Shortcuts

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 from 0 to n - 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: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

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

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

Example:

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

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources