Function Differences with torch.Tensor.repeat

torch.Tensor.repeat

torch.Tensor.repeat(*sizes)

For more information, see torch.Tensor.repeat.

mindspore.numpy.tile

mindspore.numpy.tile(a, reps)

For more information, see mindspore.numpy.tile.

Differences

MindSpore: Constructs an array by repeating a the number of times given by reps. If reps has length d, a has dimensions a.dim, the rules for repeat operation is:

If a.ndim = d: copy a for reps times in the corresponding axis ;

If a.ndim < d: a is promoted to be d-dimensional by prepending new axis, and then copied;

If a.ndim > d: The reps will be promoted to a.ndim by adding 1 in the front, and then copied.
PyTorch: The length of input args size must be greater than or equal to the dimension of the self tensor, that is, the above third case is not supported.

Code Example

MindSpore:

import mindspore.numpy as np

a = np.array([[0, 2, 1], [3, 4, 5]])

b = np.tile(a, 2)
print(b)

# out:
# [[0 2 1 0 2 1]
#  [3 4 5 3 4 5]]

c = np.tile(a, (2, 1))
print(c)

# out:
# [[0 2 1]
#  [3 4 5]
# [0 2 1]
#  [3 4 5]]

d = np.tile(a, (2, 1, 2))
print(d)

# out
# [[[0 2 1 0 2 1]
#   [3 4 5 3 4 5]]

#  [[0 2 1 0 2 1]
#   [3 4 5 3 4 5]]]

PyTorch:

import torch

a = torch.tensor([[0, 2, 1], [3, 4, 5]])

b = a.repeat(2)

# error:
# RuntimeError: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor

c = a.repeat(2, 1)
print(c)

# out:
#tensor([[0, 2, 1],
#         [3, 4, 5],
#         [0, 2, 1],
#         [3, 4, 5]])

d = a.repeat(2, 1, 2)
print(d)

# out:
#tensor([[[0, 2, 1, 0, 2, 1],
#          [3, 4, 5, 3, 4, 5]],
#
#         [[0, 2, 1, 0, 2, 1],
#          [3, 4, 5, 3, 4, 5]]])