使用 SubsetRandomSampler 拆分数据集不起作用

Question

我使用将训练数据拆分为训练 (80%) 和验证数据 (20%)。但它在拆分后显示了相同数量的图像 (4996)：

>>> print('len(train_data): ', len(train_loader.dataset))
>>> print('len(valid_data): ', len(validation_loader.dataset))

len(train_data):  4996
len(valid_data):  4996

完整代码：

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

train_transforms = transforms.Compose([transforms.ToTensor(),
                                       transforms.Normalize([0.485, 0.456, 0.406],
                                                            [0.229, 0.224, 0.225])])
dataset = datasets.ImageFolder( '/data/images/train', transform=train_transforms )

validation_split = .2
shuffle_dataset = True
random_seed= 42
batch_size = 20

dataset_size = len(dataset) #4996
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))

if shuffle_dataset :
    np.random.seed(random_seed)
    np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]

train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)

train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, sampler=train_sampler)
validation_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, sampler=valid_sampler)

Answer 1

train_loader.dataset 和 validation_loader.dataset 是 return 加载程序从中采样的基础 原始数据集 的方法（即原始数据集大小4996).

如果您遍历加载器本身，您将看到它们 return 与您在每个采样器的索引中包含的样本一样多。

使用 SubsetRandomSampler 拆分数据集不起作用

Spliting the dataset using SubsetRandomSampler not working

python

python-3.x

random

pytorch

pytorch-dataloader