-
Notifications
You must be signed in to change notification settings - Fork 0
/
test_multi_voc.py
149 lines (120 loc) · 5.77 KB
/
test_multi_voc.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
import os
import time
import argparse
import numpy as np
import cv2
import torch
from torch.backends import cudnn
from torch.nn import DataParallel
from torch.utils.data import DataLoader
from tqdm import tqdm
from torch import nn
from dataloader.voc_generate_test import VOC
#from dataloader.generator import RGBD
from dataloader.imagereader import imagefile
from utils.lr_scheduler import LRScheduler
from utils import helper
import torchvision.models as models
#================ load your net here ===================
from net.refinenet_1 import RefineNet4CascadePoolingImproved as network
#from net.refinenet_nolz import RefineNet4CascadePoolingImproved as network
#from net.refinenet import RefineNet4CascadePoolingImprovedDepth as network
#from net.refinenet import RefineNet4CascadePoolingImproved as network
import torch.nn.functional as F
def validate(data_loader,data_loader_1,data_loader_2,data_loader_3, net, loss, epoch, num_class):
root = '/home/gzx/RGBD/'
filename = root+'VOC2012/ImageSets/Segmentation/test.txt'
with open(filename, 'r') as f:
lines = f.readlines()
data_list = [c.strip() for c in lines]
net.eval()
i = 0
for batch,batch_1,batch_2,batch_3 in zip(data_loader,data_loader_1,data_loader_2,data_loader_3):
data, depth, shape = batch['image'], batch['depth'], batch['shape']
data = data.cuda(async=True)
depth = depth.cuda(async=True)
#prediction = net(data)
#prediction = net(data,depth)
prediction = net(x = data,epoch = epoch,depth = depth.squeeze(1),label = None, train=False)
prediction = F.interpolate(prediction, scale_factor=1, mode='bilinear', align_corners=False).cpu()
data, depth = batch_1['image'], batch_1['depth']
data = data.cuda(async=True)
depth = depth.cuda(async=True)
#prediction_1 = net(data)
#prediction_1 = net(data,depth)
prediction_1 = net(x = data,epoch = epoch,depth = depth.squeeze(1), label = None,train=False)
prediction_1 = F.interpolate(prediction_1, scale_factor=512/1024, mode='bilinear', align_corners=False).cpu()
data, depth = batch_2['image'], batch_2['depth']
data = data.cuda(async=True)
depth = depth.cuda(async=True)
#prediction_2 = net(data)
#prediction_2 = net(data,depth)
prediction_2 = net(x = data,epoch = epoch,depth = depth.squeeze(1), label = None,train=False)
prediction_2 = F.interpolate(prediction_2, scale_factor=512/1536, mode='bilinear', align_corners=False).cpu()
data, depth = batch_3['image'], batch_3['depth']
data = data.cuda(async=True)
depth = depth.cuda(async=True)
#prediction_3 = net(data)
#prediction_3 = net(data,depth)
prediction_3 = net(x = data,epoch = epoch,depth = depth.squeeze(1),label = None,train=False)
prediction_3 = F.interpolate(prediction_3, scale_factor=512/256, mode='bilinear', align_corners=False).cpu()
pred = torch.max((prediction+prediction_1+prediction_2+prediction_3)/4,1)[1].squeeze(0).numpy()
size = (shape[1], shape[0])
pred = cv2.resize(pred, size, interpolation=cv2.INTER_NEAREST)
cv2.imwrite('results/VOC2012/Segmentation/comp5_test_cls/%s.png'%(data_list[i]),pred)
i+=1
if __name__ == '__main__':
workers = 4
batch_size = 1
base_lr = 1e-3
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--resume', default='', help='resume training from checkpoint ...', type=str)
#parser.add_argument('-d', '--dataset', default='NYU', help='NYU or SUN', type=str)
args = parser.parse_args()
#save_dir = './%s_base/'%args.dataset
#if not os.path.exists(save_dir):
# os.mkdir(save_dir)
#epochs = {'SUN':60,'NYU':300}[args.dataset]
val_dataset = VOC(512,'test')
val_loader = DataLoader(
val_dataset, batch_size=batch_size, shuffle=False, num_workers=workers)
val_dataset_2048 = VOC(1024,'test')
val_loader_2048 = DataLoader(
val_dataset_2048, batch_size=batch_size, shuffle=False, num_workers=workers)
val_dataset_480 = VOC(1536,'test')
val_loader_480 = DataLoader(
val_dataset_480, batch_size=batch_size, shuffle=False, num_workers=workers)
val_dataset_840 = VOC(256,'test')
val_loader_840 = DataLoader(
val_dataset_840, batch_size=batch_size, shuffle=False, num_workers=workers)
num_class = 21
ignore_label = 255
loss = nn.CrossEntropyLoss(ignore_index=ignore_label)
#patience = {'SUN':15,'NYU':60}[args.dataset]
print('Val sample number: %d' % len(val_dataset))
############################################################
#net = network(640,num_classes = num_class,resnet_factory = models.resnet152, freeze_resnet=False)
net = network((3,640),num_classes = num_class,resnet_factory = models.resnet152, freeze_resnet=False)
start_epoch = 1
lr = base_lr
best_val_loss = float('inf')
log_mode = 'w'
net = net.cuda()
loss = loss.cuda()
cudnn.benchmark = True
#print(net.named_parameter())
net = DataParallel(net)
if os.path.exists(args.resume):
print('loading checkpoint %s'%(args.resume))
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch'] + 1
lr = checkpoint['lr']
best_val_loss = checkpoint['best_val_loss']
net.load_state_dict(checkpoint['state_dict'])
log_mode = 'a'
optimizer = torch.optim.SGD(
net.parameters(), lr, momentum=0.9, weight_decay=5e-4)
#lrs = LRScheduler(
# lr, patience=patience, factor=0.5, min_lr=0.5*0.5*0.5 * lr, best_loss=best_val_loss)
with torch.no_grad():
validate(val_loader,val_loader_2048,val_loader_480,val_loader_840, net, loss, 0, num_class)