Differences with torch.utils.data.SubsetRandomSampler

torch.utils.data.SubsetRandomSampler

class torch.utils.data.SubsetRandomSampler(indices, generator=None)

For more information, see torch.utils.data.SubsetRandomSampler.

mindspore.dataset.SubsetRandomSampler

class mindspore.dataset.SubsetRandomSampler(indices, num_samples=None)

For more information, see mindspore.dataset.SubsetRandomSampler.

Differences

PyTorch: Samples the elements randomly from a sequence of indices, random generator can be set manually.

MindSpore: Samples the elements randomly from a sequence of indices, random generator is not supported.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameter	Parameter 1	indices	indices	-
	Parameter 2	generator	-	Specifies sampling logic. MindSpore uses global random sampling.
	Parameter 3	-	num_samples	Specify the number of samples returned by the sampler

Code Example

import torch
from torch.utils.data import SubsetRandomSampler

torch.manual_seed(0)

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 = SubsetRandomSampler(indices=[0, 2])
dataloader = torch.utils.data.DataLoader(ds, sampler=sampler)

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

import mindspore as ms
from mindspore.dataset import SubsetRandomSampler

ms.dataset.config.set_seed(1)

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 = SubsetRandomSampler(indices=[0, 2])
dataloader = ms.dataset.GeneratorDataset(ds, column_names=["data"], sampler=sampler)

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