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营业执照识别_paddle营业执照模型

作者：爱喝兽奶帝天荒 | 2024-07-23 06:54:40

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paddle营业执照模型

目录

1、制作文本检测数据集

2、导出文本检测推理模型

3、制作语义实体识别数据集

4、训练模型

5、模型预测

6、模型导出

7、模型推理

1、制作文本检测数据集

      文本检测数据集格式如下：

  1.  
  2. dataset_dir     # 数据集根目录，目录名称可以改变
  3. ├── images      # 存放图像的目录，目录名称可以改变，但要注意和train.txt val.txt的内容对应
  4. ├── train.txt   # 训练集标注文件，文件名称不可改变，内容举例：images/img_0.jpg 	 [{"transcription": "张三公司", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
  5. └── val.txt     # 验证集标注文件，文件名称不可改变，内容举例：images/img_61.jpg 	 [{"transcription": "李四公司", "points": [[31, 10], [310, 140], [420, 220], [310, 170]]}, {...}]

2、导出文本检测推理模型

      文本检测推理模型结构如下：

3、制作语义实体识别数据集

      语义实体识别数据集格式如下：

  1. train_data/XFUND    
  2. ├── zh_train    
  3. |      ├── images
  4. |      └── train.json 
  5. ├── zh_val
  6. |      ├── images  
  7. |      └── val.json
  8. └── class_list_xfun.txt

4、训练模型

python tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml

配置文件 ser_vi_layoutxlm_xfund_zh_udml.yml

  1. Global:
  2.   use_gpu: True
  3.   epoch_num: &epoch_num 200
  4.   log_smooth_window: 10
  5.   print_batch_step: 10
  6.   save_model_dir: ./output/ser_vi_layoutxlm_xfund_zh_udml
  7.   save_epoch_step: 2000
  8.   # evaluation is run every 10 iterations after the 0th iteration
  9.   eval_batch_step: [ 0, 19 ]
  10.   cal_metric_during_train: False
  11.   save_inference_dir:
  12.   use_visualdl: False
  13.   seed: 2022
  14.   infer_img: ppstructure/docs/kie/input/zh_val_42.jpg
  15.   save_res_path: ./output/ser_layoutxlm_xfund_zh/res
  16.  
  17.  
  18. Architecture:
  19.   model_type: &model_type "kie"
  20.   name: DistillationModel
  21.   algorithm: Distillation
  22.   Models:
  23.     Teacher:
  24.       pretrained:
  25.       freeze_params: false
  26.       return_all_feats: true
  27.       model_type: *model_type
  28.       algorithm: &algorithm "LayoutXLM"
  29.       Transform:
  30.       Backbone:
  31.         name: LayoutXLMForSer
  32.         pretrained: True
  33.         # one of base or vi
  34.         mode: vi
  35.         checkpoints:
  36.         num_classes: &num_classes 5
  37.     Student:
  38.       pretrained:
  39.       freeze_params: false
  40.       return_all_feats: true
  41.       model_type: *model_type
  42.       algorithm: *algorithm
  43.       Transform:
  44.       Backbone:
  45.         name: LayoutXLMForSer
  46.         pretrained: True
  47.         # one of base or vi
  48.         mode: vi
  49.         checkpoints:
  50.         num_classes: *num_classes
  51.  
  52.  
  53. Loss:
  54.   name: CombinedLoss
  55.   loss_config_list:
  56.   - DistillationVQASerTokenLayoutLMLoss:
  57.       weight: 1.0
  58.       model_name_list: ["Student", "Teacher"]
  59.       key: backbone_out
  60.       num_classes: *num_classes
  61.   - DistillationSERDMLLoss:
  62.       weight: 1.0
  63.       act: "softmax"
  64.       use_log: true
  65.       model_name_pairs:
  66.       - ["Student", "Teacher"]
  67.       key: backbone_out
  68.   - DistillationVQADistanceLoss:
  69.       weight: 0.5
  70.       mode: "l2"
  71.       model_name_pairs:
  72.         - ["Student", "Teacher"]
  73.       key: hidden_states_5
  74.       name: "loss_5"
  75.   - DistillationVQADistanceLoss:
  76.       weight: 0.5
  77.       mode: "l2"
  78.       model_name_pairs:
  79.         - ["Student", "Teacher"]
  80.       key: hidden_states_8
  81.       name: "loss_8"
  82.   
  83.   
  84.  
  85. Optimizer:
  86.   name: AdamW
  87.   beta1: 0.9
  88.   beta2: 0.999
  89.   lr:
  90.     name: Linear
  91.     learning_rate: 0.00005
  92.     epochs: *epoch_num
  93.     warmup_epoch: 10
  94.   regularizer:
  95.     name: L2
  96.     factor: 0.00000
  97.     
  98. PostProcess:
  99.   name: DistillationSerPostProcess
  100.   model_name: ["Student", "Teacher"]
  101.   key: backbone_out
  102.   class_path: &class_path train_data/XFUND/class_list_xfun.txt
  103.  
  104. Metric:
  105.   name: DistillationMetric
  106.   base_metric_name: VQASerTokenMetric
  107.   main_indicator: hmean
  108.   key: "Student"
  109.  
  110. Train:
  111.   dataset:
  112.     name: SimpleDataSet
  113.     data_dir: train_data/XFUND/zh_train
  114.     label_file_list: 
  115.       - train_data/XFUND/zh_train/train.json
  116.     ratio_list: [ 1.0 ]
  117.     transforms:
  118.       - DecodeImage: # load image
  119.           img_mode: RGB
  120.           channel_first: False
  121.       - VQATokenLabelEncode: # Class handling label
  122.           contains_re: False
  123.           algorithm: *algorithm
  124.           class_path: *class_path
  125.           # one of [None, "tb-yx"]
  126.           order_method: &order_method "tb-yx"
  127.       - VQATokenPad:
  128.           max_seq_len: &max_seq_len 512
  129.           return_attention_mask: True
  130.       - VQASerTokenChunk:
  131.           max_seq_len: *max_seq_len
  132.       - Resize:
  133.           size: [224,224]
  134.       - NormalizeImage:
  135.           scale: 1
  136.           mean: [ 123.675, 116.28, 103.53 ]
  137.           std: [ 58.395, 57.12, 57.375 ]
  138.           order: 'hwc'
  139.       - ToCHWImage:
  140.       - KeepKeys:
  141.           keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
  142.   loader:
  143.     shuffle: True
  144.     drop_last: False
  145.     batch_size_per_card: 1
  146.     num_workers: 1
  147.  
  148. Eval:
  149.   dataset:
  150.     name: SimpleDataSet
  151.     data_dir: train_data/XFUND/zh_val
  152.     label_file_list:
  153.       - train_data/XFUND/zh_val/val.json
  154.     transforms:
  155.       - DecodeImage: # load image
  156.           img_mode: RGB
  157.           channel_first: False
  158.       - VQATokenLabelEncode: # Class handling label
  159.           contains_re: False
  160.           algorithm: *algorithm
  161.           class_path: *class_path
  162.           order_method: *order_method
  163.       - VQATokenPad:
  164.           max_seq_len: *max_seq_len
  165.           return_attention_mask: True
  166.       - VQASerTokenChunk:
  167.           max_seq_len: *max_seq_len
  168.       - Resize:
  169.           size: [224,224]
  170.       - NormalizeImage:
  171.           scale: 1
  172.           mean: [ 123.675, 116.28, 103.53 ]
  173.           std: [ 58.395, 57.12, 57.375 ]
  174.           order: 'hwc'
  175.       - ToCHWImage:
  176.       - KeepKeys:
  177.           keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
  178.   loader:
  179.     shuffle: False
  180.     drop_last: False
  181.     batch_size_per_card: 8
  182.     num_workers: 4

5、模型预测

  1. python tools/infer_kie_token_ser.py \
  2.   -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml \
  3.   -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh_udml/best_accuracy \
  4.   Global.infer_img=./train_data/XFUND/zh_train/images/51.jpg  Global.kie_det_model_dir="./infer_model/inference"

配置文件 ser_vi_layoutxlm_xfund_zh.yml

  1. Global:
  2.   use_gpu: True
  3.   epoch_num: &epoch_num 200
  4.   log_smooth_window: 10
  5.   print_batch_step: 10
  6.   save_model_dir: ./output/ser_vi_layoutxlm_xfund_zh
  7.   save_epoch_step: 2000
  8.   # evaluation is run every 10 iterations after the 0th iteration
  9.   eval_batch_step: [ 0, 19 ]
  10.   cal_metric_during_train: False
  11.   save_inference_dir:
  12.   use_visualdl: False
  13.   seed: 2022
  14.   infer_img: ppstructure/docs/kie/input/zh_val_42.jpg
  15.   d2s_train_image_shape: [3, 224, 224]
  16.   # if you want to predict using the groundtruth ocr info,
  17.   # you can use the following config
  18.   # infer_img: train_data/XFUND/zh_val/val.json
  19.   # infer_mode: False
  20.  
  21.   save_res_path: ./output/ser/xfund_zh/res
  22.   kie_rec_model_dir: 
  23.   kie_det_model_dir:
  24.   amp_custom_white_list: ['scale', 'concat', 'elementwise_add']
  25.  
  26. Architecture:
  27.   model_type: kie
  28.   algorithm: &algorithm "LayoutXLM"
  29.   Transform:
  30.   Backbone:
  31.     name: LayoutXLMForSer
  32.     pretrained: True
  33.     checkpoints:
  34.     # one of base or vi
  35.     mode: vi
  36.     num_classes: &num_classes 3
  37.  
  38. Loss:
  39.   name: VQASerTokenLayoutLMLoss
  40.   num_classes: *num_classes
  41.   key: "backbone_out"
  42.  
  43. Optimizer:
  44.   name: AdamW
  45.   beta1: 0.9
  46.   beta2: 0.999
  47.   lr:
  48.     name: Linear
  49.     learning_rate: 0.00005
  50.     epochs: *epoch_num
  51.     warmup_epoch: 2
  52.   regularizer:
  53.     name: L2
  54.     factor: 0.00000
  55.     
  56. PostProcess:
  57.   name: VQASerTokenLayoutLMPostProcess
  58.   class_path: &class_path train_data/XFUND/class_list_xfun.txt
  59.  
  60. Metric:
  61.   name: VQASerTokenMetric
  62.   main_indicator: hmean
  63.  
  64. Train:
  65.   dataset:
  66.     name: SimpleDataSet
  67.     data_dir: train_data/XFUND/zh_train
  68.     label_file_list: 
  69.       - train_data/XFUND/zh_train/train.json
  70.     ratio_list: [ 1.0 ]
  71.     transforms:
  72.       - DecodeImage: # load image
  73.           img_mode: RGB
  74.           channel_first: False
  75.       - VQATokenLabelEncode: # Class handling label
  76.           contains_re: False
  77.           algorithm: *algorithm
  78.           class_path: *class_path
  79.           use_textline_bbox_info: &use_textline_bbox_info True
  80.           # one of [None, "tb-yx"]
  81.           order_method: &order_method "tb-yx"
  82.       - VQATokenPad:
  83.           max_seq_len: &max_seq_len 512
  84.           return_attention_mask: True
  85.       - VQASerTokenChunk:
  86.           max_seq_len: *max_seq_len
  87.       - Resize:
  88.           size: [224,224]
  89.       - NormalizeImage:
  90.           scale: 1
  91.           mean: [ 123.675, 116.28, 103.53 ]
  92.           std: [ 58.395, 57.12, 57.375 ]
  93.           order: 'hwc'
  94.       - ToCHWImage:
  95.       - KeepKeys:
  96.           keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
  97.   loader:
  98.     shuffle: True
  99.     drop_last: False
  100.     batch_size_per_card: 8
  101.     num_workers: 4
  102.  
  103. Eval:
  104.   dataset:
  105.     name: SimpleDataSet
  106.     data_dir: train_data/XFUND/zh_val
  107.     label_file_list:
  108.       - train_data/XFUND/zh_val/val.json
  109.     transforms:
  110.       - DecodeImage: # load image
  111.           img_mode: RGB
  112.           channel_first: False
  113.       - VQATokenLabelEncode: # Class handling label
  114.           contains_re: False
  115.           algorithm: *algorithm
  116.           class_path: *class_path
  117.           use_textline_bbox_info: *use_textline_bbox_info
  118.           order_method: *order_method
  119.       - VQATokenPad:
  120.           max_seq_len: *max_seq_len
  121.           return_attention_mask: True
  122.       - VQASerTokenChunk:
  123.           max_seq_len: *max_seq_len
  124.       - Resize:
  125.           size: [224,224]
  126.       - NormalizeImage:
  127.           scale: 1
  128.           mean: [ 123.675, 116.28, 103.53 ]
  129.           std: [ 58.395, 57.12, 57.375 ]
  130.           order: 'hwc'
  131.       - ToCHWImage:
  132.       - KeepKeys:
  133.           keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] # dataloader will return list in this order
  134.   loader:
  135.     shuffle: False
  136.     drop_last: False
  137.     batch_size_per_card: 8
  138.     num_workers: 4

6、模型导出

python tools/export_model.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh_udml/best_accuracy Global.kie_det_model_dir="./infer_model/inference" Global.save_inference_dir=./inference/ser_vi_layoutxlm

7、模型推理

  1. python ppstructure/kie/predict_kie_token_ser.py \
  2.   --kie_algorithm=LayoutXLM \
  3.   --ser_model_dir=./inference/ser_vi_layoutxlm \
  4.   --det_model_dir=./infer_model/inference \
  5.   --image_dir=./train_data/XFUND/zh_train/images/18.jpg \
  6.   --ser_dict_path=./train_data/XFUND/class_list_xfun.txt \
  7.   --vis_font_path=./doc/fonts/simfang.ttf \
  8.   --ocr_order_method="tb-yx"

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