Question

嗨，现在我收到了数据加载代码，我不确定如何将其拆分为训练和测试数据。谁能给我建议，这是我的数据加载代码。

def __init__(self, root, specific_folder, img_extension, preprocessing_method=None, crop_size=(96, 112),train = True):
    """
    Dataloader of the LFW dataset.

    root: path to the dataset to be used.
    specific_folder: specific folder inside the same dataset.
    img_extension: extension of the dataset images.
    preprocessing_method: string with the name of the preprocessing method.
    crop_size: retrieval network specific crop size.
    """

    self.preprocessing_method = preprocessing_method
    self.crop_size = crop_size
    self.imgl_list = []
    self.classes = []
    self.people = []
    self.model_align = None
    self.arr = []

    # read the file with the names and the number of images of each people in the dataset
    with open(os.path.join(root, 'people.txt')) as f:
        people = f.read().splitlines()[1:]

    # get only the people that have more than 20 images
    for p in people:
        p = p.split('\t')
        if len(p) > 1:
            if int(p[1]) >= 20:
                for num_img in range(1, int(p[1]) + 1):
                    self.imgl_list.append(os.path.join(root, specific_folder, p[0], p[0] + '_' +
                                                       '{:04}'.format(num_img) + '.' + img_extension))
                    self.classes.append(p[0])
                    self.people.append(p[0])

    le = preprocessing.LabelEncoder()
    self.classes = le.fit_transform(self.classes)

    print(len(self.imgl_list), len(self.classes), len(self.people))

def __getitem__(self, index):
    imgl = imageio.imread(self.imgl_list[index])
    cl = self.classes[index]

    # if image is grayscale, transform into rgb by repeating the image 3 times
    if len(imgl.shape) == 2:
        imgl = np.stack([imgl] * 3, 2)

    imgl, bb = preprocess(imgl, self.preprocessing_method, crop_size=self.crop_size,
                          is_processing_dataset=True, return_only_largest_bb=True, execute_default=True)

    # append image with its reverse
    imglist = [imgl, imgl[:, ::-1, :]]

    # normalization
    for i in range(len(imglist)):
        imglist[i] = (imglist[i] - 127.5) / 128.0
        imglist[i] = imglist[i].transpose(2, 0, 1)
    imgs = [torch.from_numpy(i).float() for i in imglist]

    return imgs, cl, imgl, bb, self.imgl_list[index], self.people[index]

def __len__(self):
    return len(self.imgl_list)

我需要将其中的数据分成20％和80％的数据，这样我才可以测试我的模块，已经快一个星期了，但仍然完全不知道如何去做，如果有人可以提供帮助，将不胜感激：

Answer 1

通常使用PyTorch：

import torch
import numpy as np
from torchvision import datasets
from torchvision import transforms
from torch.utils.data.sampler import SubsetRandomSampler

dataset = yourdatahere
batch_size = 16 #change to whatever you'd like it to be
test_split = .2
shuffle_dataset = True
random_seed= 42

# Creating data indices for training and validation splits:
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(test_split * dataset_size))
if shuffle_dataset :
    np.random.seed(random_seed)
    np.random.shuffle(indices)
train_indices, test_indices = indices[split:], indices[:split]

# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
test_sampler = SubsetRandomSampler(test_indices)

train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, 
                                           sampler=train_sampler)
test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                                                sampler=test_sampler)

# Usage Example:
num_epochs = 10
for epoch in range(num_epochs):
    # Train:   
    for batch_index, (faces, labels) in enumerate(train_loader):
        # ...

请注意，您还应该将训练数据分为训练+验证数据。您可以使用上面相同的逻辑。

如何将数据分为训练和测试数据

1 个答案: