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| _base_ = [ | |
| '../../_base_/default_runtime.py', | |
| '../../_base_/recog_pipelines/satrn_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='SATRN', | |
| backbone=dict(type='ShallowCNN', input_channels=3, hidden_dim=256), | |
| encoder=dict( | |
| type='SatrnEncoder', | |
| n_layers=6, | |
| n_head=8, | |
| d_k=256 // 8, | |
| d_v=256 // 8, | |
| d_model=256, | |
| n_position=100, | |
| d_inner=256 * 4, | |
| dropout=0.1), | |
| decoder=dict( | |
| type='NRTRDecoder', | |
| n_layers=6, | |
| d_embedding=256, | |
| n_head=8, | |
| d_model=256, | |
| d_inner=256 * 4, | |
| d_k=256 // 8, | |
| d_v=256 // 8), | |
| loss=dict(type='TFLoss'), | |
| label_convertor=label_convertor, | |
| max_seq_len=25) | |
| # optimizer | |
| optimizer = dict(type='Adam', lr=3e-4) | |
| optimizer_config = dict(grad_clip=None) | |
| # learning policy | |
| lr_config = dict(policy='step', step=[3, 4]) | |
| total_epochs = 6 | |
| data = dict( | |
| samples_per_gpu=64, | |
| workers_per_gpu=4, | |
| 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') | |