gaussian-splatting-my/envs/gaussian_splatting/Lib/site-packages/torchvision-0.13.1-py3.7.egg-info/PKG-INFO

Metadata-Version: 2.1
Name: torchvision
Version: 0.13.1
Summary: image and video datasets and models for torch deep learning
Home-page: https://github.com/pytorch/vision
Author: PyTorch Core Team
Author-email: soumith@pytorch.org
License: BSD
Requires-Python: >=3.7
Provides-Extra: scipy
License-File: LICENSE

torchvision
===========

.. image:: https://pepy.tech/badge/torchvision
    :target: https://pepy.tech/project/torchvision

.. image:: https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
    :target: https://pytorch.org/vision/stable/index.html


The torchvision package consists of popular datasets, model architectures, and common image transformations for computer 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 versions.

+--------------------------+--------------------------+---------------------------------+
| ``torch``                | ``torchvision``          | ``python``                      |
+==========================+==========================+=================================+
| ``main`` / ``nightly``   | ``main`` / ``nightly``   | ``>=3.7``, ``<=3.10``           |
+--------------------------+--------------------------+---------------------------------+
| ``1.11.0``               | ``0.12.0``               | ``>=3.7``, ``<=3.10``           |
+--------------------------+--------------------------+---------------------------------+
| ``1.10.2``               | ``0.11.3``               | ``>=3.6``, ``<=3.9``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.10.1``               | ``0.11.2``               | ``>=3.6``, ``<=3.9``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.10.0``               | ``0.11.1``               | ``>=3.6``, ``<=3.9``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.9.1``                | ``0.10.1``               | ``>=3.6``, ``<=3.9``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.9.0``                | ``0.10.0``               | ``>=3.6``, ``<=3.9``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.8.2``                | ``0.9.2``                | ``>=3.6``, ``<=3.9``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.8.1``                | ``0.9.1``                | ``>=3.6``, ``<=3.9``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.8.0``                | ``0.9.0``                | ``>=3.6``, ``<=3.9``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.7.1``                | ``0.8.2``                | ``>=3.6``, ``<=3.9``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.7.0``                | ``0.8.1``                | ``>=3.6``, ``<=3.8``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.7.0``                | ``0.8.0``                | ``>=3.6``, ``<=3.8``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.6.0``                | ``0.7.0``                | ``>=3.6``, ``<=3.8``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.5.1``                | ``0.6.1``                | ``>=3.5``, ``<=3.8``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.5.0``                | ``0.6.0``                | ``>=3.5``, ``<=3.8``            |
+--------------------------+--------------------------+---------------------------------+
| ``1.4.0``                | ``0.5.0``                | ``==2.7``, ``>=3.5``, ``<=3.8`` |
+--------------------------+--------------------------+---------------------------------+
| ``1.3.1``                | ``0.4.2``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
+--------------------------+--------------------------+---------------------------------+
| ``1.3.0``                | ``0.4.1``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
+--------------------------+--------------------------+---------------------------------+
| ``1.2.0``                | ``0.4.0``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
+--------------------------+--------------------------+---------------------------------+
| ``1.1.0``                | ``0.3.0``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
+--------------------------+--------------------------+---------------------------------+
| ``<=1.0.1``              | ``0.2.2``                | ``==2.7``, ``>=3.5``, ``<=3.7`` |
+--------------------------+--------------------------+---------------------------------+

Anaconda:

.. code:: bash

    conda install torchvision -c pytorch

pip:

.. code:: bash

    pip install torchvision

From source:

.. code:: bash

    python setup.py install
    # or, for OSX
    # MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install


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.

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.

.. _libpng : http://www.libpng.org/pub/png/libpng.html
.. _Pillow : https://python-pillow.org/
.. _Pillow-SIMD : https://github.com/uploadcare/pillow-simd
.. _accimage: https://github.com/pytorch/accimage
.. _libjpeg: http://ijg.org/
.. _libjpeg-turbo: https://libjpeg-turbo.org/

Video Backend
=============
Torchvision currently supports the following video backends:

* `pyav`_ (default) - Pythonic binding for ffmpeg libraries.

.. _pyav : https://github.com/PyAV-Org/PyAV

* video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any conflicting version of ffmpeg installed. Currently, this is only supported on Linux.

.. code:: bash

     conda install -c conda-forge ffmpeg
     python setup.py install


Using the models on C++
=======================
TorchVision provides an example project for how to use the models on C++ using JIT Script.

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``.

Python linking is disabled by default when compiling TorchVision with CMake, this allows you to run models without any Python 
dependency. In some special cases where TorchVision's operators are used from Python code, you may need to link to Python. This 
can be done by passing ``-DUSE_PYTHON=on`` to CMake.

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/vision/stable/index.html

Contributing
============

See the `CONTRIBUTING <CONTRIBUTING.md>`_ file for how to help out.

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!

Pre-trained Model License
=========================

The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.

More specifically, SWAG models are released under the CC-BY-NC 4.0 license. See `SWAG LICENSE <https://github.com/facebookresearch/SWAG/blob/main/LICENSE>`_ for additional details.