膨胀卷积pytorch实现
时间: 2023-12-11 14:33:19 浏览: 67
以下是使用PyTorch实现膨胀卷积的示例代码:
```python
import torch
import torch.nn as nn
# 定义一个包含膨胀卷积的神经网络模型
class DilatedConvNet(nn.Module):
def __init__(self):
super(DilatedConvNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, dilation=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, dilation=2, padding=2)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, dilation=4, padding=4)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, dilation=8, padding=8)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, dilation=16, padding=16)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc = nn.Linear(in_features=1024, out_features=10)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = self.pool(x)
x = nn.functional.relu(self.conv2(x))
x = self.pool(x)
x = nn.functional.relu(self.conv3(x))
x = self.pool(x)
x = nn.functional.relu(self.conv4(x))
x = self.pool(x)
x = nn.functional.relu(self.conv5(x))
x = nn.functional.avg_pool2d(x, kernel_size=x.size()[2:])
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
# 定义一个输入数据并进行膨胀卷积
input_data = torch.randn(1, 3, 32, 32)
model = DilatedConvNet()
output = model(input_data)
print(output.shape) # 输出:torch.Size([1, 10])
```
在这个示例中,我们定义了一个包含膨胀卷积的神经网络模型,并使用随机生成的输入数据进行了膨胀卷积。在模型中,我们使用了5个膨胀卷积层,每个卷积层的膨胀率不同,以便提取不同尺度的特征。最后,我们使用全局平均池化和全连接层对特征进行分类。
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