model_name: selimsef_spacenet4_resnet34unet

model_path:
train: false
infer: true

pretrained: true
nn_framework:  torch
batch_size: 42

data_specs:
  width: 384
  height:  384
  dtype:
  image_type: zscore
  rescale: false
  rescale_minima: auto
  rescale_maxima: auto
  additional_inputs:
  channels: 4
  label_type: mask
  is_categorical: false
  mask_channels: 3
  val_holdout_frac: 0.2
  data_workers:  12

training_data_csv: '/path/to/training_df.csv'
validation_data_csv:
inference_data_csv: '/path/to/test_df.csv'

training_augmentation:
  augmentations:
    RandomScale:
      scale_limit:
        - 0.5
        - 1.5
      interpolation: nearest
    Rotate:
      limit:
        - 5
        - 6
      border_mode: constant
      p: 0.3
    RandomCrop:
      height: 416
      width: 416
      always_apply: true
      p: 1.0
    Normalize:
      mean:
        - 0.006479
        - 0.009328
        - 0.01123
        - 0.02082
      std:
        - 0.004986
        - 0.004964
        - 0.004950
        - 0.004878
      max_pixel_value: 65535.0
      p: 1.0
  p: 1.0
  shuffle: true

validation_augmentation:
  augmentations:
    CenterCrop:
      height: 384
      width: 384
      p: 1.0
    Normalize:
      mean:
        - 0.006479
        - 0.009328
        - 0.01123
        - 0.02082
      std:
        - 0.004986
        - 0.004964
        - 0.004950
        - 0.004878
      max_pixel_value: 65535.0
      p: 1.0
  p: 1.0

inference_augmentation:
  augmentations:
    Normalize:
      mean:
        - 0.006479
        - 0.009328
        - 0.01123
        - 0.02082
      std:
        - 0.004986
        - 0.004964
        - 0.004950
        - 0.004878
      max_pixel_value: 65535.0
      p: 1.0
  p: 1.0
training:
  epochs:  70
  steps_per_epoch:
  optimizer: AdamW
  lr: 2e-4
  opt_args:
    weight_decay: 0.0001
  loss:
    focal:
    dice:
  loss_weights:
    focal: 1
    dice: 1
  metrics:
    training:
    validation:
  checkpoint_frequency: 10
  callbacks:
    lr_schedule:
      schedule_type: 'arbitrary'
      schedule_dict:
        milestones:
          - 1
          - 5
          - 15
          - 30
          - 50
          - 60
        gamma: 0.5
    model_checkpoint:
      filepath: 'selimsef_resnet34_best.pth'
      monitor: val_loss
  model_dest_path: 'selimsef_resnet34.pth'
  verbose: true

inference:
  window_step_size_x:
  window_step_size_y:
  output_dir: 'inference_out/'