The following mapping relationships can be found in this file.
PyTorch APIs | MindSpore APIs |
---|---|
torch.all | mindspore.ops.all |
torch.Tensor.all | mindspore.Tensor.all |
torch.all(input, dim, keepdim=False, *, out=None) -> Tensor
For more information, see torch.all.
mindspore.ops.all(x, axis=(), keep_dims=False) -> Tensor
For more information, see mindspore.ops.all.
PyTorch: Perform logic AND on the elements of input
according to the specified dim
. keepdim
controls whether the output and input have the same dimension. out
can fetch the output.
MindSpore: Perform logic AND on the elements of x
according to the specified axis
. The keep_dims
has the same function as PyTorch, and MindSpore does not have the out
parameter. MindSpore has a default value for axis
, and performs the logical AND on all elements of x
if axis
is the default.
Categories | Subcategories | PyTorch | MindSpore | Differences |
---|---|---|---|---|
Parameters | Parameter 1 | input | x | Same function, different parameter names |
Parameter 2 | dim | axis | PyTorch must pass dim and only one integer. MindSpore axis can be passed as an integer, a tuples of integers or a list of integers |
|
Parameter 3 | keepdim | keep_dims | Same function, different parameter names | |
Parameter 4 | out | - | PyTorch out can get the output. MindSpore does not have this parameter |
# PyTorch
import torch
input = torch.tensor([[False, True, False, True], [False, True, False, False]])
print(torch.all(input, dim=0, keepdim=True))
# tensor([[False, True, False, False]])
# MindSpore
import mindspore
x = mindspore.Tensor([[False, True, False, True], [False, True, False, False]])
print(mindspore.ops.all(x, axis=0, keep_dims=True))
# [[False True False False]]
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