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