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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

* 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)

* Add loss, sampler, and train script

* Fix train script

* Add argparse

* Fix lint

* Change f strings to .format()

* Remove unused imports

* Change TripletMarginLoss to extend nn.Module

* Load eye uint8 tensors directly on device

* Refactor model.py to backbone=None

* Add docstring for PKSampler

* Refactor evaluate() to take loader as arg instead

* Change eval method to cat embeddings all at once

* Add dataset comments

* Add README.md

* Add tests for sampler

* Refactor threshold finder to helper method

* Refactor targets comment

* Fix lint

* Rename embedding to similarity (More consistent with existing literature)

* 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)

* Doc multigpu and propagate data path.

* Use raw doc because of backslash.

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

* cast images to PIL at instantiation instead of runtime

* add test for svhn

* added tests for remaining SVHN splits

* flake8

* rolled back changes to datasets

* 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

* Update test for detection model to test input list unmodified

Update test for detection model to test input list unmodified according to suggestion in a previous PR

* test input unchaged

* 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

* Cityscapes test; semantic segmentation getitem test

* Add multiple targets test for Cityscapes

* flake8 fix for test_datasets

* initial commit

* add more checks, fix lint, fix doc

* Fixed width multiplier

Layer channels are now rounded to a multiple of 8, as per the official tensorflow implementation.  I found this fix when looking through: https://github.com/d-li14/mobilenetv2.pytorch

* Channel multiple now a user configurable option

The official tensorflow slim mobilenet v2 implementation rounds the number of channels in each layer to a multiple of 8.  This is now user configurable - 1 turns off rounding

* Fixed whitespace error

Fixed error: ./torchvision/models/mobilenet.py:152:1: W293 blank line contains whitespace

* WIP

* WIP

* Add some documentation

* Improve tests and add GC collection

* [WIP] add timestamp getter

* Bugfixes

* Improvements and travis

* Add audio fine-grained alignment

* More doc

* Remove unecessary file

* Remove comment

* Lazy import av

* Remove hard-coded constants for the test

* Return info stats from read

* Fix for Python-2