mirror of https://github.com/open-mmlab/mmocr.git
111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
file_client_args = dict(backend='disk')
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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='ASTER',
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preprocessor=dict(
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type='STN',
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in_channels=3,
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resized_image_size=(32, 64),
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output_image_size=(32, 100),
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num_control_points=20),
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backbone=dict(
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type='ResNet',
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in_channels=3,
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stem_channels=[32],
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block_cfgs=dict(type='BasicBlock', use_conv1x1='True'),
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arch_layers=[3, 4, 6, 6, 3],
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arch_channels=[32, 64, 128, 256, 512],
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strides=[(2, 2), (2, 2), (2, 1), (2, 1), (2, 1)],
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init_cfg=[
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dict(type='Kaiming', layer='Conv2d'),
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dict(type='Constant', val=1, layer='BatchNorm2d'),
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]),
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encoder=dict(type='ASTEREncoder', in_channels=512),
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decoder=dict(
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type='ASTERDecoder',
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max_seq_len=25,
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in_channels=512,
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emb_dims=512,
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attn_dims=512,
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hidden_size=512,
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postprocessor=dict(type='AttentionPostprocessor'),
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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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),
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data_preprocessor=dict(
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type='TextRecogDataPreprocessor',
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mean=[127.5, 127.5, 127.5],
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std=[127.5, 127.5, 127.5]))
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train_pipeline = [
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dict(
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type='LoadImageFromFile',
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file_client_args=file_client_args,
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ignore_empty=True,
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min_size=5),
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dict(type='LoadOCRAnnotations', with_text=True),
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dict(type='Resize', scale=(256, 64)),
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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', file_client_args=file_client_args),
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dict(type='Resize', scale=(256, 64)),
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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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'instances'))
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]
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tta_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(
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type='TestTimeAug',
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transforms=[[
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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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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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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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], [dict(type='Resize', scale=(256, 64))],
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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', 'instances'))
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]])
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]
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