使用pytorch划分数据集和加载数据

1 划分数据集

使用pytorch需要 手动 划分数据集为训练集、验证集、测试集。以下面这个数据集为例对数据集进行划分

#导入工具包
import os
import random
import shutil
from shutil import copy2

def data_set_split(src_data_folder,target_data_folder,train_scale=0.8,val_scale=0.1,test_scale=0.1):
    '''
    src_data_folder:源文件夹（未划分的数据集）
    target_data_folder:目标文件夹（划分后的数据集）
    scale:分别为训练集、验证集、测试集所占比例
    '''

    class_names=os.listdir(src_data_folder)
    #返回源数据文件夹下各文件的名字（类名）

    #在目标文件夹下创建训练集、验证集、测试集文件夹
    split_names=['train','val','test']
    for split_name in split_names:
        split_path=os.path.join(target_data_folder,split_name)
        if os.path.isdir(split_path):
            pass
        else:
            os.makedirs(split_path)

    #在三个数据集文件夹下创建类别文件夹
    for class_name in class_names:
        class_split_path=os.path.join(split_path,class_name)
        if os.path.isdir(class_split_path):
            pass
        else:
            os.makedirs(class_split_path)

    #按照scale划分数据集，并将源文件夹的数据复制到目标文件夹
    #首先遍历数据
    for class_name in class_names:
        current_class_data_path=os.path.join(src_data_folder,class_name)
        current_class_data=os.listdir(current_class_data_path)
        current_data_length=len(current_class_data)
        current_data_index_list=list(range(current_data_length))
        random.shuffle(current_data_index_list)#将所有元素随机排序

        #添加数据
        train_folder=os.path.join(target_data_folder,'train',class_name)
        val_folder=os.path.join(target_data_folder,'val',class_name)
        test_folder=os.path.join(target_data_folder,'test',class_name)
        train_stop_flag=current_data_length*train_scale
        val_stop_flag=current_data_length*(train_scale+val_scale)
        current_idx=0
        train_num=0
        val_num=0
        test_num=0
        for i in current_data_index_list:
            src_img_path=os.path.join(current_class_data_path,current_class_data[i])
            if current_idx<=train_stop_flag:
                copy2(src_img_path,train_folder)
                train_num=train_num+1
            elif current_idx>train_stop_flag and current_idx <=val_stop_flag:
                copy2(src_img_path,val_folder)
                val_num=val_num+1
            else:
                copy2(src_img_path,test_folder)
                test_num=test_num+1
            current_idx=current_idx+1

	print("{}类按照{}:{}:{}的比例划分完成,一共{}张图片".format(class_name,train_scale,val_scale,test_scale))
    print("训练集{}:{}张".format(train_folder,train_num))
    print("验证集{}:{}张".format(val_folder,val_num))
    print("测试集{}:{}张".format(test_folder,test_num))

if __name__ =='__main__':
    src_data_folder='./hymenoptera_data/train'
    target_data_folder='./target_dataset'
    data_set_split(src_data_folder,target_data_folder)

实验结果

2 加载数据

训练时：进行数据增强和归一化
验证时：归一化

import os
import torch
from torchvision import transforms, datasets

data_transforms={
    
    
    "train":transforms.Compose([
        transforms.RandomResizedCrop(224),#随即裁剪
        transforms.RandomHorizontalFlip(),#随机水平翻转
        transforms.ToTensor(),#RGB——>tensor
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),

    "val":transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
}

data_dir='./hymenoptera_data'
image_datasets={
    
    x:datasets.ImageFolder(os.path.join(data_dir,x),
                                       data_transforms[x])
                    for x in ['train','val']}
'''
ImageFolder(root,transform=None,target_transform=None,loader=default_loader)

root: 图片总目录，子层级为各类型对应的文件目录。
transform: 对PIL image进行转换操作，
'''

dataloaders={
    
    x:torch.utils.data.DataLoader(image_datasets[x],batch_size=4,
                                           shuffle=True,num_workers=1)
             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:1" if torch.cuda.is_available() else "cpu")