Metadata-Version: 2.1
Name: vshinet
Version: 1.0.0
Summary: HINet function for VapourSynth
Home-page: https://github.com/HolyWu/vs-hinet
Author: HolyWu
License: UNKNOWN
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Multimedia :: Video
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: requests
Requires-Dist: torch (>=1.9.0)
Requires-Dist: tqdm
Requires-Dist: VapourSynth (>=55)

# HINet
Half Instance Normalization Network for Image Restoration, based on https://github.com/JingyunLiang/SwinIR.


## Dependencies
- [NumPy](https://numpy.org/install)
- [PyTorch](https://pytorch.org/get-started), preferably with CUDA. Note that `torchvision` and `torchaudio` are not required and hence can be omitted from the command.
- [VapourSynth](http://www.vapoursynth.com/)
- (Optional) [TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html). Note that `uff` and `PyCUDA` are not required and hence can be skipped from the guide.
- (Optional) [torch2trt](https://nvidia-ai-iot.github.io/torch2trt/master/getting_started.html#install-without-plugins)


## Installation
```
pip install --upgrade vshinet
python -m vshinet
```


## Usage
```python
from vshinet import HINet

ret = HINet(clip)
```

See `__init__.py` for the description of the parameters.


