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* 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

* Move RandomClipSampler to references

* Lint and bugfix

Also add docs

* Copy classification scripts for video classification

* Initial version of video classification

* add version

* Training of r2plus1d_18 on kinetics work

Gives even slightly better results than expected, with 57.336 top1 clip accuracy. But we count some clips twice in this evaluation

* Cleanups on training script

* Lint

* Minor improvements

* Remove some hacks

* Lint

* Optimize read_video_timestamps for some formats

* Add some tests

* fix stl10

* fix lsun

* Miscellaneous fixes and improvements

* Guard against videos without video stream

* Fix lint

* Add test for packed b-frames videos

* Fix missing import

* [0.4_video] models - initial commit

* addressing fmassas inline comments

* pep8 and flake8

* simplify "hacks"

* sorting out latest comments

* nitpick

* Updated tests and constructors

* Added docstrings - ready to merge

* introduced function to verify str arguments

* flake8

* added FIXME to VOC

* Fixed error message

* added test for verify_str_arg

* cleanup todos

* added option for custom error message

* fix VOC

* fixed Caltech

* Add HMDB51 and UCF101

* Remove debug code

Also extend video saving to support different codecs and options. Notably, we can now save with lossless compression

* Properly order videos with B-frames

* seek doesn't seek to pts, but dts

Find a way of overcoming this problem.

Also make logging less verbose

* Replaced asserts with TORCH_CHECK

* Fixed an error

* in_channels_stage2 from backbone.inplanes

* remove type for backward compatible

* Add VideoClips and Kinetics dataset

* Lint + add back missing line

* Adds ClipSampler following Bruno comment

* Change name following Bruno's suggestion

* Enable specifying a target framerate

* Fix test_io for new interface

* Add comment mentioning drop_last behavior

* Make compute_clips more robust

* Flake8

* Fix for Python2

* fixed call to the VisionDataset constructor

* change call from keyword arguments to positional

* changed order of arguments

* removed transforms argument once again

* Fixed call to constructor of parent class

* fixed LSUN

* fixed Caltech256

Fix the old flatten method which use the size(0) to caculate the batch size, the old method will intruduce Gather opertion in the onnx output, which will faild parsed by tensorRT 5.0 (#1134)

* Remove duplicate code

* Passed transform and target_transform to the constructor

The backslash should be escaped. Currently, [it](https://pytorch.org/docs/master/torchvision/datasets.html#usps) looks bad:

* Added mnasnet

* Fixed some stuff

* Fixed some stuff

* Finished MNASNet

* Fixed format error

* Fixed format error

updated all docstrings and code references for boxes to be consistent with the scheme (x1, y1, x2, y2) (#1110)

In-place operators should only have method variant. Update here in
preparation of future PyTorch change. For example see
https://github.com/pytorch/pytorch/issues/22707

* add float32 to keypoint_rcnn docs

* add float32 to faster_rcnn docs

* add float32 to mask_rcnn

* Update faster_rcnn.py

* Update keypoint_rcnn.py

* Update mask_rcnn.py

* Update faster_rcnn.py

* make keypoints float

* make masks uint8

* Update keypoint_rcnn.py

* make labels Int64

* make labels Int64

* make labels Int64

* Add checks for boxes, labels, masks, keypoints

* update mask dim

* remove dtype

* check only if targets is not None

* account for targets being a list

* update target to be list of dict

* Update faster_rcnn.py

* Update keypoint_rcnn.py

* allow boxes to be of float16 type as well

* remove checks on mask

* to_pil_image updates

* lint

* Update test_transforms.py

* Update test_transforms.py

* Fix bug to Random Erasing

1. Avoid forever loop for getting parameters of erase.
2. replace' img_b' by 'img_c', because it indicates the channel.
3. replace v = torch.rand([img_c, h, w]) by v = torch.empty([img_c, h, w], dtype=torch.float32).normal_(). Normally distributed achieves better performance.

* add test

* Update test_transforms.py

* Update transforms.py

* Update test_transforms.py

* Update transforms.py

* Update functional.py

* Add memory_efficient args to densenet docs

This PR adds the arg `memory_efficient` to densenet docs and also mentions the relevant publication.

* linting

This PR updates the docs of MnasNet

* Minor optimization to RandomErasing

This PR adds an additional check on `p` argument and prevents computing `img.shape` multiple times.

* linting

Fixes #1047.

* Add paper references to VGG

* Add paper references to ResNet and its variants

* fix transform for rcnns so original images are unchanged

* transform does not change input list anymore

transform does not change input list anymore. Improve code according to reviewer comment

* transform for maskrcnn no longer modify input list

transform for maskrcnn no longer modify input list. improve code according to comment

* transform for maskrcnn no longer modifies input list