基于YOLOV5 的多分类 + 关键点检测_yolo关键点检测

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一 模型介绍

yoloV5主要是用于目标的检测，针对检测过程中，一些关键点的输出也是至关重要，其中有yolo-face等目标检测+关键点模型，但是目前所有模型都是单分类+关键点的检测，为了设置多分类+关键点检测，这里我在使用单分类+关键点（yolov5-car-plate）的代码基础上进行修改，实现多分类+关键点检测。

二 模型修改

1 数据代码修改

数据代码修改主要是在 utils/plate_datasets.py 代码下

• 读取数据部分
  首先修改 LoadImagesAndLabels 类中方法，添加426行代码
  

读取数据报错，做如下修改

按照制作数据进行依次修改读取数据代码：

由于我自己的数据标签依次是 ：

class，x , y ,w,h, x1 ,y1, x2, y2, x3, y3, x4, y4


所以这部分是没有修改的，这里根据自己的关键点数量进行修改。 
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数据示例：

• 数据增强-马赛克增强
  我自己的数据和作者数据是一样的4个点，所以马赛克增强也是一样的，大家根据自己的点进行修改
  

• 输入数据维度修改
  

2 网络结构修改

网络结构代码位于 model/yolo_plate.py

• 修改总输出维度
  
  每个维度输出多4个点，也就是8个输出

• 网络结构修改
  
  主要是确定点的输出位置，后面根据点的输出计算loss

• 完整修改代码

class Detect(nn.Module):
    stride = None  # strides computed during build
    export = False  # onnx export

    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
        super(Detect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5 + 8 # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
        self.register_buffer('anchors', a)  # shape(nl,na,2)
        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv

    def forward(self, x):
        # x = x.copy()  # for profiling
        z = []  # inference output
        self.training |= self.export
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            if not self.training:  # inference
                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                y = x[i].sigmoid()
                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh

                # #landmarks
                y[..., 5+self.nc:13+self.nc] = y[..., 5+self.nc:13+self.nc] * 4 - 2
                y[..., 5+self.nc:7+self.nc] = y[..., 5+self.nc:7+self.nc] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] # landmark x1 y1
                y[..., 7+self.nc:9+self.nc] = y[..., 7+self.nc:9+self.nc] * self.anchor_grid[i]  + self.grid[i].to(x[i].device) * self.stride[i]# landmark x2 y2
                y[..., 9+self.nc:11+self.nc] = y[..., 9+self.nc:11+self.nc] * self.anchor_grid[i]  + self.grid[i].to(x[i].device) * self.stride[i]# landmark x3 y3
                y[..., 11+self.nc:13+self.nc] = y[..., 11+self.nc:13+self.nc] * self.anchor_grid[i]  + self.grid[i].to(x[i].device) * self.stride[i]# landmark x4 y4

                z.append(y.view(bs, -1, self.no))
                #print('conf: ', y[..., 4])

        return x if self.training else (torch.cat(z, 1), x)

    @staticmethod
    def _make_grid(nx=20, ny=20):
        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()


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3 loss代码修改

loss修改代码位于： utils/plate_loss.py

框出来的位置注意查看，代码修改主要是确定坐标点的位置，然后将坐标点进行loss计算，进行反向传播。

# 修改后的__call__代码块 ：

    def __call__(self, p, targets):  # predictions, targets, model
        device = targets.device
        lcls, lbox, lobj, lmark = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
        tcls, tbox, indices, anchors, tlandmarks, lmks_mask = self.build_targets(p, targets)  # targets

        # Losses
        for i, pi in enumerate(p):  # layer index, layer predictions
            b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj

            n = b.shape[0]  # number of targets
            #print('pi: ', pi.shape)
            if n:
                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets

                # Regression
                pxy = ps[:, :2].sigmoid() * 2. - 0.5
                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
                pbox = torch.cat((pxy, pwh), 1)  # predicted box
                iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
                lbox += (1.0 - iou).mean()  # iou loss

                # Objectness
                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
                #print('tobj: ',tobj[b, a, gj, gi] )

                # Classification
                if self.nc > 1:  # cls loss (only if multiple classes)
                    t = torch.full_like(ps[:, 5:5+self.nc], self.cn, device=device)  # targets
                    t[range(n), tcls[i]] = self.cp
                    lcls += self.BCEcls(ps[:, 5:5+self.nc], t)  # BCE


                plandmarks = ps[:,5+self.nc:13+self.nc].sigmoid() * 4. - 2.

                plandmarks[:, 0:2] = plandmarks[:, 0:2] * anchors[i]
                plandmarks[:, 2:4] = plandmarks[:, 2:4] * anchors[i]
                plandmarks[:, 4:6] = plandmarks[:, 4:6] * anchors[i]
                plandmarks[:, 6:8] = plandmarks[:, 6:8] * anchors[i]

                lmark += self.landmarks_loss(plandmarks, tlandmarks[i], lmks_mask[i])
            #print('tobj: ',tobj)
            obji = self.BCEobj(pi[..., 4], tobj)
            lobj += obji * self.balance[i]  # obj loss
            if self.autobalance:
                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()

        if self.autobalance:
            self.balance = [x / self.balance[self.ssi] for x in self.balance]
        lbox *= self.hyp['box']
        lobj *= self.hyp['obj']
        lcls *= self.hyp['cls']
        lmark *= self.hyp['landmark']

        bs = tobj.shape[0]  # batch size

        loss = lbox + lobj + lcls + lmark
        return loss * bs, torch.cat((lbox, lobj, lcls, lmark, loss)).detach()
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三 注意事项

1 训练问题，原代码没有修改val的代码，所以val的过程是错误的。

增加 non_max_suppression_landmark 方法修改val过程中nms计算
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 # 新建non_max_suppression_landmark 方法，  注意阅读代码后半部分文字。
  
def non_max_suppression_landmark(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=True, labels=()):
    """Performs Non-Maximum Suppression (NMS) on inference results

    Returns:
         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
    """

    nc = prediction.shape[2] - 13  # number of classes
    xc = prediction[..., 4] > conf_thres  # candidates
    #print('xc: ', prediction[..., 4] )

    # Settings
    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
    max_det = 300  # maximum number of detections per image
    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
    time_limit = 10.0  # seconds to quit after
    redundant = True  # require redundant detections
    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
    merge = False  # use merge-NMS

    t = time.time()
    output = [torch.zeros((0, nc +14 ), device=prediction.device)] * prediction.shape[0]
    for xi, x in enumerate(prediction):  # image index, image inference
        # Apply constraints
        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
        x = x[xc[xi]]  # confidence

        # Cat apriori labels if autolabelling
        if labels and len(labels[xi]):
            l = labels[xi]
            v = torch.zeros((len(l), nc + 13), device=x.device)
            v[:, :4] = l[:, 1:5]  # box
            v[:, 4] = 1.0  # conf
            v[range(len(l)), l[:, 0].long() + nc + 13] = 1.0  # cls
            x = torch.cat((x, v), 0)

        # If none remain process next image
        if not x.shape[0]:
            continue

        # Compute conf
        x[:, 5:5+nc] *= x[:, 4:5]  # conf = obj_conf * cls_conf

        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
        box = xywh2xyxy(x[:, :4])



        # Detections matrix nx6 (xyxy, conf, landmarks, cls)
        if multi_label:
            i, j = (x[:, 5:5+nc] > conf_thres).nonzero(as_tuple=False).T
            x = torch.cat((box[i], x[i, j + 5, None], x[i, 5:13+nc], j[:, None].float()), 1)   # 5+nc:12+nc
        else:  # best class only
            conf, j = x[:, 5:5+nc].max(1, keepdim=True)
            x = torch.cat((box, conf, x[:, 5:13+nc], j.float()), 1)[conf.view(-1) > conf_thres]

        # Filter by class
        if classes is not None:
            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]

        # Apply finite constraint
        # if not torch.isfinite(x).all():
        #     x = x[torch.isfinite(x).all(1)]

        # Check shape
        n = x.shape[0]  # number of boxes
        if not n:  # no boxes
            continue
        elif n > max_nms:  # excess boxes
            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence

        # Sort by confidence
        # x = x[x[:, 4].argsort(descending=True)]

        # Batched NMS
        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
        if i.shape[0] > max_det:  # limit detections
            i = i[:max_det]
        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
            try:# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
                iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
                weights = iou * scores[None]  # box weights
                x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
                if redundant:
                    i = i[iou.sum(1) > 1]  # require redundancy
            except:
                print(x, i, x.shape, i.shape)
                pass

        # 默认输出
        # output[xi] = x[i]
        # 继续对结果后处理，提升低阈值下性能（0.4）

        # 用于推理,降低框的重叠
        # 建议训练过程注释掉681-693  会大大降低val速度
        tmpx = x[i]
        # 同类别框相互包裹，去除低置信度，减少误检
        boxes, scores = tmpx[:, :4], tmpx[:, 4]
        ioumin = box_iou_min(boxes, boxes)
        cles = tmpx[:, -1]
        for class1 in range(len(cles)):
            for class2 in range(len(cles)):
                if scores[class1]>0.1 and scores[class2]>0.1 and class1 != class2 and cles[class1] == cles[class2] and ioumin[class1][class2] >0.85:
                # if scores[class1] > 0.1 and scores[class2] > 0.1 and class1 != class2 and ioumin[class1][class2] > 0.85:
                    if scores[class2] > scores[class1]:
                        scores[class1] = 0
                    else:
                        scores[class2] = 0
        output[xi] = tmpx[scores>0.1]  #outputs

    return output


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修改test.py

正常情况应该是不会报错，都修改完毕了~
大家可以实际动手试试，如果有问题后台私聊我哟~
后面代码整理好了，会把链接更新上来。

参考代码

yolov5-car-plate ：单分类+4个点输出 ，https://github.com/xialuxi/yolov5-car-plate