yolov5中loss.py代码
时间: 2023-10-18 20:56:26 浏览: 44
以下是 YOLOv5 中的 `loss.py` 文件的代码:
```python
import torch
import torch.nn.functional as F
from torch import nn
class FocalLoss(nn.Module):
def __init__(self, alpha=0.25, gamma=2.0, reduction='mean'):
super(FocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
def forward(self, inputs, targets):
ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')
pt = torch.exp(-ce_loss)
focal_loss = self.alpha * (1 - pt) ** self.gamma * ce_loss
if self.reduction == 'none':
return focal_loss
elif self.reduction == 'mean':
return focal_loss.mean()
elif self.reduction == 'sum':
return focal_loss.sum()
class CIoULoss(nn.Module):
def __init__(self, reduction='mean'):
super(CIoULoss, self).__init__()
self.reduction = reduction
def forward(self, preds, targets, weight=None):
b, _, h, w = preds.shape
pred_boxes = preds[..., :4].view(b, -1, 4)
target_boxes = targets[..., :4].view(b, -1, 4)
x1 = torch.min(pred_boxes[..., 0], target_boxes[..., 0])
y1 = torch.min(pred_boxes[..., 1], target_boxes[..., 1])
x2 = torch.max(pred_boxes[..., 2], target_boxes[..., 2])
y2 = torch.max(pred_boxes[..., 3], target_boxes[..., 3])
pred_area = (pred_boxes[..., 2] - pred_boxes[..., 0]) * (pred_boxes[..., 3] - pred_boxes[..., 1])
target_area = (target_boxes[..., 2] - target_boxes[..., 0]) * (target_boxes[..., 3] - target_boxes[..., 1])
intersect_area = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
union_area = pred_area + target_area - intersect_area
iou = intersect_area / union_area
enclose_x1y1 = torch.min(pred_boxes[..., 0], target_boxes[..., 0])
enclose_x2y2 = torch.max(pred_boxes[..., 2], target_boxes[..., 2])
enclose_y1y2 = torch.min(pred_boxes[..., 1], target_boxes[..., 1])
enclose_y2y2 = torch.max(pred_boxes[..., 3], target_boxes[..., 3])
enclose_area = (enclose_x2y2 - enclose_x1y1).clamp(0) * (enclose_y2y2 - enclose_y1y2).clamp(0)
cious = iou - (enclose_area - union_area) / enclose_area
if weight is not None:
cious = cious * weight
ciou_loss = 1 - cious
if self.reduction == 'none':
return ciou_loss
elif self.reduction == 'mean':
return ciou_loss.mean()
elif self.reduction == 'sum':
return ciou_loss.sum()
```
这个文件包含了 YOLOv5 模型中的 Focal Loss 和 CIoU Loss 的实现。Focal Loss 是一种针对目标检测任务的损失函数,用于解决类别不平衡的问题;CIoU Loss 是用于计算预测框和真实框之间的 IoU 的损失函数。这些损失函数在 YOLOv5 模型的训练过程中起到了重要的作用。