some questions

[ruti0823]

请问有友友成功复现这篇代码了吗？我复现的结果，除了第一个指标外，其余的都是零，这是什么问题呀？

[xiaoyu-s-alt]

请问有友友成功复现这篇代码了吗？我复现的结果，除了第一个指标外，其余的都是零，这是什么问题呀？
我也碰到了这个问题，很难受，请问您解决这个问题了吗

[zhen-guang]

I made some changes to the function for calculating metrics to adapt to the latest version of PyTorch, but I cannot guarantee that my changes are completely correct.

def get_sensitivity(SR, GT, threshold=0.5):
    # Sensitivity == Recall
    SR = SR > threshold
    GT = GT == torch.max(GT)

    # TP : True Positive
    # FN : False Negative
    TP = (SR == 1) & (GT == 1) 
    FN = (SR == 0) & (GT == 1)

    SE = float(torch.sum(TP)) / (float(torch.sum(TP) + torch.sum(FN)) + 1e-6)

    return SE


def get_specificity(SR, GT, threshold=0.5):
    SR = SR > threshold
    GT = GT == torch.max(GT)

    # TN : True Negative
    # FP : False Positive
    TN = (SR == 0) & (GT == 0)
    FP = (SR == 1) & (GT == 0)

    SP = float(torch.sum(TN)) / (float(torch.sum(TN) + torch.sum(FP)) + 1e-6)

    return SP


def get_precision(SR, GT, threshold=0.5):
    SR = SR > threshold
    GT = GT == torch.max(GT)

    # TP : True Positive
    # FP : False Positive
    TP = (SR == 1) & (GT == 1)
    FP = (SR == 1) & (GT == 0)

    PC = float(torch.sum(TP)) / (float(torch.sum(TP) + torch.sum(FP)) + 1e-6)

    return PC


def get_F1(SR, GT, threshold=0.5):
    # Sensitivity == Recall
    SE = get_sensitivity(SR, GT, threshold=threshold)
    PC = get_precision(SR, GT, threshold=threshold)

    F1 = 2 * SE * PC / (SE + PC + 1e-6)

    return F1


def get_JS(SR, GT, threshold=0.5):
    # JS : Jaccard similarity
    SR = SR > threshold
    GT = GT == torch.max(GT)

    Inter = torch.sum(SR & GT)
    Union = torch.sum(SR | GT)

    JS = float(Inter) / (float(Union) + 1e-6)

    return JS


def get_DC(SR, GT, threshold=0.5):
    # DC : Dice Coefficient
    SR = SR > threshold
    GT = GT == torch.max(GT)

    Inter = torch.sum(SR & GT)
    DC = float(2 * Inter) / (float(torch.sum(SR) + torch.sum(GT)) + 1e-6)

    return DC

[Wangbaoyun]

I also made some changes in evlaution.py ,but the loss of the network "U_Net" always increases,Model nonconvergence.