GitLab

* Improved error messages for transforms.functional.normalize().

Split the original TypeError into 1. a TypeError if `tensor` is not a torch.Tensor and 2. a ValueError if `tensor` does not have the correct dimensionality.

Added more detail to the error for when `tensor` has the wrong dimension to make it easier to diagnose. This is useful when this function isn't called directly by the user (e.g. when the user uses transforms.Normalize and can't directly see this functions doc string).

Deleted hanging function `_is_tensor_image()`. It isn't used in this file and isn't used internally anywhere else in torchvision that I can see. (Some users will have used it despite the underscore prefix, but a quick google search for "F._is_tensor_image" suggests this is rare).

* Value checking to prevent division by zero runtime crashes.

Added a ValueError to check for and avoid division by zero in `div_`. Not preventing the call leads to runtime crashes, at least in some environments.

* Fixed div by zero check for non-scalar inputs.

* docs for faster+mask rcnn coords is clearer

* keypoint rcnn coords format is clearer
Co-authored-by: rvirgolireply <51229032+rvirgolireply@users.noreply.github.com>

Co-authored-by: rvirgolireply <51229032+rvirgolireply@users.noreply.github.com>

* replaced mean on dimensions 2,3 by adaptive_avg_pooling2d with destination of 1, to remove hardcoded dimension ordering

* replaced reshape command by torch.squeeze after global_avg_pool2d, which is cleaner

* reshape rather than squeeze for BS=1

* remove import torch

input to parameter mode can be 'fine' or 'coarse'. The code internally converts the mode = 'fine' input to 'gtFine' (same for coarse input and gtCoarse), but the docs mention that input to mode should be 'gtFine' or 'gtCoarse' inconsistently.

* 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

* 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]``

* 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

* 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

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).

* scriptability checks

* tests upds

* linter upds

* linter upds

* upds

* tuple list changes

* linter updates

* Make R-CNN models less verbose in script mode

* Fix typo in warning message

* remove windows skips from video_utils tests, now that they pass

* replace lambda in videoclips in order to be pickled on windows and update tests