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from maskrcnn_benchmark.data import datasets
from .coco import coco_evaluation
from .voc import voc_evaluation
from .vg import vg_evaluation
from .box_aug import im_detect_bbox_aug
from .od_to_grounding import od_to_grounding_evaluation
def evaluate(dataset, predictions, output_folder, **kwargs):
"""evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
prediction results for one image.
output_folder: output folder, to save evaluation files or results.
**kwargs: other args.
Returns:
evaluation result
"""
args = dict(dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs)
if isinstance(dataset, datasets.COCODataset) or isinstance(dataset, datasets.TSVDataset):
return coco_evaluation(**args)
# elif isinstance(dataset, datasets.VGTSVDataset):
# return vg_evaluation(**args)
elif isinstance(dataset, datasets.PascalVOCDataset):
return voc_evaluation(**args)
elif isinstance(dataset, datasets.CocoDetectionTSV):
return od_to_grounding_evaluation(**args)
elif isinstance(dataset, datasets.LvisDetection):
pass
else:
dataset_name = dataset.__class__.__name__
raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name))
def evaluate_mdetr(dataset, predictions, output_folder, cfg):
args = dict(dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs)
if isinstance(dataset, datasets.COCODataset) or isinstance(dataset, datasets.TSVDataset):
return coco_evaluation(**args)
# elif isinstance(dataset, datasets.VGTSVDataset):
# return vg_evaluation(**args)
elif isinstance(dataset, datasets.PascalVOCDataset):
return voc_evaluation(**args)
elif isinstance(dataset, datasets.CocoDetectionTSV):
return od_to_grounding_evaluation(**args)
elif isinstance(dataset, datasets.LvisDetection):
pass
else:
dataset_name = dataset.__class__.__name__
raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name))
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