GitLab

* Add a warning if a clip can't be get from a video in VideoClips

* Update torchvision/datasets/video_utils.py
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* Add a test
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* Enable jit tests in all models and add warning if checkModule() tests are skipped.

* Turning on JIT tests on CI.

* Fixing broken unit-tests.

* Refactoring and cleaning up duplicate code.

* Add explicit check for number of channels

Example why you need to check it:
`M = torch.randint(low=0, high=2, size=(6, 64, 64), dtype = torch.float)`
When you put this input through to_pil_image without mode argument, it converts to uint8 here:
```
if pic.is_floating_point() and mode != 'F':
            pic = pic.mul(255).byte()
```
and change the mode to RGB here:
```
if mode is None and npimg.dtype == np.uint8:
            mode = 'RGB'
```
Image.fromarray doesn't raise if provided with mode RGB and just cut number of channels from what you have to 3

* Check number of channels before processing

* Add test for invalid number of channels

* Add explicit check for number of channels

Example why you need to check it:
`M = torch.randint(low=0, high=2, size=(6, 64, 64), dtype = torch.float)`
When you put this input through to_pil_image without mode argument, it converts to uint8 here:
```
if pic.is_floating_point() and mode != 'F':
            pic = pic.mul(255).byte()
```
and change the mode to RGB here:
```
if mode is None and npimg.dtype == np.uint8:
            mode = 'RGB'
```
Image.fromarray doesn't raise if provided with mode RGB and just cut number of channels from what you have to 3

* Check number of channels before processing

* Add test for invalid number of channels

* Put check after channel dim unsqueeze

* Add test if error message is matching

* Delete redundant code

* Bug fix in checking for bad types
Co-authored-by: Demyanchuk <demyanca@mh-hannover.local>
Co-authored-by: vfdev <vfdev.5@gmail.com>

* Add support of mode and remove channels.

* Replacing integer mode with define constants.

* Add AnchorGenerator with ground-truth outputs

* Minor fixes

* Adding output channels implementation for pngs.

* Adding tests for png.

* Adding channels in the API and documentation.

* Fixing formatting.

* Refactoring test_image.py to remove huge grace_hopper_517x606.pth file from assets and reduce duplicate code. Moving jpeg assets used by encode and write unit-tests on their separate folders.

* Adding output channels implementation for jpegs. Fix asset locations.

* Add tests for JPEG, adding the channels in the API and documentation and adding checks for inputs.

* Changing folder for unit-test.

* Fixing windows flakiness, removing duplicate test.

* Replacing components to channels.

* Adding reference for supporting CMYK.

* Minor changes: num_components to output_components, adding comments, fixing variable name etc.

* Reverting output_components to num_components.

* Replacing decoding with generic method on tests.

* Palette converted to Gray.

* Replace the Pallete with an RGB + Alpha file.

* Correct typo on name.

Revert "Change the `SUM_OF_PRETRAINED_RESNET18_PARAMS` decimal values to resolve broken tests. (#2978)" (#2990)

This reverts commit 1f4e3a46.

* Add support of different color types in readpng.

* Adding test images and unit-tests.

* Use closest possible type.

* Fix formatting.

* tries adding anchor tests

* fixes lint

* tries fixing

* tries one more time

* fixes the test

* Making quantized inception torchscriptable.

* Adding a test.

* Fix mypy warning.

* Remove model download from tests

* Refactor trainable_layers checks in detection models

* Bugfix

* Finish tests and fixes

* Fix lint

* 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

- fixed problem with error computation between results
- refactored tensor cast for resize
- fixed round usage

* Simplify the ACCEPT=True logic in assertExpected().

* Separate the expected filename estimation from assertExpected

* Unflatten expected values.

* Assert for duplicate scores if primary check fails.

* Remove custom exceptions for algorithms and add a compact function for shrinking large ouputs.

* Removing unused variables.

* Add warning and comments.

* Re-enable all autocast unit-test for detection and marking the tests as skipped in partial validation.

* Move test skip at the end.

* Changing the warning message.

* Simplify the ACCEPT=True logic in assertExpected().

* Separate the expected filename estimation from assertExpected

* Make read_file accept unicode strings on Windows

* More fixes

* Remove definitions from source files

* Move string definitions to header

* Add checks

* Fix comments

* Update macro

* Fix comments

* Fix lint

* include windows header

* Change func signature in header

* Use from_blob

* Fix fread calls

* Fix clang format

* Fix missing return

* Avoid copy

* Overwriting FrozenBN eps=0.0 if pretrained=True for detection models.

* Moving the method to detection utils and adding comments.

* Moving Autocast mtest in RoiOpTester.

* Adding autocast test on DeformConvTester.

* Adding autocast test on NMSTester.

* Fixing styles.

* 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>

* Vectorize operations, across all feaure levels.

* Remove unnecessary other_outputs variable.

* Split per feature level.

* Perform batched_nms across feature levels.

* Add extra parameter for limiting detections before and after nms.

* Restoring default threshold.

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

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

* Modify expected value and threshold for retinanet unit-test.

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

* Removing unnecessary variable.

* Refactoring common_utils.py to remove unused vars.

* Removing unused value and changing use of accept_output.
Co-authored-by: vfdev <vfdev.5@gmail.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Video reader now returns dicts

* docs update

* Minor improvements
Co-authored-by: Bruno Korbar <bjuncek@Frazz.local>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* 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>

* Minor improvements to VideoReader

* update jupyter notebook with new naming
Co-authored-by: Bruno Korbar <bjuncek@gmail.com>

* Adding checks on forward and backward passes.

* Adding unit-tests.

* initial API documentation attempt

* test the docs

* initial commit

* updating test to match the registration

* adding the warning on unsucessful import

* Try to do conditional import

* Simple fix?

* clearing up docs

* docstring commit

* Adding types in arguments
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* reverting warning commit

* addressing Francisco's comments

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

* Revert "reverting warning commit"

This reverts commit bd1a3dd4f3b97709ab59c744962e11174757f8ce.

* Revert "adding the warning on unsucessful import"

This reverts commit afef7df9eaa73bf80246e6d9114cb4c30b16f0ce.

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