Desnet pytorch实现

### DenseNet 实现概述 DenseNet 是一种卷积神经网络架构，在其中每一层与其后的每层直接相连。这种连接模式有助于缓解梯度消失问题并促进特征重用。在 PyTorch 中实现 DenseNet 可以通过定义相应的类来构建模型。 以下是基于官方文档和社区贡献的一个简化版 DenseNet 的实现方法[^1]： ```python import torch.nn as nn import torch class _DenseLayer(nn.Module): def __init__(self, num_input_features, growth_rate, bn_size, drop_rate): super(_DenseLayer, self).__init__() self.add_module('norm1', nn.BatchNorm2d(num_input_features)), self.add_module('relu1', nn.ReLU(inplace=True)), self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)), self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)), self.add_module('relu2', nn.ReLU(inplace=True)), self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)), self.drop_rate = float(drop_rate) def forward(self, x): new_features = super(_DenseLayer, self).forward(x) if self.drop_rate > 0: new_features = F.dropout(new_features, p=self.drop_rate, training=self.training) return torch.cat([x, new_features], 1) class _DenseBlock(nn.Sequential): def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate): super(_DenseBlock, self).__init__() for i in range(num_layers): layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate) self.add_module('denselayer%d' % (i + 1), layer) class DenseNet(nn.Module): r"""Densenet-BC model class""" 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(DenseNet, self).__init__() # First convolution self.features = nn.Sequential(OrderedDict([ ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)), ('norm0', nn.BatchNorm2d(num_init_features)), ('relu0', nn.ReLU(inplace=True)), ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), ])) # Each denseblock num_features = num_init_features for i, num_layers in enumerate(block_config): 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('denseblock%d' % (i + 1), block) num_features = num_features + num_layers * growth_rate if i != len(block_config) - 1: trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2) self.features.add_module('transition%d' % (i + 1), trans) num_features = num_features // 2 # Final batch norm self.features.add_module('norm5', nn.BatchNorm2d(num_features)) # Linear layer self.classifier = nn.Linear(num_features, num_classes) # Official init from torch repo. for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight.data) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.bias.data.zero_() def forward(self, x): features = self.features(x) out = F.relu(features, inplace=True) out = F.adaptive_avg_pool2d(out, (1, 1)).view(features.size(0), -1) out = self.classifier(out) return out ``` 此代码片段展示了如何利用 PyTorch 构建一个基础的 DenseNet 模型，包括密集块 `_DenseBlock` 和过渡层 `_Transition` 的定义以及整体网络结构 `DenseNet` 类的设计。为了使该模型能够正常工作，还需要导入必要的库，并确保数据预处理阶段的数据已经被转换成适合 PyTorch 处理的形式[^2]。