rec_d28_can.yml

Global:
  use_gpu: True
  epoch_num: 240
  log_smooth_window: 20
  print_batch_step: 10
  save_model_dir: ./output/rec/can/
  save_epoch_step: 1
  # evaluation is run every 1105 iterations
  eval_batch_step: [0, 1105]
  cal_metric_during_train: True
  pretrained_model: ./output/rec/can/CAN
  checkpoints: ./output/rec/can/CAN
  save_inference_dir: ./inference/rec_d28_can/
  use_visualdl: False
  infer_img: doc/imgs_hme/hme_01.jpeg
  # for data or label process
  character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
  max_text_length: 36
  infer_mode: False
  use_space_char: False
  save_res_path: ./output/rec/predicts_can.txt

Optimizer:
  name: Momentum
  momentum: 0.9
  clip_norm_global: 100.0
  lr:
    name: TwoStepCosine
    learning_rate: 0.01
    warmup_epoch: 1
  weight_decay: 0.0001

Architecture:
  model_type: rec
  algorithm: CAN
  in_channels: 1
  Transform:
  Backbone:
    name: DenseNet 
    growthRate: 24
    reduction: 0.5
    bottleneck: True
    use_dropout: True
    input_channel: 1 
    
  Head:
    name: CANHead
    in_channel: 684
    out_channel: 111
    max_text_length: 36
    ratio: 16
    attdecoder:
      is_train: True
      input_size: 256
      hidden_size: 256
      encoder_out_channel: 684
      dropout: True
      dropout_ratio: 0.5
      word_num: 111
      counting_decoder_out_channel: 111
      attention:
        attention_dim: 512
        word_conv_kernel: 1
   
Loss:
  name: CANLoss

PostProcess:
  name: SeqLabelDecode
  character: 111

Metric:
  name: CANMetric
  main_indicator: exp_rate

Train:
  dataset:
    name: HMERDataSet
    data_dir: ./train_data/CROHME/training/images/
    transforms:
      - DecodeImage:
          channel_first: False
      - GrayImageChannelFormat: 
          normalize: True
          inverse: True
      - KeepKeys:
          keep_keys: ['image', 'label']
    label_file_list: ["./train_data/CROHME/training/labels.json"]
  loader:
    shuffle: True
    batch_size_per_card: 2
    drop_last: True
    num_workers: 1
    collate_fn: DyMaskCollator

Eval:
  dataset:
    name: HMERDataSet
    data_dir: ./train_data/CROHME/evaluation/images/
    transforms: 
      - DecodeImage:
          channel_first: False
      - GrayImageChannelFormat:
          normalize: True
          inverse: True
      - KeepKeys:
          keep_keys: ['image', 'label']
    label_file_list: ["./train_data/CROHME/evaluation/labels.json"]
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 1
    num_workers: 4
    collate_fn: DyMaskCollator