mirror of
https://github.com/open-mmlab/mmocr.git
synced 2025-06-03 21:54:47 +08:00
97 lines
2.8 KiB
Python
97 lines
2.8 KiB
Python
_base_ = [
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'../../_base_/default_runtime.py',
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'../../_base_/schedules/schedule_adam_step_5e.py',
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'../../_base_/recog_models/satrn.py'
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]
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default_hooks = dict(logger=dict(type='LoggerHook', interval=50))
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# dataset settings
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dataset_type = 'OCRDataset'
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data_root = 'tests/data/ocr_toy_dataset'
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file_client_args = dict(backend='petrel')
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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=512),
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encoder=dict(
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type='SATRNEncoder',
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n_layers=12,
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n_head=8,
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d_k=512 // 8,
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d_v=512 // 8,
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d_model=512,
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n_position=100,
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d_inner=512 * 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=512,
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n_head=8,
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d_model=512,
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d_inner=512 * 4,
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d_k=512 // 8,
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d_v=512 // 8,
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loss=dict(type='CELoss', flatten=True, ignore_first_char=True),
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max_seq_len=25,
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postprocessor=dict(type='AttentionPostprocessor')))
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# optimizer
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optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=3e-4))
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train_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(type='LoadOCRAnnotations', with_text=True),
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dict(type='Resize', scale=(100, 32), keep_ratio=False),
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dict(
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type='PackTextRecogInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
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]
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# TODO Add Test Time Augmentation `MultiRotateAugOCR`
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test_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(type='Resize', scale=(100, 32), keep_ratio=False),
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dict(
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type='PackTextRecogInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio',
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'instances'))
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]
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train_dataloader = dict(
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batch_size=64,
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num_workers=8,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(img_path=None),
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ann_file='train_label.json',
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pipeline=train_pipeline))
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val_dataloader = dict(
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batch_size=64,
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num_workers=4,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(img_path=None),
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ann_file='test_label.json',
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test_mode=True,
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pipeline=test_pipeline))
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test_dataloader = val_dataloader
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val_evaluator = [
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dict(
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type='WordMetric', mode=['exact', 'ignore_case',
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'ignore_case_symbol']),
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dict(type='CharMetric')
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]
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test_evaluator = val_evaluator
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visualizer = dict(type='TextRecogLocalVisualizer', name='visualizer')
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