mirror of https://github.com/open-mmlab/mmocr.git
76 lines
2.0 KiB
Python
76 lines
2.0 KiB
Python
_base_ = [
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'_base_spts_resnet50_mmocr.py',
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'../_base_/datasets/totaltext.py',
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'../_base_/default_runtime.py',
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]
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load_from = 'work_dirs/spts_resnet50_150e_pretrain-spts/epoch_150.pth'
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num_epochs = 200
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lr = 0.00001
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default_hooks = dict(
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checkpoint=dict(
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type='CheckpointHook',
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save_best='none/hmean',
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rule='greater',
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_delete_=True),
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logger=dict(type='LoggerHook', interval=10))
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(type='AdamW', lr=lr, weight_decay=0.0001),
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paramwise_cfg=dict(custom_keys={
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'backbone': dict(lr_mult=0.1),
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}))
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train_cfg = dict(
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type='EpochBasedTrainLoop', max_epochs=num_epochs, val_interval=10)
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val_cfg = dict(type='ValLoop')
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test_cfg = dict(type='TestLoop')
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# dataset settings
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totaltext_textspotting_train = _base_.totaltext_textspotting_train
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totaltext_textspotting_train.pipeline = _base_.train_pipeline
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totaltext_textspotting_test = _base_.totaltext_textspotting_test
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totaltext_textspotting_test.pipeline = _base_.test_pipeline
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train_dataloader = dict(
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batch_size=8,
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num_workers=8,
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persistent_workers=True,
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pin_memory=True,
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sampler=dict(type='BatchAugSampler', shuffle=True, num_repeats=2),
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dataset=totaltext_textspotting_train)
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val_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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pin_memory=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=totaltext_textspotting_test)
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test_dataloader = val_dataloader
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val_cfg = dict(type='ValLoop')
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test_cfg = dict(type='TestLoop')
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val_evaluator = [
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dict(
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type='E2EPointMetric',
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prefix='none',
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word_spotting=True,
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match_dist_thr=0.4),
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dict(
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type='E2EPointMetric',
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prefix='full',
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lexicon_path='data/totaltext/lexicons/weak_voc_new.txt',
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pair_path='data/totaltext/lexicons/'
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'weak_voc_pair_list.txt',
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word_spotting=True,
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match_dist_thr=0.4),
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]
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test_evaluator = val_evaluator
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