关于ResNeXt网络的pytorch实现

1. ResNeXt网络简介

ResNeXt网络是一种结合Inception和ResNet思想的深度学习网络，其主要思想是通过构造多个相同结构的网络，从而提高网络的表达能力和准确性。其在ImageNet官方数据集上的准确率达到了96.4%，同时与ResNet拥有相同的参数量和FLOPS，但比ResNet更加高效。

1.1 ResNeXt网络结构

ResNeXt网络的核心结构是基于“split-transform-merge”（分裂-变换-合并）策略，将一个大的卷积核拆分成多个小的卷积核，由多个路径上的小卷积核同时处理输入数据，最后将输出结果合并，从而增加网络的宽度和深度。

ResNeXt的核心创意是在宽度（width）和深度（depth）两个方向上进行网络的扩张。这里的宽度和深度并不单指网络中的通道（channel）数量、层数或者指模型的规模，而是指网络对数据表征能力的两个扩张方向。这种思想允许我们设计高度模块化、可表示力强的网络。ResNeXt中的网络设计认为多个独立的子网络的结合可以构建更强大的模型表达能力，因此ResNeXt被认为是Inception和ResNet模型的结合。

import torch
import torch.nn as nn
import torch.nn.functional as F
class ResNeXtBlock(nn.Module):
    def __init__(self, in_channels, out_channels, cardinality=32, block_width=4, stride=1):
        super(ResNeXtBlock, self).__init__()
        C = cardinality * block_width
        self.split_conv_reduce = nn.Conv2d(in_channels, C, kernel_size=1, stride=1, bias=False)
        self.bn_reduce = nn.BatchNorm2d(C)
        self.split_conv_conv = nn.Conv2d(C, C, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)
        self.bn = nn.BatchNorm2d(C)
        self.squeeze_conv = nn.Conv2d(C, out_channels, kernel_size=1, stride=1, bias=False)
        self.bn_expand = 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):
        x = F.relu(self.bn_reduce(self.split_conv_reduce(x)))
        x = F.relu(self.bn(self.split_conv_conv(x)))
        x = self.bn_expand(self.squeeze_conv(x))
        residual = self.shortcut(x)
        x += residual
        x = F.relu(x)
        return x

1.2 ResNeXt网络的优点

ResNeXt网络在准确性和参数量上优于其他网络，具有以下优点：

有更多网络剪枝可能，因为分组卷积本身就是参数化的，因此能够更微润地控制网络结构。

基于相同数量的参数，ResNeXt比ResNet在ImageNet数据集上的准确率略高。

ResNeXt具有可扩展性，以原始结构作为组件，可自由增加组件个数以及细微参数改变，进一步提高模型的准确性。

2. ResNeXt的pytorch实现

2.1 ResNeXt代码架构

在pytorch中，ResNeXt的代码架构可以用以下代码实现：

import torch
import torch.nn as nn
import torch.nn.functional as F
class ResNeXt(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, cardinality=32, block_width=4):
        super(ResNeXt, self).__init__()
        self.cardinality = cardinality
        self.block_width = block_width
        self.in_channels = 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[0], 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(cardinality * block_width * 32, num_classes)
    def _make_layer(self, block, out_channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, out_channels, self.cardinality, self.block_width, stride))
            self.in_channels = out_channels * self.cardinality * self.block_width
        return nn.Sequential(*layers)
    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = F.avg_pool2d(x, 2)
        x = x.view(x.size(0), -1)
        x = self.linear(x)
        return x
def resnext50(cardinality, block_width):
    return ResNeXt(ResNeXtBlock, [3, 4, 6, 3], 10, cardinality, block_width)
model = resnext50(32, 4)
print(model)

代码中的ResNeXt50指的是ResNet50加入ResNeXt思想后的网络模型，相比于ResNet50，在准确率和参数量方面都有所提升。

2.2 数据集和训练

为了验证ResNeXt在CIFAR-10数据集上的效果，我们加载CIFAR-10数据集，设置学习率为0.1，优化器为随机梯度下降（SGD），损失函数为交叉熵（Cross Entropy）。

具体代码如下：

import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
torch.manual_seed(1)
train_transforms = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
test_transforms = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transforms)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transforms)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False, num_workers=2)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = resnext50(32, 4)
model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
def train(epoch):
    model.train()
    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(train_loader):
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()
    print('Epoch:{} | Loss:{:.3f} | Acc:{:.3f}'.format(
        epoch, train_loss / len(train_loader), 100. * correct / total))
def test(epoch):
    model.eval()
    test_loss = 0
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(test_loader):
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()
    print('Epoch:{} | Loss:{:.3f} | Acc:{:.3f}'.format(
        epoch, test_loss / len(test_loader), 100. * correct / total))
for epoch in range(1, 201):
    train(epoch)
    test(epoch)

由于时间和算力限制，我们仅训练了200次，实际训练次数要远远超过这个数字。

3. 总结

本文主要介绍了ResNeXt网络的pytorch实现，深入讲解了ResNeXt的结构和优点，并且实现了ResNeXt在CIFAR-10数据集上的训练和测试过程。从结果上来看，ResNeXt比ResNet在ImageNet数据集上的准确率略高，与ResNet相当，但其复杂度更高，训练和测试时间会更长。如果需要在计算机视觉领域获得更高的准确率，ResNeXt是值得尝试的网络。