global:
  name: exp
  phase: train
  stage: pretrain-vision
  workdir: /tmp/workdir
  seed: ~
 
dataset:
  train: {
    roots: ['data/training/MJ/MJ_train/', 
            'data/training/MJ/MJ_test/', 
            'data/training/MJ/MJ_valid/', 
            'data/training/ST'],
    batch_size: 128
  }
  test: {
    roots: ['data/evaluation/IIIT5k_3000', 
            'data/evaluation/SVT', 
            'data/evaluation/SVTP',
            'data/evaluation/IC13_857',
            'data/evaluation/IC15_1811',
            'data/evaluation/CUTE80'],
    batch_size: 128
  }
  charset_path: data/charset_36.txt
  num_workers: 4
  max_length: 25  # 30
  image_height: 32
  image_width: 128
  case_sensitive: False
  eval_case_sensitive: False
  data_aug: True
  multiscales: False
  pin_memory: True
  smooth_label: False
  smooth_factor: 0.1
  one_hot_y: True
  use_sm: False

training:
  epochs: 6
  show_iters: 50
  eval_iters: 3000
  save_iters: 20000
  start_iters: 0
  stats_iters: 100000

optimizer:
  type: Adadelta # Adadelta, Adam
  true_wd: False
  wd: 0. # 0.001
  bn_wd: False
  args: {
    # betas: !!python/tuple [0.9, 0.99], # betas=(0.9,0.99) for AdamW
    # betas: !!python/tuple [0.9, 0.999], # for default Adam 
  }
  clip_grad: 20
  lr: [1.0, 1.0, 1.0]  # lr: [0.005, 0.005, 0.005]   
  scheduler: {
    periods: [3, 2, 1],
    gamma: 0.1,
  }

model:
  name: 'modules.model_abinet.ABINetModel'
  checkpoint: ~
  strict: True