Differences with torch.poisson
torch.poisson
torch.poisson(input, generator=None)
For more information, see torch.poisson.
mindspore.ops.random_poisson
mindspore.ops.random_poisson(shape, rate, seed=None, dtype=mstype.float32)
For more information, see mindspore.ops.random_poisson.
Differences
API function of MindSpore is consistent with that of PyTorch.
PyTorch: The shape and data type of the return value are the same as input
.
MindSpore: shape
determines the shape of the random number tensor sampled under each distribution, and the shape of the return value is mindspore.concat([shape, mindspore.shape(rate)], axis=0)
. The data type of the return value is determined by dtype
.
Categories |
Subcategories |
PyTorch |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
- |
shape |
The shape of the random number tensor sampled under each distribution under MindSpore |
Parameter 2 |
input |
rate |
Parameters of the Poisson distribution |
|
Parameter 3 |
generator |
seed |
For details, see General Difference Parameter Table |
|
Parameter 4 |
- |
dtype |
The data type of the returned value in MindSpore supports int32/64, float16/32/64 |
Code Example
# PyTorch
import torch
import numpy as np
rate = torch.tensor(np.array([[5.0, 10.0], [5.0, 1.0]]), dtype=torch.float32)
output = torch.poisson(rate)
print(output.shape)
# torch.Size([2, 2])
# MindSpore
import mindspore as ms
import numpy as np
shape = ms.Tensor(np.array([1]), ms.int32)
rate = ms.Tensor(np.array([[5.0, 10.0], [5.0, 1.0]]), dtype=ms.float32)
output = ms.ops.random_poisson(shape, rate, dtype=ms.float32)
output = ms.ops.reshape(output, (2, 2))
print(output.shape)
# (2, 2)