to

paddle.Tensor. to ( self: Tensor, *args, **kwargs )

Performs Tensor dtype and/or device conversion. A paddle.dtype and place are inferred from the arguments of self.to(*args, **kwargs).

This API has three calling conventions:

  1. to(device=None, dtype=None, blocking=True, copy=False, *, non_blocking=False):

    Moves and/or casts the Tensor.

  2. to(dtype, blocking=True, copy=False, *, non_blocking=False):

    Equivalent to self.to(device=None, dtype=dtype, ...).

  3. to(other, blocking=True, copy=False, *, non_blocking=False):

    Equivalent to self.to(device=other.place, dtype=other.dtype, ...).

Note

If the self Tensor already has the correct dtype and device, then self is returned. Otherwise, the returned tensor is a copy of self with the desired dtype and device.

Parameters

  • device (str|paddle.CPUPlace()|paddle.CUDAPlace()|paddle.CUDAPinnedPlace()|paddle.XPUPlace()|None, optional) – The device to move to. Default: None.

  • dtype (str|numpy.dtype|paddle.dtype|None, optional) – The desired data type. Default: None.

  • blocking (bool, optional) – If False and the source is in pinned memory, the copy will be asynchronous with respect to the host. Default: True.

  • copy (bool, optional) – If True, a new Tensor is created even when the Tensor already matches the desired conversion. Default: False.

Keyword Arguments

non_blocking (bool, optional) – If True and the source is in pinned memory, the copy will be asynchronous with respect to the host. Default: False. non_blocking and blocking are mutually exclusive and cannot both be set at the same time.

Returns

self

Return type

Tensor

Examples

>>> 
>>> import paddle
>>> x = paddle.to_tensor([1, 2, 3])
>>> print(x)
Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
    [1, 2, 3])

>>> x = x.to("cpu")
>>> print(x.place)
Place(cpu)

>>> x = x.to("float32")
>>> print(x.dtype)
paddle.float32

>>> x = x.to("gpu", "int16")
>>> print(x)
Tensor(shape=[3], dtype=int16, place=Place(gpu:0), stop_gradient=True,
    [1, 2, 3])
>>> y = paddle.to_tensor([4, 5, 6])
>>> y
Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
    [4, 5, 6])
>>> y = y.to(x)
>>> print(y)
Tensor(shape=[3], dtype=int16, place=Place(gpu:0), stop_gradient=True,
    [4, 5, 6])