基于yolov8在hisi3536a推理

# Yolov8-Hisi yolov8在hisi3536a推理，./build.sh 编译即可。 ## yolov8源码需要修改部分 ### DFL模块修改 ```python class DFL(nn.Module): # Integral module of Distribution Focal Loss (DFL) # Proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391 def __init__(self, c1=16): super().__init__() self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False) x = torch.arange(c1, dtype=torch.float) self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1)) self.c1 = c1 def forward(self, x): b, c, a = x.shape # batch, channels, anchors return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a) # return self.conv(x.view(b, self.c1, 4, a).softmax(1)).view(b, 4, a) ``` 更换为如下： ```python class DFL(nn.Module): # Integral module of Distribution Focal Loss (DFL) # Proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391 def __init__(self, c1=16): super().__init__() self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False) x = torch.arange(c1, dtype=torch.float) self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1)) self.c1 = c1 def forward(self, x): b, c, h, w = x.shape # batch, channels, anchors return self.conv(x.permute(0,2,3,1).view(b, h*w, 4, c//4).softmax(3).permute(0,3,1,2)).view(b, h*w, 4) ``` ### Detect的forward修改为如下： ```python def forward(self, x): shape = x[0].shape # BCHW if self.export == True: for i in range(self.nl): boxs = self.dfl(self.cv2[i](x[i])) print(boxs.shape) cls = self.cv3[i](x[i]).permute(0,2,3,1) b,h,w,c = cls.shape # print(b,h,w,c) cls = cls.view(b, h*w, c) print(cls.shape) x[i] = torch.cat(((boxs, cls.sigmoid())), 2) return x else: for i in range(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 x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2) if self.export and self.format in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs'): # avoid TF FlexSplitV ops box = x_cat[:, :self.reg_max * 4] cls = x_cat[:, self.reg_max * 4:] else: box, cls = x_cat.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) ``` ## 导出模型 ```python path = "/home/huangzm/code/mycode/pytorch/ultralytics/runs/detect/GWM/weights/best.pt" yolo = YOLO(path) success = yolo.export(format="onnx",simplify = True,imgsz=[384,640]) 导出模型后在海思工具上转换om模型即可在本demo代码上使用。 ```