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data_root = 'data/mjsynth'
cache_path = 'data/cache'
train_preparer = dict(
obtainer=dict(
type='NaiveDataObtainer',
cache_path=cache_path,
files=[
dict(
url='https://thor.robots.ox.ac.uk/~vgg/data/text/'
'mjsynth.tar.gz',
save_name='mjsynth.tar.gz',
md5='7bf2b60ad935eaf64e5b606f782d68e5',
split=['train'],
content=['image', 'annotation'],
mapping=[
[
'mjsynth/mnt/ramdisk/max/90kDICT32px/*/',
'textrecog_imgs/train/'
],
[
'mjsynth/mnt/ramdisk/max/90kDICT32px/annotation.txt',
'annotations/annotation.txt'
]
]),
dict(
url='https://download.openmmlab.com/mmocr/data/1.x/recog/'
'Syn90k/subset_textrecog_train.json',
save_name='subset_textrecog_train.json',
md5='ba958d87bb170980f39e194180c15b9e',
split=['train'],
content=['annotation'])
]),
gatherer=dict(type='MonoGatherer', ann_name='annotation.txt'),
parser=dict(
type='MJSynthAnnParser',
separator=' ',
format='img num',
remove_strs=None),
packer=dict(type='TextRecogPacker'),
dumper=dict(type='JsonDumper'),
)
delete = ['mjsynth', 'annotations']
config_generator = dict(
type='TextRecogConfigGenerator',
data_root=data_root,
train_anns=[
dict(ann_file='textrecog_train.json', dataset_postfix=''),
dict(ann_file='subset_textrecog_train.json', dataset_postfix='sub'),
],
test_anns=None)
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