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# dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
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, denorm_bbox=True),
dict(type='Resize', img_scale=(1024, 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']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1024, 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=0, # workers_per_gpu > 0 may occur out of memory
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/oidv6-train-annotations-bbox.csv',
img_prefix=data_root + 'OpenImages/train/',
label_file=data_root + 'annotations/class-descriptions-boxable.csv',
hierarchy_file=data_root +
'annotations/bbox_labels_600_hierarchy.json',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/validation-annotations-bbox.csv',
img_prefix=data_root + 'OpenImages/validation/',
label_file=data_root + 'annotations/class-descriptions-boxable.csv',
hierarchy_file=data_root +
'annotations/bbox_labels_600_hierarchy.json',
meta_file=data_root + 'annotations/validation-image-metas.pkl',
image_level_ann_file=data_root +
'annotations/validation-annotations-human-imagelabels-boxable.csv',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/validation-annotations-bbox.csv',
img_prefix=data_root + 'OpenImages/validation/',
label_file=data_root + 'annotations/class-descriptions-boxable.csv',
hierarchy_file=data_root +
'annotations/bbox_labels_600_hierarchy.json',
meta_file=data_root + 'annotations/validation-image-metas.pkl',
image_level_ann_file=data_root +
'annotations/validation-annotations-human-imagelabels-boxable.csv',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric='mAP')
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