使用pytorch实现用shufflenet v2进行图像分类，并给出模型代码，训练代码，并描述整个过程

ShuffleNet V2是一种轻量级的深度神经网络，适用于移动设备和嵌入式设备上的图像分类任务。它采用了channel shuffle的思想，在保证模型精度的同时，大大减少了模型的计算量和参数量。

下面是使用PyTorch实现ShuffleNet V2进行图像分类的模型代码和训练代码：

import torch
import torch.nn as nn

class ShuffleBlock(nn.Module):
    def __init__(self, groups=2):
        super(ShuffleBlock, self).__init__()
        self.groups = groups

    def forward(self, x):
        batch_size, channels, height, width = x.size()
        channels_per_group = channels // self.groups
        x = x.view(batch_size, self.groups, channels_per_group, height, width)
        x = x.transpose(1, 2).contiguous()
        x = x.view(batch_size, -1, height, width)
        return x

class ShuffleNetV2(nn.Module):
    def __init__(self, num_classes=1000):
        super(ShuffleNetV2, self).__init__()
        self.conv1 = nn.Conv2d(3, 24, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(24)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.stage2 = self._make_stage(24, 116, 3)
        self.stage3 = self._make_stage(116, 232, 4)
        self.stage4 = self._make_stage(232, 464, 6)
        self.conv5 = nn.Conv2d(464, 1024, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn5 = nn.BatchNorm2d(1024)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(1024, num_classes)

    def _make_stage(self, in_channels, out_channels, repeat):
        layers = []
        layers.append(nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False))
        layers.append(nn.BatchNorm2d(out_channels))
        layers.append(nn.ReLU(inplace=True))
        for i in range(repeat):
            layers.append(ShuffleBlock())
            layers.append(nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False))
            layers.append(nn.BatchNorm2d(out_channels))
            layers.append(nn.ReLU(inplace=True))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = nn.ReLU(inplace=True)(x)
        x = self.maxpool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.conv5(x)
        x = self.bn5(x)
        x = nn.ReLU(inplace=True)(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

上面的代码实现了ShuffleNet V2的主体结构。通过_make_stage函数可以定义每个stage的结构，其中包含多个ShuffleBlock以及卷积、BN和ReLU激活函数等操作。在forward函数中，将主体结构按照顺序连接起来，最后通过全局平均池化和全连接层输出分类结果。

下面是使用PyTorch进行模型训练的代码：

import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# 数据预处理
transform_train = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

transform_test = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载训练集和测试集
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

# 定义模型
net = ShuffleNetV2(num_classes=10)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=0.0001)

# 训练模型
for epoch in range(200):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

    # 测试模型
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

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

上面的代码中，使用了CIFAR10数据集进行模型训练和测试。在训练过程中，定义了交叉熵损失函数和随机梯度下降优化器，并使用了数据增强技术。在每个epoch结束后，通过测试集计算模型的准确率。