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torch.randperm#

torch.randperm(n,*,generator=None,out=None,dtype=torch.int64,layout=torch.strided,device=None,requires_grad=False,pin_memory=False)→Tensor#

Returns a random permutation of integers from0 ton-1.

Parameters

n (int) – the upper bound (exclusive)

Keyword Arguments

  • generator (torch.Generator, optional) – a pseudorandom number generator for sampling

  • out (Tensor,optional) – the output tensor.

  • dtype (torch.dtype, optional) – the desired data type of returned tensor.Default:torch.int64.

  • layout (torch.layout, optional) – the desired layout of returned Tensor.Default:torch.strided.

  • device (torch.device, optional) – the desired device of returned tensor.Default: ifNone, uses the current device for the default tensor type(seetorch.set_default_device()).device will be the CPUfor CPU tensor types and the current CUDA device for CUDA tensor types.

  • requires_grad (bool,optional) – If autograd should record operations on thereturned tensor. Default:False.

  • pin_memory (bool,optional) – If set, returned tensor would be allocated inthe pinned memory. Works only for CPU tensors. Default:False.

Example:

>>>torch.randperm(4)tensor([2, 1, 0, 3])