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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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