使用pytorch实现shufflenetv2做自动识别的智能电子秤的方法及代码

ShuffleNetV2是一种轻量级的神经网络结构，适用于移动设备和嵌入式设备上的计算，较小的模型尺寸可以减少计算资源和存储空间的使用。在智能电子秤中使用ShuffleNetV2可以实现重量的自动识别，具有较高的精度和速度。

以下是使用PyTorch实现ShuffleNetV2的代码示例，其中包含训练和推理过程：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader

# 定义ShuffleNetV2网络结构
class ShuffleNetV2(nn.Module):
    def __init__(self, num_classes=10):
        super(ShuffleNetV2, self).__init__()
        self.stage1 = nn.Sequential(
            nn.Conv2d(1, 24, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(24),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        self.stage2 = nn.Sequential(
            ShuffleUnit(24, 116),
            ShuffleUnit(116, 116),
            ShuffleUnit(116, 116)
        )
        self.stage3 = nn.Sequential(
            ShuffleUnit(116, 232),
            ShuffleUnit(232, 232),
            ShuffleUnit(232, 232),
            ShuffleUnit(232, 232),
            ShuffleUnit(232, 232)
        )
        self.stage4 = nn.Sequential(
            ShuffleUnit(232, 464),
            ShuffleUnit(464, 464),
            ShuffleUnit(464, 464)
        )
        self.conv5 = nn.Sequential(
            nn.Conv2d(464, 1024, kernel_size=1, stride=1, padding=0),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True)
        )
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.stage1(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.conv5(x)
        x = nn.AdaptiveAvgPool2d(1)(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 定义ShuffleNetV2的基本单元
class ShuffleUnit(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ShuffleUnit, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.stride = 2 if self.in_channels != self.out_channels else 1
        self.bottleneck_channels = self.out_channels // 4
        self.residual = nn.Sequential(
            nn.Conv2d(self.in_channels, self.bottleneck_channels, kernel_size=1, stride=1, padding=0),
            nn.BatchNorm2d(self.bottleneck_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(self.bottleneck_channels, self.bottleneck_channels, kernel_size=3, stride=self.stride, padding=1, groups=self.bottleneck_channels),
            nn.BatchNorm2d(self.bottleneck_channels),
            nn.Conv2d(self.bottleneck_channels, self.out_channels, kernel_size=1, stride=1, padding=0),
            nn.BatchNorm2d(self.out_channels)
        )
        self.shortcut = nn.Sequential()
        if self.stride == 2:
            self.shortcut = nn.Sequential(
                nn.Conv2d(self.in_channels, self.in_channels, kernel_size=3, stride=2, padding=1, groups=self.in_channels),
                nn.BatchNorm2d(self.in_channels),
                nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, stride=1, padding=0),
                nn.BatchNorm2d(self.out_channels)
            )

    def forward(self, x):
        residual = self.residual(x)
        shortcut = self.shortcut(x)
        out = nn.ReLU(inplace=True)(residual + shortcut)
        out = channel_shuffle(out, 2)
        return out

# 定义通道混洗操作
def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()
    channels_per_group = num_channels // groups
    x = x.view(batchsize, groups, channels_per_group, height, width)
    x = torch.transpose(x, 1, 2).contiguous()
    x = x.view(batchsize, -1, height, width)
    return x

# 定义训练函数
def train(model, device, train_loader, optimizer, criterion, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 定义测试函数
def test(model, device, test_loader, criterion):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

# 定义主函数
def main():
    # 设置超参数
    batch_size = 64
    epochs = 50
    lr = 0.01
    momentum = 0.9
    weight_decay = 1e-4

    # 定义数据预处理函数
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize((0.5,), (0.5,))
    ])

    # 加载数据集
    train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
    test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

    # 定义设备
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # 定义模型、优化器和损失函数
    model = ShuffleNetV2().to(device)
    optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)
    criterion = nn.CrossEntropyLoss()

    # 训练模型
    for epoch in range(1, epochs + 1):
        train(model, device, train_loader, optimizer, criterion, epoch)
        test(model, device, test_loader, criterion)

    # 保存模型
    torch.save(model.state_dict(), "shufflenetv2.pth")

if __name__ == '__main__':
    main()

以上代码实现了使用ShuffleNetV2网络结构在MNIST数据集上进行训练和测试，其中包括数据预处理、模型定义、优化器和损失函数的设置，以及训练和测试函数的定义。可以根据实际需求对代码进行修改和调整。