mrq
/
DL-Art-School
1
3
Fork 5
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update README.md

pull/9/head
PRAGMA 2019-11-24 07:47:57 +07:00 committed by GitHub
parent 3549100670
commit 8e91d59107
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 5 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
README.md
Unescape Escape View File

@ -1,6 +1,6 @@
# MMSR
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the [open-mmlab](https://github.com/open-mmlab) project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/). MMSR is based on our previous projects: [BasicSR](https://github.com/xinntao/BasicSR), [ESRGAN](https://github.com/xinntao/ESRGAN), and [EDVR](https://github.com/xinntao/EDVR).
MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the [open-mmlab](https://github.com/open-mmlab) project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk). MMSR is based on our previous projects: [BasicSR](https://github.com/xinntao/BasicSR), [ESRGAN](https://github.com/xinntao/ESRGAN), and [EDVR](https://github.com/xinntao/EDVR).
### Highlights
- **A unified framework** suitable for image and video super-resolution tasks. It is also easy to adapt to other restoration tasks, e.g., deblurring, denoising, etc.
@ -13,17 +13,15 @@ MMSR is an open source image and video super-resolution toolbox based on PyTorch
## Dependencies and Installation
- Python 3 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux))
- [PyTorch >= 1.1](https://pytorch.org/)
- Python 3 (Recommend to use [Anaconda](https://www.anaconda.com/download))
- [PyTorch >= 1.1](https://pytorch.org)
- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
- [Deformable Convolution](https://arxiv.org/abs/1703.06211). We use [mmdetection](https://github.com/open-mmlab/mmdetection)'s dcn implementation. Please first compile it.
```
cd ./codes/models/archs/dcn
python setup.py develop
```
- Python packages: `pip install numpy opencv-python lmdb pyyaml`
- TensorBoard:
- PyTorch >= 1.1: `pip install tb-nightly future`
- Python packages: `pip install -r requirements.txt`
## Dataset Preparation
@ -39,7 +37,7 @@ Results and pre-trained models are available in the [wiki-Model Zoo](https://git
We appreciate all contributions. Please refer to [mmdetection](https://github.com/open-mmlab/mmdetection/blob/master/CONTRIBUTING.md) for contributing guideline.
**Python code style**<br/>
We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style. We use [flake8](http://flake8.pycqa.org/en/latest/) as the linter and [yapf](https://github.com/google/yapf) as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the [yapf configuration](.style.yapf) and [flake8 configuration](.flake8).
We adopt [PEP8](https://python.org/dev/peps/pep-0008) as the preferred code style. We use [flake8](http://flake8.pycqa.org/en/latest) as the linter and [yapf](https://github.com/google/yapf) as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the [yapf configuration](.style.yapf) and [flake8 configuration](.flake8).
> Before you create a PR, make sure that your code lints and is formatted by yapf.