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简介

IoU（Intersection over Union）是一种测量在特定数据集中检测相应物体准确度的一个标准，通常用于目标检测中预测框（bounding box）之间准确度的一个度量（预测框和实际目标框）。

IoU计算的是“预测的边框”和“真实的边框”的交叠率，即它们的交集和并集的比值。最理想情况是完全重叠，即比值为1。

IoU的计算方法如下：

计算两个框的交集面积，即两个框的左、上、右、下四个点的交集。
计算两个框的并集面积，即两个框的左、上、右、下四个点的并集。
计算交集面积和并集面积的比值，即为 IoU 值。
IoU的优点是可以反映预测检测框与真实检测框的检测效果，并且具有尺度不变性，即对尺度不敏感。但是，IoU也存在一些缺点，例如无法反映两个框之间的距离大小（重合度），如果两个框没有相交，则 IoU 值为 0，无法进行学习训练。

源码实现:

cpu版源码实现:

def iou_core(box1: Tensor, box2: Tensor, area_sum: Tensor):overlap_w = torch.min(box1[2],box2[2]) - torch.max(box1[0],box2[0])overlap_h = torch.min(box1[3],box2[3]) - torch.max(box1[1],box2[1])if overlap_w <= 0 or overlap_h <= 0:return 0overlap_area = overlap_h * overlap_wreturn overlap_area / (area_sum - overlap_area)def iou_cpu(box1: Tensor, box2: Tensor):box1_num = box1.size(0)box2_num = box2.size(0)box1_dim = box1.size(1)box2_dim = box2.size(1)if box1_dim != 4 or box2_dim != 4:return -1box1_area = (box1[:, 2] - box1[:, 0]) * (box1[:, 3] - box1[:, 1])box2_area = (box2[:, 2] - box2[:, 0]) * (box2[:, 3] - box2[:, 1])result = torch.zeros(size=(box1_num, box2_num))for i in range(box1_num):for j in range(box2_num):if box1_area[i] >= 0 and box2_area[j] >= 0:result[i, j] = iou_core(box1[i], box2[j], box1_area[i] + box2_area[j])else:result[i, j] = 9999return result

gpu版源码实现:

__device__ float iou_core(const float* box1 ,const float* box2){float box1_x0 = *(box1 + 0);float box1_y0 = *(box1 + 1);float box1_x1 = *(box1 + 2);float box1_y1 = *(box1 + 3);float box2_x0 = *(box2 + 0);float box2_y0 = *(box2 + 1);float box2_x1 = *(box2 + 2);float box2_y1 = *(box2 + 3);if(!(box1_x0 < box1_x1 && box1_y0 < box1_y1 && box2_x0 < box2_x1 && box2_y0 < box2_y1)){return 9999;}float inter_x0 = std::max(box1_x0, box2_x0);float inter_x1 = std::min(box1_x1, box2_x1);float inter_y0 = std::max(box1_y0, box2_y0);float inter_y1 = std::min(box1_y1, box2_y1);float inter_area = (inter_x1 - inter_x0)*(inter_y1-inter_y0);inter_area = std::max(inter_area, 0.0f);float box1_area = (box1_x1 - box1_x0)*(box1_y1-box1_y0);float box2_area = (box2_x1 - box2_x0)*(box2_y1-box2_y0);float iou = inter_area / (box1_area + box2_area - inter_area);printf("iou =%f\n",iou);return iou;
}__global__ void iou_gpu_kernel(const int box1_num,
const float* box1_ptr,
const int box2_num,
const float* box2_ptr,
float* result_ptr){const int box1_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;const int box2_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;printf("gpu: box1_idx = %d, box2_idy= %d\n",box1_idx,box2_idx);if(box1_idx>=box1_num || box2_idx>=box2_num){return;}printf("gpu: box1_idx = %d, box2_idy= %d, result_id= %d\n",box1_idx,box2_idx,box1_idx * box2_num + box2_idx);const float* box1 = box1_ptr + box1_idx * 4;const float* box2 = box2_ptr + box2_idx * 4;float iou = iou_core(box1, box2);*(result_ptr + box1_idx * box2_num + box2_idx) = iou;
}void iou_gpu_launch(const int box1_num,
const float* box1_ptr,
const int box2_num,
const float* box2_ptr,
float* result_ptr){dim3 blocks(DIVUP(box1_num, THREADS_PER_BLOCK),DIVUP(box2_num, THREADS_PER_BLOCK));//每个grid的blocksdim3 threads(THREADS_PER_BLOCK,THREADS_PER_BLOCK);//每个block里面的threadprintf("blocks=(%d %d), threads=(%d %d)\n",DIVUP(box1_num, THREADS_PER_BLOCK),DIVUP(box2_num, THREADS_PER_BLOCK),THREADS_PER_BLOCK,THREADS_PER_BLOCK);iou_gpu_kernel<<<blocks,threads>>>(box1_num,box1_ptr,box2_num,box2_ptr,result_ptr);cudaDeviceSynchronize();// waiting for gpu workprintf("gpu done\n");
}

耗时测试:

import torch
from iou import iou_gpu, iou_cpu
from utils import TicTocdevice = torch.device('cuda:0')
input1 = torch.Tensor([[0, 0, 1, 1],[0, 2, 1, 3],[0.2, 0, 1, 1],[0.1, 2, 1, 3],[0.11, 0, 1, 1],[0, 2.4, 1, 3],[0.2, 0.1, 1, 1],[0.7, 2.5, 1, 3],[0, 0, 6, 1],[1.5, 2, 1, 3]]).to(device)
input2 = torch.Tensor([[0.5, 0, 1.5, 1],[0, 0.5, 1, 1.5],[0.5, 0.5, 1.5, 1.5],[0, 0.5, 1, 2.5]]).to(device)tictic = TicToc('iou fun')
for i in range(1000):result = iou_gpu(input1, input2)
tictic.toc()
tictic.tic()
for i in range(1000):result2 = iou_cpu(input1.to('cpu'), input2.to('cpu'))
tictic.toc()
pass

具体流程说明:

IoU的计算方法如下：
计算两个框的交集面积，即两个框的左、上、右、下四个点的交集。
计算两个框的并集面积，即两个框的左、上、右、下四个点的并集。
计算交集面积和并集面积的比值，即为 IoU 值。
在实际应用中，通常设定 IoU 的阈值，例如 0.5 或 0.7 等，当 IoU 值大于阈值时，认为预测成功。通过调整阈值，可以得到不同的模型，再通过不同的评价指标（如 ROC 曲线、F1 值等）来确定最优模型。

如需获取全套代码请参考