GitLab

* feat: Added __repr__ attribute to GeneralizedRCNNTransform

Added more details to default __repr__ attribute for printing.

* fix: Put back relative imports

* style: Fixed pep8 compliance

Switched strings with  syntax to f-strings.

* test: Added test for GeneralizedRCNNTransform __repr__

Checked integrity of __repr__ attribute

* test: Fixed unittest for __repr__

Fixed the formatted strings in the __repr__ integrity check for GeneralizedRCNNTransform

* fix: Fixed f-strings for earlier python versions

Switched back f-strings to .format syntax for Python3.5 compatibility.

* fix: Fixed multi-line string

Fixed multiple-line string syntax for compatibility

* fix: Fixed GeneralizedRCNNTransform unittest

Fixed formatting of min_size argument of the resizing part

This reverts commit 28b7f8ae.

* Force object annotiation to be an array

* Remove unecessary parentheses

* Change object check

* Remove check for list

* Add test coverage to xml parsing

* Tidy up whitespace

* Fix indentation

* Add cmake config file generation for torchvision (Torch::Vision)

* Update README

* Update cmake config to export TorchVision::Vision target, adding dependencies

* Update readme

* Fix path issues and rename target to TorchVision::TorchVision

* Force object annotiation to be an array

* Remove unecessary parentheses

* Change object check

* Remove check for list

* Add test coverage to xml parsing

* Tidy up whitespace

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

* torchscriptable functions for video io (#1653)

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/1653



created new torchscriptable video io functions as part of the api: read_video_meta_data_from_memory and read_video_from_memory.

Updated the implementation of some of the internal functions to be torchscriptable.

Reviewed By: stephenyan1231

Differential Revision: D18720474

fbshipit-source-id: 4ee646b66afecd2dc338a71fd8f249f25a3263bc

* BugFix
Co-authored-by: Jon Guerin <54725679+jguerin-fb@users.noreply.github.com>

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>

* adding new_empty_tensor symbolic

* flake8

* fix for feedback

* skipping the ORT test

* fix for ORT test

Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

I have fixed a typo that was persent in the Normalize class changed line 150 from     ``input[channel] = (input[channel] - mean[channel]) / std[channel]`` to     ``output[channel] = (input[channel] - mean[channel]) / std[channel]``

* Update README to reflect strict dependency on torch==1.4.0

* 1.4 or newer.

* initial fix

* outsourced num bands lookup

* fix doc

* added pillow version requirement

* simplify number of bands extraction

* remove unrelated change

* remove indirect dependency on pillow>=5.2.0

* extend docstring to transform

* bug fix

* added test

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

Python 2.7 was EOL on January 1, 2020

The last torchvision release to support Python 2.7 was 0.5.0
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* Type of input featmap_names fixed in example.

* Added missing imports.

* update doc

* update doc

* Fixes #1716

Fixes EMNIST classes attribute is wrong #1716

* Fixed the Classes for Letters Split

* Update mnist.py

* Move classes attribute inside init definition

* Fix Linting errors

* Fix AnchorGenerator if moving from one device to another

* Fixes for the test

* Testing CI

* Disable tests for Pillow 7

Previous weights are not compatible with current PyTorch

* Fix lint following #1695

* V2

* V3

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).