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)