pytorch ResNet网络

ResNet是一种深度残差网络，可以有效地训练非常深的神经网络。下面是一个使用PyTorch实现ResNet-50的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ResNet(nn.Module):
    def __init__(self, num_classes=1000):
        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(64, 3)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 6, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * 4, num_classes)

    def _make_layer(self, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or planes != 64:
            downsample = nn.Sequential(
                nn.Conv2d(64, planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes),
            )

        layers = [ResidualBlock(64, planes, stride, downsample)]
        for i in range(1, blocks):
            layers.append(ResidualBlock(planes, 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(0), -1)
        x = self.fc(x)

        return x

class ResidualBlock(nn.Module):
    def __init__(self, in_planes, planes, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()

        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample

    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

在这个示例代码中，我们定义了一个名为ResNet的类，它继承自PyTorch中的nn.Module类。ResNet类包含了ResNet-50的所有层，并且我们可以使用它来进行训练和推理。

ResNet类的构造函数中定义了各个层的结构，包括卷积层、池化层、批归一化层、全连接层等。其中，_make_layer函数用于创建残差块，ResidualBlock类定义了残差块的结构。

在ResNet类的forward函数中，我们按照ResNet的结构依次调用各个层，并将输出传递给下一层。最终，我们使用全局平均池化层和一个全连接层来输出分类结果。

这个实现示例可以处理3通道的图像，输出1000个类别的预测结果。如果需要处理其他类型的数据，可以根据实际情况进行修改。