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- _base_ = [
- '../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- val_evaluator = dict(metric='proposal_fast')
- test_evaluator = val_evaluator
- # inference on val dataset and dump the proposals with evaluate metric
- # data_root = 'data/coco/'
- # test_evaluator = [
- # dict(
- # type='DumpProposals',
- # output_dir=data_root + 'proposals/',
- # proposals_file='rpn_r50_fpn_1x_val2017.pkl'),
- # dict(
- # type='CocoMetric',
- # ann_file=data_root + 'annotations/instances_val2017.json',
- # metric='proposal_fast',
- # backend_args={{_base_.backend_args}},
- # format_only=False)
- # ]
- # inference on training dataset and dump the proposals without evaluate metric
- # data_root = 'data/coco/'
- # test_dataloader = dict(
- # dataset=dict(
- # ann_file='annotations/instances_train2017.json',
- # data_prefix=dict(img='train2017/')))
- #
- # test_evaluator = [
- # dict(
- # type='DumpProposals',
- # output_dir=data_root + 'proposals/',
- # proposals_file='rpn_r50_fpn_1x_train2017.pkl'),
- # ]
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