|
- # dataset settings
- 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='LoadAnnotations', with_bbox=True, with_mask=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', 'gt_bboxes', 'gt_labels', 'gt_masks']),
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- 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='Collect', keys=['img']),
- ])
- ]
- data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_train2017.json',
- img_prefix=data_root + 'train2017/',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline))
- evaluation = dict(metric=['bbox', 'segm'])
|
