calculate_fid_given_paths()

`calculate_fid_given_paths()`是一个用于计算两个文件夹中图像生成模型的FID（Fréchet Inception Distance）分数的函数。它可以通过引用中的Python实现来实现。

以下是`calculate_fid_given_paths()`函数的示例代码：

import torch
from torchvision import transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
from torchvision.models import inception_v3
from scipy.linalg import sqrtm
import numpy as np

def calculate_fid_given_paths(path_real, path_fake, batch_size=50, device='cuda'):
    # 加载真实图像数据集
    dataset_real = ImageFolder(path_real, transform=transforms.ToTensor())
    dataloader_real = DataLoader(dataset_real, batch_size=batch_size, shuffle=True)

    # 加载生成的图像数据集
    dataset_fake = ImageFolder(path_fake, transform=transforms.ToTensor())
    dataloader_fake = DataLoader(dataset_fake, batch_size=batch_size, shuffle=True)

    # 加载Inception V3模型
    inception_model = inception_v3(pretrained=True, transform_input=False).to(device)
    inception_model.eval()

    # 计算真实图像数据集的Inception特征
    real_features = []
    for batch_real, _ in dataloader_real:
        batch_real = batch_real.to(device)
        features_real = inception_model(batch_real)[0].view(batch_real.shape[0], -1)
        real_features.append(features_real.detach().cpu().numpy())
    real_features = np.concatenate(real_features, axis=0)

    # 计算生成的图像数据集的Inception特征
    fake_features = []
    for batch_fake, _ in dataloader_fake:
        batch_fake = batch_fake.to(device)
        features_fake = inception_model(batch_fake)[0].view(batch_fake.shape[0], -1)
        fake_features.append(features_fake.detach().cpu().numpy())
    fake_features = np.concatenate(fake_features, axis=0)

    # 计算真实图像数据集和生成的图像数据集的FID分数
    mu_real = np.mean(real_features, axis=0)
    mu_fake = np.mean(fake_features, axis=0)
    sigma_real = np.cov(real_features, rowvar=False)
    sigma_fake = np.cov(fake_features, rowvar=False)

    diff = mu_real - mu_fake
    cov_mean, _ = sqrtm(sigma_real.dot(sigma_fake), disp=False)
    if np.iscomplexobj(cov_mean):
        cov_mean = cov_mean.real

    fid_score = np.dot(diff, diff) + np.trace(sigma_real + sigma_fake - 2 * cov_mean)

    return fid_score

请注意，为了运行上述代码，您需要安装PyTorch和SciPy库。