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Merge branch 'main' into fix-docstring-fill-param
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.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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.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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README.md

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| `torch` | `torchvision` | Python |
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| ------------------ | ------------------ | ------------------- |
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| `main` / `nightly` | `main` / `nightly` | `>=3.10`, `<=3.13` |
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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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Read more in in our [docs](https://pytorch.org/vision/stable/transforms.html).
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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/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

setup.py

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"scipy": ["scipy"],
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},
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ext_modules=extensions,
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python_requires=">=3.9",
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python_requires=">=3.10",
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cmdclass={
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"build_ext": BuildExtension.with_options(no_python_abi_suffix=True),
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"clean": clean,

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