目标检测YOLOv5运行遇到的问题及解决方法

 我此处下载YOLOv5版本的v5.0。

运行detect.py报错：

（1）AttributeError: Can't get attribute 'SPPF' on

解决方法：

        去V6版本里面的model/common.py里面去找到这个SPPF的类,把它拷过来到你的这个V5的model/common.py里面,这样你的代码就也有这个类了。然后在common.py中引入warnings包就可以了。

直接复制粘贴下面这段代码即可 ：

class SPPF(nn.Module):
    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * 4, c2, 1, 1)
        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)

    def forward(self, x):
        x = self.cv1(x)
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
            y1 = self.m(x)
            y2 = self.m(y1)
            return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))

（2）RuntimeError: The size of tensor a (80) must match the size of tensor b (56) at non-singleton dimension 3

解决方法：

        这是由于每一个版本的模型有差异，例如，你运行v5.0，但默认下载的是最新版本的yolov5s.pt就会这样(默认下载的是V6.1的)，所以你需要到运行版本的assets下载对应的模型。

（3）OSError: [WinError 1455] 页面文件太小，无法完成操作。Error loading"xxx\caffe2_detectron_ops_gpu.dll" or one of its dependencies.

解决方法：

        按住CTRL，鼠标点一下这个data_loader，就跳转到了名为data_processor.py的源码中，在其中就能看到torch.utils.data.DataLoader()的函数，把其中的参数num_workers改为0即可。GPU不足就减小batchsize。

（4）RuntimeError: result type Float can't be cast to the desired output type __int64

        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。 

复制下面的for函数替换utils/loss.py中最后那段for函数即可。

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

        return tcls, tbox, indices, anch