用pytorch写一个目标检测网络
时间: 2024-05-02 19:18:45 浏览: 10
抱歉,我是一名语言模型,无法编写代码。不过,以下是一个简单的目标检测网络的示例,供参考:
```
import torch
import torch.nn as nn
class ObjectDetectionNet(nn.Module):
def __init__(self, num_classes):
super(ObjectDetectionNet, self).__init__()
self.num_classes = num_classes
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.relu1 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.relu2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.relu3 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.relu4 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(512 * 7 * 7, 4096)
self.relu5 = nn.ReLU(inplace=True)
self.dropout1 = nn.Dropout()
self.fc2 = nn.Linear(4096, 4096)
self.relu6 = nn.ReLU(inplace=True)
self.dropout2 = nn.Dropout()
self.fc3 = nn.Linear(4096, self.num_classes * 5)
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool2(x)
x = self.conv3(x)
x = self.relu3(x)
x = self.pool3(x)
x = self.conv4(x)
x = self.relu4(x)
x = self.pool4(x)
x = x.view(x.size(0), -1)
x = self.fc1(x)
x = self.relu5(x)
x = self.dropout1(x)
x = self.fc2(x)
x = self.relu6(x)
x = self.dropout2(x)
x = self.fc3(x)
return x
```
这个网络采用了四个卷积层和三个全连接层。输出是一个大小为`[batch_size, num_classes * 5]`的张量,其中每个目标都包含5个元素:类别、置信度和四个坐标(左上角和右下角)。您可以使用这个网络来训练和测试目标检测任务。