#保姆级教学 「图像评价指标」（MSE、LPIPS）——理论+代码

• *均方误差MSE

给定一个大小为 m n的原图I和生成图K，计算均方误（ MSE*）定义为：的干净图像和噪声图像，均方误差定义为：

#原图为I，生成图为K

#pytorch ——直接调用torch.nn.MSELoss()函数
function = torch.nn.MSELoss()
mse_loss = funciton(I, K)

#tensorflow 1.x
mse_loss = tf.keras.losses.MSE(I, K)

#tensorflow 2.x
mse_loss = tf.losses.MSE(I, K)

• *LPIPS

学习感知图像块相似度(Learned Perceptual Image Patch Similarity, LPIPS)也称为" 感知损失"(perceptual loss)，用于度量两张图像之间的差别，来源于论文《The Unreasonable Effectiveness of Deep Features as a Perceptual Metric》。

论文地址：

https://arxiv.org/pdf/1801.03924.pdf

代码地址：

pytorch:https://github.com/richzhang/PerceptualSimilarity

tensorflow:https://github.com/alexlee-gk/lpips-tensorflow

计算相似度需逐层计算网络输出的对应channel的Cos Distance，然后对得到的distance进行平均（所有层，空间维度），LPIPS主要是把两个Cos Distance作为网络的输入，然后用Cross Entropy Loss训练网络学习2AFC。

计算与 之间的距离:给定不同的BaseNet F，首先计算深度嵌入，规格化通道维度中的激活，用向量缩放每个通道，取距离，然后对空间维度和所有层次求平均。

从层提取特征堆并在通道维度中进行单元标准化。通过缩放激活通道维并计算 距离

，在空间上取平均，在层上求和。

#pytorch 求LPIPS

import torch
import lpips
import os

use_gpu = False         # Whether to use GPU
spatial = True         # Return a spatial map of perceptual distance.

# Linearly calibrated models (LPIPS)
loss_fn = lpips.LPIPS(net='alex', spatial=spatial) # Can also set net = 'squeeze' or 'vgg'
# loss_fn = lpips.LPIPS(net='alex', spatial=spatial, lpips=False) # Can also set net = 'squeeze' or 'vgg'

if(use_gpu):
    loss_fn.cuda()

## Example usage with dummy tensors
rood_path = r'D:\Project\results\faces'
img0_path_list = []
img1_path_list = []
## path in net is already exist
'''
for root, _, fnames in sorted(os.walk(rood_path, followlinks=True)):
    for fname in fnames:
        path = os.path.join(root, fname)
        if '_generated' in fname:
            im0_path_list.append(path)
        elif '_real' in fname:
            im1_path_list.append(path)
'''

dist_ = []
for i in range(len(img0_path_list)):
    dummy_img0 = lpips.im2tensor(lpips.load_image(img0_path_list[i]))
    dummy_img1 = lpips.im2tensor(lpips.load_image(img1_path_list[i]))
    if(use_gpu):
        dummy_img0 = dummy_img0.cuda()
        dummy_img1 = dummy_img1.cuda()
    dist = loss_fn.forward(dummy_img0, dummy_img1)
    dist_.append(dist.mean().item())
print('Avarage Distances: %.3f' % (sum(dist_)/len(img0_path_list)))

需要注意的是tensorflow版本需要 下载.pb数据文件：

http://rail.eecs.berkeley.edu/models/lpips/

#tensorflow 求LPIPS

import numpy as np
import tensorflow as tf
import lpips_tf

batch_size = 32
image_shape = (batch_size, 64, 64, 3)
image0 = np.random.random(image_shape)  #read real image
image1 = np.random.random(image_shape)  #read generate image
image0_ph = tf.placeholder(tf.float32)
image1_ph = tf.placeholder(tf.float32)

distance_t = lpips_tf.lpips(image0_ph, image1_ph, model='net-lin', net='alex')

with tf.Session() as session:
    distance = session.run(distance_t, feed_dict={image0_ph: image0, image1_ph: image1})

Original: https://blog.csdn.net/m0_49629753/article/details/121547634
Author: 砸吧砸吧
Title: #保姆级教学 「图像评价指标」（MSE、LPIPS）——理论+代码