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requirements.txt 0 → 100644
View file @ 5efcc6ff
absl-py==2.0.0
addict==2.4.0
cachetools==5.3.1
certifi==2022.5.18.1
charset-normalizer==3.3.0
cycler==0.11.0
decorator==4.4.2
easydict==1.10
einops==0.6.1
face-alignment==1.4.1
facexlib==0.3.0
filelock==3.12.2
filterpy==1.4.5
fonttools==4.38.0
fsspec==2023.1.0
future==0.18.3
gfpgan==1.3.8
google-auth==2.23.2
google-auth-oauthlib==0.4.6
grpcio==1.59.0
huggingface-hub==0.16.4
idna==3.4
imageio==2.31.2
imageio-ffmpeg==0.4.9
importlib-metadata==6.7.0
kiwisolver==1.4.5
llvmlite==0.39.1
lmdb==1.4.1
lpips==0.1.4
Markdown==3.4.4
MarkupSafe==2.1.3
matplotlib==3.5.3
moviepy==1.0.3
networkx==2.6.3
numba==0.56.4
numpy==1.20.3
oauthlib==3.2.2
opencv-python==4.8.1.78
packaging==23.2
Pillow==9.5.0
platformdirs==3.11.0
proglog==0.1.10
protobuf==3.20.3
pyasn1==0.5.0
pyasn1-modules==0.3.0
pyparsing==3.1.1
python-dateutil==2.8.2
PyWavelets==1.3.0
PyYAML==6.0.1
realesrgan==0.3.0
requests==2.31.0
requests-oauthlib==1.3.1
rsa==4.9
safetensors==0.4.0
scikit-image==0.19.3
scipy==1.7.3
six==1.16.0
scikit-learn
tb-nightly==2.12.0a20230113
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorboardX==2.6.2.2
tifffile==2021.11.2
timm==0.9.7
tomli==2.0.1
tqdm==4.66.1
typing_extensions==4.7.1
Werkzeug==2.2.3
yapf==0.40.2
zipp==3.15.0
sr_model/.gitignore 0 → 100644
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# ignored folders
datasets/*
data
experiments/*
experiments
exp_cluster/*
results/*
results
results_local/*
tb_logger/*
tb_logger
wandb/*
tmp/*
ckpt/*
version.py
trash/*
data/*
pretrained_ckpt/*
scripts/azcopy_linux_amd64_10.16.0
pretrained_ckpt
git/*
BasicSR/*
# scripts/*
*.diff
# 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/
sr_model/Basicsr/.github/workflows/gitee-mirror.yml 0 → 100644
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name: Gitee Mirror
on: [ push, delete, create ]
jobs:
git-mirror:
runs-on: ubuntu-latest
steps:
- uses: wearerequired/git-mirror-action@v1
env:
SSH_PRIVATE_KEY: ${{ secrets.SSH_PRIVATE_KEY }}
with:
source-repo: 'git@github.com:xinntao/BasicSR.git'
destination-repo: 'git@gitee.com:xinntao/BasicSR.git'
sr_model/Basicsr/.github/workflows/publish-pip.yml 0 → 100644
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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 -r requirements.txt
- name: Build and install
run: rm -rf .eggs && pip install -e .
- name: Build for distribution
# remove bdist_wheel for pip installation with compiling cuda extensions
run: python setup.py sdist
- name: Publish distribution to PyPI
uses: pypa/gh-action-pypi-publish@master
with:
password: ${{ secrets.PYPI_API_TOKEN }}
sr_model/Basicsr/.github/workflows/pylint.yml 0 → 100644
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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: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install codespell flake8 isort yapf
- name: Lint
run: |
codespell
flake8 .
isort --check-only --diff basicsr/ options/ scripts/ tests/ inference/ setup.py
yapf -r -d basicsr/ options/ scripts/ tests/ inference/ setup.py
sr_model/Basicsr/.gitignore 0 → 100644
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# ignored folders
datasets/*
experiments/*
results/*
tb_logger/*
wandb/*
tmp/*
git/*
docs/api
scripts/__init__.py
*.DS_Store
.idea
# ignored files
version.py
# ignored files with suffix
*.html
*.png
*.jpeg
*.jpg
*.gif
*.pth
*.zip
# template
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.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/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# 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/
sr_model/Basicsr/.pre-commit-config.yaml 0 → 100644
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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"]
sr_model/Basicsr/.readthedocs.yaml 0 → 100644
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# .readthedocs.yaml
# Read the Docs configuration file
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
# Required
version: 2
# Set the version of Python and other tools you might need
build:
os: ubuntu-20.04
tools:
python: "3.8"
# You can also specify other tool versions:
# nodejs: "16"
# rust: "1.55"
# golang: "1.17"
# Build documentation in the docs/ directory with Sphinx
sphinx:
configuration: docs/conf.py
# If using Sphinx, optionally build your docs in additional formats such as PDF
# formats:
# - pdf
# Optionally declare the Python requirements required to build your docs
python:
install:
- requirements: docs/requirements.txt
sr_model/Basicsr/CITATION.cff 0 → 100644
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cff-version: 1.2.0
message: "If you use this project, please cite it as below."
title: "BasicSR: Open Source Image and Video Restoration Toolbox"
version: 1.3.5
date-released: 2022-02-16
url: "https://github.com/XPixelGroup/BasicSR"
license: Apache-2.0
authors:
- family-names: BasicSR
given-names: Authors
sr_model/Basicsr/INSTALL.md 0 → 100644
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# Installation
## Contents
- [Requirements](#requirements)
- [BASICSR_EXT and BASICSR_JIT Environment Variables](#basicsr_ext-and-basicsr_jit-environment-variables)
- [Installation Options](#installation-options)
- [Install from PyPI](#install-from-pypi)
- [Install from a local clone](#install-from-a-local-clone)
- [FAQ](#faq)
## Requirements
- 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/)
- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
- Linux (We have not tested on Windows)
## BASICSR_EXT and BASICSR_JIT Environment Variables
If you want to use PyTorch C++ extensions:<br>
&emsp;deformable convolution: [*dcn* for EDVR](basicsr/ops) (For torchvision>=0.9.0, we use the official `torchvision.ops.deform_conv2d` instead)<br>
&emsp;StyleGAN customized operators: [*upfirdn2d* and *fused_act* for StyleGAN2](basicsr/ops)<br>
you also need to:
1. **compile** the PyTorch C++ extensions during installation
2. OR **load** the PyTorch C++ extensions just-in-time (JIT)
You may choose one of the options according to your needs.
| Option | Pros| Cons | Cases | Env Variable|
| :--- | :--- | :--- | :--- |:--- |
| **Compile** PyTorch C++ extensions during installation | **Quickly load** the compiled extensions during running | May have more stringent requirements for the environment, and you may encounter annoying issues | If you need to train/inference those models for many times, it will save your time| Set `BASICSR_EXT=True` during **installation**|
| **Load** PyTorch C++ extensions just-in-time (JIT) | Have less requirements, may have less issues | Each time you run the model, it will takes several minutes to load extensions again | If you just want to do simple inferences, it is more convenient| Set `BASICSR_JIT=True` during **running** (not **installation**) |
For those who need to compile the PyTorch C++ extensions during installation, remember:
- Make sure that your gcc and g++ version: gcc & g++ >= 5
Note that:
- The `BASICSR_JIT` has higher priority, that is, even you have successfully compiled PyTorch C++ extensions during installation, it will still load the extensions just-in-time if you set `BASICSR_JIT=True` in your running commands.
- :x: Do not set `BASICSR_JIT` during installation. Installation commands are in [Installation Options](#installation-options).
- :heavy_check_mark: If you want to load PyTorch C++ extensions just-in-time (JIT), just set `BASICSR_JIT=True` before your **running** commands. For example, `BASICSR_JIT=True python inference/inference_stylegan2.py`.
If you do not need those PyTorch C++ extensions, just skip it. There is no need to set `BASICSR_EXT` or `BASICSR_JIT` environment variables.
## Installation Options
There are two options to install BASICSR, according to your needs.
- If you just want to use BASICSR as a **package** (just like [GFPGAN](https://github.com/TencentARC/GFPGAN) and []()), it is recommended to install from PyPI.
- If you want to **investigate** the details of BASICSR OR **develop** it OR **modify** it to fulfill your needs, it is better to install from a local clone.
### Install from PyPI
- If you do not need C++ extensions (more details are [here](#basicsr_ext-and-basicsr_jit-environment-variables)):
```bash
pip install basicsr
```
- If you want to use C++ extensions in **JIT mode** without compiling them during installatoin (more details are [here](#basicsr_ext-and-basicsr_jit-environment-variables)):
```bash
pip install basicsr
```
- If you want to **compile C++ extensions during installation**, please set the environment variable `BASICSR_EXT=True`:
```bash
BASICSR_EXT=True pip install basicsr
```
The compilation may fail without any error prints. If you encounter running errors, such as `ImportError: cannot import name 'deform_conv_ext' | 'fused_act_ext' | 'upfirdn2d_ext'`, you may check the compilation process by re-installation. The following command will print detailed log:
```bash
BASICSR_EXT=True pip install basicsr -vvv
```
You may also want to specify the CUDA paths:
```bash
CUDA_HOME=/usr/local/cuda \
CUDNN_INCLUDE_DIR=/usr/local/cuda \
CUDNN_LIB_DIR=/usr/local/cuda \
BASICSR_EXT=True pip install basicsr
```
### Install from a local clone
1. Clone the repo
```bash
git clone https://github.com/xinntao/BasicSR.git
```
1. Install dependent packages
```bash
cd BasicSR
pip install -r requirements.txt
```
1. Install BasicSR<br>
Please run the following commands in the **BasicSR root path** to install BasicSR:<br>
- If you do not need C++ extensions (more details are [here](#basicsr_ext-and-basicsr_jit-environment-variables)):
```bash
python setup.py develop
```
- If you want to use C++ extensions in **JIT mode** without compiling them during installatoin (more details are [here](#basicsr_ext-and-basicsr_jit-environment-variables)):
```bash
python setup.py develop
```
- If you want to **compile C++ extensions during installation**, please set the environment variable `BASICSR_EXT=True`:
```bash
BASICSR_EXT=True python setup.py develop
```
You may also want to specify the CUDA paths:
```bash
CUDA_HOME=/usr/local/cuda \
CUDNN_INCLUDE_DIR=/usr/local/cuda \
CUDNN_LIB_DIR=/usr/local/cuda \
BASICSR_EXT=True python setup.py develop
```
## FAQ
sr_model/Basicsr/LICENSE.txt 0 → 100644
View file @ 5efcc6ff
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identification within third-party archives.
Copyright 2018-2022 BasicSR Authors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
sr_model/Basicsr/LICENSE/LICENSE 0 → 100644
View file @ 5efcc6ff
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2018-2020 BasicSR Authors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
sr_model/Basicsr/LICENSE/LICENSE-NVIDIA 0 → 100644
View file @ 5efcc6ff
Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
Nvidia Source Code License-NC
=======================================================================
1. Definitions
"Licensor" means any person or entity that distributes its Work.
"Software" means the original work of authorship made available under
this License.
"Work" means the Software and any additions to or derivative works of
the Software that are made available under this License.
"Nvidia Processors" means any central processing unit (CPU), graphics
processing unit (GPU), field-programmable gate array (FPGA),
application-specific integrated circuit (ASIC) or any combination
thereof designed, made, sold, or provided by Nvidia or its affiliates.
The terms "reproduce," "reproduction," "derivative works," and
"distribution" have the meaning as provided under U.S. copyright law;
provided, however, that for the purposes of this License, derivative
works shall not include works that remain separable from, or merely
link (or bind by name) to the interfaces of, the Work.
Works, including the Software, are "made available" under this License
by including in or with the Work either (a) a copyright notice
referencing the applicability of this License to the Work, or (b) a
copy of this License.
2. License Grants
2.1 Copyright Grant. Subject to the terms and conditions of this
License, each Licensor grants to you a perpetual, worldwide,
non-exclusive, royalty-free, copyright license to reproduce,
prepare derivative works of, publicly display, publicly perform,
sublicense and distribute its Work and any resulting derivative
works in any form.
3. Limitations
3.1 Redistribution. You may reproduce or distribute the Work only
if (a) you do so under this License, (b) you include a complete
copy of this License with your distribution, and (c) you retain
without modification any copyright, patent, trademark, or
attribution notices that are present in the Work.
3.2 Derivative Works. You may specify that additional or different
terms apply to the use, reproduction, and distribution of your
derivative works of the Work ("Your Terms") only if (a) Your Terms
provide that the use limitation in Section 3.3 applies to your
derivative works, and (b) you identify the specific derivative
works that are subject to Your Terms. Notwithstanding Your Terms,
this License (including the redistribution requirements in Section
3.1) will continue to apply to the Work itself.
3.3 Use Limitation. The Work and any derivative works thereof only
may be used or intended for use non-commercially. The Work or
derivative works thereof may be used or intended for use by Nvidia
or its affiliates commercially or non-commercially. As used herein,
"non-commercially" means for research or evaluation purposes only.
3.4 Patent Claims. If you bring or threaten to bring a patent claim
against any Licensor (including any claim, cross-claim or
counterclaim in a lawsuit) to enforce any patents that you allege
are infringed by any Work, then your rights under this License from
such Licensor (including the grants in Sections 2.1 and 2.2) will
terminate immediately.
3.5 Trademarks. This License does not grant any rights to use any
Licensor's or its affiliates' names, logos, or trademarks, except
as necessary to reproduce the notices described in this License.
3.6 Termination. If you violate any term of this License, then your
rights under this License (including the grants in Sections 2.1 and
2.2) will terminate immediately.
4. Disclaimer of Warranty.
THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR
NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER
THIS LICENSE.
5. Limitation of Liability.
EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF
OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK
(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,
LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER
COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
THE POSSIBILITY OF SUCH DAMAGES.
=======================================================================
sr_model/Basicsr/LICENSE/LICENSE-stylegan2-pytorch 0 → 100644
View file @ 5efcc6ff
MIT License
Copyright (c) 2019 Kim Seonghyeon
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
sr_model/Basicsr/LICENSE/LICENSE_SwinIR 0 → 100644
View file @ 5efcc6ff
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [2021] [SwinIR Authors]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
sr_model/Basicsr/LICENSE/LICENSE_pytorch-image-models 0 → 100644
View file @ 5efcc6ff
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
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sr_model/Basicsr/LICENSE/README.md 0 → 100644
View file @ 5efcc6ff
# License and Acknowledgement
This BasicSR project is released under the Apache 2.0 license.
- StyleGAN2
- The codes are modified from the repository [stylegan2-pytorch](https://github.com/rosinality/stylegan2-pytorch). Many thanks to the author - [Kim Seonghyeon](https://rosinality.github.io/) :blush: for translating from the official TensorFlow codes to PyTorch ones. Here is the [license](LICENSE-stylegan2-pytorch) of stylegan2-pytorch.
- The official repository is https://github.com/NVlabs/stylegan2, and here is the [NVIDIA license](./LICENSE-NVIDIA).
- DFDNet
- The codes are largely modified from the repository [DFDNet](https://github.com/csxmli2016/DFDNet). Their license is [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
- DiffJPEG
- Modified from https://github.com/mlomnitz/DiffJPEG.
- [pytorch-image-models](https://github.com/rwightman/pytorch-image-models/)
- We use the implementation of `DropPath` and `trunc_normal_` from [pytorch-image-models](https://github.com/rwightman/pytorch-image-models/). The LICENSE is included as [LICENSE_pytorch-image-models](LICENSE/LICENSE_pytorch-image-models).
- [SwinIR](https://github.com/JingyunLiang/SwinIR)
- The arch implementation of SwinIR is from [SwinIR](https://github.com/JingyunLiang/SwinIR). The LICENSE is included as [LICENSE_SwinIR](LICENSE/LICENSE_SwinIR).
- [ECBSR](https://github.com/xindongzhang/ECBSR)
- The arch implementation of ECBSR is from [ECBSR](https://github.com/xindongzhang/ECBSR). The LICENSE of ECBSR is [Apache License 2.0](https://github.com/xindongzhang/ECBSR/blob/main/LICENSE)
## References
1. NIQE metric: the codes are translated from the [official MATLAB codes](http://live.ece.utexas.edu/research/quality/niqe_release.zip)
> A. Mittal, R. Soundararajan and A. C. Bovik, "Making a Completely Blind Image Quality Analyzer", IEEE Signal Processing Letters, 2012.
1. FID metric: the codes are modified from [pytorch-fid](https://github.com/mseitzer/pytorch-fid) and [stylegan2-pytorch](https://github.com/rosinality/stylegan2-pytorch).
sr_model/Basicsr/MANIFEST.in 0 → 100644
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include basicsr/ops/dcn/src/*.cu basicsr/ops/dcn/src/*.cpp
include basicsr/ops/fused_act/src/*.cu basicsr/ops/fused_act/src/*.cpp
include basicsr/ops/upfirdn2d/src/*.cu basicsr/ops/upfirdn2d/src/*.cpp
include basicsr/metrics/niqe_pris_params.npz
include VERSION
include requirements.txt
sr_model/Basicsr/README.md 0 → 100644
View file @ 5efcc6ff
<p align="center">
<img src="assets/basicsr_xpixel_logo.png" height=120>
</p>
## <div align="center"><b><a href="README.md">English</a> | <a href="README_CN.md">简体中文</a></b></div>
[![LICENSE](https://img.shields.io/github/license/xinntao/basicsr.svg)](https://github.com/xinntao/BasicSR/blob/master/LICENSE/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/basicsr)](https://pypi.org/project/basicsr/)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/xinntao/BasicSR.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/xinntao/BasicSR/context:python)
[![python lint](https://github.com/xinntao/BasicSR/actions/workflows/pylint.yml/badge.svg)](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/pylint.yml)
[![Publish-pip](https://github.com/xinntao/BasicSR/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/publish-pip.yml)
[![gitee mirror](https://github.com/xinntao/BasicSR/actions/workflows/gitee-mirror.yml/badge.svg)](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/gitee-mirror.yml)
<!-- [English](README.md) **|** [简体中文](README_CN.md) &emsp; [GitHub](https://github.com/xinntao/BasicSR) **|** [Gitee码云](https://gitee.com/xinntao/BasicSR) -->
:rocket: We add [BasicSR-Examples](https://github.com/xinntao/BasicSR-examples), which provides guidance and templates of using BasicSR as a python package. :rocket:
:loudspeaker: **技术交流QQ群**：**320960100** &emsp; 入群答案：**互帮互助共同进步**
:compass: [入群二维码](#e-mail-contact) (QQ、微信) &emsp;&emsp; [入群指南 (腾讯文档)](https://docs.qq.com/doc/DYXBSUmxOT0xBZ05u)
---
<a href="https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" height="18" alt="google colab logo"></a> Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing) <br>
:m: [Model Zoo](docs/ModelZoo.md): :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing)
:arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ) <br>
:file_folder: [Datasets](docs/DatasetPreparation.md): :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) :arrow_double_down: [百度网盘](https://pan.baidu.com/s/1AZDcEAFwwc1OC3KCd7EDnQ) (提取码:basr)<br>
:chart_with_upwards_trend: [Training curves in wandb](https://app.wandb.ai/xintao/basicsr) <br>
:computer: [Commands for training and testing](docs/TrainTest.md) <br>
:zap: [HOWTOs](#zap-howtos)
---
BasicSR (**Basic** **S**uper **R**estoration) is an open-source **image and video restoration** toolbox based on PyTorch, such as super-resolution, denoise, deblurring, JPEG artifacts removal, *etc*.
BasicSR (**Basic** **S**uper **R**estoration) 是一个基于 PyTorch 的开源 图像视频复原工具箱, 比如 超分辨率, 去噪, 去模糊, 去 JPEG 压缩噪声等.
:triangular_flag_on_post: **New Features/Updates**
- :white_check_mark: Oct 5, 2021. Add **ECBSR training and testing** codes: [ECBSR](https://github.com/xindongzhang/ECBSR).
> ACMMM21: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
- :white_check_mark: Sep 2, 2021. Add **SwinIR training and testing** codes: [SwinIR](https://github.com/JingyunLiang/SwinIR) by [Jingyun Liang](https://github.com/JingyunLiang). More details are in [HOWTOs.md](docs/HOWTOs.md#how-to-train-swinir-sr)
- :white_check_mark: Aug 5, 2021. Add NIQE, which produces the same results as MATLAB (both are 5.7296 for tests/data/baboon.png).
- :white_check_mark: July 31, 2021. Add **bi-directional video super-resolution** codes: [**BasicVSR** and IconVSR](https://arxiv.org/abs/2012.02181).
> CVPR21: BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
- **[More](docs/history_updates.md)**
:sparkles: **Projects that use BasicSR**
- [**Real-ESRGAN**](https://github.com/xinntao/Real-ESRGAN): A practical algorithm for general image restoration
- [**GFPGAN**](https://github.com/TencentARC/GFPGAN): A practical algorithm for real-world face restoration
If you use `BasicSR` in your open-source projects, welcome to contact me (by [email](#e-mail-contact) or opening an issue/pull request). I will add your projects to the above list :blush:
---
If BasicSR helps your research or work, please help to :star: this repo or recommend it to your friends. Thanks:blush: <br>
Other recommended projects:<br>
:arrow_forward: [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN): A practical algorithm for general image restoration<br>
:arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN): A practical algorithm for real-world face restoration <br>
:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions.<br>
:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison. <br>
<sub>([ESRGAN](https://github.com/xinntao/ESRGAN), [EDVR](https://github.com/xinntao/EDVR), [DNI](https://github.com/xinntao/DNI), [SFTGAN](https://github.com/xinntao/SFTGAN))</sub>
<sub>([HandyView](https://github.com/xinntao/HandyView), [HandyFigure](https://github.com/xinntao/HandyFigure), [HandyCrawler](https://github.com/xinntao/HandyCrawler), [HandyWriting](https://github.com/xinntao/HandyWriting))</sub>
---
## :zap: HOWTOs
We provide simple pipelines to train/test/inference models for a quick start.
These pipelines/commands cannot cover all the cases and more details are in the following sections.
| GAN | | | | | |
| :------------------- | :--------------------------------------------: | :----------------------------------------------------: | :------- | :--------------------------------------------: | :----------------------------------------------------: |
| StyleGAN2 | [Train](docs/HOWTOs.md#How-to-train-StyleGAN2) | [Inference](docs/HOWTOs.md#How-to-inference-StyleGAN2) | | | |
| **Face Restoration** | | | | | |
| DFDNet | - | [Inference](docs/HOWTOs.md#How-to-inference-DFDNet) | | | |
| **Super Resolution** | | | | | |
| ESRGAN | *TODO* | *TODO* | SRGAN | *TODO* | *TODO* |
| EDSR | *TODO* | *TODO* | SRResNet | *TODO* | *TODO* |
| RCAN | *TODO* | *TODO* | SwinIR | [Train](docs/HOWTOs.md#how-to-train-swinir-sr) | [Inference](docs/HOWTOs.md#how-to-inference-swinir-sr) |
| EDVR | *TODO* | *TODO* | DUF | - | *TODO* |
| BasicVSR | *TODO* | *TODO* | TOF | - | *TODO* |
| **Deblurring** | | | | | |
| DeblurGANv2 | - | *TODO* | | | |
| **Denoise** | | | | | |
| RIDNet | - | *TODO* | CBDNet | - | *TODO* |
## :wrench: Dependencies and Installation
For detailed instructions refer to [INSTALL.md](INSTALL.md).
## :hourglass_flowing_sand: TODO List
Please see [project boards](https://github.com/xinntao/BasicSR/projects).
## :turtle: Dataset Preparation
- Please refer to **[DatasetPreparation.md](docs/DatasetPreparation.md)** for more details.
- The descriptions of currently supported datasets (`torch.utils.data.Dataset` classes) are in [Datasets.md](docs/Datasets.md).
## :computer: Train and Test
- **Training and testing commands**: Please see **[TrainTest.md](docs/TrainTest.md)** for the basic usage.
- **Options/Configs**: Please refer to [Config.md](docs/Config.md).
- **Logging**: Please refer to [Logging.md](docs/Logging.md).
## :european_castle: Model Zoo and Baselines
- The descriptions of currently supported models are in [Models.md](docs/Models.md).
- **Pre-trained models and log examples** are available in **[ModelZoo.md](docs/ModelZoo.md)**.
- We also provide **training curves** in [wandb](https://app.wandb.ai/xintao/basicsr):
<p align="center">
<a href="https://app.wandb.ai/xintao/basicsr" target="_blank">
<img src="./assets/wandb.jpg" height="280">
</a></p>
## :memo: Codebase Designs and Conventions
Please see [DesignConvention.md](docs/DesignConvention.md) for the designs and conventions of the BasicSR codebase.<br>
The figure below shows the overall framework. More descriptions for each component: <br>
**[Datasets.md](docs/Datasets.md)**&emsp;|&emsp;**[Models.md](docs/Models.md)**&emsp;|&emsp;**[Config.md](docs/Config.md)**&emsp;|&emsp;**[Logging.md](docs/Logging.md)**
![overall_structure](./assets/overall_structure.png)
## :scroll: License and Acknowledgement
This project is released under the Apache 2.0 license.<br>
More details about **license** and **acknowledgement** are in [LICENSE](LICENSE/README.md).
## :earth_asia: Citations
If BasicSR helps your research or work, please consider citing BasicSR.<br>
The following is a BibTeX reference. The BibTeX entry requires the `url` LaTeX package.
``` latex
@misc{wang2020basicsr,
author = {Xintao Wang and Ke Yu and Kelvin C.K. Chan and
Chao Dong and Chen Change Loy},
title = {{BasicSR}: Open Source Image and Video Restoration Toolbox},
howpublished = {\url{https://github.com/xinntao/BasicSR}},
year = {2018}
}
```
> Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR: Open Source Image and Video Restoration Toolbox. <https://github.com/xinntao/BasicSR>, 2018.
## :e-mail: Contact
If you have any questions, please email `xintao.wang@outlook.com`.
<br>
- **QQ群**: 扫描左边二维码 或者 搜索QQ群号: 320960100   入群答案：互帮互助共同进步
- **微信群**: 我们的群一已经满500人啦，进群二可以扫描中间的二维码；如果进群遇到问题，也可以添加 Liangbin 的个人微信 (右边二维码)，他会在空闲的时候拉大家入群~
<p align="center">
<img src="https://user-images.githubusercontent.com/17445847/134879983-6f2d663b-16e7-49f2-97e1-7c53c8a5f71a.jpg" height="300"> &emsp;
<img src="https://user-images.githubusercontent.com/52127135/172553058-6cf32e10-2959-42dd-b26a-f802f09343b0.png" height="300"> &emsp;
<img src="https://user-images.githubusercontent.com/17445847/139572512-8e192aac-00fa-432b-ac8e-a33026b019df.png" height="300">
</p>
sr_model/Basicsr/README_CN.md 0 → 100644
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<p align="center">
<img src="assets/basicsr_xpixel_logo.png" height=120>
</p>
## <div align="center"><b><a href="README.md">English</a> | <a href="README_CN.md">简体中文</a></b></div>
[![LICENSE](https://img.shields.io/github/license/xinntao/basicsr.svg)](https://github.com/xinntao/BasicSR/blob/master/LICENSE/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/basicsr)](https://pypi.org/project/basicsr/)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/xinntao/BasicSR.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/xinntao/BasicSR/context:python)
[![python lint](https://github.com/xinntao/BasicSR/actions/workflows/pylint.yml/badge.svg)](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/pylint.yml)
[![Publish-pip](https://github.com/xinntao/BasicSR/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/publish-pip.yml)
[![gitee mirror](https://github.com/xinntao/BasicSR/actions/workflows/gitee-mirror.yml/badge.svg)](https://github.com/xinntao/BasicSR/blob/master/.github/workflows/gitee-mirror.yml)
<!-- [English](README.md) **|** [简体中文](README_CN.md) &emsp; [GitHub](https://github.com/xinntao/BasicSR) **|** [Gitee码云](https://gitee.com/xinntao/BasicSR) -->
:rocket: 我们添加了 [BasicSR-Examples](https://github.com/xinntao/BasicSR-examples), 它提供了使用BasicSR的指南以及模板 (以python package的形式) :rocket:
:loudspeaker: **技术交流QQ群**：**320960100** &emsp; 入群答案：**互帮互助共同进步**
:compass: [入群二维码](#e-mail-%E8%81%94%E7%B3%BB) (QQ、微信) &emsp;&emsp; [入群指南 (腾讯文档)](https://docs.qq.com/doc/DYXBSUmxOT0xBZ05u)
---
<a href="https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" height="18" alt="google colab logo"></a> Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing) <br>
:m: [模型库](docs/ModelZoo_CN.md): :arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ)
:arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing) <br>
:file_folder: [数据](docs/DatasetPreparation_CN.md): :arrow_double_down: [百度网盘](https://pan.baidu.com/s/1AZDcEAFwwc1OC3KCd7EDnQ) (提取码:basr) :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) <br>
:chart_with_upwards_trend: [wandb的训练曲线](https://app.wandb.ai/xintao/basicsr) <br>
:computer: [训练和测试的命令](docs/TrainTest_CN.md) <br>
:zap: [HOWTOs](#zap-howtos)
---
BasicSR (**Basic** **S**uper **R**estoration) 是一个基于 PyTorch 的开源图像视频复原工具箱, 比如 超分辨率, 去噪, 去模糊, 去 JPEG 压缩噪声等.
:triangular_flag_on_post: **新的特性/更新**
- :white_check_mark: Oct 5, 2021. 添加 **ECBSR 训练和测试** 代码: [ECBSR](https://github.com/xindongzhang/ECBSR).
> ACMMM21: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
- :white_check_mark: Sep 2, 2021. 添加 **SwinIR 训练和测试** 代码: [SwinIR](https://github.com/JingyunLiang/SwinIR) by [Jingyun Liang](https://github.com/JingyunLiang). 更多内容参见 [HOWTOs.md](docs/HOWTOs.md#how-to-train-swinir-sr)
- :white_check_mark: Aug 5, 2021. 添加了NIQE， 它输出和MATLAB一样的结果 (both are 5.7296 for tests/data/baboon.png).
- :white_check_mark: July 31, 2021. Add **bi-directional video super-resolution** codes: [**BasicVSR** and IconVSR](https://arxiv.org/abs/2012.02181).
> CVPR21: BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
- **[更多](docs/history_updates.md)**
:sparkles: **使用 BasicSR 的项目**
- [**Real-ESRGAN**](https://github.com/xinntao/Real-ESRGAN): 通用图像复原的实用算法
- [**GFPGAN**](https://github.com/TencentARC/GFPGAN): 真实场景人脸复原的实用算法
如果你的开源项目中使用了`BasicSR`, 欢迎联系我 ([邮件](#e-mail-%E8%81%94%E7%B3%BB)或者开一个issue/pull request)。我会将你的开源项目添加到上面的列表中 :blush:
---
如果 BasicSR 对你有所帮助，欢迎 :star: 这个仓库或推荐给你的朋友。Thanks:blush: <br>
其他推荐的项目:<br>
:arrow_forward: [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN): 通用图像复原的实用算法<br>
:arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN): 真实场景人脸复原的实用算法<br>
:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): 提供实用的人脸相关功能的集合<br>
:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): 基于PyQt5的 方便的看图比图工具<br>
<sub>([ESRGAN](https://github.com/xinntao/ESRGAN), [EDVR](https://github.com/xinntao/EDVR), [DNI](https://github.com/xinntao/DNI), [SFTGAN](https://github.com/xinntao/SFTGAN))</sub>
<sub>([HandyView](https://gitee.com/xinntao/HandyView), [HandyFigure](https://gitee.com/xinntao/HandyFigure), [HandyCrawler](https://gitee.com/xinntao/HandyCrawler), [HandyWriting](https://gitee.com/xinntao/HandyWriting))</sub>
---
## :zap: HOWTOs
我们提供了简单的流程来快速上手 训练/测试/推理 模型. 这些命令并不能涵盖所有用法, 更多的细节参见下面的部分.
| GAN | | | | | |
| :------------------- | :------------------------------------------: | :------------------------------------------: | :------- | :--------------------------------------------: | :----------------------------------------------------: |
| StyleGAN2 | [训练](docs/HOWTOs_CN.md#如何训练-StyleGAN2) | [测试](docs/HOWTOs_CN.md#如何测试-StyleGAN2) | | | |
| **Face Restoration** | | | | | |
| DFDNet | - | [测试](docs/HOWTOs_CN.md#如何测试-DFDNet) | | | |
| **Super Resolution** | | | | | |
| ESRGAN | *TODO* | *TODO* | SRGAN | *TODO* | *TODO* |
| EDSR | *TODO* | *TODO* | SRResNet | *TODO* | *TODO* |
| RCAN | *TODO* | *TODO* | SwinIR | [Train](docs/HOWTOs.md#how-to-train-swinir-sr) | [Inference](docs/HOWTOs.md#how-to-inference-swinir-sr) |
| EDVR | *TODO* | *TODO* | DUF | - | *TODO* |
| BasicVSR | *TODO* | *TODO* | TOF | - | *TODO* |
| **Deblurring** | | | | | |
| DeblurGANv2 | - | *TODO* | | | |
| **Denoise** | | | | | |
| RIDNet | - | *TODO* | CBDNet | - | *TODO* |
## :wrench: 依赖和安装
For detailed instructions refer to [INSTALL.md](INSTALL.md).
## :hourglass_flowing_sand: TODO 清单
参见 [project boards](https://github.com/xinntao/BasicSR/projects).
## :turtle: 数据准备
- 数据准备步骤, 参见 **[DatasetPreparation_CN.md](docs/DatasetPreparation_CN.md)**.
- 目前支持的数据集 (`torch.utils.data.Dataset`类), 参见 [Datasets_CN.md](docs/Datasets_CN.md).
## :computer: 训练和测试
- **训练和测试的命令**, 参见 **[TrainTest_CN.md](docs/TrainTest_CN.md)**.
- **Options/Configs**配置文件的说明, 参见 [Config_CN.md](docs/Config_CN.md).
- **Logging**日志系统的说明, 参见 [Logging_CN.md](docs/Logging_CN.md).
## :european_castle: 模型库和基准
- 目前支持的模型描述, 参见 [Models_CN.md](docs/Models_CN.md).
- **预训练模型和log样例**, 参见 **[ModelZoo_CN.md](docs/ModelZoo_CN.md)**.
- 我们也在 [wandb](https://app.wandb.ai/xintao/basicsr) 上提供了**训练曲线**等:
<p align="center">
<a href="https://app.wandb.ai/xintao/basicsr" target="_blank">
<img src="./assets/wandb.jpg" height="280">
</a></p>
## :memo: 代码库的设计和约定
参见 [DesignConvention_CN.md](docs/DesignConvention_CN.md).<br>
下图概括了整体的框架. 每个模块更多的描述参见: <br>
**[Datasets_CN.md](docs/Datasets_CN.md)**&emsp;|&emsp;**[Models_CN.md](docs/Models_CN.md)**&emsp;|&emsp;**[Config_CN.md](docs/Config_CN.md)**&emsp;|&emsp;**[Logging_CN.md](docs/Logging_CN.md)**
![overall_structure](./assets/overall_structure.png)
## :scroll: 许可
本项目使用 Apache 2.0 license.<br>
更多关于**许可**和**致谢**, 请参见 [LICENSE](LICENSE/README.md).
## :earth_asia: 引用
如果 BasicSR 对你有所帮助, 可以考虑引用BasicSR. <br>
下面是一个 BibTex 引用条目, 它需要 `url` LaTeX package.
``` latex
@misc{wang2020basicsr,
author = {Xintao Wang and Ke Yu and Kelvin C.K. Chan and
Chao Dong and Chen Change Loy},
title = {{BasicSR}: Open Source Image and Video Restoration Toolbox},
howpublished = {\url{https://github.com/xinntao/BasicSR}},
year = {2018}
}
```
> Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR: Open Source Image and Video Restoration Toolbox. <https://github.com/xinntao/BasicSR>, 2018.
## :e-mail: 联系
若有任何问题, 请电邮 `xintao.wang@outlook.com`.
<br>
- **QQ群**: 扫描左边二维码 或者 搜索QQ群号: 320960100   入群答案：互帮互助共同进步
- **微信群**: 我们的群一已经满500人啦，进群二可以扫描中间的二维码；如果进群遇到问题，也可以添加 Liangbin 的个人微信 (右边二维码)，他会在空闲的时候拉大家入群~
<p align="center">
<img src="https://user-images.githubusercontent.com/17445847/134879983-6f2d663b-16e7-49f2-97e1-7c53c8a5f71a.jpg" height="300"> &emsp;
<img src="https://user-images.githubusercontent.com/52127135/172553058-6cf32e10-2959-42dd-b26a-f802f09343b0.png" height="300"> &emsp;
<img src="https://user-images.githubusercontent.com/17445847/139572512-8e192aac-00fa-432b-ac8e-a33026b019df.png" height="300">
</p>
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