mindspore.Tensor.to

Tensor.to(dtype=None, non_blocking=False, copy=False) → Tensor[source]

Returns a tensor with the new specified data type.

Note

  • When converting complex numbers to boolean type, the imaginary part of the complex number is not taken into account. As long as the real part is non-zero, it returns True; otherwise, it returns False.

  • non_blocking and copy do not take effect in GRAPH_MODE or within jit.

Parameters

  • dtype (dtype.Number, optional) – The valid data type of the output tensor. Default: None.

  • non_blocking (bool, optional) – Data type conversion asynchronously. If True , convert data type asynchronously. If False , convert data type synchronously. Default: False .

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

Returns

Tensor, the data type of the tensor is dtype .

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
>>> input = Tensor(input_np)
>>> dtype = mindspore.int32
>>> output = input.to(dtype)
>>> print(output.dtype)
Int32
>>> print(output.shape)
(2, 3, 4, 5)
Tensor.to(device=None, dtype=None, non_blocking=False, copy=False) → Tensor[source]

Returns a tensor with the new specified data type and device type.

Note

device , non_blocking and copy do not take effect in GRAPH_MODE or within jit.

Parameters

  • device (string, optional) – The device type of the output tensor. Default: None .

  • dtype (dtype.Number, optional) – The valid data type of the output tensor. Default: None .

  • non_blocking (bool, optional) – Data type conversion asynchronously. If True , convert data type asynchronously. If False , convert data type synchronously. Default: False .

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

Returns

Tensor, the specified device type and data type of the tensor.

Supported Platforms:

Ascend CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
>>> input = Tensor(input_np)
>>> dtype = mindspore.int32
>>> output = input.to("Ascend")
>>> print(output.device)
"Ascend:0"
Tensor.to(other, non_blocking=False, copy=False) → Tensor[source]

Returns a tensor with same device and dtype as the Tensor other .

Note

non_blocking and copy do not take effect in GRAPH_MODE or within jit.

Parameters

  • other (Tensor) – The returned Tensor has the same device and dtype as other .

  • non_blocking (bool, optional) – Data type conversion asynchronously. If True , convert data type asynchronously. If False , convert data type synchronously. Default: False .

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

Returns

Tensor, same device and dtype as the Tensor other .

Supported Platforms:

Ascend CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
>>> input = Tensor(input_np)
>>> other = input.to("Ascend", dtype=mindspore.float16)
>>  output = input.to(other)
>>> print(output.device)
"Ascend:0"
>>> print(output.dtype)
float16