mindspore.numpy.pad

mindspore.numpy.pad(arr, pad_width, mode='constant', stat_length=None, constant_values=0, end_values=0, reflect_type='even', **kwargs)[source]

Pads an array.

Note

Currently, median mode is not supported. reflect and symmetric mode only supports GPU backend.

Parameters

  • arr (Union[list, tuple, Tensor]) – The array to pad.

  • pad_width (Union[int, tuple, list]) – Number of values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) creates unique pad widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes.

  • mode (string, optional) –

    One of the following string values:

    • constant (default): Pads with a constant value.

    • edge: Pads with the edge values of arr.

    • linear_ramp: Pads with the linear ramp between end_value and the arr edge value.

    • maximum: Pads with the maximum value of all or part of the vector along each axis.

    • mean: Pads with the mean value of all or part of the vector along each axis.

    • median: Pads with the median value of all or part of the vector along each axis.

    • minimum: Pads with the minimum value of all or part of the vector along each axis.

    • reflect: Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis.

    • symmetric: Pads with the reflection of the vector mirrored along the edge of the arr.

    • wrap: Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning.

    • empty: Pads with undefined values.

    • <function>: The padding function, if used, should modify and return a new 1-d tensor. It has the following signature: padding_func(tensor, iaxis_pad_width, iaxis, kwargs)

  • stat_length (Union[tuple, list, int], optional) – Used in ‘maximum’, ‘mean’, ‘median’, and ‘minimum’. Number of values at edge of each axis used to calculate the statistic value. ((before_1, after_1), ... (before_N, after_N)) creates unique statistic lengths for each axis. ((before, after),) yields same before and after statistic lengths for each axis. (stat_length,) or int is a shortcut for before = after = statistic length for all axes. Default is None, to use the entire axis.

  • constant_values (Union[tuple, list, int], optional) – Used in constant mode. The values to set the padded values for each axis. ((before_1, after_1), ... (before_N, after_N)) creates unique pad constants for each axis. ((before, after),) yields same before and after constants for each axis. (constant,) or constant is a shortcut for before = after = constant for all axes. Default is 0.

  • end_values (Union[tuple, list, int], optional) – Used in ‘linear_ramp’. The values used for the ending value of the linear_ramp and that will form the edge of the padded arr. ((before_1, after_1), ... (before_N, after_N)) unique end values for each axis. ((before, after),) yields same before and after end values for each axis. (constant,) or constant is a shortcut for before = after = constant for all axes. Default is 0.

  • reflect_type (string, optional) – ‘reflect’, and ‘symmetric’. The ‘even’ style is the default with an unaltered reflection around the edge value. For the ‘odd’ style, the extended part of the arr is created by subtracting the reflected values from two times the edge value.

Returns

Padded tensor of rank equal to arr with shape increased according to pad_width.

Raises

  • TypeError – if arr, pad_width, stat_length, constant_values or end_values have types not specified above.

  • ValueError – if mode cannot be recognized, or if pad_width, stat_length, constant_values, end_values cannot broadcast to (arr.ndim, 2), or if keyword arguments got unexpected inputs.

  • NotImplementedError – if mode is function or ‘/median’/.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> tensor = np.array([1., 2., 3., 4., 5.])
>>> print(np.pad(tensor, (3, 4)))
[0. 0. 0. 1. 2. 3. 4. 5. 0. 0. 0. 0.]
>>> print(np.pad(tensor, (3, 4), mode="wrap"))
[3. 4. 5. 1. 2. 3. 4. 5. 1. 2. 3. 4.]
>>> print(np.pad(tensor, (3, 4), mode="linear_ramp", end_values=(10, 10)))
[10.    7.    4.    1.    2.    3.    4.    5.    6.25  7.5   8.75 10.  ]