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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>

* explain lr and batch size in references/detection/train.py

* fix typo

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Actually upload wheels (please port to master)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Propagate more environment variables.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Put macos binaries in the right place
Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Add uploading support, make CUDA builds actually work.

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

`self.fc1(x)` converts the shape of `x` into "N x 1024", and `self.fc2(x)` converts into "N x num_classes".

By adding `print(x.shape)` under each comment line, the console displays as follows (batch_size is 1):

```text
torch.Size([1, 2048])
torch.Size([1, 1024])
torch.Size([1, 1024])
torch.Size([1, 1024])
torch.Size([1, 1000])
```

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Add docs for video models

* Fix docstrings for resnet and vgg

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* [WIP] Minor cleanups on R3d

* Move all models to video/resnet.py

* Remove old files

* Make tests less memory intensive

* Lint

* Fix typo and add pretraing arg to training script

* 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

* adding mixed precision training with Apex

* fix APEX default optimization level

* adding python version check for apex

* fix LINT errors and raise exceptions if apex not available

* fixing apex distributed training

* fix throughput calculation: include forward pass

* remove torch.cuda.set_device(args.gpu) as it's already called in init_distributed_mode

* fix linter: new line

* move Apex initialization code back to the beginning of main

* move apex initialization to before lr_scheduler - for peace of mind. Though, doing apex initialization after lr_scheduler seems to work fine as well