PyTorch ResNeXt50 和 ReNeXt50 模型：深入理解实现

2024-02-16 08:29:32

简介

ResNeXt50 和 ReNeXt50 模型是基于 ResNet 模型改进而来的一系列深度神经网络架构，因其在计算机视觉领域的出色表现而闻名。这些模型在 ImageNet 图像分类任务中取得了 state-of-the-art 的结果，并成功应用于目标检测、图像分割等任务。

2. ResNeXt50 模型

ResNeXt50 模型的提出是为了解决 ResNet 模型中基本模块瓶颈结构的局限性。在 ResNet 模型中，每个基本模块都由两个 3x3 卷积层和一个 1x1 卷积层组成。ResNeXt50 模型则将这两个 3x3 卷积层替换为多个 1x1 卷积层和一个分组卷积层。分组卷积层是一种特殊的卷积操作，它将输入特征图划分为多个组，然后分别对每个组进行卷积运算。这种结构可以有效地减少模型的参数数量，同时保持模型的性能。

3. ReNeXt50 模型

ReNeXt50 模型是在 ResNeXt50 模型的基础上进一步改进而来的。ReNeXt50 模型将 ResNeXt50 模型中分组卷积层的组数从 32 增加到 64，并对模型的超参数进行了调整。这些改动进一步提高了模型的性能，使其在 ImageNet 图像分类任务中的准确率达到了 93.6%。

4. PyTorch 代码示例

import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义模型
class ResNeXt50(nn.Module):

    def __init__(self):
        super(ResNeXt50, self).__init__()
        # 定义网络结构
        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)

        # 定义分组卷积模块
        class Bottleneck(nn.Module):

            def __init__(self, inplanes, planes, groups, stride=1, downsample=None):
                super(Bottleneck, self).__init__()
                # 定义分组卷积层
                self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
                self.bn1 = nn.BatchNorm2d(planes)
                self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
                self.bn2 = nn.BatchNorm2d(planes)
                self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
                self.bn3 = nn.BatchNorm2d(planes * 4)
                self.relu = nn.ReLU(inplace=True)
                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)
                out = self.relu(out)

                out = self.conv3(out)
                out = self.bn3(out)

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

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

                return out

        # 定义网络剩余部分
        self.layer1 = self._make_layer(Bottleneck, 256, 3, groups=32)
        self.layer2 = self._make_layer(Bottleneck, 512, 4, groups=32, stride=2)
        self.layer3 = self._make_layer(Bottleneck, 1024, 6, groups=32, stride=2)
        self.layer4 = self._make_layer(Bottleneck, 2048, 3, groups=32, stride=2)

        # 定义平均池化层和全连接层
        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.fc = nn.Linear(2048, 1000)

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

        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

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0001)

# 训练模型
for epoch in range(10):
    # 训练阶段
    model.train()
    for i, data in enumerate(train_loader):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    # 评估阶段
    model.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for i, data in enumerate(test_loader):
            inputs, labels = data
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

# 保存模型
torch.save(model.state_dict(), 'ResNeXt50.pth')