-
Notifications
You must be signed in to change notification settings - Fork 135
/
predict.py
157 lines (132 loc) · 5.5 KB
/
predict.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
155
156
from __future__ import print_function
import argparse
import skimage
import skimage.io
import skimage.transform
from PIL import Image
from math import log10
#from GCNet.modules.GCNet import L1Loss
import sys
import shutil
import os
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
#from models.GANet_deep import GANet
from dataloader.data import get_test_set
import numpy as np
# Training settings
parser = argparse.ArgumentParser(description='PyTorch GANet Example')
parser.add_argument('--crop_height', type=int, required=True, help="crop height")
parser.add_argument('--crop_width', type=int, required=True, help="crop width")
parser.add_argument('--max_disp', type=int, default=192, help="max disp")
parser.add_argument('--resume', type=str, default='', help="resume from saved model")
parser.add_argument('--cuda', type=bool, default=True, help='use cuda?')
parser.add_argument('--kitti', type=int, default=0, help='kitti dataset? Default=False')
parser.add_argument('--kitti2015', type=int, default=0, help='kitti 2015? Default=False')
parser.add_argument('--data_path', type=str, required=True, help="data root")
parser.add_argument('--test_list', type=str, required=True, help="training list")
parser.add_argument('--save_path', type=str, default='./result/', help="location to save result")
parser.add_argument('--model', type=str, default='GANet_deep', help="model to train")
opt = parser.parse_args()
print(opt)
if opt.model == 'GANet11':
from models.GANet11 import GANet
elif opt.model == 'GANet_deep':
from models.GANet_deep import GANet
else:
raise Exception("No suitable model found ...")
cuda = opt.cuda
#cuda = True
if cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run without --cuda")
#torch.manual_seed(opt.seed)
#if cuda:
# torch.cuda.manual_seed(opt.seed)
#print('===> Loading datasets')
print('===> Building model')
model = GANet(opt.max_disp)
if cuda:
model = torch.nn.DataParallel(model).cuda()
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
model.load_state_dict(checkpoint['state_dict'], strict=False)
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
def test_transform(temp_data, crop_height, crop_width):
_, h, w=np.shape(temp_data)
if h <= crop_height and w <= crop_width:
temp = temp_data
temp_data = np.zeros([6, crop_height, crop_width], 'float32')
temp_data[:, crop_height - h: crop_height, crop_width - w: crop_width] = temp
else:
start_x = int((w - crop_width) / 2)
start_y = int((h - crop_height) / 2)
temp_data = temp_data[:, start_y: start_y + crop_height, start_x: start_x + crop_width]
left = np.ones([1, 3,crop_height,crop_width],'float32')
left[0, :, :, :] = temp_data[0: 3, :, :]
right = np.ones([1, 3, crop_height, crop_width], 'float32')
right[0, :, :, :] = temp_data[3: 6, :, :]
return torch.from_numpy(left).float(), torch.from_numpy(right).float(), h, w
def load_data(leftname, rightname):
left = Image.open(leftname)
right = Image.open(rightname)
size = np.shape(left)
height = size[0]
width = size[1]
temp_data = np.zeros([6, height, width], 'float32')
left = np.asarray(left)
right = np.asarray(right)
r = left[:, :, 0]
g = left[:, :, 1]
b = left[:, :, 2]
temp_data[0, :, :] = (r - np.mean(r[:])) / np.std(r[:])
temp_data[1, :, :] = (g - np.mean(g[:])) / np.std(g[:])
temp_data[2, :, :] = (b - np.mean(b[:])) / np.std(b[:])
r = right[:, :, 0]
g = right[:, :, 1]
b = right[:, :, 2]
#r,g,b,_ = right.split()
temp_data[3, :, :] = (r - np.mean(r[:])) / np.std(r[:])
temp_data[4, :, :] = (g - np.mean(g[:])) / np.std(g[:])
temp_data[5, :, :] = (b - np.mean(b[:])) / np.std(b[:])
return temp_data
def test(leftname, rightname, savename):
# count=0
input1, input2, height, width = test_transform(load_data(leftname, rightname), opt.crop_height, opt.crop_width)
input1 = Variable(input1, requires_grad = False)
input2 = Variable(input2, requires_grad = False)
model.eval()
if cuda:
input1 = input1.cuda()
input2 = input2.cuda()
with torch.no_grad():
prediction = model(input1, input2)
temp = prediction.cpu()
temp = temp.detach().numpy()
if height <= opt.crop_height and width <= opt.crop_width:
temp = temp[0, opt.crop_height - height: opt.crop_height, opt.crop_width - width: opt.crop_width]
else:
temp = temp[0, :, :]
skimage.io.imsave(savename, (temp * 256).astype('uint16'))
if __name__ == "__main__":
file_path = opt.data_path
file_list = opt.test_list
f = open(file_list, 'r')
filelist = f.readlines()
for index in range(len(filelist)):
current_file = filelist[index]
if opt.kitti2015:
leftname = file_path + 'image_2/' + current_file[0: len(current_file) - 1]
rightname = file_path + 'image_3/' + current_file[0: len(current_file) - 1]
if opt.kitti:
leftname = file_path + 'colored_0/' + current_file[0: len(current_file) - 1]
rightname = file_path + 'colored_1/' + current_file[0: len(current_file) - 1]
savename = opt.save_path + current_file[0: len(current_file) - 1]
test(leftname, rightname, savename)