这篇教程pytorch 如何把图像数据集进行划分成train,test和val写得很实用，希望能帮到您。
1、手上目前拥有数据集是一大坨，没有train，test，val的划分如图所示
2、目录结构：|---data |---dslr |---images |---back_pack |---a.jpg |---b.jpg ... 3、转换后的格式如图
目录结构为： |---datanews |---dslr |---images |---test |---train |---valid |---back_pack |---a.jpg |---b.jpg ... 4、代码如下：4.1 先创建同样结构的层级结构 4.2 然后讲原始数据按照比例划分 4.3 移入到对应的文件目录里面 import os, random, shutildef make_dir(source, target): ''' 创建和源文件相似的文件路径函数 :param source: 源文件位置 :param target: 目标文件位置 ''' dir_names = os.listdir(source) for names in dir_names: for i in ['train', 'valid', 'test']: path = target + '/' + i + '/' + names if not os.path.exists(path): os.makedirs(path)def divideTrainValiTest(source, target): ''' 创建和源文件相似的文件路径 :param source: 源文件位置 :param target: 目标文件位置 ''' # 得到源文件下的种类 pic_name = os.listdir(source) # 对于每一类里的数据进行操作 for classes in pic_name: # 得到这一种类的图片的名字 pic_classes_name = os.listdir(os.path.join(source, classes)) random.shuffle(pic_classes_name) # 按照8：1：1比例划分 train_list = pic_classes_name[0:int(0.8 * len(pic_classes_name))] valid_list = pic_classes_name[int(0.8 * len(pic_classes_name)):int(0.9 * len(pic_classes_name))] test_list = pic_classes_name[int(0.9 * len(pic_classes_name)):] # 对于每个图片，移入到对应的文件夹里面 for train_pic in train_list: shutil.copyfile(source + '/' + classes + '/' + train_pic, target + '/train/' + classes + '/' + train_pic) for validation_pic in valid_list: shutil.copyfile(source + '/' + classes + '/' + validation_pic, target + '/valid/' + classes + '/' + validation_pic) for test_pic in test_list: shutil.copyfile(source + '/' + classes + '/' + test_pic, target + '/test/' + classes + '/' + test_pic)if __name__ == '__main__': filepath = r'../data/dslr/images' dist = r'../datanews/dslr/images' make_dir(filepath, dist) divideTrainValiTest(filepath, dist) 补充：pytorch中数据集的划分方法及eError: take(): argument 'index' (position 1) must be Tensor, not numpy.ndarray错误原因 在使用pytorch框架时，难免需要对数据集进行训练集和验证集的划分，一般使用sklearn.model_selection中的train_test_split方法 该方法使用如下：from sklearn.model_selection import train_test_splitimport numpy as npimport torchimport torch.autograd import Variablefrom torch.utils.data import DataLoader traindata = np.load(train_path) # image_num * W * Htrainlabel = np.load(train_label_path)train_data = traindata[:, np.newaxis, ...]train_label_data = trainlabel[:, np.newaxis, ...] x_tra, x_val, y_tra, y_val = train_test_split(train_data, train_label_data, test_size=0.1, random_state=0) # 训练集和验证集使用9:1 x_tra = Variable(torch.from_numpy(x_tra))x_tra = x_tra.float()y_tra = Variable(torch.from_numpy(y_tra))y_tra = y_tra.float() x_val = Variable(torch.from_numpy(x_val))x_val = x_val.float()y_val = Variable(torch.from_numpy(y_val))y_val = y_val.float() # 训练集的DataLoadertraindataset = torch.utils.data.TensorDataset(x_tra, y_tra)trainloader = DataLoader(dataset=traindataset, num_workers=opt.threads, batch_size=8, shuffle=True) # 验证集的DataLoadervalidataset = torch.utils.data.TensorDataset(x_val, y_val)valiloader = DataLoader(dataset=validataset, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True) 注意：如果按照如下方式使用，就会报eError: take(): argument 'index' (position 1) must be Tensor, not numpy.ndarray错误 from sklearn.model_selection import train_test_splitimport numpy as npimport torchimport torch.autograd import Variablefrom torch.utils.data import DataLoader traindata = np.load(train_path) # image_num * W * Htrainlabel = np.load(train_label_path) train_data = traindata[:, np.newaxis, ...]train_label_data = trainlabel[:, np.newaxis, ...] x_train = Variable(torch.from_numpy(train_data))x_train = x_train.float()y_train = Variable(torch.from_numpy(train_label_data))y_train = y_train.float()# 将原始的训练数据集分为训练集和验证集，后面就可以使用早停机制x_tra, x_val, y_tra, y_val = train_test_split(x_train, y_train, test_size=0.1) # 训练集和验证集使用9:1 报错原因：train_test_split方法接受的x_train,y_train格式应该为numpy.ndarray 而不应该是Tensor，这点需要注意。 以上为个人经验，希望能给大家一个参考，也希望大家多多支持51zixue.net。
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