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_base_ = ['./ndl.py'] |
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dataset_type = 'CocoDataset' |
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classes = ('line_main', 'line_none', 'line_inote', 'line_hnote', 'line_caption', |
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'block_fig', 'block_table', 'block_pillar', 'block_folio', 'block_rubi', |
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'block_chart', 'block_eqn', 'block_cfm', 'block_eng', |
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'char', 'void') |
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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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image_size = 1024 |
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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=(image_size, image_size), 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=(image_size, image_size), |
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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=1, |
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workers_per_gpu=1, |
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train=dict( |
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type=dataset_type, |
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classes=classes, |
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ann_file='/tmp/generated/dataset_kindai_preprocessed_train.json', |
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img_prefix='/tmp/dataset_kindai_preprocessed_out', |
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pipeline=train_pipeline), |
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val=dict( |
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type=dataset_type, |
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classes=classes, |
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ann_file='/tmp/generated/dataset_kindai_preprocessed_test.json', |
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img_prefix='/tmp/dataset_kindai_preprocessed_out', |
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pipeline=test_pipeline), |
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test=dict( |
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type=dataset_type, |
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classes=classes, |
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ann_file='/tmp/generated/dataset_kindai_preprocessed_test.json', |
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img_prefix='/tmp/dataset_kindai_preprocessed_out', |
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pipeline=test_pipeline)) |
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evaluation = dict(interval=10, metric=['bbox'], classwise=True) |
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