GitLab

* Update docs

* Update docs/source/ops.rst
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add Deformable Convolution in Doc

* Add PSRoIPool and PSRoIAlign in Doc

* guess documentation location

* document mean/std origins

* fix artifact

* update for clarity

* fix typo

* Update requirements.txt

* Update conf.py

* Update conf.py

* almost working...

* respond to comments

* add empty tensor op, handle different output types in generalized rcnn

* clean ups

* address comments

* more changes

* it's working!

* torchscript bugs

* add script/ eager test

* eval script model

* fix flake

* division import

* py2 compat

* update test, fix arange bug

* import division statement

* fix linter

* fixes

* changes needed for JIT master

* cleanups

* remove imagelist_to

* requested changes

* Make FPN backwards-compatible and torchscript compatible

We remove support for feature channels=0, but support for it was already a bit limited

* Fix ONNX regression

* Add docs for video models

* Fix docstrings for resnet and vgg

* Expose docs for io and ops package

Had do modify the docstrings to use Napoleon NumPy style, because Napoleon Google Style doesn't support multiple return arguments

* Add video section

* Add docs to UCF101 and HMDB51 datasets

* Remove extra line

* Lint

Also add docs

* add wide resnet

* add docstring for wide resnet

* update WRN-50-2 model

* add docs

* extend WRN docstring

* use pytorch storage for WRN

* fix rebase

* fix typo in docs

* add erase function

* add Random Erasing

* Update transforms.py

* Update transforms.py

* add test for random erasing

* Update test_transforms.py

* fix flake8

* Update test_transforms.py

* Update functional.py

* Update test_transforms.py

* fix bug for per-pixel erasing

* Update transforms.py

* specific for coordinate (x, y)

* add raise TypeError for img

* Update transforms.py

* Update transforms.rst

* Add initial mnasnet impl

* Remove all type hints, comply with PyTorch overall style

* Expose models

* Remove avgpool from features() and add separately

* Fix python3-only stuff, replace subclasses with functions

* fix __all__

* Fix typo

* Remove conditional dropout

* Make dropout functional

* Addressing @fmassa's feedback, round 1

* Replaced adaptive avgpool with mean on H and W to prevent collapsing the batch dimension

* Partially address feedback

* YAPF

* Removed redundant class vars

* Update urls to releases

* Add information to models.rst

* Replace init with kaiming_normal_ in fan-out mode

* Use load_state_dict_from_url

Values from random.random() are always in the range [0.0, 1.0), so the statement `random.random() > 5` will never be true.

* Added general reader for sn3 tensors in "pascalvincent" format

* Added class QMNIST into mnist.py

* QMNIST dataset: make some pt files smaller

* Change request from fmassa.

* read_sn3_pascalvincent_tensor: cse

* read_sn3_pascalvincent_tensor: check file size (when strict!=False)

* Fix lint

* More lint

* Add documentation and expose QMNIST to dataset namespace

* add USPS dataset

* minor fixes

* Improvements to the USPS dataset

Add it to the documentation, expose it to torchvision.datasets
and inherit from VisionDataset

Also move weights from ShuffleNet to PyTorch bucket. Additionally, rename shufflenet to make it consistent with the other models

* Add ShuffleNet v2

Added 4 configurations: x0.5, x1, x1.5, x2
Add 2 pretrained models: x0.5, x1

* fix lint

* Change globalpool to torch.mean() call

* Added Semantic Boundaries Dataset

* Updated docs and datasets importing module

* Improved docs

* Add GoogLeNet (Inception v1)

* Fix missing padding

* Add missing ReLu to aux classifier

* Add Batch normalized version of GoogLeNet

* Use ceil_mode instead of padding and initialize weights using "xavier"

* Match BVLC GoogLeNet zero initialization of classifier

* Small cleanup

* use adaptive avg pool

* adjust network to match TensorFlow

* Update url of pre-trained model and add classification results on ImageNet

* Bugfix that improves performance by 1 point

* flake_fixes

* flake_fixes2

* Add KMNIST

* Add documentation

* Add KMNIST to module's __all__ list

* Add Cityscapes Dataset

* Rename 'label' to 'semantic' to make the meaning more clear

* Add support for gtCoarse target set

* pad updated

* checked pad

* pad checked

* pad checked

* docs changed

* VOC Dataset, linted, flak8 passing, samples on gist.

* Double backtick on values.

* Apply suggestions from code review

Add suggestions from @ellisbrown, using dict of dicts instead of array index.
Co-Authored-By: bpinaya <bpg_92@hotmail.com>

* Fixed errors with the new comments.

* Added documentation on RST

* Removed getBB, added parse_voc_xml, variable naming change.

* Removed unused variable, removed VOC_CLASSES, two new gists for test.

* Add Flickr8k and Flickr30k Datasets

* Add Flickr to the dataset docs

* Sort ids, glob during construction

* annFile -> ann_file

* Fix undefined variable name bug

* Add SBU Captioned Photo Dataset

* Add SBU to the dataset docs