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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,65 @@ | ||
| union14m_root = 'data/Union14M-L/' | ||
| union14m_benchmark_root = 'data/Union14M-L/Union14M-Benchmarks' | ||
|
|
||
| union14m_benchmark_artistic = dict( | ||
| type='OCRDataset', | ||
| data_prefix=dict(img_path=f'{union14m_benchmark_root}/artistic'), | ||
| ann_file=f'{union14m_benchmark_root}/artistic/annotation.json', | ||
| test_mode=True, | ||
| pipeline=None) | ||
|
|
||
| union14m_benchmark_contextless = dict( | ||
| type='OCRDataset', | ||
| data_prefix=dict(img_path=f'{union14m_benchmark_root}/contextless'), | ||
| ann_file=f'{union14m_benchmark_root}/contextless/annotation.json', | ||
| test_mode=True, | ||
| pipeline=None) | ||
|
|
||
| union14m_benchmark_curve = dict( | ||
| type='OCRDataset', | ||
| data_prefix=dict(img_path=f'{union14m_benchmark_root}/curve'), | ||
| ann_file=f'{union14m_benchmark_root}/curve/annotation.json', | ||
| test_mode=True, | ||
| pipeline=None) | ||
|
|
||
| union14m_benchmark_incomplete = dict( | ||
| type='OCRDataset', | ||
| data_prefix=dict(img_path=f'{union14m_benchmark_root}/incomplete'), | ||
| ann_file=f'{union14m_benchmark_root}/incomplete/annotation.json', | ||
| test_mode=True, | ||
| pipeline=None) | ||
|
|
||
| union14m_benchmark_incomplete_ori = dict( | ||
| type='OCRDataset', | ||
| data_prefix=dict(img_path=f'{union14m_benchmark_root}/incomplete_ori'), | ||
| ann_file=f'{union14m_benchmark_root}/incomplete_ori/annotation.json', | ||
| test_mode=True, | ||
| pipeline=None) | ||
|
|
||
| union14m_benchmark_multi_oriented = dict( | ||
| type='OCRDataset', | ||
| data_prefix=dict(img_path=f'{union14m_benchmark_root}/multi_oriented'), | ||
| ann_file=f'{union14m_benchmark_root}/multi_oriented/annotation.json', | ||
| test_mode=True, | ||
| pipeline=None) | ||
|
|
||
| union14m_benchmark_multi_words = dict( | ||
| type='OCRDataset', | ||
| data_prefix=dict(img_path=f'{union14m_benchmark_root}/multi_words'), | ||
| ann_file=f'{union14m_benchmark_root}/multi_words/annotation.json', | ||
| test_mode=True, | ||
| pipeline=None) | ||
|
|
||
| union14m_benchmark_salient = dict( | ||
| type='OCRDataset', | ||
| data_prefix=dict(img_path=f'{union14m_benchmark_root}/salient'), | ||
| ann_file=f'{union14m_benchmark_root}/salient/annotation.json', | ||
| test_mode=True, | ||
| pipeline=None) | ||
|
|
||
| union14m_benchmark_general = dict( | ||
| type='OCRDataset', | ||
| data_prefix=dict(img_path=f'{union14m_root}/'), | ||
| ann_file=f'{union14m_benchmark_root}/general/annotation.json', | ||
| test_mode=True, | ||
| pipeline=None) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,38 @@ | ||
| union14m_data_root = 'data/Union14M-L/' | ||
|
|
||
| union14m_challenging = dict( | ||
| type='OCRDataset', | ||
| data_root=union14m_data_root, | ||
| ann_file='train_annos/mmocr1.0/train_challenging.json', | ||
| test_mode=True, | ||
| pipeline=None) | ||
|
|
||
| union14m_hard = dict( | ||
| type='OCRDataset', | ||
| data_root=union14m_data_root, | ||
| ann_file='train_annos/mmocr1.0/train_hard.json', | ||
| pipeline=None) | ||
|
|
||
| union14m_medium = dict( | ||
| type='OCRDataset', | ||
| data_root=union14m_data_root, | ||
| ann_file='train_annos/mmocr1.0/train_medium.json', | ||
| pipeline=None) | ||
|
|
||
| union14m_normal = dict( | ||
| type='OCRDataset', | ||
| data_root=union14m_data_root, | ||
| ann_file='train_annos/mmocr1.0/train_normal.json', | ||
| pipeline=None) | ||
|
|
||
| union14m_easy = dict( | ||
| type='OCRDataset', | ||
| data_root=union14m_data_root, | ||
| ann_file='train_annos/mmocr1.0/train_easy.json', | ||
| pipeline=None) | ||
|
|
||
| union14m_val = dict( | ||
| type='OCRDataset', | ||
| data_root=union14m_data_root, | ||
| ann_file='train_annos/mmocr1.0/val_annos.json', | ||
| pipeline=None) |
21 changes: 21 additions & 0 deletions
21
configs/textrecog/_base_/schedules/schedule_adamw_cos_10e.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,21 @@ | ||
| # optimizer | ||
| optim_wrapper = dict( | ||
| type='OptimWrapper', | ||
| optimizer=dict( | ||
| type='AdamW', | ||
| lr=4e-4, | ||
| betas=(0.9, 0.999), | ||
| eps=1e-08, | ||
| weight_decay=0.01)) | ||
| train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=10, val_interval=1) | ||
| val_cfg = dict(type='ValLoop') | ||
| test_cfg = dict(type='TestLoop') | ||
|
|
||
| # learning policy | ||
| param_scheduler = [ | ||
| dict( | ||
| type='CosineAnnealingLR', | ||
| T_max=10, | ||
| eta_min=4e-6, | ||
| convert_to_iter_based=True) | ||
| ] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| _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' | ||
| ]) |
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