写一个YOLOV7截取锚框的程序

以下是一个基本的YOLOv7截取锚框的程序，仅供参考：

import torch
import numpy as np

# 定义锚框
anchors = torch.tensor([
    [10, 13], [16, 30], [33, 23],
    [30, 61], [62, 45], [59, 119],
    [116, 90], [156, 198], [373, 326]
])

# 定义网格大小
grid_size = torch.tensor([13, 13])

# 定义预测的边界框
pred_boxes = torch.rand((1, 255, 4))

# 计算边界框的中心坐标和宽高
xywh = pred_boxes[..., :4].clone()
xywh[:, :, :2] = torch.sigmoid(xywh[:, :, :2]) + torch.arange(grid_size[1]).view(1, -1, 1).type_as(xywh)
xywh[:, :, :2] /= grid_size.view(1, 1, -1)
xywh[:, :, 2:] = torch.exp(xywh[:, :, 2:]) * anchors.view(1, -1, 2) / grid_size.view(1, 1, -1)

# 计算边界框的左上角和右下角坐标
bboxes = torch.zeros_like(xywh)
bboxes[:, :, 0] = xywh[:, :, 0] - xywh[:, :, 2] / 2  # x1
bboxes[:, :, 1] = xywh[:, :, 1] - xywh[:, :, 3] / 2  # y1
bboxes[:, :, 2] = xywh[:, :, 0] + xywh[:, :, 2] / 2  # x2
bboxes[:, :, 3] = xywh[:, :, 1] + xywh[:, :, 3] / 2  # y2

# 截取锚框
best_anchor = np.argmax(pred_boxes[..., 4] * pred_boxes[..., 5:], axis=-1)
best_anchor = best_anchor.reshape(-1)
bboxes = bboxes.reshape(-1, 4)
bboxes = bboxes[best_anchor, :]

这个程序中，首先定义了锚框和网格大小，接着计算了预测的边界框的中心坐标和宽高，然后计算了边界框的左上角和右下角坐标。最后，根据预测的边界框的置信度和类别预测值，选择最佳的锚框，并截取对应的边界框。