mindspore.ops.Lerp

class mindspore.ops.Lerp[source]

Does a linear interpolation of two tensors start and end based on a float or tensor weight.

If weight is a tensor, the shapes of three inputs need to be broadcast; If weight is a float, the shapes of start and end need to be broadcast.

\[output_{i} = start_{i} + weight_{i} * (end_{i} - start_{i})\]
Inputs:

  • start (Tensor) - The tensor with the starting points. Data type must be float16 or float32.

  • end (Tensor) - The tensor with the ending points. Data type must be float16 or float32.

  • weight (Union[float, Tensor]) – The weight for the interpolation formula. Must be a float or a scalar tensor with float16 or float32 data type.

Outputs:

Tensor, has the same type and shape as input start.

Raises

  • TypeError – If start or end is not a tensor.

  • TypeError – If weight is neither float nor tensor.

  • TypeError – If dtype of start or end is neither float16 nor float32.

  • TypeError – If dtype of weight is neither float16 nor float32 when it is a tensor.

  • TypeError – If start and end have different data types.

  • TypeError – If start, end and weight have different data types when weight is a tensor.

  • ValueError – If end could not be broadcast to a tensor with shape of start.

  • ValueError – If weight could not be broadcast to tensors with shapes of start and end when it is a tensor.

Supported Platforms:

Ascend

Examples

>>> start = Tensor(np.array([1., 2., 3., 4.]), mindspore.float32)
>>> end = Tensor(np.array([10., 10., 10., 10.]), mindspore.float32)
>>> lerp = ops.Lerp()
>>> output = lerp(start, end, 0.5)
>>> print(output)
[5.5 6. 6.5 7. ]