深度学习|RuntimeError: result type Float can‘t be cast to the desired output type __int64报错解决方法
小白刚开始学习YOLOv5，跟随老哥的步骤走了一遍目标检测--手把手教你搭建自己的YOLOv5目标检测平台
【深度学习|RuntimeError: result type Float can‘t be cast to the desired output type __int64报错解决方法】最后训练最后一步出现RuntimeError: result type Float can‘t be cast to the desired output type __int64报错
解决方法：找到5.0版报错的loss.py中最后那段for函数，将其整体替换为yolov5-master版中loss.py最后一段for函数即可正常运行
for i in range(self.nl):
anchors, shape = self.anchors[i], p[i].shape
gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]]# xyxy gain# Match targets to anchors
t = targets * gain# shape(3,n,7)
if nt:
# Matches
r = t[..., 4:6] / anchors[:, None]# wh ratio
j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t']# compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']# iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j]# filter# Offsets
gxy = t[:, 2:4]# grid xy
gxi = gain[[2, 3]] - gxy# inverse
j, k = ((gxy % 1 < g) & (gxy > 1)).T
l, m = ((gxi % 1 < g) & (gxi > 1)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0# Define
bc, gxy, gwh, a = t.chunk(4, 1)# (image, class), grid xy, grid wh, anchors
a, (b, c) = a.long().view(-1), bc.long().T# anchors, image, class
gij = (gxy - offsets).long()
gi, gj = gij.T# grid indices# Append
indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1)))# image, anchor, grid
tbox.append(torch.cat((gxy - gij, gwh), 1))# box
anch.append(anchors[a])# anchors
tcls.append(c)# class推荐阅读
- 深度学习|训练yolov5过程中报错RuntimeError: result type Float can‘t be cast to the desired output type __int64
- python|RuntimeError: result type Float can‘t be cast to the desired output type __int64 yolov5 V6.x
- 软件安装|Anaconda虚拟环境+jupyter内核配置（详解）
- 论文|Unet论文总结
- 人工智能|10 个开源 Python OpenCV 小项目，YouTube热门
- 人工智能教程|第四课（神经网络是如何进行预测的）
- 预测模型及优化|BP神经网络时间序列预测-附代码
- 人工智能|AI遮天传 DL-多层感知机
- 人工智能|2022年最受欢迎的几本人工智能书