mirror of https://github.com/open-mmlab/mmocr.git
64 lines
1.6 KiB
Python
64 lines
1.6 KiB
Python
_base_ = [
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'../../_base_/default_runtime.py',
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'../../_base_/schedules/schedule_adam_step_5e.py',
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'../../_base_/recog_pipelines/sar_pipeline.py',
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'../../_base_/recog_datasets/ST_SA_MJ_real_train.py',
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'../../_base_/recog_datasets/academic_test.py'
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]
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train_list = {{_base_.train_list}}
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test_list = {{_base_.test_list}}
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train_pipeline = {{_base_.train_pipeline}}
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test_pipeline = {{_base_.test_pipeline}}
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max_seq_len = 30
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label_convertor = dict(
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type='AttnConvertor',
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dict_type='DICT90',
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with_unknown=True,
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max_seq_len=max_seq_len)
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model = dict(
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type='SARNet',
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backbone=dict(type='ResNet31OCR'),
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encoder=dict(
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type='SAREncoder',
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enc_bi_rnn=False,
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enc_do_rnn=0.1,
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enc_gru=False,
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),
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decoder=dict(
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type='SequentialSARDecoder',
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enc_bi_rnn=False,
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dec_bi_rnn=False,
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dec_do_rnn=0,
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dec_gru=False,
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pred_dropout=0.1,
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d_k=512,
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pred_concat=True),
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loss=dict(type='SARLoss'),
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label_convertor=label_convertor,
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max_seq_len=max_seq_len)
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data = dict(
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samples_per_gpu=64,
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workers_per_gpu=2,
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val_dataloader=dict(samples_per_gpu=1),
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test_dataloader=dict(samples_per_gpu=1),
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train=dict(
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type='UniformConcatDataset',
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datasets=train_list,
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pipeline=train_pipeline),
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val=dict(
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type='UniformConcatDataset',
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datasets=test_list,
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pipeline=test_pipeline),
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test=dict(
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type='UniformConcatDataset',
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datasets=test_list,
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pipeline=test_pipeline))
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evaluation = dict(interval=1, metric='acc')
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