基于pytorch人脸表情识别

人脸表情识别是一种应用广泛的计算机视觉任务。基于 PyTorch 的人脸表情识别可以使用 CNN（卷积神经网络）来实现。以下是一个简单的 PyTorch 代码示例，用于训练和测试 CNN 模型以识别人脸表情：

1. 导入所需的库

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

2. 定义数据预处理和增强

transform = transforms.Compose([
    transforms.Resize((48, 48)),
    transforms.Grayscale(num_output_channels=1),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

3. 加载数据集

train_dataset = ImageFolder('train', transform=transform)
test_dataset = ImageFolder('test', transform=transform)

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

4. 定义 CNN 模型

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 12 * 12, 128)
        self.fc2 = nn.Linear(128, 7)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 64 * 12 * 12)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

5. 训练和测试模型

cnn = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(cnn.parameters(), lr=0.001)

for epoch in range(10):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = cnn(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 50 == 49:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 50))
            running_loss = 0.0

    correct = 0
    total = 0
    with torch.no_grad():
        for inputs, labels in test_loader:
            outputs = cnn(inputs)
            _, 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))

通过以上代码，我们可以训练一个简单的 CNN 模型来识别人脸表情。然而，要获得更高的准确率，需要使用更复杂的模型，并将其与更大的数据集一起训练。