main_deepclusterv2.py [296:313]:
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        losses.update(loss.item(), inputs[0].size(0))
        batch_time.update(time.time() - end)
        end = time.time()
        if args.rank ==0 and it % 50 == 0:
            logger.info(
                "Epoch: [{0}][{1}]\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"
                "Lr: {lr:.4f}".format(
                    epoch,
                    it,
                    batch_time=batch_time,
                    data_time=data_time,
                    loss=losses,
                    lr=optimizer.optim.param_groups[0]["lr"],
                )
            )
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main_swav.py [332:349]:
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        losses.update(loss.item(), inputs[0].size(0))
        batch_time.update(time.time() - end)
        end = time.time()
        if args.rank ==0 and it % 50 == 0:
            logger.info(
                "Epoch: [{0}][{1}]\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"
                "Lr: {lr:.4f}".format(
                    epoch,
                    it,
                    batch_time=batch_time,
                    data_time=data_time,
                    loss=losses,
                    lr=optimizer.optim.param_groups[0]["lr"],
                )
            )
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