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jdsgomesmalfet
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Update hardcoded WIN-CUDA from 11.1 with 11.3 (#5451) (#5467)
Also, remove reference to conda-forge, all CUDA toolchain should be available from NVIDIA channel Install h5py from pip on Windows and skip failing gaussian_blur tests if Win+CUDA11.3 is used Co-authored-by: Nikita Shulga <[email protected]>
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.circleci/config.yml

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.circleci/config.yml.in

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@@ -816,7 +816,7 @@ jobs:
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executor:
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name: windows-gpu
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environment:
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CUDA_VERSION: "11.1"
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CUDA_VERSION: "11.3"
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PYTHON_VERSION: << parameters.python_version >>
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steps:
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- checkout

.circleci/unittest/windows/scripts/environment.yml

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@@ -9,10 +9,11 @@ dependencies:
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- libpng
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- jpeg
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- ca-certificates
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- h5py
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- hdf5
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- pip:
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- future
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- pillow >=5.3.0, !=8.3.*
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- scipy
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- av
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- dataclasses
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- h5py

.circleci/unittest/windows/scripts/install.sh

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@@ -28,7 +28,7 @@ else
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fi
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printf "Installing PyTorch with %s\n" "${cudatoolkit}"
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conda install -y -c "pytorch-${UPLOAD_CHANNEL}" -c conda-forge "pytorch-${UPLOAD_CHANNEL}"::pytorch[build="*${version}*"] "${cudatoolkit}" pytest
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conda install -y -c "pytorch-${UPLOAD_CHANNEL}" -c nvidia "pytorch-${UPLOAD_CHANNEL}"::pytorch[build="*${version}*"] "${cudatoolkit}" pytest
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torch_cuda=$(python -c "import torch; print(torch.cuda.is_available())")
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echo torch.cuda.is_available is $torch_cuda

test/test_transforms_tensor.py

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import os
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import sys
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import numpy as np
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import pytest
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)
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@pytest.mark.parametrize("channels", [1, 3])
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def test_gaussian_blur(device, channels, meth_kwargs):
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if all(
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[
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device == "cuda",
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channels == 1,
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meth_kwargs["kernel_size"] in [23, [23]],
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torch.version.cuda == "11.3",
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sys.platform in ("win32", "cygwin"),
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]
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):
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pytest.skip("Fails on Windows, see https://github.com/pytorch/vision/issues/5464")
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tol = 1.0 + 1e-10
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torch.manual_seed(12)
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_test_class_op(

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