PyTorch搭建DenseNet
介绍
在深度学习领域，DenseNet是一种非常流行的卷积神经网络架构。它通过直接连接不同层的特征图来加强信息的传递和重用，使得网络更加紧凑且易于训练。本文将介绍如何使用PyTorch框架搭建一个DenseNet网络，并提供相应的代码示例。
导入相关库
首先，我们需要导入PyTorch和其他必要的库。
import torch
import torch.nn as nn
import torch.optim as optim
构建基本模块
我们将首先构建DenseNet的基本模块——DenseBlock和TransitionBlock。
DenseBlock
DenseBlock是DenseNet的核心部分，由多个密集连接的卷积层组成。每个卷积层的输入是前面所有层的输出的连结。
class DenseBlock(nn.Module):
def __init__(self, in_channels, growth_rate, num_layers):
super(DenseBlock, self).__init__()
self.layers = nn.ModuleList()
for i in range(num_layers):
in_channels += i * growth_rate
self.layers.append(self._make_dense_layer(in_channels, growth_rate))
def _make_dense_layer(self, in_channels, growth_rate):
return nn.Sequential(
nn.BatchNorm2d(in_channels),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels, growth_rate, kernel_size=3, padding=1, bias=False)
)
def forward(self, x):
for layer in self.layers:
out = layer(x)
x = torch.cat((x, out), dim=1)
return x
TransitionBlock
TransitionBlock用于将DenseBlock的输出通道数减少，并进行下采样。
class TransitionBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(TransitionBlock, self).__init__()
self.transition = nn.Sequential(
nn.BatchNorm2d(in_channels),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
nn.AvgPool2d(kernel_size=2, stride=2)
)
def forward(self, x):
return self.transition(x)
构建DenseNet
我们将使用上述基本模块构建整个DenseNet网络。
class DenseNet(nn.Module):
def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_classes=10):
super(DenseNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
num_channels = 64
self.dense_blocks = nn.ModuleList()
self.transition_blocks = nn.ModuleList()
for i, num_layers in enumerate(block_config):
block = DenseBlock(num_channels, growth_rate, num_layers)
self.dense_blocks.append(block)
num_channels += num_layers * growth_rate
if i != len(block_config) - 1:
transition = TransitionBlock(num_channels, num_channels // 2)
self.transition_blocks.append(transition)
num_channels = num_channels // 2
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(num_channels, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
for dense_block, transition_block in zip(self.dense_blocks, self.transition_blocks):
x = dense_block(x)
x = transition_block(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
流程图
下面是整个DenseNet网络的流程图：
flowchart TD
A[输入] --> B[卷积层]
B --> C[Batch Normalization]
C --> D[ReLU]
D --> E[最大池化]
E --> F[密集块1]
F --> G[过渡块1]
G --> H[密集块2]
H --> I[过渡块2]
I --> J[密集块3]
