逐行注释下面这段代码: dx = delta[:, 0] / self.weights[0] dy = delta[:, 1] / self.weights[1] dw = delta[:, 2] / self.weights[2] dh = delta[:, 3] / self.weights[3] dw = torch.clamp(dw, max=self.bbox_xform_clip) dh = torch.clamp(dh, max=self.bbox_xform_clip) width = box[:, 2] - box[:, 0] height = box[:, 3] - box[:, 1] ctr_x = box[:, 0] + 0.5 * width ctr_y = box[:, 1] + 0.5 * height pred_ctr_x = dx * width + ctr_x pred_ctr_y = dy * height + ctr_y pred_w = torch.exp(dw) * width pred_h = torch.exp(dh) * height xmin = pred_ctr_x - 0.5 * pred_w ymin = pred_ctr_y - 0.5 * pred_h xmax = pred_ctr_x + 0.5 * pred_w ymax = pred_ctr_y + 0.5 * pred_h target = torch.stack((xmin, ymin, xmax, ymax), dim=1) return target
时间: 2023-07-16 21:16:01 浏览: 65
这段代码是一个目标检测算法中的回归部分,用于预测目标框的位置。下面是每一行代码的注释:
```python
dx = delta[:, 0] / self.weights[0] # 计算 delta_x
dy = delta[:, 1] / self.weights[1] # 计算 delta_y
dw = delta[:, 2] / self.weights[2] # 计算 delta_w
dh = delta[:, 3] / self.weights[3] # 计算 delta_h
# 对 dw、dh 进行剪裁,限制在一个范围内
dw = torch.clamp(dw, max=self.bbox_xform_clip)
dh = torch.clamp(dh, max=self.bbox_xform_clip)
# 计算目标框的宽度、高度、中心点坐标
width = box[:, 2] - box[:, 0]
height = box[:, 3] - box[:, 1]
ctr_x = box[:, 0] + 0.5 * width
ctr_y = box[:, 1] + 0.5 * height
# 根据 delta 值和原始目标框计算预测的目标框的中心点坐标、宽度和高度
pred_ctr_x = dx * width + ctr_x
pred_ctr_y = dy * height + ctr_y
pred_w = torch.exp(dw) * width
pred_h = torch.exp(dh) * height
# 根据预测的目标框的中心点坐标、宽度和高度计算目标框的四个坐标
xmin = pred_ctr_x - 0.5 * pred_w
ymin = pred_ctr_y - 0.5 * pred_h
xmax = pred_ctr_x + 0.5 * pred_w
ymax = pred_ctr_y + 0.5 * pred_h
# 将目标框的四个坐标组成一个 tensor,并返回
target = torch.stack((xmin, ymin, xmax, ymax), dim=1)
return target
```