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# training schedule for 1x
_base_ = [
'_base_aster.py',
'../_base_/datasets/union14m_train.py',
'../_base_/datasets/union14m_benchmark.py',
'../_base_/datasets/cute80.py',
'../_base_/datasets/iiit5k.py',
'../_base_/datasets/svt.py',
'../_base_/datasets/svtp.py',
'../_base_/datasets/icdar2013.py',
'../_base_/datasets/icdar2015.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_adamw_cos_6e.py',
]
dictionary = dict(
type='Dictionary',
dict_file= # noqa
'{{ fileDirname }}/../../../dicts/english_digits_symbols_space.txt',
with_padding=True,
with_unknown=True,
same_start_end=True,
with_start=True,
with_end=True)
# dataset settings
train_list = [
_base_.union14m_challenging, _base_.union14m_hard, _base_.union14m_medium,
_base_.union14m_normal, _base_.union14m_easy
]
val_list = [
_base_.cute80_textrecog_test, _base_.iiit5k_textrecog_test,
_base_.svt_textrecog_test, _base_.svtp_textrecog_test,
_base_.icdar2013_textrecog_test, _base_.icdar2015_textrecog_test
]
test_list = [
_base_.union14m_benchmark_artistic,
_base_.union14m_benchmark_multi_oriented,
_base_.union14m_benchmark_contextless,
_base_.union14m_benchmark_curve,
_base_.union14m_benchmark_incomplete,
_base_.union14m_benchmark_incomplete_ori,
_base_.union14m_benchmark_multi_words,
_base_.union14m_benchmark_salient,
_base_.union14m_benchmark_general,
]
default_hooks = dict(logger=dict(type='LoggerHook', interval=50))
auto_scale_lr = dict(base_batch_size=512)
train_dataset = dict(
type='ConcatDataset', datasets=train_list, pipeline=_base_.train_pipeline)
test_dataset = dict(
type='ConcatDataset', datasets=test_list, pipeline=_base_.test_pipeline)
val_dataset = dict(
type='ConcatDataset', datasets=val_list, pipeline=_base_.test_pipeline)
train_dataloader = dict(
batch_size=512,
num_workers=12,
persistent_workers=True,
pin_memory=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=train_dataset)
test_dataloader = dict(
batch_size=128,
num_workers=4,
persistent_workers=True,
pin_memory=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=test_dataset)
val_dataloader = dict(
batch_size=128,
num_workers=4,
persistent_workers=True,
pin_memory=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=val_dataset)
val_evaluator = dict(
dataset_prefixes=['CUTE80', 'IIIT5K', 'SVT', 'SVTP', 'IC13', 'IC15'])
test_evaluator = dict(dataset_prefixes=[
'artistic', 'multi-oriented', 'contextless', 'curve', 'incomplete',
'incomplete-ori', 'multi-words', 'salient', 'general'
])
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