GitLab

* Add flag to ignore ffmpeg in python build.

* Update setup.py to avoid potential collisions with other projects that are using FFMPEG
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Added base tests for rotate_image_tensor

* Updated resize_image_tensor API and tests and fixed a bug with max_size

* Refactored and modified private api for resize functional op

* Fixed failures

* More updates

* Updated proto functional op: resize_image_*

* Added max_size arg to resize_bounding_box and updated basic tests

* Update functional.py

* Reverted fill/center order for rotate
Other nits

* Refactored and modified private api for resize functional op

* Fixed failures

* More updates

* Fixed flake8

* Add rough raft-stereo implementation on prototype/models

* Add standard raft_stereo builder, and modify context_encoder to be more similar with original implementation

* Follow original implementation on pre-convolve context

* Fix to make sure we can load original implementation weight and got same output

* reusing component from raft

* Make the raft_stereo_fast able to load original weight implementation

* Format with ufmt and update some comment

* Use raft FlowHead

* clean up comments

* Remove unnecessary import and use ufmt format

* Add __all__ and more docs for RaftStereo class

* Only accept param and not module for raft stereo builder

* Cleanup comment

* Adding typing to raft_stereo

* Update some of raft code and reuse on raft stereo

* Use bool instead of int

* Make standard raft_stereo model jit scriptable

* Make the function _make_out_layer using boolean with_block and init the block_layer with identity

* Separate corr_block into two modules for pyramid and building corr features

* Use tuple if input is not variable size, also remove default value if using List

* Format using ufmt and update ConvGRU to not inherit from raft in order to satisfy both jit script and mypy

* Change RaftStereo docs input type

* Ufmt format raft

* revert back convgru to see mypy errors, add test for jit and fx, make the model fx compatible

* ufmt format

* Specify device for new tensor, dont init module then overwrite and put if-else instead

* Ignore mypy problem on override, put back num_iters on forward

* Revert some effort to make it fx compatible but unnecessary now

* refactor code and remove num_iters from RaftStereo constructor

* Change to raft_stereo_realtime, and specify device directly for tensor creation

* Add description for raft_stereo_realtime

* Update the test for raft_stereo

* Fix raft stereo prototype test to properly test jit script

* Ufmt format

* Test against expected file, change name from raft_stereo to raft_stereo_builder to prevent import error

* Revert __init__.py changes

* Add default value for non-list param on model builder

* Add checking on out_with_block length, add more docs on the encoder

* Use base instead of basic since it is more commonly used

* rename expect file to base as well

* rename on test

* Revert the revert of __init__.py, also revert the adding default value to _raft_stereo to follow the standard pattern

* ufmt format __init__.py

Summary:
fixes two sporadic issues from missing attributes:

- breaking circular imports
- submodule not being imported explicitly

Reviewed By: ehhuang

Differential Revision: D37071652

fbshipit-source-id: 0680f098384b0fd21076339750e9d1a96186ede3
Co-authored-by: Edward Wang (EcoF) <hack@fb.com>

* added simple POC

* added jitter and crop options

* added references

* moved simplecopypaste to detection module

* working POC for simple copy paste in detection

* added comments

* remove transforms from class
updated the labels
added gaussian blur

* removed loop for mask calculation

* replaced Gaussian blur with functional api

* added inplace operations

* added changes to accept tuples instead of tensors

* - make copy paste functional
- make only one copy of batch and target

* add inplace support within copy paste functional

* Updated code for copy-paste transform

* Fixed code formatting

* [skip ci] removed manual thresholding

* Replaced cropping by resizing data to paste

* Removed inplace arg (as useless) and put a check on iscrowd target

* code-formatting

* Updated copypaste op to make it torch scriptable
Added fallbacks to support LSJ

* Fixed flake8

* Updates according to the review
Co-authored-by: vfdev-5 <vfdev.5@gmail.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Make sure we are building against test chanell for release

* Cleanup

* Cleanup

* Refactoring logic

* Remove tagged tests

* Add new .. betastatus:: directive to document Beta APIs

* Also add it for the fine-grained video API

* Add directive for all builders and pages of Detection module

* Also segmentation and video models

* Added elastic augment

* ufmt formatting

* updated comments

* fixed circular dependency issue and bare except error

* Fixed three type checking errors in functional_tensor.py

* ufmt formatted

* changed elastic_deformation to a more common implementation

Implementation uses alpha and sigma to control strength and smoothness of the displacement vectors in elastic_deformation instead of control_point_spacings and sigma.

* ufmt formatting

* Some performance updates

Put random offset vectors to device before gaussian_blur is applied speeds it up 3-fold.

* fixed type error

* fixed again a type error

* Update torchvision/transforms/functional_tensor.py
Co-authored-by: vfdev <vfdev.5@gmail.com>

* Added some requested changes

- pil image support similar to GaussianBlur
- changed interpolation arg to InterpolationMode
- added a wrapper in torchvision.transforms.functional.py that gets called by the class in transforms.py
-renamed it to ElasticTransform
- handled sigma = 0 case

* added img docstring

* added some tests

* Updated tests and the code

* Added the requested changes to the arguments of F.elastic_transform

Added random_state and displacement as arguments to F.elastic_transform

* fixed the type error

* Fixed tests and docs

* implemented requested changes

Changes:
1) alpha AND sigma OR displacement must be given as arguments to transforms.functional_tensor.elastic_transform instead of alpha AND sigma AND displacement
2) displacements are accepted in transforms.functional.elastic_transform as np.array and torch.Tensor instead of only accepting torch.Tensor

* ufmt formatting

* trochscript error resolved

replaced torch.from_numpy() to torch.Tensor() to make it compatible to torchscript

* revert to torch.from_numpy()

* updated argument checks and errors

- In F.elastic_transform added check to see if both user inputs img and displacement are either of type PIL Image and ndarray or both of type tensor.
- In F_t.elastic_transform added check if alpha and sigma are None if displacement is given or vice versa.

* fixed seed error

changed torch.seed to torch.manual_seed in F_t.elastic_transform

* Reverted displacement type and other cosmetics

* Other minor improvements

* changed gaussian_blur filter size

changed gaussian_blur filter size
from
4 * int(sigma) + 1
to
int(8 * sigma + 1)
to make it consistent with ernestums implementation

* resolved merge error

* Revert "resolved merge error"

This reverts commit 6a4a4e74ff4d078e2c2753d359185f9a81c415d0.

* resolve merge error

* ufmt formatted

* ufmt formated once again..

* fixed unsupported operand error

* Update API and removed random_state from functional part

* Added default values

* Added ElasticTransform to gallery and updated the docstring

* Updated gallery and added _log_api_usage_once
BTW, matplotlib.pylab is deprecated

* Updated gallery transforms code

* Updates according to review
Co-authored-by: vfdev <vfdev.5@gmail.com>

s#ref/heads/#refs/heads/#

I should have noticed it while copy-n-pasting the condition. Unfortunately there are no way to test is other than in prod, but nightly builds are still not getting pushed, see https://github.com/pytorch/vision/runs/6860407007?check_suite_focus=true for example

* Update _pil_constants.py

* Update _pil_constants.py

* Fix flake8

* Fixed two related warnings in tests

* switch dir with hasattr
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

Replace `steps.extract_branch.outputs.branch` (which were probably taken from
https://stackoverflow.com/questions/58033366/how-to-get-the-current-branch-within-github-actions ) with straightforward `github.event.ref`

Tested in
https://github.com/malfet/deleteme/runs/6822647720?check_suite_focus=true and https://github.com/malfet/deleteme/runs/6822691158?check_suite_focus=true

* Revert "Indicate Celeba download parameter is deprecated and will be removed (#6059)"

This reverts commit 49496c4f.

* Revert "Deactivate CelebA download (#6052)"

This reverts commit ac56f52e.

* Adding tagged builds

* Testing

* Testing

* Testing

* Testing

* Adding conda builds

As @vfdev-5 proposed in https://github.com/pytorch/vision/issues/6138#issuecomment-1150398081

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

By passing `--pre` option to `pip install`, otherwise torchvision were always tested against last PyTorch release

* [quant][core][better-engineering] Rename files in quantized directory to conform with non-quantized countertpart filenames (#77037)

Summary:
X-link: https://github.com/pytorch/pytorch/pull/77037



Names of analogous files in quantized directory (previously snake case) were inconsistent with
their non-quantized filename counterparts (pascal case). This is the first of a series of PRs that changes
all files in quantized (and sub-directories) dir to have pascal case.

`aten/src/ATen/native/quantized/qconv_unpack.cpp` has not been renamed yet
because (for reasons currently unknown) after making the name change, `import torch` produces the below error (`qlinear_unpack.cpp` renaming also seems to fail some phabricator CI tests for similar reasons). We suspect that these may be undefined errors and will revisit naming these files in a future PR.

```
terminate called after throwing an instance of 'c10::Error'
  what():  Type c10::intrusive_ptr<ConvPackedParamsBase<2> > could not be converted to any of the known types.
Exception raised from operator() at ../aten/src/ATen/core/jit_type.h:1735 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x55 (0x7f26745c0c65 in /data/users/dzdang/pytorch/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xb1 (0x7f26745bdcd1 in /data/users/dzdang/pytorch/torch/lib/libc10.so)
frame #2: <unknown function> + 0x1494e24 (0x7f2663b14e24 in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so)
frame #3: <unknown function> + 0xfed0bc (0x7f266366d0bc in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so)
frame #4: c10::detail::infer_schema::make_function_schema(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&&, c10::ArrayRef<c10::detail::infer_schema::ArgumentDef>, c10::ArrayRef<c10::detail::infer_schema::ArgumentDef>) + 0x5a (0x7f266366d71a in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so)
frame #5: c10::detail::infer_schema::make_function_schema(c10::ArrayRef<c10::detail::infer_schema::ArgumentDef>, c10::ArrayRef<c10::detail::infer_schema::ArgumentDef>) + 0x7b (0x7f266366e06b in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so)
frame #6: <unknown function> + 0x1493f32 (0x7f2663b13f32 in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so)
frame #7: <unknown function> + 0xe227dd (0x7f26634a27dd in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so)
frame #8: <unknown function> + 0x14e0a (0x7f268c934e0a in /lib64/ld-linux-x86-64.so.2)
..........................truncated.............
```

Reviewed By: malfet

Differential Revision: D36862332

Pulled By: dzdang

fbshipit-source-id: 598c36656b4e71f906d940e7ff19ecf82d43031d

* empty commit

* empty commit

* empty commit
Co-authored-by: dzdang <dzdang@umich.edu>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

Clean up Conda build folder before every run
Enable artifact upload to GitHub for every workflow run, but upload to Conda/S3 only on nightly pushes

Test plan: `conda install -c pytorch-nightly torchvision; python -c "import torchvision;print(torchvision.io.read_image('hummingbird.jpg').shape)"`

* Add M1 testing job

* libjpeg -> jpeg<=9b in test-m1.yml

* Added export PATH=~/miniconda3/bin... from 6122

* Tests were OK, let's see if we can remove the pinning

* Docs: build with Sphinx 5

* Sphinx 5 no longer accepts language = None
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Fix the issue :)

* Intellij vs ufmt battle

* remove .item()

Explicitly set PATH to point to `conda` binary, otherwise libjpeg detection logic does not work
Pin libjpeg to the same version on x86 and m1
Add simple tests that jpeg can be decoded by a generated wheel

* [BE] Unify version computation

Instead of hardcoding dev version in various script, use one from
`version.txt` if `setup_build_version` is called without arguments

Also, pass `--pre` option to M1 build/test pip install commands to build
TorchVision against nightly pytorch

* Pin torchvision dependency to a specific pytorch version

* Add missing `_version` to the MLPBlock

* fix linter

This would package libpng and libjpeg.dylib into wheel files
Add a very simple test step, copied from https://github.com/pytorch/pytorch.github.io/blob/1eaa33a3d3f1b83b64c5031c6dd04dbb238d6105/scripts/test_install.py#L78
Cherry-picked from https://github.com/pytorch/builder/blob/d0bc74cc363a9da5a8b6a40e883d40d25d050036/build_m1_domains.sh#L22

Differential Revision: D36671123

fbshipit-source-id: eb99f293978ed150bca60073115971586f0fa02a
Co-authored-by: Oleksandr Voietsa <ovoietsa@fb.com>
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

New version of git checks folder owner and errors with 'fatal: unsafe repository' if owner is different.
Thus, `root_dir="$(git rev-parse --show-toplevel)"` is failing.

To fix the issue, just added all floder as safe directories
git rev-parse --show-toplevel

* Added tests for other padding modes

* Fixed expected mask dtype

* Applied comments from review

* Use List[int] instead of int for window_size and shift_size

* Make PatchMerging and SwinTransformerBlock able to handle 2d and 3d cases

* Separate patch embedding from SwinTransformer and enable to get model without head by specifying num_heads=None

* Dont use if before padding so it is fx friendly

* Put the handling on window_size edge cases on separate function and wrap with torch.fx.wrap so it is excluded from tracing

* Update the weight url to the converted weight with new structure

* Update the accuracy of swin_transformer

* Change assert to Exception and nit

* Make num_classes optional

* Add typing output for _fix_window_and_shift_size function

* init head to None to make it jit scriptable

* Revert the change to make num_classes optional

* Revert unneccesarry changes that might be risky

* Remove self.head declaration