Add task type information when importing#1422
Add task type information when importing#1422wonjuleee merged 26 commits intoopen-edge-platform:developfrom
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| caption = 11 | ||
| super_resolution = 12 | ||
| depth_estimation = 13 | ||
| mixed = 14 |
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Is there a way for users to know what tasks are possible when TaskType is mixed?
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Mixed task can be transformed to any task types. The reason why we are providing mixed is because Datumaro format can have any AnnotationType when importing.
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I could see there are many changes in plugins/data_format. However, I'd rather revert them and let a set of annotation types existing in the dataset to be managed by DatasetStorage (Dataset's dataset item container) or StreamDatasetStorage (a correspondent to StreamDataset). This is because
- This implementation makes a hidden constraint that every dataset extractor (
DatasetBase) should implement an annotation type gatherer such as
https://github.com/openvinotoolkit/datumaro/blob/f97820b6e6d6de107af8561c829db4321e7b7f70/src/datumaro/plugins/data_formats/ade20k2017.py#L72 - This implementation is not aligned with our dataset transform logics. It currently compute
task_typeatDatasetBase. Let's assume that someDatasetBasedecides that a given dataset has two annotation types,LabelandBbox. However, if an arbitrary dataset transform is applied on top of it and it drops everyBbox, we must re-compute a set of annotation types existed in the dataset after transformation. This should be done byDatasetStorageorStreamDatasetStorage.
Following this idea, it would be
class DatasetStorage:
def __init__(self):
...
self._set_of_ann_types: set | None = None
...
@property
def set_of_ann_types(self):
if self._set_of_ann_types is None:
self._set_of_ann_types = set()
# If reset or not computed, run its iterator to compute
for item in self:
for ann in item.annotations:
self._set_of_ann_types.add(ann.type)
return self._set_of_ann_types
@property
def task_type(self):
return infer_task_type_from_set_of_ann_types(self.set_of_ann_types)
...
def _iter_init_cache_unchecked(self) -> Iterable[DatasetItem]:
# Merges the source, source transforms and patch, caches the result
# and provides an iterator for the resulting item sequence.
...
# Reset if there is a possible change
self._set_of_ann_types = NoneThere was a problem hiding this comment.
Thank you for the good idea. When I approached with this way, it needs to have a single iteration of whole dataset for obtaining available task information. So, I have turned to obtain available task during importing. What do you think?
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I have a question. I'm curious why datasets are designed to have a single task_type. For example, if a dataset has both label and bbox annotations, it can be used for both classification and detection tasks. (And even for anomaly cls/det. tasks if the labels are anomalous and normal). However, according to your implementation, it seems like the task_type becomes detection. |
Hi @jihyeonyi, thank you for the question. We are able to identify the mapping between annotation types and tasks in |
wonjuleee
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## develop #1422 +/- ##
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+ Coverage 80.85% 80.98% +0.12%
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Files 271 272 +1
Lines 30689 31137 +448
Branches 6197 6279 +82
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+ Hits 24815 25216 +401
- Misses 4489 4505 +16
- Partials 1385 1416 +31
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| # # when adding a new item, task_type will be updated automatically | ||
| # for ann in item.annotations: | ||
| # self._set_of_ann_types.add(ann.type) | ||
| # self._task_type = TaskAnnotationMapping().get_task(self._set_of_ann_types) |
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||
| def __iter__(self) -> Iterator[DatasetItem]: | ||
| yield from self.stacked_transform | ||
| # yield from self.stacked_transform |
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