计算两组图片之间fid指标的代码并真实图片路径为./tank，虚假图片路径为./fake

要计算两组图片之间的FID指标，需要使用两组图片的特征向量来计算。以下是计算FID指标的示例代码，需要使用PyTorch和NumPy库：

import torch
import numpy as np
from scipy import linalg

def calculate_activation_statistics(images, model, batch_size=50, dims=2048):
    model.eval()
    act = np.empty((len(images), dims))
    
    with torch.no_grad():
        for i in range(0, len(images), batch_size):
            batch = images[i:i+batch_size]
            batch = torch.FloatTensor(batch).cuda()
            pred = model(batch)[0].view(batch.shape[0], -1)
            act[i:i+batch_size] = pred.cpu().data.numpy()

    mu = np.mean(act, axis=0)
    sigma = np.cov(act, rowvar=False)
    
    return mu, sigma

def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
    mu1 = np.atleast_1d(mu1)
    mu2 = np.atleast_1d(mu2)

    sigma1 = np.atleast_2d(sigma1)
    sigma2 = np.atleast_2d(sigma2)

    assert mu1.shape == mu2.shape, 'Training and test mean vectors have different lengths'
    assert sigma1.shape == sigma2.shape, 'Training and test covariances have different dimensions'

    diff = mu1 - mu2

    # Product might be almost singular
    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
    if not np.isfinite(covmean).all():
        print('fid calculation produces singular product; adding %s to diagonal of cov estimates' % eps)
        offset = np.eye(sigma1.shape[0]) * eps
        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))

    # Numerical error might give slight imaginary component
    if np.iscomplexobj(covmean):
        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
            m = np.max(np.abs(covmean.imag))
            raise ValueError('Imaginary component {}'.format(m))
        covmean = covmean.real

    tr_covmean = np.trace(covmean)

    return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean

def calculate_fid(images1, images2, model, batch_size=50, dims=2048):
    images1 = np.asarray(images1)
    images2 = np.asarray(images2)

    assert (len(images1) > 0)
    assert (len(images1) == len(images2))

    mu1, sigma1 = calculate_activation_statistics(images1, model, batch_size, dims)
    mu2, sigma2 = calculate_activation_statistics(images2, model, batch_size, dims)
    fid_value = calculate_frechet_distance(mu1, sigma1, mu2, sigma2)

    return fid_value

你需要将真实图片路径和虚假图片路径分别传入这个函数，以计算FID指标：

from torchvision.models import inception_v3
from torchvision.datasets import ImageFolder
from torchvision.transforms import transforms

model = inception_v3(pretrained=True, transform_input=False).cuda()

real_dataset = ImageFolder('./tank', transform=transforms.Compose([
    transforms.Resize(299),
    transforms.CenterCrop(299),
    transforms.ToTensor()
]))

fake_dataset = ImageFolder('./fake', transform=transforms.Compose([
    transforms.Resize(299),
    transforms.CenterCrop(299),
    transforms.ToTensor()
]))

real_images = [img[0].numpy() for img in real_dataset]
fake_images = [img[0].numpy() for img in fake_dataset]

fid_score = calculate_fid(real_images, fake_images, model)
print('FID score:', fid_score)

请注意，这段代码假定您已经将真实图片和虚假图片下载到了本地文件系统上，并且使用了PyTorch的ImageFolder类来读取这些图片。如果您的图片不在本地文件系统上，您需要修改代码以读取它们从其他来源。