pspnet.yaml

EVAL_CROP_SIZE: (2049, 1025) # (width, height), for unpadding rangescaling and stepscaling
TRAIN_CROP_SIZE: (713, 713) # (width, height), for unpadding rangescaling and stepscaling
AUG:
    AUG_METHOD: "stepscaling" # choice unpadding rangescaling and stepscaling
    FIX_RESIZE_SIZE: (640, 640) # (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: 2.0  # for stepscaling
    MIN_SCALE_FACTOR: 0.5  # for stepscaling
    SCALE_STEP_SIZE: 0.25  # for stepscaling
    MIRROR: True
BATCH_SIZE: 4
DATASET:
    DATA_DIR: "./dataset/cityscapes/"
    IMAGE_TYPE: "rgb"  # choice rgb or rgba
    NUM_CLASSES: 19
    TEST_FILE_LIST: "dataset/cityscapes/val.list"
    TRAIN_FILE_LIST: "dataset/cityscapes/train.list"
    VAL_FILE_LIST: "dataset/cityscapes/val.list"
    IGNORE_INDEX: 255
FREEZE:
    MODEL_FILENAME: "model"
    PARAMS_FILENAME: "params"
MODEL:
    MODEL_NAME: "pspnet"
    DEFAULT_NORM_TYPE: "bn"
    PSPNET:
        DEPTH_MULTIPLIER: 1
        LAYERS: 50
TEST:
    TEST_MODEL: "snapshots/cityscapes_pspnet50/final"
TRAIN:
    MODEL_SAVE_DIR: "snapshots/cityscapes_pspnet50/"
    PRETRAINED_MODEL_DIR: u"pretrained_model/pspnet50_bn_cityscapes/"
    SNAPSHOT_EPOCH: 10
SOLVER:
    LR: 0.001
    LR_POLICY: "poly"
    OPTIMIZER: "sgd"
    NUM_EPOCHS: 700