layoutlmv2.yml

Global:
  use_gpu: True
  epoch_num: &epoch_num 200
  log_smooth_window: 10
  print_batch_step: 10
  save_model_dir: ./output/re_layoutlmv2/
  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: 2048
  infer_img: doc/vqa/input/zh_val_21.jpg
  save_res_path: ./output/re/

Architecture:
  model_type: vqa
  algorithm: &algorithm "LayoutLMv2"
  Transform:
  Backbone:
    name: LayoutLMv2ForRe
    pretrained: True
    checkpoints: 

Loss:
  name: LossFromOutput
  key: loss
  reduction: mean

Optimizer:
  name: AdamW
  beta1: 0.9
  beta2: 0.999
  clip_norm: 10
  lr:
    # name: Piecewise
    # values: [0.000005, 0.00005]
    # decay_epochs: [10]
    # warmup_epoch: 0
    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/XFUND/zh_train/image
    label_file_list: 
      - train_data/XFUND/zh_train/xfun_normalize_train.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 ppstructure/vqa/labels/labels_ser.txt
      - VQATokenPad:
          max_seq_len: &max_seq_len 512
          return_attention_mask: True
      - VQAReTokenRelation:
      - VQAReTokenChunk:
          max_seq_len: *max_seq_len
      - Resize:
          size: [224,224]
      - NormalizeImage:
          scale: 1./255.
          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]
          order: 'hwc'
      - ToCHWImage:
      - KeepKeys:
          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
  loader:
    shuffle: True
    drop_last: False
    batch_size_per_card: 8
    num_workers: 8
    collate_fn: ListCollator

Eval:
  dataset:
    name: SimpleDataSet
    data_dir: train_data/XFUND/zh_val/image
    label_file_list:
      - train_data/XFUND/zh_val/xfun_normalize_val.json
    transforms:
      - DecodeImage: # load image
          img_mode: RGB
          channel_first: False
      - VQATokenLabelEncode: # Class handling label
          contains_re: True
          algorithm: *algorithm
          class_path: *class_path
      - VQATokenPad:
          max_seq_len: *max_seq_len
          return_attention_mask: True
      - VQAReTokenRelation:
      - VQAReTokenChunk:
          max_seq_len: *max_seq_len
      - Resize:
          size: [224,224]
      - NormalizeImage:
          scale: 1./255.
          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]
          order: 'hwc'
      - ToCHWImage:
      - KeepKeys:
          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 8
    num_workers: 8
    collate_fn: ListCollator