-
Notifications
You must be signed in to change notification settings - Fork 0
/
validation.py
60 lines (43 loc) · 1.69 KB
/
validation.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
import torch
from torch.autograd import Variable
import time
import torch.nn.functional as F
import sys
import os
import json
from dataset_utils import AverageMeter, calculate_accuracy
def val_epoch(epoch, params, data_loader, model, criterion, opt, logger):
print('Validation at epoch {}'.format(epoch))
model.eval()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
accuracies = AverageMeter()
end_time = time.time()
for i, (inputs, targets) in enumerate(data_loader):
data_time.update(time.time() - end_time)
if not opt.no_cuda:
targets = targets.cuda(async=True)
inputs = Variable(inputs.cuda(), volatile=True)
targets = Variable(targets, volatile=True)
_, _, _, outputs, _, _ = model.forward_base(inputs)
loss = criterion(outputs, targets)
acc = calculate_accuracy(outputs, targets)
losses.update(loss.data[0], inputs.size(0))
accuracies.update(acc, inputs.size(0))
batch_time.update(time.time() - end_time)
end_time = time.time()
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc {acc.val:.3f} ({acc.avg:.3f})'.format(
epoch,
i + 1,
len(data_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
acc=accuracies))
logger.log({'epoch': epoch, 'loss': losses.avg, 'acc': accuracies.avg})
return losses.avg