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| _base_ = [ | |
| '../../_base_/default_runtime.py', | |
| '../../_base_/schedules/schedule_adam_step_20e.py', | |
| '../../_base_/recog_pipelines/abinet_pipeline.py', | |
| '../../_base_/recog_datasets/toy_data.py' | |
| # '../../_base_/recog_datasets/ST_MJ_alphanumeric_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}} | |
| # Model | |
| num_chars = 37 | |
| max_seq_len = 26 | |
| label_convertor = dict( | |
| type='ABIConvertor', | |
| dict_type='DICT36', | |
| with_unknown=False, | |
| with_padding=False, | |
| lower=True, | |
| ) | |
| model = dict( | |
| type='ABINet', | |
| backbone=dict(type='ResNetABI'), | |
| encoder=dict( | |
| type='ABIVisionModel', | |
| encoder=dict( | |
| type='TransformerEncoder', | |
| n_layers=3, | |
| n_head=8, | |
| d_model=512, | |
| d_inner=2048, | |
| dropout=0.1, | |
| max_len=8 * 32, | |
| ), | |
| decoder=dict( | |
| type='ABIVisionDecoder', | |
| in_channels=512, | |
| num_channels=64, | |
| attn_height=8, | |
| attn_width=32, | |
| attn_mode='nearest', | |
| use_result='feature', | |
| num_chars=num_chars, | |
| max_seq_len=max_seq_len, | |
| init_cfg=dict(type='Xavier', layer='Conv2d')), | |
| ), | |
| loss=dict( | |
| type='ABILoss', | |
| enc_weight=1.0, | |
| dec_weight=1.0, | |
| fusion_weight=1.0, | |
| num_classes=num_chars), | |
| label_convertor=label_convertor, | |
| max_seq_len=max_seq_len, | |
| iter_size=1) | |
| data = dict( | |
| samples_per_gpu=192, | |
| workers_per_gpu=8, | |
| 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') | |