unet_pet.yaml

TRAIN_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
EVAL_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
AUG:
    AUG_METHOD: "unpadding" # choice unpadding rangescaling and stepscaling
    FIX_RESIZE_SIZE: (512, 512) # (width, height), for unpadding

    INF_RESIZE_VALUE: 500  # for rangescaling
    MAX_RESIZE_VALUE: 600  # for rangescaling
    MIN_RESIZE_VALUE: 400  # for rangescaling

    MAX_SCALE_FACTOR: 1.25  # for stepscaling
    MIN_SCALE_FACTOR: 0.75  # for stepscaling
    SCALE_STEP_SIZE: 0.25  # for stepscaling
    MIRROR: True
BATCH_SIZE: 4
DATASET:
    DATA_DIR: "./dataset/mini_pet/"
    IMAGE_TYPE: "rgb"  # choice rgb or rgba
    NUM_CLASSES: 3
    TEST_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
    TRAIN_FILE_LIST: "./dataset/mini_pet/file_list/train_list.txt"
    VAL_FILE_LIST: "./dataset/mini_pet/file_list/val_list.txt"
    VIS_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
    IGNORE_INDEX: 255
    SEPARATOR: " "
FREEZE:
    MODEL_FILENAME: "__model__"
    PARAMS_FILENAME: "__params__"
MODEL:
    MODEL_NAME: "unet"
    DEFAULT_NORM_TYPE: "bn"
TEST:
    TEST_MODEL: "./saved_model/unet_pet/final/"
TRAIN:
    MODEL_SAVE_DIR: "./saved_model/unet_pet/"
    PRETRAINED_MODEL_DIR: "./test/models/unet_coco_init/"
    SNAPSHOT_EPOCH: 10
SOLVER:
    NUM_EPOCHS: 100
    LR: 0.005
    LR_POLICY: "poly"
    OPTIMIZER: "adam"