fcb04b6bf9e0a9c712a97ef1bd9d8c3829c60894,examples/imagenet_eval.py,,validate,#,211

Before Change

    for i, (input, target) in enumerate(val_loader):
        target = target.cuda()
        input = input.cuda()
        input_var = torch.autograd.Variable(input, volatile=True)
        target_var = torch.autograd.Variable(target, volatile=True)

        // compute output
        output = model(input_var)
        loss = criterion(output, target_var)

After Change

def validate(val_loader, model, criterion):
    with torch.no_grad():
        batch_time = AverageMeter()
        losses = AverageMeter()
        top1 = AverageMeter()
        top5 = AverageMeter()

        // switch to evaluate mode
        model.eval()

        end = time.time()
        for i, (input, target) in enumerate(val_loader):
            target = target.cuda()
            input = input.cuda()

            // compute output
            output = model(input)
            loss = criterion(output, target)

            // measure accuracy and record loss
            prec1, prec5 = accuracy(output.data, target.data, topk=(1, 5))
            losses.update(loss.data[0], input.size(0))
            top1.update(prec1[0], input.size(0))
            top5.update(prec5[0], input.size(0))

            // measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                print("Test: [{0}/{1}]\t"
                      "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
                      "Loss {loss.val:.4f} ({loss.avg:.4f})\t"
                      "Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t"
                      "Acc@5 {top5.val:.3f} ({top5.avg:.3f})".format(
                       i, len(val_loader), batch_time=batch_time, loss=losses,
                       top1=top1, top5=top5))

        print(" * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}"
              .format(top1=top1, top5=top5))

        return top1.avg, top5.avg


def save_checkpoint(state, is_best, filename="checkpoint.pth.tar"):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, "model_best.pth.tar")

Instances