GitLab

* ops.masks_to_bounding_boxes

* test fixtures

* unit test

* ignore lint e201 and e202 for in-lined matrix

* ignore e121 and e241 linting rules for in-lined matrix

* draft gallery example text

* removed type annotations from pytest fixtures

* inlined fixture

* renamed masks_to_bounding_boxes to masks_to_boxes

* reformat inline array

* import cleanup

* moved masks_to_boxes into boxes module

* docstring cleanup

* updated docstring

* fix formatting issue

* gallery example

* use torch

* use torch

* use torch

* use torch

* updated docs and test

* cleanup

* updated import

* use torch

* Update gallery/plot_repurposing_annotations.py
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* Update gallery/plot_repurposing_annotations.py
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* Update gallery/plot_repurposing_annotations.py
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* Autodoc

* use torch instead of numpy in tests

* fix build_docs failure

* Closing quotes.
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Aditya Oke <47158509+oke-aditya@users.noreply.github.com>

* Updated annotation to be a Union of Tensor and List

* Updated check_roi_boxes_shape.

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

* Adding code skeleton

* Adding MBConvConfig.

* Extend SqueezeExcitation to support custom min_value and activation.

* Implement MBConv.

* Replace stochastic_depth with operator.

* Adding the rest of the EfficientNet implementation

* Update torchvision/models/efficientnet.py

* Replacing 1st activation of SE with SiLU.

* Adding efficientnet_b3.

* Replace mobilenetv3 assets with custom.

* Switch to standard sigmoid and reconfiguring BN.

* Reconfiguration of efficientnet.

* Add repr

* Add weights.

* Update weights.

* Adding B5-B7 weights.

* Update docs and hubconf.

* Fix doc link.

* Fix typo on comment.

* Adding operator.

* Adding tests

* switching order of `p` and `mode`.

* Remove seed setting.

* Replace stats import with pytest.importorskip.

* Fix doc

* Apply suggestions from code review
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Fixing indentation.

* Adding operator in the documentation.

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

* correct the size of returne tensor

* Styling changes
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Edited roi transforms docs

* remove incorrect param desc

* Fixed confusion  about feature maps according to comment

* WIP

* clang

* docs

* extracted out common utils

* Use better quantization function and pass tensors as parameters

* proper dequantization

* Some tests

* Dequantization optimization, seems to gain a few ms

* clang-format

* again

* more correct test. Had to remove optimization although it almost works

* Also test aligned=True

* remove useless part

* more docs and comments

* Put back optimization with more robust test

* Added check for index upper bound

* avoid possible overflow

* Move common function into common.h

* oops

* scale=1,zero_point=0 makes more sense

* Force batch size of 1 to prevent any indexingbug

* format

* format again

* updated docstring

* put back description comment for pre_calc_bilinear_interpolate

* revert most changes to docstring as it's taken care of in another PR

* some doc improvements

* flake8
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* add tests

* fix bug

* remove tests

* fix comment

* fix comment

* add warning

* fix syntax error

* fix python lint
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* new batched_nms implem

* flake8

* hopefully fix torchscipt tests

* Use where instead of nonzero

* Use same threshold (4k) for CPU and GPU

* Remove use of argsort

* use views again

* remove print

* trying stuff, I don't know what's going on

* previous passed onnx checks so the error isn't in _vanilla func. Trying to return vanilla now

* add tracing decorators

* cleanup

* wip

* ignore new path with ONNX

* use vanilla if tracing...????

* Remove script_if_tracing decorator as it was conflicting with _is_tracing

* flake8

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

* Specify coordinate constraints

* some more

* flake8

* Replace type T with accumulator.

* Upcast tensors of box ops to avoid overflow in multiplications.

* Simplify code and remove used vars.

* Simplify expressions and remove used parenthesis.

* Jit fixes.

* Making check more readable.

* fixing styles

* Exposing LevelMapper params in MultiScaleRoIAlign

Ability to set the `canonical_scale` and `canonical_level` of LevelMapper from the init method of MultiScaleRoIAlign. 
Not sure if this is the right place to expose those parameters. 
Also, additional tests should be written.

Discussion in issue #3129

* MultiScaleRoIAlign keyword-only parameters + docs (pytorch#3129)

* Linter fix (trailing whitespace)
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Replacing all torch.jit.annotations with typing

* Replacing remaining typing

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

* [DOC] Added paper citation for DeformConv2d

* Fixes Pylint error

* Added citation of the previous paper

* Change children() to modules() to ensure init happens in all blocks.

* Update expected values of all detection models.

* Revert "Update expected values of all detection models."

This reverts commit 050b64ae

* Update expecting values.

* Add modulation input for DeformConv2D

* lint

* Patch for GPU CI

* Remove bad cache on CI

* Change default eps value of FrozenBN.

* Update the unit-tests.`

* Update the expected values.

* Revert the expected value and use original eps=0 value for flaky tests.

* Post init change of eps.

* Styles.

* feat: Updated FrozenBN eps to align with BatchNorm

* feat: Added eps to __repr__ of FrozenBN

* test: Updated unittest of __repr__ for FrozenBN

* test: Updated unittest for eps value in BN and FrozenBN

* fix: Revert FrozenBN eps value

* test: Revert test on eps alignment between FrozenBN and BN

* feat: Added __repr__ to MultiScaleRoIAlign

* test: Added unittest for __repr__ of MultiScaleRoIAlign

* feat: Added feature map names in __repr__

* test: Updated unittest
Co-authored-by: vfdev <vfdev.5@gmail.com>

* Added rois shape check in C++

* Fixes code formatting

* Remove accidental include

* - Updated code according to the review
- Replaced old AT_ASSERT/ERROR by new TORCH_CHECK

* Add rough implementation of RetinaNet.

* Move AnchorGenerator to a seperate file.

* Move box similarity to Matcher.

* Expose extra blocks in FPN.

* Expose retinanet in __init__.py.

* Use P6 and P7 in FPN for retinanet.

* Use parameters from retinanet for anchor generation.

* General fixes for retinanet model.

* Implement loss for retinanet heads.

* Output reshaped outputs from retinanet heads.

* Add postprocessing of detections.

* Small fixes.

* Remove unused argument.

* Remove python2 invocation of super.

* Add postprocessing for additional outputs.

* Add missing import of ImageList.

* Remove redundant import.

* Simplify class correction.

* Fix pylint warnings.

* Remove the label adjustment for background class.

* Set default score threshold to 0.05.

* Add weight initialization for regression layer.

* Allow training on images with no annotations.

* Use smooth_l1_loss with beta value.

* Add more typehints for TorchScript conversions.

* Fix linting issues.

* Fix type hints in postprocess_detections.

* Fix type annotations for TorchScript.

* Fix inconsistency with matched_idxs.

* Add retinanet model test.

* Add missing JIT annotations.

* Remove redundant model construction

Make tests pass

* Fix bugs during training on newer PyTorch and unused params in DDP

Needs cleanup and to add back support for images with no annotations

* Cleanup resnet_fpn_backbone

* Use L1 loss for regression

Gives 1mAP improvement over smooth l1

* Disable support for images with no annotations

Need to fix distributed first

* Fix retinanet tests

Need to deduplicate those box checks

* Fix Lint

* Add pretrained model

* Add training info for retinanet
Co-authored-by: Hans Gaiser <hansg91@gmail.com>
Co-authored-by: Hans Gaiser <hans.gaiser@robovalley.com>
Co-authored-by: Hans Gaiser <hans.gaiser@robohouse.com>

* Add compatibility checks for C++ extensions

* Fix lint

* fixies small bug in box_convert

* activates jit test

* Passes JIT test

* fixes typo

* adds error tests, removes assert

* implements to proposal2

* adds boxes conversion

* adds documentation

* adds xywh tests

* fixes small typo

* adds tests

* Remove sphinx theme

* corrects assertions

* cleans code as per suggestion
Signed-off-by: Aditya Oke <okeaditya315@gmail.com>

* reverts assertion

* fixes to assertEqual

* fixes inplace operations

* Adds docstrings

* added documentation

* changes tests

* moves code to box_convert

* adds more tests

* Apply suggestions from code review

Let's leave those changes to a separate PR

* fixes documentation
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