写一个pytorch版本的CSPGhostNet的代码

时间: 2024-03-08 18:47:39 浏览: 67

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pytorch环境下ResNet示例代码

在PyTorch环境中实现ResNet模型是深度学习领域的一个常见任务，ResNet（残差网络）因其独特的架构设计，能够解决深度神经网络中的梯度消失问题，从而在图像分类、物体检测等任务中表现出色。本示例代码将详细介绍如何在PyTorch框架下构建并训练一个基本的ResNet模型。我们需要导入必要的库，包括`torch`和`torchvision`。`torch`是PyTorch的核心库，提供了张量操作和计算图等功能；`torchvision`则包含了一些常用的图像处理和数据集。 ```python import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms ``` 接着，定义ResNet的基本构建块——残差块（Residual Block）。每个残差块包含两个卷积层，以及一个跳跃连接（skip connection），其核心思想是通过直接传递输入信号到输出，使得网络可以更容易地学习身份映射。 ```python class ResidualBlock(nn.Module): def __init__(self, in_channels, out_channels, stride=1): super(ResidualBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) if in_channels != out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_channels) ) else: self.shortcut = nn.Identity() def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) return F.relu(out) ``` 然后，我们可以构建整个ResNet模型。这里以ResNet18为例，它由四个阶段组成，每个阶段的输出通道数逐渐增加，步长(stride)在特定层改变以保持输出尺寸。 ```python class ResNet18(nn.Module): def __init__(self, num_classes=10): super(ResNet18, self).__init__() self.in_channels = 64 self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1) self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2) self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2) self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2) self.avg_pool = nn.AvgPool2d(4) self.fc = nn.Linear(512, num_classes) def make_layer(self, block, out_channels, blocks, stride=1): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels for _ in range(1, blocks): layers.append(block(out_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avg_pool(out) out = out.view(out.size(0), -1) out = self.fc(out) return out ``` 为了训练模型，我们需要准备数据集，通常使用`torchvision.datasets.CIFAR10`或`CIFAR100`，并进行预处理。接下来，定义损失函数（如交叉熵损失）和优化器（如SGD），然后进行训练和验证循环。 ```python # 数据预处理 transform = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) # 加载CIFAR10数据集 train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) train_loader = DataLoader(train_dataset, batch_size=100, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=100, shuffle=False) # 创建ResNet18模型 model = ResNet18(num_classes=10) # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) # 训练和验证模型 num_epochs = 10 for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): # 前向传播 outputs = model(images) loss = criterion(outputs, labels) # 反向传播和优化 optimizer.zero_grad() loss.backward() optimizer.step() # 验证模型 with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print(f'Epoch {epoch + 1}/{num_epochs}, Accuracy: {100 * correct / total}%') ``` 以上就是在PyTorch环境中实现ResNet18模型的基本步骤。通过这个示例，你可以理解ResNet的核心思想和结构，以及如何在实际项目中应用PyTorch进行深度学习模型的开发和训练。对于更复杂的ResNet变体，如ResNet50、ResNet101等，只需要调整模型结构中的层数量和通道数即可。

以下是基于PyTorch实现的CSPGhostNet模型代码示例： ```python import torch import torch.nn as nn class GhostModule(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True): super(GhostModule, self).__init__() self.primary_conv = nn.Sequential( nn.Conv2d(in_channels, out_channels // ratio, kernel_size, stride, kernel_size // 2, bias=False), nn.BatchNorm2d(out_channels // ratio), nn.ReLU(inplace=True) if relu else nn.Sequential() ) self.cheap_operation = nn.Sequential( nn.Conv2d(out_channels // ratio, out_channels // ratio, dw_size, 1, dw_size // 2, groups=out_channels // ratio, bias=False), nn.BatchNorm2d(out_channels // ratio), nn.ReLU(inplace=True) if relu else nn.Sequential(), nn.Conv2d(out_channels // ratio, out_channels // ratio, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels // ratio), nn.ReLU(inplace=True) if relu else nn.Sequential() ) self.fusion_conv = nn.Conv2d(out_channels // ratio, out_channels, 1, 1, 0, bias=False) self.bn = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(inplace=True) if relu else nn.Sequential() def forward(self, x): primary_out = self.primary_conv(x) cheap_out = self.cheap_operation(primary_out) fusion_out = self.fusion_conv(cheap_out) out = self.bn(fusion_out + primary_out) return self.relu(out) class CSPGhostNet(nn.Module): def __init__(self, num_classes=1000, width_mult=1.0): super(CSPGhostNet, self).__init__() self.init_channel = int(16 * width_mult) self.features = nn.Sequential( nn.Conv2d(3, self.init_channel, kernel_size=3, stride=2, padding=1, bias=False), nn.BatchNorm2d(self.init_channel), nn.ReLU(inplace=True), GhostModule(self.init_channel, int(16 * width_mult), kernel_size=3, stride=1, relu=True), nn.Conv2d(int(16 * width_mult), int(24 * width_mult), kernel_size=3, stride=2, padding=1, bias=False), nn.BatchNorm2d(int(24 * width_mult)), nn.ReLU(inplace=True), GhostModule(int(24 * width_mult), int(24 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(24 * width_mult), int(24 * width_mult), kernel_size=3, stride=1, relu=True), nn.Conv2d(int(24 * width_mult), int(40 * width_mult), kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(int(40 * width_mult)), nn.ReLU(inplace=True), GhostModule(int(40 * width_mult), int(40 * width_mult), kernel_size=3, stride=1, relu=True), nn.Conv2d(int(40 * width_mult), int(80 * width_mult), kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(int(80 * width_mult)), nn.ReLU(inplace=True), GhostModule(int(80 * width_mult), int(80 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(80 * width_mult), int(80 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(80 * width_mult), int(80 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(80 * width_mult), int(112 * width_mult), kernel_size=1, stride=1, relu=True), nn.Conv2d(int(112 * width_mult), int(160 * width_mult), kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(int(160 * width_mult)), nn.ReLU(inplace=True), GhostModule(int(160 * width_mult), int(160 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(160 * width_mult), int(160 * width_mult), kernel_size=3, stride=2, relu=True), GhostModule(int(160 * width_mult), int(160 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(160 * width_mult), int(192 * width_mult), kernel_size=1, stride=1, relu=True), GhostModule(int(192 * width_mult), int(192 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(192 * width_mult), int(192 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(192 * width_mult), int(192 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(192 * width_mult), int(240 * width_mult), kernel_size=1, stride=1, relu=True), GhostModule(int(240 * width_mult), int(240 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(240 * width_mult), int(240 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(240 * width_mult), int(240 * width_mult), kernel_size=3, stride=1, relu=True), GhostModule(int(240 * width_mult), int(200 * width_mult), kernel_size=1, stride=1, relu=True), nn.Conv2d(int(200 * width_mult), int(1280 * width_mult), kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(int(1280 * width_mult)), nn.ReLU(inplace=True), ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(int(1280 * width_mult), num_classes) def forward(self, x): x = self.features(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x ``` CSPGhostNet模型是GhostNet系列中的一种变体，它使用了CSP（cross stage partial）结构来加速模型训练和推理速度。该模型使用了Ghost Module作为基本构建模块，其中包含了一个主要的卷积层和一个轻量级的操作。在模型中，使用了多个CSP结构和特征层的跨层连接，以提高模型性能。通过设置不同的width_mult参数，可以在保持模型精度的情况下，调整模型大小。

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