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dictionary = dict(
type='Dictionary',
dict_file='{{ fileDirname }}/../../../dicts/english_digits_symbols.txt',
with_padding=True,
with_unknown=True,
same_start_end=True,
with_start=True,
with_end=True)
model = dict(
type='MASTER',
backbone=dict(
type='ResNet',
in_channels=3,
stem_channels=[64, 128],
block_cfgs=dict(
type='BasicBlock',
plugins=dict(
cfg=dict(
type='GCAModule',
ratio=0.0625,
n_head=1,
pooling_type='att',
is_att_scale=False,
fusion_type='channel_add'),
position='after_conv2')),
arch_layers=[1, 2, 5, 3],
arch_channels=[256, 256, 512, 512],
strides=[1, 1, 1, 1],
plugins=[
dict(
cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)),
stages=(True, True, False, False),
position='before_stage'),
dict(
cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)),
stages=(False, False, True, False),
position='before_stage'),
dict(
cfg=dict(
type='ConvModule',
kernel_size=3,
stride=1,
padding=1,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU')),
stages=(True, True, True, True),
position='after_stage')
],
init_cfg=[
dict(type='Kaiming', layer='Conv2d'),
dict(type='Constant', val=1, layer='BatchNorm2d'),
]),
encoder=None,
decoder=dict(
type='MasterDecoder',
d_model=512,
n_head=8,
attn_drop=0.,
ffn_drop=0.,
d_inner=2048,
n_layers=3,
feat_pe_drop=0.2,
feat_size=6 * 40,
postprocessor=dict(type='AttentionPostprocessor'),
module_loss=dict(
type='CEModuleLoss', reduction='mean', ignore_first_char=True),
max_seq_len=30,
dictionary=dictionary),
data_preprocessor=dict(
type='TextRecogDataPreprocessor',
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5]))
train_pipeline = [
dict(type='LoadImageFromFile', ignore_empty=True, min_size=2),
dict(type='LoadOCRAnnotations', with_text=True),
dict(
type='RescaleToHeight',
height=48,
min_width=48,
max_width=160,
width_divisor=16),
dict(type='PadToWidth', width=160),
dict(
type='PackTextRecogInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RescaleToHeight',
height=48,
min_width=48,
max_width=160,
width_divisor=16),
dict(type='PadToWidth', width=160),
# 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='RescaleToHeight',
height=48,
min_width=48,
max_width=160,
width_divisor=16)
],
[dict(type='PadToWidth', width=160)],
# 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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