修复非极大值抑制函数中的张量维度问题，并更新compute_ap函数以使用numpy.trapezoid替代已弃用的numpy.trapz

utils/util.py

@@ -151,7 +151,7 @@ def non_max_suppression(outputs, confidence_threshold=0.001, iou_threshold=0.65)
         box = wh2xy(box)  # (cx, cy, w, h) to (x1, y1, x2, y2)
         if nc > 1:
             i, j = (cls > confidence_threshold).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, 4 + j, None], j[:, None].float()), dim=1)
+            x = torch.cat((box[i], x[i, 4 + j].unsqueeze(1), j[:, None].float()), dim=1)
         else:  # best class only
             conf, j = cls.max(1, keepdim=True)
             x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > confidence_threshold]
@@ -296,7 +296,8 @@ def compute_ap(tp, conf, output, target, plot=False, names=(), eps=1e-16):
 
             # Integrate area under curve
             x = numpy.linspace(start=0, stop=1, num=101)  # 101-point interp (COCO)
-            ap[ci, j] = numpy.trapz(numpy.interp(x, m_rec, m_pre), x)  # integrate
+            # numpy.trapz is deprecated in numpy 2.0.0 or after version, use numpy.trapezoid instead
+            ap[ci, j] = numpy.trapezoid(numpy.interp(x, m_rec, m_pre), x)  # integrate
             if plot and j == 0:
                 py.append(numpy.interp(px, m_rec, m_pre))  # precision at mAP@0.5