Python代码：在PyTorch中计算真负例（TN）

在给定的代码中，TN的计算应该是在每个类别的循环中进行的。可以在每个类别循环的最后添加以下代码来计算TN：

TN = batchshape - TP - FP - FN

这样就可以计算出每个类别的TN值了。完整的代码如下：

with torch.no_grad():
    for i, b in enumerate(batch(dataset, batch_size)):

        imgs = np.array([k[0] for k in b]).astype(np.float32)
        true_masks = np.array([k[1] for k in b])

        imgs = torch.from_numpy(imgs)
        imgs = imgs.unsqueeze(1)
        true_masks = torch.from_numpy(true_masks)

        pre_masks_eval = torch.zeros(true_masks.shape[0],14,256,256)
        true_masks_eval = torch.zeros(true_masks.shape[0],14,256,256)
        batchshape = true_masks.shape[0]

        batch_dice = torch.zeros(14).cuda()
        if gpu:
            imgs = imgs.cuda()
            true_masks = true_masks.cuda()
            net.cuda()

        output_img = net(imgs)
        input = output_img.cuda()
        pre_masks = input.max(1)[1].float() #索引代表像素所属类别的数字
        for ak in range(14):
            if ak == 0:
                continue
            pre_masks_eval[:,ak] = (pre_masks==ak)
            true_masks_eval[:,ak] = (true_masks==ak)
            premasks = pre_masks_eval[:,ak].view(true_masks.shape[0],-1)
            truemasks = true_masks_eval[:,ak].view(true_masks.shape[0],-1)

            intersection = premasks * truemasks
            TP = intersection.sum(1)
            FP = premasks.sum(1) - TP
            FN = truemasks.sum(1) - TP

            TN = batchshape - TP - FP - FN

            for bk in range(true_masks.shape[0]):
                if TP[bk] == 0 and FP[bk] == 0 and FN[bk] == 0:
                    NE[ak] += 1
                    JNE[ak] += 1