python实现将两个文件夹合并至另一个文件夹(制作数据集)

#导入一些进行该操作需要的库
import numpy as np
import os
import random
import shutil

path=r'C:UsersAdministrator.SKY-20180518VHYDesktoprxore'#原始数据集的路径
data=os.listdir(path)
#listdir该操作（[添加链接描述](https://www.zalou.cn/article/184106.htm)）在我的上篇文章中有所介绍，此操作能读取的内容为A01、A02、A03、B01、B02、B03这些文件夹
#print(data)

root=path#复制原始数据路径path

c=[]
d=[]#创建两个空列表
for i in range(len(data)):
 a=data[i][0]
 if (a=='A'):
  c.append(data[i])
 else:
  d.append(data[i])
#print(d)

train_root='C:UsersAdministrator.SKY-20180518VHYDesktopmyselftrain'
val_root='C:UsersAdministrator.SKY-20180518VHYDesktopmyselfval'
vall_root='C:UsersAdministrator.SKY-20180518VHYDesktopmyselfvalb'
valb_root='C:UsersAdministrator.SKY-20180518VHYDesktopmyselfvall'


for i in range(len(c)):
 qqq=os.path.exists(train_root+'/'+c[i][1:])
 if (not qqq):
  os.mkdir(train_root+'/'+c[i][1:])
  qq=os.path.exists(val_root+'/'+c[i][1:])
  if (not qq):
   os.mkdir(val_root+'/'+c[i][1:])
   qq=os.path.exists(vall_root+'/'+c[i][1:])
   if (not qq):
    os.mkdir(vall_root+'/'+c[i][1:])
    qq=os.path.exists(valb_root+'/'+c[i][1:])
    if (not qq):
     os.mkdir(valb_root+'/'+c[i][1:])
#f=[]
#g=[]
aq='C:UsersAdministrator.SKY-20180518VHYDesktoprxore'
train_root1='C:UsersAdministrator.SKY-20180518VHYDesktopmyselftrain'
val_root1='C:UsersAdministrator.SKY-20180518VHYDesktopmyselfval'
vall_root1='C:UsersAdministrator.SKY-20180518VHYDesktopmyselfvalb'
valb_root1='C:UsersAdministrator.SKY-20180518VHYDesktopmyselfvall'
for i in range(len(c)):
 a=c[i]
 data_0=os.listdir(aq+a)
# f.append(data_0)
# g.append(aq+a)
#print(f)
#print(g) 
 random.shuffle(data_0)#打乱A中数据
 for j in range(len(d)):
  b=d[j]
  if(a[1:]==b[1:]):
   data_1=os.listdir(aq+b)
   #print(aq+b);
   random.shuffle(data_1)
   #print(data_1)
   #print(data_0,data_1)
   for z in range(len(data_0)):
    #print(z)
    pic_path=aq+a+'/'+data_0[z]
    
    if z<int(len(data_0)*0.7):
     obj_path=train_root1+a[1:]+'/'+data_0[z]
     
    else:
     obj_path=val_root1+a[1:]+'/'+data_0[z]
     obl_path=vall_root1+a[1:]+'/'+data_0[z]
     shutil.copyfile(pic_path,obl_path)
   #print(len(data_0),len(data_0)*0.7)
    #if (os.path.exists(pic_path)):
    shutil.copyfile(pic_path,obj_path)
   for z in range(len(data_1)):
    pic_path=aq+b+'/'+data_1[z]
    if z<int(len(data_1)*0.7):
     obj_path=train_root1+b[1:]+'/'+data_1[z]
     
    else:
     obj_path=val_root1+b[1:]+'/'+data_1[z]
     obl_path=valb_root1+a[1:]+'/'+data_1[z]
     shutil.copyfile(pic_path,obl_path)
    #if (os.path.exists(pic_path)):
    shutil.copyfile(pic_path,obj_path)#shutil.copyfile( src, dst) 

from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
import math
import torch.nn.functional as F

D=299

data_transforms = {
 'train': transforms.Compose([
#  transforms.RandomResizedCrop(D),
  transforms.Resize(D),
  transforms.RandomCrop(D),
  transforms.RandomHorizontalFlip(),
  transforms.ToTensor(),
  transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
 ]),
 'val': transforms.Compose([
  transforms.Resize(D),
  transforms.CenterCrop(D),
  transforms.ToTensor(),
  transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
 ]),
}

data_dir = r'C:UsersAdministrator.SKY-20180518VHYDesktopmyself'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
           data_transforms[x])
     for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=200,
            shuffle=True, num_workers=4)
    for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#print(image_datasets['train'][0])
img, label = image_datasets['val'][11] 
print(label)#输出为2即第三类