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dictionary = dict(
type='Dictionary',
dict_file='{{ fileDirname }}/../../../dicts/lower_english_digits.txt',
with_start=True,
with_end=True,
same_start_end=True,
with_padding=False,
with_unknown=False)
model = dict(
type='ABINet',
backbone=dict(type='ResNetABI'),
encoder=dict(
type='ABIEncoder',
n_layers=3,
n_head=8,
d_model=512,
d_inner=2048,
dropout=0.1,
max_len=8 * 32,
),
decoder=dict(
type='ABIFuser',
vision_decoder=dict(
type='ABIVisionDecoder',
in_channels=512,
num_channels=64,
attn_height=8,
attn_width=32,
attn_mode='nearest',
init_cfg=dict(type='Xavier', layer='Conv2d')),
module_loss=dict(type='ABIModuleLoss', letter_case='lower'),
postprocessor=dict(type='AttentionPostprocessor'),
dictionary=dictionary,
max_seq_len=26,
),
data_preprocessor=dict(
type='TextRecogDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]))
train_pipeline = [
dict(type='LoadImageFromFile', ignore_empty=True, min_size=2),
dict(type='LoadOCRAnnotations', with_text=True),
dict(type='Resize', scale=(128, 32)),
dict(
type='RandomApply',
prob=0.5,
transforms=[
dict(
type='RandomChoice',
transforms=[
dict(
type='RandomRotate',
max_angle=15,
),
dict(
type='TorchVisionWrapper',
op='RandomAffine',
degrees=15,
translate=(0.3, 0.3),
scale=(0.5, 2.),
shear=(-45, 45),
),
dict(
type='TorchVisionWrapper',
op='RandomPerspective',
distortion_scale=0.5,
p=1,
),
])
],
),
dict(
type='RandomApply',
prob=0.25,
transforms=[
dict(type='PyramidRescale'),
dict(
type='mmdet.Albu',
transforms=[
dict(type='GaussNoise', var_limit=(20, 20), p=0.5),
dict(type='MotionBlur', blur_limit=7, p=0.5),
]),
]),
dict(
type='RandomApply',
prob=0.25,
transforms=[
dict(
type='TorchVisionWrapper',
op='ColorJitter',
brightness=0.5,
saturation=0.5,
contrast=0.5,
hue=0.1),
]),
dict(
type='PackTextRecogInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(128, 32)),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
dict(type='LoadOCRAnnotations', with_text=True),
dict(
type='PackTextRecogInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
]
tta_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='TestTimeAug',
transforms=[
[
dict(
type='ConditionApply',
true_transforms=[
dict(
type='ImgAugWrapper',
args=[dict(cls='Rot90', k=0, keep_size=False)])
],
condition="results['img_shape'][1]<results['img_shape'][0]"
),
dict(
type='ConditionApply',
true_transforms=[
dict(
type='ImgAugWrapper',
args=[dict(cls='Rot90', k=1, keep_size=False)])
],
condition="results['img_shape'][1]<results['img_shape'][0]"
),
dict(
type='ConditionApply',
true_transforms=[
dict(
type='ImgAugWrapper',
args=[dict(cls='Rot90', k=3, keep_size=False)])
],
condition="results['img_shape'][1]<results['img_shape'][0]"
),
],
[dict(type='Resize', scale=(128, 32))],
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
[dict(type='LoadOCRAnnotations', with_text=True)],
[
dict(
type='PackTextRecogInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape',
'valid_ratio'))
]
])
]
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