config_2d.yml

model_params:
  model: &model VNet
  encoder_name: &encoder_name "resnet34"
  group_norm: &group_norm False
  classes: 7
  center: !!str &center 'none'
  attention_type: !!str &attention_type 'none'
  reslink: False
  multi_task: &multi_task False


args:
  expdir: "src"
  logdir: &logdir "./logs/structseg"
  baselogdir: "./logs/structseg"

distributed_params:
  opt_level: O1


stages:

  state_params:
    main_metric: &reduce_metric dice
    minimize_metric: False

  criterion_params:
    criterion: &criterion MultiDiceLoss
    activation: 'Softmax'
    weight: [0.1, 0.2, 0.2, 0.3, 0.4, 0.4, 0.4]
    dice_weight: 0.9

  data_params:
    batch_size: 16
    num_workers: 4
    drop_last: False
    # drop_last: True

    image_size: &image_size 512
    train_csv: "./csv/train_0.csv"
    valid_csv: "./csv/valid_0.csv"
    data: "2D"

  stage1:

    optimizer_params:
      optimizer: Adam
      lr: 0.0001

    scheduler_params:
      scheduler: OneCycleLRWithWarmup
      num_steps: &num_epochs 50
      lr_range: [0.0005, 0.00001]
      warmup_steps: 5
      momentum_range: [0.85, 0.95]

    state_params:
      num_epochs: *num_epochs

    callbacks_params:
      loss:
        callback: CriterionCallback
      optimizer:
        callback: OptimizerCallback
        accumulation_steps: 1
      dice:
        callback: MultiDiceCallback
        activation: 'Softmax'
      scheduler:
        callback: SchedulerCallback
        reduce_metric: *reduce_metric
        # mode: 'batch'
      saver:
        callback: CheckpointCallback

monitoring_params:
  project: "SS_Task3"
  tags: [*model, *encoder_name, *criterion]