mirror of https://github.com/open-mmlab/mmocr.git
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
dictionary = dict(
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type='Dictionary',
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dict_file='{{ fileDirname }}/../../../dicts/english_digits_symbols.txt',
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with_padding=True,
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with_unknown=True,
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same_start_end=True,
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with_start=True,
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with_end=True)
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model = dict(
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type='SATRN',
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backbone=dict(type='ShallowCNN', input_channels=3, hidden_dim=512),
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encoder=dict(
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type='SATRNEncoder',
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n_layers=12,
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n_head=8,
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d_k=512 // 8,
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d_v=512 // 8,
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d_model=512,
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n_position=100,
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d_inner=512 * 4,
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dropout=0.1),
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decoder=dict(
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type='NRTRDecoder',
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n_layers=6,
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d_embedding=512,
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n_head=8,
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d_model=512,
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d_inner=512 * 4,
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d_k=512 // 8,
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d_v=512 // 8,
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module_loss=dict(
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type='CEModuleLoss', flatten=True, ignore_first_char=True),
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dictionary=dictionary,
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max_seq_len=25,
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postprocessor=dict(type='AttentionPostprocessor')),
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data_preprocessor=dict(
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type='TextRecogDataPreprocessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375]))
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train_pipeline = [
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dict(type='LoadImageFromFile', ignore_empty=True, min_size=2),
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dict(type='LoadOCRAnnotations', with_text=True),
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dict(type='Resize', scale=(100, 32), keep_ratio=False),
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dict(
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type='PackTextRecogInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', scale=(100, 32), keep_ratio=False),
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# add loading annotation after ``Resize`` because ground truth
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# does not need to do resize data transform
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dict(type='LoadOCRAnnotations', with_text=True),
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dict(
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type='PackTextRecogInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
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]
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tta_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='TestTimeAug',
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transforms=[
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[
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dict(
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type='ConditionApply',
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true_transforms=[
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dict(
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type='ImgAugWrapper',
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args=[dict(cls='Rot90', k=0, keep_size=False)])
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],
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condition="results['img_shape'][1]<results['img_shape'][0]"
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),
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dict(
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type='ConditionApply',
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true_transforms=[
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dict(
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type='ImgAugWrapper',
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args=[dict(cls='Rot90', k=1, keep_size=False)])
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],
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condition="results['img_shape'][1]<results['img_shape'][0]"
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),
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dict(
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type='ConditionApply',
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true_transforms=[
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dict(
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type='ImgAugWrapper',
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args=[dict(cls='Rot90', k=3, keep_size=False)])
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],
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condition="results['img_shape'][1]<results['img_shape'][0]"
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),
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],
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[dict(type='Resize', scale=(100, 32), keep_ratio=False)],
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# add loading annotation after ``Resize`` because ground truth
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# does not need to do resize data transform
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[dict(type='LoadOCRAnnotations', with_text=True)],
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[
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dict(
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type='PackTextRecogInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape',
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'valid_ratio'))
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]
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])
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]
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