mindspore.ops.lerp
- mindspore.ops.lerp(input, end, weight)[source]
Does a linear interpolation of two tensors input 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 input and end need to be broadcast.
\[output_{i} = input_{i} + weight_{i} * (end_{i} - input_{i})\]- Parameters
input (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 the same as input.
weight (Union[float, Tensor]) – The weight for the interpolation formula. Must be a float or a scalar tensor with float16 or float32 data type.
- Returns
Tensor, has the same type and shape as input input.
- Raises
TypeError – If input or end is not a tensor.
TypeError – If weight is neither scalar(float) nor tensor.
TypeError – If dtype of input or end is neither float16 nor float32.
TypeError – If dtype of weight is neither float16 nor float32 when it is a tensor.
TypeError – If input and end have different data types.
TypeError – If input, 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 input.
ValueError – If weight could not be broadcast to tensors with shapes of input and end when it is a tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> input = Tensor(np.array([1., 2., 3., 4.]), mindspore.float32) >>> end = Tensor(np.array([10., 10., 10., 10.]), mindspore.float32) >>> output = ops.lerp(input, end, 0.5) >>> print(output) [5.5 6. 6.5 7. ]