mirror of https://github.com/open-mmlab/mmocr.git
161 lines
4.6 KiB
Python
161 lines
4.6 KiB
Python
_base_ = ['../../_base_/default_runtime.py']
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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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label_convertor = dict(
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type='SegConvertor', dict_type='DICT36', with_unknown=True, lower=True)
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model = dict(
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type='SegRecognizer',
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backbone=dict(
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type='ResNet31OCR',
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layers=[1, 2, 5, 3],
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channels=[32, 64, 128, 256, 512, 512],
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out_indices=[0, 1, 2, 3],
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stage4_pool_cfg=dict(kernel_size=2, stride=2),
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last_stage_pool=True),
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neck=dict(
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type='FPNOCR', in_channels=[128, 256, 512, 512], out_channels=256),
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head=dict(
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type='SegHead',
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in_channels=256,
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upsample_param=dict(scale_factor=2.0, mode='nearest')),
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loss=dict(
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type='SegLoss', seg_downsample_ratio=1.0, seg_with_loss_weight=True),
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label_convertor=label_convertor)
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find_unused_parameters = True
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img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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gt_label_convertor = dict(
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type='SegConvertor', dict_type='DICT36', with_unknown=True, lower=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='RandomPaddingOCR',
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max_ratio=[0.15, 0.2, 0.15, 0.2],
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box_type='char_quads'),
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dict(type='OpencvToPil'),
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dict(
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type='RandomRotateImageBox',
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min_angle=-17,
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max_angle=17,
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box_type='char_quads'),
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dict(type='PilToOpencv'),
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dict(
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type='ResizeOCR',
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height=64,
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min_width=64,
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max_width=512,
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keep_aspect_ratio=True),
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dict(
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type='OCRSegTargets',
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label_convertor=gt_label_convertor,
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box_type='char_quads'),
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dict(type='RandomRotateTextDet', rotate_ratio=0.5, max_angle=15),
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dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
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dict(type='ToTensorOCR'),
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dict(type='FancyPCA'),
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dict(type='NormalizeOCR', **img_norm_cfg),
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dict(
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type='CustomFormatBundle',
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keys=['gt_kernels'],
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visualize=dict(flag=False, boundary_key=None),
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call_super=False),
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dict(
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type='Collect',
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keys=['img', 'gt_kernels'],
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meta_keys=['filename', 'ori_shape', 'img_shape'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='ResizeOCR',
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height=64,
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min_width=64,
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max_width=None,
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keep_aspect_ratio=True),
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dict(type='ToTensorOCR'),
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dict(type='NormalizeOCR', **img_norm_cfg),
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dict(type='CustomFormatBundle', call_super=False),
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dict(
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type='Collect',
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keys=['img'],
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meta_keys=['filename', 'ori_shape', 'img_shape'])
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]
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train_img_root = 'data/mixture/'
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train_img_prefix = train_img_root + 'SynthText'
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train_ann_file = train_img_root + 'SynthText/instances_train.txt'
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train = dict(
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type='OCRSegDataset',
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img_prefix=train_img_prefix,
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ann_file=train_ann_file,
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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='LineJsonParser', keys=['file_name', 'annotations', 'text'])),
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pipeline=train_pipeline,
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test_mode=False)
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dataset_type = 'OCRDataset'
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test_prefix = 'data/mixture/'
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test_img_prefix1 = test_prefix + 'IIIT5K/'
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test_img_prefix2 = test_prefix + 'svt/'
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test_img_prefix3 = test_prefix + 'icdar_2013/'
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test_img_prefix4 = test_prefix + 'ct80/'
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test_ann_file1 = test_prefix + 'IIIT5K/test_label.txt'
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test_ann_file2 = test_prefix + 'svt/test_label.txt'
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test_ann_file3 = test_prefix + 'icdar_2013/test_label_1015.txt'
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test_ann_file4 = test_prefix + 'ct80/test_label.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=test_pipeline,
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test_mode=True)
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test2 = {key: value for key, value in test1.items()}
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test2['img_prefix'] = test_img_prefix2
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test2['ann_file'] = test_ann_file2
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test3 = {key: value for key, value in test1.items()}
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test3['img_prefix'] = test_img_prefix3
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test3['ann_file'] = test_ann_file3
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test4 = {key: value for key, value in test1.items()}
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test4['img_prefix'] = test_img_prefix4
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test4['ann_file'] = test_ann_file4
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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(type='ConcatDataset', datasets=[train]),
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val=dict(type='ConcatDataset', datasets=[test1, test2, test3, test4]),
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test=dict(type='ConcatDataset', datasets=[test1, test2, test3, test4]))
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evaluation = dict(interval=1, metric='acc')
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