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) . When the value of shape is Tensor([]), the shape of the return value is the same as that in PyTorch, which is the same as the shape of rate. 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, the shape of the return value is the same as PyTorch when the value Tensor([])
	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([]), 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)
print(output.shape)
# (2, 2)