GitLab

* fix shape error for deform conv gpu op

recover shape of columns for next iteration in for loops, previous version will cause error when batch_sz / n_parallel_imgs > 1

* fix shape error for deform conv cpu op

recover shape of columns for next iteration in for loops, previous version will cause error when batch_sz / n_parallel_imgs > 1

* Fix C++ lint

* More fixes

* Base decoder for video. (#1747)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1747

Pull Request resolved: https://github.com/pytorch/vision/pull/1746

Added the implementation of ffmpeg based decoder with functionality that can be used in VUE and TorchVision.

Reviewed By: fmassa

Differential Revision: D19358914

fbshipit-source-id: abb672f89bfaca6351dda2354f0d35cf8e47fa0f

* Integrated base decoder into VideoReader class and video_utils.py (#1766)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1766

Replaced FfmpegDecoder (incompativle with VUE) by base decoder (compatible with VUE).
Modified python utilities video_utils.py for internal simplification. Public interface got preserved.

Reviewed By: fmassa

Differential Revision: D19415903

fbshipit-source-id: 4d7a0158bd77bac0a18732fe4183fdd9a57f6402

* Optimizating base decoder performance. (#1852)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1852

Changed base decoder internals for a faster clip processing.

Reviewed By: stephenyan1231

Differential Revision: D19748379

fbshipit-source-id: 58a435f0a0b25545e7bd1a3edb0b1d558176a806

* Minor fix and decoder class members access.

Summary:
Found and fix a bug in cropping algorithm (simple mistyping).
Also derived classes need access to some decoder class members, like initialization parameters - make it protected.

Reviewed By: stephenyan1231, fmassa

Differential Revision: D19895076

fbshipit-source-id: 691336c8e18526b085ae5792ac3546bc387a6db9

* Added missing header for less dependencies. (#1898)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1898

Include streams/samplers shouldn't depend on decoder headers. Add dependencies directly to the place where they are required.

Reviewed By: stephenyan1231

Differential Revision: D19911404

fbshipit-source-id: ef322a053708405c02cee4562b456b1602fb12fc

* Implemented VUE Asynchronous Decoder

Summary: For Mothership we have found that asynchronous decoder provides a better performance.

Differential Revision: D20026194

fbshipit-source-id: 627b91844b4e3f917002031dd32cb19c239f4ba8

* fix a bug in API read_video_from_memory (#1942)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1942

In D18720474, it introduces a bug in `read_video_from_memory` API. Thank weiyaowang for reporting it.

Reviewed By: weiyaowang

Differential Revision: D20270179

fbshipit-source-id: 66348c99a5ad1f9129b90e934524ddfaad59de03

* extend decoder to support new video_max_dimension argument (#1924)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1924

Extend `video reader` decoder python API in Torchvision to support a new argument `video_max_dimension`. This enables the new video decoding use cases. When setting `video_width=0`, `video_height=0`, `video_min_dimension != 0`, and `video_max_dimension != 0`, we can rescale the video clips so that its spatial resolution (height, width) becomes
 - (video_min_dimension, video_max_dimension) if original height < original width
 - (video_max_dimension, video_min_dimension) if original height >= original width

This is useful at video model testing stage, where we perform fully convolution evaluation and take entire video frames without cropping as input. Previously, for instance we can only set `video_width=0`, `video_height=0`, `video_min_dimension = 128`, which will preserve aspect ratio. In production dataset, there are a small number of videos where aspect ratio is either extremely large or small, and when the shorter edge is rescaled to 128, the longer edge is still large. This will easily cause GPU memory OOM when we sample multiple video clips, and put them in a single minibatch.

Now, we can set (for instance) `video_width=0`, `video_height=0`, `video_min_dimension = 128` and `video_max_dimension = 171` so that the rescale resolution is either (128, 171) or (171, 128) depending on whether original height is larger than original width. Thus, we are less likely to have gpu OOM because the spatial size of video clips is determined.

Reviewed By: putivsky

Differential Revision: D20182529

fbshipit-source-id: f9c40afb7590e7c45e6908946597141efa35f57c

* Fixing samplers initialization (#1967)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1967



No-ops for torchvision diff, which fixes samplers.

Differential Revision: D20397218

fbshipit-source-id: 6dc4d04364f305fbda7ca4f67a25ceecd73d0f20

* Exclude C++ test files
Co-authored-by: Yuri Putivsky <yuri@fb.com>
Co-authored-by: Zhicheng Yan <zyan3@fb.com>

* Added code to support creating extension on ROCm

* max -> fmaxf conversion for hipification

* added WITH_HIP flag for hipExtension

* added appropriate headers for HIP build

* use USE_ROCM in condition to build

* change fmaxf and fminf calls

* fminf -> min

* fix the check for ROCM_HOME

* more robust checking for rocm pytorch

* add check for pytorch version before using HIP extensions

* conditional reading of ROCM_HOME

* Aligned flag in the interfaces

* Aligned flag in the impl, and remove unused comments

* Handling empty bin in forward

* Remove raise error in roi_width

* Aligned flag in the Testcodes

This reverts commit 28b7f8ae.

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1747

Pull Request resolved: https://github.com/pytorch/vision/pull/1746



Added the implementation of ffmpeg based decoder with functionality that can be used in VUE and TorchVision.

Reviewed By: fmassa

Differential Revision: D19358914

fbshipit-source-id: abb672f89bfaca6351dda2354f0d35cf8e47fa0f
Co-authored-by: Yuri Putivsky <yuri@fb.com>

1. Let the IOU function compare with threshold. This avoid a division. Similar strategy is also used in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/non_max_suppression_op.cu.cc
2. Only compute the upper triangle of the mask.

This speeds up the kernel about 20% (tested on GTX 1080Ti, with 20 input cases dumped from a Mask R-CNN inference job).

* Add Deformable Convolution operation.

This adds the deformable convolution operation, as described in Deformable Convolutional Networks (https://arxiv.org/abs/1703.06211).

- The code is based on https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda.cpp ; the whole code was modified and refactored to remove redundancies and increase clarity, and to adapt it to torchvision.

- The CPU part is a direct copy of the CUDA code; it might make sense to do follow-up adjustments in the CPU code to simplify it / optimize it, or to reuse functionality between CPU and CUDA..

- We also add tests (with a non-trivial set of parameters); they can be made more robust by randomizing the parameters and executing multiple times.

* Update DeformConv to be more consistent w/ Conv2d

* rename some variables and arguments to match Conv2d;
* add optional bias;
* add weight, offset and bias as module parameters;
* remove the n_parallel_imgs parameter;
* Fix __repr__;
* etc..

Initialization of weight and bias is the same as in Conv2d, and
initialization of offsets to zero is the same as in the paper.

This also includes some other small unrelated fixes/improvements.

* Apply clang-format in DeformConv files.

* Import Optional type annotation

* Remove offset param from DeformConv2d module

- We pass the offset in the forward of DeformConv2d, instead of having
an internal parameter. This adds some complexity to creating the module
(e.g. now you have to worry about the output size, to create the
offset), but it gives more flexibility.
- We also use make_tuple for tuple creation, in an attempt to fix error
w/ older compilers.

* Replace abs by std::abs

Old gcc versions were giving wrong results here, because they would
resolve abs as int -> int, thus causing undesired truncation. Replacing
abs by std::abs should allow for correct overloading of abs as float -> float.

* Reorder declarations for clarity

* Reorder weight and offset args in deform_conv2d

We place offset arg before the weight arg, to be more
consistent with DeformConv2d.forward(input, offset)

* Replace abs by std::abs in DeformConv_cuda

* almost working...

* respond to comments

* add empty tensor op, handle different output types in generalized rcnn

* clean ups

* address comments

* more changes

* it's working!

* torchscript bugs

* add script/ eager test

* eval script model

* fix flake

* division import

* py2 compat

* update test, fix arange bug

* import division statement

* fix linter

* fixes

* changes needed for JIT master

* cleanups

* remove imagelist_to

* requested changes

* Make FPN backwards-compatible and torchscript compatible

We remove support for feature channels=0, but support for it was already a bit limited

* Fix ONNX regression

Rename with_bias() to bias(), and output_channels() to out_channels() in C++ conv layer options usage (#1576)

* Fix inconsistent NMS implementation

* Improve tests for NMS

* Remove unnecessary using statement

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

* fixed linter errors

* fixed linter errors 2

* fixed linter errors 3

* 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.

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}

* [video reader] inception commit

* add method save_metadata to class VideoClips in video_utils.py

* add load_metadata() method to VideoClips class

* add Exception to not catch unexpected events such as memory erros, interrupt

* fix bugs in video_plus.py

* [video reader]remove logging. update setup.py

* remove time measurement in test_video_reader.py

* Remove glog and try making ffmpeg finding more robust

* Add ffmpeg to conda build

* Add ffmpeg to conda build [again]

* Make library path finding more robust

* Missing import

* One more missing fix for import

* Py2 compatibility and change package to av to avoid version conflict with ffmpeg

* Fix for python2

* [video reader] support to decode one stream only (e.g. video/audio stream)

* remove argument _precomputed_metadata_filepath

* remove save_metadata method

* add get_metadata method

* expose _precomputed_metadata and frame_rate arguments in video dataset __init__ method

* remove ssize_t

* remove size_t to pass CI check on Windows

* add PyInit__video_reader function to pass CI check on Windows

* minor fix to define PyInit_video_reader symbol

* Make c++ video reader optional

* Temporarily revert changes to test_io

* Revert changes to python files

* Rename files to make it private

* Fix python lint

* Fix C++ lint

* add a functor object EnumClassHash to make Enum class instances usable as key type of std::unordered_map

* fix cpp format check

* Remove C++ extensions in favor of custom ops

* Remove unused custom_ops.cpp file

* Rename _custom_ops.py

* Reorganize functions

* Minor improvements and fixes

* Fix lint

* Fully scriptable ops

* Import types used by annotations

* Make custom ops differentiable

and replace autograd.Function. Use ops unconditionally.

We may consider removing the extension functions in a follow-up.

The code-path is tested by the exisitng tests for differentiability.

* add scripting gradchecks tests and use intlist

* fix implicit tuple conversion for gcc-5

* fix merge

* Revert "Revert "Register Torchvision Ops as Cutom Ops (#1267)" (#1316)"

This reverts commit fe234fc8.

* Make import of C++ extensions lazy

* define python initialization functions for extension

* Fix lint

This reverts commit 78f169b5.

* Register torchvision ops

* install ORT only with python 3

* remane lib + address other comments

* fix lint

* fix lib copy

* find file with pattern instead of suffix

* use relative path

* revert rename and use imp to find lib

* fix typo

* Use Tensor.data_ptr instead of .data

* use pytorch-nightly in CI

* Replaced asserts with TORCH_CHECK

* Fixed an error

* Added mnasnet

* Fixed some stuff

* Fixed some stuff

* Finished MNASNet

* Fixed format error

* Fixed format error

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