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# dataset settings
custom_imports = dict(imports=[
'openpsg.datasets',
'openpsg.datasets.pipelines',
],
allow_failed_imports=False)
dataset_type = 'SceneGraphDataset'
ann_file = 'data/vg/data_openpsg.json'
img_dir = 'data/vg/VG_100K'
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='LoadSceneGraphAnnotations', 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', 'gt_bboxes', 'gt_labels']),
]
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=ann_file,
img_prefix=img_dir,
pipeline=train_pipeline,
split='train'),
val=dict(type=dataset_type,
ann_file=ann_file,
img_prefix=img_dir,
pipeline=test_pipeline,
split='test'),
test=dict(type=dataset_type,
ann_file=ann_file,
img_prefix=img_dir,
pipeline=test_pipeline,
split='test'))
evaluation = dict(interval=1, metric='bbox')
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