model_params:

  model: &model CNNFinetuneModels

  model_name: &model_name resnet50

  num_classes: 6

args:

  expdir: "src"

  logdir: &logdir "./logs/rsna"

  baselogdir: "./logs/rsna"

distributed_params:

  opt_level: O1

stages:

  state_params:

    main_metric: &reduce_metric loss

    minimize_metric: True

  criterion_params:

    criterion: &criterion LogLoss

    weight: [1,1,1,1,1,2]

  data_params:

    batch_size: 64

    num_workers: 4

    drop_last: False

    train_csv: "./csv/stratified_kfold/train_0.csv.gz"

    valid_csv: "./csv/stratified_kfold/valid_0.csv.gz"

    with_any: True

    root: "/data/stage_1_train_3w/"

    image_type: "jpg"

    # root: "/data/png/train/adjacent-brain-cropped/"

    # image_type: "png"

  callbacks_params: &callbacks_params

    loss:

      callback: CriterionCallback

    optimizer:

      callback: OptimizerCallback

      accumulation_steps: 1

    scheduler:

      callback: SchedulerCallback

      reduce_metric: *reduce_metric

    saver:

      callback: CheckpointCallback

      save_n_best: 5

    early_stoping:

      callback: EarlyStoppingCallback

      patience: 2

  warmup:

    data_params:

      image_size: [512, 512]

    optimizer_params:

      optimizer: AdamW

      lr: 0.001

    scheduler_params:

      scheduler: MultiStepLR

      milestones: [10]

      gamma: 0.3

    state_params:

      num_epochs: 3

    callbacks_params:

      loss:

        callback: CriterionCallback

      optimizer:

        callback: OptimizerCallback

        accumulation_steps: 1

      scheduler:

        callback: SchedulerCallback

        reduce_metric: *reduce_metric

      saver:

        callback: CheckpointCallback

        save_n_best: 5

      early_stoping:

        callback: EarlyStoppingCallback

        patience: 3

  train512:

    data_params:

      image_size: [512, 512]

    optimizer_params:

      optimizer: AdamW

      lr: 0.0001

    scheduler_params:

#      scheduler: MultiStepLR

#      milestones: [3]

#      gamma: 0.1

      scheduler: ReduceLROnPlateau

      patience: 0

      min_lr: 0.00001

    state_params:

      num_epochs: 20

    callbacks_params:

      loss:

        callback: CriterionCallback

      optimizer:

        callback: OptimizerCallback

        accumulation_steps: 1

      scheduler:

        callback: SchedulerCallback

        reduce_metric: *reduce_metric

      saver:

        callback: CheckpointCallback

        save_n_best: 5

      early_stoping:

        callback: EarlyStoppingCallback

        patience: 3

#        metric: "accuracy01"

#        minimize: False

    # callbacks_params: *callbacks_params

monitoring_params:

  project: "Kaggle-RSNA"

  tags: [*model, *model_name, *criterion]