FID代码pytorch环境下的

FID（Fréchet Inception Distance）是一种用于评估生成对抗网络（GAN）生成图像质量的指标。以下是在PyTorch环境下计算FID的示例代码：

import torch
from torch.nn.functional import adaptive_avg_pool2d
from torchvision.models import inception_v3
import numpy as np
from scipy import linalg

def get_activations(images, model, batch_size=50, dims=2048):
    """计算图像集的Inception-v3特征向量"""
    model.eval()

    # 激活值列表
    act = np.zeros((images.shape[0], dims))

    # 加载批次
    for i in range(0, images.shape[0], batch_size):
        batch = torch.from_numpy(images[i:i+batch_size]).type(torch.FloatTensor)
        batch = batch.cuda()
        with torch.no_grad():
            pred = model(batch)[0]

        # pool到一个维度
        pred = adaptive_avg_pool2d(pred, output_size=(1, 1)).squeeze(dim=2).squeeze(dim=2).cpu().numpy()
        act[i:i+batch_size] = pred

    return act

def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
    """计算两个高斯分布之间的FID"""
    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, 'mu1和mu2的形状不同'
    assert sigma1.shape == sigma2.shape, 'sigma1和sigma2的形状不同'

    diff = mu1 - mu2

    # sqrtm并不总是稳定，所以需要尝试/捕捉异常
    try:
        sqrtm = linalg.sqrtm(np.dot(sigma1, sigma2))
    except:
        print('FID计算过程中出现奇异值；添加eps以提高数值稳定性')
        offset = np.eye(sigma1.shape[0]) * eps
        sqrtm = linalg.sqrtm(np.dot(sigma1 + offset, sigma2 + offset))

    # 检查sqrtm是否为虚数
    if np.iscomplexobj(sqrtm):
        if not np.allclose(np.diagonal(sqrtm).imag, 0, atol=1e-3):
            m = np.max(np.abs(sqrtm.imag))
            raise ValueError('Imaginary component {}'.format(m))
        sqrtm = sqrtm.real

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

def calculate_fretchet(images_real, images_fake, batch_size):
    """计算真实和生成图像之间的FID"""
    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]
    model = InceptionV3([block_idx])

    # 计算真实图像的激活值
    act_real = get_activations(images_real, model, batch_size)
    # 计算生成图像的激活值
    act_fake = get_activations(images_fake, model, batch_size)

    # 计算真实图像的mu和sigma
    mu_real = np.mean(act_real, axis=0)
    sigma_real = np.cov(act_real, rowvar=False)

    # 计算生成图像的mu和sigma
    mu_fake = np.mean(act_fake, axis=0)
    sigma_fake = np.cov(act_fake, rowvar=False)

    # 计算FID
    fid = calculate_frechet_distance(mu_real, sigma_real, mu_fake, sigma_fake)

    return fid

要使用此代码，您需要安装PyTorch和torchvision。然后，您可以将真实和生成的图像作为Numpy数组传递给`calculate_fretchet`函数，并指定批处理大小。函数将返回FID分数。