我正在尝试将 Pytorch 自定义数据集 (MNIST) 拆分为训练集和验证集,如下所示:
def get_train_valid_splits(data_dir,
batch_size,
random_seed=1,
valid_size=0.2,
shuffle=True,
num_workers=4,
pin_memory=False):
normalize = transforms.Normalize((0.1307,), (0.3081,)) # MNIST
# define transforms
valid_transform = transforms.Compose([
transforms.ToTensor(),
normalize
])
train_transform = transforms.Compose([
transforms.ToTensor(),
normalize
])
# load the dataset
train_dataset = datasets.MNIST(root=data_dir, train=True,
download=True, transform=train_transform)
valid_dataset = datasets.MNIST(root=data_dir, train=True,
download=True, transform=valid_transform)
dataset_size = len(train_dataset)
indices = list(range(dataset_size))
split = int(np.floor(valid_size * dataset_size))
if shuffle == True:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_idx, valid_idx = indices[split:], indices[:split]
train_sampler = sampler.SubsetRandomSampler(train_idx)
valid_sampler = sampler.SubsetRandomSampler(valid_idx)
print(len(train_sampler))
print(len(valid_sampler))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size, sampler=train_sampler,
num_workers=num_workers, pin_memory=pin_memory)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=batch_size, sampler=valid_sampler,
num_workers=num_workers, pin_memory=pin_memory)
print(len(train_loader.dataset))
print(len(valid_loader.dataset))
return (train_loader, valid_loader)
调用该函数后,我注意到采样索引的结果看起来是正确的,48000 和 12000:
print(len(train_sampler))
print(len(valid_sampler))
但是当我查看与 train_loader 和 valid_loader 相关的数据集的长度时:
print(len(train_loader.dataset))
print(len(valid_loader.dataset))
我得到的两者长度相同:60000!知道这是怎么回事吗?为什么它给两个相同的长度,即使我明确地按索引分割它?
这是因为数据加载器不会修改您传递给它的数据集,而是在您尝试通过迭代访问数据时“应用”批量大小、采样器等内容。您的问题是
len(loader.dataset)
,当您真正想要 len(loader)
时,它会为您提供未修改的所提供数据集的长度,这是“应用”批量大小和采样器等内容后的数据集长度。
import torch
import numpy as np
dataset = np.random.rand(100,200)
sampler = torch.utils.data.SubsetRandomSampler(list(range(70)))
loader = torch.utils.data.DataLoader(dataset, sampler=sampler)
print(len(loader))
>>> 70
print(len(loader.dataset))
>>> 100
注意: len 的结果会受到批量大小的影响:
# with batch size
loader = torch.utils.data.DataLoader(dataset, sampler=sampler, batch_size=2)
print(len(loader))
>>> 35
print(len(loader.dataset))
>>> 100
train_loader
和valid_loader
的长度相同是因为您对train_dataset
和valid_dataset
使用了相同的数据。
你想要
valid_dataset = datasets.MNIST(root=data_dir, train=False,
download=True, transform=valid_transform)
(不是
train=True
)下载验证集。