-
Notifications
You must be signed in to change notification settings - Fork 8
/
Copy pathinference.py
185 lines (168 loc) · 8.32 KB
/
inference.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
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
import argparse
import os
import cv2
import numpy as np
from tqdm import tqdm
from dataloaders import make_data_loader
from modeling.unet import *
class Trainer(object):
def __init__(self, args):
self.args = args
# Define Dataloader
kwargs = {'num_workers': args.workers, 'pin_memory': True}
self.train_loader, self.val_loader, self.test_loader, self.nclass = make_data_loader(args, **kwargs)
# Define network
self.model = Unet(n_channels=4, n_classes=14)
# Using cuda
if args.cuda:
self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids)
self.model = self.model.cuda()
# Resuming checkpoint
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume))
checkpoint = torch.load(args.resume)
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
def inferrence(self, mode, nclass):
"""inferrence and save images, mode: grey, color"""
self.model.eval()
save_dir = '/home/user/disk4T/dataset/SensatUrban/BEV/data_11/pred5697/'
label_color_map13 = [[255,248,220], [220,220,220], [139, 71, 38],
[238,197,145], [ 70,130,180], [179,238, 58],
[110,139, 61], [105,105,105], [ 0, 0,128],
[205, 92, 92], [244,164, 96], [147,112,219],
[255,228,225]]
label_color_map14 = [[0,0,0], [255,248,220], [220,220,220], [139, 71, 38],
[238,197,145], [ 70,130,180], [179,238, 58],
[110,139, 61], [105,105,105], [ 0, 0,128],
[205, 92, 92], [244,164, 96], [147,112,219],
[255,228,225]]
tbar = tqdm(self.test_loader, desc='\r')
for j, sample in enumerate(tbar):
image, fname = sample['image'], sample['fname']
if self.args.cuda:
image = image.cuda()
with torch.no_grad():
output = self.model(image)
pred = output.data.cpu().numpy()
pred = np.argmax(pred, axis=1)
# colorize prediction
edge = self.args.base_size
for i in range(self.args.batch_size):
if i>pred.shape[0]-1:
continue
if mode=='grey':
out = int(pred[i])
elif mode=='color':
if nclass==13:
label_color_map = label_color_map13
elif nclass==14:
label_color_map = label_color_map14
# provide two types of color prediction output methods
for x in range(edge):
for y in range(edge):
out[x, y, 2] = label_color_map[int(pred[0, x, y])][0] # R
out[x, y, 1] = label_color_map[int(pred[0, x, y])][1] # G
out[x, y, 0] = label_color_map[int(pred[0, x, y])][2] # B
# fill = lambda x: label_color_map[x]
# grey = int(pred[i]).reshape(1,-1)[0]
# out = np.array(list(map(fill, grey))).reshape(edge, edge, 3)
# out = cv2.cvtColor(cv2.COLOR_RGB2BGR, out)
cv2.imwrite(os.path.join(save_dir, fname[i]), out)
def main():
parser = argparse.ArgumentParser(description="PyTorch Unet Training")
parser.add_argument('--dataset', type=str, default='cityscapes',
choices=['pascal', 'coco', 'cityscapes'],
help='dataset name (default: pascal)')
parser.add_argument('--workers', type=int, default=4,
metavar='N', help='dataloader threads')
parser.add_argument('--base-size', type=int, default=500,
help='base image size')
parser.add_argument('--crop-size', type=int, default=500,
help='crop image size')
parser.add_argument('--loss-type', type=str, default='ce',
choices=['ce', 'focal'],
help='loss func type (default: ce)')
# training hyper params
parser.add_argument('--epochs', type=int, default=200, metavar='N',
help='number of epochs to train (default: auto)')
parser.add_argument('--start_epoch', type=int, default=0,
metavar='N', help='start epochs (default:0)')
parser.add_argument('--batch-size', type=int, default=None,
metavar='N', help='input batch size for \
training (default: auto)')
parser.add_argument('--test-batch-size', type=int, default=None,
metavar='N', help='input batch size for \
testing (default: auto)')
parser.add_argument('--use-balanced-weights', action='store_true', default=False,
help='whether to use balanced weights (default: False)')
# optimizer params
parser.add_argument('--lr', type=float, default=None, metavar='LR',
help='learning rate (default: auto)')
parser.add_argument('--lr-scheduler', type=str, default='poly',
choices=['poly', 'step', 'cos','elr'],
help='lr scheduler mode: (default: poly)')
parser.add_argument('--momentum', type=float, default=0.9,
metavar='M', help='momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=1e-4,
metavar='M', help='w-decay (default: 4e-5)')
parser.add_argument('--nesterov', action='store_true', default=False,
help='whether use nesterov (default: False)')
# cuda, seed and logging
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--gpu-ids', type=str, default='0,1',
help='use which gpu to train, must be a \
comma-separated list of integers only (default=0)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
# checking point
parser.add_argument('--resume', type=str, default=None,
help='put the path to resuming file if needed')
parser.add_argument('--checkname', type=str, default=None,
help='set the checkpoint name')
# finetuning pre-trained models
parser.add_argument('--ft', action='store_true', default=False,
help='finetuning on a different dataset')
# evaluation option
parser.add_argument('--eval-interval', type=int, default=1,
help='evaluuation interval (default: 1)')
parser.add_argument('--no-val', action='store_true', default=False,
help='skip validation during training')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
# default settings for epochs, batch_size and lr
if args.epochs is None:
epoches = {
'coco': 30,
'cityscapes': 200,
'pascal': 400,
}
args.epochs = epoches[args.dataset.lower()]
if args.batch_size is None:
args.batch_size = 4 * len(args.gpu_ids)
if args.test_batch_size is None:
args.test_batch_size = args.batch_size
if args.lr is None:
lrs = {
'coco': 0.1,
'cityscapes': 0.01,
'pascal': 0.05,
}
args.lr = lrs[args.dataset.lower()] / (2 * len(args.gpu_ids)) * args.batch_size
print(args)
torch.manual_seed(args.seed)
trainer = Trainer(args)
trainer.inferrence()
if __name__ == "__main__":
main()