计算fid需要多少张图片

计算FID (Fréchet Inception Distance) 的方法是通过比较两个图像分布的特征向量来评估生成模型的质量。为了计算FID，我们需要先使用生成模型生成一定数量的图像样本作为生成分布，然后再从真实图像数据集中选取一定数量的图像样本作为真实分布。这些图像样本可以是任意数量，一般情况下，选择的样本数量越多，FID的计算结果越准确。

在计算FID时，一般建议生成分布和真实分布的样本数量相当，这样可以保证对两种分布的全面比较。通常情况下，样本数量在几千至几万之间是比较常见的选择。然而，具体需要多少张图片取决于所研究的问题、实验设计以及计算资源的限制。

需要注意的是，计算FID需要使用深度学习模型，如Inception网络来提取图像特征。对于大规模数据集，计算FID可能需要较长的时间和更多的计算资源。因此，在计算FID时，需要权衡计算时间和计算资源的可用性，从而确定生成分布和真实分布的样本数量。

相关问题

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。

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。