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config_v2.py
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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
z_min = -3.0
z_max = 1.0
# 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, **kwargs):
super(GridParameters, self).__init__(**kwargs)
class DataParameters:
# classes_map = {"Car": 0,
# "Pedestrian": 1,
# "Person_sitting": 1,
# "Cyclist": 2,
# "Truck": 3,
# "Van": 3,
# "Tram": 3,
# "Misc": 3,
# }
# for Car and Pedestrian
# map_classes = {
# 0: "Car",
# 1: "Pedestrian"
# }
# classes_map = {"Car": 0,
# "Pedestrian": 1,
# "Person_sitting": 1,
# # "Cyclist": 2,
# # "Truck": 3,
# # "Van": 3,
# # "Tram": 3,
# # "Misc": 3,
# }
# for Car only
map_classes = {
0: "Car"
}
classes_map = {"Car": 0
}
# # for Pedestrian only
# map_classes = {
# 0: "Pedestrian"
# }
# classes_map = {
# "Pedestrian": 0,
# "Person_sitting": 0,
# }
nb_classes = len(np.unique(list(classes_map.values())))
assert nb_classes == np.max(np.unique(list(classes_map.values()))) + 1, 'Starting class indexing at zero.'
# 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())))
# assert nb_classes == np.max(np.unique(list(classes.values()))) + 1, 'Starting class indexing at zero.'
def __init__(self, **kwargs):
super(DataParameters, self).__init__(**kwargs)
class NetworkParameters:
max_points_per_pillar = 100
max_pillars = 12000
nb_features = 9
nb_channels = 64
downscaling_factor = 2
# length (x), width (y), height (z), z-center, orientation
# for car and pedestrian
# anchor_dims = np.array([[3.9, 1.6, 1.56, -1, 0],
# [3.9, 1.6, 1.56, -1, np.pi/2],
# [0.8, 0.6, 1.73, -0.6, 0],
# [0.8, 0.6, 1.73, -0.6, np.pi/2],
# ], dtype=np.float32).tolist()
# for car only
anchor_dims = np.array([[3.9, 1.6, 1.56, -1, 0],
[3.9, 1.6, 1.56, -1, np.pi/2]], dtype=np.float32).tolist()
# for pedestrian only
# anchor_dims = np.array([[0.8, 0.6, 1.73, -0.6, 0],
# [0.8, 0.6, 1.73, -0.6, np.pi/2],
# ], dtype=np.float32).tolist()
nb_dims = 3
# for car
positive_iou_threshold = 0.6
negative_iou_threshold = 0.3
# for pedestrian
# positive_iou_threshold = 0.5
# negative_iou_threshold = 0.35
# batch_size = 1
batch_size = 4
total_training_epochs = 160
# iters_to_decay = 101040. # 15 * 4 * ceil(6733. / 4) --> every 15 epochs on 6733 kitti samples, cf. pillar paper
iters_to_decay = 100500
learning_rate = 2e-4
decay_rate = 1e-8
L1 = 0
L2 = 0
alpha = 0.25
gamma = 2.0
# original pillars paper values
focal_weight = 1.0 # 1.0
loc_weight = 2.0 # 2.0
size_weight = 2.0 # 2.0
angle_weight = 2.0 # 2.0
heading_weight = 0.2 # 0.2
class_weight = 0.5 # 0.2
def __init__(self, **kwargs):
super(NetworkParameters, self).__init__(**kwargs)
class Parameters(GridParameters, DataParameters, NetworkParameters):
def __init__(self, **kwargs):
super(Parameters, self).__init__(**kwargs)