mindspore.ops.UpsampleNearest3D
- class mindspore.ops.UpsampleNearest3D[source]
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 [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.]]]]]