pix2pix、pix2pixHD 通过损失日志进行训练可视化

article/2025/8/26 22:23:58

背景

代码

结果

总结

背景

pix2pix(HD)代码在训练时会自动保存一个损失变化的txt文件，通过该文件能够对训练过程进行一个简单的可视化，代码如下。

训练的损失文件如图，对其进行可视化。

代码

#coding:utf-8
##
#author: QQ：1913434222 WeChat：Alocus
##
import matplotlib.pyplot as plt
import re
import numpy as np
import os,sysfile = "loss_logA - 副本.txt"
savepath = r"J:\混合样本去雾\调试代码&脚本&可视化\visualiz\\"
#dirs = os.listdir( filepath )
#for file in dirs:
#    if file.endswith('txt'):
#        f  = open(filepath+file,'r')
f  = open(file,'r')
lines = f.readlines()
G_GAN = []
G_GAN_Feat = []
G_VGG = []
G_KL= []
D_real = []
D_fake = []
G_featD = []
featD_real = []
featD_fake = []
total_data =[]for line in lines:if "(epoch" in line:if "G_GAN" in line :G_GAN_list = line.split()G_GAN.append(float(G_GAN_list[9]))if "G_GAN_Feat" in line :G_GAN_Feat_list = line.split()G_GAN_Feat.append(float(G_GAN_Feat_list[11]))if "G_VGG" in line :G_VGG_list = line.split()G_VGG.append(float(G_VGG_list[13]))if "G_KL" in line :G_KL_list = line.split()G_KL.append(float(G_KL_list[15]))if "D_real" in line:D_real_list = line.split()D_real.append(float(D_real_list[17]))if "D_fake" in line:D_fake_list = line.split()D_fake.append(float(D_fake_list[19]))if "G_featD" in line:G_featD_list = line.split()G_featD.append(float(G_featD_list[21]))if "featD_real" in line:featD_real_list = line.split()featD_real.append(float(featD_real_list[23]))if "featD_fake" in line:featD_fake_list = line.split()featD_fake.append(float(featD_fake_list[25]))total_data = [(G_GAN [i] + G_GAN_Feat[i]+G_VGG[i]+G_KL[i]+D_real[i]+D_fake[i]+G_featD[i]+featD_real[i]+featD_fake[i])  for i in range(0, len(featD_fake))]fig = plt.figure()#(figsize=(50,6))ax = np.linspace(0,len(featD_fake),len(featD_fake))plt.plot(ax, total_data, label="total")plt.plot(ax,G_GAN,label="G_GAN")plt.plot(ax,G_GAN_Feat,label="G_GAN_Feat")plt.plot(ax,G_VGG,label="G_VGG")plt.plot(ax,D_real,label="D_real")plt.plot(ax,D_real,label="D_fake")plt.plot(ax,G_KL,label="G_KL")plt.plot(ax,G_featD,label="G_featD")plt.plot(ax,featD_real,label="featD_real")plt.plot(ax,featD_fake,label="featD_fake")plt.grid(color='gray', linestyle='--', linewidth=1, alpha=0.3)
plt.legend()
plt.title('VAE$_1$')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.savefig(savepath+file.split('.txt')[0]+'.png')#G_GAN: 0.973 G_GAN_Feat: 7.709 G_VGG: 8.092 G_KL: 1.172 D_real: 0.908 D_fake: 0.870
# G_Feat_L2: 46.512 G_GAN: 3.917 G_GAN_Feat: 14.005 G_VGG: 8.741 D_real: 3.828 D_fake: 3.098