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author | lilac | 2022-04-21 04:46:35 +0800 |
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committer | lilac | 2022-04-21 04:46:35 +0800 |
commit | ff29187a0882f6d9c6b0b630c40b99e3eaf9fe45 (patch) | |
tree | 97ce033c3bb0d1f343ec6a20f0978ba55644b72b | |
parent | 0ea6eb5c246d4a3b264020aa60fa71312ff72c82 (diff) | |
download | aur-ff29187a0882f6d9c6b0b630c40b99e3eaf9fe45.tar.gz |
[lilac] updated to 0.12.0-4
-rw-r--r-- | .SRCINFO | 6 | ||||
-rw-r--r-- | PKGBUILD | 21 |
2 files changed, 21 insertions, 6 deletions
@@ -1,7 +1,7 @@ pkgbase = python-torchvision pkgdesc = Datasets, transforms, and models specific to computer vision pkgver = 0.12.0 - pkgrel = 3 + pkgrel = 4 url = https://github.com/pytorch/vision arch = x86_64 license = BSD @@ -20,7 +20,11 @@ pkgbase = python-torchvision optdepends = python-av: video reader backend (the default one) optdepends = python-pycocotools: support for MS-COCO dataset source = vision-0.12.0.tar.gz::https://github.com/pytorch/vision/archive/v0.12.0.tar.gz + source = https://github.com/NVIDIA/DALI/raw/main/dali/operators/reader/loader/video/nvdecode/cuviddec.h + source = https://github.com/NVIDIA/DALI/raw/main/dali/operators/reader/loader/video/nvdecode/nvcuvid.h sha512sums = ebc48a9e9ef58cc93c1b095e565c67feb2bc1bf06551e8f891a0369c211c6732e10bf191298b0633a05664371fa6dc637aab851b01a57f6b3e0d5936e87ee8ae + sha512sums = 8f97deedab5b0de1154ce7f8486eadcc7556a9cbb01fb44a988729da80b982daafbfe8da32b7f3ced78c1544b3ac696a569c50a6b4cb244f502b07e615b4de10 + sha512sums = 89f8d4410a238dc52b27200dfb8db9ff1a58777bdfebb346f3d157e16108930dc3b56f18b611f5de1cb081afa6be6768b52e2486cca57703b490194305dc1c67 pkgname = python-torchvision depends = python-numpy @@ -4,8 +4,10 @@ # Based on python-torchvision-git; original contributors: # Contributor: Stephen Zhang <zsrkmyn at gmail dot com> # -# to build with GPU deocder, you need to add https://aur.archlinux.org/packages/nvidia-sdk to depends -# set environment variable `TORCHVISION_INCLUDE` and `TORCHVISION_LIBRARY` +# NOTE: +# to build with GPU deocder, we use nvidia-sdk header files from https://github.com/NVIDIA/DALI for convenient +# you could also use https://aur.archlinux.org/packages/nvidia-sdk +# just update environment variable `TORCHVISION_INCLUDE` and `TORCHVISION_LIBRARY` # see also https://github.com/pytorch/vision/blob/main/torchvision/csrc/io/decoder/gpu/README.rst # @@ -13,7 +15,7 @@ _CUDA_ARCH_LIST="5.2;5.3;6.0;6.1;6.2;7.0;7.2;7.5;8.0;8.6;8.6+PTX" pkgname=('python-torchvision' 'python-torchvision-cuda') _pkgname=vision pkgver=0.12.0 -pkgrel=3 +pkgrel=4 pkgdesc='Datasets, transforms, and models specific to computer vision' arch=('x86_64') url='https://github.com/pytorch/vision' @@ -38,8 +40,13 @@ makedepends=( python-setuptools qt5-base ) -source=("${_pkgname}-${pkgver}.tar.gz::https://github.com/pytorch/vision/archive/v${pkgver}.tar.gz") -sha512sums=('ebc48a9e9ef58cc93c1b095e565c67feb2bc1bf06551e8f891a0369c211c6732e10bf191298b0633a05664371fa6dc637aab851b01a57f6b3e0d5936e87ee8ae') +source=("${_pkgname}-${pkgver}.tar.gz::https://github.com/pytorch/vision/archive/v${pkgver}.tar.gz" + "https://github.com/NVIDIA/DALI/raw/main/dali/operators/reader/loader/video/nvdecode/cuviddec.h" + "https://github.com/NVIDIA/DALI/raw/main/dali/operators/reader/loader/video/nvdecode/nvcuvid.h" +) +sha512sums=('ebc48a9e9ef58cc93c1b095e565c67feb2bc1bf06551e8f891a0369c211c6732e10bf191298b0633a05664371fa6dc637aab851b01a57f6b3e0d5936e87ee8ae' + '8f97deedab5b0de1154ce7f8486eadcc7556a9cbb01fb44a988729da80b982daafbfe8da32b7f3ced78c1544b3ac696a569c50a6b4cb244f502b07e615b4de10' + '89f8d4410a238dc52b27200dfb8db9ff1a58777bdfebb346f3d157e16108930dc3b56f18b611f5de1cb081afa6be6768b52e2486cca57703b490194305dc1c67') get_pyver() { python -c 'import sys; print(str(sys.version_info[0]) + "." + str(sys.version_info[1]))' @@ -63,6 +70,8 @@ build() { python setup.py build cd "${srcdir}/${_pkgname}-cuda-${pkgver}" + TORCHVISION_INCLUDE=${srcdir} \ + TORCHVISION_LIBRARY=/usr/lib \ FORCE_CUDA=1 \ TORCH_CUDA_ARCH_LIST=${_CUDA_ARCH_LIST} \ python setup.py build @@ -83,6 +92,8 @@ package_python-torchvision-cuda() { conflicts+=(python-torchvision=${pkgver}) cd "${_pkgname}-cuda-${pkgver}" + TORCHVISION_INCLUDE=${srcdir} \ + TORCHVISION_LIBRARY=/usr/lib \ FORCE_CUDA=1 \ TORCH_CUDA_ARCH_LIST=${_CUDA_ARCH_LIST} \ python setup.py install --root="${pkgdir}" --optimize=1 --skip-build |