2
import math
import numpy as np
import os
import os.path as osp
import random
import time
import torch
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.data.transforms import augment
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data
@DATASET_REGISTRY.register()
class RealESRGANDataset(data.Dataset):
"""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
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']
if not self.gt_folder.endswith('.lmdb'):
raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
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().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'] # a list for each kernel probability
self.blur_sigma = opt['blur_sigma']
self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels
self.betap_range = opt['betap_range'] # betap used in plateau blur kernels
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']
self.kernel_list2 = opt['kernel_list2']
self.kernel_prob2 = opt['kernel_prob2']
self.blur_sigma2 = opt['blur_sigma2']
self.betag_range2 = opt['betag_range2']
self.betap_range2 = opt['betap_range2']
self.sinc_prob2 = opt['sinc_prob2']
# a final sinc filter
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
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
# -------------------------------- Load gt images -------------------------------- #
# Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
gt_path = self.paths[index]
# avoid errors caused by high latency in reading files
retry = 3
while retry > 0:
try:
img_bytes = self.file_client.get(gt_path, 'gt')
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
index = random.randint(0, self.__len__())
gt_path = self.paths[index]
time.sleep(1) # sleep 1s for occasional server congestion
else:
break
finally:
retry -= 1
img_gt = imfrombytes(img_bytes, float32=True)
# -------------------- Do augmentation for training: flip, rotation -------------------- #
img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])
# 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
if h < crop_pad_size or w < crop_pad_size:
pad_h = max(0, crop_pad_size - h)
pad_w = max(0, crop_pad_size - w)
img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101)
# crop
if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
h, w = img_gt.shape[0:2]
# randomly choose top and left coordinates
top = random.randint(0, h - crop_pad_size)
left = random.randint(0, w - crop_pad_size)
img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...]
# ------------------------ Generate kernels (used in the first degradation) ------------------------ #
kernel_size = random.choice(self.kernel_range)
if np.random.uniform() < self.opt['sinc_prob']:
# this sinc filter setting is for kernels ranging from [7, 21]
if kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else:
kernel = random_mixed_kernels(
self.kernel_list,
self.kernel_prob,
kernel_size,
self.blur_sigma,
self.blur_sigma, [-math.pi, math.pi],
self.betag_range,
self.betap_range,
noise_range=None)
# pad kernel
pad_size = (21 - kernel_size) // 2
kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))
# ------------------------ Generate kernels (used in the second degradation) ------------------------ #
kernel_size = random.choice(self.kernel_range)
if np.random.uniform() < self.opt['sinc_prob2']:
if kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else:
kernel2 = random_mixed_kernels(
self.kernel_list2,
self.kernel_prob2,
kernel_size,
self.blur_sigma2,
self.blur_sigma2, [-math.pi, math.pi],
self.betag_range2,
self.betap_range2,
noise_range=None)
# pad kernel
pad_size = (21 - kernel_size) // 2
kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))
# ------------------------------------- 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)
sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
sinc_kernel = torch.FloatTensor(sinc_kernel)
else:
sinc_kernel = self.pulse_tensor
# BGR to RGB, HWC to CHW, numpy to tensor
img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]
kernel = torch.FloatTensor(kernel)
kernel2 = torch.FloatTensor(kernel2)
return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path}
return return_d
def __len__(self):
return len(self.paths)
================================================
FILE: realesrgan/data/realesrgan_paired_dataset.py
================================================
import os
from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
from basicsr.data.transforms import augment, paired_random_crop
from basicsr.utils import FileClient, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data
from torchvision.transforms.functional import normalize
@DATASET_REGISTRY.register()
class RealESRGANPairedDataset(data.Dataset):
"""Paired image dataset for image restoration.
Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.
There are three modes:
1. 'lmdb': Use lmdb files.
If opt['io_backend'] == lmdb.
2. 'meta_info': Use meta information file to generate paths.
If opt['io_backend'] != lmdb and opt['meta_info'] is not None.
3. 'folder': Scan folders to generate paths.
The rest.
Args:
opt (dict): Config for train datasets. It contains the following keys:
dataroot_gt (str): Data root path for gt.
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 template excludes the file extension.
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
and w for implementation).
scale (bool): Scale, which will be added automatically.
phase (str): 'train' or 'val'.
"""
def __init__(self, opt):
super(RealESRGANPairedDataset, self).__init__()
self.opt = opt
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']
self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'
# 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 = []
for path in paths:
gt_path, lq_path = path.split(', ')
gt_path = os.path.join(self.gt_folder, gt_path)
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):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
scale = self.opt['scale']
# Load gt and lq images. Dimension order: HWC; channel order: BGR;
# image range: [0, 1], float32.
gt_path = self.paths[index]['gt_path']
img_bytes = self.file_client.get(gt_path, 'gt')
img_gt = imfrombytes(img_bytes, float32=True)
lq_path = self.paths[index]['lq_path']
img_bytes = self.file_client.get(lq_path, 'lq')
img_lq = imfrombytes(img_bytes, float32=True)
# augmentation for training
if self.opt['phase'] == 'train':
gt_size = self.opt['gt_size']
# random crop
img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
# flip, rotation
img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])
# BGR to RGB, HWC to CHW, numpy to tensor
img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
# normalize
if self.mean is not None or self.std is not None:
normalize(img_lq, self.mean, self.std, inplace=True)
normalize(img_gt, self.mean, self.std, inplace=True)
return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
def __len__(self):
return len(self.paths)
================================================
FILE: realesrgan/models/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp
# automatically scan and import model modules for registry
# scan all the files that end with '_model.py' under the model folder
model_folder = osp.dirname(osp.abspath(__file__))
model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
# import all the model modules
_model_modules = [importlib.import_module(f'realesrgan.models.{file_name}') for file_name in model_filenames]
================================================
FILE: realesrgan/models/realesrgan_model.py
================================================
import numpy as np
import random
import torch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
from basicsr.models.srgan_model import SRGANModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from collections import OrderedDict
from torch.nn import functional as F
@MODEL_REGISTRY.register()
class RealESRGANModel(SRGANModel):
"""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() # simulate JPEG compression artifacts
self.usm_sharpener = USMSharp().cuda() # do usm sharpening
self.queue_size = opt.get('queue_size', 180)
@torch.no_grad()
def _dequeue_and_enqueue(self):
"""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, 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: # 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 first b samples
lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
# update the queue
self.queue_lr[0:b, :, :, :] = self.lq.clone()
self.queue_gt[0:b, :, :, :] = self.gt.clone()
self.lq = lq_dequeue
self.gt = gt_dequeue
else:
# only do enqueue
self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
self.queue_ptr = self.queue_ptr + b
@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)
self.gt_usm = self.usm_sharpener(self.gt)
self.kernel1 = data['kernel1'].to(self.device)
self.kernel2 = data['kernel2'].to(self.device)
self.sinc_kernel = data['sinc_kernel'].to(self.device)
ori_h, ori_w = self.gt.size()[2:4]
# ----------------------- The first degradation process ----------------------- #
# blur
out = filter2D(self.gt_usm, self.kernel1)
# random resize
updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
if updown_type == 'up':
scale = np.random.uniform(1, self.opt['resize_range'][1])
elif updown_type == 'down':
scale = np.random.uniform(self.opt['resize_range'][0], 1)
else:
scale = 1
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = F.interpolate(out, scale_factor=scale, mode=mode)
# 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(
out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
else:
out = random_add_poisson_noise_pt(
out,
scale_range=self.opt['poisson_scale_range'],
gray_prob=gray_noise_prob,
clip=True,
rounds=False)
# JPEG compression
jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
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 ----------------------- #
# blur
if np.random.uniform() < self.opt['second_blur_prob']:
out = filter2D(out, self.kernel2)
# random resize
updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
if updown_type == 'up':
scale = np.random.uniform(1, self.opt['resize_range2'][1])
elif updown_type == 'down':
scale = np.random.uniform(self.opt['resize_range2'][0], 1)
else:
scale = 1
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)
# 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(
out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
else:
out = random_add_poisson_noise_pt(
out,
scale_range=self.opt['poisson_scale_range2'],
gray_prob=gray_noise_prob,
clip=True,
rounds=False)
# JPEG compression + the final sinc filter
# We also need to resize images to desired sizes. We group [resize back + sinc filter] together
# as one operation.
# We consider two orders:
# 1. [resize back + sinc filter] + JPEG compression
# 2. JPEG compression + [resize back + sinc filter]
# Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
if np.random.uniform() < 0.5:
# resize back + the final sinc filter
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
out = filter2D(out, self.sinc_kernel)
# JPEG compression
jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
out = torch.clamp(out, 0, 1)
out = self.jpeger(out, quality=jpeg_p)
else:
# JPEG compression
jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
out = torch.clamp(out, 0, 1)
out = self.jpeger(out, quality=jpeg_p)
# resize back + the final sinc filter
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
out = filter2D(out, self.sinc_kernel)
# clamp and round
self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.
# random crop
gt_size = self.opt['gt_size']
(self.gt, self.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size,
self.opt['scale'])
# training pair pool
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)
self.gt_usm = self.usm_sharpener(self.gt)
def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
# do not use the synthetic process during validation
self.is_train = False
super(RealESRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
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
if self.opt['l1_gt_usm'] is False:
l1_gt = self.gt
if self.opt['percep_gt_usm'] is False:
percep_gt = self.gt
if self.opt['gan_gt_usm'] is False:
gan_gt = self.gt
# optimize net_g
for p in self.net_d.parameters():
p.requires_grad = False
self.optimizer_g.zero_grad()
self.output = self.net_g(self.lq)
l_g_total = 0
loss_dict = OrderedDict()
if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
# pixel loss
if self.cri_pix:
l_g_pix = self.cri_pix(self.output, l1_gt)
l_g_total += l_g_pix
loss_dict['l_g_pix'] = l_g_pix
# perceptual loss
if self.cri_perceptual:
l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt)
if l_g_percep is not None:
l_g_total += l_g_percep
loss_dict['l_g_percep'] = l_g_percep
if l_g_style is not None:
l_g_total += l_g_style
loss_dict['l_g_style'] = l_g_style
# gan loss
fake_g_pred = self.net_d(self.output)
l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
l_g_total += l_g_gan
loss_dict['l_g_gan'] = l_g_gan
l_g_total.backward()
self.optimizer_g.step()
# optimize net_d
for p in self.net_d.parameters():
p.requires_grad = True
self.optimizer_d.zero_grad()
# real
real_d_pred = self.net_d(gan_gt)
l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
loss_dict['l_d_real'] = l_d_real
loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
l_d_real.backward()
# fake
fake_d_pred = self.net_d(self.output.detach().clone()) # clone for pt1.9
l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
loss_dict['l_d_fake'] = l_d_fake
loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
l_d_fake.backward()
self.optimizer_d.step()
if self.ema_decay > 0:
self.model_ema(decay=self.ema_decay)
self.log_dict = self.reduce_loss_dict(loss_dict)
================================================
FILE: realesrgan/models/realesrnet_model.py
================================================
import numpy as np
import random
import torch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
from basicsr.models.sr_model import SRModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from torch.nn import functional as F
@MODEL_REGISTRY.register()
class RealESRNetModel(SRModel):
"""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() # simulate JPEG compression artifacts
self.usm_sharpener = USMSharp().cuda() # do usm sharpening
self.queue_size = opt.get('queue_size', 180)
@torch.no_grad()
def _dequeue_and_enqueue(self):
"""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, 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: # 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 first b samples
lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
# update the queue
self.queue_lr[0:b, :, :, :] = self.lq.clone()
self.queue_gt[0:b, :, :, :] = self.gt.clone()
self.lq = lq_dequeue
self.gt = gt_dequeue
else:
# only do enqueue
self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
self.queue_ptr = self.queue_ptr + b
@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 sharpen the GT images
if self.opt['gt_usm'] is True:
self.gt = self.usm_sharpener(self.gt)
self.kernel1 = data['kernel1'].to(self.device)
self.kernel2 = data['kernel2'].to(self.device)
self.sinc_kernel = data['sinc_kernel'].to(self.device)
ori_h, ori_w = self.gt.size()[2:4]
# ----------------------- The first degradation process ----------------------- #
# blur
out = filter2D(self.gt, self.kernel1)
# random resize
updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
if updown_type == 'up':
scale = np.random.uniform(1, self.opt['resize_range'][1])
elif updown_type == 'down':
scale = np.random.uniform(self.opt['resize_range'][0], 1)
else:
scale = 1
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = F.interpolate(out, scale_factor=scale, mode=mode)
# 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(
out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
else:
out = random_add_poisson_noise_pt(
out,
scale_range=self.opt['poisson_scale_range'],
gray_prob=gray_noise_prob,
clip=True,
rounds=False)
# JPEG compression
jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
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 ----------------------- #
# blur
if np.random.uniform() < self.opt['second_blur_prob']:
out = filter2D(out, self.kernel2)
# random resize
updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
if updown_type == 'up':
scale = np.random.uniform(1, self.opt['resize_range2'][1])
elif updown_type == 'down':
scale = np.random.uniform(self.opt['resize_range2'][0], 1)
else:
scale = 1
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)
# 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(
out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
else:
out = random_add_poisson_noise_pt(
out,
scale_range=self.opt['poisson_scale_range2'],
gray_prob=gray_noise_prob,
clip=True,
rounds=False)
# JPEG compression + the final sinc filter
# We also need to resize images to desired sizes. We group [resize back + sinc filter] together
# as one operation.
# We consider two orders:
# 1. [resize back + sinc filter] + JPEG compression
# 2. JPEG compression + [resize back + sinc filter]
# Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
if np.random.uniform() < 0.5:
# resize back + the final sinc filter
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
out = filter2D(out, self.sinc_kernel)
# JPEG compression
jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
out = torch.clamp(out, 0, 1)
out = self.jpeger(out, quality=jpeg_p)
else:
# JPEG compression
jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
out = torch.clamp(out, 0, 1)
out = self.jpeger(out, quality=jpeg_p)
# resize back + the final sinc filter
mode = random.choice(['area', 'bilinear', 'bicubic'])
out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
out = filter2D(out, self.sinc_kernel)
# clamp and round
self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.
# random crop
gt_size = self.opt['gt_size']
self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale'])
# 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)
self.gt_usm = self.usm_sharpener(self.gt)
def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
# do not use the synthetic process during validation
self.is_train = False
super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
self.is_train = True
================================================
FILE: realesrgan/train.py
================================================
# flake8: noqa
import os.path as osp
from basicsr.train import train_pipeline
import realesrgan.archs
import realesrgan.data
import realesrgan.models
if __name__ == '__main__':
root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
train_pipeline(root_path)
================================================
FILE: realesrgan/utils.py
================================================
import cv2
import math
import numpy as np
import os
import queue
import threading
import torch
from basicsr.utils.download_util import load_file_from_url
from torch.nn import functional as F
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. 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,
dni_weight=None,
model=None,
tile=0,
tile_pad=10,
pre_pad=10,
half=False,
device=None,
gpu_id=None):
self.scale = scale
self.tile_size = tile
self.tile_pad = tile_pad
self.pre_pad = pre_pad
self.mod_scale = None
self.half = half
# initialize model
if gpu_id:
self.device = torch.device(
f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu') if device is None else device
else:
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
if isinstance(model_path, list):
# dni
assert len(model_path) == len(dni_weight), 'model_path and dni_weight should have the save length.'
loadnet = self.dni(model_path[0], model_path[1], dni_weight)
else:
# if the model_path starts with https, it will first download models to the folder: weights
if model_path.startswith('https://'):
model_path = load_file_from_url(
url=model_path, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
loadnet = torch.load(model_path, map_location=torch.device('cpu'))
# prefer to use params_ema
if 'params_ema' in loadnet:
keyname = 'params_ema'
else:
keyname = 'params'
model.load_state_dict(loadnet[keyname], strict=True)
model.eval()
self.model = model.to(self.device)
if self.half:
self.model = self.model.half()
def dni(self, net_a, net_b, dni_weight, key='params', loc='cpu'):
"""Deep network interpolation.
``Paper: Deep Network Interpolation for Continuous Imagery Effect Transition``
"""
net_a = torch.load(net_a, map_location=torch.device(loc))
net_b = torch.load(net_b, map_location=torch.device(loc))
for k, v_a in net_a[key].items():
net_a[key][k] = dni_weight[0] * v_a + dni_weight[1] * net_b[key][k]
return net_a
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:
self.img = self.img.half()
# pre_pad
if self.pre_pad != 0:
self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
# mod pad for divisible borders
if self.scale == 2:
self.mod_scale = 2
elif self.scale == 1:
self.mod_scale = 4
if self.mod_scale is not None:
self.mod_pad_h, self.mod_pad_w = 0, 0
_, _, h, w = self.img.size()
if (h % self.mod_scale != 0):
self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
if (w % self.mod_scale != 0):
self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
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):
"""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
output_width = width * self.scale
output_shape = (batch, channel, output_height, output_width)
# start with black image
self.output = self.img.new_zeros(output_shape)
tiles_x = math.ceil(width / self.tile_size)
tiles_y = math.ceil(height / self.tile_size)
# loop over all tiles
for y in range(tiles_y):
for x in range(tiles_x):
# extract tile from input image
ofs_x = x * self.tile_size
ofs_y = y * self.tile_size
# input tile area on total image
input_start_x = ofs_x
input_end_x = min(ofs_x + self.tile_size, width)
input_start_y = ofs_y
input_end_y = min(ofs_y + self.tile_size, height)
# input tile area on total image with padding
input_start_x_pad = max(input_start_x - self.tile_pad, 0)
input_end_x_pad = min(input_end_x + self.tile_pad, width)
input_start_y_pad = max(input_start_y - self.tile_pad, 0)
input_end_y_pad = min(input_end_y + self.tile_pad, height)
# input tile dimensions
input_tile_width = input_end_x - input_start_x
input_tile_height = input_end_y - input_start_y
tile_idx = y * tiles_x + x + 1
input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
# upscale tile
try:
with torch.no_grad():
output_tile = self.model(input_tile)
except RuntimeError as error:
print('Error', error)
print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
# output tile area on total image
output_start_x = input_start_x * self.scale
output_end_x = input_end_x * self.scale
output_start_y = input_start_y * self.scale
output_end_y = input_end_y * self.scale
# output tile area without padding
output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
# put tile into output image
self.output[:, :, output_start_y:output_end_y,
output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
output_start_x_tile:output_end_x_tile]
def post_process(self):
# remove extra pad
if self.mod_scale is not None:
_, _, h, w = self.output.size()
self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
# remove prepad
if self.pre_pad != 0:
_, _, h, w = self.output.size()
self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
return self.output
@torch.no_grad()
def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
h_input, w_input = img.shape[0:2]
# img: numpy
img = img.astype(np.float32)
if np.max(img) > 256: # 16-bit image
max_range = 65535
print('\tInput is a 16-bit image')
else:
max_range = 255
img = img / max_range
if len(img.shape) == 2: # gray image
img_mode = 'L'
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
elif img.shape[2] == 4: # RGBA image with alpha channel
img_mode = 'RGBA'
alpha = img[:, :, 3]
img = img[:, :, 0:3]
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if alpha_upsampler == 'realesrgan':
alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
else:
img_mode = 'RGB'
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# ------------------- process image (without the alpha channel) ------------------- #
self.pre_process(img)
if self.tile_size > 0:
self.tile_process()
else:
self.process()
output_img = self.post_process()
output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
if img_mode == 'L':
output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
# ------------------- process the alpha channel if necessary ------------------- #
if img_mode == 'RGBA':
if alpha_upsampler == 'realesrgan':
self.pre_process(alpha)
if self.tile_size > 0:
self.tile_process()
else:
self.process()
output_alpha = self.post_process()
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: # 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)
# merge the alpha channel
output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
output_img[:, :, 3] = output_alpha
# ------------------------------ return ------------------------------ #
if max_range == 65535: # 16-bit image
output = (output_img * 65535.0).round().astype(np.uint16)
else:
output = (output_img * 255.0).round().astype(np.uint8)
if outscale is not None and outscale != float(self.scale):
output = cv2.resize(
output, (
int(w_input * outscale),
int(h_input * outscale),
), interpolation=cv2.INTER_LANCZOS4)
return output, img_mode
class PrefetchReader(threading.Thread):
"""Prefetch images.
Args:
img_list (list[str]): A image list of image paths to be read.
num_prefetch_queue (int): Number of prefetch queue.
"""
def __init__(self, img_list, num_prefetch_queue):
super().__init__()
self.que = queue.Queue(num_prefetch_queue)
self.img_list = img_list
def run(self):
for img_path in self.img_list:
img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
self.que.put(img)
self.que.put(None)
def __next__(self):
next_item = self.que.get()
if next_item is None:
raise StopIteration
return next_item
def __iter__(self):
return self
class IOConsumer(threading.Thread):
def __init__(self, opt, que, qid):
super().__init__()
self._queue = que
self.qid = qid
self.opt = opt
def run(self):
while True:
msg = self._queue.get()
if isinstance(msg, str) and msg == 'quit':
break
output = msg['output']
save_path = msg['save_path']
cv2.imwrite(save_path, output)
print(f'IO worker {self.qid} is done.')
================================================
FILE: requirements.txt
================================================
basicsr>=1.4.2
facexlib>=0.2.5
gfpgan>=1.3.5
numpy
opencv-python
Pillow
torch>=1.7
torchvision
tqdm
================================================
FILE: scripts/extract_subimages.py
================================================
import argparse
import cv2
import numpy as np
import os
import sys
from basicsr.utils import scandir
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm
def main(args):
"""A multi-thread tool to crop large images to sub-images for faster IO.
opt (dict): Configuration dict. It contains:
n_thread (int): Thread number.
compression_level (int): CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size
and longer compression time. 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 than thresh_size will be dropped.
Usage:
For each folder, run this script.
Typically, there are GT folder and LQ folder to be processed for DIV2K dataset.
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
opt['input_folder'] = args.input
opt['save_folder'] = args.output
opt['crop_size'] = args.crop_size
opt['step'] = args.step
opt['thresh_size'] = args.thresh_size
extract_subimages(opt)
def extract_subimages(opt):
"""Crop images to subimages.
Args:
opt (dict): Configuration dict. It contains:
input_folder (str): Path to the input folder.
save_folder (str): Path to save folder.
n_thread (int): Thread number.
"""
input_folder = opt['input_folder']
save_folder = opt['save_folder']
if not osp.exists(save_folder):
os.makedirs(save_folder)
print(f'mkdir {save_folder} ...')
else:
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')
pool = Pool(opt['n_thread'])
for path in img_list:
pool.apply_async(worker, args=(path, opt), callback=lambda arg: pbar.update(1))
pool.close()
pool.join()
pbar.close()
print('All processes done.')
def worker(path, opt):
"""Worker for each process.
Args:
path (str): Image path.
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 than thresh_size will be dropped.
save_folder (str): Path to save folder.
compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.
Returns:
process_info (str): Process information displayed in progress bar.
"""
crop_size = opt['crop_size']
step = opt['step']
thresh_size = opt['thresh_size']
img_name, extension = osp.splitext(osp.basename(path))
# remove the x2, x3, x4 and x8 in the filename for DIV2K
img_name = img_name.replace('x2', '').replace('x3', '').replace('x4', '').replace('x8', '')
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
h, w = img.shape[0:2]
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)
w_space = np.arange(0, w - crop_size + 1, step)
if w - (w_space[-1] + crop_size) > thresh_size:
w_space = np.append(w_space, w - crop_size)
index = 0
for x in h_space:
for y in w_space:
index += 1
cropped_img = img[x:x + crop_size, y:y + crop_size, ...]
cropped_img = np.ascontiguousarray(cropped_img)
cv2.imwrite(
osp.join(opt['save_folder'], f'{img_name}_s{index:03d}{extension}'), cropped_img,
[cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
process_info = f'Processing {img_name} ...'
return process_info
if __name__ == '__main__':
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_HR_sub', help='Output folder')
parser.add_argument('--crop_size', type=int, default=480, help='Crop size')
parser.add_argument('--step', type=int, default=240, help='Step for overlapped sliding window')
parser.add_argument(
'--thresh_size',
type=int,
default=0,
help='Threshold size. Patches whose size is lower than thresh_size will be dropped.')
parser.add_argument('--n_thread', type=int, default=20, help='Thread number.')
parser.add_argument('--compression_level', type=int, default=3, help='Compression level')
args = parser.parse_args()
main(args)
================================================
FILE: scripts/generate_meta_info.py
================================================
import argparse
import cv2
import glob
import os
def main(args):
txt_file = open(args.meta_info, 'w')
for folder, root in zip(args.input, args.root):
img_paths = sorted(glob.glob(os.path.join(folder, '*')))
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 (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')
if __name__ == '__main__':
"""Generate meta info (txt file) for only Ground-Truth images.
It can also generate meta info from several folders into one txt file.
"""
parser = argparse.ArgumentParser()
parser.add_argument(
'--input',
nargs='+',
default=['datasets/DF2K/DF2K_HR', 'datasets/DF2K/DF2K_multiscale'],
help='Input folder, can be a list')
parser.add_argument(
'--root',
nargs='+',
default=['datasets/DF2K', 'datasets/DF2K'],
help='Folder root, should have the length as input folders')
parser.add_argument(
'--meta_info',
type=str,
default='datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt',
help='txt path for meta info')
parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
args = parser.parse_args()
assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
f'{len(args.input)} and {len(args.root)}.')
os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
main(args)
================================================
FILE: scripts/generate_meta_info_pairdata.py
================================================
import argparse
import glob
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], '*')))
assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
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}')
txt_file.write(f'{img_name_gt}, {img_name_lq}\n')
if __name__ == '__main__':
"""This script is used to generate meta info (txt file) for paired images.
"""
parser = argparse.ArgumentParser()
parser.add_argument(
'--input',
nargs='+',
default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
help='Input folder, should be [gt_folder, lq_folder]')
parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
parser.add_argument(
'--meta_info',
type=str,
default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
help='txt path for meta info')
args = parser.parse_args()
assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
for i in range(2):
if args.input[i].endswith('/'):
args.input[i] = args.input[i][:-1]
if args.root[i] is None:
args.root[i] = os.path.dirname(args.input[i])
main(args)
================================================
FILE: scripts/generate_multiscale_DF2K.py
================================================
import argparse
import glob
import os
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]
shortest_edge = 400
path_list = sorted(glob.glob(os.path.join(args.input, '*')))
for path in path_list:
print(path)
basename = os.path.splitext(os.path.basename(path))[0]
img = Image.open(path)
width, height = img.size
for idx, scale in enumerate(scale_list):
print(f'\t{scale:.2f}')
rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))
# save the smallest image which the shortest edge is 400
if width < height:
ratio = height / width
width = shortest_edge
height = int(width * ratio)
else:
ratio = width / height
height = shortest_edge
width = int(height * ratio)
rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))
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')
args = parser.parse_args()
os.makedirs(args.output, exist_ok=True)
main(args)
================================================
FILE: scripts/pytorch2onnx.py
================================================
import argparse
import torch
import torch.onnx
from basicsr.archs.rrdbnet_arch import RRDBNet
def main(args):
# 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()
# An example input
x = torch.rand(1, 3, 64, 64)
# 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)
================================================
FILE: setup.cfg
================================================
[flake8]
ignore =
# line break before binary operator (W503)
W503,
# line break after binary operator (W504)
W504,
max-line-length=120
[yapf]
based_on_style = pep8
column_limit = 120
blank_line_before_nested_class_or_def = true
split_before_expression_after_opening_paren = true
[isort]
line_length = 120
multi_line_output = 0
known_standard_library = pkg_resources,setuptools
known_first_party = realesrgan
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/
================================================
FILE: setup.py
================================================
#!/usr/bin/env python
from setuptools import find_packages, setup
import os
import subprocess
import time
version_file = 'realesrgan/version.py'
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
def get_git_hash():
def _minimal_ext_cmd(cmd):
# construct minimal environment
env = {}
for k in ['SYSTEMROOT', 'PATH', 'HOME']:
v = os.environ.get(k)
if v is not None:
env[k] = v
# LANGUAGE is used on win32
env['LANGUAGE'] = 'C'
env['LANG'] = 'C'
env['LC_ALL'] = 'C'
out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
return out
try:
out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
sha = out.strip().decode('ascii')
except OSError:
sha = 'unknown'
return sha
def get_hash():
if os.path.exists('.git'):
sha = get_git_hash()[:7]
else:
sha = 'unknown'
return sha
def write_version_py():
content = """# GENERATED VERSION FILE
# TIME: {}
__version__ = '{}'
__gitsha__ = '{}'
version_info = ({})
"""
sha = get_hash()
with open('VERSION', 'r') as f:
SHORT_VERSION = f.read().strip()
VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
with open(version_file, 'w') as f:
f.write(version_file_str)
def get_version():
with open(version_file, 'r') as f:
exec(compile(f.read(), version_file, 'exec'))
return locals()['__version__']
def get_requirements(filename='requirements.txt'):
here = os.path.dirname(os.path.realpath(__file__))
with open(os.path.join(here, filename), 'r') as f:
requires = [line.replace('\n', '') for line in f.readlines()]
return requires
if __name__ == '__main__':
write_version_py()
setup(
name='realesrgan',
version=get_version(),
description='Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration',
long_description=readme(),
long_description_content_type='text/markdown',
author='Xintao Wang',
author_email='xintao.wang@outlook.com',
keywords='computer vision, pytorch, image restoration, super-resolution, esrgan, real-esrgan',
url='https://github.com/xinntao/Real-ESRGAN',
include_package_data=True,
packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
classifiers=[
'Development Status :: 4 - Beta',
'License :: OSI Approved :: Apache Software License',
'Operating System :: OS Independent',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
],
license='BSD-3-Clause License',
setup_requires=['cython', 'numpy'],
install_requires=get_requirements(),
zip_safe=False)
================================================
FILE: tests/data/gt.lmdb/meta_info.txt
================================================
baboon.png (480,500,3) 1
comic.png (360,240,3) 1
================================================
FILE: tests/data/lq.lmdb/meta_info.txt
================================================
baboon.png (120,125,3) 1
comic.png (80,60,3) 1
================================================
FILE: tests/data/meta_info_gt.txt
================================================
baboon.png
comic.png
================================================
FILE: tests/data/meta_info_pair.txt
================================================
gt/baboon.png, lq/baboon.png
gt/comic.png, lq/comic.png
================================================
FILE: tests/data/test_realesrgan_dataset.yml
================================================
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
================================================
FILE: tests/data/test_realesrgan_model.yml
================================================
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
================================================
FILE: tests/data/test_realesrgan_paired_dataset.yml
================================================
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
================================================
FILE: tests/data/test_realesrnet_model.yml
================================================
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
================================================
FILE: tests/test_dataset.py
================================================
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)
================================================
FILE: tests/test_discriminator_arch.py
================================================
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)
================================================
FILE: tests/test_model.py
================================================
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()))
================================================
FILE: tests/test_utils.py
================================================
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'