config.yaml

pl_trainer:
  max_epochs: 120
  devices:
  - 2
  accelerator: gpu

# should match arguments in the BrainPatchesDataModule class
dataset:
  patches:
    window_size: 64
    stride: 32
    img_threshold: 0.5
    normalization: z_score
    denoiser: synthseg_merged # False, synthseg or synthseg_merged
    # False - use 1 channel of original image
    # synthseg - use as a prior synthseg segmentation with 32 labels as a second channel
    # synthseg_merged - use as a prior synthseg segmentation with 4 labels as a second channel
    augmentation: True
    use_all_data: True # if True uses train + val images for training

  train_num_workers: 8
  train_batch_size: 64
  val_num_workers: 8
  val_batch_size: 64

# should match arguments in the module class
model:
  n_classes: 4
  in_channels: 2
  feature_scale: 4

  loss: focal # dice, cross_entropy, focal, tversky
  lr: 0.001
  model: "UNet_3Plus" # UNet_3Plus, UNet_2Plus or UNet

exp_name: unet3p_augm_focal_64-32_05_synthseg_merged_lrsch_fullset