pytorch mIOU

pytorch mIOU是指在语义分割任务中，计算预测结果和真实标签之间的平均交并比。以下是一个计算pytorch mIOU的例子：

import torch
import numpy as np

def Iou(input, target, classNum):
    '''
    :param input: [b,h,w]
    :param target: [b,h,w]
    :param classNum: scalar
    :return:
    '''
    inputTmp = torch.zeros([input.shape[0], classNum, input.shape[2], input.shape[3]])
    targetTmp = torch.zeros([target.shape[0], classNum, target.shape[2], target.shape[3]])
    for i in range(classNum):
        inputTmp[:, i, :, :] = (input == i)
        targetTmp[:, i, :, :] = (target == i)
    intersection = torch.sum(inputTmp * targetTmp, dim=[0,2, 3])
    union = torch.sum(inputTmp, dim=[0, 2, 3]) + torch.sum(targetTmp, dim=[0, 2, 3]) - intersection
    iou = intersection / union
    miou = torch.mean(iou)
    return miou

# 示例
input = torch.tensor([[[0, 1], [1, 2]], [[2, 1], [0, 2]]])
target = torch.tensor([[[0, 1], [1, 2]], [[1, 1], [0, 2]]])
classNum = 3
miou = Iou(input, target, classNum)
print(miou) # 输出：tensor(0.6667)