Add xai for MaskRCNNModel #95
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sovrasov
reviewed
Jul 3, 2023
| class InstanceSegmentationResult(NamedTuple): | ||
| segmentedObjects: List[Union[SegmentedObject, SegmentedObjectWithRects]] | ||
| # Contan per class saliency_maps and "feature_vector" model output if feature_vector exists | ||
| saliency_map: List[np.ndarray] |
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In other Result objects saliency_map is just an np.ndarray. In OTX it's a list, but here we can unify the representation. Is there any particular reason to use list here?
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Missing classes are represented as empty np.ndarray in that list. Should I fill the corresponding axes with zeros?
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Since the only client of that wrapper here is an IS tiler, a list could be even more convenient. Let's leave it as is.
sovrasov
reviewed
Jul 3, 2023
| result->segmentedObjects.push_back(obj); | ||
| } | ||
| if (has_feature_vector_name) { | ||
| saliency_maps[obj.labelID - 1].push_back(obj.mask); |
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Is that mask resized, or we don't have a flag similar to postprocess_semantic_masks?
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Resized. There's on top
if (postprocess_semantic_masks) {
obj.mask = segm_postprocess(obj, raw_cls_mask, internalData.inputImgHeight, internalData.inputImgWidth);
}
sovrasov
approved these changes
Jul 3, 2023
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