未验证 提交 d2f9e193 编写于 作者: G Guo Sheng 提交者: GitHub

Merge pull request #9582 from guoshengCS/add-python-pad

Add python wrapper for pad_op
......@@ -494,6 +494,12 @@ reshape
.. autofunction:: paddle.fluid.layers.reshape
:noindex:
pad
---
.. autofunction:: paddle.fluid.layers.pad
:noindex:
scale
-----
......
......@@ -76,6 +76,7 @@ __all__ = [
'reshape',
'lod_reset',
'lrn',
'pad',
]
......@@ -3379,6 +3380,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[2, 4, 6], dtype='float32')
reshaped = fluid.layers.reshape(
......@@ -3580,3 +3582,62 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
"beta": beta})
return lrn_out
def pad(x, paddings, pad_value=0., name=None):
"""
Pads a tensor with a constant value given by :attr:`pad_value`, and the
padded width is specified by :attr:`paddings`.
Specifically, the number of values padded before the contents of :attr:`x`
in dimension :attr:`i` is indicated by :attr:`paddings[i]`, and the number
of values padded after the contents of :attr:`x` in dimension :attr:`i` is
indicated by :attr:`paddings[i+1]`.
See below for an example.
.. code-block:: text
Given:
x = [[1, 2], [3, 4]]
paddings = [0, 1, 1, 2]
pad_value = 0
Return:
out = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]]
Args:
x (Variable): The input tensor variable.
paddings (list): A list of integers. Its elements specify the padded
width before and after for each dimension in turn.
The length of :attr:paddings must be
:math:`rank(x) \\times 2`.
pad_value (float): The constant value used to pad.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The padded tensor variable.
Examples:
.. code-block:: python
# x is a rank 2 tensor variable.
out = fluid.layers.pad(
x=x, paddings=[0, 1, 1, 2], pad_value=0.)
"""
helper = LayerHelper('pad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='pad',
inputs={'X': x},
outputs={'Out': out},
attrs={'paddings': paddings,
'pad_value': float(pad_value)})
return out
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