-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtest_our.py
More file actions
151 lines (118 loc) · 5.94 KB
/
test_our.py
File metadata and controls
151 lines (118 loc) · 5.94 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
import argparse
import shutil
import time
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import torch
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import loaddata
import copy
import numpy as np
import random
from utils import util
from models import modules, net, mobilenetv2
import pdb
parser = argparse.ArgumentParser(description='CSMSF: inference')
parser.add_argument('--data_path', type=str, default='/media/hujunjie/nami/multi-sensor-fusion/MS2_dataset/')
parser.add_argument('--file', type=str, default='test_day_list.csv')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def main():
global args
args = parser.parse_args()
original_model = mobilenetv2.mobilenet_v2(pretrained=True)
encoder_v = modules.E_mvnet2_img(original_model)
model_v = net.model(encoder_v, block_channel=[24, 32, 96, 160]).to(device)
checkpoint = torch.load("./runs/model_v.pth.tar")
model_v.load_state_dict(checkpoint['state_dict'])
original_model2 = mobilenetv2.mobilenet_v2(pretrained=True)
encoder_l = modules.E_mvnet2_lidar(original_model2)
model_l = net.model(encoder_l, block_channel=[24, 32, 96, 160]).to(device)
checkpoint2 = torch.load("./runs/model_l.pth.tar")
model_l.load_state_dict(checkpoint2['state_dict'])
encoder_t = copy.deepcopy(encoder_v)
model_t = net.model(encoder_t, block_channel=[24, 32, 96, 160]).to(device)
checkpoint3 = torch.load("./runs/model_t.pth.tar")
model_t.load_state_dict(checkpoint3['state_dict'])
######################### load module for image lidar fusion ###########################################
model_vl = net.fusion_2sensors(block_channel=[24, 32, 96, 160]).to(device)
checkpoint = torch.load("./runs/model_vl.pth.tar")
model_vl.load_state_dict(checkpoint['state_dict'])
######################### load module for image thr fusion ###########################################
model_vt = net.fusion_2sensors(block_channel=[24, 32, 96, 160]).to(device)
checkpoint = torch.load("./runs/model_vt.pth.tar")
model_vt.load_state_dict(checkpoint['state_dict'])
######################### load module for thr lidar fusion ###########################################
model_lt = net.fusion_2sensors(block_channel=[24, 32, 96, 160]).to(device)
checkpoint = torch.load("./runs/model_lt.pth.tar")
model_lt.load_state_dict(checkpoint['state_dict'])
######################### load module for img lidar thr fusion ###########################################
model_vlt = net.fusion_3sensors(block_channel=[24, 32, 96, 160]).to(device)
checkpoint = torch.load("./runs/model_vlt.pth.tar")
model_vlt.load_state_dict(checkpoint['state_dict'])
batch_size = 1
cudnn.benchmark = True
test_loader = loaddata.getTestingData(batch_size, args.data_path, args.file)
#############possible noise type: {}
sensor_type = random.choice(['v','l','t','vl','vt','lt','vlt'])
test(test_loader, model_v, model_l, model_t, model_vl, model_vt, model_lt, model_vlt, sensor_type)
def test(test_loader, model_v, model_l, model_t, model_vl, model_vt, model_lt, model_vlt, sensor_type):
model_v.eval()
model_l.eval()
model_t.eval()
model_vl.eval()
model_vt.eval()
model_lt.eval()
model_vlt.eval()
totalNumber = 0
errorSum = {'MSE': 0, 'RMSE': 0, 'ABS_REL': 0, 'LG10': 0,
'MAE': 0, 'DELTA1': 0, 'DELTA2': 0, 'DELTA3': 0}
for i, sample in enumerate(test_loader):
img, thr, lidar, gt_img, gt_thr = sample['img'], sample['thr'], sample['lidar_thr_sd'], sample['lidar_img_gt'], sample['lidar_thr_gt']
img, thr, lidar, gt_img, gt_thr = img.cuda(), thr.cuda(), lidar.cuda(), gt_img.cuda(), gt_thr.cuda()
with torch.no_grad():
_,_,x_img_1,x_img_2,x_img_3,x_img_4 = model_v(img)
_,_,x_lidar_1, x_lidar_2, x_lidar_3, x_lidar_4 = model_l(lidar)
_,_,x_thr_1, x_thr_2, x_thr_3, x_thr_4 = model_t(thr)
valid_camera = True
valid_lidar = True
valid_thr = True
if(sensor_type=='vlt'): # all sensors are valid
output, _ = model_vlt(x_img_1,x_img_2,x_img_3,x_img_4,x_lidar_1, x_lidar_2, x_lidar_3, x_lidar_4,x_thr_1, x_thr_2, x_thr_3, x_thr_4)
elif(sensor_type=='vl'):
valid_thr = False
output, _ = model_vl(x_img_1,x_img_2,x_img_3,x_img_4,x_lidar_1, x_lidar_2, x_lidar_3, x_lidar_4)
elif(sensor_type=='vt'):
valid_lidar = False
output, _ = model_vt(x_img_1,x_img_2,x_img_3,x_img_4,x_thr_1, x_thr_2, x_thr_3, x_thr_4)
elif(sensor_type=='lt'):
valid_camera = False
output, _ = model_lt(x_lidar_1, x_lidar_2, x_lidar_3, x_lidar_4,x_thr_1, x_thr_2, x_thr_3, x_thr_4)
elif(sensor_type=='t'):
valid_camera = False
valid_lidar = False
output, _, _, _,_,_ = model_t(thr)
elif(sensor_type=='l'):
valid_camera = False
valid_thr = False
output, _, _, _,_,_= model_l(lidar)
elif(sensor_type=='v'):
valid_lidar = False
valid_thr = False
output, _,_, _,_,_ = model_v(img)
if(valid_camera and not valid_lidar and not valid_thr): ######## should use ground truth of image view when only visual camera is valid
gt = gt_img
else:
gt = gt_thr
output = F.upsample(output, size=[gt.size(2),gt.size(3)], mode='bilinear', align_corners=True)
batchSize = img.size(0)
totalNumber = totalNumber + batchSize
mask = (gt > 0)
gt = gt[mask]
output = output[mask]
errors = util.evaluateError(output, gt)
errorSum = util.addErrors(errorSum, errors, batchSize)
averageError = util.averageErrors(errorSum, totalNumber)
print(averageError)
if __name__ == '__main__':
main()