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_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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|
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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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|
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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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|
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train_list = {{_base_.train_list}} |
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test_list = {{_base_.test_list}} |
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|
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train_pipeline = {{_base_.train_pipeline}} |
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test_pipeline = {{_base_.test_pipeline}} |
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|
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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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|
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evaluation = dict(interval=1, metric='acc') |
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|
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cudnn_benchmark = True |
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