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mindspore.ops.UpsampleNearest3D

class mindspore.ops.UpsampleNearest3D

Performs nearest neighbor upsampling operation.

This operator scale up the volumetric input with specified output_size or scales factors, using nearest neighbor algorithm.

One of output_size or scales must be given, and can not specified both at the same time.

Inputs:

• x (Tensor) - 5D tensor of shape $(N,C,D_{in},H_{in},W_{in})$. Supporting types: [float16, float32, float64].

• output_size (Union[tuple[int], list[int]]): A tuple or list of int specifying the output volumetric size. Default: None.

• scales (Union[tuple[float], list[float]]): A tuple or list of float specifying the upsampling factors. Default: None.

Outputs:

• y (Tensor) - Upsampled output with the same type as x , whose shape is $(N,C,D_{out},H_{out},W_{out})$.

Raises

• TypeError – When output_size is not None and output_size is not list[int] or tuple[int].

• TypeError – When scales is not None and scales is not list[float] or tuple[float].

• TypeError – If dtype of x is not int [uint8, float16, float32, float64].

• ValueError – If any value of output_size is negative or zero when output_size is not None.

• ValueError – If any value of scales is negative or zero when scales is not None.

• ValueError – If shape of x is not 5D.

• ValueError – If none of scales and output_size is specified or both specified.

• ValueError – If size of scales is not equal 3 when scales is specified.

• ValueError – If size of output_size is not equal 3 when output_size is specified.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> x = Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])
...       .reshape([1, 1, 2, 2, 4]), mstype.float32)
>>> output_size = [3, 4, 5]
>>> net = ops.UpsampleNearest3D()
>>> output = net(x, output_size, None)
>>> print(output)
[[[[[ 1.  1.  2.  3.  4.]
    [ 1.  1.  2.  3.  4.]
    [ 5.  5.  6.  7.  8.]
    [ 5.  5.  6.  7.  8.]]
   [[ 1.  1.  2.  3.  4.]
    [ 1.  1.  2.  3.  4.]
    [ 5.  5.  6.  7.  8.]
    [ 5.  5.  6.  7.  8.]]
   [[ 9.  9. 10. 11. 12.]
    [ 9.  9. 10. 11. 12.]
    [13. 13. 14. 15. 16.]
    [13. 13. 14. 15. 16.]]]]]