yolov8 瑞芯微RKNN和地平线Horizon芯片仿真测试部署

特别说明：参考官方开源的yolov8代码、瑞芯微官方文档、地平线的官方文档，如有侵权告知删，谢谢。

模型和完整仿真测试代码，放在github上参考链接 模型和代码。

跟上技术的步伐，yolov8 首个板端芯片部署。

1 模型和训练

  训练代码参考官方开源的yolov8训练代码，由于SiLU在有些板端芯片上还不支持，因此将其改为ReLU。

2 导出 yolov8 onnx

   后处理中有些算在板端芯片上效率低或者不支持，导出 onnx 需要将板端芯片不友好或不支持算子规避掉。导出onnx修改的部分。

第一步：
进行预测将pt只保存权重，增加代码如下图。

# 保存权重值import torch
        self.model.fuse()
        self.model.eval()
        torch.save(self.model.state_dict(),'./weights/Yolov8_dict.pt')# self.model.load_state_dict(torch.load('./weights/Yolov8_dict.pt', map_location='cpu'))

修改后运行以下代码：

from ultralytics import YOLO
model = YOLO('./weights/yolov8n_coco128.pt')
results = model(task='detect', mode='predict', source='./images/test.jpg', line_thickness=3, show=True, save=True, device='cpu')

第二步：
导出onnx，去除不需要的算子。修改代码如下。

# headsclassDetect(nn.Module):# YOLOv8 Detect head for detection models
    dynamic =False# force grid reconstruction
    export =False# export mode
    shape =None
    anchors = torch.empty(0)# init
    strides = torch.empty(0)# initdef__init__(self, nc=80, ch=()):# detection layersuper().__init__()
        self.nc = nc  # number of classes
        self.nl =len(ch)# number of detection layers
        self.reg_max =16# DFL channels (ch[0] // 16 to scale 4/8/12/16/20 for n/s/m/l/x)
        self.no = nc + self.reg_max *4# number of outputs per anchor
        self.stride = torch.zeros(self.nl)# strides computed during build
        c2, c3 =max((16, ch[0]//4, self.reg_max *4)),max(ch[0], self.nc)# channels
        self.cv2 = nn.ModuleList(
            nn.Sequential(Conv(x, c2,3), Conv(c2, c2,3), nn.Conv2d(c2,4* self.reg_max,1))for x in ch)
        self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3,3), Conv(c3, c3,3), nn.Conv2d(c3, self.nc,1))for x in ch)
        self.dfl = DFL(self.reg_max)if self.reg_max >1else nn.Identity()# 导出 onnx 增加
        self.conv1x1 = nn.Conv2d(16,1,1, bias=False).requires_grad_(False)
        x = torch.arange(16, dtype=torch.float)
        self.conv1x1.weight.data[:]= nn.Parameter(x.view(1,16,1,1))defforward(self, x):
        shape = x[0].shape  # BCHW
        y =[]for i inrange(self.nl):
            t1 = self.cv2[i](x[i])
            t2 = self.cv3[i](x[i])
            y.append(self.conv1x1(t1.view(t1.shape[0],4,16,-1).transpose(2,1).softmax(1)))# y.append(t2.sigmoid())
            y.append(t2)return y
        for i inrange(self.nl):
            x[i]= torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])),1)if self.training:return x
        elif self.dynamic or self.shape != shape:
            self.anchors, self.strides =(x.transpose(0,1)for x in make_anchors(x, self.stride,0.5))
            self.shape = shape
        box, cls = torch.cat([xi.view(shape[0], self.no,-1)for xi in x],2).split((self.reg_max *4, self.nc),1)
        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1)* self.strides
        y = torch.cat((dbox, cls.sigmoid()),1)return y if self.export else(y, x)defbias_init(self):# Initialize Detect() biases, WARNING: requires stride availability
        m = self  # self.model[-1]  # Detect() module# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequencyfor a, b, s inzip(m.cv2, m.cv3, m.stride):# from
            a[-1].bias.data[:]=1.0# box
            b[-1].bias.data[:m.nc]= math.log(5/ m.nc /(640/ s)**2)# cls (.01 objects, 80 classes, 640 img)

增加保存onnx模型代码，如下：

# 导出 onnx 增加import torch
        self.model.fuse()
        self.model.eval()
        self.model.load_state_dict(torch.load('./weights/Yolov8_dict.pt', map_location='cpu'), strict=False)print("===========  onnx =========== ")
        dummy_input = torch.randn(1,3,640,640)
        input_names =["data"]
        output_names =["cls1","reg1","cls2","reg2","cls3","reg3"]
        torch.onnx.export(self.model, dummy_input,"./weights/yolov8n_ZQ.onnx", verbose=False, input_names=input_names, output_names=output_names, opset_version=11)print("======================== convert onnx Finished! .... ")

以上修改后完运行以下代码：

from ultralytics import YOLO
model = YOLO('./ultralytics/models/v8/yolov8n.yaml')
results = model(task='detect', mode='predict', source='./images/test3.jpg', line_thickness=3, show=False, save=True, device='cpu')

3 yolov8 onnx 测试效果

onnx模型和测试完整代码，放在github上代码。

注：图片来源coco128

4 yolov8导出瑞芯微rknn和地平线horizon仿真测试

4.1 瑞芯微 rknn 仿真

  瑞芯微环境搭建和详细步骤参考上一篇 【瑞芯微RKNN模型转换和PC端仿真】。
  yolov8导出rknn模型代码和后处理参考 yolov8_rknn

4.2 地平线仿真

  地平线环境搭建和详细步骤参考上一篇 【地平线Horizon模型转换和PC端仿真测试】。
  yolov8导出地平线模型代码和后处理参考 yolov8_horizon