基于加权类的PyTorch语义分割

我正在研究如何对图像进行语义分割，但我想给这些类分配一个权重。我正在使用它，在实际实现中，不可能对类应用权重。 我试图像下面报告的代码那样更改util/functional.py，但培训阶段并没有提高性能。尤其是IOU_分数在0.26值上振荡，没有任何良好表现

我正在使用示例文件夹中报告的笔记本，它具有多类配置

有谁能帮助我了解如何更改度量或如何使用不同的度量以及分配给类的权重

谢谢你的帮助

def iou(pr, gt, eps=1e-7, threshold=None, ignore_channels=None, weights=None):
    """Calculate Intersection over Union between ground truth and prediction
    Args:
        pr (torch.Tensor): predicted tensor
        gt (torch.Tensor):  ground truth tensor
        eps (float): epsilon to avoid zero division
        threshold: threshold for outputs binarization
    Returns:
        float: IoU (Jaccard) score
    """

    pr = _threshold(pr, threshold=threshold)
    pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)

    #print('PR', pr.shape, type(pr))
    #print('GT', gt.shape, type(gt))

    pr1 = torch.zeros(pr.shape[0], pr.shape[1], pr.shape[2], pr.shape[3])
    gt1 = torch.zeros(gt.shape[0], gt.shape[1], gt.shape[2], gt.shape[3])

    pr1[:, 0, :, :] = pr[:, 0, :, :] / 100
    pr1[:, 1, :, :] = pr[:, 1, :, :] / 2
    pr1[:, 2, :, :] = pr[:, 2, :, :] / 2
    pr1[:, 3, :, :] = pr[:, 3, :, :] / 100
    pr1[:, 4, :, :] = pr[:, 4, :, :] / 100

    gt1[:, 0, :, :] = gt[:, 0, :, :] / 100
    gt1[:, 1, :, :] = gt[:, 1, :, :] / 2
    gt1[:, 2, :, :] = gt[:, 2, :, :] / 2
    gt1[:, 3, :, :] = gt[:, 3, :, :] / 100
    gt1[:, 4, :, :] = gt[:, 4, :, :] / 100

    intersection = torch.sum(gt1 * pr1)
    union = torch.sum(gt1) + torch.sum(pr1) - intersection + eps
    return (intersection + eps) / union


jaccard = iou


def f_score(pr, gt, beta=1, eps=1e-7, threshold=None, ignore_channels=None, weights=None):
    """Calculate F-score between ground truth and prediction
    Args:
        pr (torch.Tensor): predicted tensor
        gt (torch.Tensor):  ground truth tensor
        beta (float): positive constant
        eps (float): epsilon to avoid zero division
        threshold: threshold for outputs binarization
    Returns:
        float: F score
    """

    pr = _threshold(pr, threshold=threshold)
    pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)

    #print('PR', pr.shape, type(pr))
    #print('GT', gt.shape, type(gt))

    pr1 = torch.zeros(pr.shape[0], pr.shape[1], pr.shape[2], pr.shape[3])
    gt1 = torch.zeros(gt.shape[0], gt.shape[1], gt.shape[2], gt.shape[3])

    pr1[:, 0, :, :] = pr[:, 0, :, :] / 100
    pr1[:, 1, :, :] = pr[:, 1, :, :] / 2
    pr1[:, 2, :, :] = pr[:, 2, :, :] / 2
    pr1[:, 3, :, :] = pr[:, 3, :, :] / 100
    pr1[:, 4, :, :] = pr[:, 4, :, :] / 100

    gt1[:, 0, :, :] = gt[:, 0, :, :] / 100
    gt1[:, 1, :, :] = gt[:, 1, :, :] / 2
    gt1[:, 2, :, :] = gt[:, 2, :, :] / 2
    gt1[:, 3, :, :] = gt[:, 3, :, :] / 100
    gt1[:, 4, :, :] = gt[:, 4, :, :] / 100

    tp = torch.sum(gt1 * pr1)
    fp = torch.sum(pr1) - tp
    fn = torch.sum(gt1) - tp

    score = ((1 + beta ** 2) * tp + eps) \
            / ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + eps)

    return score