写一个用resnet18分析jaffe数据的代码

好的，下面是使用 PyTorch 写的基于 ResNet18 分析 JAFFE 数据集的代码：

import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, models
from PIL import Image
import numpy as np
import pandas as pd

class JAFFEDataset(Dataset):
    def __init__(self, csv_file, root_dir, transform=None):
        self.annotations = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform

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

    def __getitem__(self, idx):
        img_path = self.root_dir + '/' + self.annotations.iloc[idx, 0]
        image = Image.open(img_path)

        emotion = self.annotations.iloc[idx, 1]
        labels_dict = {'NE':0, 'AN':1, 'DI':2, 'FE':3, 'HA':4, 'SA':5, 'SU':6}
        label = labels_dict[emotion]

        if self.transform:
            image = self.transform(image)

        return image, label

transform_train = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

transform_test = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

train_set = JAFFEDataset('jaffe_train.csv', 'jaffe', transform_train)
test_set = JAFFEDataset('jaffe_test.csv', 'jaffe', transform_test)

train_loader = DataLoader(train_set, batch_size=32, shuffle=True)
test_loader = DataLoader(test_set, batch_size=32, shuffle=False)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = models.resnet18(pretrained=True)
model.fc = nn.Linear(512, 7)
model.to(device)

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

num_epochs = 10
for epoch in range(num_epochs):
    for images, labels in train_loader:
        images = images.to(device)
        labels = labels.to(device)

        outputs = model(images)
        loss = criterion(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)

        outputs = model(images)
        _, 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))

以上是基于 ResNet18 分析 JAFFE 数据集的代码，该代码可用于训练并测试一个情绪识别模型。您可以将代码保存为 `.py` 文件并在命令行中运行。注意，您需要确保已经下载了 JAFFE 数据集，并相应修改代码中的文件名和路径。