Merge branch 'main' of github.com:pytorch/vision into ufmt

Author: NicolasHug

.circleci/config.yml

@@ -200,6 +200,7 @@ jobs:
             pip install --user --progress-bar off mypy
             pip install --user --progress-bar off types-requests
             pip install --user --progress-bar off --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
+            pip install --user --progress-bar off git+https://github.com/pytorch/data.git
             pip install --user --progress-bar off --no-build-isolation --editable .
             mypy --config-file mypy.ini
 
@@ -739,7 +740,7 @@ jobs:
     executor:
       name: windows-gpu
     environment:
-      CUDA_VERSION: "10.2"
+      CUDA_VERSION: "11.1"
       PYTHON_VERSION: << parameters.python_version >>
     steps:
       - checkout
@@ -763,6 +764,12 @@ 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
@@ -835,7 +842,7 @@ jobs:
     <<: *binary_common
     machine:
       image: ubuntu-1604-cuda-10.2:202012-01
-    resource_class: gpu.small
+    resource_class: gpu.nvidia.small
     environment:
       PYTHON_VERSION: << parameters.python_version >>
       PYTORCH_VERSION: << parameters.pytorch_version >>

.circleci/config.yml.in

@@ -26,6 +26,10 @@ fi
 printf "Installing PyTorch with %s\n" "${cudatoolkit}"
 conda install -y -c "pytorch-${UPLOAD_CHANNEL}" "pytorch-${UPLOAD_CHANNEL}"::pytorch "${cudatoolkit}" pytest
 
+printf "Installing torchdata from source"
+pip install git+https://github.com/pytorch/data.git
+
+
 if [ $PYTHON_VERSION == "3.6" ]; then
     printf "Installing minimal PILLOW version\n"
     # Install the minimal PILLOW version. Otherwise, let setup.py install the latest

.circleci/unittest/linux/scripts/install.sh

@@ -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 )"
 
@@ -26,13 +26,30 @@ else
 fi
 
 printf "Installing PyTorch with %s\n" "${cudatoolkit}"
-conda install -y -c "pytorch-${UPLOAD_CHANNEL}" "pytorch-${UPLOAD_CHANNEL}"::pytorch "${cudatoolkit}" pytest
+# conda-forge channel is required for cudatoolkit 11.1 on Windows, see https://github.com/pytorch/vision/issues/4458
+conda install -y -c "pytorch-${UPLOAD_CHANNEL}" -c conda-forge "pytorch-${UPLOAD_CHANNEL}"::pytorch "${cudatoolkit}" pytest
+
+printf "Installing torchdata from source"
+pip install git+https://github.com/pytorch/data.git
+
 
 if [ $PYTHON_VERSION == "3.6" ]; then
     printf "Installing minimal PILLOW version\n"
     # Install the minimal PILLOW version. Otherwise, let setup.py install the latest
     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

@@ -12,7 +12,7 @@ body:
     description: |
       Please provide a clear and concise description of what the bug is.
 
-      If relevant, add a minimal example so that we can reproduce the error by running the code. It is very important for he snippet to be as succinct (minimal) as possible, so please take time to trim down any irrelevant code to help us debug efficiently. We are going to copy-paste your code and we expect to get the same result as you did: avoid any external data, and include the relevant imports, etc. For example:
+      If relevant, add a minimal example so that we can reproduce the error by running the code. It is very important for the snippet to be as succinct (minimal) as possible, so please take time to trim down any irrelevant code to help us debug efficiently. We are going to copy-paste your code and we expect to get the same result as you did: avoid any external data, and include the relevant imports, etc. For example:
 
       ```python
       # All necessary imports at the beginning

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -3,7 +3,7 @@ name: pr-labels
 on:
   push:
     branches:
-      - master
+      - main
 
 jobs:
   is-properly-labeled:

.github/workflows/pr-labels.yml

@@ -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

@@ -1,6 +1,6 @@
-sphinx==3.5.4
-sphinx-gallery>=0.9.0
-sphinx-copybutton>=0.3.1
 matplotlib
 numpy
+sphinx-copybutton>=0.3.1
+sphinx-gallery>=0.9.0
+sphinx==3.5.4
 -e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme

docs/requirements.txt

@@ -19,8 +19,8 @@ It works by following roughly these steps:
 
 1. Symbolically tracing the model to get a graphical representation of
    how it transforms the input, step by step.
-2. Setting the user-selected graph nodes as ouputs.
-3. Removing all redundant nodes (anything downstream of the ouput nodes).
+2. Setting the user-selected graph nodes as outputs.
+3. Removing all redundant nodes (anything downstream of the output nodes).
 4. Generating python code from the resulting graph and bundling that into a
    PyTorch module together with the graph itself.
 
@@ -30,6 +30,39 @@ The `torch.fx documentation <https://pytorch.org/docs/stable/fx.html>`_
 provides a more general and detailed explanation of the above procedure and
 the inner workings of the symbolic tracing.
 
+.. _about-node-names:
+
+**About Node Names**
+
+In order to specify which nodes should be output nodes for extracted
+features, one should be familiar with the node naming convention used here
+(which differs slightly from that used in ``torch.fx``). A node name is
+specified as a ``.`` separated path walking the module hierarchy from top level
+module down to leaf operation or leaf module. For instance ``"layer4.2.relu"``
+in ResNet-50 represents the output of the ReLU of the 2nd block of the 4th
+layer of the ``ResNet`` module. Here are some finer points to keep in mind:
+
+- When specifying node names for :func:`create_feature_extractor`, you may
+  provide a truncated version of a node name as a shortcut. To see how this
+  works, try creating a ResNet-50 model and printing the node names with
+  ``train_nodes, _ = get_graph_node_names(model) print(train_nodes)`` and
+  observe that the last node pertaining to ``layer4`` is
+  ``"layer4.2.relu_2"``. One may specify ``"layer4.2.relu_2"`` as the return
+  node, or just ``"layer4"`` as this, by convention, refers to the last node
+  (in order of execution) of ``layer4``.
+- If a certain module or operation is repeated more than once, node names get
+  an additional ``_{int}`` postfix to disambiguate. For instance, maybe the
+  addition (``+``) operation is used three times in the same ``forward``
+  method. Then there would be ``"path.to.module.add"``,
+  ``"path.to.module.add_1"``, ``"path.to.module.add_2"``. The counter is
+  maintained within the scope of the direct parent. So in ResNet-50 there is
+  a ``"layer4.1.add"`` and a ``"layer4.2.add"``. Because the addition
+  operations reside in different blocks, there is no need for a postfix to
+  disambiguate.
+
+
+**An Example**
+
 Here is an example of how we might extract features for MaskRCNN:
 
 .. code-block:: python
@@ -80,10 +113,10 @@ Here is an example of how we might extract features for MaskRCNN:
   # Now you can build the feature extractor. This returns a module whose forward
   # method returns a dictionary like:
   # {
-  #     'layer1': ouput of layer 1,
-  #     'layer2': ouput of layer 2,
-  #     'layer3': ouput of layer 3,
-  #     'layer4': ouput of layer 4,
+  #     'layer1': output of layer 1,
+  #     'layer2': output of layer 2,
+  #     'layer3': output of layer 3,
+  #     'layer4': output of layer 4,
   # }
   create_feature_extractor(m, return_nodes=return_nodes)