2023-08-18 nightly release (87d54c4)

.github/workflows/docs.yml

@@ -118,5 +118,6 @@ jobs:
         
         git config user.name 'pytorchbot'
         git config user.email 'soumith+bot@pytorch.org'
+        git config http.postBuffer 524288000
         git commit -m "auto-generating sphinx docs" || true
         git push

CONTRIBUTING.md

@@ -30,30 +30,49 @@ clear and has sufficient instructions to be able to reproduce the issue.
 
 ## Development installation
 
-### Install PyTorch Nightly 
+
+### Dependencies
+
+Start by installing the **nightly** build of PyTorch following the [official
+instructions](https://pytorch.org/get-started/locally/).
+
+**Optionally**, install `libpng` and `libjpeg-turbo` if you want to enable
+support for
+native encoding / decoding of PNG and JPEG formats in
+[torchvision.io](https://pytorch.org/vision/stable/io.html#image):
 
 ```bash
-conda install pytorch -c pytorch-nightly
-# or with pip (see https://pytorch.org/get-started/locally/)
-# pip install numpy
-# pip install --pre torch -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html
+conda install libpng libjpeg-turbo -c pytorch
 ```
 
-### Install Torchvision
+Note: you can use the `TORCHVISION_INCLUDE` and `TORCHVISION_LIBRARY`
+environment variables to tell the build system where to find those libraries if
+they are in specific locations. Take a look at
+[setup.py](https://github.com/pytorch/vision/blob/main/setup.py) for more
+details.
+
+### Clone and install torchvision
 
 ```bash
 git clone https://github.com/pytorch/vision.git
 cd vision
-python setup.py develop
+python setup.py develop  # use install instead of develop if you don't care about development.
 # or, for OSX
 # MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py develop
-# for C++ debugging, please use DEBUG=1
+# for C++ debugging, use DEBUG=1
 # DEBUG=1 python setup.py develop
-pip install flake8 typing mypy pytest pytest-mock scipy
 ```
-You may also have to install `libpng-dev` and `libjpeg-turbo8-dev` libraries:
-```bash
-conda install libpng jpeg
+
+By default, GPU support is built if CUDA is found and `torch.cuda.is_available()` is true. It's possible to force
+building GPU support by setting `FORCE_CUDA=1` environment variable, which is useful when building a docker image.
+
+We don't officially support building from source using `pip`, but _if_ you do, you'll need to use the
+`--no-build-isolation` flag.
+
+Other development dependencies include:
+
+```
+pip install flake8 typing mypy pytest pytest-mock scipy
 ```
 
 ## Development Process
@@ -192,7 +211,7 @@ Please refer to the guidelines in [Contributing to Torchvision - Models](https:/
 
 ### New dataset
 
-More details on how to add a new dataset will be provided later. Please, do not send any PR with a new dataset without discussing 
+Please, do not send any PR with a new dataset without discussing 
 it in an issue as, most likely, it will not be accepted.
 
 ### Pull Request

README.md

@@ -8,8 +8,14 @@ vision.
 
 ## Installation
 
-We recommend Anaconda as Python package management system. Please refer to [pytorch.org](https://pytorch.org/) for the
-detail of PyTorch (`torch`) installation. The following is the corresponding `torchvision` versions and supported Python
+Please refer to the [official
+instructions](https://pytorch.org/get-started/locally/) to install the stable
+versions of `torch` and `torchvision` on your system.
+
+To build source, refer to our [contributing
+page](https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation).
+
+The following is the corresponding `torchvision` versions and supported Python
 versions.
 
 | `torch`            | `torchvision`      | Python              |
@@ -39,54 +45,18 @@ versions.
 
 </details>
 
-Anaconda:
-
-```
-conda install torchvision -c pytorch
-```
-
-pip:
-
-```
-pip install torchvision
-```
+## Image Backends
 
-From source:
-
-```
-python setup.py install
-# or, for OSX
-# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
-```
-
-We don't officially support building from source using `pip`, but _if_ you do, you'll need to use the
-`--no-build-isolation` flag. In case building TorchVision from source fails, install the nightly version of PyTorch
-following the linked guide on the
-[contributing page](https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation) and retry the
-install.
-
-By default, GPU support is built if CUDA is found and `torch.cuda.is_available()` is true. It's possible to force
-building GPU support by setting `FORCE_CUDA=1` environment variable, which is useful when building a docker image.
+Torchvision currently supports the following image backends:
 
-## Image Backend
+- torch tensors
+- PIL images:
+    - [Pillow](https://python-pillow.org/)
+    - [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD.
 
-Torchvision currently supports the following image backends:
+Read more in in our [docs](https://pytorch.org/vision/stable/transforms.html).
 
-- [Pillow](https://python-pillow.org/) (default)
-- [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD.
-  If installed will be used as the default.
-- [accimage](https://github.com/pytorch/accimage) - if installed can be activated by calling
-  `torchvision.set_image_backend('accimage')`
-- [libpng](http://www.libpng.org/pub/png/libpng.html) - can be installed via conda `conda install libpng` or any of the
-  package managers for debian-based and RHEL-based Linux distributions.
-- [libjpeg](http://ijg.org/) - can be installed via conda `conda install jpeg` or any of the package managers for
-  debian-based and RHEL-based Linux distributions. [libjpeg-turbo](https://libjpeg-turbo.org/) can be used as well.
-
-**Notes:** `libpng` and `libjpeg` must be available at compilation time in order to be available. Make sure that it is
-available on the standard library locations, otherwise, add the include and library paths in the environment variables
-`TORCHVISION_INCLUDE` and `TORCHVISION_LIBRARY`, respectively.
-
-## Video Backend
+## [UNSTABLE] Video Backend
 
 Torchvision currently supports the following video backends:
 

docs/source/conf.py

@@ -29,6 +29,7 @@
 import pytorch_sphinx_theme
 import torchvision
 import torchvision.models as M
+from sphinx_gallery.sorting import ExplicitOrder
 from tabulate import tabulate
 
 sys.path.append(os.path.abspath("."))
@@ -61,6 +62,7 @@
 sphinx_gallery_conf = {
     "examples_dirs": "../../gallery/",  # path to your example scripts
     "gallery_dirs": "auto_examples",  # path to where to save gallery generated output
+    "subsection_order": ExplicitOrder(["../../gallery/v2_transforms", "../../gallery/others"]),
     "backreferences_dir": "gen_modules/backreferences",
     "doc_module": ("torchvision",),
     "remove_config_comments": True,

docs/source/datapoints.rst

@@ -6,7 +6,7 @@ Datapoints
 Datapoints are tensor subclasses which the :mod:`~torchvision.transforms.v2` v2 transforms use under the hood to
 dispatch their inputs to the appropriate lower-level kernels. Most users do not
 need to manipulate datapoints directly and can simply rely on dataset wrapping -
-see e.g. :ref:`sphx_glr_auto_examples_plot_transforms_v2_e2e.py`.
+see e.g. :ref:`sphx_glr_auto_examples_v2_transforms_plot_transforms_v2_e2e.py`.
 
 .. autosummary::
     :toctree: generated/

docs/source/io.rst

@@ -1,11 +1,37 @@
-Reading/Writing images and videos
-=================================
+Decoding / Encoding images and videos
+=====================================
 
 .. currentmodule:: torchvision.io
 
 The :mod:`torchvision.io` package provides functions for performing IO
-operations. They are currently specific to reading and writing video and
-images.
+operations. They are currently specific to reading and writing images and
+videos.
+
+Images
+------
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    read_image
+    decode_image
+    encode_jpeg
+    decode_jpeg
+    write_jpeg
+    encode_png
+    decode_png
+    write_png
+    read_file
+    write_file
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    ImageReadMode
+
+
 
 Video
 -----
@@ -20,7 +46,7 @@ Video
 
 
 Fine-grained video API
-----------------------
+^^^^^^^^^^^^^^^^^^^^^^
 
 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.
@@ -61,28 +87,3 @@ Example of inspecting a video:
     # the constructor we select a default video stream, but
     # in practice, we can set whichever stream we would like 
     video.set_current_stream("video:0")
-
-
-Image
------
-
-.. autosummary::
-    :toctree: generated/
-    :template: class.rst
-
-    ImageReadMode
-
-.. autosummary::
-    :toctree: generated/
-    :template: function.rst
-
-    read_image
-    decode_image
-    encode_jpeg
-    decode_jpeg
-    write_jpeg
-    encode_png
-    decode_png
-    write_png
-    read_file
-    write_file

docs/source/transforms.rst

@@ -13,7 +13,7 @@ Transforming and augmenting images
     are fully backward compatible with the current ones, and you'll see them
     documented below with a `v2.` prefix. To get started with those new
     transforms, you can check out
-    :ref:`sphx_glr_auto_examples_plot_transforms_v2_e2e.py`.
+    :ref:`sphx_glr_auto_examples_v2_transforms_plot_transforms_v2_e2e.py`.
     Note that these transforms are still BETA, and while we don't expect major
     breaking changes in the future, some APIs may still change according to user
     feedback. Please submit any feedback you may have `here
@@ -54,15 +54,15 @@ across calls. For reproducible transformations across calls, you may use
 
 The following examples illustrate the use of the available transforms:
 
-    * :ref:`sphx_glr_auto_examples_plot_transforms.py`
+    * :ref:`sphx_glr_auto_examples_others_plot_transforms.py`
 
-        .. figure:: ../source/auto_examples/images/sphx_glr_plot_transforms_001.png
+        .. figure:: ../source/auto_examples/others/images/sphx_glr_plot_transforms_001.png
             :align: center
             :scale: 65%
 
-    * :ref:`sphx_glr_auto_examples_plot_scripted_tensor_transforms.py`
+    * :ref:`sphx_glr_auto_examples_others_plot_scripted_tensor_transforms.py`
 
-        .. figure:: ../source/auto_examples/images/sphx_glr_plot_scripted_tensor_transforms_001.png
+        .. figure:: ../source/auto_examples/others/images/sphx_glr_plot_scripted_tensor_transforms_001.png
             :align: center
             :scale: 30%
 
@@ -237,6 +237,7 @@ Conversion
     v2.ConvertImageDtype
     v2.ToDtype
     v2.ConvertBoundingBoxFormat
+    v2.ToPureTensor
 
 Auto-Augmentation
 -----------------
@@ -268,7 +269,7 @@ CutMix and MixUp are special transforms that
 are meant to be used on batches rather than on individual images, because they
 are combining pairs of images together. These can be used after the dataloader
 (once the samples are batched), or part of a collation function. See
-:ref:`sphx_glr_auto_examples_plot_cutmix_mixup.py` for detailed usage examples.
+:ref:`sphx_glr_auto_examples_v2_transforms_plot_cutmix_mixup.py` for detailed usage examples.
 
 .. autosummary::
     :toctree: generated/

docs/source/utils.rst

@@ -4,7 +4,7 @@ Utils
 =====
 
 The ``torchvision.utils`` module contains various utilities, mostly :ref:`for
-visualization <sphx_glr_auto_examples_plot_visualization_utils.py>`.
+visualization <sphx_glr_auto_examples_others_plot_visualization_utils.py>`.
 
 .. currentmodule:: torchvision.utils
 

gallery/README.rst

@@ -1,4 +1,2 @@
-Example gallery
-===============
-
-Below is a gallery of examples
+Examples and tutorials
+======================

gallery/others/README.rst

@@ -0,0 +1,2 @@
+Others
+------

gallery/plot_repurposing_annotations.py → gallery/others/plot_repurposing_annotations.py

@@ -20,7 +20,7 @@
 import torchvision.transforms.functional as F
 
 
-ASSETS_DIRECTORY = "assets"
+ASSETS_DIRECTORY = "../assets"
 
 plt.rcParams["savefig.bbox"] = "tight"
 

gallery/plot_scripted_tensor_transforms.py → gallery/others/plot_scripted_tensor_transforms.py

@@ -49,16 +49,16 @@ def show(imgs):
 # The :func:`~torchvision.io.read_image` function allows to read an image and
 # directly load it as a tensor
 
-dog1 = read_image(str(Path('assets') / 'dog1.jpg'))
-dog2 = read_image(str(Path('assets') / 'dog2.jpg'))
+dog1 = read_image(str(Path('../assets') / 'dog1.jpg'))
+dog2 = read_image(str(Path('../assets') / 'dog2.jpg'))
 show([dog1, dog2])
 
 # %%
 # Transforming images on GPU
 # --------------------------
 # Most transforms natively support tensors on top of PIL images (to visualize
 # the effect of the transforms, you may refer to see
-# :ref:`sphx_glr_auto_examples_plot_transforms.py`).
+# :ref:`sphx_glr_auto_examples_others_plot_transforms.py`).
 # Using tensor images, we can run the transforms on GPUs if cuda is available!
 
 import torch.nn as nn
@@ -121,7 +121,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 import json
 
-with open(Path('assets') / 'imagenet_class_index.json') as labels_file:
+with open(Path('../assets') / 'imagenet_class_index.json') as labels_file:
     labels = json.load(labels_file)
 
 for i, (pred, pred_scripted) in enumerate(zip(res, res_scripted)):

gallery/plot_transforms.py → gallery/others/plot_transforms.py

@@ -19,7 +19,7 @@
 
 
 plt.rcParams["savefig.bbox"] = 'tight'
-orig_img = Image.open(Path('assets') / 'astronaut.jpg')
+orig_img = Image.open(Path('../assets') / 'astronaut.jpg')
 # if you change the seed, make sure that the randomly-applied transforms
 # properly show that the image can be both transformed and *not* transformed!
 torch.manual_seed(0)

gallery/plot_visualization_utils.py → gallery/others/plot_visualization_utils.py

@@ -41,8 +41,8 @@ def show(imgs):
 from torchvision.io import read_image
 from pathlib import Path
 
-dog1_int = read_image(str(Path('assets') / 'dog1.jpg'))
-dog2_int = read_image(str(Path('assets') / 'dog2.jpg'))
+dog1_int = read_image(str(Path('../assets') / 'dog1.jpg'))
+dog2_int = read_image(str(Path('../assets') / 'dog2.jpg'))
 dog_list = [dog1_int, dog2_int]
 
 grid = make_grid(dog_list)
@@ -360,7 +360,7 @@ def show(imgs):
 from torchvision.models.detection import keypointrcnn_resnet50_fpn, KeypointRCNN_ResNet50_FPN_Weights
 from torchvision.io import read_image
 
-person_int = read_image(str(Path("assets") / "person1.jpg"))
+person_int = read_image(str(Path("../assets") / "person1.jpg"))
 
 weights = KeypointRCNN_ResNet50_FPN_Weights.DEFAULT
 transforms = weights.transforms()

gallery/v2_transforms/README.rst

@@ -0,0 +1,2 @@
+V2 transforms
+-------------