config.yaml

# project root working directory, automatically read by hydra (.../UNet3P)
WORK_DIR: ${hydra:runtime.cwd}

DATA_PREPARATION:
    # unprocessed LiTS scan data paths, for custom data training skip this section details
    SCANS_TRAIN_DATA_PATH: "/data/Training Batch 2/"
    SCANS_VAL_DATA_PATH: "/data/Training Batch 1/"

    # Resize scans to model input size
    RESIZED_HEIGHT: ${INPUT.HEIGHT}
    RESIZED_WIDTH: ${INPUT.WIDTH}

    # Clip scans value in given range
    SCAN_MIN_VALUE: -200
    SCAN_MAX_VALUE: 250

DATASET:
    # paths should be relative from project root path
    TRAIN:
        IMAGES_PATH: "/data/train/images"
        MASK_PATH: "/data/train/mask"
    VAL:
        IMAGES_PATH: "/data/val/images"
        MASK_PATH: "/data/val/mask"
    TEST:
        IMAGES_PATH: "/data/test/images"
        MASK_PATH: None

MODEL:
    # available variants are unet3plus, unet3plus_deepsup, unet3plus_deepsup_cgm
    TYPE: "unet3plus_deepsup"
    WEIGHTS_FILE_NAME: model_${MODEL.TYPE}

SEED: 5 # for result's reproducibility
VERBOSE: 1 # For logs printing details, available options are 0, 1, 2
DATALOADER_WORKERS: 3 # number of workers used for data loading
SHOW_CENTER_CHANNEL_IMAGE: True # only true for UNet3+ for custom dataset it should be False

# Model input shape
INPUT:
    HEIGHT: 640
    WIDTH: 640
    CHANNELS: 3

# Model output classes
OUTPUT:
    CLASSES: 1

HYPER_PARAMETERS:
    EPOCHS: 500
    BATCH_SIZE: 2 # specify per gpu batch size
    LEARNING_RATE: 2e-5 # 1e-3

CALLBACKS:
    # paths should be relative from project root path
    LOGGING:
        PATH: "/checkpoint/logs"

    EARLY_STOPPING:
        PATIENCE: 20
        DELTA: 0.001

    MODEL_CHECKPOINT:
        PATH: "/checkpoint"
        SAVE_WEIGHTS_ONLY: True
        SAVE_BEST_ONLY: True

    CSV_LOGGER:
        PATH: "/checkpoint"
        APPEND_LOGS: False

PREPROCESS_DATA:
    RESIZE:
        VALUE: True # if True, resize to input height and width
        HEIGHT: ${INPUT.HEIGHT}
        WIDTH: ${INPUT.WIDTH}

    IMAGE_PREPROCESSING_TYPE: "normalize"

    NORMALIZE_MASK:
        VALUE: True # if True, divide mask by given value
        NORMALIZE_VALUE: 255

    SHUFFLE:
        TRAIN:
            VALUE: True
        VAL:
            VALUE: False
        TEST:
            VALUE: False

USE_MULTI_GPUS:
    VALUE: False # If True use multiple gpus for training
    # GPU_IDS: Could be integer or list of integers.
    # In case Integer: if integer value is -1 then it uses all available gpus.
    # otherwise if positive number, then use given number of gpus.
    # In case list of Integers: each integer will be considered as gpu id
    # e.g. [4, 5, 7] means use gpu 5,6 and 8 for training/evaluation
    GPU_IDS: 3

GPU_ID: 1

RESULT_DIR: "/outputs"

# to stop hydra from storing logs files
defaults:
    - _self_
    - override hydra/hydra_logging: disabled
    - override hydra/job_logging: disabled

hydra:
    output_subdir: null