pytorch人脸表情识别代码

PyTorch是一个开源的深度学习框架，可以用于构建和训练神经网络模型。人脸表情识别是一个常见的计算机视觉任务，可以通过PyTorch来实现。

以下是一个简单的PyTorch人脸表情识别代码示例：

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

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc1 = nn.Linear(64 * 5 * 5, 128)
        self.fc2 = nn.Linear(128, 7)  # 7个表情类别

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        x = x.view(-1, 64 * 5 * 5)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 数据预处理和加载
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

train_dataset = datasets.FER2013(root='./data', train=True, transform=transform, download=True)
test_dataset = datasets.FER2013(root='./data', train=False, transform=transform, download=True)

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# 初始化模型和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 10
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

for epoch in range(num_epochs):
    model.train()
    for batch_idx, (data, targets) in enumerate(train_loader):
        data = data.to(device)
        targets = targets.to(device)

        # 前向传播和计算损失
        outputs = model(data)
        loss = criterion(outputs, targets)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # 在测试集上评估模型性能
    model.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for data, targets in test_loader:
            data = data.to(device)
            targets = targets.to(device)

            outputs = model(data)
            _, predicted = torch.max(outputs.data, 1)
            total += targets.size(0)
            correct += (predicted == targets).sum().item()

        accuracy = 100 * correct / total
        print(f"Epoch [{epoch+1}/{num_epochs}], Test Accuracy: {accuracy:.2f}%")

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

这段代码实现了一个简单的卷积神经网络模型，用于人脸表情识别。它使用FER2013数据集进行训练和测试，并在每个epoch结束时输出测试集的准确率。最后，模型会被保存为`emotion_model.pth`文件。