mirror of https://github.com/open-mmlab/mmocr.git
81 lines
2.1 KiB
Python
81 lines
2.1 KiB
Python
_base_ = [
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'../../_base_/default_runtime.py',
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'../../_base_/schedules/schedule_adam_step_20e.py',
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'../../_base_/recog_pipelines/abinet_pipeline.py',
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'../../_base_/recog_datasets/ST_MJ_alphanumeric_train.py',
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'../../_base_/recog_datasets/academic_test.py'
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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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train_pipeline = {{_base_.train_pipeline}}
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test_pipeline = {{_base_.test_pipeline}}
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# Model
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num_chars = 37
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max_seq_len = 26
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label_convertor = dict(
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type='ABIConvertor',
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dict_type='DICT36',
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with_unknown=False,
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with_padding=False,
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lower=True,
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max_seq_len=max_seq_len)
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model = dict(
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type='ABINet',
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backbone=dict(type='ResNetABI'),
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encoder=dict(
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type='ABIVisionModel',
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encoder=dict(
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type='TransformerEncoder',
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n_layers=3,
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n_head=8,
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d_model=512,
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d_inner=2048,
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dropout=0.1,
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max_len=8 * 32,
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),
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decoder=dict(
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type='ABIVisionDecoder',
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in_channels=512,
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num_channels=64,
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attn_height=8,
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attn_width=32,
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attn_mode='nearest',
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use_result='feature',
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num_chars=num_chars,
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max_seq_len=max_seq_len,
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init_cfg=dict(type='Xavier', layer='Conv2d')),
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),
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loss=dict(
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type='ABILoss',
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enc_weight=1.0,
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dec_weight=1.0,
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fusion_weight=1.0,
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num_classes=num_chars),
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label_convertor=label_convertor,
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max_seq_len=max_seq_len,
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iter_size=1)
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data = dict(
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samples_per_gpu=192,
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workers_per_gpu=8,
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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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evaluation = dict(interval=1, metric='acc')
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