4441480fde64e42a9c4af205bf2ab8003511172e,test.py,,kNN,#,69

Before Change

    top5 = 0.
    end = time.time()
    for batch_idx, (inputs, targets, indexes) in enumerate(testloader):
        inputs, targets = inputs.cuda(), targets.cuda()
        inputs, targets = Variable(inputs, volatile=True), Variable(targets)
        batchSize = inputs.size(0)
        features = net(inputs)
        net_time.update(time.time() - end)

After Change

    top1 = 0.
    top5 = 0.
    end = time.time()
    with torch.no_grad():
        for batch_idx, (inputs, targets, indexes) in enumerate(testloader):
            inputs, targets = inputs.to(device), targets.to(device)
            batchSize = inputs.size(0)
            features = net(inputs)
            net_time.update(time.time() - end)
            end = time.time()

            dist = torch.mm(features, trainFeatures)

            yd, yi = dist.topk(K, dim=1, largest=True, sorted=True)
            candidates = trainLabels.view(1,-1).expand(batchSize, -1)
            retrieval = torch.gather(candidates, 1, yi)

            retrieval_one_hot = torch.zeros(batchSize * K, C, device=device)
            retrieval_one_hot.scatter_(1, retrieval.view(-1, 1), 1)
            yd_transform = yd.clone().div_(sigma).exp_()
            probs = torch.sum(torch.mul(retrieval_one_hot.view(batchSize, -1 , C), yd_transform.view(batchSize, -1, 1)), 1)
            _, predictions = probs.sort(1, True)

            // Find which predictions match the target
            correct = predictions.eq(targets.data.view(-1,1))

            top1 = top1 + correct.narrow(1,0,1).sum().item()
            top5 = top5 + correct.narrow(1,0,5).sum().item()

            total += targets.size(0)

            cls_time.update(time.time() - end)
            end = time.time()

            print("Test [{}/{}]\t"
                  "Net Time {net_time.val:.3f} ({net_time.avg:.3f})\t"
                  "Cls Time {cls_time.val:.3f} ({cls_time.avg:.3f})\t"
                  "Top1: {:.2f}  Top5: {:.2f}".format(
                  total, testsize, top1*100./total, top5*100./total, net_time=net_time, cls_time=cls_time))

    print(top1*100./total)

    return top1/total

Instances