mirror of https://github.com/open-mmlab/mmocr.git
185 lines
5.2 KiB
Python
185 lines
5.2 KiB
Python
_base_ = [
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'../../_base_/default_runtime.py', '../../_base_/recog_models/satrn.py'
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]
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label_convertor = dict(
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type='AttnConvertor', dict_type='DICT90', with_unknown=True)
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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='TFDecoder',
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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='TFLoss'),
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label_convertor=label_convertor,
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max_seq_len=25)
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# optimizer
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optimizer = dict(type='Adam', lr=3e-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 = 6
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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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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='ResizeOCR',
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height=32,
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min_width=100,
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max_width=100,
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keep_aspect_ratio=False,
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width_downsample_ratio=0.25),
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dict(type='ToTensorOCR'),
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dict(type='NormalizeOCR', **img_norm_cfg),
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dict(
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type='Collect',
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keys=['img'],
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meta_keys=[
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'filename', 'ori_shape', 'img_shape', 'text', 'valid_ratio',
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'resize_shape'
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]),
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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='MultiRotateAugOCR',
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rotate_degrees=[0, 90, 270],
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transforms=[
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dict(
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type='ResizeOCR',
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height=32,
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min_width=100,
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max_width=100,
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keep_aspect_ratio=False,
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width_downsample_ratio=0.25),
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dict(type='ToTensorOCR'),
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dict(type='NormalizeOCR', **img_norm_cfg),
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dict(
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type='Collect',
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keys=['img'],
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meta_keys=[
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'filename', 'ori_shape', 'img_shape', 'valid_ratio',
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'resize_shape'
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]),
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])
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]
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dataset_type = 'OCRDataset'
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train_prefix = 'data/mixture/'
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train_img_prefix1 = train_prefix + \
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'SynthText/synthtext/SynthText_patch_horizontal'
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train_img_prefix2 = train_prefix + 'Syn90k/mnt/ramdisk/max/90kDICT32px'
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train_ann_file1 = train_prefix + 'SynthText/label.lmdb'
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train_ann_file2 = train_prefix + 'Syn90k/label.lmdb'
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train1 = dict(
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type=dataset_type,
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img_prefix=train_img_prefix1,
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ann_file=train_ann_file1,
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loader=dict(
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type='LmdbLoader',
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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=False)
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train2 = {key: value for key, value in train1.items()}
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train2['img_prefix'] = train_img_prefix2
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train2['ann_file'] = train_ann_file2
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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 + 'icdar_2015/'
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test_img_prefix5 = test_prefix + 'svtp/'
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test_img_prefix6 = 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 + 'icdar_2015/test_label.txt'
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test_ann_file5 = test_prefix + 'svtp/test_label.txt'
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test_ann_file6 = 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=None,
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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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test5 = {key: value for key, value in test1.items()}
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test5['img_prefix'] = test_img_prefix5
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test5['ann_file'] = test_ann_file5
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test6 = {key: value for key, value in test1.items()}
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test6['img_prefix'] = test_img_prefix6
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test6['ann_file'] = test_ann_file6
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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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train=dict(
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type='UniformConcatDataset',
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datasets=[train1, train2],
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pipeline=train_pipeline),
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val=dict(
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type='UniformConcatDataset',
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datasets=[test1, test2, test3, test4, test5, test6],
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pipeline=test_pipeline),
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test=dict(
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type='UniformConcatDataset',
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datasets=[test1, test2, test3, test4, test5, test6],
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pipeline=test_pipeline))
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evaluation = dict(interval=1, metric='acc')
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