Merge branch 'main' into mvitv1

Author: datumbox

.github/workflows/build-m1-binaries.yml

@@ -7,7 +7,14 @@ on:
     branches:
       - nightly
       - main
+      - release/*
+    tags:
+        # NOTE: Binary build pipelines should only get triggered on release candidate builds
+        # Release candidate tags look like: v1.11.0-rc1
+        - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
   workflow_dispatch:
+env:
+  CHANNEL: "nightly"
 jobs:
   build_wheels:
     name: "Build TorchVision M1 wheels"
@@ -18,6 +25,17 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v2
+      - name: Set CHANNEL (only for tagged pushes)
+        if: ${{ github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags/') }}
+        run: |
+          # reference ends with an RC suffix
+          if [[ ${GITHUB_REF_NAME} = *-rc[0-9]* ]]; then
+            echo "CHANNEL=test" >> "$GITHUB_ENV"
+          fi
+      - name: Set Release CHANNEL (for release)
+        if: ${{ (github.event_name == 'pull_request' && startsWith(github.base_ref, 'release')) || startsWith(github.ref, 'refs/heads/release') }}
+        run: |
+          echo "CHANNEL=test" >> "$GITHUB_ENV"
       - name: Build TorchVision M1 wheel
         shell: arch -arch arm64 bash {0}
         env:
@@ -29,10 +47,16 @@ jobs:
           export PATH=~/miniconda3/bin:$PATH
           set -ex
           . packaging/pkg_helpers.bash
-          setup_build_version
+          # if we are uploading to test channell, our version consist only of the base: 0.x.x - no date string or suffix added
+          if [[ $CHANNEL == "test" ]]; then
+            setup_base_build_version
+          else
+            setup_build_version
+          fi
+
           WHL_NAME=torchvision-${BUILD_VERSION}-cp${PY_VERS/.}-cp${PY_VERS/.}-macosx_11_0_arm64.whl
           conda create -yp ${ENV_NAME} python=${PY_VERS} numpy libpng jpeg wheel pkg-config
-          conda run -p ${ENV_NAME} python3 -mpip install torch --pre --extra-index-url=https://download.pytorch.org/whl/nightly
+          conda run -p ${ENV_NAME} python3 -mpip install torch --pre --extra-index-url=https://download.pytorch.org/whl/${CHANNEL}
           conda run -p ${ENV_NAME} python3 -mpip install delocate
           conda run -p ${ENV_NAME} python3 setup.py bdist_wheel
           export PYTORCH_VERSION="$(conda run -p ${ENV_NAME} python3 -mpip show torch | grep ^Version: | sed 's/Version:  *//')"
@@ -47,27 +71,89 @@ jobs:
           . ~/miniconda3/etc/profile.d/conda.sh
           set -ex
           conda create -yp ${ENV_NAME} python=${PY_VERS} numpy
-          conda run -p ${ENV_NAME} python3 -mpip install torch --pre --extra-index-url=https://download.pytorch.org/whl/nightly
+          conda run -p ${ENV_NAME} python3 -mpip install torch --pre --extra-index-url=https://download.pytorch.org/whl/${CHANNEL}
           conda run -p ${ENV_NAME} python3 -mpip install dist/*.whl
           # Test torch is importable, by changing cwd and running import commands
           conda run --cwd /tmp -p ${ENV_NAME} python3 -c "import torchvision;print('torchvision version is ', torchvision.__version__)"
           conda run --cwd /tmp -p ${ENV_NAME} python3 -c "import torch;import torchvision;print('Is torchvision useable?', all(x is not None for x in [torch.ops.image.decode_png, torch.ops.torchvision.roi_align]))"
           conda run --cwd /tmp -p ${ENV_NAME} python3 -c "import torchvision;print(torchvision.io.read_image('${PWD}/gallery/assets/dog1.jpg').shape)"
           conda env remove -p ${ENV_NAME}
       - name: Upload wheel to GitHub
-        if: ${{ github.event_name == 'push' && steps.extract_branch.outputs.branch == 'nightly' }}
         uses: actions/upload-artifact@v3
         with:
           name: torchvision-py${{ matrix.py_vers }}-macos11-m1
           path: dist/
       - name: Upload wheel to S3
-        if: ${{ github.event_name == 'push' && steps.extract_branch.outputs.branch == 'nightly' }}
+        if: ${{ github.event_name == 'push' && (github.event.ref == 'refs/heads/nightly' || startsWith(github.event.ref, 'refs/tags/')) }}
         shell: arch -arch arm64 bash {0}
         env:
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
           AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}
-          CHANNEL: nightly
         run: |
           for pkg in dist/*; do
             aws s3 cp "$pkg" "s3://pytorch/whl/${CHANNEL}/cpu/" --acl public-read
           done
+  build_conda:
+    name: "Build TorchVision M1 conda packages"
+    runs-on: macos-m1
+    strategy:
+      matrix:
+        py_vers: [ "3.8", "3.9", "3.10" ]
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v2
+      - name: Set CHANNEL (only for tagged pushes)
+        if: ${{ github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags/') }}
+        run: |
+          # reference ends with an RC suffix
+          if [[ ${GITHUB_REF_NAME} = *-rc[0-9]* ]]; then
+            echo "CHANNEL=test" >> "$GITHUB_ENV"
+          fi
+      - name: Set CHANNEL Release (for release)
+        if: ${{ (github.event_name == 'pull_request' && startsWith(github.base_ref, 'release')) || startsWith(github.ref, 'refs/heads/release') }}
+        run: |
+          echo "CHANNEL=test" >> "$GITHUB_ENV"
+      - name: Install conda-build and purge previous artifacts
+        shell: arch -arch arm64 bash {0}
+        run: |
+          . ~/miniconda3/etc/profile.d/conda.sh
+          conda install -yq conda-build
+          conda build purge-all
+
+      - name: Build TorchVision M1 conda package
+        shell: arch -arch arm64 bash {0}
+        env:
+          ENV_NAME: conda-env-${{ github.run_id }}
+          PYTHON_VERSION: ${{ matrix.py_vers }}
+          CU_VERSION: cpu
+        run: |
+          . ~/miniconda3/etc/profile.d/conda.sh
+          set -ex
+          . packaging/pkg_helpers.bash
+
+          if [[ $CHANNEL == "test" ]]; then
+            setup_base_build_version
+          else
+            setup_build_version
+          fi
+
+          setup_conda_pytorch_constraint
+          export SOURCE_ROOT_DIR=$(pwd)
+          conda build -c defaults $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchvision
+          mkdir -p dist
+          cp ~/miniconda3/conda-bld/osx-arm64/*.tar.bz2 dist/
+      - name: Upload package to GitHub
+        uses: actions/upload-artifact@v3
+        with:
+          name: torchvision-py${{ matrix.py_vers }}-macos11-m1-conda
+          path: dist/
+      - name: Upload package to conda
+        if: ${{ github.event_name == 'push' && (github.event.ref == 'refs/heads/nightly' || startsWith(github.event.ref, 'refs/tags/')) }}
+        shell: arch -arch arm64 bash {0}
+        env:
+          CONDA_PYTORCHBOT_TOKEN: ${{ secrets.CONDA_PYTORCHBOT_TOKEN }}
+        run: |
+          . ~/miniconda3/etc/profile.d/conda.sh
+          conda install -yq anaconda-client
+          set -x
+          anaconda  -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/miniconda3/conda-bld/osx-arm64/*.tar.bz2 -u "pytorch-${CHANNEL}" --label main --no-progress --force

.github/workflows/test-m1.yml

@@ -7,7 +7,10 @@ on:
     branches:
       - nightly
       - main
+      - release/*
   workflow_dispatch:
+env:
+  CHANNEL: "nightly"
 jobs:
   tests:
     name: "Unit-tests on M1"
@@ -19,6 +22,10 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v2
+      - name: Set Release CHANNEL (for release)
+        if: ${{ (github.event_name == 'pull_request' && startsWith(github.base_ref, 'release')) || startsWith(github.ref, 'refs/heads/release') }}
+        run: |
+          echo "CHANNEL=test" >> "$GITHUB_ENV"
       - name: Install TorchVision
         shell: arch -arch arm64 bash {0}
         env:
@@ -30,7 +37,7 @@ jobs:
           export PATH=~/miniconda3/bin:$PATH
           set -ex
           conda create -yp ${ENV_NAME} python=${PY_VERS} numpy libpng jpeg scipy
-          conda run -p ${ENV_NAME} python3 -mpip install torch --extra-index-url=https://download.pytorch.org/whl/nightly
+          conda run -p ${ENV_NAME} python3 -mpip install --pre torch --extra-index-url=https://download.pytorch.org/whl/${CHANNEL}
           conda run -p ${ENV_NAME} python3 setup.py develop
           conda run -p ${ENV_NAME} python3 -mpip install pytest pytest-mock av
       - name: Run tests

docs/source/beta_status.py

@@ -0,0 +1,20 @@
+from docutils import nodes
+from docutils.parsers.rst import Directive
+
+
+class BetaStatus(Directive):
+    has_content = True
+
+    def run(self):
+        api_name = " ".join(self.content)
+        text = f"The {api_name} is in Beta stage, and backward compatibility is not guaranteed."
+        return [nodes.warning("", nodes.paragraph("", "", nodes.Text(text)))]
+
+
+def setup(app):
+    app.add_directive("betastatus", BetaStatus)
+    return {
+        "version": "0.1",
+        "parallel_read_safe": True,
+        "parallel_write_safe": True,
+    }

docs/source/conf.py

@@ -21,6 +21,7 @@
 # sys.path.insert(0, os.path.abspath('.'))
 
 import os
+import sys
 import textwrap
 from copy import copy
 from pathlib import Path
@@ -30,6 +31,7 @@
 import torchvision.models as M
 from tabulate import tabulate
 
+sys.path.append(os.path.abspath("."))
 
 # -- General configuration ------------------------------------------------
 
@@ -50,6 +52,7 @@
     "sphinx.ext.duration",
     "sphinx_gallery.gen_gallery",
     "sphinx_copybutton",
+    "beta_status",
 ]
 
 sphinx_gallery_conf = {

docs/source/io.rst

@@ -26,6 +26,8 @@ In addition to the :mod:`read_video` function, we provide a high-performance
 lower-level API for more fine-grained control compared to the :mod:`read_video` function.
 It does all this whilst fully supporting torchscript.
 
+.. betastatus:: fine-grained video API
+
 .. autosummary::
     :toctree: generated/
     :template: class.rst

docs/source/models.rst

@@ -272,6 +272,8 @@ Semantic Segmentation
 
 .. currentmodule:: torchvision.models.segmentation
 
+.. betastatus:: segmentation module
+
 The following semantic segmentation models are available, with or without
 pre-trained weights:
 
@@ -334,6 +336,8 @@ keypoint detection are initialized with the classification models
 in torchvision. The models expect a list of ``Tensor[C, H, W]``.
 Check the constructor of the models for more information.
 
+.. betastatus:: detection module
+
 Object Detection
 ----------------
 
@@ -453,6 +457,8 @@ Video Classification
 
 .. currentmodule:: torchvision.models.video
 
+.. betastatus:: video module
+
 The following video classification models are available, with or without
 pre-trained weights:
 

docs/source/models/deeplabv3.rst

@@ -6,6 +6,8 @@ DeepLabV3
 The DeepLabV3 model is based on the `Rethinking Atrous Convolution for Semantic
 Image Segmentation <https://arxiv.org/abs/1706.05587>`__ paper.
 
+.. betastatus:: segmentation module
+
 
 Model builders
 --------------

docs/source/models/faster_rcnn.rst

@@ -3,10 +3,12 @@ Faster R-CNN
 
 .. currentmodule:: torchvision.models.detection
 
+
 The Faster R-CNN model is based on the `Faster R-CNN: Towards Real-Time Object Detection 
 with Region Proposal Networks <https://arxiv.org/abs/1506.01497>`__
 paper.
 
+.. betastatus:: detection module
 
 Model builders
 --------------

docs/source/models/fcn.rst

@@ -7,6 +7,8 @@ The FCN model is based on the `Fully Convolutional Networks for Semantic
 Segmentation <https://arxiv.org/abs/1411.4038>`__
 paper.
 
+.. betastatus:: segmentation module
+
 
 Model builders
 --------------

docs/source/models/fcos.rst

@@ -6,6 +6,8 @@ FCOS
 The RetinaNet model is based on the `FCOS: Fully Convolutional One-Stage Object Detection
 <https://arxiv.org/abs/1904.01355>`__ paper.
 
+.. betastatus:: detection module
+
 Model builders
 --------------
 

docs/source/models/keypoint_rcnn.rst

@@ -6,6 +6,8 @@ Keypoint R-CNN
 The Keypoint R-CNN model is based on the `Mask R-CNN
 <https://arxiv.org/abs/1703.06870>`__ paper.
 
+.. betastatus:: detection module
+
 
 Model builders
 --------------

docs/source/models/lraspp.rst

@@ -5,6 +5,8 @@ LRASPP
 
 The LRASPP model is based on the `Searching for MobileNetV3 <https://arxiv.org/abs/1905.02244>`_ paper.
 
+.. betastatus:: segmentation module
+
 Model builders
 --------------
 

docs/source/models/mask_rcnn.rst

@@ -6,6 +6,8 @@ Mask R-CNN
 The Mask R-CNN model is based on the `Mask R-CNN <https://arxiv.org/abs/1703.06870>`__
 paper.
 
+.. betastatus:: detection module
+
 
 Model builders
 --------------

docs/source/models/resnext.rst

@@ -23,3 +23,4 @@ more details about this class.
 
     resnext50_32x4d
     resnext101_32x8d
+    resnext101_64x4d

docs/source/models/retinanet.rst

@@ -6,6 +6,8 @@ RetinaNet
 The RetinaNet model is based on the `Focal Loss for Dense Object Detection
 <https://arxiv.org/abs/1708.02002>`__ paper.
 
+.. betastatus:: detection module
+
 Model builders
 --------------
 

docs/source/models/ssd.rst

@@ -6,6 +6,8 @@ SSD
 The SSD model is based on the `SSD: Single Shot MultiBox Detector
 <https://arxiv.org/abs/1512.02325>`__ paper.
 
+.. betastatus:: detection module
+
 
 Model builders
 --------------

docs/source/models/ssdlite.rst

@@ -8,6 +8,7 @@ The SSDLite model is based on the `SSD: Single Shot MultiBox Detector
 <https://arxiv.org/abs/1905.02244>`__ and `MobileNetV2: Inverted Residuals and Linear
 Bottlenecks <https://arxiv.org/abs/1801.04381>__` papers.
 
+.. betastatus:: detection module
 
 Model builders
 --------------

docs/source/models/video_resnet.rst

@@ -6,6 +6,8 @@ Video ResNet
 The VideoResNet model is based on the `A Closer Look at Spatiotemporal
 Convolutions for Action Recognition <https://arxiv.org/abs/1711.11248>`__ paper.
 
+.. betastatus:: video module
+
 
 Model builders
 --------------

gallery/plot_optical_flow.py

@@ -180,7 +180,7 @@ def preprocess(img1_batch, img2_batch):
 #     # Note: it would be faster to predict batches of flows instead of individual flows
 #     img1, img2 = preprocess(img1, img2)
 
-#     list_of_flows = model(img1.to(device), img1.to(device))
+#     list_of_flows = model(img1.to(device), img2.to(device))
 #     predicted_flow = list_of_flows[-1][0]
 #     flow_img = flow_to_image(predicted_flow).to("cpu")
 #     output_folder = "/tmp/"  # Update this to the folder of your choice

gallery/plot_transforms.py

@@ -149,6 +149,17 @@ def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
 affine_imgs = [affine_transfomer(orig_img) for _ in range(4)]
 plot(affine_imgs)
 
+####################################
+# ElasticTransform
+# ~~~~~~~~~~~~~~~~
+# The :class:`~torchvision.transforms.ElasticTransform` transform
+# (see also :func:`~torchvision.transforms.functional.elastic_transform`)
+# Randomly transforms the morphology of objects in images and produces a
+# see-through-water-like effect.
+elastic_transformer = T.ElasticTransform(alpha=250.0)
+transformed_imgs = [elastic_transformer(orig_img) for _ in range(2)]
+plot(transformed_imgs)
+
 ####################################
 # RandomCrop
 # ~~~~~~~~~~