merge v0.16.0

.github/workflows/build-wheels-linux.yml

+name: Build Linux Wheels
+
+on:
+  pull_request:
+  push:
+    branches:
+      - nightly
+      - main
+      - release/*
+    tags:
+        # NOTE: Binary build pipelines should only get triggered on release candidate builds
+        # Release candidate tags look like: v1.11.0-rc1
+        - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+  workflow_dispatch:
+
+jobs:
+  generate-matrix:
+    uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@release/2.1
+    with:
+      package-type: wheel
+      os: linux
+      test-infra-repository: pytorch/test-infra
+      test-infra-ref: release/2.1
+  build:
+    needs: generate-matrix
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - repository: pytorch/vision
+            pre-script: packaging/pre_build_script.sh
+            post-script: packaging/post_build_script.sh
+            smoke-test-script: test/smoke_test.py
+            package-name: torchvision
+    name: ${{ matrix.repository }}
+    uses: pytorch/test-infra/.github/workflows/build_wheels_linux.yml@release/2.1
+    with:
+      repository: ${{ matrix.repository }}
+      ref: ""
+      test-infra-repository: pytorch/test-infra
+      test-infra-ref: release/2.1
+      build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+      pre-script: ${{ matrix.pre-script }}
+      post-script: ${{ matrix.post-script }}
+      package-name: ${{ matrix.package-name }}
+      smoke-test-script: ${{ matrix.smoke-test-script }}
+      trigger-event: ${{ github.event_name }}
+    secrets:
+      AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
+      AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}

.github/workflows/build-wheels-m1.yml

+name: Build M1 Wheels
+
+on:
+  pull_request:
+  push:
+    branches:
+      - nightly
+      - main
+      - release/*
+    tags:
+        # NOTE: Binary build pipelines should only get triggered on release candidate builds
+        # Release candidate tags look like: v1.11.0-rc1
+        - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+  workflow_dispatch:
+
+jobs:
+  generate-matrix:
+    uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@release/2.1
+    with:
+      package-type: wheel
+      os: macos-arm64
+      test-infra-repository: pytorch/test-infra
+      test-infra-ref: release/2.1
+  build:
+    needs: generate-matrix
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - repository: pytorch/vision
+            pre-script: packaging/pre_build_script.sh
+            post-script: packaging/post_build_script.sh
+            smoke-test-script: test/smoke_test.py
+            package-name: torchvision
+    name: ${{ matrix.repository }}
+    uses: pytorch/test-infra/.github/workflows/build_wheels_macos.yml@release/2.1
+    with:
+      repository: ${{ matrix.repository }}
+      ref: ""
+      test-infra-repository: pytorch/test-infra
+      test-infra-ref: release/2.1
+      build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+      pre-script: ${{ matrix.pre-script }}
+      post-script: ${{ matrix.post-script }}
+      package-name: ${{ matrix.package-name }}
+      runner-type: macos-m1-12
+      smoke-test-script: ${{ matrix.smoke-test-script }}
+      trigger-event: ${{ github.event_name }}
+    secrets:
+      AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
+      AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}

.github/workflows/build-wheels-macos.yml

+name: Build Macos Wheels
+
+on:
+  pull_request:
+  push:
+    branches:
+      - nightly
+      - main
+      - release/*
+    tags:
+        # NOTE: Binary build pipelines should only get triggered on release candidate builds
+        # Release candidate tags look like: v1.11.0-rc1
+        - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+  workflow_dispatch:
+
+jobs:
+  generate-matrix:
+    uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@release/2.1
+    with:
+      package-type: wheel
+      os: macos
+      test-infra-repository: pytorch/test-infra
+      test-infra-ref: release/2.1
+  build:
+    needs: generate-matrix
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - repository: pytorch/vision
+            pre-script: packaging/pre_build_script.sh
+            post-script: packaging/post_build_script.sh
+            smoke-test-script: test/smoke_test.py
+            package-name: torchvision
+    name: ${{ matrix.repository }}
+    uses: pytorch/test-infra/.github/workflows/build_wheels_macos.yml@release/2.1
+    with:
+      repository: ${{ matrix.repository }}
+      ref: ""
+      test-infra-repository: pytorch/test-infra
+      test-infra-ref: release/2.1
+      build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+      pre-script: ${{ matrix.pre-script }}
+      post-script: ${{ matrix.post-script }}
+      package-name: ${{ matrix.package-name }}
+      runner-type: macos-12
+      smoke-test-script: ${{ matrix.smoke-test-script }}
+      trigger-event: ${{ github.event_name }}
+    secrets:
+      AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
+      AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}

.github/workflows/build-wheels-windows.yml

+name: Build Windows Wheels
+
+on:
+  pull_request:
+  push:
+    branches:
+      - nightly
+      - main
+      - release/*
+    tags:
+        # NOTE: Binary build pipelines should only get triggered on release candidate builds
+        # Release candidate tags look like: v1.11.0-rc1
+        - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+  workflow_dispatch:
+
+jobs:
+  generate-matrix:
+    uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@release/2.1
+    with:
+      package-type: wheel
+      os: windows
+      test-infra-repository: pytorch/test-infra
+      test-infra-ref: release/2.1
+  build:
+    needs: generate-matrix
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - repository: pytorch/vision
+            pre-script: packaging/pre_build_script.sh
+            env-script: packaging/windows/internal/vc_env_helper.bat
+            post-script: "python packaging/wheel/relocate.py"
+            smoke-test-script: test/smoke_test.py
+            package-name: torchvision
+    name: ${{ matrix.repository }}
+    uses: pytorch/test-infra/.github/workflows/build_wheels_windows.yml@release/2.1
+    with:
+      repository: ${{ matrix.repository }}
+      ref: ""
+      test-infra-repository: pytorch/test-infra
+      test-infra-ref: release/2.1
+      build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+      pre-script: ${{ matrix.pre-script }}
+      env-script: ${{ matrix.env-script }}
+      post-script: ${{ matrix.post-script }}
+      package-name: ${{ matrix.package-name }}
+      smoke-test-script: ${{ matrix.smoke-test-script }}
+      trigger-event: ${{ github.event_name }}
+    secrets:
+      AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
+      AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}

.github/workflows/docs.yml

+name: Docs
+
+on:
+  pull_request:
+  push:
+    branches:
+      - nightly
+      - main
+      - release/*
+    tags:
+      - v[0-9]+.[0-9]+.[0-9]
+      - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+  workflow_dispatch:
+
+jobs:
+  build:
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      upload-artifact: docs
+      script: |
+        set -euo pipefail
+
+        export PYTHON_VERSION=3.8
+        export GPU_ARCH_TYPE=cpu
+        export GPU_ARCH_VERSION=''
+        ./.github/scripts/setup-env.sh
+
+        # Prepare conda
+        CONDA_PATH=$(which conda)
+        eval "$(${CONDA_PATH} shell.bash hook)"
+        conda activate ci
+        # FIXME: not sure why we need this. `ldd torchvision/video_reader.so` shows that it
+        #  already links against the one pulled from conda. However, at runtime it pulls from
+        #  /lib64
+        # Should we maybe always do this in `./.github/scripts/setup-env.sh` so that we don't
+        # have to pay attention in all other workflows?
+        export LD_LIBRARY_PATH="${CONDA_PREFIX}/lib:${LD_LIBRARY_PATH}"
+
+        cd docs
+
+        echo '::group::Install doc requirements'
+        pip install --progress-bar=off -r requirements.txt
+        echo '::endgroup::'
+
+        if [[ ${{ github.event_name }} == push && (${{ github.ref_type }} == tag || (${{ github.ref_type }} == branch && ${{ github.ref_name }} == release/*)) ]]; then
+          echo '::group::Enable version string sanitization'
+          # This environment variable just has to exist and must not be empty. The actual value is arbitrary.
+          # See docs/source/conf.py for details
+          export TORCHVISION_SANITIZE_VERSION_STR_IN_DOCS=1
+          echo '::endgroup::'
+        fi
+
+        # The runner does not have sufficient memory to run with as many processes as there are
+        # cores (`-j auto`). Thus, we limit to a single process (`-j 1`) here.
+        sed -i -e 's/-j auto/-j 1/' Makefile
+        make html
+
+        # Below is an imperfect way for us to add "try on collab" links to all of our gallery examples.
+        # sphinx-gallery will convert all gallery examples to .ipynb notebooks and stores them in
+        # build/html/_downloads/<some_hash>/<example_name>.ipynb
+        # We copy all those ipynb files in a more convenient folder so that we can more easily link to them.
+        mkdir build/html/_generated_ipynb_notebooks
+        for file in `find build/html/_downloads`; do
+          if [[ $file == *.ipynb ]]; then
+            cp $file build/html/_generated_ipynb_notebooks/
+          fi
+        done
+
+        cp -r build/html "${RUNNER_ARTIFACT_DIR}"
+
+        # On PRs we also want to upload the docs into our S3 bucket for preview.
+        if [[ ${{ github.event_name == 'pull_request' }} ]]; then
+          cp -r build/html/* "${RUNNER_DOCS_DIR}"
+        fi
+
+  upload:
+    needs: build
+    if: github.repository == 'pytorch/vision' && github.event_name == 'push' && 
+        ((github.ref_type == 'branch' && github.ref_name == 'main') || github.ref_type == 'tag')
+    permissions:
+      contents: write
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      download-artifact: docs
+      ref: gh-pages
+      script: |
+        set -euo pipefail
+
+        REF_TYPE=${{ github.ref_type }}
+        REF_NAME=${{ github.ref_name }}
+
+        if [[ "${REF_TYPE}" == branch ]]; then
+          TARGET_FOLDER="${REF_NAME}"
+        elif [[ "${REF_TYPE}" == tag ]]; then
+          case "${REF_NAME}" in
+            *-rc*)
+              echo "Aborting upload since this is an RC tag: ${REF_NAME}"
+              exit 0
+              ;;
+            *)
+              # Strip the leading "v" as well as the trailing patch version. For example:
+              # 'v0.15.2' -> '0.15'
+              TARGET_FOLDER=$(echo "${REF_NAME}" | sed 's/v\([0-9]\+\)\.\([0-9]\+\)\.[0-9]\+/\1.\2/')
+              ;;
+          esac
+        fi
+        echo "Target Folder: ${TARGET_FOLDER}"
+
+        mkdir -p "${TARGET_FOLDER}"
+        rm -rf "${TARGET_FOLDER}"/*
+        mv "${RUNNER_ARTIFACT_DIR}"/html/* "${TARGET_FOLDER}"
+        git add "${TARGET_FOLDER}" || true
+
+        if [[ "${TARGET_FOLDER}" == main ]]; then
+          mkdir -p _static
+          rm -rf _static/*
+          cp -r "${TARGET_FOLDER}"/_static/* _static
+          git add _static || true
+        fi
+
+        git config user.name 'pytorchbot'
+        git config user.email 'soumith+bot@pytorch.org'
+        git config http.postBuffer 524288000
+        git commit -m "auto-generating sphinx docs" || true
+        git push

.github/workflows/lint.yml

+name: Lint
+
+on:
+  pull_request:
+  push:
+    branches:
+      - nightly
+      - main
+      - release/*
+  workflow_dispatch:
+
+jobs:
+  python-source-and-configs:
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      script: |
+        set -euo pipefail
+
+        echo '::group::Setup environment'
+        CONDA_PATH=$(which conda)
+        eval "$(${CONDA_PATH} shell.bash hook)"
+        conda create --name ci --quiet --yes python=3.8 pip
+        conda activate ci
+        echo '::endgroup::'
+
+        echo '::group::Install lint tools'
+        pip install --progress-bar=off pre-commit
+        echo '::endgroup::'
+
+        set +e
+        pre-commit run --all-files
+
+        if [ $? -ne 0 ]; then
+          git --no-pager diff
+          exit 1
+        fi
+
+  c-source:
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      script: |
+        set -euo pipefail
+
+        echo '::group::Setup environment'
+        CONDA_PATH=$(which conda)
+        eval "$(${CONDA_PATH} shell.bash hook)"
+        # clang-format needs some shared libraries that conflict with the system ones. Thus, we install them from conda
+        # and prepend the libraries to linker path to prioritize them. `ncurses=5` is only available on the conda-forge
+        # channel. Since we are not building or testing here, this is fine.
+        conda create --name ci --quiet --yes -c conda-forge python=3.8 ncurses=5 libgcc
+        conda activate ci
+        export LD_LIBRARY_PATH="${CONDA_PREFIX}/lib:${LD_LIBRARY_PATH}"
+        echo '::endgroup::'
+
+        echo '::group::Install lint tools'
+        curl https://oss-clang-format.s3.us-east-2.amazonaws.com/linux64/clang-format-linux64 -o ./clang-format
+        chmod +x ./clang-format
+        echo '::endgroup::'
+
+        echo '::group::Lint C source'
+        set +e
+        ./.github/scripts/run-clang-format.py -r torchvision/csrc --clang-format-executable ./clang-format
+
+        if [ $? -ne 0 ]; then
+          git --no-pager diff
+          exit 1
+        fi
+        echo '::endgroup::'
+
+  python-types:
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      script: |
+        set -euo pipefail
+
+        export PYTHON_VERSION=3.8
+        export GPU_ARCH_TYPE=cpu
+        export GPU_ARCH_VERSION=''
+
+        ./.github/scripts/setup-env.sh
+
+        CONDA_PATH=$(which conda)
+        eval "$(${CONDA_PATH} shell.bash hook)"
+        conda activate ci
+
+        echo '::group::Install lint tools'
+        pip install --progress-bar=off mypy
+        echo '::endgroup::'
+
+        echo '::group::Lint Python types'
+        mypy --install-types --non-interactive --config-file mypy.ini
+        echo '::endgroup::'
+
+  bc:
+    if: github.event.pull_request
+    runs-on: ubuntu-latest
+    steps:
+      - name: Run BC Lint Action
+        uses: pytorch/test-infra/.github/actions/bc-lint@release/2.1
+        with:
+          repo: ${{ github.event.pull_request.head.repo.full_name }}
+          base_sha: ${{ github.event.pull_request.base.sha }}
+          head_sha: ${{ github.event.pull_request.head.sha }}

.github/workflows/test-m1.yml

-name: Unit-tests on M1
-on:
-  pull_request:
-  push:
-    branches:
-      - nightly
-      - main
-      - release/*
-  workflow_dispatch:
-env:
-  CHANNEL: "nightly"
-jobs:
-  tests:
-    name: "Unit-tests on M1"
-    runs-on: macos-m1-12
-    strategy:
-      matrix:
-        py_vers: [ "3.8"]
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v2
-      - name: Set Release CHANNEL (for release)
-        if: ${{ (github.event_name == 'pull_request' && startsWith(github.base_ref, 'release')) || startsWith(github.ref, 'refs/heads/release') }}
-        run: |
-          echo "CHANNEL=test" >> "$GITHUB_ENV"
-      - name: Install TorchVision
-        shell: arch -arch arm64 bash {0}
-        env:
-          ENV_NAME: conda-env-${{ github.run_id }}
-          PY_VERS: ${{ matrix.py_vers }}
-        run: |
-          . ~/miniconda3/etc/profile.d/conda.sh
-          # Needed for JPEG library detection as setup.py detects conda presence by running `shutil.which('conda')`
-          export PATH=~/miniconda3/bin:$PATH
-          set -ex
-          conda create -yp ${ENV_NAME} python=${PY_VERS} numpy libpng jpeg scipy
-          conda run -p ${ENV_NAME} python3 -mpip install --pre torch --extra-index-url=https://download.pytorch.org/whl/${CHANNEL}
-          conda run -p ${ENV_NAME} python3 setup.py develop
-          conda run -p ${ENV_NAME} python3 -mpip install pytest pytest-mock av
-      - name: Run tests
-        shell: arch -arch arm64 bash {0}
-        env:
-          ENV_NAME: conda-env-${{ github.run_id }}
-          PY_VERS: ${{ matrix.py_vers }}
-        run: |
-          . ~/miniconda3/etc/profile.d/conda.sh
-          set -ex
-          conda run -p ${ENV_NAME} --no-capture-output python3 -u -mpytest -v --tb=long --durations 20
-          conda env remove -p ${ENV_NAME}

.github/workflows/tests-schedule.yml

@@ -18,11 +18,14 @@ jobs:
       - name: Set up python
         uses: actions/setup-python@v2
         with:
-          python-version: 3.7
+          python-version: 3.8
 
       - name: Upgrade system packages
         run: python -m pip install --upgrade pip setuptools wheel
 
+      - name: SSL
+        run: python -c 'import ssl; print(ssl.OPENSSL_VERSION)'
+
       - name: Checkout repository
         uses: actions/checkout@v2
 

.github/workflows/tests.yml

+name: Tests
+
+on:
+  pull_request:
+  push:
+    branches:
+      - nightly
+      - main
+      - release/*
+  workflow_dispatch:
+
+jobs:
+  unittests-linux:
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+        runner: ["linux.12xlarge"]
+        gpu-arch-type: ["cpu"]
+        include:
+          - python-version: 3.8
+            runner: linux.g5.4xlarge.nvidia.gpu
+            gpu-arch-type: cuda
+            gpu-arch-version: "11.8"
+      fail-fast: false
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      runner: ${{ matrix.runner }}
+      gpu-arch-type: ${{ matrix.gpu-arch-type }}
+      gpu-arch-version: ${{ matrix.gpu-arch-version }}
+      timeout: 120
+      script: |
+        set -euo pipefail
+
+        export PYTHON_VERSION=${{ matrix.python-version }}
+        export GPU_ARCH_TYPE=${{ matrix.gpu-arch-type }}
+        export GPU_ARCH_VERSION=${{ matrix.gpu-arch-version }}
+
+        ./.github/scripts/unittest.sh
+
+  unittests-macos:
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+        runner: ["macos-12"]
+        include:
+          - python-version: "3.8"
+            runner: macos-m1-12
+      fail-fast: false
+    uses: pytorch/test-infra/.github/workflows/macos_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      # We need an increased timeout here, since the macos-12 runner is the free one from GH
+      # and needs roughly 2 hours to just run the test suite
+      timeout: 240
+      runner: ${{ matrix.runner }}
+      script: |
+        set -euo pipefail
+
+        export PYTHON_VERSION=${{ matrix.python-version }}
+        export GPU_ARCH_TYPE=cpu
+        export GPU_ARCH_VERSION=''
+
+        ./.github/scripts/unittest.sh
+
+  unittests-windows:
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+        runner: ["windows.4xlarge"]
+        gpu-arch-type: ["cpu"]
+        include:
+          - python-version: "3.8"
+            runner: windows.g5.4xlarge.nvidia.gpu
+            gpu-arch-type: cuda
+            gpu-arch-version: "11.8"
+      fail-fast: false
+    uses: pytorch/test-infra/.github/workflows/windows_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      runner: ${{ matrix.runner }}
+      gpu-arch-type: ${{ matrix.gpu-arch-type }}
+      gpu-arch-version: ${{ matrix.gpu-arch-version }}
+      timeout: 120
+      script: |
+        set -euxo pipefail
+
+        export PYTHON_VERSION=${{ matrix.python-version }}
+        export VC_YEAR=2019
+        export VSDEVCMD_ARGS=""
+        export GPU_ARCH_TYPE=${{ matrix.gpu-arch-type }}
+        export GPU_ARCH_VERSION=${{ matrix.gpu-arch-version }}
+
+        ./.github/scripts/unittest.sh
+
+  onnx:
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      script: |
+        set -euo pipefail
+
+        export PYTHON_VERSION=3.8
+        export GPU_ARCH_TYPE=cpu
+        export GPU_ARCH_VERSION=''
+
+        ./.github/scripts/setup-env.sh
+
+        # Prepare conda
+        CONDA_PATH=$(which conda)
+        eval "$(${CONDA_PATH} shell.bash hook)"
+        conda activate ci
+
+        echo '::group::Install ONNX'
+        pip install --progress-bar=off onnx onnxruntime
+        echo '::endgroup::'
+
+        echo '::group::Install testing utilities'
+        pip install --progress-bar=off pytest
+        echo '::endgroup::'
+
+        echo '::group::Run ONNX tests'
+        pytest --junit-xml="${RUNNER_TEST_RESULTS_DIR}/test-results.xml" -v --durations=25 test/test_onnx.py
+        echo '::endgroup::'
+
+  unittests-extended:
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      script: |
+        set -euo pipefail
+
+        export PYTHON_VERSION=3.8
+        export GPU_ARCH_TYPE=cpu
+        export GPU_ARCH_VERSION=''
+
+        ./.github/scripts/setup-env.sh
+
+        # Prepare conda
+        CONDA_PATH=$(which conda)
+        eval "$(${CONDA_PATH} shell.bash hook)"
+        conda activate ci
+
+        echo '::group::Pre-download model weights'
+        pip install --progress-bar=off aiohttp aiofiles tqdm
+        python scripts/download_model_urls.py
+        echo '::endgroup::'
+
+        echo '::group::Install testing utilities'
+        pip install --progress-bar=off pytest
+        echo '::endgroup::'
+
+        echo '::group::Run extended unittests'
+        export PYTORCH_TEST_WITH_EXTENDED=1
+        pytest --junit-xml="${RUNNER_TEST_RESULTS_DIR}/test-results.xml" -v --durations=25 test/test_extended_*.py
+        echo '::endgroup::'

.github/workflows/update-viablestrict.yml

+name: Update viable/strict
+
+on:
+  pull_request:
+    paths:
+      - .github/workflows/update-viablestrict.yml
+  schedule:
+    - cron: 10,40 * * * *
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}
+  cancel-in-progress: false
+
+jobs:
+  do_update_viablestrict:
+    uses: pytorch/test-infra/.github/workflows/update-viablestrict.yml@release/2.1
+    with:
+      repository: pytorch/vision
+      required_checks: "Build Linux,Build M1,Build Macos,Build Windows,Tests,CMake,Lint,Docs"
+    secrets:
+      ROCKSET_API_KEY: ${{ secrets.ROCKSET_API_KEY }}
+      GITHUB_DEPLOY_KEY : ${{ secrets.VISION_GITHUB_DEPLOY_KEY }}

.pre-commit-config.yaml

@@ -6,19 +6,21 @@ repos:
       - id: check-toml
       - id: check-yaml
         exclude: packaging/.*
+        args:
+          - --allow-multiple-documents
       - id: mixed-line-ending
         args: [--fix=lf]
       - id: end-of-file-fixer
 
   - repo: https://github.com/omnilib/ufmt
-    rev: v1.3.2
+    rev: v1.3.3
     hooks:
       - id: ufmt
         additional_dependencies:
           - black == 22.3.0
           - usort == 1.0.2
 
-  - repo: https://gitlab.com/pycqa/flake8
+  - repo: https://github.com/PyCQA/flake8
     rev: 5.0.4
     hooks:
       - id: flake8

CITATION.cff

+cff-version: 1.2.0
+title: "TorchVision: PyTorch's Computer Vision library"
+message: >-
+  If you find TorchVision useful in your work, please
+  consider citing the following BibTeX entry.
+type: software
+authors:
+  - given-names: TorchVision maintainers and contributors
+url: "https://github.com/pytorch/vision"
+license: "BSD-3-Clause"
+date-released: "2016-11-06"
+journal: "GitHub repository"
+publisher: "GitHub"
+key: "torchvision2016"

CMakeLists.txt

-cmake_minimum_required(VERSION 3.12)
+cmake_minimum_required(VERSION 3.18)
 project(torchvision)
-set(CMAKE_CXX_STANDARD 14)
+set(CMAKE_CXX_STANDARD 17)
 file(STRINGS version.txt TORCHVISION_VERSION)
 
 option(WITH_CUDA "Enable CUDA support" OFF)
+option(WITH_MPS "Enable MPS support" OFF)
 option(WITH_PNG "Enable features requiring LibPNG." ON)
 option(WITH_JPEG "Enable features requiring LibJPEG." ON)
 option(USE_PYTHON "Link to Python when building" OFF)
@@ -13,11 +14,11 @@ if(WITH_CUDA)
   add_definitions(-D__CUDA_NO_HALF_OPERATORS__)
   add_definitions(-DWITH_CUDA)
   set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr")
-  # CUDA-11.x can not be compiled using C++14 standard on Windows
-  string(REGEX MATCH "^[0-9]+" CUDA_MAJOR ${CMAKE_CUDA_COMPILER_VERSION})
-  if(${CUDA_MAJOR} GREATER 10 AND MSVC)
-    set(CMAKE_CXX_STANDARD 17)
-  endif()
+endif()
+
+if(WITH_MPS)
+  enable_language(OBJC OBJCXX)
+  add_definitions(-DWITH_MPS)
 endif()
 
 find_package(Torch REQUIRED)
@@ -84,6 +85,9 @@ list(APPEND ALLOW_LISTED ${TVCPP} ${TVCPP}/io/image ${TVCPP}/io/image/cpu ${TVCP
 if(WITH_CUDA)
     list(APPEND ALLOW_LISTED ${TVCPP}/ops/cuda ${TVCPP}/ops/autocast)
 endif()
+if(WITH_MPS)
+    list(APPEND ALLOW_LISTED ${TVCPP}/ops/mps)
+endif()
 
 FOREACH(DIR ${ALLOW_LISTED})
     file(GLOB ALL_SOURCES ${ALL_SOURCES} ${DIR}/*.*)

CONTRIBUTING.md

@@ -4,22 +4,22 @@ We want to make contributing to this project as easy and transparent as possible
 
 ## TL;DR
 
-We appreciate all contributions. If you are interested in contributing to Torchvision, there are many ways to help out. 
+We appreciate all contributions. If you are interested in contributing to Torchvision, there are many ways to help out.
 Your contributions may fall into the following categories:
 
-- It helps the project if you could 
+- It helps the project if you could
     - Report issues you're facing
-    - Give a :+1: on issues that others reported and that are relevant to you 
+    - Give a :+1: on issues that others reported and that are relevant to you
 
 - Answering queries on the issue tracker, investigating bugs are very valuable contributions to the project.
 
-- You would like to improve the documentation. This is no less important than improving the library itself! 
+- You would like to improve the documentation. This is no less important than improving the library itself!
 If you find a typo in the documentation, do not hesitate to submit a GitHub pull request.
 
 - If you would like to fix a bug
     - please pick one from the [list of open issues labelled as "help wanted"](https://github.com/pytorch/vision/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22)
     - comment on the issue that you want to work on this issue
-    - send a PR with your fix, see below. 
+    - send a PR with your fix, see below.
 
 - If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
 
@@ -30,30 +30,49 @@ clear and has sufficient instructions to be able to reproduce the issue.
 
 ## Development installation
 
-### Install PyTorch Nightly 
+
+### Dependencies
+
+Start by installing the **nightly** build of PyTorch following the [official
+instructions](https://pytorch.org/get-started/locally/).
+
+**Optionally**, install `libpng` and `libjpeg-turbo` if you want to enable
+support for
+native encoding / decoding of PNG and JPEG formats in
+[torchvision.io](https://pytorch.org/vision/stable/io.html#image):
 
 ```bash
-conda install pytorch -c pytorch-nightly
-# or with pip (see https://pytorch.org/get-started/locally/)
-# pip install numpy
-# pip install --pre torch -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html
+conda install libpng libjpeg-turbo -c pytorch
 ```
 
-### Install Torchvision
+Note: you can use the `TORCHVISION_INCLUDE` and `TORCHVISION_LIBRARY`
+environment variables to tell the build system where to find those libraries if
+they are in specific locations. Take a look at
+[setup.py](https://github.com/pytorch/vision/blob/main/setup.py) for more
+details.
+
+### Clone and install torchvision
 
 ```bash
 git clone https://github.com/pytorch/vision.git
 cd vision
-python setup.py develop
+python setup.py develop  # use install instead of develop if you don't care about development.
 # or, for OSX
 # MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py develop
-# for C++ debugging, please use DEBUG=1
+# for C++ debugging, use DEBUG=1
 # DEBUG=1 python setup.py develop
-pip install flake8 typing mypy pytest pytest-mock scipy
 ```
-You may also have to install `libpng-dev` and `libjpeg-turbo8-dev` libraries:
-```bash
-conda install libpng jpeg
+
+By default, GPU support is built if CUDA is found and `torch.cuda.is_available()` is true. It's possible to force
+building GPU support by setting `FORCE_CUDA=1` environment variable, which is useful when building a docker image.
+
+We don't officially support building from source using `pip`, but _if_ you do, you'll need to use the
+`--no-build-isolation` flag.
+
+Other development dependencies include:
+
+```
+pip install flake8 typing mypy pytest pytest-mock scipy
 ```
 
 ## Development Process
@@ -66,12 +85,12 @@ If you plan to modify the code or documentation, please follow the steps below:
 4. Ensure the test suite passes.
 5. Make sure your code passes the formatting checks (see below).
 
-For more details about pull requests, 
-please read [GitHub's guides](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request). 
+For more details about pull requests,
+please read [GitHub's guides](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request).
 
-If you would like to contribute a new model, please see [here](#New-model).
+If you would like to contribute a new model, please see [here](#New-architecture-or-improved-model-weights).
 
-If you would like to contribute a new dataset, please see [here](#New-dataset). 
+If you would like to contribute a new dataset, please see [here](#New-dataset).
 
 ### Code formatting and typing
 
@@ -83,7 +102,7 @@ Instead of relying directly on `black` however, we rely on
 [ufmt](https://github.com/omnilib/ufmt), for compatibility reasons with Facebook
 internal infrastructure.
 
-To format your code, install `ufmt` with `pip install ufmt==1.3.2 black==22.3.0 usort==1.0.2` and use e.g.:
+To format your code, install `ufmt` with `pip install ufmt==1.3.3 black==22.3.0 usort==1.0.2` and use e.g.:
 
 ```bash
 ufmt format torchvision
@@ -126,8 +145,8 @@ mypy --config-file mypy.ini
 
 ### Unit tests
 
-If you have modified the code by adding a new feature or a bug-fix, please add unit tests for that. To run a specific 
-test: 
+If you have modified the code by adding a new feature or a bug-fix, please add unit tests for that. To run a specific
+test:
 ```bash
 pytest test/<test-module.py> -vvv -k <test_myfunc>
 # e.g. pytest test/test_transforms.py -vvv -k test_center_crop
@@ -136,7 +155,7 @@ pytest test/<test-module.py> -vvv -k <test_myfunc>
 If you would like to run all tests:
 ```bash
 pytest test -vvv
-``` 
+```
 
 Tests that require internet access should be in
 `test/test_internet.py`.
@@ -189,18 +208,18 @@ with "transforms" in their name.
 ### New architecture or improved model weights
 
 Please refer to the guidelines in [Contributing to Torchvision - Models](https://github.com/pytorch/vision/blob/main/CONTRIBUTING_MODELS.md).
- 
+
 ### New dataset
 
-More details on how to add a new dataset will be provided later. Please, do not send any PR with a new dataset without discussing 
+Please, do not send any PR with a new dataset without discussing
 it in an issue as, most likely, it will not be accepted.
 
 ### Pull Request
 
-If all previous checks (flake8, mypy, unit tests) are passing, please send a PR. Submitted PR will pass other tests on 
-different operation systems, python versions and hardwares.
+If all previous checks (flake8, mypy, unit tests) are passing, please send a PR. Submitted PR will pass other tests on
+different operating systems, python versions and hardware.
 
-For more details about pull requests workflow, 
+For more details about pull requests workflow,
 please read [GitHub's guides](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request).
 
 ## License

CONTRIBUTING_MODELS.md

@@ -20,13 +20,13 @@ So, before starting any work and submitting a PR there are a few critical things
 
 ### 1. Preparation work
 
-- Start by looking into this [issue](https://github.com/pytorch/vision/issues/2707) in order to have an idea of the models that are being considered, express your willingness to add a new model and discuss with the community whether or not this model should be included in TorchVision. It is very important at this stage to make sure that there is an agreement on the value of having this model in TorchVision and there is no one else already working on it.
+- Start by looking into this [issue](https://github.com/pytorch/vision/issues/2707) in order to have an idea of the models that are being considered, express your willingness to add a new model and discuss with the community whether this model should be included in TorchVision. It is very important at this stage to make sure that there is an agreement on the value of having this model in TorchVision and there is no one else already working on it.
 
 - If the decision is to include the new model, then please create a new ticket which will be used for all design and implementation discussions prior to the PR. One of the TorchVision maintainers will reach out at this stage and this will be your POC from this point onwards in order to provide support, guidance and regular feedback.
 
 ### 2.  Implement the model
 
-Please take a look at existing models in TorchVision to get familiar with the idioms. Also please look at recent contributions for new models. If in doubt about any design decisions you can ask for feedback on the issue created in step 1.  Example of things to take into account:
+Please take a look at existing models in TorchVision to get familiar with the idioms. Also, please look at recent contributions for new models. If in doubt about any design decisions you can ask for feedback on the issue created in step 1.  Example of things to take into account:
 
 - The implementation should be as close as possible to the canonical implementation/paper
 - The PR must include the code implementation, documentation and tests
@@ -34,7 +34,7 @@ Please take a look at existing models in TorchVision to get familiar with the id
 - The weights need to reproduce closely the results of the paper in terms of accuracy, even though the final weights to be deployed will be those trained by the TorchVision maintainers
 - The PR description should include commands/configuration used to train the model, so that the TorchVision maintainers can easily run them to verify the implementation and generate the final model to be released
 - Make sure we re-use existing components as much as possible (inheritance)
-- New primitives (transforms, losses, etc) can be added if necessary, but the final location will be determined after discussion with the dedicated maintainer
+- New primitives (transforms, losses, etc.) can be added if necessary, but the final location will be determined after discussion with the dedicated maintainer
 - Please take a look at the detailed [implementation and documentation guidelines](https://github.com/pytorch/vision/issues/5319) for a fine grain list of things not to be missed
 
 ### 3. Train the model with reference scripts

MANIFEST.in

-include README.rst
+include README.md
 include LICENSE
 
 recursive-exclude * __pycache__

README.md

@@ -47,7 +47,7 @@ pip3 install torchvision*
 ##### 编译安装
 
 ```bash
-git clone -b dtk-23.10-v0.14.1 http://developer.hpccube.com/codes/aicomponent/vision.git
+git clone -b dtk-23.10-v0.16.0 http://developer.hpccube.com/codes/aicomponent/vision.git
 cd vision
 PYTORCH_ROCM_ARCH="gfx906;gfx926" python3 setup.py bdist_wheel
 pip3 install dist/vision*

README_ORIGIN.md

+# torchvision
+
+[![total torchvision downloads](https://pepy.tech/badge/torchvision)](https://pepy.tech/project/torchvision)
+[![documentation](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/vision/stable/index.html)
+
+The torchvision package consists of popular datasets, model architectures, and common image transformations for computer
+vision.
+
+## Installation
+
+Please refer to the [official
+instructions](https://pytorch.org/get-started/locally/) to install the stable
+versions of `torch` and `torchvision` on your system.
+
+To build source, refer to our [contributing
+page](https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation).
+
+The following is the corresponding `torchvision` versions and supported Python
+versions.
+
+| `torch`            | `torchvision`      | Python              |
+| ------------------ | ------------------ | ------------------- |
+| `main` / `nightly` | `main` / `nightly` | `>=3.8`, `<=3.11`   |
+| `2.1`              | `0.16`             | `>=3.8`, `<=3.11`   |
+| `2.0`              | `0.15`             | `>=3.8`, `<=3.11`   |
+| `1.13`             | `0.14`             | `>=3.7.2`, `<=3.10` |
+
+<details>
+    <summary>older versions</summary>
+
+| `torch` | `torchvision`     | Python                    |
+|---------|-------------------|---------------------------|
+| `1.12`  | `0.13`            | `>=3.7`, `<=3.10`         |
+| `1.11`  | `0.12`            | `>=3.7`, `<=3.10`         |
+| `1.10`  | `0.11`            | `>=3.6`, `<=3.9`          |
+| `1.9`   | `0.10`            | `>=3.6`, `<=3.9`          |
+| `1.8`   | `0.9`             | `>=3.6`, `<=3.9`          |
+| `1.7`   | `0.8`             | `>=3.6`, `<=3.9`          |
+| `1.6`   | `0.7`             | `>=3.6`, `<=3.8`          |
+| `1.5`   | `0.6`             | `>=3.5`, `<=3.8`          |
+| `1.4`   | `0.5`             | `==2.7`, `>=3.5`, `<=3.8` |
+| `1.3`   | `0.4.2` / `0.4.3` | `==2.7`, `>=3.5`, `<=3.7` |
+| `1.2`   | `0.4.1`           | `==2.7`, `>=3.5`, `<=3.7` |
+| `1.1`   | `0.3`             | `==2.7`, `>=3.5`, `<=3.7` |
+| `<=1.0` | `0.2`             | `==2.7`, `>=3.5`, `<=3.7` |
+
+</details>
+
+## Image Backends
+
+Torchvision currently supports the following image backends:
+
+- torch tensors
+- PIL images:
+    - [Pillow](https://python-pillow.org/)
+    - [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD.
+
+Read more in in our [docs](https://pytorch.org/vision/stable/transforms.html).
+
+## [UNSTABLE] Video Backend
+
+Torchvision currently supports the following video backends:
+
+- [pyav](https://github.com/PyAV-Org/PyAV) (default) - Pythonic binding for ffmpeg libraries.
+- video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any
+  conflicting version of ffmpeg installed. Currently, this is only supported on Linux.
+
+```
+conda install -c conda-forge ffmpeg
+python setup.py install
+```
+
+# Using the models on C++
+
+TorchVision provides an example project for how to use the models on C++ using JIT Script.
+
+Installation From source:
+
+```
+mkdir build
+cd build
+# Add -DWITH_CUDA=on support for the CUDA if needed
+cmake ..
+make
+make install
+```
+
+Once installed, the library can be accessed in cmake (after properly configuring `CMAKE_PREFIX_PATH`) via the
+`TorchVision::TorchVision` target:
+
+```
+find_package(TorchVision REQUIRED)
+target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
+```
+
+The `TorchVision` package will also automatically look for the `Torch` package and add it as a dependency to
+`my-target`, so make sure that it is also available to cmake via the `CMAKE_PREFIX_PATH`.
+
+For an example setup, take a look at `examples/cpp/hello_world`.
+
+Python linking is disabled by default when compiling TorchVision with CMake, this allows you to run models without any
+Python dependency. In some special cases where TorchVision's operators are used from Python code, you may need to link
+to Python. This can be done by passing `-DUSE_PYTHON=on` to CMake.
+
+### TorchVision Operators
+
+In order to get the torchvision operators registered with torch (eg. for the JIT), all you need to do is to ensure that
+you `#include <torchvision/vision.h>` in your project.
+
+## Documentation
+
+You can find the API documentation on the pytorch website: <https://pytorch.org/vision/stable/index.html>
+
+## Contributing
+
+See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.
+
+## Disclaimer on Datasets
+
+This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets,
+vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to
+determine whether you have permission to use the dataset under the dataset's license.
+
+If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset
+to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML
+community!
+
+## Pre-trained Model License
+
+The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the
+dataset used for training. It is your responsibility to determine whether you have permission to use the models for your
+use case.
+
+More specifically, SWAG models are released under the CC-BY-NC 4.0 license. See
+[SWAG LICENSE](https://github.com/facebookresearch/SWAG/blob/main/LICENSE) for additional details.
+
+## Citing TorchVision
+
+If you find TorchVision useful in your work, please consider citing the following BibTeX entry:
+
+```bibtex
+@software{torchvision2016,
+    title        = {TorchVision: PyTorch's Computer Vision library},
+    author       = {TorchVision maintainers and contributors},
+    year         = 2016,
+    journal      = {GitHub repository},
+    publisher    = {GitHub},
+    howpublished = {\url{https://github.com/pytorch/vision}}
+}
+```

README_ORIGIN.rst

-torchvision
-===========
-
-.. image:: https://pepy.tech/badge/torchvision
-    :target: https://pepy.tech/project/torchvision
-
-.. image:: https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
-    :target: https://pytorch.org/vision/stable/index.html
-
-
-The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
-
-
-Installation
-============
-
-We recommend Anaconda as Python package management system. Please refer to `pytorch.org <https://pytorch.org/>`_
-for the detail of PyTorch (``torch``) installation. The following is the corresponding ``torchvision`` versions and
-supported Python versions.
-
-+--------------------------+--------------------------+---------------------------------+
-| ``torch``                | ``torchvision``          | ``python``                      |
-+==========================+==========================+=================================+
-| ``main`` / ``nightly``   | ``main`` / ``nightly``   | ``>=3.7``, ``<=3.10``           |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.12.0``               | ``0.13.0``               | ``>=3.7``, ``<=3.10``           |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.11.0``               | ``0.12.0``               | ``>=3.7``, ``<=3.10``           |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.10.2``               | ``0.11.3``               | ``>=3.6``, ``<=3.9``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.10.1``               | ``0.11.2``               | ``>=3.6``, ``<=3.9``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.10.0``               | ``0.11.1``               | ``>=3.6``, ``<=3.9``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.9.1``                | ``0.10.1``               | ``>=3.6``, ``<=3.9``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.9.0``                | ``0.10.0``               | ``>=3.6``, ``<=3.9``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.8.2``                | ``0.9.2``                | ``>=3.6``, ``<=3.9``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.8.1``                | ``0.9.1``                | ``>=3.6``, ``<=3.9``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.8.0``                | ``0.9.0``                | ``>=3.6``, ``<=3.9``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.7.1``                | ``0.8.2``                | ``>=3.6``, ``<=3.9``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.7.0``                | ``0.8.1``                | ``>=3.6``, ``<=3.8``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.7.0``                | ``0.8.0``                | ``>=3.6``, ``<=3.8``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.6.0``                | ``0.7.0``                | ``>=3.6``, ``<=3.8``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.5.1``                | ``0.6.1``                | ``>=3.5``, ``<=3.8``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.5.0``                | ``0.6.0``                | ``>=3.5``, ``<=3.8``            |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.4.0``                | ``0.5.0``                | ``==2.7``, ``>=3.5``, ``<=3.8`` |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.3.1``                | ``0.4.2``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.3.0``                | ``0.4.1``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.2.0``                | ``0.4.0``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
-+--------------------------+--------------------------+---------------------------------+
-| ``1.1.0``                | ``0.3.0``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
-+--------------------------+--------------------------+---------------------------------+
-| ``<=1.0.1``              | ``0.2.2``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
-+--------------------------+--------------------------+---------------------------------+
-
-Anaconda:
-
-.. code:: bash
-
-    conda install torchvision -c pytorch
-
-pip:
-
-.. code:: bash
-
-    pip install torchvision
-
-From source:
-
-.. code:: bash
-
-    python setup.py install
-    # or, for OSX
-    # MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
-
-
-We don't officially support building from source using ``pip``, but *if* you do,
-you'll need to use the ``--no-build-isolation`` flag.
-In case building TorchVision from source fails, install the nightly version of PyTorch following
-the linked guide on the  `contributing page <https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation>`_ and retry the install.
-
-By default, GPU support is built if CUDA is found and ``torch.cuda.is_available()`` is true.
-It's possible to force building GPU support by setting ``FORCE_CUDA=1`` environment variable,
-which is useful when building a docker image.
-
-Image Backend
-=============
-Torchvision currently supports the following image backends:
-
-* `Pillow`_ (default)
-
-* `Pillow-SIMD`_ - a **much faster** drop-in replacement for Pillow with SIMD. If installed will be used as the default.
-
-* `accimage`_ - if installed can be activated by calling :code:`torchvision.set_image_backend('accimage')`
-
-* `libpng`_ - can be installed via conda :code:`conda install libpng` or any of the package managers for debian-based and RHEL-based Linux distributions.
-
-* `libjpeg`_ - can be installed via conda :code:`conda install jpeg` or any of the package managers for debian-based and RHEL-based Linux distributions. `libjpeg-turbo`_ can be used as well.
-
-**Notes:** ``libpng`` and ``libjpeg`` must be available at compilation time in order to be available. Make sure that it is available on the standard library locations,
-otherwise, add the include and library paths in the environment variables ``TORCHVISION_INCLUDE`` and ``TORCHVISION_LIBRARY``, respectively.
-
-.. _libpng : http://www.libpng.org/pub/png/libpng.html
-.. _Pillow : https://python-pillow.org/
-.. _Pillow-SIMD : https://github.com/uploadcare/pillow-simd
-.. _accimage: https://github.com/pytorch/accimage
-.. _libjpeg: http://ijg.org/
-.. _libjpeg-turbo: https://libjpeg-turbo.org/
-
-Video Backend
-=============
-Torchvision currently supports the following video backends:
-
-* `pyav`_ (default) - Pythonic binding for ffmpeg libraries.
-
-.. _pyav : https://github.com/PyAV-Org/PyAV
-
-* video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any conflicting version of ffmpeg installed. Currently, this is only supported on Linux.
-
-.. code:: bash
-
-     conda install -c conda-forge ffmpeg
-     python setup.py install
-
-
-Using the models on C++
-=======================
-TorchVision provides an example project for how to use the models on C++ using JIT Script.
-
-Installation From source:
-
-.. code:: bash
-
-    mkdir build
-    cd build
-    # Add -DWITH_CUDA=on support for the CUDA if needed
-    cmake ..
-    make
-    make install
-
-Once installed, the library can be accessed in cmake (after properly configuring ``CMAKE_PREFIX_PATH``) via the :code:`TorchVision::TorchVision` target:
-
-.. code:: rest
-
-	find_package(TorchVision REQUIRED)
-	target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
-
-The ``TorchVision`` package will also automatically look for the ``Torch`` package and add it as a dependency to ``my-target``,
-so make sure that it is also available to cmake via the ``CMAKE_PREFIX_PATH``.
-
-For an example setup, take a look at ``examples/cpp/hello_world``.
-
-Python linking is disabled by default when compiling TorchVision with CMake, this allows you to run models without any Python 
-dependency. In some special cases where TorchVision's operators are used from Python code, you may need to link to Python. This 
-can be done by passing ``-DUSE_PYTHON=on`` to CMake.
-
-TorchVision Operators
----------------------
-In order to get the torchvision operators registered with torch (eg. for the JIT), all you need to do is to ensure that you
-:code:`#include <torchvision/vision.h>` in your project.
-
-Documentation
-=============
-You can find the API documentation on the pytorch website: https://pytorch.org/vision/stable/index.html
-
-Contributing
-============
-
-See the `CONTRIBUTING <CONTRIBUTING.md>`_ file for how to help out.
-
-Disclaimer on Datasets
-======================
-
-This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
-
-If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
-
-Pre-trained Model License
-=========================
-
-The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
-
-More specifically, SWAG models are released under the CC-BY-NC 4.0 license. See `SWAG LICENSE <https://github.com/facebookresearch/SWAG/blob/main/LICENSE>`_ for additional details.

android/README.md

+## Status
+
+The Android demo of TorchVision is currently unmaintained, untested and likely out-of-date.