比较与torch.nn.functional.normalize的功能差异

torch.nn.functional.normalize

torch.nn.functional.normalize(
    input,
    p=2,
    dim=1,
    eps=1e-12,
    out=None
)

更多内容详见torch.nn.functional.normalize。

mindspore.ops.L2Normalize

class mindspore.ops.L2Normalize(
    axis=0,
    epsilon=1e-4
)(input_x)

更多内容详见mindspore.ops.L2Normalize。

使用方式

PyTorch：支持通过指定参数p来使用Lp范式。

MindSpore：仅支持L2范式。

代码示例

import mindspore
from mindspore import Tensor
import mindspore.ops as ops
import torch
import numpy as np

# In MindSpore, you can directly pass data into the function, and the default dimension is 0.
l2_normalize = ops.L2Normalize()
input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
output = l2_normalize(input_x)
print(output)
# Out：
# [0.2673 0.5345 0.8018]

# In torch, parameter p should be set to determine it is a lp normalization, and the default dimension is 1.
input_x = torch.tensor(np.array([1.0, 2.0, 3.0]))
outputL2 = torch.nn.functional.normalize(input=input_x, p=2, dim=0)
outputL3 = torch.nn.functional.normalize(input=input_x, p=3, dim=0)
print(outputL2)
print(outputL3)
# Out：
# tensor([0.2673, 0.5345, 0.8018], dtype=torch.float64)
# tensor([0.3029, 0.6057, 0.9086], dtype=torch.float64)