add v0.19.1 release

hubconf.py

 # Optional list of dependencies required by the package
-dependencies = ['torch']
+dependencies = ["torch"]
 
-# classification
+from torchvision.models import get_model_weights, get_weight
 from torchvision.models.alexnet import alexnet
-from torchvision.models.densenet import densenet121, densenet169, densenet201, densenet161
-from torchvision.models.inception import inception_v3
-from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet101, resnet152,\
-    resnext50_32x4d, resnext101_32x8d, wide_resnet50_2, wide_resnet101_2
-from torchvision.models.squeezenet import squeezenet1_0, squeezenet1_1
-from torchvision.models.vgg import vgg11, vgg13, vgg16, vgg19, vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn
+from torchvision.models.convnext import convnext_base, convnext_large, convnext_small, convnext_tiny
+from torchvision.models.densenet import densenet121, densenet161, densenet169, densenet201
+from torchvision.models.efficientnet import (
+    efficientnet_b0,
+    efficientnet_b1,
+    efficientnet_b2,
+    efficientnet_b3,
+    efficientnet_b4,
+    efficientnet_b5,
+    efficientnet_b6,
+    efficientnet_b7,
+    efficientnet_v2_l,
+    efficientnet_v2_m,
+    efficientnet_v2_s,
+)
 from torchvision.models.googlenet import googlenet
-from torchvision.models.shufflenetv2 import shufflenet_v2_x0_5, shufflenet_v2_x1_0
+from torchvision.models.inception import inception_v3
+from torchvision.models.maxvit import maxvit_t
+from torchvision.models.mnasnet import mnasnet0_5, mnasnet0_75, mnasnet1_0, mnasnet1_3
 from torchvision.models.mobilenetv2 import mobilenet_v2
 from torchvision.models.mobilenetv3 import mobilenet_v3_large, mobilenet_v3_small
-from torchvision.models.mnasnet import mnasnet0_5, mnasnet0_75, mnasnet1_0, \
-    mnasnet1_3
-
-# segmentation
-from torchvision.models.segmentation import fcn_resnet50, fcn_resnet101, \
-    deeplabv3_resnet50, deeplabv3_resnet101, deeplabv3_mobilenet_v3_large, lraspp_mobilenet_v3_large
+from torchvision.models.optical_flow import raft_large, raft_small
+from torchvision.models.regnet import (
+    regnet_x_16gf,
+    regnet_x_1_6gf,
+    regnet_x_32gf,
+    regnet_x_3_2gf,
+    regnet_x_400mf,
+    regnet_x_800mf,
+    regnet_x_8gf,
+    regnet_y_128gf,
+    regnet_y_16gf,
+    regnet_y_1_6gf,
+    regnet_y_32gf,
+    regnet_y_3_2gf,
+    regnet_y_400mf,
+    regnet_y_800mf,
+    regnet_y_8gf,
+)
+from torchvision.models.resnet import (
+    resnet101,
+    resnet152,
+    resnet18,
+    resnet34,
+    resnet50,
+    resnext101_32x8d,
+    resnext101_64x4d,
+    resnext50_32x4d,
+    wide_resnet101_2,
+    wide_resnet50_2,
+)
+from torchvision.models.segmentation import (
+    deeplabv3_mobilenet_v3_large,
+    deeplabv3_resnet101,
+    deeplabv3_resnet50,
+    fcn_resnet101,
+    fcn_resnet50,
+    lraspp_mobilenet_v3_large,
+)
+from torchvision.models.shufflenetv2 import (
+    shufflenet_v2_x0_5,
+    shufflenet_v2_x1_0,
+    shufflenet_v2_x1_5,
+    shufflenet_v2_x2_0,
+)
+from torchvision.models.squeezenet import squeezenet1_0, squeezenet1_1
+from torchvision.models.swin_transformer import swin_b, swin_s, swin_t, swin_v2_b, swin_v2_s, swin_v2_t
+from torchvision.models.vgg import vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn
+from torchvision.models.video import (
+    mc3_18,
+    mvit_v1_b,
+    mvit_v2_s,
+    r2plus1d_18,
+    r3d_18,
+    s3d,
+    swin3d_b,
+    swin3d_s,
+    swin3d_t,
+)
+from torchvision.models.vision_transformer import vit_b_16, vit_b_32, vit_h_14, vit_l_16, vit_l_32

ios/CMakeLists.txt

 cmake_minimum_required(VERSION 3.4.1)
 set(TARGET torchvision_ops)
 project(${TARGET} CXX)
-set(CMAKE_CXX_STANDARD 14)
+set(CMAKE_CXX_STANDARD 17)
 set(LIBTORCH_HEADER_ROOT ${LIBTORCH_HEADER_ROOT})
 set(LIBRARY_OUTPUT_PATH ../lib)
 
@@ -11,13 +11,6 @@ file(GLOB VISION_SRCS
     ../torchvision/csrc/ops/*.h
     ../torchvision/csrc/ops/*.cpp)
 
-# Remove interpolate_aa sources as they are temporary code
-# see https://github.com/pytorch/vision/pull/3761
-# and using TensorIterator unavailable with iOS
-list(REMOVE_ITEM VISION_SRCS "${CMAKE_CURRENT_LIST_DIR}/../torchvision/csrc/ops/cpu/interpolate_aa_kernels.cpp")
-list(REMOVE_ITEM VISION_SRCS "${CMAKE_CURRENT_LIST_DIR}/../torchvision/csrc/ops/interpolate_aa.cpp")
-list(REMOVE_ITEM VISION_SRCS "${CMAKE_CURRENT_LIST_DIR}/../torchvision/csrc/ops/interpolate_aa.h")
-
 add_library(${TARGET} STATIC
   ${VISION_SRCS}
 )

ios/LibTorchvision.podspec

+pytorch_version = '2.0.0'
+
+Pod::Spec.new do |s|
+    s.name             = 'LibTorchvision'
+    s.version          = '0.15.1'
+    s.authors          = 'PyTorch Team'
+    s.license          = { :type => 'BSD' }
+    s.homepage         = 'https://github.com/pytorch/vision'
+    s.source           = { :http => "https://ossci-ios.s3.amazonaws.com/libtorchvision_ops_ios_#{s.version}.zip" }
+    s.summary          = '"The C++ library of TorchVision ops for iOS'
+    s.description      = <<-DESC
+        The C++ library of TorchVision ops for iOS.
+        This version (#{s.version}) requires the installation of LibTorch #{pytorch_version} or LibTorch-Lite #{pytorch_version}.
+    DESC
+    s.ios.deployment_target = '12.0'
+    s.vendored_libraries = 'install/lib/*.a'
+    s.user_target_xcconfig = {
+        'VALID_ARCHS' => 'x86_64 arm64',
+        'OTHER_LDFLAGS' => '$(inherited) -force_load "$(PODS_ROOT)/LibTorchvision/install/lib/libtorchvision_ops.a"',
+        'CLANG_CXX_LANGUAGE_STANDARD' => 'c++14',
+        'CLANG_CXX_LIBRARY' => 'libc++'
+    }
+    s.library = ['c++', 'stdc++']
+end

ios/README.md

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

ios/VisionTestApp/VisionTestApp/ViewController.h

@@ -5,4 +5,3 @@
 
 
 @end
-

ios/VisionTestApp/make_assets.py

 import torch
-import torchvision
 from torch.utils.mobile_optimizer import optimize_for_mobile
+from torchvision.models.detection import (
+    fasterrcnn_mobilenet_v3_large_320_fpn,
+    FasterRCNN_MobileNet_V3_Large_320_FPN_Weights,
+)
 
 print(torch.__version__)
 
-model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(
-    pretrained=True,
+model = fasterrcnn_mobilenet_v3_large_320_fpn(
+    weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.DEFAULT,
     box_score_thresh=0.7,
     rpn_post_nms_top_n_test=100,
     rpn_score_thresh=0.4,
-    rpn_pre_nms_top_n_test=150)
+    rpn_pre_nms_top_n_test=150,
+)
 
 model.eval()
 script_model = torch.jit.script(model)

maintainer_guide.md

+# Torchvision maintainers guide
+
+This document aims at documenting user-facing policies / principles used when
+developing and maintaining torchvision. Other maintainer info (e.g. release
+process) can be found in the meta-internal wiki.
+
+### What is public and what is private?
+
+For the Python API, torchvision largely follows the [PyTorch
+policy](https://github.com/pytorch/pytorch/wiki/Public-API-definition-and-documentation)
+which is consistent with other major packages
+([numpy](https://numpy.org/neps/nep-0023-backwards-compatibility.html),
+[scikit-learn](https://scikit-learn.org/dev/glossary.html#term-API) etc.).
+We recognize that his policy is somewhat imperfect for some edge cases, and that
+it's difficult to come up with an accurate technical definition. In broad terms,
+which are usually well understood by users, the policy is that:
+
+- modules that can be accessed without leading underscore are public
+- objects in a public file that don't have a leading underscore are public
+- class attributes are public iff they have no leading underscore
+- the rest of the modules / objects / class attributes are considered private
+
+The public API has backward-compatible (BC) guarantees defined in our
+deprecation policy (see below). The private API has not BC guarantees.
+
+For C++, code is private. For Meta employees: if a C++ change breaks fbcode, fix
+fbcode or revert the change. We should be careful about models running in
+production and relying on torchvision ops.
+
+The `test` folder is not importable and is **private.** Even meta-internal
+projects should *not* rely on it (it has happened in the past and is now
+programmatically impossible).
+
+The training references do not have BC guarantees. Breaking changes are
+possible, but we should make sure that the tutorials are still running properly,
+and that their intended narrative is preserved (by e.g. checking outputs,
+etc.).
+
+The rest of the folders (build, android, ios, etc.) are private and have no BC
+guarantees.
+
+### Deprecation policy.
+
+Because they're disruptive, **deprecations should only be used sparingly**.
+
+We largely follow the [PyTorch
+policy](https://github.com/pytorch/pytorch/wiki/PyTorch's-Python-Frontend-Backward-and-Forward-Compatibility-Policy):
+breaking changes require a deprecation period of at least 2 versions.
+
+Deprecations should clearly indicate their deadline in the docs and warning
+messages. Avoid not committing to a deadline, or keeping deprecated APIs for too
+long: it gives no incentive for users to update their code, sends conflicting
+messages ("why was this API removed while this other one is still around?"), and
+accumulates debt in the project.
+
+### Should this attribute be public? Should this function be private?
+
+When designing an API it’s not always obvious what should be exposed as public,
+and what should be kept as a private implementation detail. The following
+guidelines can be useful:
+
+* Functional consistency throughout the library is a top priority, for users and
+  developers’ sake. In doubt and unless it’s clearly wrong, expose what other
+  similar classes expose.
+* Think really hard about the users and their use-cases, and try to expose what
+  they would need to address those use-cases. Aggressively keep everything else
+  private. Remember that the “private -> public” direction is way smoother than
+  the “public -> private” one: in doubt, keep it private.
+* When thinking about use-cases, the general API motto applies: make what’s
+  simple and common easy, and make what’s complex possible (80% / 20% rule).
+  There might be a ~1% left that’s not addressed: that’s OK. Also, **make what’s
+  wrong very hard**, if not impossible.
+
+As a good practice, always create new files and even classes with a leading
+underscore in their name. This way, everything is private by default and the
+only public surface is explicitly present in an `__init__.py` file.

mypy.ini

@@ -3,12 +3,19 @@
 files = torchvision
 show_error_codes = True
 pretty = True
+allow_redefinition = True
+no_implicit_optional = True
+warn_redundant_casts = True
 
-[mypy-torchvision.io._video_opt.*]
+[mypy-torchvision.io.image.*]
 
 ignore_errors = True
 
-[mypy-torchvision.io.*]
+[mypy-torchvision.io.video.*]
+
+ignore_errors = True
+
+[mypy-torchvision.io.video_reader]
 
 ignore_errors = True
 
@@ -16,11 +23,43 @@ ignore_errors = True
 
 ignore_errors=True
 
-[mypy-torchvision.models.detection.*]
+[mypy-torchvision.models.detection.anchor_utils]
+
+ignore_errors = True
+
+[mypy-torchvision.models.detection.transform]
+
+ignore_errors = True
+
+[mypy-torchvision.models.detection.roi_heads]
+
+ignore_errors = True
+
+[mypy-torchvision.models.detection.faster_rcnn]
 
 ignore_errors = True
 
-[mypy-torchvision.models.quantization.*]
+[mypy-torchvision.models.detection.mask_rcnn]
+
+ignore_errors = True
+
+[mypy-torchvision.models.detection.keypoint_rcnn]
+
+ignore_errors = True
+
+[mypy-torchvision.models.detection.retinanet]
+
+ignore_errors = True
+
+[mypy-torchvision.models.detection.ssd]
+
+ignore_errors = True
+
+[mypy-torchvision.models.detection.ssdlite]
+
+ignore_errors = True
+
+[mypy-torchvision.models.detection.fcos]
 
 ignore_errors = True
 
@@ -28,7 +67,15 @@ ignore_errors = True
 
 ignore_errors = True
 
-[mypy-torchvision.transforms.*]
+[mypy-torchvision.transforms._functional_pil]
+
+ignore_errors = True
+
+[mypy-torchvision.transforms.functional.*]
+
+ignore_errors = True
+
+[mypy-torchvision.transforms.transforms.*]
 
 ignore_errors = True
 
@@ -52,10 +99,6 @@ ignore_missing_imports = True
 
 ignore_missing_imports = True
 
-[mypy-pandas.*]
-
-ignore_missing_imports = True
-
 [mypy-accimage.*]
 
 ignore_missing_imports = True
@@ -67,3 +110,15 @@ ignore_missing_imports = True
 [mypy-defusedxml.*]
 
 ignore_missing_imports = True
+
+[mypy-torchdata.*]
+
+ignore_missing_imports = True
+
+[mypy-h5py.*]
+
+ignore_missing_imports = True
+
+[mypy-gdown.*]
+
+ignore_missing_imports = True

packaging/README.md

-# Building torchvision packages for release
-
-## Anaconda packages
-
-### Linux
-
-```bash
-nvidia-docker run -it --ipc=host --rm -v $(pwd):/remote soumith/conda-cuda bash
-pushd remote/conda
-
-./build_vision.sh 9.0
-./build_vision.sh 10.0
-./build_vision.sh cpu
-
-# copy packages over to /remote
-# exit docker
-# anaconda upload -u pytorch torchvision*.bz2
-```
-
-### OSX
-
-```bash
-# create a fresh anaconda environment / install and activate it
-conda install -y conda-build anaconda-client
-./build_vision.sh cpu
-
-# copy packages over to /remote
-# exit docker
-# anaconda upload -u pytorch torchvision*.bz2
-```
-
-### Windows
-
-```bash
-# Open `Git Bash` and change dir to `conda`
-./build_vision.sh 9.0
-./build_vision.sh 10.0
-./build_vision.sh cpu
-
-# copy packages to a output directory
-# anaconda upload -u pytorch torchvision*.bz2
-```
-
-## Wheels
-
-### Linux
-
-pushd wheel
-
-```bash
-nvidia-docker run -it --ipc=host --rm -v $(pwd):/remote soumith/manylinux-cuda90:latest bash
-cd remote
-./linux_manywheel.sh cu90
-
-rm -rf /usr/local/cuda*
-./linux_manywheel.sh cpu
-```
-
-```bash
-nvidia-docker run -it --ipc=host --rm -v $(pwd):/remote soumith/manylinux-cuda100:latest bash
-cd remote
-./linux_manywheel.sh cu100
-```
-
-wheels are in the folders `cpu`, `cu90`, `cu100`.
-
-You can upload the `cu90` wheels to twine with `twine upload *.whl`.
-Which wheels we upload depends on which wheels PyTorch uploads as default, and right now, it's `cu90`.
-
-### OSX
-
-```bash
-pushd wheel
-./osx_wheel.sh
-```
-
-### Windows
-
-```cmd
-set PYTORCH_REPO=pytorch
-
-pushd windows
-call build_vision.bat 90 0.3.0 1
-call build_vision.bat 100 0.3.0 1
-call build_vision.bat cpu 0.3.0 1
-```
-
-wheels are in the current folder.
-
-You can upload them to twine with `twine upload *.whl`

packaging/build_cmake.sh

-#!/bin/bash
-set -ex
-
-PARALLELISM=8
-if [ -n "$MAX_JOBS" ]; then
-    PARALLELISM=$MAX_JOBS
-fi
-
-if [[ "$(uname)" != Darwin && "$OSTYPE" != "msys" ]]; then
-    eval "$(./conda/bin/conda shell.bash hook)"
-    conda activate ./env
-fi
-
-script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-. "$script_dir/pkg_helpers.bash"
-
-export BUILD_TYPE=conda
-setup_env 0.10.0
-export SOURCE_ROOT_DIR="$PWD"
-setup_conda_pytorch_constraint
-setup_conda_cudatoolkit_plain_constraint
-
-if [[ "$OSTYPE" == "msys" ]]; then
-    conda install -yq conda-build cmake pillow>=5.3.0 future
-    pip install dataclasses
-fi
-
-setup_visual_studio_constraint
-setup_junit_results_folder
-
-conda install -yq pytorch=$PYTORCH_VERSION $CONDA_CUDATOOLKIT_CONSTRAINT $CONDA_CPUONLY_FEATURE  -c "pytorch-${UPLOAD_CHANNEL}"
-TORCH_PATH=$(dirname $(python -c "import torch; print(torch.__file__)"))
-
-if [[ "$(uname)" == Darwin || "$OSTYPE" == "msys" ]]; then
-    conda install -yq libpng jpeg
-else
-    yum install -y libpng-devel libjpeg-turbo-devel
-fi
-
-mkdir cpp_build
-pushd cpp_build
-
-# Generate libtorchvision files
-cmake .. -DTorch_DIR=$TORCH_PATH/share/cmake/Torch -DWITH_CUDA=$CMAKE_USE_CUDA
-
-# Compile and install libtorchvision
-if [[ "$OSTYPE" == "msys" ]]; then
-    "$script_dir/windows/internal/vc_env_helper.bat" "$script_dir/windows/internal/build_cmake.bat" $PARALLELISM
-    CONDA_PATH=$(dirname $(which python))
-    cp -r "C:/Program Files (x86)/torchvision/include/torchvision" $CONDA_PATH/include
-else
-    make -j$PARALLELISM
-    make install
-
-    if [[ "$(uname)" == Darwin ]]; then
-        CONDA_PATH=$(dirname $(dirname $(which python)))
-        cp -r /usr/local/include/torchvision $CONDA_PATH/include/
-        export C_INCLUDE_PATH=/usr/local/include
-        export CPLUS_INCLUDE_PATH=/usr/local/include
-    fi
-fi
-
-popd
-
-# Install torchvision locally
-python setup.py develop
-
-# Trace, compile and run project that uses Faster-RCNN
-pushd test/tracing/frcnn
-mkdir build
-
-# Trace model
-python trace_model.py
-cp fasterrcnn_resnet50_fpn.pt build
-
-cd build
-cmake .. -DTorch_DIR=$TORCH_PATH/share/cmake/Torch -DWITH_CUDA=$CMAKE_USE_CUDA
-if [[ "$OSTYPE" == "msys" ]]; then
-    "$script_dir/windows/internal/vc_env_helper.bat" "$script_dir/windows/internal/build_frcnn.bat" $PARALLELISM
-    mv fasterrcnn_resnet50_fpn.pt Release
-    cd Release
-    export PATH=$(cygpath "C:/Program Files (x86)/torchvision/bin"):$(cygpath $TORCH_PATH)/lib:$PATH
-else
-    make -j$PARALLELISM
-fi
-
-# Run traced program
-./test_frcnn_tracing
-
-# Compile and run the CPP example
-popd
-cd examples/cpp/hello_world
-
-mkdir build
-cd build
-cmake .. -DTorch_DIR=$TORCH_PATH/share/cmake/Torch
-
-if [[ "$OSTYPE" == "msys" ]]; then
-    "$script_dir/windows/internal/vc_env_helper.bat" "$script_dir/windows/internal/build_cpp_example.bat" $PARALLELISM
-    cd Release
-else
-    make -j$PARALLELISM
-fi
-
-# Run CPP example
-./hello-world

packaging/build_conda.sh

-#!/bin/bash
-set -ex
-
-script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-. "$script_dir/pkg_helpers.bash"
-
-export BUILD_TYPE=conda
-setup_env 0.10.0
-export SOURCE_ROOT_DIR="$PWD"
-setup_conda_pytorch_constraint
-setup_conda_cudatoolkit_constraint
-setup_visual_studio_constraint
-setup_junit_results_folder
-conda build $CONDA_CHANNEL_FLAGS -c defaults -c conda-forge --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchvision

packaging/build_wheel.sh

-#!/bin/bash
-set -ex
-
-script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-. "$script_dir/pkg_helpers.bash"
-
-export BUILD_TYPE=wheel
-setup_env 0.10.0
-setup_wheel_python
-pip_install numpy pyyaml future ninja
-setup_pip_pytorch_version
-python setup.py clean
-
-# Copy binaries to be included in the wheel distribution
-if [[ "$(uname)" == Darwin || "$OSTYPE" == "msys" ]]; then
-    python_exec="$(which python)"
-    bin_path=$(dirname $python_exec)
-    env_path=$(dirname $bin_path)
-    if [[ "$(uname)" == Darwin ]]; then
-        # Install delocate to relocate the required binaries
-        pip_install delocate
-    else
-        cp "$bin_path/Library/bin/libpng16.dll" torchvision
-        cp "$bin_path/Library/bin/libjpeg.dll" torchvision
-    fi
-else
-    # Install auditwheel to get some inspection utilities
-    pip_install auditwheel
-
-    # Point to custom libraries
-    export LD_LIBRARY_PATH=$(pwd)/ext_libraries/lib:$LD_LIBRARY_PATH
-    export TORCHVISION_INCLUDE=$(pwd)/ext_libraries/include
-    export TORCHVISION_LIBRARY=$(pwd)/ext_libraries/lib
-fi
-
-download_copy_ffmpeg
-
-if [[ "$OSTYPE" == "msys" ]]; then
-    IS_WHEEL=1 "$script_dir/windows/internal/vc_env_helper.bat" python setup.py bdist_wheel
-else
-    IS_WHEEL=1 python setup.py bdist_wheel
-fi
-
-
-if [[ "$(uname)" == Darwin ]]; then
-    pushd dist/
-    python_exec="$(which python)"
-    bin_path=$(dirname $python_exec)
-    env_path=$(dirname $bin_path)
-    for whl in *.whl; do
-        DYLD_FALLBACK_LIBRARY_PATH="$env_path/lib/:$DYLD_FALLBACK_LIBRARY_PATH" delocate-wheel -v $whl
-    done
-else
-    if [[ "$OSTYPE" == "msys" ]]; then
-        "$script_dir/windows/internal/vc_env_helper.bat" python $script_dir/wheel/relocate.py
-    else
-        LD_LIBRARY_PATH="/usr/local/lib:$LD_LIBRARY_PATH" python $script_dir/wheel/relocate.py
-    fi
-fi

packaging/cut_release.sh

+#!/usr/bin/env bash
+#
+# Usage (run from root of project):
+# TEST_INFRA_BRANCH=release/2.1 RELEASE_BRANCH=release/2.1 RELEASE_VERSION=2.1.0 packaging/cut_release.sh
+#
+# TEST_INFRA_BRANCH: The release branch of test-infra that houses all reusable
+# workflows
+#
+# RELEASE_BRANCH: The name of the release branch for this repo
+#
+# RELEASE_VERSION: Version of this current release
+
+set -eou pipefail
+
+# Create and Check out to Release Branch
+git checkout -b "${RELEASE_BRANCH}"
+
+# Change all GitHub Actions to reference the test-infra release branch
+# as opposed to main.
+for i in .github/workflows/*.yml; do 
+  if [[ "$OSTYPE" == "darwin"* ]]; then
+    sed -i '' -e s#@main#@"${TEST_INFRA_BRANCH}"# $i;
+    sed -i '' -e s#test-infra-ref:[[:space:]]main#"test-infra-ref: ${TEST_INFRA_BRANCH}"# $i;
+  else
+    sed -i -e s#@main#@"${TEST_INFRA_BRANCH}"# $i;
+    sed -i -e s#test-infra-ref:[[:space:]]main#"test-infra-ref: ${TEST_INFRA_BRANCH}"# $i;
+  fi
+done
+
+# Update the Release Version in version.txt
+echo "${RELEASE_VERSION}" >version.txt
+
+# Optional
+# git add ./github/workflows/*.yml version.txt
+# git commit -m "[RELEASE-ONLY CHANGES] Branch Cut for Release {RELEASE_VERSION}"
+# git push origin "${RELEASE_BRANCH}"

packaging/pkg_helpers.bash

-# A set of useful bash functions for common functionality we need to do in
-# many build scripts
-
-
-# Setup CUDA environment variables, based on CU_VERSION
-#
-# Inputs:
-#   CU_VERSION (cpu, cu92, cu100)
-#   NO_CUDA_PACKAGE (bool)
-#   BUILD_TYPE (conda, wheel)
-#
-# Outputs:
-#   VERSION_SUFFIX (e.g., "")
-#   PYTORCH_VERSION_SUFFIX (e.g., +cpu)
-#   WHEEL_DIR (e.g., cu100/)
-#   CUDA_HOME (e.g., /usr/local/cuda-9.2, respected by torch.utils.cpp_extension)
-#   FORCE_CUDA (respected by torchvision setup.py)
-#   NVCC_FLAGS (respected by torchvision setup.py)
-#
-# Precondition: CUDA versions are installed in their conventional locations in
-# /usr/local/cuda-*
-#
-# NOTE: Why VERSION_SUFFIX versus PYTORCH_VERSION_SUFFIX?  If you're building
-# a package with CUDA on a platform we support CUDA on, VERSION_SUFFIX ==
-# PYTORCH_VERSION_SUFFIX and everyone is happy.  However, if you are building a
-# package with only CPU bits (e.g., torchaudio), then VERSION_SUFFIX is always
-# empty, but PYTORCH_VERSION_SUFFIX is +cpu (because that's how you get a CPU
-# version of a Python package.  But that doesn't apply if you're on OS X,
-# since the default CU_VERSION on OS X is cpu.
-setup_cuda() {
-
-  # First, compute version suffixes.  By default, assume no version suffixes
-  export VERSION_SUFFIX=""
-  export PYTORCH_VERSION_SUFFIX=""
-  export WHEEL_DIR=""
-  # Wheel builds need suffixes (but not if they're on OS X, which never has suffix)
-  if [[ "$BUILD_TYPE" == "wheel" ]] && [[ "$(uname)" != Darwin ]]; then
-    # The default CUDA has no suffix
-    if [[ "$CU_VERSION" != "cu102" ]]; then
-      export PYTORCH_VERSION_SUFFIX="+$CU_VERSION"
-    fi
-    # Match the suffix scheme of pytorch, unless this package does not have
-    # CUDA builds (in which case, use default)
-    if [[ -z "$NO_CUDA_PACKAGE" ]]; then
-      export VERSION_SUFFIX="$PYTORCH_VERSION_SUFFIX"
-      export WHEEL_DIR="$CU_VERSION/"
-    fi
-  fi
-
-  # Now work out the CUDA settings
-  case "$CU_VERSION" in
-    cu112)
-      if [[ "$OSTYPE" == "msys" ]]; then
-        export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2"
-      else
-        export CUDA_HOME=/usr/local/cuda-11.2/
-      fi
-      export FORCE_CUDA=1
-      export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6"
-      ;;
-    cu111)
-      if [[ "$OSTYPE" == "msys" ]]; then
-        export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1"
-      else
-        export CUDA_HOME=/usr/local/cuda-11.1/
-      fi
-      export FORCE_CUDA=1
-      export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6"
-      ;;
-    cu110)
-      if [[ "$OSTYPE" == "msys" ]]; then
-        export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.0"
-      else
-        export CUDA_HOME=/usr/local/cuda-11.0/
-      fi
-      export FORCE_CUDA=1
-      export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0"
-      ;;
-    cu102)
-      if [[ "$OSTYPE" == "msys" ]]; then
-        export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
-      else
-        export CUDA_HOME=/usr/local/cuda-10.2/
-      fi
-      export FORCE_CUDA=1
-      export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5"
-      ;;
-    cu101)
-      if [[ "$OSTYPE" == "msys" ]]; then
-        export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.1"
-      else
-        export CUDA_HOME=/usr/local/cuda-10.1/
-      fi
-      export FORCE_CUDA=1
-      export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5"
-      ;;
-    cu100)
-      if [[ "$OSTYPE" == "msys" ]]; then
-        export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.0"
-      else
-        export CUDA_HOME=/usr/local/cuda-10.0/
-      fi
-      export FORCE_CUDA=1
-      export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5"
-      ;;
-    cu92)
-      if [[ "$OSTYPE" == "msys" ]]; then
-        export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.2"
-      else
-        export CUDA_HOME=/usr/local/cuda-9.2/
-      fi
-      export FORCE_CUDA=1
-      export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0"
-      ;;
-    cpu)
-      ;;
-    rocm*)
-      export FORCE_CUDA=1
-      ;;
-    *)
-      echo "Unrecognized CU_VERSION=$CU_VERSION"
-      exit 1
-      ;;
-  esac
-}
-
-# Populate build version if necessary, and add version suffix
-#
-# Inputs:
-#   BUILD_VERSION (e.g., 0.2.0 or empty)
-#   VERSION_SUFFIX (e.g., +cpu)
-#
-# Outputs:
-#   BUILD_VERSION (e.g., 0.2.0.dev20190807+cpu)
-#
-# Fill BUILD_VERSION if it doesn't exist already with a nightly string
-# Usage: setup_build_version 0.2.0
-setup_build_version() {
-  if [[ -z "$BUILD_VERSION" ]]; then
-    export BUILD_VERSION="$1.dev$(date "+%Y%m%d")$VERSION_SUFFIX"
-  else
-    export BUILD_VERSION="$BUILD_VERSION$VERSION_SUFFIX"
-  fi
-
-  # Set build version based on tag if on tag
-  if [[ -n "${CIRCLE_TAG}" ]]; then
-    # Strip tag
-    export BUILD_VERSION="$(echo "${CIRCLE_TAG}" | sed -e 's/^v//' -e 's/-.*$//')${VERSION_SUFFIX}"
-  fi
-}
-
-# Set some useful variables for OS X, if applicable
-setup_macos() {
-  if [[ "$(uname)" == Darwin ]]; then
-    export MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++
-  fi
-}
-
-
-# Top-level entry point for things every package will need to do
-#
-# Usage: setup_env 0.2.0
-setup_env() {
-  setup_cuda
-  setup_build_version "$1"
-  setup_macos
-}
-
-# Function to retry functions that sometimes timeout or have flaky failures
-retry () {
-    $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
-}
-
-# Inputs:
-#   PYTHON_VERSION (2.7, 3.5, 3.6, 3.7)
-#   UNICODE_ABI (bool)
-#
-# Outputs:
-#   PATH modified to put correct Python version in PATH
-#
-# Precondition: If Linux, you are in a soumith/manylinux-cuda* Docker image
-setup_wheel_python() {
-  if [[ "$(uname)" == Darwin || "$OSTYPE" == "msys" ]]; then
-    eval "$(conda shell.bash hook)"
-    conda env remove -n "env$PYTHON_VERSION" || true
-    if [[ "$PYTHON_VERSION" == 3.9 ]]; then
-      export CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS} -c=conda-forge"
-    fi
-    conda create ${CONDA_CHANNEL_FLAGS} -yn "env$PYTHON_VERSION" python="$PYTHON_VERSION"
-    conda activate "env$PYTHON_VERSION"
-    # Install libpng from Anaconda (defaults)
-    conda install ${CONDA_CHANNEL_FLAGS} -c conda-forge libpng "jpeg<=9b" -y
-  else
-    # Install native CentOS libJPEG, LAME, freetype and GnuTLS
-    yum install -y libjpeg-turbo-devel lame freetype gnutls
-    case "$PYTHON_VERSION" in
-      2.7)
-        if [[ -n "$UNICODE_ABI" ]]; then
-          python_abi=cp27-cp27mu
-        else
-          python_abi=cp27-cp27m
-        fi
-        ;;
-      3.5) python_abi=cp35-cp35m ;;
-      3.6) python_abi=cp36-cp36m ;;
-      3.7) python_abi=cp37-cp37m ;;
-      3.8) python_abi=cp38-cp38 ;;
-      3.9) python_abi=cp39-cp39 ;;
-      *)
-        echo "Unrecognized PYTHON_VERSION=$PYTHON_VERSION"
-        exit 1
-        ;;
-    esac
-    # Download all the dependencies required to compile image and video_reader
-    # extensions
-
-    mkdir -p ext_libraries
-    pushd ext_libraries
-    popd
-    export PATH="/opt/python/$python_abi/bin:$(pwd)/ext_libraries/bin:$PATH"
-  fi
-}
-
-# Install with pip a bit more robustly than the default
-pip_install() {
-  retry pip install --progress-bar off "$@"
-}
-
-# Install torch with pip, respecting PYTORCH_VERSION, and record the installed
-# version into PYTORCH_VERSION, if applicable
-setup_pip_pytorch_version() {
-  if [[ -z "$PYTORCH_VERSION" ]]; then
-    # Install latest prerelease version of torch, per our nightlies, consistent
-    # with the requested cuda version
-    pip_install --pre torch -f "https://download.pytorch.org/whl/nightly/${WHEEL_DIR}torch_nightly.html"
-    if [[ "$CUDA_VERSION" == "cpu" ]]; then
-      # CUDA and CPU are ABI compatible on the CPU-only parts, so strip
-      # in this case
-      export PYTORCH_VERSION="$(pip show torch | grep ^Version: | sed 's/Version:  *//' | sed 's/+.\+//')"
-    else
-      export PYTORCH_VERSION="$(pip show torch | grep ^Version: | sed 's/Version:  *//')"
-    fi
-  else
-    pip_install "torch==$PYTORCH_VERSION$PYTORCH_VERSION_SUFFIX" \
-      -f "https://download.pytorch.org/whl/${CU_VERSION}/torch_stable.html" \
-      -f "https://download.pytorch.org/whl/${UPLOAD_CHANNEL}/${CU_VERSION}/torch_${UPLOAD_CHANNEL}.html"
-  fi
-}
-
-# Fill PYTORCH_VERSION with the latest conda nightly version, and
-# CONDA_CHANNEL_FLAGS with appropriate flags to retrieve these versions
-#
-# You MUST have populated PYTORCH_VERSION_SUFFIX before hand.
-setup_conda_pytorch_constraint() {
-  if [[ -z "$PYTORCH_VERSION" ]]; then
-    export CONDA_CHANNEL_FLAGS="-c pytorch-nightly -c pytorch"
-    export PYTORCH_VERSION="$(conda search --json 'pytorch[channel=pytorch-nightly]' | \
-                              python -c "import os, sys, json, re; cuver = os.environ.get('CU_VERSION'); \
-                               cuver_1 = cuver.replace('cu', 'cuda') if cuver != 'cpu' else cuver; \
-                               cuver_2 = (cuver[:-1] + '.' + cuver[-1]).replace('cu', 'cuda') if cuver != 'cpu' else cuver; \
-                               print(re.sub(r'\\+.*$', '', \
-                                [x['version'] for x in json.load(sys.stdin)['pytorch'] \
-                                  if (x['platform'] == 'darwin' or cuver_1 in x['fn'] or cuver_2 in x['fn']) \
-                                    and 'py' + os.environ['PYTHON_VERSION'] in x['fn']][-1]))")"
-    if [[ -z "$PYTORCH_VERSION" ]]; then
-      echo "PyTorch version auto detection failed"
-      echo "No package found for CU_VERSION=$CU_VERSION and PYTHON_VERSION=$PYTHON_VERSION"
-      exit 1
-    fi
-  else
-    export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-${UPLOAD_CHANNEL}"
-  fi
-  if [[ "$CU_VERSION" == cpu ]]; then
-    export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==$PYTORCH_VERSION${PYTORCH_VERSION_SUFFIX}"
-    export CONDA_PYTORCH_CONSTRAINT="- pytorch==$PYTORCH_VERSION"
-  else
-    export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==${PYTORCH_VERSION}${PYTORCH_VERSION_SUFFIX}"
-    export CONDA_PYTORCH_CONSTRAINT="- pytorch==${PYTORCH_VERSION}${PYTORCH_VERSION_SUFFIX}"
-  fi
-  if [[ "$OSTYPE" == msys && "$CU_VERSION" == cu92 ]]; then
-    export CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS} -c defaults -c numba/label/dev"
-  fi
-  if [[ "$PYTHON_VERSION" == 3.9 ]]; then
-    export CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS} -c=conda-forge"
-  fi
-}
-
-# Translate CUDA_VERSION into CUDA_CUDATOOLKIT_CONSTRAINT
-setup_conda_cudatoolkit_constraint() {
-  export CONDA_CPUONLY_FEATURE=""
-  if [[ "$(uname)" == Darwin ]]; then
-    export CONDA_CUDATOOLKIT_CONSTRAINT=""
-  else
-    case "$CU_VERSION" in
-      cu112)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=11.2,<11.3 # [not osx]"
-        ;;
-      cu111)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=11.1,<11.2 # [not osx]"
-        ;;
-      cu110)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=11.0,<11.1 # [not osx]"
-        ;;
-      cu102)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=10.2,<10.3 # [not osx]"
-        ;;
-      cu101)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=10.1,<10.2 # [not osx]"
-        ;;
-      cu100)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=10.0,<10.1 # [not osx]"
-        ;;
-      cu92)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=9.2,<9.3 # [not osx]"
-        ;;
-      cpu)
-        export CONDA_CUDATOOLKIT_CONSTRAINT=""
-        export CONDA_CPUONLY_FEATURE="- cpuonly"
-        ;;
-      *)
-        echo "Unrecognized CU_VERSION=$CU_VERSION"
-        exit 1
-        ;;
-    esac
-  fi
-}
-
-setup_conda_cudatoolkit_plain_constraint() {
-  export CONDA_CPUONLY_FEATURE=""
-  export CMAKE_USE_CUDA=1
-  if [[ "$(uname)" == Darwin ]]; then
-    export CONDA_CUDATOOLKIT_CONSTRAINT=""
-    export CMAKE_USE_CUDA=0
-  else
-    case "$CU_VERSION" in
-      cu112)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="cudatoolkit=11.2"
-        ;;
-      cu111)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="cudatoolkit=11.1"
-        ;;
-      cu102)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="cudatoolkit=10.2"
-        ;;
-      cu101)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="cudatoolkit=10.1"
-        ;;
-      cu100)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="cudatoolkit=10.0"
-        ;;
-      cu92)
-        export CONDA_CUDATOOLKIT_CONSTRAINT="cudatoolkit=9.2"
-        ;;
-      cpu)
-        export CONDA_CUDATOOLKIT_CONSTRAINT=""
-        export CONDA_CPUONLY_FEATURE="cpuonly"
-        export CMAKE_USE_CUDA=0
-        ;;
-      *)
-        echo "Unrecognized CU_VERSION=$CU_VERSION"
-        exit 1
-        ;;
-    esac
-  fi
-}
-
-# Build the proper compiler package before building the final package
-setup_visual_studio_constraint() {
-  if [[ "$OSTYPE" == "msys" ]]; then
-      export VSTOOLCHAIN_PACKAGE=vs$VC_YEAR
-      conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload packaging/$VSTOOLCHAIN_PACKAGE
-      cp packaging/$VSTOOLCHAIN_PACKAGE/conda_build_config.yaml packaging/torchvision/conda_build_config.yaml
-  fi
-}
-
-setup_junit_results_folder() {
-  if [[ "$CI" == "true" ]]; then
-    export CONDA_PYTORCH_BUILD_RESULTS_DIRECTORY="${SOURCE_ROOT_DIR}/build_results/results.xml"
-  fi
-}
-
-
-download_copy_ffmpeg() {
-  if [[ "$OSTYPE" == "msys" ]]; then
-    # conda install -yq ffmpeg=4.2 -c pytorch
-    # curl -L -q https://anaconda.org/pytorch/ffmpeg/4.3/download/win-64/ffmpeg-4.3-ha925a31_0.tar.bz2 --output ffmpeg-4.3-ha925a31_0.tar.bz2
-    # bzip2 --decompress --stdout ffmpeg-4.3-ha925a31_0.tar.bz2 | tar -x --file=-
-    # cp Library/bin/*.dll ../torchvision
-    echo "FFmpeg is disabled currently on Windows"
-  else
-    if [[ "$(uname)" == Darwin ]]; then
-      conda install -yq ffmpeg=4.2 -c pytorch
-      conda install -yq wget
-    else
-      # pushd ext_libraries
-      # wget -q https://anaconda.org/pytorch/ffmpeg/4.2/download/linux-64/ffmpeg-4.2-hf484d3e_0.tar.bz2
-      # tar -xjvf ffmpeg-4.2-hf484d3e_0.tar.bz2
-      # rm -rf ffmpeg-4.2-hf484d3e_0.tar.bz2
-      # ldconfig
-      # which ffmpeg
-      # popd
-      echo "FFmpeg is disabled currently on Linux"
-    fi
-  fi
-}

packaging/post_build_script.sh

+#!/bin/bash
+LD_LIBRARY_PATH="/usr/local/lib:$CUDA_HOME/lib64:$LD_LIBRARY_PATH" python packaging/wheel/relocate.py

packaging/pre_build_script.sh

+#!/bin/bash
+if [[ "$(uname)" == Darwin ]]; then
+  # Uninstall Conflicting jpeg brew formulae
+  jpeg_packages=$(brew list | grep jpeg)
+  echo "Existing Jpeg-related Brew libraries"
+  echo $jpeg_packages
+  for pkg in $jpeg_packages; do
+    brew uninstall --ignore-dependencies --force $pkg || true
+  done
+
+  conda install -yq wget
+fi
+
+if [[ "$(uname)" == Darwin || "$OSTYPE" == "msys" ]]; then
+  # Install libpng from Anaconda (defaults)
+  conda install libpng -yq
+  conda install -yq ffmpeg=4.2 libjpeg-turbo -c pytorch
+
+  # Copy binaries to be included in the wheel distribution
+  if [[ "$OSTYPE" == "msys" ]]; then
+      python_exec="$(which python)"
+      bin_path=$(dirname $python_exec)
+      cp "$bin_path/Library/bin/libjpeg.dll" torchvision
+  fi
+else
+
+  if [[ "$ARCH" == "aarch64" ]]; then
+    conda install libpng -yq
+    conda install -yq ffmpeg=4.2 libjpeg-turbo -c pytorch-nightly
+  fi
+
+  # Install native CentOS libJPEG, freetype and GnuTLS
+  yum install -y libjpeg-turbo-devel freetype gnutls
+
+  # Download all the dependencies required to compile image and video_reader
+  # extensions
+  mkdir -p ext_libraries
+  pushd ext_libraries
+  popd
+  export PATH="$(pwd)/ext_libraries/bin:$PATH"
+  pip install auditwheel
+
+  # Point to custom libraries
+  export LD_LIBRARY_PATH=$(pwd)/ext_libraries/lib:$LD_LIBRARY_PATH
+  export TORCHVISION_INCLUDE=$(pwd)/ext_libraries/include
+  export TORCHVISION_LIBRARY=$(pwd)/ext_libraries/lib
+fi
+
+pip install numpy pyyaml future ninja
+pip install --upgrade setuptools==72.1.0

packaging/torchvision/conda_build_config.yaml

@@ -7,8 +7,7 @@ c_compiler:
 cxx_compiler:
   - vs2017                     # [win]
 python:
-  - 3.5
-  - 3.6
+  - 3.8
 # This differs from target_platform in that it determines what subdir the compiler
 #    will target, not what subdir the compiler package will be itself.
 #    For example, we need a win-64 vs2008_win-32 package, so that we compile win-32

packaging/torchvision/meta.yaml

+{% set build_variant = environ.get('CONDA_BUILD_VARIANT', 'cpu') %}
 package:
   name: torchvision
   version: "{{ environ.get('BUILD_VERSION') }}"
@@ -9,27 +10,36 @@ requirements:
   build:
     - {{ compiler('c') }} # [win]
     - libpng
-    # NOTE: Pinned to fix issues with size_t on Windows
-    - jpeg <=9b
-    # NOTE: The only ffmpeg version that we build is actually 4.2
-    - ffmpeg >=4.2  # [not win]
+    - libjpeg-turbo
+    - ffmpeg >=4.2.2, <5.0.0  # [linux]
 
   host:
     - python
     - setuptools
-    {{ environ.get('CONDA_PYTORCH_BUILD_CONSTRAINT') }}
-    {{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT') }}
-    {{ environ.get('CONDA_CPUONLY_FEATURE') }}
+    - pytorch-mutex 1.0 {{ build_variant }}  # [not osx ]
+    {{ environ.get('CONDA_PYTORCH_BUILD_CONSTRAINT', 'pytorch') }}
+    {{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT', '') }}
 
   run:
     - python
+    - defaults::numpy >=1.11 # [py <= 310]
+    - numpy >=1.23.5 # [py >= 311]
+    - requests
     - libpng
-    - ffmpeg >=4.2  # [not win]
-    # NOTE: Pinned to fix issues with size_t on Windows
-    - jpeg <=9b
-    - pillow >=5.3.0
-    {{ environ.get('CONDA_PYTORCH_CONSTRAINT') }}
-    {{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT') }}
+    - ffmpeg >=4.2.2, <5.0.0  # [linux]
+    - libjpeg-turbo
+    - pillow >=5.3.0, !=8.3.*
+    - pytorch-mutex 1.0 {{ build_variant }}  # [not osx ]
+    {{ environ.get('CONDA_PYTORCH_CONSTRAINT', 'pytorch') }}
+    {{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT', '') }}
+
+  {% if build_variant == 'cpu' %}
+  run_constrained:
+    - cpuonly
+  {% elif not osx %}
+  run_constrained:
+     - cpuonly <0
+  {% endif %}
 
 build:
   string: py{{py}}_{{ environ['CU_VERSION'] }}
@@ -39,8 +49,7 @@ build:
     - FORCE_CUDA
     - BUILD_VERSION
     - TORCH_CUDA_ARCH_LIST
-  features:
-    {{ environ.get('CONDA_CPUONLY_FEATURE') }}
+    - MACOSX_DEPLOYMENT_TARGET
 
 test:
   imports:
@@ -52,9 +61,7 @@ test:
   requires:
     - pytest
     - scipy
-    - av
-    # NOTE: Pinned to fix issues with size_t on Windows
-    - jpeg <=9b
+    - libjpeg-turbo
     - ca-certificates
 
 

packaging/vs2017/activate.bat

-:: Set env vars that tell distutils to use the compiler that we put on path
-SET DISTUTILS_USE_SDK=1
-SET MSSdk=1
-
-SET "VS_VERSION=15.0"
-SET "VS_MAJOR=15"
-SET "VS_YEAR=2017"
-
-set "MSYS2_ARG_CONV_EXCL=/AI;/AL;/OUT;/out"
-set "MSYS2_ENV_CONV_EXCL=CL"
-
-:: For Python 3.5+, ensure that we link with the dynamic runtime.  See
-:: http://stevedower.id.au/blog/building-for-python-3-5-part-two/ for more info
-set "PY_VCRUNTIME_REDIST=%PREFIX%\\bin\\vcruntime140.dll"
-
-for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [15^,16^) -property installationPath`) do (
-    if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" (
-        set "VSINSTALLDIR=%%i\"
-        goto :vswhere
-    )
-)
-
-:vswhere
-
-:: Shorten PATH to avoid the `input line too long` error.
-SET MyPath=%PATH%
-
-setlocal EnableDelayedExpansion
-
-SET TempPath="%MyPath:;=";"%"
-SET var=
-FOR %%a IN (%TempPath%) DO (
-    IF EXIST %%~sa (
-        SET "var=!var!;%%~sa"
-    )
-)
-
-set "TempPath=!var:~1!"
-endlocal & set "PATH=%TempPath%"
-
-:: Shorten current directory too
-FOR %%A IN (.) DO CD "%%~sA"
-
-:: other things added by install_activate.bat at package build time