class Model(nn.Module): def init(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes super().init() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.safe_load(f) # model dict # Define model ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels if nc and nc != self.yaml['nc']: LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") self.yaml['nc'] = nc # override yaml value if anchors: LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') self.yaml['anchors'] = round(anchors) # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml['nc'])] # default names self.inplace = self.yaml.get('inplace', True)

这是一个使用 PyTorch 框架实现的 YOLOv5 模型，可以根据配置文件加载模型并对输入图像进行目标检测。其中，`init` 函数接受四个参数，分别是配置文件路径 `cfg`、输入图像通道数 `ch`、目标类别数 `nc` 和锚框信息 `anchors`。如果配置文件是一个字典，直接使用该字典初始化模型；否则，从配置文件中加载模型参数并初始化模型。在初始化过程中，还会根据输入参数更新模型配置。模型的前向传播过程在 `forward` 函数中实现。