torch.repeat_interleave#

torch.repeat_interleave(input,repeats,dim=None,*,output_size=None)→Tensor#

Repeat elements of a tensor.

Warning

This is different fromtorch.Tensor.repeat() but similar tonumpy.repeat.

Parameters:

• input (Tensor) – the input tensor.

• repeats (Tensor orint) – The number of repetitions for each element.repeats is broadcasted to fit the shape of the given axis.

• dim (int,optional) – The dimension along which to repeat values.By default, use the flattened input array, and return a flat outputarray.

Keyword Arguments:

output_size (int,optional) – Total output size for the given axis( e.g. sum of repeats). If given, it will avoid stream synchronizationneeded to calculate output shape of the tensor.

Returns:

Repeated tensor which has the same shape as input, except along the given axis.

Return type:

Tensor

Example:

>>>x=torch.tensor([1,2,3])>>>x.repeat_interleave(2)tensor([1, 1, 2, 2, 3, 3])>>>y=torch.tensor([[1,2],[3,4]])>>>torch.repeat_interleave(y,2)tensor([1, 1, 2, 2, 3, 3, 4, 4])>>>torch.repeat_interleave(y,3,dim=1)tensor([[1, 1, 1, 2, 2, 2],        [3, 3, 3, 4, 4, 4]])>>>torch.repeat_interleave(y,torch.tensor([1,2]),dim=0)tensor([[1, 2],        [3, 4],        [3, 4]])>>>torch.repeat_interleave(y,torch.tensor([1,2]),dim=0,output_size=3)tensor([[1, 2],        [3, 4],        [3, 4]])

If therepeats istensor([n1, n2, n3, …]), then the output will betensor([0, 0, …, 1, 1, …, 2, 2, …, …]) where0 appearsn1 times,1 appearsn2 times,2 appearsn3 times, etc.

torch.repeat_interleave(repeats,*)→Tensor

Repeats 0 repeats[0] times, 1 repeats[1] times, 2 repeats[2] times, etc.

Parameters:

repeats (Tensor) – The number of repetitions for each element.

Returns:

Repeated tensor of sizesum(repeats).

Return type:

Tensor

Example:

>>>torch.repeat_interleave(torch.tensor([1,2,3]))tensor([0, 1, 1, 2, 2, 2])