mirror of https://github.com/open-mmlab/mmocr.git
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
_base_ = [
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'../../_base_/default_runtime.py',
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'../../_base_/recog_pipelines/crnn_pipeline.py',
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'../../_base_/recog_datasets/toy_data.py',
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'../../_base_/schedules/schedule_adadelta_5e.py'
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]
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label_convertor = dict(
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type='CTCConvertor', dict_type='DICT36', with_unknown=True, lower=True)
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model = dict(
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type='CRNNNet',
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preprocessor=None,
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backbone=dict(type='VeryDeepVgg', leaky_relu=False, input_channels=1),
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encoder=None,
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decoder=dict(type='CRNNDecoder', in_channels=512, rnn_flag=True),
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loss=dict(type='CTCLoss'),
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label_convertor=label_convertor,
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pretrained=None)
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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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data = dict(
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samples_per_gpu=32,
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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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cudnn_benchmark = True
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