pytorch iou相关训练代码
时间: 2024-10-08 09:17:38 浏览: 11
在PyTorch中,Intersection over Union (IoU) 或者称为 Jaccard 系数是一种常用的评价二分类或多类别目标检测模型性能的指标。以下是一个简单的例子,展示如何计算并优化模型训练过程中的IoU:
```python
import torch
from torch import nn
# 假设我们有一个预测边界框的tensor和实际标签边界框的tensor
pred_boxes = torch.tensor([[x1, y1, x2, y2], ...]) # 预测框
gt_boxes = torch.tensor([[gx1, gy1, gx2, gy2], ...]) # 实际标注框
def compute_iou(pred_box, gt_box):
intersection_area = min(pred_box[2], gt_box[2]) * min(pred_box[3], gt_box[3])
union_area = pred_box[2] * pred_box[3] + gt_box[2] * gt_box[3] - intersection_area
return intersection_area / union_area if union_area > 0 else 0
iou = compute_iou(pred_boxes, gt_boxes)
ious = [compute_iou(p, g) for p, g in zip(pred_boxes, gt_boxes)]
# 如果您想将其整合到损失函数中,例如对于目标检测任务:
class IoULoss(nn.Module):
def forward(self, pred_boxes, gt_boxes):
losses = 1 - ious
return losses.mean()
optimizer = torch.optim.Adam(model.parameters())
loss_fn = IoULoss()
outputs = model(inputs)
loss = loss_fn(outputs, gt_boxes)
loss.backward()
optimizer.step()
```