Differences with torch.ger
The following mapping relationships can be found in this file.
PyTorch APIs |
MindSpore APIs |
---|---|
torch.ger |
mindspore.ops.ger |
torch.Tensor.ger |
mindspore.Tensor.ger |
torch.ger
torch.ger(input, vec2, *, out=None)
For more information, see torch.ger.
mindspore.ops.ger
mindspore.ops.ger(input, other)
For more information, see mindspore.ops.ger.
Differences
API function of MindSpore is not consistent with that of PyTorch.
PyTorch: The parameters input
and vec2
support all data types of uint, int and float, and can be different data types. The data type of the return value selects a larger range of data types in the input parameter.
MindSpore: The data types of parameters input
and other
support float16/32/64, and must be the same data type. The data type of the return value is the same as the input.
There is no difference in function.
Categories |
Subcategories |
PyTorch |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
input |
input |
PyTorch supports all data types of uint, int and float, and MindSpore only supports float16/32/64 |
Parameter 2 |
vec2 |
other |
PyTorch supports all data types of uint, int and float, and MindSpore only supports float16/32/64 |
|
Parameter 3 |
out |
- |
For details, see General Difference Parameter Table |
Code Example 1
The data type of the input is int, and the data type of the return value is also int.
# PyTorch
import torch
import numpy as np
x1 = np.arange(3)
x2 = np.arange(6)
input = torch.tensor(x1, dtype=torch.int32)
other = torch.tensor(x2, dtype=torch.int32)
output = torch.ger(input, other)
print(output)
# tensor([[ 0, 0, 0, 0, 0, 0],
# [ 0, 1, 2, 3, 4, 5],
# [ 0, 2, 4, 6, 8, 10]], dtype=torch.int32)
print(output.dtype)
# torch.int32
# MindSpore doesn't support this feature currently.
Code Example 2
The data type of the input is float, and the data type of the return value is also float.
# PyTorch
import torch
import numpy as np
x1 = np.arange(3)
x2 = np.arange(6)
input = torch.tensor(x1, dtype=torch.float32)
other = torch.tensor(x2, dtype=torch.float32)
output = torch.ger(input, other)
print(output)
# tensor([[ 0., 0., 0., 0., 0., 0.],
# [ 0., 1., 2., 3., 4., 5.],
# [ 0., 2., 4., 6., 8., 10.]])
print(output.dtype)
# torch.float32
# MindSpore
import mindspore as ms
import numpy as np
x1 = np.arange(3)
x2 = np.arange(6)
input = ms.Tensor(x1, ms.float32)
other = ms.Tensor(x2, ms.float32)
output = ms.ops.ger(input, other)
print(output)
# [[ 0. 0. 0. 0. 0. 0.]
# [ 0. 1. 2. 3. 4. 5.]
# [ 0. 2. 4. 6. 8. 10.]]
print(output.dtype)
# Float32