Merge pull request #46 from deepghs/dev/restore

name: Sync Waifu2x Models

on:

  #  push:

  workflow_dispatch:

  schedule:

    - cron: '30 16 * * *'

jobs:

  sync:

    name: Sync Waifu2x ONNX

    runs-on: ${{ matrix.os }}

    strategy:

      fail-fast: false

      matrix:

        os:

          - 'ubuntu-latest'

        python-version:

          - '3.8'

    steps:

      - name: Checkout code

        uses: actions/checkout@v3

        with:

          fetch-depth: 20

      - name: Set up python ${{ matrix.python-version }}

        uses: actions/setup-python@v4

        with:

          python-version: ${{ matrix.python-version }}

      - name: Set up python dependences

        run: |

          pip install --upgrade pip

          pip install --upgrade flake8 setuptools wheel twine

          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi

          if [ -f requirements-build.txt ]; then pip install -r requirements-build.txt; fi

          if [ -f requirements-test.txt ]; then pip install -r requirements-test.txt; fi

          if [ -f requirements-test.txt ]; then pip install -r requirements-zoo.txt; fi

          pip install --upgrade build

      - name: Sync Models

        env:

          HF_TOKEN: ${{ secrets.HF_TOKEN }}

          GH_ACCESS_TOKEN: ${{ secrets.GH_ACCESS_TOKEN }}

        run: |

          python -m zoo.waifu2x sync

imgutils.restore

========================

.. currentmodule:: imgutils.restore

.. automodule:: imgutils.restore

.. toctree::

    :maxdepth: 3

    nafnet

    scunet

imgutils.restore.nafnet

====================================

.. currentmodule:: imgutils.restore.nafnet

.. automodule:: imgutils.restore.nafnet

restore_with_nafnet

------------------------

.. autofunction:: restore_with_nafnet

import random

from benchmark import BaseBenchmark, create_plot_cli

from imgutils.restore.nafnet import NafNetModelTyping, restore_with_nafnet

class NafNetBenchmark(BaseBenchmark):

    def __init__(self, model: NafNetModelTyping):

        BaseBenchmark.__init__(self)

        self.model = model

    def load(self):

        from imgutils.restore.nafnet import _open_nafnet_model

        _open_nafnet_model(self.model)

    def unload(self):

        from imgutils.restore.nafnet import _open_nafnet_model

        _open_nafnet_model.cache_clear()

    def run(self):

        image_file = random.choice(self.all_images)

        _ = restore_with_nafnet(image_file, model=self.model)

if __name__ == '__main__':

    create_plot_cli(

        [

            ('NafNet-REDS', NafNetBenchmark('REDS')),

            ('NafNet-GoPro', NafNetBenchmark('GoPro')),

            ('NafNet-SIDD', NafNetBenchmark('SIDD')),

        ],

        title='Benchmark for NafNet Models',

        run_times=5,

        try_times=10,

    )()