db4fc0e5816a504a87e4a2a603b065c179406a04,xnornet_plusplus/main.py,,validate,#Any#Any#Any#Any#,263

Before Change

    model.eval()

    end = time.time()
    if args.quantize:
        bin_op.binarization()
    for i, (input, target) in enumerate(val_loader):
        target = target.cuda(async=True)
        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)

        // measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.item(), 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:
            logging.info("Test: [{0}/{1}]\t"
                  "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
                  "Loss {loss.val:.4f} ({loss.avg:.4f})\t"
                  "Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t"
                  "Prec@5 {top5.val:.3f} ({top5.avg:.3f})".format(
                   i, len(val_loader), batch_time=batch_time, loss=losses,
                   top1=top1, top5=top5))
    if args.quantize:
        bin_op.restore()

    logging.info(" * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}"
          .format(top1=top1, top5=top5))
    tb_writer.add_scalar("test/loss", losses.avg, epoch)
    tb_writer.add_scalar("test/top1", top1.avg, epoch)

After Change

        loss = criterion(output[0]["logits"], target_var)

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

Instances