比较与torch.utils.data.RandomSampler的差异
torch.utils.data.RandomSampler
class torch.utils.data.RandomSampler(data_source, replacement=False, num_samples=None, generator=None)
mindspore.dataset.RandomSampler
class mindspore.dataset.RandomSampler(replacement=False, num_samples=None)
差异对比
PyTorch:随机采样器,支持指定采样逻辑。
MindSpore:随机采样器,不支持指定采样逻辑。
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 |
replacement |
replacement |
- |
|
参数3 |
num_samples |
num_samples |
- |
|
参数4 |
generator |
- |
指定额外的采样逻辑,MindSpore为全局随机采样 |
代码示例
import torch
from torch.utils.data import RandomSampler
torch.manual_seed(1)
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 = RandomSampler(ds, num_samples=2, replacement=True)
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 RandomSampler
ms.dataset.config.set_seed(3)
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 = RandomSampler(num_samples=2, replacement=True)
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)]