pytorch miou

PyTorch没有直接提供计算Mean Intersection over Union (mIoU)的函数，但可以通过一些步骤来计算。首先，你需要根据模型的输出和目标标签计算每个类别的Intersection和Union。然后，通过求和得到所有类别的Intersection和Union，并计算mIoU。

以下是一个示例代码，演示如何计算mIoU：

import torch

def compute_iou(outputs, targets, num_classes):
    # Convert outputs and targets to binary masks
    outputs = torch.argmax(outputs, dim=1)
    targets = targets.squeeze(1)
    
    ious = torch.zeros(num_classes)
    
    for cls in range(num_classes):
        intersection = ((outputs == cls) & (targets == cls)).float().sum()
        union = ((outputs == cls) | (targets == cls)).float().sum()
        
        iou = intersection / (union + 1e-6)  # Add a small epsilon to avoid division by zero
        ious[cls] = iou
    
    return ious

def compute_miou(outputs, targets, num_classes):
    ious = compute_iou(outputs, targets, num_classes)
    miou = ious.mean()
    
    return miou