Differences with torch.broadcast_to
The following mapping relationships can be found in this file.
PyTorch APIs |
MindSpore APIs |
---|---|
torch.broadcast_to |
mindspore.ops.broadcast_to |
torch.Tensor.broadcast_to |
mindspore.Tensor.broadcast_to |
torch.broadcast_to
torch.broadcast_to(input, shape) -> Tensor
For more information, see torch.broadcast_to.
mindspore.ops.broadcast_to
mindspore.ops.broadcast_to(input, shape) -> Tensor
For more information, see mindspore.ops.broadcast_to.
Differences
PyTorch: Broadcast the input shape to the target shape.
MindSpore: MindSpore API basically implements the same function as PyTorch, with additional support for the -1 dimension in the shape. If there is a -1 dimension in the target shape, it is replaced by the value of the input shape in that dimension. If there is a -1 dimension in the target shape, the -1 dimension cannot be located in a dimension that does not exist.
Categories |
Subcategories |
PyTorch |
MindSpore |
Differences |
---|---|---|---|---|
Input |
Single input |
input |
input |
Same function, different parameter names |
Parameter |
Parameter 1 |
shape |
shape |
Same function |
Code Example 1
# PyTorch
import torch
shape = (2, 3)
x = torch.tensor([[1], [2]]).float()
torch_output = torch.broadcast_to(x, shape)
print(torch_output.numpy())
# [[1. 1. 1.]
# [2. 2. 2.]]
# MindSpore
import mindspore
from mindspore import Tensor
import numpy as np
shape = (2, 3)
x = Tensor(np.array([[1], [2]]).astype(np.float32))
output = mindspore.ops.function.broadcast_to(x, shape)
print(output)
# [[1. 1. 1.]
# [2. 2. 2.]]