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parameters.py
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parameters.py
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#############################################################################################################
##
## Parameters
##
#############################################################################################################
import numpy as np
class Parameters():
n_epoch = 1000
l_rate = 0.0001
weight_decay=0
save_path = "savefile/"
model_path = "savefile/"
batch_size = 8
x_size = 512
y_size = 256
resize_ratio = 8
grid_x = x_size/resize_ratio #64
grid_y = y_size/resize_ratio #32
feature_size = 4
regression_size = 110
mode = 3
threshold_point = 0.81
threshold_instance = 0.22
#loss function parameter
K1 = 1.0 # ####################################
K2 = 2.0
constant_offset = 1.0
constant_exist = 1.0 #2
constant_nonexist = 1.0 # 1.5 last 200epoch
constant_angle = 1.0
constant_similarity = 1.0
constant_alpha = 1.0 #in SGPN paper, they increase this factor by 2 every 5 epochs
constant_beta = 1.0
constant_gamma = 1.0
constant_back = 1.0
constant_l = 1.0
constant_lane_loss = 1.0 # 1.5 last 200epoch
constant_instance_loss = 1.0
#data loader parameter
flip_ratio=0.4
translation_ratio=0.6
rotate_ratio=0.6
noise_ratio=0.4
intensity_ratio=0.4
shadow_ratio=0.6
scaling_ratio=0.2
flip_indices=[(0,34),(1,35),(2,36),(3,37),(4,38),(5,39),(6,40),(7,41),(8,42),(9,43),(10,44),(11,45),(12,46),(13,47),(14,48),(15,49),(16,50),(17,51)
,(18,52),(19,53),(20,54),(21,55),(22,56),(23,57),(24,58),(25,59),(26,60),(27,61),(28,62),(29,63),(30,64),(31,65)
,(32,66),(33,67),(68,68),(69,69),(70,72),(71,73)]
train_root_url="TuSimple_dataset/train_set/"
test_root_url="TuSimple_dataset/test_set/"
# test parameter
color = [(0,0,0), (255,0,0), (0,255,0),(0,0,255),(255,255,0),(255,0,255),(0,255,255),(255,255,255),(100,255,0),(100,0,255),(255,100,0),(0,100,255),(255,0,100),(0,255,100)]
grid_location = np.zeros((grid_y, grid_x, 2))
for y in range(grid_y):
for x in range(grid_x):
grid_location[y][x][0] = x
grid_location[y][x][1] = y
num_iter = 30
threshold_RANSAC = 0.1
ratio_inliers = 0.1