GitLab

* 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

* Unify video backend interfaces

* Remove reference cycle

* Make functions private and enable tests on OSX

* Disable test if video_reader backend not available

* Lint

* Fix import after refactoring

* Fix lint

* 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

* Make crop torchscriptable

Relevant #1375

* Invert x and y axis

* fix lint

* Add crop test

* revert deletion of space in functional

* add import random

* add dimension in doc

* add import

* fix flake8

* change to self.assert*

* convert to uint8

* assertTrue

* lint

* doc-build fixed

* deprecation fixes

* deprecation updates

* onnx esport faster rcnn

* test

* address PR comments

* revert unbind workaround

* disable tests for older versions of pytorch

* vflip and hflip tensor

* vflip and hflip tensor

* changes made

* lint

* lint

* lint failing

* added PSRoiAlign and PSRoiPool with C++ autograd and torch ops

* fixed linter errors

* fixed linter errors 2

* fixed linter errors 3

* Handle corrupted video headers in io

* Catch exceptions while decoding partly-corrupted files

* Add more tests

* Support Exporting RPN to ONNX

* address PR comments

* fix cat

* add flatten

* replace cat by stack

* update test to run only on rpn module

* use tolerate_small_mismatch

* extend video reader to support fast video probing

* fix c++ lint

* small fix

* allow to accept input video of type torch.Tensor

Revert "Change all torch::nn::init::Nonlinearity::{name} and torch::nn::init::FanMode::{name} to torch::k{name} (#1394)" (#1428)

This reverts commit 8c3cea7f.

* Fix DeprecationWarning for collections.Iterable import

* Simplify version comparison

* Fix hmdb51.py typo

* Fix ucf101.py typo

* Support Exporting MultiScaleRoiAlign to ONNX

* remove cast

* fix dtype

* move cast

* 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

* add support for video resnet models, restructure script test to just ignore RCNN models

* switch back to testing subset of the models

Change all torch::nn::init::Nonlinearity::{name} and torch::nn::init::FanMode::{name} to torch::k{name} (#1394)

* Change all torch::nn::init::Nonlinearity::{name} and torch::nn::init::FanMode::{name} to torch::k{name}

* empty commit

* fix lint

* fix lint

* fix lint

Revert "Change all torch::nn::init::Nonlinearity::{name} and torch::nn::init::FanMode::{name} usage to torch::k{name}"

This reverts commit e22c105c.

Change all torch::nn::init::Nonlinearity::{name} and torch::nn::init::FanMode::{name} usage to torch::k{name}

modified code of io.read_video and io.read_video_timestamps to intepret pts values in seconds (#1331)

* modified code of io.read_video and io.read_video_timestamps to interpret pts values in seconds

* changed default value for pts_unit to pts, corrected formatting

* hanndliing both fractions and floats for start_pts and end_pts, added test cases for pts_unit sec

* moved unit conversion logic to _read_from_stream method

* make googlnet scriptable

* Remove typing import in favor of torch.jit.annotations

* add inceptionnet

* flake fixes

* fix asssert true

* add import division for torchscript

* fix script compilation

* fix flake, py2 division error

* fix py2 division error

* 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

* video transforms

* [video transforms]in ToTensorVideo, divide value by 255.0

* [video transforms] fix a bug

* fix linting

* Make changes backwards-compatible

* 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

* format param added

* lint fixes

* lint fixes

* lint fixes

* 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

* Fix depth scaling on first 2 layers

* Restore initialization

* Match reference implementation initialization for dense layer

* Meant to use Kaiming

* Remove spurious relu

* Point to the newest 0.5 checkpoint

* Latest pretrained checkpoint

* Restore 1.0 checkpoint

* YAPF

* Implement backwards compat as suggested by Soumith

* Update checkpoint URL

* Move warnings up

* Record a couple more function parameters

* Update comment

* Set the correct version such that if the BC-patched model is saved, it could be reloaded with BC patching again

* Set a member var, not class var

* Update mnasnet.py

Remove unused member var as per review.

* Update the path to weights