计算两组图片之间fid指标的代码

计算两组图片之间FID指标需要使用两个步骤：首先计算每组图片的特征向量，然后计算它们之间的FID距离。以下是一个Python代码示例，使用PyTorch和NumPy库实现：

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))
    start_idx = 0
    while start_idx < len(images):
        end_idx = min(start_idx + batch_size, len(images))
        batch = torch.stack(images[start_idx:end_idx]).cuda()
        with torch.no_grad():
            pred = model(batch)[0].squeeze(2).squeeze(2).cpu().numpy()
            act[start_idx:end_idx] = pred
        start_idx += batch_size
    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():
        msg = f'fid calculation produces singular product; adding {eps} to diagonal of cov estimates'
        print(msg)
        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(f'Imaginary component {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):
    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
    model = InceptionV3([block_idx]).cuda()
    mu1, sigma1 = calculate_activation_statistics(images1, model, batch_size, dims)
    mu2, sigma2 = calculate_activation_statistics(images2, model, batch_size, dims)
    fid = calculate_frechet_distance(mu1, sigma1, mu2, sigma2)
    return fid

其中`calculate_activation_statistics`函数计算图片集合的特征向量的均值和协方差矩阵，`calculate_frechet_distance`函数计算两个特征向量集合之间的FID距离，`calculate_fid`函数将前两个函数组合起来计算FID指标。您需要将`images1`和`images2`替换为两个图片集合的路径或张量列表，`model`替换为一个预训练的ImageNet分类模型（例如Inception V3），`batch_size`和`dims`为可选参数，分别表示每批次的图像数量和特征向量的维度。