Res2net pytorch代码实现

Res2Net是一种深度残差网络的变体，通过引入二维空间内的卷积，增强了特征图的空间分辨率。在PyTorch中实现Res2Net，你需要先安装相关的库，如torch、torchvision等，并理解基本的卷积神经网络（CNN）原理。

以下是一个简单的Res2Net模块的示例代码：

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

class Bottleneck(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, cardinality=32, base_width=4, scale=4):
        super(Bottleneck, self).__init__()
        width = int(out_channels * (base_width / 64.0)) * cardinality
        self.conv1 = nn.Conv2d(in_channels, width, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width)
        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)
        self.bn2 = nn.BatchNorm2d(width)
        self.conv3 = nn.Conv2d(width, out_channels, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels)
        self.shortcut = nn.Sequential()
        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)
            )

    def forward(self, x):
        identity = x
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(identity)
        out = F.relu(out)
        return out

class Res2Net(nn.Module):
    def __init__(self, block, layers, num_classes=1000, scale=4):
        super(Res2Net, self).__init__()
        self.cardinality = 32
        self.base_width = 4
        self.scale = scale
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self.make_layer(block, 64, layers)
        self.layer2 = self.make_layer(block, 128, layers, stride=2)
        self.layer3 = self.make_layer(block, 256, layers, stride=2)
        self.layer4 = self.make_layer(block, 512, layers, stride=1, scale=scale)
        self.avg_pool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def make_layer(self, block, planes, blocks, stride=1, scale=None):
        if scale is None:
            scale = self.scale
        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, self.cardinality, self.base_width, scale))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, self.cardinality, self.base_width))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avg_pool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 使用示例
res2net_50_26w_4s = Res2Net(Bottleneck, [3, 4, 6, 3], scale=4)

这是一个基础的Res2Net结构，你可以根据实际项目需求调整模型参数。记得在实际应用前，你需要导入其他必要的库，并根据训练数据对模型进行适当的配置，如设定优化器、损失函数等。