eval_linear.py [300:325]:
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        loss = criterion(output, target)

        # compute the gradients
        optimizer.zero_grad()
        loss.backward()

        # step
        optimizer.step()

        # update stats
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), inp.size(0))
        top1.update(acc1[0], inp.size(0))
        top5.update(acc5[0], inp.size(0))

        batch_time.update(time.perf_counter() - end)
        end = time.perf_counter()

        # verbose
        if args.rank == 0 and iter_epoch % 50 == 0:
            logger.info(
                "Epoch[{0}] - Iter: [{1}/{2}]\t"
                "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
                "Data {data_time.val:.3f} ({data_time.avg:.3f})\t"
                "Loss {loss.val:.4f} ({loss.avg:.4f})\t"
                "Prec {top1.val:.3f} ({top1.avg:.3f})\t"
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eval_semisup.py [259:284]:
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        loss = criterion(output, target)

        # compute the gradients
        optimizer.zero_grad()
        loss.backward()

        # step
        optimizer.step()

        # update stats
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), inp.size(0))
        top1.update(acc1[0], inp.size(0))
        top5.update(acc5[0], inp.size(0))

        batch_time.update(time.perf_counter() - end)
        end = time.perf_counter()

        # verbose
        if args.rank == 0 and iter_epoch % 50 == 0:
            logger.info(
                "Epoch[{0}] - Iter: [{1}/{2}]\t"
                "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
                "Data {data_time.val:.3f} ({data_time.avg:.3f})\t"
                "Loss {loss.val:.4f} ({loss.avg:.4f})\t"
                "Prec {top1.val:.3f} ({top1.avg:.3f})\t"
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