HINet

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

Dependencies

NumPy
PyTorch, preferably with CUDA. Note that torchvision and torchaudio are not required and hence can be omitted from the command.
VapourSynth
(Optional) TensorRT. Note that uff and PyCUDA are not required and hence can be skipped from the guide.
(Optional) torch2trt

pip install --upgrade vshinet
python -m vshinet

from vshinet import HINet

ret = HINet(clip)

See __init__.py for the description of the parameters.

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Table of built distributions (wheels) for vshinet 1.0.0
File	Interpreter	ABI	Platform	Reset
vshinet-1.0.0-py3-none-any.whl 8.7 kB Nov 12, 2021	Python 3	none	any	Details

Download URL	vshinet-1.0.0-py3-none-any.whl
Size	8.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`9966134d8b38405440c57a79643ca16cb061580a6ec980187986e94916464f16`
BLAKE2b-256 checksum How to use checksums	`6401e5baa7b27ffaf31ec7200aab9892f7ce367725ba210beecbbc68d751b929`
Upload date	Nov 12, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.5.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7`

This release

1.0.0 This release