mirror of
https://github.com/open-mmlab/mmocr.git
synced 2025-06-03 21:54:47 +08:00
96 lines
2.9 KiB
Python
96 lines
2.9 KiB
Python
_base_ = [
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'../../_base_/recog_datasets/mjsynth.py',
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'../../_base_/recog_datasets/synthtext.py',
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'../../_base_/recog_datasets/cute80.py',
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'../../_base_/recog_datasets/iiit5k.py',
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'../../_base_/recog_datasets/svt.py',
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'../../_base_/recog_datasets/svtp.py',
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'../../_base_/recog_datasets/icdar2013.py',
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'../../_base_/recog_datasets/icdar2015.py',
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'../../_base_/default_runtime.py',
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'../../_base_/schedules/schedule_adam_step_6e.py',
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'nrtr_r31.py',
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]
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# optimizer settings
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optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=3e-4))
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# dataset settings
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train_list = [_base_.mj_rec_train, _base_.st_rec_train]
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test_list = [
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_base_.cute80_rec_test, _base_.iiit5k_rec_test, _base_.svt_rec_test,
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_base_.svtp_rec_test, _base_.ic13_rec_test, _base_.ic15_rec_test
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]
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file_client_args = dict(backend='disk')
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default_hooks = dict(logger=dict(type='LoggerHook', interval=50), )
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model = dict(backbone=dict(last_stage_pool=False))
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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(
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type='RescaleToHeight',
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height=32,
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min_width=32,
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max_width=160,
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width_divisor=4),
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dict(type='PadToWidth', width=160),
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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(
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type='RescaleToHeight',
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height=32,
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min_width=32,
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max_width=160,
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width_divisor=16),
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dict(type='PadToWidth', width=160),
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# add loading annotation after ``Resize`` because ground truth
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# does not need to do resize data transform
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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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]
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train_dataloader = dict(
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batch_size=384,
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num_workers=32,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=dict(
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type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
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test_dataloader = dict(
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batch_size=1,
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num_workers=4,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
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val_dataloader = test_dataloader
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val_evaluator = dict(
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type='MultiDatasetsEvaluator',
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metrics=[
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dict(
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type='WordMetric',
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mode=['exact', 'ignore_case', 'ignore_case_symbol']),
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dict(type='CharMetric')
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],
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dataset_prefixes=['CUTE80', 'IIIT5K', 'SVT', 'SVTP', 'IC13', 'IC15'])
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test_evaluator = val_evaluator
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visualizer = dict(type='TextRecogLocalVisualizer', name='visualizer')
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