56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
# dataset settings
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dataset_type = 'VOCDataset'
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data_root = 'data/VOCdevkit/'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1000, 600),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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type='RepeatDataset',
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times=3,
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dataset=dict(
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type=dataset_type,
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ann_file=[
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data_root + 'VOC2007/ImageSets/Main/trainval.txt',
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data_root + 'VOC2012/ImageSets/Main/trainval.txt'
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],
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img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
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pipeline=train_pipeline)),
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val=dict(
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type=dataset_type,
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ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
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img_prefix=data_root + 'VOC2007/',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
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img_prefix=data_root + 'VOC2007/',
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pipeline=test_pipeline))
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evaluation = dict(interval=1, metric='mAP')
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