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The following mapping relationships can be found in this file.
| PyTorch APIs | MindSpore APIs |
|---|---|
| torch.scatter | mindspore.ops.scatter |
| torch.Tensor.scatter | mindspore.Tensor.scatter |
torch.scatter(input, dim, index, src)
For more information, see torch.scatter.
mindspore.ops.scatter(input, axis, index, src)
For more information, see mindspore.ops.scatter.
API function of MindSpore is not consistent with that of PyTorch.
PyTorch: For all dimensions d , index.size(d) <= src.size(d) is required, i.e. index can select some or all of the data of src to be scattered into input .
MindSpore: The shape of index must be the same as the shape of src , i.e. all data of src will be scattered into input by index .
| Categories | Subcategories | PyTorch | MindSpore | Differences |
|---|---|---|---|---|
| Parameters | Parameter 1 | input | input | Consistent |
| Parameter 2 | dim | axis | Different parameter names | |
| Parameter 3 | index | index | For MindSpore, the shape of index must be the same as the shape of src . For PyTorch, index.size(d) <= src.size(d) is required for all dimensions d
|
|
| Parameter 4 | src | src | Consistent |
Scatter operation on part of
srcdata.
# PyTorch
import torch
import numpy as np
input = torch.tensor(np.zeros((5, 5)), dtype=torch.float32)
src = torch.tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), dtype=torch.float32)
index = torch.tensor(np.array([[0, 1], [0, 1], [0, 1]]), dtype=torch.int64)
out = torch.scatter(input=input, dim=1, index=index, src=src)
print(out)
# tensor([[1., 2., 0., 0., 0.],
# [4., 5., 0., 0., 0.],
# [7., 8., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]])
# MindSpore doesn't support this feature currently.
Scatter operation on all of
srcdata. The output values of singular value decomposition are not unique.
import torch
import numpy as np
input = torch.tensor(np.zeros((5, 5)), dtype=torch.float32)
src = torch.tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), dtype=torch.float32)
index = torch.tensor(np.array([[0, 1], [0, 1], [0, 1]]), dtype=torch.int64)
out = torch.scatter(input=input, dim=1, index=index, src=src)
print(out)
# tensor([[1., 2., 3., 0., 0.],
# [4., 5., 6., 0., 0.],
# [7., 8., 9., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]])
# MindSpore
import mindspore as ms
import numpy as np
input = ms.Tensor(np.zeros((5, 5)), dtype=ms.float32)
src = ms.Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), dtype=ms.float32)
index = ms.Tensor(np.array([[0, 1, 2], [0, 1, 2], [0, 1, 2]]), dtype=ms.int64)
out = ms.ops.scatter(input=input, axis=1, index=index, src=src)
print(out)
# [[1. 2. 3. 0. 0.]
# [4. 5. 6. 0. 0.]
# [7. 8. 9. 0. 0.]
# [0. 0. 0. 0. 0.]
# [0. 0. 0. 0. 0.]]
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