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input to parameter mode can be 'fine' or 'coarse'. The code internally converts the mode = 'fine' input to 'gtFine' (same for coarse input and gtCoarse), but the docs mention that input to mode should be 'gtFine' or 'gtCoarse' inconsistently.

* Force object annotiation to be an array

* Remove unecessary parentheses

* Change object check

* Remove check for list

* Add test coverage to xml parsing

* Tidy up whitespace

* torchscriptable functions for video io (#1653)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1653



created new torchscriptable video io functions as part of the api: read_video_meta_data_from_memory and read_video_from_memory.

Updated the implementation of some of the internal functions to be torchscriptable.

Reviewed By: stephenyan1231

Differential Revision: D18720474

fbshipit-source-id: 4ee646b66afecd2dc338a71fd8f249f25a3263bc

* BugFix
Co-authored-by: Jon Guerin <54725679+jguerin-fb@users.noreply.github.com>

* Fixes #1716

Fixes EMNIST classes attribute is wrong #1716

* Fixed the Classes for Letters Split

* Update mnist.py

* Move classes attribute inside init definition

* Fix Linting errors

* remove windows skips from video_utils tests, now that they pass

* replace lambda in videoclips in order to be pickled on windows and update tests

* tgz updates

* tgz updates

* tgz updates

The current link to the dataset is dead. The change links to the dataset's author's personal page, which describe the dataset and is also referenced at https://github.com/fyu/lsun.

* Unify video metadata in VideoClips

* Bugfix

* Make tests a bit more robust

* Removed unnecessary class variables.

* The integrity of dataset files is now being checked right after the download finished. Thus making sure that a corrupt file is not being extracted. In case of corruption we throw a RuntimeError.

* Added missing md5 hashes to MNIST, FashionMNIST, KMNIST, EMNIST and QMNIST datasets.

* Removed printing of error message when integrity check failed.
Reformulated error message.

* Reformatted code to be lint conform.

* Fixed formatting in utils.py

* extend DistributedSampler to support group_size

* Fix lint

* remove download process

* address comments

* fix logic error

* bug fixes

* removed unused import

* add docstrings

* flake8

* remove download BC

* fix test

* removed unused code

* flake 8

* add MD5 verification before extraction

* add mock to test

* * unify _verify_archive() method and function
* remove force flag for parse_*_archive functions
* cleanup

* flake8

* Fix DeprecationWarning for collections.Iterable import

* Simplify version comparison

* Fix hmdb51.py typo

* Fix ucf101.py typo

* move sampler into TV core. Update UniformClipSampler

* Fix reference training script

* Skip test if pyav not available

* change interpolation from round() to floor() as round(0.5) behaves differently between py2 and py3

* add metadata to video dataset classes. bug fix. more robustness

* query video backend within VideoClips class

* Fix tests

* Fix lint

* Fix nightly builds

* Fix lint

* Disable video_reader tests on Travis

* Disable one more test that segfaults on Travis

* Fix typo in expression

* Expose num_workers in VideoClips

* add documentation for num_workers in VideoClips

* add documentation for num_workers in VideoClips

* add documentation for num_workers in VideoClips

* add _backend argument to __init__() of class VideoClips

* minor fix

* minor fix

* Make backend private in VideoClips

* Fix lint

* add tar.xz archive handler

* update unittest for tar.xz archive

* remove .tar.xz unittest

* add separate .tar.xz unittest

* update PY2 compatibility

* Support empty target_type for CelebA dataset

* Return (X, None) for interface consistency

* Document behavior for target_type=[]

* Simplify diff

* Raise exception on meaningless parameters

* fix url

* update comment

* fixed typo

* fixed some more typos and grammer

Fixed video labelling after `subset` call for HMDB51 dataset (hmdb51.py) (EDIT: UCF101 as well) (#1240)

* Fixed video labelling after subset for HMDB51 dataset

* Fixed video labelling after subset for HMDB51 dataset
Co-authored-by: Eric Tang <etang21@stanford.edu>
Co-authored-by: Ryan Cao <ryancao@stanford.edu>

* UCF 101 Labeling fixes

- Analogous fix to HMDB51 to maintain correct labels after the train-test split
- Additional change to the `select_fold` method in `ucf101.py` to correctly reflect the annotation format
Co-authored-by: Ryan Cao <ryancao@stanford.edu>
Co-authored-by: Eric Tang <etang21@stanford.edu>

Following a similar line of inquiry to pytorch/audio#217