| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| max_scale, min_scale = 1024, 512 | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='Resize', img_scale=(max_scale, min_scale), keep_ratio=True), | |
| dict(type='RandomFlip', flip_ratio=0.), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='KIEFormatBundle'), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'relations', 'texts', 'gt_bboxes', 'gt_labels']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='Resize', img_scale=(max_scale, min_scale), keep_ratio=True), | |
| dict(type='RandomFlip', flip_ratio=0.), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='KIEFormatBundle'), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'relations', 'texts', 'gt_bboxes'], | |
| meta_keys=[ | |
| 'img_norm_cfg', 'img_shape', 'ori_filename', 'filename', | |
| 'ori_texts' | |
| ]) | |
| ] | |
| dataset_type = 'KIEDataset' | |
| data_root = 'data/wildreceipt' | |
| loader = dict( | |
| type='HardDiskLoader', | |
| repeat=1, | |
| parser=dict( | |
| type='LineJsonParser', | |
| keys=['file_name', 'height', 'width', 'annotations'])) | |
| train = dict( | |
| type=dataset_type, | |
| ann_file=f'{data_root}/train.txt', | |
| pipeline=train_pipeline, | |
| img_prefix=data_root, | |
| loader=loader, | |
| dict_file=f'{data_root}/dict.txt', | |
| test_mode=False) | |
| test = dict( | |
| type=dataset_type, | |
| ann_file=f'{data_root}/test.txt', | |
| pipeline=test_pipeline, | |
| img_prefix=data_root, | |
| loader=loader, | |
| dict_file=f'{data_root}/dict.txt', | |
| test_mode=True) | |
| data = dict( | |
| samples_per_gpu=4, | |
| workers_per_gpu=4, | |
| val_dataloader=dict(samples_per_gpu=1), | |
| test_dataloader=dict(samples_per_gpu=1), | |
| train=train, | |
| val=test, | |
| test=test) | |
| evaluation = dict( | |
| interval=1, | |
| metric='macro_f1', | |
| metric_options=dict( | |
| macro_f1=dict( | |
| ignores=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25]))) | |
| model = dict( | |
| type='SDMGR', | |
| backbone=dict(type='UNet', base_channels=16), | |
| bbox_head=dict( | |
| type='SDMGRHead', visual_dim=16, num_chars=92, num_classes=26), | |
| visual_modality=True, | |
| train_cfg=None, | |
| test_cfg=None, | |
| class_list=f'{data_root}/class_list.txt') | |
| optimizer = dict(type='Adam', weight_decay=0.0001) | |
| optimizer_config = dict(grad_clip=None) | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=1, | |
| warmup_ratio=1, | |
| step=[40, 50]) | |
| total_epochs = 60 | |
| checkpoint_config = dict(interval=1) | |
| log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) | |
| dist_params = dict(backend='nccl') | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| workflow = [('train', 1)] | |
| find_unused_parameters = True | |