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- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊
目标
具体实现
（一）环境
语言环境：Python 3.10
编 译 器: PyCharm
框 架: Pytorch
（二）具体步骤
1. DenseNet121.py
import torch
import torch.nn as nn
import torch.nn.functional as F
import math # 实现DenseLayer（密集连接层）
class DenseLayer(nn.Module): def __init__(self, num_input_features, growth_rate, bn_size, drop_rate): super(DenseLayer, self).__init__() # BN -> ReLU -> Conv(1x1) -> BN -> ReLU -> Conv(3x3) self.norm1 = nn.BatchNorm2d(num_input_features) # 第一个批归一化层 self.relu1 = nn.ReLU(inplace=True) # 第一个ReLU激活函数 self.conv1 = nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False) # 1x1卷积层 self.norm2 = nn.BatchNorm2d(bn_size * growth_rate) # 第二个批归一化层 self.relu2 = nn.ReLU(inplace=True) # 第二个ReLU激活函数 self.conv2 = nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False) # 3x3卷积层 self.drop_rate = drop_rate # Dropout率 def forward(self, x): # 保存输入特征，用于后续的密集连接 new_features = self.norm1(x) new_features = self.relu1(new_features) new_features = self.conv1(new_features) new_features = self.norm2(new_features) new_features = self.relu2(new_features) new_features = self.conv2(new_features) # 如果设置了dropout，则应用dropout if self.drop_rate > 0: new_features = F.dropout(new_features, p=self.drop_rate, training=self.training) # 将新特征与输入特征进行拼接，实现密集连接 return torch.cat([x, new_features], 1) # 实现DenseBlock（密集块）
class DenseBlock(nn.Module): def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate): super(DenseBlock, self).__init__() # 创建指定数量的DenseLayer，每一层的输入特征数量都会增加 self.layers = nn.ModuleList() for i in range(num_layers): layer = DenseLayer( num_input_features + i * growth_rate, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate ) self.layers.append(layer) def forward(self, x): # 依次通过所有的DenseLayer features = x for layer in self.layers: features = layer(features) return features # 实现TransitionLayer（过渡层）
class TransitionLayer(nn.Module): def __init__(self, num_input_features, num_output_features): super(TransitionLayer, self).__init__() # BN -> Conv(1x1) -> AvgPool(2x2) self.norm = nn.BatchNorm2d(num_input_features) # 批归一化层 self.relu = nn.ReLU(inplace=True) # ReLU激活函数 self.conv = nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False) # 1x1卷积层 self.pool = nn.AvgPool2d(kernel_size=2, stride=2) # 平均池化层 def forward(self, x): x = self.norm(x) x = self.relu(x) x = self.conv(x) x = self.pool(x) return x # 实现完整的DenseNet121模型
class DenseNet121(nn.Module): def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000): super(DenseNet121, self).__init__() # 首先是一个7x7的卷积层，步长为2 self.features = nn.Sequential() self.features.add_module('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)) # 初始卷积层 self.features.add_module('norm0', nn.BatchNorm2d(num_init_features)) # 批归一化层 self.features.add_module('relu0', nn.ReLU(inplace=True)) # ReLU激活函数 self.features.add_module('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) # 最大池化层 # 依次添加DenseBlock和TransitionLayer num_features = num_init_features for i, num_layers in enumerate(block_config): # 添加DenseBlock block = DenseBlock( num_layers=num_layers, num_input_features=num_features, bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate ) self.features.add_module(f'denseblock{i + 1}', block) num_features = num_features + num_layers * growth_rate # 如果不是最后一个block，则添加TransitionLayer if i != len(block_config) - 1: # 过渡层将特征图数量减半 trans = TransitionLayer( num_input_features=num_features, num_output_features=num_features // 2 ) self.features.add_module(f'transition{i + 1}', trans) num_features = num_features // 2 # 最后添加一个BatchNorm self.features.add_module('norm5', nn.BatchNorm2d(num_features)) # 最终的批归一化层 # 全局平均池化和分类器 self.classifier = nn.Linear(num_features, num_classes) # 全连接分类器 # 初始化权重 for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight) # 使用Kaiming初始化卷积层权重 elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) # 初始化批归一化层的权重为1 nn.init.constant_(m.bias, 0) # 初始化批归一化层的偏置为0 elif isinstance(m, nn.Linear): nn.init.constant_(m.bias, 0) # 初始化全连接层的偏置为0 def forward(self, x): features = self.features(x) # 提取特征 out = F.relu(features, inplace=True) # 应用ReLU激活函数 out = F.adaptive_avg_pool2d(out, (1, 1)) # 全局平均池化 out = torch.flatten(out, 1) # 展平特征 out = self.classifier(out) # 分类 return out # 创建DenseNet121模型实例
def create_densenet121(num_classes=1000, pretrained=False): model = DenseNet121(num_classes=num_classes) return model # 使用示例
if __name__ == "__main__": # 创建模型 model = create_densenet121() print(model) # 创建随机输入张量 (batch_size, channels, height, width) x = torch.randn(1, 3, 224, 224) # 前向传播 output = model(x) print(f"Input shape: {x.shape}") print(f"Output shape: {output.shape}")
