GitLab

* Add libpng requirement into conda recipe

* Try to install libjpeg-turbo

* Add PNG reading capabilities

* Remove newline

* Add image extension to compilation instructions

* Include png functions as part of the main library

* Update CMakeLists

* Detect if building on conda-build

* Debug

* More debug messages

* Print globbed libreries

* Print globbed libreries

* Point to correct PNG path

* Remove libJPEG preventively

* Debug extension loading

* Link libpng explicitly

* Link with PNG

* Add PNG reading capabilities

* Add libpng requirement into conda recipe

* Try to install libjpeg-turbo

* Remove newline

* Add image extension to compilation instructions

* Include png functions as part of the main library

* Update CMakeLists

* Detect if building on conda-build

* Debug

* More debug messages

* Print globbed libreries

* Print globbed libreries

* Point to correct PNG path

* Remove libJPEG preventively

* Debug extension loading

* Link libpng explicitly

* Link with PNG

* Install libpng on conda-based wheel distributions

* Add -y flag

* Add -y flag to yum

* Locate LibPNG on windows conda

* Remove empty else

* Copy libpng16.so

* Copy dylib on Mac

* Improve check on Windows

* Try to install ninja using conda on windows

* Use libpng on Windows

* Package lib on windows wheel

* Point library to the correct place

* Include binaries as part of wheel

* Copy libpng.so on linux

* Look for png.h on Windows when using conda-build

* Do not skip png tests on Mac/Win

* Restore libjpeg-turbo

* Install jpeg-turbo on wheel distributions

* Install libjpeg-turbo from conda-forge on wheel distributions

* Do not pull av on conda-build

* Add pillow disclaimer

* Vendors libjpeg-turbo 2.0.4

* Merge JPEG work

* Remove submodules

* Regenerate circle config

* Fix style issues

* Fix C++ style issues

* More style corrections

* Add JPEG-turbo to linking libraries

* More style corrections

* More style corrections

* More style corrections

* Install libjpeg-turbo-devel

* Install libturbo-jpeg on typing pipeline

* Update Circle template

* Windows and Unix turbojpeg have the same linking name

* Install turbojpeg-devel instead of libjpeg-turbo

* Copy TurboJPEG binaries to wheel

* Move test image

* Move back test image

* Update JPEG test path

* Remove dot from extension

* Move image functions to extension

* Use stdout arg in subprocess

* Disable image extension if libpng or turbojpeg are not found

* Append libpng stdout

* Prevent list appending on lists

* Minor path correction

* Minor error correction

* Add linking flags

* Style issues correction

* Address minor review corrections

* Refactor library search

* Restore access index

* Fix JPEG tests

* Update libpng version in Travis

* Add -y flag

* Remove dot

* Update libpng using apt

* Check libpng version

* Change libturbojpeg binary

* Update import

* Change call

* Restore av in conda recipe

* Minor error correction

* Remove unused comment in travis.yml

* Update README

* Fix missing links

* Remove fixes for 16.04

* Remove JPEG-related code

* Remove installation references to turbojpeg

* Remove further references to turbojpeg

* Fix c++ style issues

* Fix c++ style issues

* Fix libpng-config include flag parsing

* Remove conda-forge

* Remove include dirs from main extension

* Do not pass extra include and library paths to main torchvision extension

* Add libpng to environment.yml

* Remove inexistent imports

* Add instructions regarding environment variables to README
Co-authored-by: Ryad ZENINE <r.zenine@gmail.com>

This reverts commit 766721b1.

* Add libpng requirement into conda recipe

* Try to install libjpeg-turbo

* Add PNG reading capabilities

* Remove newline

* Add image extension to compilation instructions

* Include png functions as part of the main library

* Update CMakeLists

* Detect if building on conda-build

* Debug

* More debug messages

* Print globbed libreries

* Print globbed libreries

* Point to correct PNG path

* Remove libJPEG preventively

* Debug extension loading

* Link libpng explicitly

* Link with PNG

* Add PNG reading capabilities

* Add libpng requirement into conda recipe

* Try to install libjpeg-turbo

* Remove newline

* Add image extension to compilation instructions

* Include png functions as part of the main library

* Update CMakeLists

* Detect if building on conda-build

* Debug

* More debug messages

* Print globbed libreries

* Print globbed libreries

* Point to correct PNG path

* Remove libJPEG preventively

* Debug extension loading

* Link libpng explicitly

* Link with PNG

* Install libpng on conda-based wheel distributions

* Add -y flag

* Add -y flag to yum

* Locate LibPNG on windows conda

* Remove empty else

* Copy libpng16.so

* Copy dylib on Mac

* Improve check on Windows

* Try to install ninja using conda on windows

* Use libpng on Windows

* Package lib on windows wheel

* Point library to the correct place

* Include binaries as part of wheel

* Copy libpng.so on linux

* Look for png.h on Windows when using conda-build

* Do not skip png tests on Mac/Win

* Restore libjpeg-turbo

* Install jpeg-turbo on wheel distributions

* Install libjpeg-turbo from conda-forge on wheel distributions

* Do not pull av on conda-build

* Add pillow disclaimer

* Vendors libjpeg-turbo 2.0.4

* Merge JPEG work

* Remove submodules

* Regenerate circle config

* Fix style issues

* Fix C++ style issues

* More style corrections

* Add JPEG-turbo to linking libraries

* More style corrections

* More style corrections

* More style corrections

* Install libjpeg-turbo-devel

* Install libturbo-jpeg on typing pipeline

* Update Circle template

* Windows and Unix turbojpeg have the same linking name

* Install turbojpeg-devel instead of libjpeg-turbo

* Copy TurboJPEG binaries to wheel

* Move test image

* Move back test image

* Update JPEG test path

* Remove dot from extension

* Move image functions to extension

* Use stdout arg in subprocess

* Disable image extension if libpng or turbojpeg are not found

* Append libpng stdout

* Prevent list appending on lists

* Minor path correction

* Minor error correction

* Add linking flags

* Style issues correction

* Address minor review corrections

* Refactor library search

* Restore access index

* Fix JPEG tests

* Update libpng version in Travis

* Add -y flag

* Remove dot

* Update libpng using apt

* Check libpng version

* Change libturbojpeg binary

* Update import

* Change call

* Restore av in conda recipe

* Minor error correction

* Remove unused comment in travis.yml

* Update README

* Fix missing links

* Remove fixes for 16.04
Co-authored-by: Ryad ZENINE <r.zenine@gmail.com>

Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* Fix missing compilation files for video-reader

* Disable IO tests in travis

* Add CircleCI job for python lint

* Break lint

* Fix

* Fix lint

* Re-enable all tests and remove travis python lint

* remove sys.version_info == 2

* remove sys.version_info < 3

* remove from __future__ imports

* remove six from python code

* remove six from setup.py

* remove six from tests

* remove six from references

* remove six from packaging

* revert str to torch._six._string_classes

* revert str to torch._six._string_classes

* 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

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>

Previously, when doing a CUDA build, we would pass -O0 to build cpu
bits. This PR removes that `-O0` (so we build in `-O3` instead.

Test Plan:
- wait for CI

* Disable C++ models from being compiled without explicitly being asked for

* Fix import in tests, which are already disabled

* [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

* 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.

* [WIP] Add CircleCI for CI

* Make jobs only run on master

* Add initial CI

* [wip] testing if works

* Trying some basic GPU tests

* [WIP] maybe it will work?

* [WIP] One more try

* Pin versions

* Simplify and reuse

* Fix

* [WIP] testing windows

* [WIP] testing windows

* Try windows

* Try Windows

* Try windows

* Try windows

* Try windows

* Try windows

* Try windows

* Try windows

* Windows speedup

* Windows multicores

* Add parallel flags for Windows

* Skip some tests on Windows

* Sync config.yml and config.yml.in

* Regenerate

* Run all tests

* Limit python and cuda version for finding pytorch

* Skip darwin for previous check

* Add description

* Fix logic

* Remove space

* Add CUDA test back

* Add back .travis.yml for now and remove duplicate test

* Add newline

* 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

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

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

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

* Added the existing code

* Added squeezenet and fixed some stuff in the other models

* Wrote DenseNet and a part of InceptionV3

Going to clean and check all of the models and finish inception

* Fixed some errors in the models

Next step is writing inception and comparing with python code again.

* Completed inception and changed models directory

* Fixed and wrote some stuff

* fixed maxpoool2d and avgpool2d and adaptiveavgpool2d

* Fixed a few stuff

Moved cmakelists to root and changed the namespace to vision and wrote weight initialization in inception

* Added models namespace and changed cmakelists

the project is now installable

* Removed some comments

* Changed style to pytorch style, added some comments and fixed some minor errors

* Removed truncated normal init

* Changed classes to structs and fixed a few errors

* Replaced modelsimpl structs with functional wherever possible

* Changed adaptive average pool from struct to function

* Wrote a max_pool2d wrapper and added some comments

* Replaced xavier init with kaiming init

* Fixed an error in kaiming inits

* Added model conversion and tests

* Fixed a typo in alexnet and removed tests from cmake

* Made an extension of tests and added module names to Densenet

* Added python tests

* Added MobileNet and GoogLeNet models

* Added tests and conversions for new models and fixed a few errors

* Updated Alexnet ad VGG

* Updated Densenet, Squeezenet and Inception

* Added ResNexts and their conversions

* Added tests for ResNexts

* Wrote tools nessesary to write ShuffleNet

* Added ShuffleNetV2

* Fixed some errors in ShuffleNetV2

* Added conversions for shufflenetv2

* Fixed the errors in test_models.cpp

* Updated setup.py

* Fixed flake8 error on test_cpp_models.py

* Changed view to reshape in forward of ResNet

* Updated ShuffleNetV2

* Split extensions to tests and ops

* Fixed test extension

* Fixed image path in test_cpp_models.py

* Fixed image path in test_cpp_models.py

* Fixed a few things in test_cpp_models.py

* Put the test models in evaluation mode

* Fixed registering error in GoogLeNet

* Updated setup.py

* write test_cpp_models.py with unittest

* Fixed a problem with pytest in test_cpp_models.py

* Fixed a lint problem

* Allow forcing GPU build with FORCE_CUDA=1

This is convenient to e.g. build with GPU support inside a docker image

* Document FORCE_CUDA

This reverts commit 06b9b0fc.

This reverts commit b384c4e7.

This is the same logic and motivation as in
https://github.com/pytorch/pytorch/pull/8244/
that is to build a docker image (which has no GPU available at build time),
and then use it with nvidia-docker

* Initial layout for layers with cpp extensions

* Move files around

* Fix import after move

* Add support for multiple types to ROIAlign

* Different organization

CUDA extensions work now

* Cleanups

* Reduce memory requirements for backwards

* Replace runtime_error by AT_ERROR

* Add nms test

* Add support for compilation using CPP extensions

* Change folder structure

* Add ROIPool cuda

* Cleanups

* Add roi_pool.py

* Fix lint

* Add initial structures folder for bounding boxes

* Assertion macros compatible with pytorch master (#540)

* Support for ROI Pooling (#592)

* ROI Pooling with tests. Fix for cuda context in ROI Align.

* renamed bottom and top to follow torch conventions

* remove .type().tensor() calls in favor of the new approach to tensor initialization (#626)

* Consistent naming for rois variable (#627)

* remove .type().tensor() calls in favor of the new approach to tensor initialization

* Consistent naming for rois variable in ROIPool

* ROIPool: Support for all datatypes (#632)

* Use of torch7 naming scheme for ROIAlign forward and backward

* use common cuda helpers in ROIAlign

* use .options() in favor of .type() where applicable

* Added tests for forward pass of ROIAlign, as well as more consistent naming scheme for CPU vs CUDA

* working ROIAlign cuda backwards pass

* working ROIAlign backwards pass for CPU

* added relevant headers for ROIAlign backwards

* tests for ROIAlign layer

* replace .type() with .options() for tensor initialization in ROIAlign layers

* support for Half types in ROIAlign

* gradcheck tests for ROIAlign

* updated ROIPool on CPU to work with all datatypes

* updated and cleaned tests for ROI Pooling

* Fix rebase problem

* Remove structures folder

* Improve cleanup and bugfix in test_layers

* Update C++ headers

* Add CUDAGuard to cu files

* Add more checks to layers

* Add CUDA NMS and tests

* Add multi-type support for NMS CUDA

* Avoid using THCudaMalloc

* Add clang-format and reformat c++ code

* Remove THC includes

* Rename layers to ops

* Add documentation and rename functions

* Improve the documentation a bit

* Fix some lint errors

* Fix remaining lint inssues

* Area computation doesn't add +1 in NMS

* Update CI to use PyTorch nightly

* Make NMS return indices sorted according to the score

* Address reviewer comments

* Lint fixes

* Improve doc for roi_align and roi_pool

* move to xenial

* Fix bug pointed by @lopuhin

* Fix RoIPool reference implementation in Python 2

Also fixes a bug in the clip_boxes_to_image -- this function needs a test!

* Remove change in .travis

Remove explicit Python interpreter name.