Merge branch 'master' of https://github.com/pytorch/vision into rand_gauss_blur_patch

Author: vfdev-5

.circleci/config.yml

@@ -212,7 +212,7 @@ jobs:
   binary_macos_wheel:
     <<: *binary_common
     macos:
-      xcode: "9.0"
+      xcode: "9.4.1"
     steps:
       - checkout_merge
       - run:
@@ -234,7 +234,7 @@ jobs:
   binary_macos_conda:
     <<: *binary_common
     macos:
-      xcode: "9.0"
+      xcode: "9.4.1"
     steps:
       - checkout_merge
       - run:
@@ -551,6 +551,76 @@ jobs:
       - store_test_results:
           path: test-results
 
+  cmake_linux_cpu:
+    <<: *binary_common
+    docker:
+      - image: "pytorch/manylinux-cuda102"
+    resource_class: 2xlarge+
+    steps:
+      - checkout_merge
+      - run:
+          name: Setup conda
+          command: .circleci/unittest/linux/scripts/setup_env.sh
+      - run: packaging/build_cmake.sh
+
+  cmake_linux_gpu:
+    <<: *binary_common
+    machine:
+      image: ubuntu-1604-cuda-10.1:201909-23
+    resource_class: gpu.small
+    environment:
+      PYTHON_VERSION: << parameters.python_version >>
+      PYTORCH_VERSION: << parameters.pytorch_version >>
+      UNICODE_ABI: << parameters.unicode_abi >>
+      CU_VERSION: << parameters.cu_version >>
+    steps:
+      - checkout_merge
+      - run:
+          name: Setup conda
+          command: docker run -e CU_VERSION -e PYTHON_VERSION -e UNICODE_ABI -e PYTORCH_VERSION -t --gpus all -v $PWD:$PWD -w $PWD << parameters.wheel_docker_image >> .circleci/unittest/linux/scripts/setup_env.sh
+      - run:
+          name: Build torchvision C++ distribution and test
+          command: docker run -e CU_VERSION -e PYTHON_VERSION -e UNICODE_ABI -e PYTORCH_VERSION -t --gpus all -v $PWD:$PWD -w $PWD << parameters.wheel_docker_image >> packaging/build_cmake.sh
+
+  cmake_macos_cpu:
+    <<: *binary_common
+    macos:
+      xcode: "9.0"
+    steps:
+      - checkout_merge
+      - run:
+          command: |
+            curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
+            sh conda.sh -b
+            source $HOME/miniconda3/bin/activate
+            conda install -yq conda-build cmake
+            packaging/build_cmake.sh
+
+  cmake_windows_cpu:
+    <<: *binary_common
+    executor:
+      name: windows-cpu
+    steps:
+      - checkout_merge
+      - run:
+          command: |
+            set -ex
+            source packaging/windows/internal/vc_install_helper.sh
+            packaging/build_cmake.sh
+
+  cmake_windows_gpu:
+    <<: *binary_common
+    executor:
+      name: windows-gpu
+    steps:
+      - checkout_merge
+      - run:
+          command: |
+            set -ex
+            source packaging/windows/internal/vc_install_helper.sh
+            packaging/windows/internal/cuda_install.bat
+            packaging/build_cmake.sh
+
 workflows:
   build:
 {%- if True %}
@@ -564,6 +634,11 @@ workflows:
   unittest:
     jobs:
       {{ unittest_workflows() }}
+
+  cmake:
+    jobs:
+      {{ cmake_workflows() }}
+
   nightly:
 {%- endif %}
     jobs:

.circleci/config.yml.in

@@ -27,7 +27,10 @@ def build_workflows(prefix='', filter_branch=None, upload=False, indentation=6,
     for btype in ["wheel", "conda"]:
         for os_type in ["linux", "macos", "win"]:
             python_versions = PYTHON_VERSIONS
-            cu_versions = (["cpu", "cu92", "cu101", "cu102"] if os_type == "linux" or os_type == "win" else ["cpu"])
+            cu_versions_dict = {"linux": ["cpu", "cu92", "cu101", "cu102", "cu110"],
+                                "win": ["cpu", "cu101", "cu102", "cu110"],
+                                "macos": ["cpu"]}
+            cu_versions = cu_versions_dict[os_type]
             for python_version in python_versions:
                 for cu_version in cu_versions:
                     for unicode in ([False, True] if btype == "wheel" and python_version == "2.7" else [False]):
@@ -66,6 +69,7 @@ def workflow_pair(btype, os_type, python_version, cu_version, unicode, prefix=''
     "cu92": "pytorch/manylinux-cuda92",
     "cu101": "pytorch/manylinux-cuda101",
     "cu102": "pytorch/manylinux-cuda102",
+    "cu110": "pytorch/manylinux-cuda110",
 }
 
 
@@ -181,6 +185,25 @@ def unittest_workflows(indentation=6):
     return indent(indentation, jobs)
 
 
+def cmake_workflows(indentation=6):
+    jobs = []
+    python_version = '3.8'
+    for os_type in ['linux', 'windows', 'macos']:
+        # Right now CMake builds are failling on Windows (GPU)
+        device_types = ['cpu', 'gpu'] if os_type == 'linux' else ['cpu']
+        for device in device_types:
+            job = {
+                'name': f'cmake_{os_type}_{device}',
+                'python_version': python_version
+            }
+
+            job['cu_version'] = 'cu101' if device == 'gpu' else 'cpu'
+            if device == 'gpu':
+                job['wheel_docker_image'] = 'pytorch/manylinux-cuda101'
+            jobs.append({f'cmake_{os_type}_{device}': job})
+    return indent(indentation, jobs)
+
+
 if __name__ == "__main__":
     d = os.path.dirname(__file__)
     env = jinja2.Environment(
@@ -193,4 +216,5 @@ def unittest_workflows(indentation=6):
         f.write(env.get_template('config.yml.in').render(
             build_workflows=build_workflows,
             unittest_workflows=unittest_workflows,
+            cmake_workflows=cmake_workflows,
         ))

.circleci/regenerate.py

@@ -6,4 +6,4 @@ eval "$(./conda/bin/conda shell.bash hook)"
 conda activate ./env
 
 python -m torch.utils.collect_env
-pytest --cov=torchvision --junitxml=test-results/junit.xml -v --durations 20 test
+pytest --cov=torchvision --junitxml=test-results/junit.xml -v --durations 20 test --ignore=test/test_datasets_download.py

.circleci/unittest/linux/scripts/run_test.sh

@@ -14,3 +14,4 @@ dependencies:
     - pillow>=4.1.1
     - scipy==1.4.1
     - av
+    - dataclasses

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

@@ -6,4 +6,4 @@ eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')"
 conda activate ./env
 
 python -m torch.utils.collect_env
-pytest --cov=torchvision --junitxml=test-results/junit.xml -v --durations 20 test
+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

@@ -0,0 +1,11 @@
+---
+title: Scheduled workflow {{ env.WORKFLOW }}/{{ env.JOB }} failed
+labels: bug, module: datasets
+---
+
+Oh no, something went wrong in the scheduled workflow {{ env.WORKFLOW }}/{{ env.JOB }}. 
+Please look into it:
+
+https://github.com/{{ env.REPO }}/actions/runs/{{ env.ID }}
+
+Feel free to close this if this was just a one-off error.

.github/failed_schedule_issue_template.md

@@ -0,0 +1,46 @@
+name: tests
+
+on:
+  pull_request:
+    - "test/test_datasets_download.py"
+    - ".github/failed_schedule_issue_template.md"
+    - ".github/workflows/tests-schedule.yml"
+
+  schedule:
+    - cron: "0 9 * * *"
+
+jobs:
+  download:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Set up python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.6
+
+      - name: Upgrade pip
+        run: python -m pip install --upgrade pip
+
+      - name: Install PyTorch from the nightlies
+        run: |
+          pip install numpy
+          pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
+
+      - name: Install tests requirements
+        run: pip install pytest pytest-subtests
+
+      - name: Run tests
+        run: pytest test/test_datasets_download.py
+
+      - uses: JasonEtco/[email protected]
+        name: Create issue if download tests failed
+        if: failure()
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          REPO: ${{ github.repository }}
+          WORKFLOW: ${{ github.workflow }}
+          JOB: ${{ github.job }}
+          ID: ${{ github.run_id }}
+        with:
+          filename: .github/failed_schedule_issue_template.md

.github/workflows/tests-schedule.yml

@@ -22,7 +22,7 @@ before_install:
   # Useful for debugging any issues with conda
   - conda info -a
 
-  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION pytorch scipy -c pytorch-nightly
+  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION cpuonly pytorch scipy -c pytorch-nightly
   - source activate test-environment
   - |
     if [[ "$IMAGE_BACKEND" == "Pillow-SIMD" ]]; then
@@ -55,7 +55,7 @@ install:
     cd -
 
 script:
-  - pytest --cov-config .coveragerc --cov torchvision --cov $TV_INSTALL_PATH -k 'not TestVideoReader and not TestVideoTransforms and not TestIO' test
+  - pytest --cov-config .coveragerc --cov torchvision --cov $TV_INSTALL_PATH -k 'not TestVideoReader and not TestVideoTransforms and not TestIO' test --ignore=test/test_datasets_download.py
   - pytest test/test_hub.py
 
 after_success:

.travis.yml

@@ -31,7 +31,9 @@ endif()
 if(NOT TARGET Python3::Python)
 find_package(Python3 COMPONENTS Development)
 endif()
-target_link_libraries(TorchVision::TorchVision INTERFACE ${TORCH_LIBRARIES} Python3::Python)
+
+set_target_properties(TorchVision::TorchVision PROPERTIES INTERFACE_INCLUDE_DIRECTORIES "${PACKAGE_PREFIX_DIR}/@CMAKE_INSTALL_INCLUDEDIR@" INTERFACE_LINK_LIBRARIES "torch;Python3::Python" )
+
 
 if(@WITH_CUDA@)
   target_compile_definitions(TorchVision::TorchVision INTERFACE WITH_CUDA)

cmake/TorchVisionConfig.cmake.in

@@ -104,7 +104,7 @@ to::
     mean = torch.mean(torch.tensor(means))
     std = torch.mean(torch.tensor(stds))
 
-Unfortunately, the concret `subset` that was used is lost. For more
+Unfortunately, the concrete `subset` that was used is lost. For more
 information see `this discussion <https://github.com/pytorch/vision/issues/1439>`_
 or `these experiments <https://github.com/pytorch/vision/pull/1965>`_.
 

docs/source/models.rst

@@ -10,6 +10,11 @@ torchvision.ops
 
 
 .. autofunction:: nms
+.. autofunction:: batched_nms
+.. autofunction:: remove_small_boxes
+.. autofunction:: clip_boxes_to_image
+.. autofunction:: box_area
+.. autofunction:: box_iou
 .. autofunction:: roi_align
 .. autofunction:: ps_roi_align
 .. autofunction:: roi_pool

docs/source/ops.rst

@@ -9,6 +9,31 @@ Functional transforms give fine-grained control over the transformations.
 This is useful if you have to build a more complex transformation pipeline
 (e.g. in the case of segmentation tasks).
 
+All transformations accept PIL Image, Tensor Image or batch of Tensor Images as input. Tensor Image is a tensor with
+``(C, H, W)`` shape, where ``C`` is a number of channels, ``H`` and ``W`` are image height and width. Batch of
+Tensor Images is a tensor of ``(B, C, H, W)`` shape, where ``B`` is a number of images in the batch. Deterministic or
+random transformations applied on the batch of Tensor Images identically transform all the images of the batch.
+
+
+Scriptable transforms
+^^^^^^^^^^^^^^^^^^^^^
+
+In order to script the transformations, please use ``torch.nn.Sequential`` instead of :class:`Compose`.
+
+.. code:: python
+
+    transforms = torch.nn.Sequential(
+        transforms.CenterCrop(10),
+        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+    )
+    scripted_transforms = torch.jit.script(transforms)
+
+Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor`` and does not require
+`lambda` functions or ``PIL.Image``.
+
+For any custom transformations to be used with ``torch.jit.script``, they should be derived from ``torch.nn.Module``.
+
+
 .. autoclass:: Compose
 
 Transforms on PIL Image

docs/source/transforms.rst

@@ -11,4 +11,6 @@ add_executable(hello-world main.cpp)
 # We now need to link the TorchVision library to our executable.
 # We can do that by using the TorchVision::TorchVision target,
 # which also adds all the necessary torch dependencies.
+target_compile_features(hello-world PUBLIC cxx_range_for)
 target_link_libraries(hello-world TorchVision::TorchVision)
+set_property(TARGET hello-world PROPERTY CXX_STANDARD 14)

examples/cpp/hello_world/CMakeLists.txt

@@ -1,5 +1,4 @@
 #include <iostream>
-
 #include <torchvision/models/resnet.h>
 
 int main()
@@ -11,5 +10,14 @@ int main()
   auto in = torch::rand({1, 3, 10, 10});
   auto out = model->forward(in);
 
-  std::cout << out;
+  std::cout << out.sizes();
+
+  if (torch::cuda::is_available()) {
+    // Move model and inputs to GPU
+    model->to(torch::kCUDA);
+    auto gpu_in = in.to(torch::kCUDA);
+    auto gpu_out = model->forward(gpu_in);
+
+    std::cout << gpu_out.sizes();
+  }
 }

examples/cpp/hello_world/main.cpp

@@ -4,15 +4,23 @@ files = torchvision
 show_error_codes = True
 pretty = True
 
-;[mypy-torchvision.datasets.*]
+[mypy-torchvision.io._video_opt.*]
 
-;ignore_errors = True
+ignore_errors = True
 
 [mypy-torchvision.io.*]
 
 ignore_errors = True
 
-[mypy-torchvision.models.*]
+[mypy-torchvision.models.detection.*]
+
+ignore_errors = True
+
+[mypy-torchvision.models.densenet.*]
+
+ignore_errors = True
+
+[mypy-torchvision.models.quantization.*]
 
 ignore_errors = True
 
@@ -51,3 +59,7 @@ ignore_missing_imports = True
 [mypy-accimage.*]
 
 ignore_missing_imports = True
+
+[mypy-av.*]
+
+ignore_missing_imports = True