import numpy
from chainer import cuda
from chainer import function
from chainer.utils import type_check
class Pad(function.Function):
"""Padding of an array"""
def __init__(self, pad_width, mode, **keywords):
self.mode = mode
self.keywords = keywords
self.pad_width = pad_width
self.pad_bw = numpy.asarray(pad_width)
if self.pad_bw.size == 1:
self.pad_bw = numpy.repeat(self.pad_bw, 2)
def check_type_forward(self, in_types):
# Depending on the arguments, pad_width and keywords, the input value
# may be inappropriate. In that case, numpy.pad or cupy.pad will raise
# errors, so that only check the size and the dtype in this function.
type_check.expect(in_types.size() == 1)
x_type = in_types[0]
type_check.expect(x_type.dtype.kind == 'f')
def forward(self, inputs):
xp = cuda.get_array_module(*inputs)
return xp.pad(inputs[0], self.pad_width, mode=self.mode,
**self.keywords),
def backward(self, inputs, grads):
xp = cuda.get_array_module(*inputs)
gy = grads[0]
array = inputs[0]
ndims = array.ndim
if self.pad_bw.ndim == 1:
self.pad_bw = numpy.tile(self.pad_bw, (ndims, 1))
for i in range(ndims):
gy = xp.take(gy,
indices=numpy.arange(self.pad_bw[i][0],
self.pad_bw[i][0]
+ array.shape[i]),
axis=i)
return gy,
[docs]def pad(x, pad_width, mode, **keywords):
"""Pad an input variable.
Args:
x (chainer.Variable or :class:``numpy.ndarray`` or cupy.ndarray):
Input data.
pad_width (int or array-like):
Number of values padded to the edges of each axis.
mode (str):
Specifies how the function fills the periphery of the array.
`constant`
Pads with a constant values.
constant_values (int or array-like):
The values are padded for each axis.
Returns:
~chainer.Variable: Output variable.
"""
return Pad(pad_width, mode, **keywords)(x)