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from detectron2.data.catalog import Metadata
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from detectron2.evaluation import COCOEvaluator
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from densepose.data.datasets.coco import (
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get_contiguous_id_to_category_id_map,
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maybe_filter_categories_cocoapi,
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)
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def _maybe_add_iscrowd_annotations(cocoapi) -> None:
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for ann in cocoapi.dataset["annotations"]:
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if "iscrowd" not in ann:
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ann["iscrowd"] = 0
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class Detectron2COCOEvaluatorAdapter(COCOEvaluator):
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def __init__(
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self,
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dataset_name,
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output_dir=None,
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distributed=True,
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):
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super().__init__(dataset_name, output_dir=output_dir, distributed=distributed)
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maybe_filter_categories_cocoapi(dataset_name, self._coco_api)
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_maybe_add_iscrowd_annotations(self._coco_api)
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if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
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self._maybe_substitute_metadata()
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def _maybe_substitute_metadata(self):
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cont_id_2_cat_id = get_contiguous_id_to_category_id_map(self._metadata)
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cat_id_2_cont_id = self._metadata.thing_dataset_id_to_contiguous_id
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if len(cont_id_2_cat_id) == len(cat_id_2_cont_id):
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return
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cat_id_2_cont_id_injective = {}
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for cat_id, cont_id in cat_id_2_cont_id.items():
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if (cont_id in cont_id_2_cat_id) and (cont_id_2_cat_id[cont_id] == cat_id):
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cat_id_2_cont_id_injective[cat_id] = cont_id
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metadata_new = Metadata(name=self._metadata.name)
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for key, value in self._metadata.__dict__.items():
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if key == "thing_dataset_id_to_contiguous_id":
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setattr(metadata_new, key, cat_id_2_cont_id_injective)
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else:
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setattr(metadata_new, key, value)
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self._metadata = metadata_new
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