| _base_ = [ | |
| '../../_base_/default_runtime.py', | |
| '../../_base_/schedules/schedule_adam_step_6e.py', | |
| '../../_base_/recog_pipelines/nrtr_pipeline.py', | |
| '../../_base_/recog_datasets/ST_MJ_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='NRTR', | |
| backbone=dict( | |
| type='ResNet31OCR', | |
| layers=[1, 2, 5, 3], | |
| channels=[32, 64, 128, 256, 512, 512], | |
| stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)), | |
| last_stage_pool=False), | |
| encoder=dict(type='NRTREncoder'), | |
| decoder=dict(type='NRTRDecoder'), | |
| loss=dict(type='TFLoss'), | |
| label_convertor=label_convertor, | |
| max_seq_len=40) | |
| data = dict( | |
| samples_per_gpu=64, | |
| workers_per_gpu=4, | |
| 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') | |