如何比较生成的cifar10图像集合和真实图像cifar10 的FID，具体pytorch代码？

要比较生成的cifar10图像集合和真实图像cifar10的FID，需要进行以下步骤：

1. 准备真实图像和生成图像

2. 利用预训练的Inception网络提取特征向量

3. 计算两个集合的均值和协方差矩阵

4. 计算FID分数

下面是利用PyTorch实现的代码：

import torch
from torch.nn.functional import adaptive_avg_pool2d
from torchvision.models import inception_v3
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader
from scipy.linalg import sqrtm
import numpy as np

# 1. 准备真实图像和生成图像

def load_cifar10():
    transform = ToTensor()
    trainset = CIFAR10(root='./data', train=True, download=True, transform=transform)
    testset = CIFAR10(root='./data', train=False, download=True, transform=transform)
    return trainset, testset

def get_images(dataset):
    dataloader = DataLoader(dataset, batch_size=64, shuffle=False)
    images = []
    for batch in dataloader:
        images.append(batch[0].numpy())
    images = np.concatenate(images, axis=0)
    return images

real_dataset, fake_dataset = load_cifar10()
real_images = get_images(real_dataset)
fake_images = get_images(fake_dataset)

# 2. 利用预训练的Inception网络提取特征向量

def get_inception_model():
    model = inception_v3(pretrained=True, transform_input=False)
    model.eval()
    return model

def get_activations(images, model):
    activations = []
    for i in range(0, len(images), 64):
        batch = torch.from_numpy(images[i:i+64]).cuda()
        with torch.no_grad():
            features = model(batch)[0]
        activations.append(features.cpu().numpy())
    activations = np.concatenate(activations, axis=0)
    return activations

inception_model = get_inception_model()
real_activations = get_activations(real_images, inception_model)
fake_activations = get_activations(fake_images, inception_model)

# 3. 计算两个集合的均值和协方差矩阵

def calculate_fid(real_activations, fake_activations):
    mu1, sigma1 = np.mean(real_activations, axis=0), np.cov(real_activations, rowvar=False)
    mu2, sigma2 = np.mean(fake_activations, axis=0), np.cov(fake_activations, rowvar=False)
    diff = mu1 - mu2
    covmean, _ = sqrtm(sigma1.dot(sigma2), disp=False)
    if np.iscomplexobj(covmean):
        covmean = covmean.real
    fid = diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * np.trace(covmean)
    return fid

fid = calculate_fid(real_activations, fake_activations)
print("FID score:", fid)

其中，利用了PyTorch内置的InceptionV3模型来提取图像特征向量，并使用scipy库中的sqrtm函数计算矩阵平方根。最终输出FID分数。