mirror of https://github.com/open-mmlab/mmocr.git
41 lines
1.0 KiB
Python
41 lines
1.0 KiB
Python
_base_ = [
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'../../_base_/default_runtime.py',
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'../../_base_/recog_pipelines/seg_pipeline.py',
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'../../_base_/recog_models/seg.py',
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'../../_base_/recog_datasets/ST_charbox_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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# optimizer
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optimizer = dict(type='Adam', lr=1e-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 = 5
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find_unused_parameters = True
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data = dict(
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samples_per_gpu=16,
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workers_per_gpu=2,
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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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