pytorch里有densenet

时间: 2024-01-17 11:04:27 浏览: 85

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使用pytorch写的Densenet代码，详细注释，可以生成训练集和测试集的损失和准确率的折线图

在本文中，我们将深入探讨如何使用PyTorch框架构建DenseNet神经网络，并结合详细的代码注释理解其实现过程。DenseNet是一种高效的卷积神经网络（CNN），它通过连接每一层的输出到后续所有层的输入来促进信息的流动，从而在减少参数数量的同时提高模型性能。 DenseNet的核心特性是稠密块（Dense Block）和过渡层（Transition Layer）。稠密块由多个卷积层组成，每个卷积层的输出都会连接到后续所有层的输入，形成密集的连接结构。过渡层则包括一个批量归一化（Batch Normalization）、一个激活函数（通常为ReLU）以及一个2x2的平均池化层，用于控制模型的复杂度并减小输出尺寸。在PyTorch中，我们首先需要导入必要的库，如`torch`, `torch.nn` 和 `torch.optim`： ```python import torch import torch.nn as nn import torch.optim as optim ``` 接下来，定义DenseBlock类，其中包含了卷积层、批量归一化和ReLU激活： ```python class DenseBlock(nn.Module): def __init__(self, in_channels, growth_rate, num_convs): super(DenseBlock, self).__init__() self.layers = nn.Sequential(*[ nn.Conv2d(in_channels + i * growth_rate, growth_rate, kernel_size=3, padding=1), nn.BatchNorm2d(growth_rate), nn.ReLU(inplace=True) for i in range(num_convs) ]) def forward(self, x): return torch.cat([x, self.layers(x)], dim=1) ``` 然后定义TransitionLayer类，包含平均池化和线性层： ```python class TransitionLayer(nn.Module): def __init__(self, in_channels, out_channels): super(TransitionLayer, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) self.bn = nn.BatchNorm2d(out_channels) self.pool = nn.AvgPool2d(kernel_size=2, stride=2) def forward(self, x): x = self.conv(self.bn(x)) x = self.pool(x) return x ``` 现在我们可以定义整个DenseNet模型，这里假设`num_classes`是分类的类别数，`growth_rate`是每个稠密块增加的通道数，`num_blocks`是每个阶段的稠密块数量，`num_convs`是每个稠密块中的卷积层数，`compress_rate`是过渡层的通道压缩比例： ```python class DenseNet(nn.Module): def __init__(self, num_classes, growth_rate, num_blocks, num_convs, compress_rate=0.5): super(DenseNet, self).__init__() self.in_channels = 64 # 输入通道数 self.growth_rate = growth_rate self.num_blocks = num_blocks self.num_convs = num_convs self.compress_rate = compress_rate # 构建网络结构 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.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.dense_block1 = self._make_dense_block(64, num_convs) self.transition1 = self._make_transition_layer(64, compress_rate) self.dense_block2 = self._make_dense_block(128, num_convs) self.transition2 = self._make_transition_layer(128, compress_rate) self.dense_block3 = self._make_dense_block(256, num_convs) self.transition3 = self._make_transition_layer(256, compress_rate) self.dense_block4 = self._make_dense_block(512, num_convs) self.bn2 = nn.BatchNorm2d(512 * growth_rate) self.avg_pool = nn.AvgPool2d(7) self.fc = nn.Linear(512 * growth_rate, num_classes) def _make_dense_block(self, num_channels, num_convs): block = nn.Sequential() for _ in range(num_convs): block.add_module('dense_layer', DenseBlock(num_channels, self.growth_rate, self.num_convs)) num_channels += self.growth_rate return block def _make_transition_layer(self, num_channels, compress_rate): return TransitionLayer(num_channels, int(num_channels * compress_rate)) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.pool(x) x = self.dense_block1(x) x = self.transition1(x) x = self.dense_block2(x) x = self.transition2(x) x = self.dense_block3(x) x = self.transition3(x) x = self.dense_block4(x) x = self.bn2(x) x = self.relu(x) x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x ``` 在训练过程中，我们需要定义损失函数（如交叉熵损失）和优化器（如Adam），并实现训练和验证循环。同时，可以使用matplotlib库绘制训练集和测试集的损失及准确率折线图，以便监控模型的训练效果。 ```python def train(model, dataloader, criterion, optimizer, device): model.train() losses, accuracies = [], [] for inputs, targets in dataloader: inputs, targets = inputs.to(device), targets.to(device) optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() _, preds = torch.max(outputs.data, 1) accuracy = torch.sum(preds == targets.data).item() / len(targets) losses.append(loss.item()) accuracies.append(accuracy) return losses, accuracies def validate(model, dataloader, criterion, device): model.eval() losses, accuracies = [], [] with torch.no_grad(): for inputs, targets in dataloader: inputs, targets = inputs.to(device), targets.to(device) outputs = model(inputs) loss = criterion(outputs, targets) _, preds = torch.max(outputs.data, 1) accuracy = torch.sum(preds == targets.data).item() / len(targets) losses.append(loss.item()) accuracies.append(accuracy) return losses, accuracies # ... 加载数据集、设置设备、定义损失函数和优化器 ... # 训练和验证模型 for epoch in range(num_epochs): train_losses, train_accuracies = train(model, train_loader, criterion, optimizer, device) val_losses, val_accuracies = validate(model, val_loader, criterion, device) # 绘制损失和准确率折线图 plot_train_val_loss(train_losses, val_losses, 'Loss') plot_train_val_acc(train_accuracies, val_accuracies, 'Accuracy') ``` 在这个例子中，我们不仅了解了DenseNet的基本结构，还学习了如何在PyTorch中实现这个模型，并使用matplotlib进行可视化。通过这种方式，我们可以更好地理解DenseNet的工作原理，以及如何在实际项目中运用它。这个DenseNet实现的代码详细注释有助于初学者掌握深度学习框架和网络结构，进一步提升他们在深度学习领域的技能。

在PyTorch中，确实有DenseNet模型的实现。你可以使用PyTorch的torchvision库来加载和使用DenseNet模型。以下是一个示例代码，展示了如何在PyTorch中使用DenseNet模型： ```python import torch import torchvision.models as models # 加载预训练的DenseNet模型 model = models.densenet121(pretrained=True) # 将模型设置为评估模式 model.eval() # 输入数据 input_data = torch.randn(1, 3, 224, 224) # 前向传播 output = model(input_data) # 打印输出的形状 print(output.shape) ``` 在这个示例中，我们使用`models.densenet121(pretrained=True)`来加载预训练的DenseNet-121模型。然后，我们将模型设置为评估模式，并传入输入数据进行前向传播。最后，我们打印输出的形状。请注意，PyTorch中还提供了其他版本的DenseNet模型，如DenseNet-169、DenseNet-201和DenseNet-264。你可以根据需要选择适合的模型。 \[1\]: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py \[2\]: https://arxiv.org/abs/1608.06993 \[3\]: https://zhuanlan.zhihu.com/p/41423739 #### 引用[.reference_title] - *1* *2* *3* [【深度学习基础】PyTorch实现DenseNet亲身实践](https://blog.csdn.net/EasonCcc/article/details/108602022)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] [ .reference_list ]

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