def __init__(self): super(DarkNet53_conv_body, self).__init__() self.stages = DarkNet_cfg[53] self.stages = self.stages[0:5]
时间: 2023-03-29 17:04:14 浏览: 63
这是一个 Python 代码段,用于初始化 DarkNet53_conv_body 类的对象。它使用 DarkNet_cfg[53] 中的配置信息来设置网络的不同阶段,并将前五个阶段存储在 self.stages 中。
相关问题
super(DarkNet, self).__init__()
这是一个 Python 中的 super() 函数的使用示例。它用于调用父类的方法,这里的 DarkNet 类是继承自另一个类。通过使用 super() 函数,DarkNet 类可以调用其父类的 __init__() 方法,以便执行必要的初始化操作。在这个例子中,__init__() 是指 DarkNet 类的构造函数。
cv.dnn.readNet(self._modelPath)
`cv.dnn.readNet(model, config=None, framework='')` is a function in OpenCV that is used to load a pre-trained deep learning model from disk, and returns a `cv.dnn_Net` object that can be used for forward inference.
`self._modelPath` is the path to the model file, which can be in different formats supported by different deep learning frameworks such as Caffe, TensorFlow, Darknet, etc. Depending on the format of the model file, you may also need to provide a configuration file and the name of the framework as parameters.
Here is an example that loads a pre-trained face detection model using the Caffe framework:
```
import cv2 as cv
model_path = 'path/to/face_detection_model.caffemodel'
config_path = 'path/to/face_detection_model.prototxt'
net = cv.dnn.readNet(model_path, config_path, framework='Caffe')
```
You can replace the model file path and the framework name with the ones that suit your needs.