比较与torch.utils.data.SequentialSampler的差异

torch.utils.data.SequentialSampler

class torch.utils.data.SequentialSampler(data_source)

更多内容详见torch.utils.data.SequentialSampler。

mindspore.dataset.SequentialSampler

class mindspore.dataset.SequentialSampler(start_index=None, num_samples=None)

更多内容详见mindspore.dataset.SequentialSampler。

差异对比

PyTorch：按数据集的顺序采样数据集样本。

MindSpore：按数据集的顺序采样数据集样本，支持指定顺序索引和样本数量。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	data_source	-	被采样的数据集对象，MindSpore不需要传入
	参数2	-	start_index	采样的起始样本索引
	参数3	-	num_samples	获取的样本数，可用于部分获取采样得到的样本

代码示例

import torch
from torch.utils.data import SequentialSampler

class MyMapDataset(torch.utils.data.Dataset):
    def __init__(self):
        super(MyMapDataset).__init__()
        self.data = [i for i in range(4)]
    def __getitem__(self, index):
        return self.data[index]
    def __len__(self):
        return len(self.data)

ds = MyMapDataset()
sampler = SequentialSampler(ds)
dataloader = torch.utils.data.DataLoader(ds, sampler=sampler)

for data in dataloader:
    print(data)
# Out:
# tensor([0])
# tensor([1])
# tensor([2])
# tensor([3])

import mindspore as ms
from mindspore.dataset import SequentialSampler

class MyMapDataset():
    def __init__(self):
        super(MyMapDataset).__init__()
        self.data = [i for i in range(4)]
    def __getitem__(self, index):
        return self.data[index]
    def __len__(self):
        return len(self.data)

ds = MyMapDataset()
sampler = SequentialSampler()
dataloader = ms.dataset.GeneratorDataset(ds, column_names=["data"], sampler=sampler)

for data in dataloader:
    print(data)
# Out:
# [Tensor(shape=[], dtype=Int64, value= 0)]
# [Tensor(shape=[], dtype=Int64, value= 1)]
# [Tensor(shape=[], dtype=Int64, value= 2)]
# [Tensor(shape=[], dtype=Int64, value= 3)]