基于pytorch搭建resnet模型的步骤

好的，下面是基于PyTorch搭建ResNet模型的步骤：

1. 导入必要的库和模块

   import torch.nn as nn

2. 定义ResNet的基本模块

ResNet的基本模块是BasicBlock，包含两个卷积层和一个shortcut连接。其中，shortcut连接可以是一个恒等映射或者一个1×1卷积层。这里我们使用恒等映射作为shortcut连接。

   class BasicBlock(nn.Module):
       expansion = 1
   
       def __init__(self, in_planes, planes, stride=1):
           super(BasicBlock, self).__init__()
           self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
           self.bn1 = nn.BatchNorm2d(planes)
           self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
           self.bn2 = nn.BatchNorm2d(planes)
   
           self.shortcut = nn.Sequential()
           if stride != 1 or in_planes != self.expansion*planes:
               self.shortcut = nn.Sequential(
                   nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                   nn.BatchNorm2d(self.expansion*planes)
               )
   
       def forward(self, x):
           out = nn.ReLU()(self.bn1(self.conv1(x)))
           out = self.bn2(self.conv2(out))
           out += self.shortcut(x)
           out = nn.ReLU()(out)
           return out

3. 定义ResNet的整体结构

ResNet的整体结构由多个BasicBlock组成，包含一个卷积层、一个BatchNorm层、四个BasicBlock层、一个全局平均池化层和一个全连接层。

   class ResNet(nn.Module):
       def __init__(self, block, num_blocks, num_classes=10):
           super(ResNet, self).__init__()
           self.in_planes = 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(block, 64, num_blocks[0], stride=1)
           self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
           self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
           self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
           self.linear = nn.Linear(512*block.expansion, num_classes)
   
       def _make_layer(self, block, planes, num_blocks, stride):
           strides = [stride] + [1]*(num_blocks-1)
           layers = []
           for stride in strides:
               layers.append(block(self.in_planes, planes, stride))
               self.in_planes = planes * block.expansion
           return nn.Sequential(*layers)
   
       def forward(self, x):
           out = nn.ReLU()(self.bn1(self.conv1(x)))
           out = self.layer1(out)
           out = self.layer2(out)
           out = self.layer3(out)
           out = self.layer4(out)
           out = nn.AvgPool2d(4)(out)
           out = out.view(out.size(0), -1)
           out = self.linear(out)
           return out

4. 定义ResNet18模型

ResNet18模型是一个包含多个BasicBlock的ResNet模型，其中包括两个3×3卷积层和一个1×1卷积层。我们可以使用ResNet类来定义ResNet18模型。

   def ResNet18():
       return ResNet(BasicBlock, [2,2,2,2])

5. 实例化模型并进行训练

在实例化ResNet18模型之后，我们可以使用PyTorch提供的优化器和损失函数对模型进行训练，如下所示：

   import torch.optim as optim
   
   device = 'cuda' if torch.cuda.is_available() else 'cpu'
   
   net = ResNet18().to(device)
   
   criterion = nn.CrossEntropyLoss()
   optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
   
   for epoch in range(100):
       net.train()
       for batch_idx, (inputs, targets) in enumerate(trainloader):
           inputs, targets = inputs.to(device), targets.to(device)
           optimizer.zero_grad()
           outputs = net(inputs)
           loss = criterion(outputs, targets)
           loss.backward()
           optimizer.step()
   
       net.eval()
       test_loss = 0
       correct = 0
       with torch.no_grad():
           for batch_idx, (inputs, targets) in enumerate(testloader):
               inputs, targets = inputs.to(device), targets.to(device)
               outputs = net(inputs)
               test_loss += criterion(outputs, targets).item()
               _, predicted = outputs.max(1)
               correct += predicted.eq(targets).sum().item()
   
       test_loss /= len(testloader.dataset)
       print('Epoch: {} | Test Loss: {:.3f} | Test Acc: {:.3f}'.format(epoch, test_loss, 100.*correct/len(testloader.dataset)))

在训练过程中，我们首先将模型移动到GPU上，然后定义交叉熵损失函数和随机梯度下降优化器。在每个epoch中，我们先将模型设置为训练模式，然后对训练集进行迭代，并在每个batch上计算损失和梯度，并更新模型的参数。接着，我们将模型设置为评估模式，对测试集进行迭代，并在每个batch上计算损失和精度。最后，我们输出每个epoch的测试损失和测试精度。