forked from blackjack2015/EASNet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathkitti_submission.py
160 lines (122 loc) · 5.26 KB
/
kitti_submission.py
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
152
153
154
from __future__ import print_function
import argparse
import os, sys
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torch.nn.functional as F
import skimage
import skimage.io
import numpy as np
import time
import math
from ofa.stereo_matching.data_providers import transforms
from ofa.stereo_matching.run_manager import StereoRunConfig, RunManager
from ofa.stereo_matching.elastic_nn.networks.ofa_aanet import OFAAANet
from ofa.stereo_matching.elastic_nn.utils import set_running_statistics
from ofa.stereo_matching.elastic_nn.training.progressive_shrinking import load_models
# 2012 data /media/jiaren/ImageNet/data_scene_flow_2012/testing/
parser = argparse.ArgumentParser(description='FADNet')
parser.add_argument('--KITTI', default='2015',
help='KITTI version')
parser.add_argument('--datapath', default='/media/jiaren/ImageNet/data_scene_flow_2015/testing/',
help='select model')
parser.add_argument('--loadmodel', default=None,
help='loading model')
parser.add_argument('--savepath', default='results/',
help='path to save the results.')
parser.add_argument('--maxdisp', type=int, default=192,
help='maxium disparity')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='enables CUDA training')
parser.add_argument('--devices', type=str, help='indicates CUDA devices, e.g. 0,1,2', default='0')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
if not os.path.exists(args.savepath):
os.makedirs(args.savepath)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.KITTI == '2015':
from dataloader import KITTI_submission_loader as DA
else:
from dataloader import KITTI_submission_loader2012 as DA
test_left_img, test_right_img = DA.dataloader(args.datapath)
devices = [int(item) for item in args.devices.split(',')]
ngpus = len(devices)
fullnet = OFAAANet(ks_list=[3,5,7], expand_ratio_list=[2,4,6,8], depth_list=[2,3,4], scale_list=[2,3,4])
model_file = args.loadmodel
init = torch.load(model_file, map_location='cpu')
model_dict = init['state_dict']
fullnet.load_state_dict(model_dict)
d = 2
e = 8
ks = 7
s = 4
fullnet.set_active_subnet(ks=ks, d=d, e=e, s=s)
model = fullnet.get_active_subnet(preserve_weight=True)
# set the batch norm values with testing data
from ofa.stereo_matching.data_providers.stereo import StereoDataProvider
if args.KITTI == '2015':
StereoDataProvider.DEFAULT_PATH = '/datasets/kitti2015/'
dataname='KITTI_2015_TEST'
else:
StereoDataProvider.DEFAULT_PATH = '/datasets/kitti2012/'
dataname='KITTI_2012_TEST'
run_config = StereoRunConfig(test_batch_size=4, n_worker=4, dataname=dataname)
run_manager = RunManager('.tmp/eval_subnet', model, run_config, init=False)
run_manager.reset_running_statistics(net=model, subset_size=200, subset_batch_size=16)
model = nn.DataParallel(model, device_ids=devices)
model.cuda()
def test(imgL,imgR):
model.eval()
if args.cuda:
imgL = imgL.cuda()
imgR = imgR.cuda()
#print(imgL.size(), imgR.size())
with torch.no_grad():
output = model(imgL, imgR)[-1]
output = torch.squeeze(output)
pred_disp = output.data.cpu().numpy()
print(pred_disp.shape)
#print('larger than 192: %s' % pred_disp[pred_disp>0.75].shape)
print('min: %f, max: %f, mean: %f' % (np.min(pred_disp), np.max(pred_disp), np.mean(pred_disp)))
return pred_disp
def main():
for inx in range(len(test_left_img)):
print('image: %s'%test_left_img[inx])
imgL_o = (skimage.io.imread(test_left_img[inx]).astype('float32'))
imgR_o = (skimage.io.imread(test_right_img[inx]).astype('float32'))
imgs = {'left':imgL_o, 'right':imgR_o}
val_transform_list = [
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]
val_transforms = transforms.Compose(val_transform_list)
rgb_transform = val_transforms
imgs = rgb_transform(imgs)
imgL = imgs['left'].unsqueeze(0)
imgR = imgs['right'].unsqueeze(0)
# pad to resize (384, 1280)
top_pad = 384-imgL.shape[2]
right_pad = 1280-imgL.shape[3]
# imgL = np.lib.pad(imgL,((0,0),(0,0),(top_pad,0),(0,right_pad)),mode='constant',constant_values=0)
# imgR = np.lib.pad(imgR,((0,0),(0,0),(top_pad,0),(0,right_pad)),mode='constant',constant_values=0)
imgL = F.pad(imgL,(0, right_pad, top_pad, 0),mode='constant',value=0)
imgR = F.pad(imgR,(0, right_pad, top_pad, 0),mode='constant',value=0)
start_time = time.time()
pred_disp = test(imgL,imgR)
print('time = %.2f' %(time.time() - start_time))
img = pred_disp[top_pad:,:-right_pad]
round_img = np.round(img*256)
skimage.io.imsave(os.path.join(args.savepath, test_left_img[inx].split('/')[-1]),round_img.astype('uint16'))
if __name__ == '__main__':
main()