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

1. 数据准备

首先，需要准备一个图像分类的数据集。在本例中，我们使用了labelimg数据集。

2. 模型选择

接下来，选择适合数据集的深度学习模型。在本例中，我们选择使用shufflenet v2模型。

3. 模型代码

下面是使用pytorch实现的shufflenet v2模型代码：

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

class ShuffleUnit(nn.Module):
    def __init__(self, in_channels, out_channels, groups=3, stride=1):
        super(ShuffleUnit, self).__init__()

        mid_channels = out_channels // 4

        self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1,
                               groups=1, stride=1, padding=0, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_channels)
        self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3,
                               groups=groups, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_channels)
        self.conv3 = nn.Conv2d(mid_channels, out_channels, kernel_size=1,
                               groups=1, stride=1, padding=0, 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,
                          groups=1, stride=stride, padding=0, bias=False),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        shortcut = self.shortcut(x)
        out = F.relu(torch.cat([out, shortcut], dim=1))

        return out


class ShuffleNetV2(nn.Module):
    def __init__(self, input_size=224, n_class=1000, scale_factor=1.0):
        super(ShuffleNetV2, self).__init__()

        assert input_size % 32 == 0

        self.scale_factor = scale_factor

        self.stage_repeats = [4, 8, 4]
        self.stage_out_channels = [24, 116, 232, 464, 1024]

        if self.scale_factor != 1.0:
            self.stage_out_channels = [int(c * self.scale_factor) for c in self.stage_out_channels]

        self.conv1 = nn.Conv2d(3, 24, kernel_size=3,
                               groups=1, 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(2, stride=2)
        self.stage3 = self._make_stage(3, stride=2)
        self.stage4 = self._make_stage(4, stride=2)
        self.globalpool = nn.AvgPool2d(int(input_size / 32))
        self.fc = nn.Linear(self.stage_out_channels[-1], n_class)

    def _make_stage(self, stage, stride):
        modules = OrderedDict()
        stage_name = "ShuffleUnit_Stage{}".format(stage)
        previous_channels = self.stage_out_channels[stage - 2]
        current_channels = self.stage_out_channels[stage - 1]

        for i in range(self.stage_repeats[stage - 2]):
            if i == 0:
                modules[stage_name + "_{}".format(i)] = ShuffleUnit(
                    previous_channels, current_channels, stride=stride)
            else:
                modules[stage_name + "_{}".format(i)] = ShuffleUnit(
                    current_channels, current_channels, stride=1)

        return nn.Sequential(modules)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.maxpool(x)

        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)

        x = self.globalpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

4. 训练代码

接下来，编写训练代码。在本例中，我们使用交叉熵损失函数，并使用Adam优化器进行模型优化。

import torch.optim as optim
from torch.utils.data import DataLoader

# 定义超参数
batch_size = 32
learning_rate = 0.001
epochs = 10

# 加载数据集
dataset = MyDataset()
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 定义模型、损失函数和优化器
model = ShuffleNetV2().cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(epochs):
    running_loss = 0.0
    for i, data in enumerate(dataloader, 0):
        inputs, labels = data
        inputs, labels = inputs.cuda(), labels.cuda()

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print('[%d] loss: %.3f' %
          (epoch + 1, running_loss / len(dataloader)))

print('Finished Training')

5. 整个过程

整个过程如下：

1. 准备数据集（labelimg数据集）。

2. 选择适合数据集的深度学习模型（shufflenet v2）。

3. 编写模型代码（pytorch实现的shufflenet v2模型代码）。

4. 编写训练代码（使用交叉熵损失函数和Adam优化器进行模型优化）。

5. 训练模型，输出训练损失。

6. 完成训练，得到训练好的模型，可以对新的图片进行分类预测。