config.py

import numpy as np


class GridParameters:
    x_min = 0.0
    x_max = 80.64
    x_step = 0.16

    y_min = -40.32
    y_max = 40.32
    y_step = 0.16

    z_min = -1.0
    z_max = 3.0

    # derived parameters
    Xn_f = float(x_max - x_min) / x_step
    Yn_f = float(y_max - y_min) / y_step
    Xn = int(Xn_f)
    Yn = int(Yn_f)

    def __init__(self):
        super(GridParameters, self).__init__()


class DataParameters:

    classes = {"Car":               0,
               "Pedestrian":        1,
               "Person_sitting":    1,
               "Cyclist":           2,
               "Truck":             3,
               "Van":               3,
               "Tram":              3,
               "Misc":              3,
               }

    nb_classes = len(np.unique(list(classes.values())))

    def __init__(self):
        super(DataParameters, self).__init__()


class NetworkParameters:

    max_points_per_pillar = 100
    max_pillars = 12000
    nb_features = 7
    nb_channels = 64
    downscaling_factor = 2

    # length, width, height, z-center, orientation
    anchor_dims = np.array([[3.9, 1.6, 1.56, -1, 0],
                            [3.9, 1.6, 1.56, -1, 1.5708],
                            [0.8, 0.6, 1.73, -0.6, 0],
                            [0.8, 0.6, 1.73, -0.6, 1.5708],
                            ], dtype=np.float32).tolist()

    positive_iou_threshold = 0.6
    negative_iou_threshold = 0.3
    batch_size = 4
    total_training_epochs = 160
    learning_rate = 2e-4
    decay_rate = 1e-8
    L1 = 0
    L2 = 0
    alpha = 0.25
    gamma = 2.0

    focal_weight = 1.0
    loc_weight = 2.0
    size_weight = 2.0
    angle_weight = 2.0
    heading_weight = 0.2
    class_weight = 0.2

    def __init__(self):
        super(NetworkParameters, self).__init__()


class Parameters(GridParameters, DataParameters, NetworkParameters):

    def __init__(self):
        super(Parameters, self).__init__()