def validate(data_loader, G, F1, F2, args):
batch_time = AverageMeter('Time', ':6.3f')
top1_1 = AverageMeter('Acc_1', ':6.2f')
top1_2 = AverageMeter('Acc_2', ':6.2f')
progress = ProgressMeter(len(data_loader),
[batch_time, top1_1, top1_2],
prefix='Test: ')
G.eval()
F1.eval()
F2.eval()
if args.per_class_eval:
classes = data_loader.dataset.classes
confmat = ConfusionMatrix(len(classes))
else:
confmat = None
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(data_loader):
images = images.to(device)
target = target.to(device)
g = G(images)
y1, y2 = F1(g), F2(g)
acc1, = accuracy(y1, target)
acc2, = accuracy(y2, target)
if confmat:
confmat.update(target, y1.argmax(1))
top1_1.update(acc1.item(), images.size(0))
top1_2.update(acc2.item(), images.size(0))
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
print(' * Acc1 {top1_1.avg:.3f} Acc2 {top1_2.avg:.3f}'.format(top1_1=top1_1, top1_2=top1_2))
if confmat:
print(confmat.format(classes))
return top1_1.avg, top1_2.avg
def validate(dataloader, target_iter, classifier, device, args):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(len(target_iter),
[batch_time, losses, top1, top5],
prefix='Test: ')
classifier.eval()
if args.per_class_eval:
classes = dataloader.dataset.classes
confmat = ConfusionMatrix(len(classes))
else:
confmat = None
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(dataloader):
images, target = images.to(device), target.to(device)
output, _ = classifier(images)
loss = F.cross_entropy(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
if confmat:
confmat.update(target, output.argmax(1))
losses.update(loss.item(), images.size(0))
top1.update(acc1.item(), images.size(0))
top5.update(acc5.item(), images.size(0))
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(top1=top1,
top5=top5))
if confmat:
print(confmat.format(classes))
return top1.avg