mirror of https://github.com/open-mmlab/mmocr.git
100 lines
2.7 KiB
Python
100 lines
2.7 KiB
Python
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dataset_type = 'KIEDataset'
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data_root = 'data/wildreceipt'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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max_scale, min_scale = 1024, 512
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(max_scale, min_scale), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='KIEFormatBundle'),
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dict(
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type='Collect',
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keys=['img', 'relations', 'texts', 'gt_bboxes', 'gt_labels'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(max_scale, min_scale), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='KIEFormatBundle'),
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dict(type='Collect', keys=['img', 'relations', 'texts', 'gt_bboxes'])
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]
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vocab_file = 'dict.txt'
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class_file = 'class_list.txt'
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data = dict(
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samples_per_gpu=4,
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workers_per_gpu=0,
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train=dict(
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type=dataset_type,
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ann_file='train.txt',
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pipeline=train_pipeline,
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data_root=data_root,
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vocab_file=vocab_file,
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class_file=class_file),
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val=dict(
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type=dataset_type,
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ann_file='test.txt',
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pipeline=test_pipeline,
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data_root=data_root,
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vocab_file=vocab_file,
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class_file=class_file),
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test=dict(
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type=dataset_type,
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ann_file='test.txt',
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pipeline=test_pipeline,
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data_root=data_root,
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vocab_file=vocab_file,
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class_file=class_file))
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evaluation = dict(
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interval=1,
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metric='macro_f1',
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metric_options=dict(
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macro_f1=dict(
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ignores=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25])))
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model = dict(
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type='SDMGR',
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backbone=dict(type='UNet', base_channels=16),
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bbox_head=dict(
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type='SDMGRHead', visual_dim=16, num_chars=92, num_classes=26),
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visual_modality=False,
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train_cfg=None,
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test_cfg=None)
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optimizer = dict(type='Adam', weight_decay=0.0001)
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optimizer_config = dict(grad_clip=None)
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lr_config = dict(
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policy='step',
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warmup='linear',
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warmup_iters=1,
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warmup_ratio=1,
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step=[40, 50])
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total_epochs = 60
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checkpoint_config = dict(interval=1)
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log_config = dict(
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interval=50,
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hooks=[
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dict(type='TextLoggerHook'),
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# dict(
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# type='PaviLoggerHook',
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# add_last_ckpt=True,
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# interval=5,
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# init_kwargs=dict(project='kie')),
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])
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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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