/
evaluate.py
62 lines (45 loc) · 1.82 KB
/
evaluate.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
import torch
from loss import LossCalculator
from utils import AverageMeter, get_data_set
def test_network(network, args):
if network is None:
return
device = torch.device("cuda" if args.gpu_flag is True else "cpu")
network.to(device)
data_set = get_data_set(args, train_flag=False)
data_loader = torch.utils.data.DataLoader(data_set, batch_size=100, shuffle=False)
test_top1, test_top5, test_loss = test_step(network, data_loader, device)
print("-*-" * 10 + "\n\t\tTest network\n" + "-*-" * 10)
test_acc_str = 'Top1: %2.4f, Top5: %2.4f, ' % (test_top1, test_top5)
test_loss_str = 'Loss: %.4f. ' % test_loss
print(test_acc_str + test_loss_str)
return
def test_step(network, data_loader, device):
network.eval()
top1 = AverageMeter()
top5 = AverageMeter()
loss_calculator = LossCalculator()
with torch.no_grad():
for inputs, targets in data_loader:
inputs, targets = inputs.to(device), targets.to(device)
outputs = network(inputs)
loss_calculator.calc_loss(outputs, targets)
prec1, prec5 = accuracy(outputs, targets, topk=(1, 5))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
return top1.avg, top5.avg, loss_calculator.get_loss_log()
def accuracy(output, target, topk=(1,)):
"""
Computes the precision@k for the specified values of k
ref: https://github.com/chengyangfu/pytorch-vgg-cifar10
"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res