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| _base_ = [ | |
| '../_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_adam_step_5e.py', | |
| '_base_sar_resnet31_parallel-decoder.py', | |
| ] | |
| _base_.pop('model') | |
| 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) | |
| model = dict( | |
| type='SARNet', | |
| data_preprocessor=dict( | |
| type='TextRecogDataPreprocessor', | |
| mean=[127, 127, 127], | |
| std=[127, 127, 127]), | |
| backbone=dict(type='ResNet31OCR'), | |
| encoder=dict( | |
| type='SAREncoder', | |
| enc_bi_rnn=False, | |
| enc_do_rnn=0.1, | |
| enc_gru=False, | |
| ), | |
| decoder=dict( | |
| type='SequentialSARDecoder', | |
| enc_bi_rnn=False, | |
| dec_bi_rnn=False, | |
| dec_do_rnn=0, | |
| dec_gru=False, | |
| pred_dropout=0.1, | |
| d_k=512, | |
| pred_concat=True, | |
| postprocessor=dict(type='AttentionPostprocessor'), | |
| module_loss=dict( | |
| type='CEModuleLoss', ignore_first_char=True, reduction='mean'), | |
| dictionary=dictionary, | |
| max_seq_len=30)) | |
| # 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, | |
| ] | |
| 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=128, | |
| num_workers=24, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| dataset=train_dataset) | |
| test_dataloader = dict( | |
| batch_size=128, | |
| num_workers=4, | |
| persistent_workers=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' | |
| ]) | |