注意力机制进行人脸表情识别的代码

以下是使用注意力机制进行人脸表情识别的代码示例，主要使用了PyTorch框架和FER2013数据集：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader

class AttentionModel(nn.Module):
    def __init__(self):
        super(AttentionModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.conv2 = nn.Conv2d(10, 20, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.conv3 = nn.Conv2d(20, 40, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.attention_conv = nn.Conv2d(40, 1, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0))
        self.fc1 = nn.Linear(40, 7)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, kernel_size=(2, 2))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, kernel_size=(2, 2))
        x = F.relu(self.conv3(x))
        x = F.max_pool2d(x, kernel_size=(2, 2))
        attention_map = F.sigmoid(self.attention_conv(x))
        x = x * attention_map
        x = x.view(x.size(0), -1)
        x = F.softmax(self.fc1(x), dim=1)
        return x

class FERDataset(Dataset):
    def __init__(self, csv_file, transform=None):
        self.data = pd.read_csv(csv_file)
        self.transform = transform

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        image = np.asarray([int(p) for p in self.data.iloc[idx, 1].split()]).reshape(48, 48, 1)
        label = int(self.data.iloc[idx, 0])
        if self.transform:
            image = self.transform(image)
        return image, label

transformations = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])

train_dataset = FERDataset('train.csv', transformations)
train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4)

model = AttentionModel().cuda()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
    for i, (images, labels) in enumerate(train_dataloader):
        images, labels = images.cuda(), labels.cuda()
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, i+1, len(train_dataloader), loss.item()))

# Testing the model on test dataset
test_dataset = FERDataset('test.csv', transformations)
test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=True, num_workers=4)

with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_dataloader:
        images, labels = images.cuda(), labels.cuda()
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))

这段代码实现了一个基于注意力机制的人脸表情识别模型，训练集和测试集均来自FER2013数据集。在模型训练过程中，最后输出的准确率可作为模型表现的评价指标。