如何用pytorch实现resnet

可以使用PyTorch中的torchvision模块来实现ResNet。首先，需要导入torchvision和torch.nn模块。然后，可以使用torchvision.models.resnet模块来加载预训练的ResNet模型，或者使用torch.nn模块中的Conv2d、BatchNorm2d和ReLU等函数来手动实现ResNet。

以下是一个使用torchvision.models.resnet模块加载预训练的ResNet模型的示例代码：

import torch
import torchvision.models as models

# 加载预训练的ResNet模型
resnet = models.resnet18(pretrained=True)

# 打印模型结构
print(resnet)

这将输出ResNet-18模型的结构。可以使用resnet.forward()函数来进行前向传播，输入为一个大小为[batch_size, 3, 224, 224]的张量。

如果想手动实现ResNet，可以使用以下代码：

import torch
import torch.nn as nn

# 定义ResNet的基本块
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

# 定义ResNet模型
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[], 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(), -1)
        out = self.linear(out)
        return out

# 创建ResNet-18模型
def ResNet18():
    return ResNet(BasicBlock, [2,2,2,2])

# 创建ResNet-34模型
def ResNet34():
    return ResNet(BasicBlock, [3,4,6,3])

这里定义了BasicBlock和ResNet两个类，其中BasicBlock是ResNet的基本块，ResNet是整个模型。可以使用ResNet18()和ResNet34()函数来创建ResNet-18和ResNet-34模型。