Merge branch 'main' into transforms/scale_jitter

Author: datumbox

docs/source/transforms.rst

@@ -198,6 +198,7 @@ The new transform can be used standalone or mixed-and-matched with existing tran
     AutoAugment
     RandAugment
     TrivialAugmentWide
+    AugMix
 
 .. _functional_transforms:
 

gallery/plot_transforms.py

@@ -263,6 +263,14 @@ def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
 imgs = [augmenter(orig_img) for _ in range(4)]
 plot(imgs)
 
+####################################
+# AugMix
+# ~~~~~~
+# The :class:`~torchvision.transforms.AugMix` transform automatically augments the data.
+augmenter = T.AugMix()
+imgs = [augmenter(orig_img) for _ in range(4)]
+plot(imgs)
+
 ####################################
 # Randomly-applied transforms
 # ---------------------------

packaging/README.md

@@ -1,90 +1,6 @@
 # Building torchvision packages for release
 
-## Anaconda packages
+TorchVision release packages are built by using `build_wheel.sh` and `build_conda.sh` for all permutations of
+supported operating systems, compute platforms and python versions.
 
-### Linux
-
-```bash
-nvidia-docker run -it --ipc=host --rm -v $(pwd):/remote soumith/conda-cuda bash
-pushd remote/conda
-
-./build_vision.sh 9.0
-./build_vision.sh 10.0
-./build_vision.sh cpu
-
-# copy packages over to /remote
-# exit docker
-# anaconda upload -u pytorch torchvision*.bz2
-```
-
-### OSX
-
-```bash
-# create a fresh anaconda environment / install and activate it
-conda install -y conda-build anaconda-client
-./build_vision.sh cpu
-
-# copy packages over to /remote
-# exit docker
-# anaconda upload -u pytorch torchvision*.bz2
-```
-
-### Windows
-
-```bash
-# Open `Git Bash` and change dir to `conda`
-./build_vision.sh 9.0
-./build_vision.sh 10.0
-./build_vision.sh cpu
-
-# copy packages to a output directory
-# anaconda upload -u pytorch torchvision*.bz2
-```
-
-## Wheels
-
-### Linux
-
-pushd wheel
-
-```bash
-nvidia-docker run -it --ipc=host --rm -v $(pwd):/remote soumith/manylinux-cuda90:latest bash
-cd remote
-./linux_manywheel.sh cu90
-
-rm -rf /usr/local/cuda*
-./linux_manywheel.sh cpu
-```
-
-```bash
-nvidia-docker run -it --ipc=host --rm -v $(pwd):/remote soumith/manylinux-cuda100:latest bash
-cd remote
-./linux_manywheel.sh cu100
-```
-
-wheels are in the folders `cpu`, `cu90`, `cu100`.
-
-You can upload the `cu90` wheels to twine with `twine upload *.whl`.
-Which wheels we upload depends on which wheels PyTorch uploads as default, and right now, it's `cu90`.
-
-### OSX
-
-```bash
-pushd wheel
-./osx_wheel.sh
-```
-
-### Windows
-
-```cmd
-set PYTORCH_REPO=pytorch
-
-pushd windows
-call build_vision.bat 90 0.3.0 1
-call build_vision.bat 100 0.3.0 1
-call build_vision.bat cpu 0.3.0 1
-```
-
-wheels are in the current folder.
-
-You can upload them to twine with `twine upload *.whl`
+OS/Python/Compute matrix is defined in https://github.com/pytorch/vision/blob/main/.circleci/regenerate.py

packaging/wheel/linux_manywheel.sh

@@ -22,6 +22,8 @@ def __init__(
                 trans.append(autoaugment.RandAugment(interpolation=interpolation))
             elif auto_augment_policy == "ta_wide":
                 trans.append(autoaugment.TrivialAugmentWide(interpolation=interpolation))
+            elif auto_augment_policy == "augmix":
+                trans.append(autoaugment.AugMix(interpolation=interpolation))
             else:
                 aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy)
                 trans.append(autoaugment.AutoAugment(policy=aa_policy, interpolation=interpolation))

packaging/wheel/osx_wheel.sh

@@ -99,7 +99,6 @@ def test_no_vanilla_tensors(self, test_home, dataset_mock, config):
                 f"{sequence_to_str(sorted(vanilla_tensors), separate_last='and ')} contained vanilla tensors."
             )
 
-    @pytest.mark.xfail
     @parametrize_dataset_mocks(DATASET_MOCKS)
     def test_transformable(self, test_home, dataset_mock, config):
         dataset_mock.prepare(test_home, config)