about:Report an issue related to https://pytorch.org/docs
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## 📚 Documentation
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about:Submit a proposal/request for a new torchvision feature
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## 🚀 Feature
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## Motivation
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## Pitch
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-pytest --cov-config .coveragerc --cov torchvision --cov $TV_INSTALL_PATH -k 'not TestVideo and not TestVideoReader and not TestVideoTransforms and not TestIO' test --ignore=test/test_datasets_download.py
-pytest test/test_hub.py
after_success:
# Necessary to run coverage combine to rewrite paths from
# /travis/env/path/site-packages/torchvision to actual path
若报错Downloading: "https://download.pytorch.org/models/resnet50-19c8e357.pth" to .cache/torch/checkpoints/resnet50-19c8e357.pth失败，则需提前下载resnet50-19c8e357.pth，拷贝至.cache/torch/checkpoints/。
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.
Image Backend
=============
Torchvision currently supports the following image backends:
* `Pillow`_ (default)
* `Pillow-SIMD`_ - a **much faster** drop-in replacement for Pillow with SIMD. If installed will be used as the default.
* `accimage`_ - if installed can be activated by calling :code:`torchvision.set_image_backend('accimage')`
* `libpng`_ - can be installed via conda :code:`conda install libpng` or any of the package managers for debian-based and RHEL-based Linux distributions.
* `libjpeg`_ - can be installed via conda :code:`conda install jpeg` or any of the package managers for debian-based and RHEL-based Linux distributions. `libjpeg-turbo`_ 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.
TorchVision also offers a C++ API that contains C++ equivalent of python models.
Installation From source:
.. code:: bash
mkdir build
cd build
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
Once installed, the library can be accessed in cmake (after properly configuring ``CMAKE_PREFIX_PATH``) via the :code:`TorchVision::TorchVision` target:
.. code:: rest
find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
The ``TorchVision`` package will also automatically look for the ``Torch`` package and add it as a dependency to ``my-target``,
so make sure that it is also available to cmake via the ``CMAKE_PREFIX_PATH``.
For an example setup, take a look at ``examples/cpp/hello_world``.
TorchVision Operators
---------------------
In order to get the torchvision operators registered with torch (eg. for the JIT), all you need to do is to ensure that you
:code:`#include <torchvision/vision.h>` in your project.
Documentation
=============
You can find the API documentation on the pytorch website: https://pytorch.org/docs/stable/torchvision/index.html
Contributing
============
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
Disclaimer on Datasets
======================
This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
