May 31, 2018 - This was unexpected to me. This may be unexpected to other people. For me, this failed silently in my model, welp. numpy.repeat [1, 2, 3] → [1, 1, 2, 2, 3, 3] torch.repeat [1, 2, 3] → [1, 2, 3, 1, 2, 3]

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]]] ... 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]]])

Continue to ../../2.14/generated/torch.Tensor.repeat.html

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]]] ... 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]]])

March 11, 2017 - Assume that I have got a tensor like below: var = Variable(torch.randn(1,7,7)) I would like to know, how can I copy this variable and make a new variable with size 512*7*7. Actually the dimension 0 is the number of c…

January 3, 2021 - high prioritymodule: deprecationmodule: numpyRelated to numpy support, and also numpy compatibility of our operatorsRelated to numpy support, and also numpy compatibility of our operatorstriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module ... torch.Tensor.repeat, as its documentation notes, is divergent from np.repeat but similar to np.tile.

Numpy · PyTorch · np.put · x.put · x.put_ x = np.array([1, 2, 3]) x.repeat(2) # [1, 1, 2, 2, 3, 3] x = torch.tensor([1, 2, 3]) x.repeat_interleave(2) # [1, 1, 2, 2, 3, 3] x.repeat(2) # [1, 2, 3, 1, 2, 3] x.repeat(2).reshape(2, -1).transpose(1, 0).reshape(-1) # [1, 1, 2, 2, 3, 3] np.tile(x, (3, 2)) x.repeat(3, 2) x = np.array([[0, 1], [2, 3], [4, 5]]) idxs = np.array([0, 2]) np.choose(idxs, x) # [0, 5] x = torch.tensor([[0, 1], [2, 3], [4, 5]]) idxs = torch.tensor([0, 2]) x[idxs, torch.arange(x.shape[1])] # [0, 5] torch.gather(x, 0, idxs[None, :])[0] # [0, 5] np.sort ·

March 31, 2019 - Hi everybody, Is there a current API that’s behave similarly to numpy, where we can repeat each row individually ? x = np.array([[1,2],[3,4]]) np.repeat(x, [1, 2], axis=0) array([[1, 2], [3, 4], [3, 4]]) …

January 19, 2025 - In some cases, torch.tile() offers a cleaner, often more efficient alternative, especially when readability or large repetitions are involved. ... While both functions create repeated tensors, tile differs subtly. It aligns well with NumPy-like syntax and is sometimes more intuitive for extending arrays over larger dimensions.

January 1, 2023 - repeat() behaves differently from numpy.repeat, but is more similar to numpy.tile. For the operator similar to numpy.repeat, see torch.repeat_interleave(). Parameters: repeat (torch.Size, int..., tuple of int or list of int) – The number of times to repeat this tensor along each dimension ·

This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters ... Repeat elements of a tensor. ... This is different from :func:`torch.repeat` but similar to `numpy.repeat`.

Continue to ../../2.14/generated/torch.repeat_interleave.html

July 23, 2025 - Comparison Between torch. repeat() and torch. expand() Tensor repetition involves duplicating the elements of a tensor along one or more dimensions. This operation is analogous to the concept of tiling in NumPy and can be used to expand the ...

April 7, 2023 - In the above syntax, we use repeat function as shown, here specified torch means the actual variable name of tensor and specified dimension means the required dimension that we need to change.

November 5, 2025 - This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.

>>> w=np.random.randn(1,2) >>> w=torch.tensor(w) >>> w tensor([[ 0.8654, -2.3015]], dtype=torch.float64) >>> w.repeat(3,1) tensor([[ 0.8654, -2.3015], [ 0.8654, -2.3015], [ 0.8654, -2.3015]], dtype=torch.float64) ... tile tile(array, number of repetitions) tile(array, (number of repetitions of the first dimension, number of repetitions of the second dimension, ..., number of repetitions of the nth dimension)) exam... repeat Create Observables that can emit specific items multiple times 1 repeat Set the number of repetitions Repeat 5 times Repeat output 1 time in 5 seconds, 3 repeatUntil set repeat, until the...