KIE:训练re报错 浮点数例外 (核心已转储)

[hbo-lambda]

请提供下述完整信息以便快速定位问题/Please provide the following information to quickly locate the problem

• 系统环境/System Environment：Aistudio

• 版本号/Version：Paddle2.4： PaddleOCR2.6： kie re：

• 运行指令/Command Code：

  python3 tools/train.py -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml

• 完整报错/Complete Error Message：

配置文件

Global:
  use_gpu: True
  epoch_num: &epoch_num 130
  log_smooth_window: 10
  print_batch_step: 10
  save_model_dir: ./output/re_vi_layoutxlm_xfund_zh
  save_epoch_step: 2000
  # evaluation is run every 10 iterations after the 0th iteration
  eval_batch_step: [ 0, 19 ]
  cal_metric_during_train: False
  save_inference_dir:
  use_visualdl: False
  seed: 2022
  infer_img: ppstructure/docs/kie/input/zh_val_21.jpg
  save_res_path: ./output/re/xfund_zh/with_gt
  kie_rec_model_dir: 
  kie_det_model_dir:

Architecture:
  model_type: kie
  algorithm: &algorithm "LayoutXLM"
  Transform:
  Backbone:
    name: LayoutXLMForRe
    pretrained: True
    mode: vi
    checkpoints:

Loss:
  name: LossFromOutput
  key: loss
  reduction: mean

Optimizer:
  name: AdamW
  beta1: 0.9
  beta2: 0.999
  clip_norm: 10
  lr:
    learning_rate: 0.00005
    warmup_epoch: 10
  regularizer:
    name: L2
    factor: 0.00000

PostProcess:
  name: VQAReTokenLayoutLMPostProcess

Metric:
  name: VQAReTokenMetric
  main_indicator: hmean

Train:
  dataset:
    name: SimpleDataSet
    data_dir: train_data/Business/zh_train/image
    label_file_list: 
      - train_data/Business/zh_train/linking.json
    ratio_list: [ 1.0 ]
    transforms:
      - DecodeImage: # load image
          img_mode: RGB
          channel_first: False
      - VQATokenLabelEncode: # Class handling label
          contains_re: True
          algorithm: *algorithm
          class_path: &class_path train_data/Business/class_Business.txt
          use_textline_bbox_info: &use_textline_bbox_info True
          order_method: &order_method "tb-yx"
      - VQATokenPad:
          max_seq_len: &max_seq_len 512
          return_attention_mask: True
      - VQAReTokenRelation:
      - VQAReTokenChunk:
          max_seq_len: *max_seq_len
      - TensorizeEntitiesRelations:
      - Resize:
          size: [224,224]
      - NormalizeImage:
          scale: 1
          mean: [ 123.675, 116.28, 103.53 ]
          std: [ 58.395, 57.12, 57.375 ]
          order: 'hwc'
      - ToCHWImage:
      - KeepKeys:
          keep_keys: [ 'input_ids', 'bbox','attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
  loader:
    shuffle: True
    drop_last: False
    batch_size_per_card: 2
    num_workers: 2

Eval:
  dataset:
    name: SimpleDataSet
    data_dir: train_data/Business/zh_train/image
    label_file_list:
      - train_data/Business/zh_train/linking.json
    transforms:
      - DecodeImage: # load image
          img_mode: RGB
          channel_first: False
      - VQATokenLabelEncode: # Class handling label
          contains_re: True
          algorithm: *algorithm
          class_path: *class_path
          use_textline_bbox_info: *use_textline_bbox_info
          order_method: *order_method
      - VQATokenPad:
          max_seq_len: *max_seq_len
          return_attention_mask: True
      - VQAReTokenRelation:
      - VQAReTokenChunk:
          max_seq_len: *max_seq_len
      - TensorizeEntitiesRelations:
      - Resize:
          size: [224,224]
      - NormalizeImage:
          scale: 1
          mean: [ 123.675, 116.28, 103.53 ]
          std: [ 58.395, 57.12, 57.375 ]
          order: 'hwc'
      - ToCHWImage:
      - KeepKeys:
          keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'entities', 'relations'] # dataloader will return list in this order
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 2
    num_workers: 2

class_Business.txt

OTHER
QUESTION
ANSWER
HEADER

样本数据举例

1.jpg   [{"transcription": "注册号", "points": [[397, 430], [469, 431], [468, 454], [396, 453]], "label": "QUESTION", "id": 1, "linking": [[1, 2]]}, {"transcription": "4201020004458351", "points": [[473, 434], [615, 437], [614, 455], [472, 452]], "label": "ANSWER", "id": 2, "linking": [[1, 2]]}]

[tink2123]

AI studio 需要关闭多进程训练：

num_workers：0

[hbo-lambda]

AI studio 需要关闭多进程训练：

num_workers：0

跟这儿没关系，我改成0也一样报错。主要是数据集这里的label有问题，QUESTION和ANSWER改成小写就没事了。源码没细究，我记着文档里好像允许大写的