基于pytorch实现resnet34的代码

### 回答1：
以下是基于 PyTorch 实现 ResNet34 的代码：

import torch.nn as nn
import torch.utils.model_zoo as model_zoo


__all__ = ['ResNet', 'resnet34']


model_urls = {
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
}


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=100):
        self.inplanes = 64
        super(ResNet, self).__init__()
        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.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, )

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(), -1)
        x = self.fc(x)

        return x


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model

希望对你有所帮助！

### 回答2：
以下是一个基于PyTorch实现ResNet34的代码示例：

import torch
import torch.nn as nn

# 定义残差块
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.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.stride = stride

        if stride != 1 or 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.Sequential()

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += self.shortcut(residual)
        out = self.relu(out)

        return out


# 定义ResNet34
class ResNet34(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet34, self).__init__()
        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.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 64, 3)
        self.layer2 = self._make_layer(64, 128, 4, stride=2)
        self.layer3 = self._make_layer(128, 256, 6, stride=2)
        self.layer4 = self._make_layer(256, 512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, in_channels, out_channels, num_blocks, stride=1):
        layers = []
        layers.append(ResidualBlock(in_channels, out_channels, stride))
        for _ in range(1, num_blocks):
            layers.append(ResidualBlock(out_channels, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

# 创建ResNet34的实例
model = ResNet34()

# 使用模型进行训练或推理
input_data = torch.randn(1, 3, 224, 224)
output = model(input_data)
print(output.shape)

这段代码实现了基于PyTorch的ResNet34模型。它定义了一个残差块（ResidualBlock）的类和一个ResNet34模型的类。在ResidualBlock中，通过两个卷积层和批归一化层实现残差连接。ResNet34类包含多个残差块组成的层。最后，模型通过全局平均池化层和全连接层生成预测结果。在使用模型之前，可以创建一个ResNet34的实例，并使用输入数据进行训练或推理。

### 回答3：
基于PyTorch实现ResNet34的代码如下：

import torch
import torch.nn as nn
import torchvision.models as models

# 定义ResNet34网络结构
class ResNet34(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet34, self).__init__()
        self.resnet34 = models.resnet34(pretrained=True)
        # 冻结所有卷积层参数
        for param in self.resnet34.parameters():
            param.requires_grad = False
        # 替换最后一层全连接层
        self.resnet34.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.resnet34(x)
        return x

# 创建ResNet34模型实例
model = ResNet34()

# 加载预训练权重
model.load_state_dict(torch.load('resnet34.pth'))

# 输入数据
input_data = torch.randn(1, 3, 224, 224)

# 前向传播
output = model(input_data)

# 打印输出结果
print(output)

在代码中，我们使用了PyTorch的torchvision库，其中包含了常用的深度学习模型，包括ResNet。首先，我们定义了一个名为ResNet34的类，继承自nn.Module。在类的构造函数中，我们使用`models.resnet34(pretrained=True)`加载了预训练的ResNet34模型，并将其赋值给self.resnet34。

然后，我们通过遍历self.resnet34的参数来冻结所有的卷积层参数，这是因为我们只需要训练最后一层全连接层。接下来，我们替换了最后一层全连接层，将输出类别数目设为num_classes。

在前向传播函数forward中，我们调用了self.resnet34进行前向传播，并返回输出结果。

最后，我们创建了一个ResNet34的实例model，并加载了预训练权重。然后，我们创建一个输入数据input_data，并进行前向传播，得到输出结果output。最后，我们打印输出结果。

这样，我们就实现了基于PyTorch的ResNet34模型的代码。