Experimental datumaro implementation #4920

AlbertvanHouten · 2025-10-20T08:10:13Z

This pull request introduces the new experimental dataset into the dataset classes of OTX. A lot of logic has been migrated to datumaro such as management of image color channels and the tiling implementation.

Summary

How to test

Checklist

The PR title and description are clear and descriptive
I have manually tested the changes
All changes are covered by automated tests
All related issues are linked to this PR (if applicable)
Documentation has been updated (if applicable)

Signed-off-by: Albert van Houten <[email protected]> Co-authored-by: Leonardo Lai <[email protected]>

…ing_extensions into feature/datumaro

Signed-off-by: Albert van Houten <[email protected]>

…4751) Signed-off-by: Albert van Houten <[email protected]> Co-authored-by: Grégoire Payen de La Garanderie <[email protected]>

…ing_extensions into feature/datumaro # Conflicts: # library/src/otx/data/dataset/base_new.py # library/src/otx/data/dataset/classification_new.py # library/src/otx/data/dataset/detection_new.py # library/src/otx/data/dataset/instance_segmentation_new.py # library/src/otx/data/dataset/keypoint_detection_new.py # library/src/otx/data/dataset/segmentation_new.py # library/src/otx/data/entity/sample.py

Signed-off-by: Albert van Houten <[email protected]>

…ing_extensions into feature/datumaro

Signed-off-by: Albert van Houten <[email protected]>

Signed-off-by: Albert van Houten <[email protected]> Co-authored-by: Albert van Houten <[email protected]>

Signed-off-by: Albert van Houten <[email protected]>

…orm/training_extensions into feature/datumaro

Signed-off-by: Albert van Houten <[email protected]>

…ing_extensions into feature/datumaro Signed-off-by: Albert van Houten <[email protected]> # Conflicts: # library/pyproject.toml # library/src/otx/data/dataset/anomaly.py # library/src/otx/data/dataset/base.py # library/src/otx/data/dataset/classification.py # library/src/otx/data/dataset/detection.py # library/src/otx/data/dataset/instance_segmentation.py # library/src/otx/data/dataset/keypoint_detection.py # library/src/otx/data/dataset/segmentation.py # library/src/otx/data/dataset/tile.py # library/src/otx/data/factory.py # library/src/otx/data/module.py # library/src/otx/data/transform_libs/torchvision.py # library/tests/unit/data/samplers/test_class_incremental_sampler.py # library/tests/unit/data/utils/test_utils.py

Copilot

Pull Request Overview

This PR implements an experimental Datumaro dataset integration for OTX, transitioning from legacy Datumaro components to the new experimental Dataset API. The changes introduce a new sample-based architecture while maintaining compatibility with existing OTX functionality.

Key changes:

Migration from legacy Datumaro components to experimental Dataset API with schema-based conversion
Introduction of new OTXSample-based data entities with PyTree registration for TorchVision compatibility
Replacement of legacy polygon handling with numpy ragged arrays for better performance
Comprehensive test updates and new test implementations for the updated dataset architecture

Reviewed Changes

Copilot reviewed 59 out of 59 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
pyproject.toml	Updates Datumaro dependency to experimental branch version
library/src/otx/data/dataset/*.py	Implements new dataset classes using experimental Datumaro with sample-based architecture
library/src/otx/data/entity/sample.py	Adds new OTXSample classes with PyTree registration for transform compatibility
library/tests/unit/types/test_label.py	Updates label tests to use new hierarchical label categories
library/tests/unit/data/transform_libs/test_torchvision.py	Converts polygon handling from Datumaro objects to numpy arrays

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

Copilot · 2025-10-29T09:25:48Z

library/tests/unit/data/transform_libs/test_torchvision.py

+    fake_polygons = np.array(
+        [
+            np.array([[10, 10], [50, 10], [50, 50], [10, 50]]),  # Rectangle polygon for first object
+            np.array([[60, 60], [100, 60], [100, 100], [60, 100]]),  # Rectangle polygon for second object
+        ]
+    )


The polygon data structure has changed from Datumaro Polygon objects to numpy ragged arrays, but the test doesn't validate that the new format maintains the same geometric properties. Consider adding assertions to verify that the polygon areas and vertex coordinates are equivalent between the old and new formats.

Copilot · 2025-10-29T09:25:48Z

library/tests/unit/data/transform_libs/test_torchvision.py

-            p = np.asarray(polygon.points)
-            p[0::2] = width - p[0::2]
-            return p.tolist()
+        def revert_hflip(polygon: np.ndarray, width: int) -> np.ndarray:


The function modifies the input polygon array in-place, which could cause issues if the original array is used elsewhere. The function should create a copy before modification to avoid unintended side effects.

Suggested change

def revert_hflip(polygon: np.ndarray, width: int) -> np.ndarray:

def revert_hflip(polygon: np.ndarray, width: int) -> np.ndarray:

polygon = polygon.copy()

Copilot · 2025-10-29T09:25:49Z

library/src/otx/data/transform_libs/utils.py

+            rescaled_polygons[i] = scaled_points
+        else:
+            # Handle empty or invalid polygons
+            rescaled_polygons[i] = np.array([[0, 0], [0, 0], [0, 0]], dtype=np.float32)


[nitpick] Creating a dummy polygon with three identical points may not be the best approach for handling empty/invalid polygons. Consider using an empty array or a clearly marked invalid polygon structure that can be properly filtered out later in the pipeline.

Suggested change

rescaled_polygons[i] = np.array([[0, 0], [0, 0], [0, 0]], dtype=np.float32)

rescaled_polygons[i] = np.empty((0, 2), dtype=np.float32)

why exactly 3 points BTW? Why not just empty tensor or 0 tensor?

Not entirely sure why Greg applied it in this manner, but I guess there is a validation step somewhere that requires 3 points for a valid polygon.

library/src/otx/data/transform_libs/utils.py

# Conflicts: # library/tests/unit/data/dataset/test_base.py # library/tests/unit/data/test_tiling.py

Signed-off-by: Albert van Houten <[email protected]>

…ing_extensions into feature/datumaro

…atumaro

Signed-off-by: Albert van Houten <[email protected]>

Copilot

Pull request overview

Copilot reviewed 98 out of 99 changed files in this pull request and generated 9 comments.

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

library/tests/unit/types/test_label.py

Copilot · 2025-12-16T14:56:36Z

library/src/otx/types/label.py

+            object.__setattr__(
+                label_group, "labels", [id_to_name_mapping.get(label, label) for label in label_group.labels]
+            )


Using object.__setattr__ to mutate frozen dataclass attributes suggests that the upstream dataclass (HierarchicalLabelCategories or LabelGroup) is frozen. Consider whether these objects should be mutable by design or if there's a better approach (e.g., creating new instances rather than mutating in place) to avoid circumventing immutability guarantees.

Copilot · 2025-12-16T14:56:36Z

library/src/otx/data/transform_libs/utils.py

+        if img.ndim == 3 and img.shape[0] <= 4 and img.shape[0] < min(img.shape[1:]):
+            # Image is in CHW format, transpose to HWC
+            return np.ascontiguousarray(img.transpose(1, 2, 0))


The heuristic assumes that if the first dimension is ≤4 and smaller than other dimensions, the image is in CHW format. This may fail for edge cases where an image legitimately has height or width of 4 or less. Consider adding additional validation or a more robust channel detection mechanism to prevent incorrect transpositions.

Copilot · 2025-12-16T14:56:36Z

library/src/otx/data/transform_libs/torchvision.py

+    def _is_native_torchvision_transform(self, transform: tvt_v2.Transform) -> bool:
+        """Check if the transform is a native torchvision transform."""
+        module = type(transform).__module__
+        return module.startswith("torchvision.")


The check relies on the module name starting with 'torchvision.'. If third-party packages or custom transforms mimic this naming convention, they may be incorrectly classified. Consider a more robust mechanism, such as checking against a known set of torchvision transform types or using an interface/base class check.

Suggested change

return module.startswith("torchvision.")

# Only treat as native if defined in torchvision.transforms.v2 or its _transforms submodule

return module == "torchvision.transforms.v2" or module == "torchvision.transforms.v2._transforms"

Copilot · 2025-12-16T14:56:37Z

library/src/otx/backend/openvino/models/segmentation.py

+                    if (ph, pw) == (tw, th):
+                        pm = pm.transpose(-1, -2)
+                    # Else if target seems swapped relative to prediction, transpose target
+                    elif (th, tw) == (pw, ph):


The dimension alignment logic attempts to fix spatial mismatches by transposing. However, this heuristic may not handle all cases correctly, especially if dimensions are neither equal nor swapped. Consider logging a warning when dimensions mismatch and a transpose is applied, so that unexpected dimension mismatches can be detected and investigated.

Suggested change

if (ph, pw) == (tw, th):

pm = pm.transpose(-1, -2)

# Else if target seems swapped relative to prediction, transpose target

elif (th, tw) == (pw, ph):

if (ph, pw) == (tw, th):

log.warning(

f"Predicted mask shape {pm.shape} does not match target mask shape {tm.shape}. "

"Transposing predicted mask to align spatial dimensions."

)

pm = pm.transpose(-1, -2)

# Else if target seems swapped relative to prediction, transpose target

elif (th, tw) == (pw, ph):

log.warning(

f"Target mask shape {tm.shape} does not match predicted mask shape {pm.shape}. "

"Transposing target mask to align spatial dimensions."

)

library/tests/integration/api/test_engine_api.py

Copilot · 2025-12-16T14:56:37Z

library/tests/integration/api/test_augmentation.py

        "RandomPhotometricDistort",
        "RandomGaussianBlur",
        "RandomGaussianNoise",


The removed 'TopdownAffine' augmentation from the configurable_augs list may indicate that this augmentation is no longer supported or has been moved elsewhere. Ensure that test coverage for this augmentation exists in another test or that its removal is intentional and documented.

Why did you remove TopdownAffine from configurable augs list? We actually provide possibility to turn on/off this augmentation in Geti

iirc, TopdownAffine can't be an optional augmentation since it is responsible for resizing the image. Otherwise, at least for this test, the images were in the wrong size.

Signed-off-by: Albert van Houten <[email protected]>

…ing_extensions into feature/datumaro Signed-off-by: Albert van Houten <[email protected]> # Conflicts: # library/uv.lock

AlbertvanHouten · 2025-12-17T12:42:21Z

library/src/otx/recipe/semantic_segmentation/dino_v2_tile.yaml

@kprokofi please review if these changes make sense. The old logic only worked when tiling with square images.

I think padding do nothing here. We resize and pad to the same size.

Without the padding, tiling only works if the image is square (which it was previously in the integration test). I have replaced it with another dataset, as the polygons in the dataset were self-intersecting, which isn't supported, and this new dataset does have rectangle images, which broke specifically this tiling recipe.

leoll2 · 2025-12-17T16:51:28Z

.github/workflows/lib-lint-and-test.yaml

+      # Unit test models separately using --forked flag so that each is run in a separate process.
+      # Running these models after each other from the same process can cause segfaults
+      - name: Unit testing models
        working-directory: library
-        run: uv run pytest tests/unit --cov --cov-report=xml
+        env:
+          OMP_NUM_THREADS: "1"
+        run: uv run pytest tests/unit/backend/native/models --cov --cov-report=xml --cov-append --forked


Do you have any idea what causes the segfaults?

Unclear to me. I've tried a few different things to see if it would fix it, like clearing up pytorch memory usage, but this was the only way that I could fix it.

library/src/otx/backend/native/callbacks/gpu_mem_monitor.py

leoll2 · 2025-12-17T17:01:47Z

library/src/otx/data/factory.py

+        # TODO(gdlg): Add support for ignore_index again
+        ignore_index: int = 255,  # noqa: ARG003


There is a TODO here. Do you know what's the purpose of this ignore_index parameter?

Ignore index is necessary for Semantic Segmentation tasks. This index helps to ignore marked pixels.

Is there any use case for the ignore index being different from the value used for background pixels? Or is it always the same?

It is always the same if we don't count background as training class. Sometimes (in some datasets), ignore_index = -1 is used to indicate invalid pixels instead of 255.

However in the datasets like Pascal VOC we use background in training and mark it as 0 class

library/src/otx/recipe/detection/rtmdet_tiny.yaml

kprokofi

First round of comments

library/src/otx/backend/native/models/detection/rtdetr.py

kprokofi · 2025-12-19T10:46:54Z

library/src/otx/backend/native/tools/tile_merge.py

        self,
        batch_tile_preds: list[OTXPredBatch],
-        batch_tile_attrs: list[list[dict]],
+        batch_tile_attrs: list[list[TileInfo]],


Since you are changing name tile_attrs to tile_info, could you change it everywhere for consistency?

kprokofi · 2025-12-19T10:49:32Z

library/src/otx/backend/openvino/models/segmentation.py

-            for pred_mask, target_mask in zip(preds.masks, inputs.masks)
-        ]
+        aligned: list[dict[str, Any]] = []
+        for pred_mask, target_mask in zip(preds.masks, inputs.masks):


Why not just pm, tm then? (for pm, tm in ..)

kprokofi · 2025-12-19T11:00:36Z

library/src/otx/backend/openvino/models/segmentation.py

+            if tm.ndim == 2:
+                tm = tm.unsqueeze(0)
+
+            # Align spatial dimensions if swapped (e.g., [1, W, H] vs [1, H, W])


Why spatial dimensions can be misaligned and swapped? Code here looks as a hack for me. We need to align targets and predictions on the dataset preparation / output preprocessing stage, not here.

Let me recheck this after my break. I have made many changes to get training to work, and probably some of these checks can be removed. I do think it's still good to make some of this code more robust against channel orders.

kprokofi · 2025-12-19T12:11:28Z

library/src/otx/data/entity/sample.py

+
+
+@register_pytree_node
+class InstanceSegmentationSampleWithMask(OTXSample):


Can't we combine InstanceSegmentationSample and InstanceSegmentationSampleWithMask? The difference in mask field only.

Why do we still include polygons in InstanceSegmentationSampleWithMask?

kprokofi · 2025-12-19T12:45:01Z

library/src/otx/data/factory.py

+        cls,
        task: OTXTaskType,
-        dm_subset: DmDataset,
+        dm_subset: DmDataset | DatasetNew,


Is it for backward compatibility? I don't think we need that. cc @leoll2
Same for "convert_from_legacy".

GetiTune will depend on the new Datumaro Dataset, Geti classic has no dependency on the develop branch. OTX uses file system, not datumaro directly. I propose to support only the latest version if no stoppers.

Legacy datumaro datasets can still be passed here since the conversion to the new datumaro dataset schema happens in this factory. We will remove this in the future, but as long as we still support the legacy datasets in datumaro it also makes sense to keep it here. If you already have a new datumaro Dataset, you can initialize the tasks dataset immediately.

It also reduces the scope for this PR a bit.

kprokofi · 2025-12-19T13:46:05Z

library/src/otx/data/transform_libs/torchvision.py

+                elif isinstance(image, torch.Tensor):
+                    # Handle tensor in HWC format - convert to CHW
+                    # If the last dimension is <= 4 and smaller than other dims, it's likely HWC format
+                    if image.ndim == 3 and image.shape[-1] <= 4 and image.shape[-1] < min(image.shape[:-1]):


Why do we need to convert tensors from CHW (what is actually needed for training and transforms) to HWC?

library/src/otx/data/transform_libs/torchvision.py

kprokofi · 2025-12-19T14:00:17Z

library/src/otx/data/transform_libs/utils.py

    if isinstance(img, np.ndarray):
+        # Check if the numpy array is in CHW format (channels should be <= 4 typically)
+        # If the first dimension is small (<=4) and smaller than other dimensions, it's likely CHW format
+        if img.ndim == 3 and img.shape[0] <= 4 and img.shape[0] < min(img.shape[1:]):


Same comment as above. I think we should always stick to CHW format.

kprokofi · 2025-12-19T14:04:13Z

library/src/otx/data/transform_libs/utils.py

+            rescaled_polygons[i] = scaled_points
+        else:
+            # Handle empty or invalid polygons
+            rescaled_polygons[i] = np.array([[0, 0], [0, 0], [0, 0]], dtype=np.float32)


why exactly 3 points BTW? Why not just empty tensor or 0 tensor?

kprokofi

Second round of comments

kprokofi · 2025-12-19T14:28:17Z

library/src/otx/recipe/semantic_segmentation/dino_v2_tile.yaml

I think padding do nothing here. We resize and pad to the same size.

kprokofi · 2025-12-19T14:38:48Z

library/src/otx/types/label.py


    @classmethod
-    def from_dm_label_groups(cls, dm_label_categories: LabelCategories) -> LabelInfo:
+    def from_dm_label_groups(cls, dm_label_categories: HierarchicalLabelCategories) -> LabelInfo:


Since we deleted this in OTXDataset and we define LabelInfo directly, could you clean this class? Specifically, we don't need "from_dm_label_groups", "from_dm_label_groups_arrow", etc.

kprokofi · 2025-12-19T14:41:11Z

library/tests/conftest.py

+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        if is_xpu_available():
+            import intel_extension_for_pytorch as ipex


We don't use IPEX. It is outdated

kprokofi · 2025-12-19T14:44:41Z

library/tests/integration/api/test_augmentation.py

        "RandomPhotometricDistort",
        "RandomGaussianBlur",
        "RandomGaussianNoise",


Why did you remove TopdownAffine from configurable augs list? We actually provide possibility to turn on/off this augmentation in Geti

Signed-off-by: Albert van Houten <[email protected]>

AlbertvanHouten and others added 16 commits September 4, 2025 10:10

Classification Dataset refactor (#4606)

9bd3fa7

Signed-off-by: Albert van Houten <[email protected]> Co-authored-by: Leonardo Lai <[email protected]>

Merge branch 'develop' of https://github.com/open-edge-platform/train…

373a046

…ing_extensions into feature/datumaro

Detection dataset updated to use new datumaro version (#4675)

9f26bb8

Signed-off-by: Albert van Houten <[email protected]>

Update the segmentation dataset to use new Datumaro dataset (#4676)

8b2fe8b

Instance segmentation support with the new Datumaro dataset (#4686)

3d84b50

Migrate OTX Keypoint detection to use the new experimental dataset (#…

d1ff0dc

…4751) Signed-off-by: Albert van Houten <[email protected]> Co-authored-by: Grégoire Payen de La Garanderie <[email protected]>

Move new dataset files to library folder

a3d82dd

Signed-off-by: Albert van Houten <[email protected]>

Update OTX classification task to use legacy converter (#4769)

c1b8084

Signed-off-by: Albert van Houten <[email protected]>

Merge branch 'develop' of https://github.com/open-edge-platform/train…

9fd7abc

…ing_extensions into feature/datumaro

Migrate anomaly task to use the new Datumaro dataset (#4770)

4751cb2

Merge branch 'develop' of https://github.com/open-edge-platform/train…

5ff3b59

…ing_extensions into feature/datumaro

Point datumaro deps to the dev branch

6088088

Signed-off-by: Albert van Houten <[email protected]>

Tilling support with the experimental Datumaro dataset (#4858)

7827ca5

Signed-off-by: Albert van Houten <[email protected]> Co-authored-by: Albert van Houten <[email protected]>

Use new datumaro for multi label and hierarchical classification (#4815)

64e58ca

Signed-off-by: Albert van Houten <[email protected]>

Merge branch 'feature/datumaro' of https://github.com/open-edge-platf…

fdb6afe

…orm/training_extensions into feature/datumaro

github-actions bot added DEPENDENCY Any changes in any dependencies (new dep or its version) should be produced via Change Request on PM TEST Any changes in tests BUILD labels Oct 20, 2025

AlbertvanHouten added 4 commits October 23, 2025 11:55

Replace old datasets with new one

bdaa084

Signed-off-by: Albert van Houten <[email protected]>

Update hierarchical label tests to use new datumaro categories

eb6fbda

Signed-off-by: Albert van Houten <[email protected]>

Cleanup remaining tests

a7d0c51

Signed-off-by: Albert van Houten <[email protected]>

AlbertvanHouten changed the title ~~Feature/datumaro~~ Experimental datumaro implementation Oct 29, 2025

AlbertvanHouten marked this pull request as ready for review October 29, 2025 09:24

AlbertvanHouten requested a review from a team as a code owner October 29, 2025 09:24

Copilot AI review requested due to automatic review settings October 29, 2025 09:24

Copilot AI reviewed Oct 29, 2025

View reviewed changes

leoll2 added 2 commits November 1, 2025 10:13

Merge branch 'develop' into feature/datumaro

3efe5aa

# Conflicts: # library/tests/unit/data/dataset/test_base.py # library/tests/unit/data/test_tiling.py

fix random generator

715bbb5

AlbertvanHouten and others added 18 commits December 8, 2025 16:33

Fix bug in integration workflow causing only segmentation tests to run.

fecdba9

Signed-off-by: Albert van Houten <[email protected]>

Force multiprocessing start method.

7f8efeb

Signed-off-by: Albert van Houten <[email protected]>

Mock dataloader in engine test

c077100

Signed-off-by: Albert van Houten <[email protected]>

Remove tiling tests as the tiling logic has moved to datumaro.

89bf2a6

Signed-off-by: Albert van Houten <[email protected]>

Make transforms more robust to different channel orders

8de7a1e

Signed-off-by: Albert van Houten <[email protected]>

Further channel order improvements

d2fe10b

Signed-off-by: Albert van Houten <[email protected]>

Fix polygon reshape issue

6627388

Signed-off-by: Albert van Houten <[email protected]>

Merge branch 'develop' into feature/datumaro

56070d8

Merge branch 'develop' of https://github.com/open-edge-platform/train…

9155bd7

…ing_extensions into feature/datumaro

Merge remote-tracking branch 'origin/feature/datumaro' into feature/d…

9c84d81

…atumaro

Update lock file

3296092

Signed-off-by: Albert van Houten <[email protected]>

Add rust install step

80b34da

Signed-off-by: Albert van Houten <[email protected]>

Test with another datasetset

9e49023

Signed-off-by: Albert van Houten <[email protected]>

Test with num workers = 0

38f50f6

Signed-off-by: Albert van Houten <[email protected]>

Test grayscale fix

b0e2f01

Signed-off-by: Albert van Houten <[email protected]>

Test grayscale fix

5cc68bf

Signed-off-by: Albert van Houten <[email protected]>

Test grayscale fix

7444866

Signed-off-by: Albert van Houten <[email protected]>

Further fixes for channel alignment.

c0887a2

Signed-off-by: Albert van Houten <[email protected]>

AlbertvanHouten requested a review from Copilot December 16, 2025 14:53

Copilot AI reviewed Dec 16, 2025

View reviewed changes

AlbertvanHouten added 4 commits December 16, 2025 16:04

Revert skipping of tests

c29055c

Signed-off-by: Albert van Houten <[email protected]>

Fix dino v2 tile recipe

6ceeb48

Signed-off-by: Albert van Houten <[email protected]>

Cleanup

e443fff

Signed-off-by: Albert van Houten <[email protected]>

Merge branch 'develop' of https://github.com/open-edge-platform/train…

6318155

…ing_extensions into feature/datumaro Signed-off-by: Albert van Houten <[email protected]> # Conflicts: # library/uv.lock

AlbertvanHouten commented Dec 17, 2025

View reviewed changes

leoll2 reviewed Dec 17, 2025

View reviewed changes

AlbertvanHouten requested a review from kprokofi December 18, 2025 09:00

kprokofi reviewed Dec 19, 2025

View reviewed changes

addres a few PR comments.

1c14e75

Signed-off-by: Albert van Houten <[email protected]>

	def revert_hflip(polygon: np.ndarray, width: int) -> np.ndarray:
	def revert_hflip(polygon: np.ndarray, width: int) -> np.ndarray:
	polygon = polygon.copy()

	rescaled_polygons[i] = np.array([[0, 0], [0, 0], [0, 0]], dtype=np.float32)
	rescaled_polygons[i] = np.empty((0, 2), dtype=np.float32)

	return module.startswith("torchvision.")
	# Only treat as native if defined in torchvision.transforms.v2 or its _transforms submodule
	return module == "torchvision.transforms.v2" or module == "torchvision.transforms.v2._transforms"

		# TODO(gdlg): Add support for ignore_index again
		ignore_index: int = 255, # noqa: ARG003



		@register_pytree_node
		class InstanceSegmentationSampleWithMask(OTXSample):

Experimental datumaro implementation #4920

Are you sure you want to change the base?

Experimental datumaro implementation #4920

Uh oh!

Conversation

AlbertvanHouten commented Oct 20, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Summary

How to test

Checklist

Uh oh!

Copilot AI left a comment

Choose a reason for hiding this comment

Pull Request Overview

Reviewed Changes

Uh oh!

Copilot AI Oct 29, 2025

Choose a reason for hiding this comment

Uh oh!

Copilot AI Oct 29, 2025

Choose a reason for hiding this comment

Uh oh!

Copilot AI Oct 29, 2025

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Copilot AI left a comment

Choose a reason for hiding this comment

Pull request overview

Uh oh!

Uh oh!

Uh oh!

Copilot AI Dec 16, 2025

Choose a reason for hiding this comment

Uh oh!

Copilot AI Dec 16, 2025

Choose a reason for hiding this comment

Uh oh!

Copilot AI Dec 16, 2025

Choose a reason for hiding this comment

Uh oh!

Copilot AI Dec 16, 2025

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Uh oh!

Copilot AI Dec 16, 2025

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

AlbertvanHouten Dec 19, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

AlbertvanHouten Dec 19, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

kprokofi Dec 19, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

AlbertvanHouten commented Oct 20, 2025 •

edited

Loading

AlbertvanHouten Dec 19, 2025 •

edited

Loading

AlbertvanHouten Dec 19, 2025 •

edited

Loading

kprokofi Dec 19, 2025 •

edited

Loading

AlbertvanHouten Dec 19, 2025 •

edited

Loading