weighted_loader = DataLoader(dataset, batch_size=2, sampler=weighted_sampler) # 使用BatchSampler将样本索引分成多个批次 batch_sampler = torch.utils.data.sampler.BatchSampler(SequentialSampler(dataset), batch_size=2, drop_last=False) batch_loader = DataLoader(dataset, batch_sampler=batch_sampler) # ...
下面是使用上述Sampler类和函数的示例代码: import torchfrom torch.utils.data import DataLoaderfrom torch.utils.data.sampler import RandomSampler, SequentialSampler, SubsetRandomSampler, WeightedRandomSampler# 创建一个数据集dataset = torch.utils.data.TensorDataset(torch.randn(10, 3), torch.randint(0, 2,...
1. 自由地转换numpyarray 和torchtensorimport torchimportnumpyas np# convertnumpyto tensor or vise versanp_data = np.arange(6).reshape((2, 3))torch_data =torch.from_numpy(np_data)tensor2array =torch_data.numpy()print( '\nnumpy array:', np_d ...
---> 1 import torch.utils.data as data 2 from PIL import Image 3 import os 4 import os.path 5 import six ~/anaconda3/lib/python3.7/site-packages/torch/utils/data/__init__.py in <module> 3 from .distributed import DistributedSampler 4 from .dataset import Dataset, TensorDataset, Concat...
♦ 分布式需要指定 sampler DistIterSampler 的代码如下: """ Modified from torch.utils.data.distributed.DistributedSampler Support enlarging the dataset for *iter-oriented* training, for saving time when restart the dataloader after each epoch """ import math import torch import torch.distributed as ...
(indices) from torch.utils.data.sampler import SubsetRandomSampler # With the indices randomly shuffled, # grab the first 20% of the shuffled indices, and store them in the training index list # grab the remainder of the shuffled indices, and store them in the testing index list # Given...
例如,如果DistributedSampler实际上在torch.utils.data.distributed中,你应该这样导入它: python from torch.utils.data.distributed import DistributedSampler 但是,请注意,这只是一个示例,实际的导入路径可能因库和版本而异。 综上所述,解决这个问题的关键是确认DistributedSampler的正确来源和导入路径。如果它是...
import torch import torch.nn.functional as F from torch.utils.data import Dataset, DataLoader # NEW imports: import os import platform from torch.utils.data.distributed import DistributedSampler from torch.nn.parallel import DistributedDataParallel as DDP from torch.distributed import init_process_group...
importnumpyasnpimporttorchimporttorch.nnasnnfromtorchvisionimportdatasetsfromtorchvisionimporttransformsfromtorch.utils.data.samplerimportSubsetRandomSampler# Device configurationdevice=torch.device('cuda'iftorch.cuda.is_available()else'cpu') Copy Loading the Data ...
import pandas as pd from pytorch_pretrained_bert.tokenization import BertTokenizer from torch.nn import MSELoss from torch.nn import MSELoss, CrossEntropyLoss from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset) TensorDataset) from tqdm import tqdm_notebook as tqdm...