Function Differences with torch.meshgrid
torch.meshgrid
torch.meshgrid(
*tensors)
For more information, see torch.meshgrid.
mindspore.ops.meshgrid
mindspore.ops.meshgrid(*inputs, indexing='xy')
For more information, see mindspore.ops.meshgrid.
Differences
PyTorch: Generate the grid matrix from the given tensors. If it is a list of scalars, the scalar will automatically be considered as a tensor of size (1,).
MindSpore: MindSpore API basically implements the same function as TensorFlow. The inputs parameter is only supported for Tensor, not for scalar.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
|---|---|---|---|---|
Parameters |
Parameter 1 |
tensors |
inputs |
Same function |
Parameter 2 |
- |
indexing |
torch.meshgrid v1.8.1 has no parameter |
Code Example 1
# PyTorch
import numpy as np
import torch
x = torch.tensor(np.array([1, 2, 3, 4]).astype(np.int32))
y = torch.tensor(np.array([5, 6, 7]).astype(np.int32))
z = torch.tensor(np.array([8, 9, 0, 1, 2]).astype(np.int32))
output = torch.meshgrid(x, y, z)
print(output)
# (tensor([[[1, 1, 1, 1, 1],
# [1, 1, 1, 1, 1],
# [1, 1, 1, 1, 1]],
# [[2, 2, 2, 2, 2],
# [2, 2, 2, 2, 2],
# [2, 2, 2, 2, 2]],
# [[3, 3, 3, 3, 3],
# [3, 3, 3, 3, 3],
# [3, 3, 3, 3, 3]],
# [[4, 4, 4, 4, 4],
# [4, 4, 4, 4, 4],
# [4, 4, 4, 4, 4]]], dtype=torch.int32), tensor([[[5, 5, 5, 5, 5],
# [6, 6, 6, 6, 6],
# [7, 7, 7, 7, 7]],
# [[5, 5, 5, 5, 5],
# [6, 6, 6, 6, 6],
# [7, 7, 7, 7, 7]],
# [[5, 5, 5, 5, 5],
# [6, 6, 6, 6, 6],
# [7, 7, 7, 7, 7]],
# [[5, 5, 5, 5, 5],
# [6, 6, 6, 6, 6],
# [7, 7, 7, 7, 7]]], dtype=torch.int32), tensor([[[8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2]],
# [[8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2]],
# [[8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2]],
# [[8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2]]], dtype=torch.int32))
# MindSpore
import mindspore
import numpy as np
from mindspore import Tensor
x = Tensor(np.array([1, 2, 3, 4]).astype(np.int32))
y = Tensor(np.array([5, 6, 7]).astype(np.int32))
z = Tensor(np.array([8, 9, 0, 1, 2]).astype(np.int32))
output = mindspore.ops.meshgrid(x, y, z, indexing='ij')
print(output)
# (Tensor(shape=[4, 3, 5], dtype=Int32, value=
# [[[1, 1, 1, 1, 1],
# [1, 1, 1, 1, 1],
# [1, 1, 1, 1, 1]],
# [[2, 2, 2, 2, 2],
# [2, 2, 2, 2, 2],
# [2, 2, 2, 2, 2]],
# [[3, 3, 3, 3, 3],
# [3, 3, 3, 3, 3],
# [3, 3, 3, 3, 3]],
# [[4, 4, 4, 4, 4],
# [4, 4, 4, 4, 4],
# [4, 4, 4, 4, 4]]]), Tensor(shape=[4, 3, 5], dtype=Int32, value=
# [[[5, 5, 5, 5, 5],
# [6, 6, 6, 6, 6],
# [7, 7, 7, 7, 7]],
# [[5, 5, 5, 5, 5],
# [6, 6, 6, 6, 6],
# [7, 7, 7, 7, 7]],
# [[5, 5, 5, 5, 5],
# [6, 6, 6, 6, 6],
# [7, 7, 7, 7, 7]],
# [[5, 5, 5, 5, 5],
# [6, 6, 6, 6, 6],
# [7, 7, 7, 7, 7]]]), Tensor(shape=[4, 3, 5], dtype=Int32, value=
# [[[8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2]],
# [[8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2]],
# [[8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2]],
# [[8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2],
# [8, 9, 0, 1, 2]]]))