config.yml

basic_trainer: # trains model on the whole train dataset
  active: True # toggle if training should be activated
  val_path: PATH/TO/VAL/DATASET # path to validation dataset, JSON file or directory containing dataset
  train_path: PATH/TO/TRAIN/DATASET # path to train datset, JSON file or directory containing dataset

oof_trainer: # trains three models with the oof strategy
  active: False # toggle if training should be activated
  dataset_path: PATH/TO/WHOLE/DATASET # JSON file or direcotry containing dataset
  oof_p: 0.33

network_config:
  epochs: 5 # number of epochs to train
  batch_size: 1 # batch size for training
  learning_rate: 0.00001 # initial learning rate
  load_from_model: None|PATH/TO/MODEL # either "None" or a file path to model weights
  wandb: True # if wandb should be activated for monitoring
  save: True # if checkpoints of the model should be saved
  evaluation_dir: PATH/TO/EVALUATION/DIR # directory all evaluation files will be saved to
  bilinear: True # if bilinear connections should be used
  amp: False # amp active/deactive
  validation_interval: 200 # validation interval in samples
  loss_type: abs_l1_loss # which loss to use. possible losses are (abs_l1_loss,mean_l1_loss,mean_l2_loss)
  optimizer_name: rmsprop # name of optimizer to be used 'adam', 'rmpsprop'

dataset_config:
  scale_images: 0.5 # percentage all images get scaled with (1.0 implies no scaling)
  enable_augmentation: False # if images augmentation should be applied
  add_nan_mask_to_input: True # if nan mask should be concatented to input
  add_region_mask_to_input: True # if region mask should be concatenated to input
  normalize_depths: 
    active: False # normalized input and label depth d with (d - mean) / std channel wise
    min: 0
    max: 0
  resize_region_to_fill_input: False