mirror of https://github.com/open-mmlab/mmocr.git
80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
_base_ = [
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'dbnet_r18_fpnc.py',
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'../../_base_/default_runtime.py',
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'../../_base_/schedules/schedule_sgd_1200e.py',
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]
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default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=20), )
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train_pipeline_r18 = [
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(
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type='LoadOCRAnnotations',
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with_polygon=True,
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with_bbox=True,
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with_label=True,
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),
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dict(
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type='TorchVisionWrapper',
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op='ColorJitter',
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brightness=32.0 / 255,
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saturation=0.5),
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dict(
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type='ImgAugWrapper',
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args=[['Fliplr', 0.5],
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dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]),
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dict(type='RandomCrop', min_side_ratio=0.1),
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dict(type='Resize', scale=(640, 640), keep_ratio=True),
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dict(type='Pad', size=(640, 640)),
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dict(
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type='PackTextDetInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape'))
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]
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test_pipeline_1333_736 = [
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(type='Resize', scale=(1333, 736), keep_ratio=True),
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dict(
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type='PackTextDetInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor',
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'instances'))
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]
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dataset_type = 'OCRDataset'
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data_root = 'data/icdar2015'
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train_dataset = dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='instances_training.json',
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data_prefix=dict(img_path='imgs/'),
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filter_cfg=dict(filter_empty_gt=True, min_size=32),
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pipeline=train_pipeline_r18)
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test_dataset = dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='instances_test.json',
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data_prefix=dict(img_path='imgs/'),
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test_mode=True,
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pipeline=test_pipeline_1333_736)
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train_dataloader = dict(
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batch_size=16,
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num_workers=8,
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persistent_workers=False,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=train_dataset)
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val_dataloader = dict(
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batch_size=16,
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num_workers=8,
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persistent_workers=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=test_dataset)
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test_dataloader = val_dataloader
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val_evaluator = dict(type='HmeanIOUMetric')
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test_evaluator = val_evaluator
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visualizer = dict(type='TextDetLocalVisualizer', name='visualizer')
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