mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
a2f82874bb
* init * update UT * fix UT except SAR * update to latest 2.0 * fix ncnn UT * export info
112 lines
2.8 KiB
Python
Executable File
112 lines
2.8 KiB
Python
Executable File
# Copyright (c) OpenMMLab. All rights reserved.
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_base_ = []
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checkpoint_config = dict(interval=1)
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# yapf:disable
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log_config = dict(
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interval=1,
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hooks=[
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dict(type='TextLoggerHook')
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])
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# yapf:enable
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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# model
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label_convertor = dict(
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type='CTCConvertor', dict_type='DICT36', with_unknown=False, lower=True)
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dictionary = dict(
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type='Dictionary',
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dict_file='tests/test_codebase/test_mmocr/data/lower_english_digits.txt',
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with_padding=True)
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default_scope = 'mmocr'
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model = dict(
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type='mmocr.CRNN',
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preprocessor=None,
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backbone=dict(type='MiniVGG', leaky_relu=False, input_channels=1),
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encoder=None,
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decoder=dict(
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type='CRNNDecoder',
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in_channels=512,
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rnn_flag=True,
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module_loss=dict(type='CTCModuleLoss', letter_case='lower'),
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postprocessor=dict(type='CTCPostProcessor'),
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dictionary=dictionary),
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data_preprocessor=dict(
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type='TextRecogDataPreprocessor', mean=[127], std=[127]))
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train_cfg = None
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test_cfg = None
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# optimizer
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optimizer = dict(type='Adadelta', lr=1.0)
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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=[])
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total_epochs = 5
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# data
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img_norm_cfg = dict(mean=[127], std=[127])
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test_pipeline = [
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dict(
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type='RescaleToHeight',
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height=32,
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min_width=32,
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max_width=None,
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width_divisor=16),
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dict(type='LoadOCRAnnotations', with_text=True),
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dict(
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type='PackTextRecogInputs',
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meta_keys=('ori_shape', 'img_shape', 'valid_ratio'))
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]
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val_cfg = dict(type='MultiValLoop')
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test_cfg = dict(type='MultiTestLoop')
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param_scheduler = [dict(type='ConstantLR', factor=1.0)]
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file_client_args = dict(backend='disk')
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dataset_type = 'OCRDataset'
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test_prefix = 'tests/test_codebase/test_mmocr/data/'
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test_img_prefix1 = test_prefix
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test_ann_file1 = test_prefix + 'text_recognition.txt'
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test1 = dict(
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type=dataset_type,
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img_prefix=test_img_prefix1,
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ann_file=test_ann_file1,
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loader=dict(
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type='HardDiskLoader',
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repeat=1,
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parser=dict(
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type='LineStrParser',
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keys=['filename', 'text'],
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keys_idx=[0, 1],
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separator=' ')),
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pipeline=None,
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test_mode=True)
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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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val=dict(
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type='UniformConcatDataset', datasets=[test1], pipeline=test_pipeline),
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test=dict(
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type='UniformConcatDataset', datasets=[test1], pipeline=test_pipeline))
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evaluation = dict(interval=1, metric='acc')
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cudnn_benchmark = True
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visualizer = dict(type='TextRecogLocalVisualizer', name='visualizer')
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