基于pytorch实现的 3D DenseNet 代码
时间: 2023-07-30 20:04:47 浏览: 134
DenseNet.zip
以下是基于PyTorch实现的3D DenseNet代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, in_channels, growth_rate):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm3d(in_channels)
self.conv1 = nn.Conv3d(in_channels, 4 * growth_rate, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm3d(4 * growth_rate)
self.conv2 = nn.Conv3d(4 * growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)
def forward(self, x):
out = self.conv1(F.relu(self.bn1(x)))
out = self.conv2(F.relu(self.bn2(out)))
out = torch.cat([out, x], 1)
return out
class DenseBlock(nn.Module):
def __init__(self, in_channels, growth_rate, num_layers):
super(DenseBlock, self).__init__()
self.layers = nn.ModuleList([Bottleneck(in_channels + i * growth_rate, growth_rate) for i in range(num_layers)])
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
class Transition(nn.Module):
def __init__(self, in_channels, out_channels):
super(Transition, self).__init__()
self.bn = nn.BatchNorm3d(in_channels)
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=1, bias=False)
self.pool = nn.AvgPool3d(kernel_size=2, stride=2)
def forward(self, x):
out = self.conv(F.relu(self.bn(x)))
out = self.pool(out)
return out
class DenseNet3D(nn.Module):
def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_classes=100):
super(DenseNet3D, self).__init__()
self.conv1 = nn.Conv3d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm3d(64)
self.pool1 = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
self.dense1 = DenseBlock(64, growth_rate, block_config[0])
self.trans1 = Transition(256, 128)
self.dense2 = DenseBlock(128, growth_rate, block_config[1])
self.trans2 = Transition(512, 256)
self.dense3 = DenseBlock(256, growth_rate, block_config[2])
self.trans3 = Transition(1024, 512)
self.dense4 = DenseBlock(512, growth_rate, block_config[3])
self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1))
self.fc = nn.Linear(1024, num_classes)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = F.relu(out)
out = self.pool1(out)
out = self.dense1(out)
out = self.trans1(out)
out = self.dense2(out)
out = self.trans2(out)
out = self.dense3(out)
out = self.trans3(out)
out = self.dense4(out)
out = self.avgpool(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
```
这个代码实现了一个3D DenseNet模型,与2D DenseNet相似,但在处理3D数据时使用了3D卷积和3D池化操作。模型中的Bottleneck、DenseBlock、Transition和DenseNet部分与2D版本相同,只是相关操作被调整为适应3D数据。在实现过程中,我们使用了PyTorch提供的3D版本的各种模块和函数,如nn.BatchNorm3d、nn.Conv3d、nn.AvgPool3d、nn.AdaptiveAvgPool3d等,这些工具和接口可用于构建和训练3D DenseNet模型。
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