| _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'), | |
| # ] | |