GitLab

* add download tests for Caltech(101|256)

* lint

* Add macos unittest

* Add wget

Change the default anchor generator settings to default Faster RCNN version(without FPN), so it is hopefully less misleading. Also add an assert to grad_anchors functions to make sure people use it as it is aspected.

Otherwise, torch jit scripted function raises exception on save

* Readme for video models.

* Adressing @fmassa comments
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add encode/write_png functions

* Do not redefine

* Style issues correction

* Comply with low-level interface

* Minor comment correction

* Add python frontend functions

* Add encode_png test

* Pass compession level to encode_png

* Do not compare output buffers

* Convert to bytes

* Compare pil image instead of buffer

* Add error tests

* Add test_write_png

* Remove png test assets

* Register writePNG correctly

* Update write_png docstring

* Do not preserve PIL image beyond the scope
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add decode_image op

* Fix lint

* More lint

* Add C10_EXPORT

* fix triggers for scheduled workflow

* more fix

* add missing repository checkout

* try fix label in template

* rewrite test infrastructure

* trigger issue generation

* try fix issue template

* try remove quotes

* remove buggy label

* try fix title

* cleanup

* add more test details

* reenable issue creation
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

This reverts commit 81d76487.

* Add decode and write JPEG ops

* Fix styling issues

* Use int64_t instead of int

* Use std::string

* Use jpegcommon.h for read_jpeg

* Minor updates to error handling in read

* Include header only once

* Reverse header inclusion

* Update common header

* Add common definitions

* Include string

* Include header?

* Include header?

* Add Python frontend calls

* Use unsigned long directly

* Fix style issues

* Include cstddef

* Ignore clang-format on cstddef

* Also include stdio

* Add JPEG and PNG include dirs

* Use C10_EXPORT

* Add JPEG encoding test

* Set quality to 75 by default and add write jpeg test

* Minor error correction

* Use assertEquals by assertEqual

* Remove test results

* Use pre-saved PIL output

* Remove extra PIL call

* Use read_jpeg instead of PIL

* Add error tests

* Address review comments

* Fix style issues

* Set test case to uint8

* Update test error check

* Apply suggestions from code review

* Fix clang-format

* Fix lint

* Fix test

* Remove unused file

* Fix regex error message

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

* tries adding genaralized_iou

* fixes linting

* Adds docs for giou, iou and box area

* fixes lint

* removes docs to fixup in other PR

* linter fix

* Cleans comments

* Adds tests for box area, iou and giou

* typo fix for testCase

* fixes typo

* fixes box area test

* fixes implementation

* updates tests to tolerance

Replace nonzero by where, now that it works with just a condition

* [WIP] All transforms are now derived from torch.nn.Module
- Compose, RandomApply, Normalize can be jit scripted

* Fixed flake8

* Updated code and docs
- added getattr to Lambda and tests
- updated code and docs of Compose
- added failing test with append/extend on Composed.transforms

* Fixed flake8

* Updated code, tests and docs

* partially enable mypy for .models

* fix existing errors

* ignore error instead of using Union

* run download tests on a daily schedule

* add blank line

* add issue template as workflow trigger

* try broaden workflow triggers

* run scheduled test after nightlies are published

* try remove schedule as workflow trigger

* restore workflow triggers

* cleanup

* Revert "remove dummy workflow"

This reverts commit d094e4bf2c3da2392bf1298450b97d9d63a15bfc.

* cleanup

This reverts commit cd4761f4.

* Refactor transforms input checks
- added _setup_size to check and setup size argument

* More refactor

Co-authored-by: Your Name <you@example.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Adds docs and fixes improts for ops and io

* reverts io and init for images

Co-authored-by: vfdev <vfdev.5@gmail.com>

* Make JPEG/PNG return images in CHW format

* Use int array

* [WIP] Added tests on batch of tensors

* Updated tests on batch of images

* All functional transforms can work with (..., C, H, W) format

* Added transforms tests on batch tensors

* Added batch tests for five/ten crop
- updated docs

* Run C++ torchvision example as part of cmake tests

* Enable C++14 support

* Minor error correction

* Add GPU forward on example

* Use arrow operator

* Fixes #2547

- Added warning to the documentation of F_pil and F_t functions

* Update functional_tensor.py

* Improved functional tensor geom transforms to work on floatX dtype
- Fixes #2600
- added tests
- refactored test_affine

* Removed float16/cpu case

* enable typing check for torchvision.io

* fix existing errors

* Update torchvision/io/_video_opt.py
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* add ignores for FileFinder

* use python 3 type hints

* lint

* video_opt

* video

* try quote av type hints

* revert from .dim() to .ndim

* revert changes to _video_opt.py and ignore errors

* fix type hints

* fix type hints for read_video_timestamps

* change offset int to float

* remove unused import
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add CMake build pipeline

* Add CMake build workflow

* Add executable permissions to script

* Install cmake on Windows/MacOS

* Install conda-build before setting up MSVC

* Install PyTorch from nightly

* Do not use conda-build variables

* Add path to CMake

* Install libpng and libjpeg

* Perform make

* Call msbuild on Windows

* Add missing yq

* Use vc_env_helper

* Use string instruction

* Escape configuration option

* Remove configuration flag

* Try to pass -p

* Use caret to escape equal sign

* Escape string option in Windows

* Try to call other bat

* Remove Windows/GPU CMake

* Add tracing cpp test

* Script model instead of tracing it

* Try to register operators manually

* Use manylinux-cuda102

* Activate conda env on Linux

* Build and run sample tracing test

* Add empty echo

* Remove unnecessary register

* Copy headers on Mac

* Revert to 2xlarge

* Include /usr/local/include on Mac

* Install pillow on Windows

* Install future

* Install torchvision on Windows

* Set include flag

* Add torchlib to PATH

* Normalize path via cygpath

* Register ops on Windows

* Minor error correction

* Register CPU/GPU ops on DLL library and register ops via reference

* Install dataclasses

* Install dataclasses using pip

* Address clang formatting issue

* Try to use an actual GPU instance on Linux

* Remove extra environment section

* Declare environment explicitly

* Regenerate

* Pass env variables to Dokcer

* Regenerate circleci

* Test tracing on GPU

* Use GPU medium

* Regenerate

* Use cuda101

* Regenerate

* Do not use pre-trained weights

Avoids having to download pretrained files, which could cause flaky tests
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* split off tests for dataset downloadability

* ignore download tests during normal test suite

* lint

* add retry mechanic