比较与torch.utils.data.WeightedRandomSampler的差异
torch.utils.data.WeightedRandomSampler
class torch.utils.data.WeightedRandomSampler(weights, num_samples, replacement=True, generator=None)
mindspore.dataset.WeightedRandomSampler
class mindspore.dataset.WeightedRandomSampler(weights, num_samples=None, replacement=True)
差异对比
PyTorch:给定样本的权重列表,根据权重的大小对样本进行采样,支持指定采样逻辑。
MindSpore:给定样本的权重列表,根据权重的大小对样本进行采样,不支持指定采样逻辑。
分类 |
子类 |
PyTorch |
MindSpore |
差异 |
---|---|---|---|---|
参数 |
参数1 |
weights |
weights |
- |
参数2 |
num_samples |
num_samples |
- |
|
参数3 |
replacement |
replacement |
- |
|
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 WeightedRandomSampler
torch.manual_seed(0)
class MyMapDataset(torch.utils.data.Dataset):
def __init__(self):
super(MyMapDataset).__init__()
self.data = [i for i in range(1, 5)]
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)
ds = MyMapDataset()
sampler = WeightedRandomSampler(weights=[0.1, 0.1, 0.9, 0.9], num_samples=4)
dataloader = torch.utils.data.DataLoader(ds, sampler=sampler)
for data in dataloader:
print(data)
# Out:
# tensor([4])
# tensor([3])
# tensor([4])
# tensor([4])
import mindspore as ms
from mindspore.dataset import WeightedRandomSampler
ms.dataset.config.set_seed(4)
class MyMapDataset():
def __init__(self):
super(MyMapDataset).__init__()
self.data = [i for i in range(1, 5)]
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)
ds = MyMapDataset()
sampler = WeightedRandomSampler(weights=[0.1, 0.1, 0.9, 0.9], num_samples=4)
dataloader = ms.dataset.GeneratorDataset(ds, column_names=["data"], sampler=sampler)
for data in dataloader:
print(data)
# Out:
# [Tensor(shape=[], dtype=Int64, value= 4)]
# [Tensor(shape=[], dtype=Int64, value= 3)]
# [Tensor(shape=[], dtype=Int64, value= 4)]
# [Tensor(shape=[], dtype=Int64, value= 4)]