mirror of
https://github.com/open-mmlab/mmocr.git
synced 2025-06-03 21:54:47 +08:00
0a1787d6bc
* base refactor * update config * modify implementation of nrtr * add config file * add mask * add operation order * fix contiguous bug * update config * fix pytest * fix pytest * update readme * update readme and metafile * update docstring * fix norm cfg and dict size * rm useless * use mmocr builder instead * update pytest * update * remove useless * fix ckpt name * fix path * include all config file into pytest * update inference * Update test_recog_config.py
69 lines
1.7 KiB
Python
69 lines
1.7 KiB
Python
_base_ = [
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'../../_base_/default_runtime.py',
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'../../_base_/recog_pipelines/satrn_pipeline.py',
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'../../_base_/recog_datasets/ST_MJ_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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label_convertor = dict(
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type='AttnConvertor', dict_type='DICT90', with_unknown=True)
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model = dict(
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type='SATRN',
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backbone=dict(type='ShallowCNN', input_channels=3, hidden_dim=256),
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encoder=dict(
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type='SatrnEncoder',
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n_layers=6,
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n_head=8,
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d_k=256 // 8,
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d_v=256 // 8,
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d_model=256,
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n_position=100,
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d_inner=256 * 4,
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dropout=0.1),
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decoder=dict(
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type='NRTRDecoder',
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n_layers=6,
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d_embedding=256,
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n_head=8,
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d_model=256,
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d_inner=256 * 4,
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d_k=256 // 8,
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d_v=256 // 8),
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loss=dict(type='TFLoss'),
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label_convertor=label_convertor,
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max_seq_len=25)
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# optimizer
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optimizer = dict(type='Adam', lr=3e-4)
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optimizer_config = dict(grad_clip=None)
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# learning policy
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lr_config = dict(policy='step', step=[3, 4])
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total_epochs = 6
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data = dict(
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samples_per_gpu=64,
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workers_per_gpu=4,
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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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