def SqueezeSegV2Config():
mc = EasyDict()
mc.CLASSES = [
'Road', 'Sidewalk', 'Building', 'Pole', 'Vegetation', 'Person',
'TwoWheeler', 'Car', 'Truck', 'Bus', "None"
]
mc.NUM_CLASS = len(mc.CLASSES)
mc.CLS_2_ID = dict(zip(mc.CLASSES, range(len(mc.CLASSES))))
mc.CLS_LOSS_WEIGHT = np.array(
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
mc.CLS_COLOR_MAP = np.array([
[128, 64, 128], # Road
[244, 35, 232], # Sidewalk
[70, 70, 70], # Building
[153, 153, 153], # Pole
[107, 142, 35], # Vegetation
[220, 20, 60], # Person
[255, 0, 0], # Two Wheeler
[0, 0, 142], # Car
[0, 0, 70], # Truck
[0, 60, 100], # Bus
[0, 0, 0] # None
]) / 255.0
# Input Shape
mc.BATCH_SIZE = 8
mc.AZIMUTH_LEVEL = 240
mc.ZENITH_LEVEL = 32
mc.NUM_FEATURES = 6
# Loss
mc.FOCAL_GAMMA = 2.0
mc.CLS_LOSS_COEF = 15.0
mc.DENOM_EPSILON = 1e-12 # small value used in denominator to prevent division by 0
# Gradient Decent
mc.LEARNING_RATE = 0.05
mc.LR_DECAY_STEPS = 500
mc.LR_DECAY_FACTOR = 0.9
mc.MAX_GRAD_NORM = 100.0
# Network
mc.L2_WEIGHT_DECAY = 0.05
mc.DROP_RATE = 0.1
mc.BN_MOMENTUM = 0.9
mc.REDUCTION = 16
# Dataset
mc.DATA_AUGMENTATION = True
mc.RANDOM_FLIPPING = True
mc.RANDOM_SHIFT = True
# x, y, z, intensity, distance
mc.INPUT_MEAN = np.array([[[24.810, 0.819, 0.000, 16.303, 25.436]]])
mc.INPUT_STD = np.array([[[30.335, 7.807, 2.058, 25.208, 30.897]]])
return mc
