pad_constant_like¶

paddle.fluid.layers.nn. pad_constant_like ( x, y, pad_value=0.0, name=None ) [source]

Pad y with pad_value, the number of values padded to the edges of each axis is specified by the difference of the shape of x and y . ((0, shape_x_0 - shape_y_0), … (0, shape_x_n - shape_y_n)) specify padding widths for each axis. The input should be a k-D tensor(k > 0 and k < 7).

See below for an example.

Given:
    X = [[[[ 0,  1,  2],
           [ 3,  4,  5]],
          [[ 6,  7,  8],
           [ 9, 10, 11]],
          [[12, 13, 14],
           [15, 16, 17]]],
         [[[18, 19, 20],
           [21, 22, 23]],
          [[24, 25, 26],
           [27, 28, 29]],
          [[30, 31, 32],
           [33, 34, 35]]]]

    X.shape = (2, 3, 2, 3)

    Y = [[[[35, 36, 37]],
          [[38, 39, 40]],
          [[41, 42, 43]]]]

    Y.shape = (1, 3, 1, 3)

And
    pad_value = 0.

Return:
    Out = [[[[35, 36, 37],
             [ 0,  0,  0]],
            [[38, 39, 40],
             [ 0,  0,  0]],
            [[41, 42, 43],
             [ 0,  0,  0]]],
           [[[ 0,  0,  0],
             [ 0,  0,  0]],
            [[ 0,  0,  0],
             [ 0,  0,  0]],
            [[ 0,  0,  0],
             [ 0,  0,  0]]]]

    Out.shape = [2, 3, 2, 3]

Parameters

• x (Variable) – Tensor, its shape specifies the shape of output.

• y (Variable) – Tensor, its rank is the same with x, and for each dimension \(i\) , \(y\_shape[i] <= x\_shape[i]\) . The data type can be float32 or float64.

• pad_value (float) – The constant value used to pad.

• name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name

Returns

The padded tensor, with the same shape as x and the same data type as y

Return Type:

Variable

Examples

# x is a rank 4 tensor variable, x.shape = (2, 3, 2, 3)
# y is a rank 4 tensor variable, y.shape = (1, 3, 1, 3)
import paddle.fluid as fluid
x = fluid.data(name='x', shape=[2,3,2,3], dtype='float32')
y = fluid.data(name='y', shape=[1,3,1,3], dtype='float32')
out = fluid.layers.pad_constant_like(x=x, y=y, pad_value=0.)
# out is a rank 4 tensor variable, and out.shape = [2, 3 ,2 , 3]