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* Added ResNet variants * ResNet50 example * Simplified example * fixed resnet config * [Automated] Updated coverage badge --------- Co-authored-by: Martin Kozlovsky <[email protected]> Co-authored-by: GitHub Actions <[email protected]>
* fixed incorrect class property call * fixed exporter uploading * uploadCheckpoint uploads on every checkpoint epoch * fix temp files names * updated callback readme * pre-commit run
* option to source custom code in CLI * removed empty dicts * [Automated] Updated coverage badge --------- Co-authored-by: GitHub Actions <[email protected]>
* option to source custom code in CLI * removed empty dicts * fixed issue with removed tensor metadata in match case statements
* forbid extra fields in config * fixed configs
* automatic inference of attach index based on type signature * added inference for input and x names
* fixed link in docs * fixed repvgg backbone * fixed efficientnet
* upload logs to mlflow * added mlflwo instance * multithread log upload * fixed upload logs * fixed log file path * removed exceptions * logging exceptions * fixed typo * reverted exception * moved line * replaced warning with error log * Update trainer.py
* add archiver CLI * add archiver callback * add max_det parameter to EfficientBBoxHead * add enum to categorize tasks for the implemented heads * add archiver tests * adjust Archiver to new nn archive format * pre-comit formatting * add LDF creation and adjust to new nn archive format * update requirements.txt * add opencv-python to requirements.txt * add support for ImplicitKeypointBBoxHead * remove support for ObjectDetectionSSD * Update requirements.txt * Added mlflow and removed opencv * [Automated] Updated coverage badge * add support for SegmentationHead and BiSeNetHead * base archiver tests on model from luxonis-train instead of torchvision * adjust head parameters to changes in NN Archive * adjust keypoint detection head parameters to changes in NN Archive * bugfix - make sure self.max_det is used in nms * add max_det parameter to ImplicitKeypointBBoxHead * adjust task categorization for ImplicitKeypointBBoxHead * fixing Windows PermissionError occuring on file deletion * fixing Windows PermissionError occuring on file deletion due to unreleased logging handlers * add method to remove file handlers keeping the log file open * add a logging statement at the end of archiving * add optuna_integration to requirements.txt * add hard-coded solution to determining is_softmax parameter * added help --------- Co-authored-by: Martin Kozlovský <[email protected]> Co-authored-by: GitHub Actions <[email protected]>
* extend NN Archive generation test coverage to cover all implemented heads * [Automated] Updated coverage badge --------- Co-authored-by: GitHub Actions <[email protected]>
* uploading all checkpoints * fix names * removed comment
* handling SIGTERM signal * resume argument takes path
* handling SIGTERM signal * resume argument takes path * basic task group labels support * updated requirements * fixed tests * fixed loader test * Update luxonis_train/models/luxonis_model.py Co-authored-by: conorsim <[email protected]> --------- Co-authored-by: conorsim <[email protected]>
* option to set torch matmul precision for tensor cores * updated readme
* fixed reset not being called * added metric resets * removed inheritance * proper oks reset * removed unnecessary resets * added annotations
* Added seed to config for reproducibility * fixed seg drawing when using torch deterministic backend * added deterministic order of creating nodes * removed seed from example config * added reproducability to inspect * formatting --------- Co-authored-by: DrejcPesjak <[email protected]> Co-authored-by: Martin Kozlovský <[email protected]>
* support for custom loaders and datasets * updated configs * custom loaders in inspect command * updated inspect for multi-task labels * removed custom loader from test config * deleted comment * deleted comment * removed custom dataset * removed comment * skipping archiver test untill fixed in luxonis-ml * [Automated] Updated coverage badge --------- Co-authored-by: GitHub Actions <[email protected]>
Co-authored-by: GitHub Actions <[email protected]>
Co-authored-by: GitHub Actions <[email protected]>
Co-authored-by: GitHub Actions <[email protected]>
Co-authored-by: GitHub Actions <[email protected]> Co-authored-by: Martin Kozlovsky <[email protected]>
Co-authored-by: Martin Kozlovsky <[email protected]> Co-authored-by: Michal Sejak <[email protected]> Co-authored-by: GitHub Actions <[email protected]>
Co-authored-by: Martin Kozlovsky <[email protected]>
Test Results2 files 2 suites 18s ⏱️ For more details on these errors, see this check. Results for commit 4265845. |
Co-authored-by: GitHub Actions <[email protected]>
kozlov721
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Sep 20, 2024
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There's a lot of code duplication, confusing commented code and incorrect docstrings. Also please make sure the CI pipeline passes (see CONTRIBUTING.md for more info).
Otherwise looks good
| return assigned_labels, assigned_bboxes, assigned_scores | ||
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| class RotatedTaskAlignedAssigner(TaskAlignedAssigner): |
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I'd move this to a separate file
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And the name could be changed to OrientedTaskAlignedAssigner to be consistent with the OBB naming.
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| class VarifocalLoss(nn.Module): |
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The VarifocalLoss is already implemented in luxonis_train.attached_modules.losses.adaptive_detection_loss
luxonis_train/assigners/utils.py
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| """Select the positive anchor center in gt for rotated bounding | ||
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| Args: |
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The docstring must be in epytext
| """ | ||
| super().__init__(**kwargs) | ||
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| if not isinstance(self.node, EfficientOBBoxHead): |
Collaborator
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This is not necessary; the check is done automatically in BaseAttachedModule.
| gt_cxcywhr = target[:, :, 1:] | ||
| mask_gt = (gt_cxcywhr.sum(-1, keepdim=True) > 0).float() | ||
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| # TODO: log change of assigner (once common Logger) |
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There is no change of assigner to log
| return stack([pt1, pt2, pt3, pt4], -2) | ||
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| def xyxy2xywh(x: Tensor) -> Tensor: |
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There's already torchvision.ops.box_convert for this
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| def xywh2xyxy(x: Tensor) -> Tensor: |
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torchision.ops.box_convert
| return iou | ||
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| def probiou( |
Collaborator
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more readable function name
| return iou | ||
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| def batch_probiou(obb1: Tensor, obb2: Tensor, eps: float = 1e-7) -> Tensor: |
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This function is almost identical to probiou, why do we need both?
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| def non_max_suppression_obb( |
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This is almost identical to non_max_suppression. Can you improve non_max_suppression to also support oriented bboxes instead of having this function?
Co-authored-by: Martin Kozlovsky <[email protected]>
Co-authored-by: Martin Kozlovsky <[email protected]> Co-authored-by: GitHub Actions <[email protected]>
Collaborator
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Closing for now as the priorities shifted and the code is at this point too far behind the current main branch. We might revisit the implementation of this task type + model in the future |
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This PR introduces oriented bounding box functionality for object detection tasks, built on top of
DetectionModel.Implemented classes