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AntoineSimoulinAntoineSimoulin
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Merge branch 'main' into benchmarks
2 parents 73af38d + 1752fe6 commit a85db8e

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‎.github/scripts/setup-env.sh‎

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@@ -23,15 +23,13 @@ case $(uname) in
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esac
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echo '::group::Create build environment'
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# See https://github.com/pytorch/vision/issues/7296 for ffmpeg
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conda create \
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--name ci \
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--quiet --yes \
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python="${PYTHON_VERSION}" pip \
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ninja cmake \
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libpng \
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libwebp \
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'ffmpeg<4.3'
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libwebp
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conda activate ci
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conda install --quiet --yes libjpeg-turbo -c pytorch
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pip install --progress-bar=off --upgrade setuptools==72.1.0

‎.github/workflows/build-cmake.yml‎

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script: |
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set -euo pipefail
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export PYTHON_VERSION=3.9
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export PYTHON_VERSION=3.10
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export GPU_ARCH_TYPE=${{ matrix.gpu-arch-type }}
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export GPU_ARCH_VERSION=${{ matrix.gpu-arch-version }}
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./.github/scripts/cmake.sh
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script: |
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set -euo pipefail
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export PYTHON_VERSION=3.9
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export PYTHON_VERSION=3.10
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export GPU_ARCH_TYPE=cpu
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export GPU_ARCH_VERSION=''
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${CONDA_RUN} ./.github/scripts/cmake.sh
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windows:
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strategy:
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matrix:
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include:
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- runner: windows.4xlarge
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gpu-arch-type: cpu
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- runner: windows.g5.4xlarge.nvidia.gpu
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gpu-arch-type: cuda
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gpu-arch-version: "12.6"
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fail-fast: false
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uses: pytorch/test-infra/.github/workflows/windows_job.yml@main
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with:
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repository: pytorch/vision
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runner: ${{ matrix.runner }}
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gpu-arch-type: ${{ matrix.gpu-arch-type }}
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gpu-arch-version: ${{ matrix.gpu-arch-version }}
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test-infra-ref: main
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script: |
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set -euo pipefail
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# windows:
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# strategy:
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# matrix:
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# include:
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# - runner: windows.4xlarge
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# gpu-arch-type: cpu
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# - runner: windows.g5.4xlarge.nvidia.gpu
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# gpu-arch-type: cuda
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# gpu-arch-version: "12.6"
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# fail-fast: false
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# uses: pytorch/test-infra/.github/workflows/windows_job.yml@main
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# with:
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# repository: pytorch/vision
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# runner: ${{ matrix.runner }}
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# gpu-arch-type: ${{ matrix.gpu-arch-type }}
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# gpu-arch-version: ${{ matrix.gpu-arch-version }}
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# test-infra-ref: main
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# script: |
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# set -euo pipefail
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export PYTHON_VERSION=3.9
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export VC_YEAR=2022
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export VSDEVCMD_ARGS=""
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export GPU_ARCH_TYPE=${{ matrix.gpu-arch-type }}
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export GPU_ARCH_VERSION=${{ matrix.gpu-arch-version }}
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# export PYTHON_VERSION=3.9
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# export VC_YEAR=2022
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# export VSDEVCMD_ARGS=""
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# export GPU_ARCH_TYPE=${{ matrix.gpu-arch-type }}
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# export GPU_ARCH_VERSION=${{ matrix.gpu-arch-version }}
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./.github/scripts/cmake.sh
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# ./.github/scripts/cmake.sh

‎.github/workflows/prototype-tests-linux-gpu.yml‎

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strategy:
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matrix:
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python-version:
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- "3.9"
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- "3.10"
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- "3.11"
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- "3.12"
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runner: ["linux.12xlarge"]
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gpu-arch-type: ["cpu"]
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include:
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- python-version: "3.9"
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- python-version: "3.10"
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runner: linux.g5.4xlarge.nvidia.gpu
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gpu-arch-type: cuda
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gpu-arch-version: "12.6"

‎.github/workflows/tests-schedule.yml‎

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steps:
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- name: Set up python
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uses: actions/setup-python@v2
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uses: actions/setup-python@v5
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with:
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python-version: 3.9
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python-version: "3.10"
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- name: Upgrade system packages
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run: python -m pip install --upgrade pip setuptools wheel

‎.github/workflows/tests.yml‎

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strategy:
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matrix:
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python-version:
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- "3.9"
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- "3.10"
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- "3.11"
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- "3.12"
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runner: ["linux.12xlarge"]
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gpu-arch-type: ["cpu"]
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include:
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- python-version: 3.9
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- python-version: "3.10"
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runner: linux.g5.4xlarge.nvidia.gpu
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gpu-arch-type: cuda
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gpu-arch-version: "12.6"
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strategy:
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matrix:
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python-version:
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- "3.9"
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- "3.10"
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# TODO put back 3.11 (See blame)
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# - "3.11"
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strategy:
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matrix:
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python-version:
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- "3.9"
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- "3.10"
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- "3.11"
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- "3.12"
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script: |
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set -euo pipefail
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export PYTHON_VERSION=3.9
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export PYTHON_VERSION="3.10"
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export GPU_ARCH_TYPE=cpu
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export GPU_ARCH_VERSION=''
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‎CMakeLists.txt‎

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@@ -11,8 +11,6 @@ option(WITH_JPEG "Enable features requiring LibJPEG." ON)
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# untested. Since building from cmake is very low pri anyway, this is OK. If
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# you're a user and you need this, please open an issue (and a PR!).
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option(WITH_WEBP "Enable features requiring LibWEBP." OFF)
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# Same here
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option(WITH_AVIF "Enable features requiring LibAVIF." OFF)
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if(WITH_CUDA)
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enable_language(CUDA)
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if (WITH_WEBP)
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add_definitions(-DWEBP_FOUND)
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find_package(WEBP REQUIRED)
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endif()
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if (WITH_AVIF)
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add_definitions(-DAVIF_FOUND)
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find_package(AVIF REQUIRED)
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find_package(WebP REQUIRED)
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endif()
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function(CUDA_CONVERT_FLAGS EXISTING_TARGET)

‎README.md‎

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| `torch` | `torchvision` | Python |
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| ------------------ | ------------------ | ------------------- |
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| `main` / `nightly` | `main` / `nightly` | `>=3.9`, `<=3.12` |
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| `2.5` | `0.20` | `>=3.9`, `<=3.12` |
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| `2.4` | `0.19` | `>=3.8`, `<=3.12` |
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| `2.3` | `0.18` | `>=3.8`, `<=3.12` |
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| `2.2` | `0.17` | `>=3.8`, `<=3.11` |
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| `2.1` | `0.16` | `>=3.8`, `<=3.11` |
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| `2.0` | `0.15` | `>=3.8`, `<=3.11` |
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| `main` / `nightly` | `main` / `nightly` | `>=3.10`, `<=3.14` |
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| `2.9` | `0.24` | `>=3.10`, `<=3.14` |
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| `2.8` | `0.23` | `>=3.9`, `<=3.13` |
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| `2.7` | `0.22` | `>=3.9`, `<=3.13` |
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| `2.6` | `0.21` | `>=3.9`, `<=3.12` |
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<details>
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<summary>older versions</summary>
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| `torch` | `torchvision` | Python |
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|---------|-------------------|---------------------------|
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| `2.5` | `0.20` | `>=3.9`, `<=3.12` |
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| `2.4` | `0.19` | `>=3.8`, `<=3.12` |
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| `2.3` | `0.18` | `>=3.8`, `<=3.12` |
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| `2.2` | `0.17` | `>=3.8`, `<=3.11` |
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| `2.1` | `0.16` | `>=3.8`, `<=3.11` |
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| `2.0` | `0.15` | `>=3.8`, `<=3.11` |
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| `1.13` | `0.14` | `>=3.7.2`, `<=3.10` |
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| `1.12` | `0.13` | `>=3.7`, `<=3.10` |
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| `1.11` | `0.12` | `>=3.7`, `<=3.10` |
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Read more in in our [docs](https://pytorch.org/vision/stable/transforms.html).
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## [UNSTABLE] Video Backend
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Torchvision currently supports the following video backends:
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- [pyav](https://github.com/PyAV-Org/PyAV) (default) - Pythonic binding for ffmpeg libraries.
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- video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any
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conflicting version of ffmpeg installed. Currently, this is only supported on Linux.
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```
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conda install -c conda-forge 'ffmpeg<4.3'
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pip install . -v --no-build-isolation
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```
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# Using the models on C++
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Refer to [example/cpp](https://github.com/pytorch/vision/tree/main/examples/cpp).
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**DISCLAIMER**: the `libtorchvision` library includes the torchvision
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custom ops as well as most of the C++ torchvision APIs. Those APIs do not come
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with any backward-compatibility guarantees and may change from one version to
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the next. Only the Python APIs are stable and with backward-compatibility
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guarantees. So, if you need stability within a C++ environment, your best bet is
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to export the Python APIs via torchscript.
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## Documentation
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You can find the API documentation on the pytorch website: <https://pytorch.org/vision/stable/index.html>

‎docs/source/conf.py‎

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"plot_transforms_e2e.py",
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"plot_cutmix_mixup.py",
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"plot_rotated_box_transforms.py",
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"plot_keypoints_transforms.py",
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"plot_custom_transforms.py",
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"plot_tv_tensors.py",
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"plot_custom_tv_tensors.py",

‎docs/source/transforms.rst‎

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v2.RandomErasing
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v2.Lambda
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v2.SanitizeBoundingBoxes
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v2.SanitizeKeyPoints
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v2.ClampBoundingBoxes
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v2.ClampKeyPoints
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v2.UniformTemporalSubsample
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v2.functional.normalize
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v2.functional.erase
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v2.functional.sanitize_bounding_boxes
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v2.functional.sanitize_keypoints
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v2.functional.clamp_bounding_boxes
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v2.functional.clamp_keypoints
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v2.functional.uniform_temporal_subsample
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v2.query_size
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v2.query_chw
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v2.get_bounding_boxes
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v2.get_keypoints
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V1 API Reference

‎gallery/assets/pottery.jpg‎

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