def validate(val_loader: DataLoader, G: nn.Module, F1: ImageClassifierHead,
F2: ImageClassifierHead, args: argparse.Namespace) -> Tuple[float, float]:
batch_time = AverageMeter('Time', ':6.3f')
top1_1 = AverageMeter('Acc_1', ':6.2f')
top1_2 = AverageMeter('Acc_2', ':6.2f')
progress = ProgressMeter(
len(val_loader),
[batch_time, top1_1, top1_2],
prefix='Test: ')
# switch to evaluate mode
G.eval()
F1.eval()
F2.eval()
if args.per_class_eval:
classes = val_loader.dataset.classes
confmat = ConfusionMatrix(len(classes))
else:
confmat = None
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
images = images.to(device)
target = target.to(device)
# compute output
g = G(images)
y1, y2 = F1(g), F2(g)
# measure accuracy and record loss
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))
# measure elapsed time
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(val_loader: DataLoader, model: ImageClassifier, args: argparse.Namespace) -> float:
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(val_loader),
[batch_time, losses, top1, top5],
prefix='Test: ')
# switch to evaluate mode
model.eval()
if args.per_class_eval:
classes = val_loader.dataset.classes
confmat = ConfusionMatrix(len(classes))
else:
confmat = None
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
images = images.to(device)
target = target.to(device)
# compute output
output, _ = model(images)
loss = F.cross_entropy(output, target)
# measure accuracy and record loss
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))
# measure elapsed time
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
def validate(val_loader: DataLoader, model: Classifier,
args: argparse.Namespace) -> float:
batch_time = AverageMeter('Time', ':6.3f')
classes = val_loader.dataset.classes
confmat = ConfusionMatrix(len(classes))
progress = ProgressMeter(len(val_loader), [batch_time], prefix='Test: ')
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
images = images.to(device)
target = target.to(device)
# compute output
output, _ = model(images)
softmax_output = F.softmax(output, dim=1)
softmax_output[:, -1] = args.threshold
# measure accuracy and record loss
confmat.update(target, softmax_output.argmax(1))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
acc_global, accs, iu = confmat.compute()
all_acc = torch.mean(accs).item() * 100
known = torch.mean(accs[:-1]).item() * 100
unknown = accs[-1].item() * 100
h_score = 2 * known * unknown / (known + unknown)
if args.per_class_eval:
print(confmat.format(classes))
print(
' * All {all:.3f} Known {known:.3f} Unknown {unknown:.3f} H-score {h_score:.3f}'
.format(all=all_acc, known=known, unknown=unknown,
h_score=h_score))
return h_score