Differences with torch.cat
torch.cat
torch.cat(
tensors,
dim=0,
*,
out=None
) -> Tensor
For more information, see torch.cat.
mindspore.ops.cat
mindspore.ops.cat(tensors, axis=0) -> Tensor
For more information, see mindspore.ops.cat.
Differences
API function of MindSpore is consistent with that of PyTorch.
PyTorch: Splice the input Tensor on the specified axis. When the data precision of the input Tensors is different, the low precision Tensor will be automatically converted to high precision Tensor.
MindSpore: Currently, the data type and precision of the the input Tensors are required to remain the same. If not, the low-precision Tensor can be converted to a high-precision Tensor through ops.cast and then call the concat operator.
Categories |
Subcategories |
PyTorch |
MindSpore |
Differences |
---|---|---|---|---|
Input |
Single input |
tensors |
tensors |
The data type and precision of the |
Parameters |
Parameter 1 |
dim |
axis |
Different parameter names |
Parameter 2 |
out |
- |
For details, see General Difference Parameter Table |
Code Example
MindSpore currently requires that the data type and precision of the input Tensors are consistent. If it is inconsistent, the low-precision tensor can be converted to a high-precision type through ops.cast before calling the concat operator.
# PyTorch
import torch
torch_x1 = torch.Tensor([[0, 1], [2, 3]]).type(torch.float32)
torch_x2 = torch.Tensor([[0, 1], [2, 3]]).type(torch.float32)
torch_x3 = torch.Tensor([[0, 1], [2, 3]]).type(torch.float16)
torch_output = torch.cat((torch_x1, torch_x2, torch_x3))
print(torch_output.numpy())
# [[0. 1.]
# [2. 3.]
# [0. 1.]
# [2. 3.]
# [0. 1.]
# [2. 3.]]
# MindSpore
import mindspore
import numpy as np
from mindspore import Tensor
# In MindSpore,converting low precision to high precision is needed before cat.
ms_x1 = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float32))
ms_x2 = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float32))
ms_x3 = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float16))
ms_x3 = mindspore.ops.cast(ms_x2, mindspore.float32)
output = mindspore.ops.cat((ms_x1, ms_x2, ms_x3))
print(output)
# [[0. 1.]
# [2. 3.]
# [0. 1.]
# [2. 3.]
# [0. 1.]
# [2. 3.]]