tensorplay.functional.empty_permuted

tensorplay.functional.empty_permuted(size, physical_layout, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) → Tensor[source]

Creates an uninitialized, non-overlapping and dense tensor with the specified size, with physical_layout specifying how the dimensions are physically laid out in memory (each logical dimension is listed from outermost to innermost). physical_layout is a generalization of NCHW/NHWC notation: if each dimension is assigned a number according to what order they occur in size (N=0, C=1, H=2, W=3), then NCHW is (0, 1, 2, 3) while NHWC is (0, 2, 3, 1). Equivalently, the strides of the output tensor t are such that t.stride(physical_layout[i]) == contiguous_strides[i] (notably, this function is not equivalent to tensorplay.empty(size).permute(physical_layout)).

Unlike tensorplay.empty_strided(), this is guaranteed to produce a dense tensor with no overlaps. If possible, prefer using this function over tensorplay.empty_strided() or manual use of tensorplay.as_strided().

Note

The returned storage is uninitialized, and tensorplay.use_deterministic_algorithms() does not change that. If the contents matter, allocate with tensorplay.zeros() or fill the tensor before reading it, otherwise the output becomes a source of nondeterminism. Floating point and complex tensors are filled with NaN, and integer tensors are filled with the maximum value.

Parameters:

• size (tuple of int) – the shape of the output tensor

• physical_layout (tuple of int) – the ordering of dimensions physically in memory

Keyword Arguments:

• dtype (tensorplay.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see tensorplay.set_default_dtype()).

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

• device (tensorplay.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see tensorplay.set_default_device()). 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.

Examples

>>> tensorplay.empty((2, 3, 5, 7)).stride()
(105, 35, 7, 1)
>>> tensorplay.empty_permuted((2, 3, 5, 7), (0, 1, 2, 3)).stride()
(105, 35, 7, 1)
>>> tensorplay.empty((2, 3, 5, 7), memory_format=tensorplay.channels_last).stride()
(105, 1, 21, 3)
>>> tensorplay.empty_permuted((2, 3, 5, 7), (0, 2, 3, 1)).stride()
(105, 1, 21, 3)
>>> tensorplay.empty_permuted((2, 3, 5, 7), (0, 2, 3, 1)).dim_order()
(0, 2, 3, 1)