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[MODEL] Add Union14M trained models (#1960)
* [Update] Add ABINet * [Update] Add NRTR * [Update] Add SATRN * [Update] Add configs
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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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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,91 @@ | ||
| # 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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114 changes: 114 additions & 0 deletions
114
configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_union14m.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,114 @@ | ||
| _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_base.py', | ||
| '_base_nrtr_resnet31.py', | ||
| ] | ||
|
|
||
| # optimizer settings | ||
| train_cfg = dict(max_epochs=6) | ||
| # learning policy | ||
| param_scheduler = [ | ||
| dict(type='MultiStepLR', milestones=[3, 4], end=6), | ||
| ] | ||
|
|
||
| _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='NRTR', | ||
| backbone=dict( | ||
| type='ResNet31OCR', | ||
| layers=[1, 2, 5, 3], | ||
| channels=[32, 64, 128, 256, 512, 512], | ||
| stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)), | ||
| last_stage_pool=False), | ||
| encoder=dict(type='NRTREncoder'), | ||
| decoder=dict( | ||
| type='NRTRDecoder', | ||
| module_loss=dict( | ||
| type='CEModuleLoss', ignore_first_char=True, flatten=True), | ||
| postprocessor=dict(type='AttentionPostprocessor'), | ||
| dictionary=dictionary, | ||
| max_seq_len=30, | ||
| ), | ||
| 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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