DataLoader其实就是先根据sampler方法先采样,再切分出batch(比如样本有10个,SubsetRandomSampler返回一个下标,比如0到7,那么取出这8个数据,然后按照batch_size切分出一个个的batch) 实际应用: from torch.utils.data import DataLoader from torch.utils.data import sampler train_data = CriteoDataset('./data', tr...
这篇文章记录一个采样器都随机地从原始的数据集中抽样数据。抽样数据采用permutation。 生成任意一个下标重排,从而利用下标来提取dataset中的数据的方法class SubsetRandomSampler(Sampler[int]): r""…
按照网上可以搜集到的资料,Subset Random Sampler应该用于训练集、测试集和验证集的划分,下面将data划分为train和val两个部分,再次指出__iter__()返回的的不是索引,而是索引对应的数据: sub_sampler_train = sampler.SubsetRandomSampler(indices=data[0:2]) sub_sampler_val= sampler.SubsetRandomSampler(indices=da...
sample_size =len(train_dataset) sampler1 = torch.utils.data.sampler.SubsetRandomSampler( np.random.choice(range(len(train_dataset)), sample_size)) AI代码助手复制代码 代码详解 np.random.choice()#numpy.random.choice(a, size=None, replace=True, p=None) #从a(只要是ndarray都可以,但必须是一维...
from torch.utils.data import Subsetimport numpy as np # 创建一个子集,包含原始数据集的前20%的数据dataset_size = len(dataset)subset_size = int(0.2 * dataset_size)subset_indices = np.random.choice(dataset_size, subset_size, repl...
3、SubsetRandomSampler 代码语言:javascript 复制 classSubsetRandomSampler(Sampler):r"""Samples elements randomly from a given listofindices,without replacement.Arguments:indices(sequence):a sequenceofindices""" def__init__(self,indices):self.indices=indices ...
RandomSampler初始化形式:torch.utils.data.RandomSampler(data_source, replacement=False, num_samples=None, generator=None)。RandomSampler初始化参数除了data_source还有以下2个。 num_samples: 指定采样的数量,默认是所有。 replacement: 默认是False,若为True,则表示可以重复采样,即同一个样本可以重复采样,这样可能...
子类Sampler: 子类包含: Sequential Sampler(顺序采样) Random Sampler(随机采样) Subset Random Sampler(子集随机采样) Weighted Random Sampler(加权随机采样) Sequential Sampler classSequentialSampler(Sampler):r"""Samples elements sequentially, always in the same order.Arguments:data_source (Dataset): dataset ...
pytorch随机采样操作SubsetRandomSampler（）
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, ...