您好，在多风格非配对图场景，能否交换source和target的位置，并将得到的output_A和output_B进一步经过enhancer,得到recover_A和recover_B。最后计算l1_loss(source, recover_A)和l1_loss(target, recover_B)及Triplet_loss(output_A，target, source) 和 Triplet_loss(output_B，source，target)

def train(train_loader, mapping, enhancer, criterion, optimizer):
    losses = AverageMeter()
    criterionTriplet = torch.nn.TripletMarginLoss(margin=1.0, p=2)
    FEModel = Feature_Extract_Model().cuda()

    mapping.train()
    enhancer.train()

    for (source_img, source_center, target_img, target_center) in train_loader:
        source_img = source_img.cuda(non_blocking=True)
        source_center = source_center.cuda(non_blocking=True)
        target_img = target_img.cuda(non_blocking=True)
        target_center = target_center.cuda(non_blocking=True)

        style_A = mapping(source_center)
        style_B = mapping(target_center)

        output_A = enhancer(source_img, style_A, style_B)
        output_B = enhancer(target_img, style_B, style_A)
        recoverA = enhancer(output_A, style_B, style_A)
        recoverB = enhancer(output_B, style_A, style_B)

        source_img_feature = FEModel(source_img)
        target_img_feature = FEModel(target_img)
        output_A_feature = FEModel(output_A)
        output_B_feature = FEModel(output_B)

        loss_l1 = criterion(recoverA, source_img) + criterion(recoverB, target_img)
        loss_triplet = criterionTriplet(output_B_feature, source_img_feature, target_img_feature) + \
                       criterionTriplet(output_A_feature, target_img_feature, source_img_feature)
        loss = loss_l1 + loss_triplet

        losses.update(loss.item(), args.t_batch_size)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    return losses.avg

您好，我想用您的工程跑一下自己的数据，现在有输入，输出一组数据对，训练数据里面A-E剩下的4种效果是怎样生成的呢，这些目标效果数据能否是非成对的呢？如果只有一种风格，能否A-E目标效果都拷贝成一样的数据呢，在train_enhancer.py所训练的单风格脚本是需要embeddings.npy文件，这个文件在单风格训练时是必须的吗

你好，我在您提供的fivek.lrcat没找到 DeepUPE issue里的"(default) input with ExpertC"。请问单风格实验的输入是下图中的“InputAsShotZeroed”还是“(Q)InputZeroed with ExpertC WhiteBalance”

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Some peers have emailed me asking about the performance of the open source model that does not agree with the results in the paper. As stated in the README, the model is not the model of the paper, but the performance is similar. The exact result should be: PSNR: 25.41, SSIM: 0.942, LPIPS: 0.085

If you find that your result is not this, then it may be that the JPEG codec is different, which is related to the version of opencv and how it is installed.

You can uninstall your opencv (either with pip or conda) and reinstall it using pip (it must be pip, because conda installs a different JPEG codec):

pip install opencv-python==4.5.5.62​