Merge branch 'main' into models/replace_se

Author: datumbox

.circleci/config.yml

@@ -763,6 +763,12 @@ jobs:
           paths:
             - conda
             - env
+      - run:
+          name: Install CUDA
+          command: packaging/windows/internal/cuda_install.bat
+      - run:
+          name: Update CUDA driver
+          command: packaging/windows/internal/driver_update.bat
       - run:
           name: Install torchvision
           command: .circleci/unittest/windows/scripts/install.sh

.circleci/config.yml.in

@@ -5,7 +5,7 @@ unset PYTORCH_VERSION
 # so no need to set PYTORCH_VERSION.
 # In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config.
 
-set -e
+set -ex
 
 this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
@@ -35,5 +35,17 @@ if [ $PYTHON_VERSION == "3.6" ]; then
     pip install pillow>=5.3.0
 fi
 
+torch_cuda=$(python -c "import torch; print(torch.cuda.is_available())")
+echo torch.cuda.is_available is $torch_cuda
+
+if [ ! -z "${CUDA_VERSION:-}" ] ; then
+    if [ "$torch_cuda" == "False" ]; then
+        echo "torch with cuda installed but torch.cuda.is_available() is False"
+        exit 1
+    fi
+fi
+
+source "$this_dir/set_cuda_envs.sh"
+
 printf "* Installing torchvision\n"
 "$this_dir/vc_env_helper.bat" python setup.py develop

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

@@ -5,6 +5,9 @@ set -e
 eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')"
 conda activate ./env
 
+this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+source "$this_dir/set_cuda_envs.sh"
+
 export PYTORCH_TEST_WITH_SLOW='1'
 python -m torch.utils.collect_env
 pytest --cov=torchvision --junitxml=test-results/junit.xml -v --durations 20 test --ignore=test/test_datasets_download.py

.circleci/unittest/windows/scripts/run_test.sh

@@ -1,35 +1,48 @@
 #!/usr/bin/env bash
+set -ex
 
-if [ "${CU_VERSION:-}" == "cpu" ] ; then
-    exit 0
-fi
+echo CU_VERSION is "${CU_VERSION}"
+echo CUDA_VERSION is "${CUDA_VERSION}"
 
-if [[ ${#CU_VERSION} -eq 5 ]]; then
-    CUDA_VERSION="${CU_VERSION:2:2}.${CU_VERSION:4:1}"
+# Currenly, CU_VERSION and CUDA_VERSION are not consistent. 
+# to understand this code, see https://github.com/pytorch/vision/issues/4443
+version="cpu"
+if [[ ! -z "${CUDA_VERSION}" ]] ; then
+    version="$CUDA_VERSION"
+else
+    if [[ ${#CU_VERSION} -eq 5 ]]; then
+        version="${CU_VERSION:2:2}.${CU_VERSION:4:1}"
+    fi
 fi
 
-# It's a log to see if CU_VERSION exists, if not, we use environment CUDA_VERSION directly
-# in unittest_windows_gpu, there's no CU_VERSION, but CUDA_VERSION.
-echo "Using CUDA $CUDA_VERSION, CU_VERSION is $CU_VERSION now"
+# Don't use if [[ "$version" == "cpu" ]]; then exit 0 fi.
+# It would exit the shell. One result is cpu tests would not run if the shell exit.
+# Unless there's an error, Don't exit.
+if [[ "$version" != "cpu" ]]; then
+    # set cuda envs
+    export PATH="/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${version}/bin:/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${version}/libnvvp:$PATH"
+    export CUDA_PATH_V${version/./_}="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v${version}"
+    export CUDA_PATH="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v${version}"
 
-version=$CUDA_VERSION
+    if  [ ! -d "$CUDA_PATH" ]; then
+        echo "$CUDA_PATH" does not exist
+        exit 1
+    fi
 
-# set cuda envs
-export PATH="/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${version}/bin:/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${version}/libnvvp:$PATH"
-export CUDA_PATH_V${version/./_}="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v${version}"
-export CUDA_PATH="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v${version}"
+    if [ ! -f "${CUDA_PATH}\include\nvjpeg.h" ]; then
+        echo "nvjpeg does not exist"
+        exit 1
+    fi
 
-if  [ ! -d "$CUDA_PATH" ]
-then
-    echo "$CUDA_PATH" does not exist
-    exit 1
-fi
+    # check cuda driver version
+    for path in '/c/Program Files/NVIDIA Corporation/NVSMI/nvidia-smi.exe' /c/Windows/System32/nvidia-smi.exe; do
+        if [[ -x "$path" ]]; then
+            "$path" || echo "true";
+            break
+        fi
+    done
 
-# check cuda driver version
-for path in '/c/Program Files/NVIDIA Corporation/NVSMI/nvidia-smi.exe' /c/Windows/System32/nvidia-smi.exe; do
-    if [[ -x "$path" ]]; then
-        "$path" || echo "true";
-        break
-    fi
-done
-which nvcc
+    which nvcc
+    nvcc --version
+    env | grep CUDA
+fi

.circleci/unittest/windows/scripts/set_cuda_envs.sh

@@ -106,6 +106,20 @@ otherwise, add the include and library paths in the environment variables ``TORC
 .. _libjpeg: http://ijg.org/
 .. _libjpeg-turbo: https://libjpeg-turbo.org/
 
+Video Backend
+=============
+Torchvision currently supports the following video backends:
+
+* [pyav](https://github.com/PyAV-Org/PyAV) (default) - Pythonic binding for ffmpeg libraries. 
+
+* video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any conflicting version of ffmpeg installed. Currently, this is only supported on Linux.
+
+.. code:: bash
+
+     conda install -c conda-forge ffmpeg
+     python setup.py install
+
+
 Using the models on C++
 =======================
 TorchVision provides an example project for how to use the models on C++ using JIT Script.

README.rst

@@ -1,3 +1,5 @@
+.. _ops:
+
 torchvision.ops
 ===============
 

docs/source/ops.rst

@@ -1,3 +1,5 @@
+.. _utils:
+
 torchvision.utils
 =================