Spaces:
Sleeping
Sleeping
| _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_adamw_cos_10e.py', | |
| '_base_abinet.py', | |
| ] | |
| load_from = 'https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth' # noqa | |
| _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='ABINet', | |
| backbone=dict(type='ResNetABI'), | |
| encoder=dict( | |
| type='ABIEncoder', | |
| n_layers=3, | |
| n_head=8, | |
| d_model=512, | |
| d_inner=2048, | |
| dropout=0.1, | |
| max_len=8 * 32, | |
| ), | |
| decoder=dict( | |
| type='ABIFuser', | |
| vision_decoder=dict( | |
| type='ABIVisionDecoder', | |
| in_channels=512, | |
| num_channels=64, | |
| attn_height=8, | |
| attn_width=32, | |
| attn_mode='nearest', | |
| init_cfg=dict(type='Xavier', layer='Conv2d')), | |
| module_loss=dict(type='ABIModuleLoss'), | |
| postprocessor=dict(type='AttentionPostprocessor'), | |
| dictionary=dictionary, | |
| max_seq_len=26, | |
| ), | |
| data_preprocessor=dict( | |
| type='TextRecogDataPreprocessor', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375])) | |
| # 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' | |
| ]) | |