| _base_ = [ | |
| '../_base_/models/fast_rcnn_r50_fpn.py', | |
| '../_base_/datasets/coco_detection.py', | |
| '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' | |
| ] | |
| dataset_type = 'CocoDataset' | |
| data_root = 'data/coco/' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadProposals', num_max_proposals=2000), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadProposals', num_max_proposals=None), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='ToTensor', keys=['proposals']), | |
| dict( | |
| type='ToDataContainer', | |
| fields=[dict(key='proposals', stack=False)]), | |
| dict(type='Collect', keys=['img', 'proposals']), | |
| ]) | |
| ] | |
| data = dict( | |
| samples_per_gpu=2, | |
| workers_per_gpu=2, | |
| train=dict( | |
| proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_train2017.pkl', | |
| pipeline=train_pipeline), | |
| val=dict( | |
| proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', | |
| pipeline=test_pipeline), | |
| test=dict( | |
| proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', | |
| pipeline=test_pipeline)) | |