FID pytorch代码
时间: 2024-03-10 12:41:41 浏览: 31
FID(Fréchet Inception Distance)是一种用于评生成模型和真实数据分布之间差异的指标。下面是一个使用PyTorch实现FID的代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import inception_v3
from torchvision import transforms
from scipy.linalg import sqrtm
import numpy as np
def calculate_activation_statistics(images, model, batch_size=50, dims=2048):
model.eval()
act_values = np.zeros((images.shape[0], dims))
dataloader = torch.utils.data.DataLoader(images, batch_size=batch_size)
for i, batch in enumerate(dataloader, 0):
batch = batch.cuda()
with torch.no_grad():
pred = model(batch)[0]
act_values[i * batch_size: i * batch_size + batch.size(0)] = pred.cpu().data.numpy().reshape(batch.size(0), -1)
mu = np.mean(act_values, axis=0)
sigma = np.cov(act_values, rowvar=False)
return mu, sigma
def calculate_frechet_distance(mu1, sigma1, mu2, sigma2):
diff = mu1 - mu2
covmean, _ = sqrtm(sigma1.dot(sigma2), disp=False)
if np.iscomplexobj(covmean):
covmean = covmean.real
return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * np.trace(covmean)
def calculate_fid_score(real_images, fake_images, batch_size=50):
assert len(real_images) == len(fake_images), "The number of real and fake images must be the same."
model = inception_v3(pretrained=True, transform_input=False).cuda()
model.fc = nn.Identity()
real_mu, real_sigma = calculate_activation_statistics(real_images, model, batch_size)
fake_mu, fake_sigma = calculate_activation_statistics(fake_images, model, batch_size)
fid_score = calculate_frechet_distance(real_mu, real_sigma, fake_mu, fake_sigma)
return fid_score
# 示例用法
real_images = ... # 真实图像数据
fake_images = ... # 生成的图像数据
fid_score = calculate_fid_score(real_images, fake_images)
print("FID score:", fid_score)
```
这段代码使用了PyTorch和torchvision库来加载预训练的Inception V3模型,并计算真实图像和生成图像的FID分数。首先,通过`calculate_activation_statistics`函数计算真实图像和生成图像在Inception V3模型的中间层的激活值的均值和协方差矩阵。然后,使用`calculate_frechet_distance`函数计算两个分布之间的Fréchet距离。最后,通过`calculate_fid_score`函数计算真实图像和生成图像的FID分数。
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