Differences with torch.Tensor.scatter_
torch.Tensor.scatter_
torch.Tensor.scatter_(dim, index, src, reduce) -> Tensor
For more information, see torch.Tensor.scatter_.
mindspore.ops.tensor_scatter_elements
mindspore.ops.tensor_scatter_elements(
input_x,
indices,
updates,
axis=0,
reduction='none'
) -> Tensor
For more information, see mindspore.ops.tensor_scatter_elements.
Differences
PyTorch: Replaces the element at the specified index position in the Tensor with the given value.
MindSpore: MindSpore API implements the same function as PyTorch, which is a Tensor interface with a slightly different invocation method in PyTorch.
Categories |
Subcategories |
PyTorch |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
dim |
axis |
Same function, different parameter names |
Parameter 2 |
index |
indices |
Same function, different parameter names |
|
Parameter 3 |
src |
updates |
Same function, different parameter names |
|
Parameter 4 |
reduce |
reduction |
Specification computation method. MindSpore only supports "none" and "add" modes. |
|
Parameter 5 |
- |
input_x |
This interface is the Tensor interface in PyTorch |
Code Example
The two APIs achieve the same function.
# PyTorch
import torch
t = torch.zeros((3, 4), dtype=torch.float32)
indices = torch.tensor([[1, 2], [0, 1]])
values = torch.tensor([[3, 4], [5, 6]], dtype=torch.float32)
t.scatter_(0, indices, values)
print(t)
# tensor([[5., 0., 0., 0.],
# [3., 6., 0., 0.],
# [0., 4., 0., 0.]])
# MindSpore
import numpy as np
import mindspore
from mindspore import Tensor, Parameter
from mindspore import ops
input_x = Parameter(Tensor(np.zeros((3, 4)), mindspore.float32), name="x")
indices = Tensor(np.array([[1, 2], [0, 1]]), mindspore.int32)
updates = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32)
axis = 0
reduction = "none"
output = ops.tensor_scatter_elements(input_x, indices, updates, axis, reduction)
print(output)
# [[5. 0. 0. 0.]
# [3. 6. 0. 0.]
# [0. 4. 0. 0.]]