/
prelu.py
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/
prelu.py
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import numpy
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer.utils import type_check
def _fwd_kern():
return cuda.elementwise(
'T x, T cond, T W', 'T y',
'y = cond >= 0 ? x : (T)(x * W)', 'prelu')
def _get_extended_shape(W, x):
return (1,) + W.shape + (1,) * (x.ndim - W.ndim - 1)
def _get_reduce_axes(W, x):
return (0,) + tuple(range(1 + W.ndim, x.ndim))
class PReLUFunction(function_node.FunctionNode):
"""Parametric Rectified Linear Unit function."""
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x', 'W'))
x_type, W_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
W_type.dtype == x_type.dtype,
x_type.ndim >= W_type.ndim + 1,
x_type.shape[1:1 + type_check.eval(W_type.ndim)] == W_type.shape
)
def forward_cpu(self, inputs):
x, W = inputs
y = x.copy()
masked = numpy.ma.masked_greater_equal(y, 0, copy=False)
shape = _get_extended_shape(W, y)
masked *= W.reshape(shape)
self.retain_inputs((0, 1))
return y,
def forward_gpu(self, inputs):
x, W = inputs
shape = _get_extended_shape(W, x)
y = _fwd_kern()(x, x, W.reshape(shape))
self.retain_inputs((0, 1))
return y,
def backward(self, indexes, grad_outputs):
x, W = self.get_retained_inputs()
gy, = grad_outputs
return PReLUFunctionGrad(
x.data, _get_reduce_axes(W, x),
_get_extended_shape(W, x)).apply((x, W, gy))
class PReLUFunctionGrad(function_node.FunctionNode):
"""Parametric Rectified Linear Unit gradient function."""
def __init__(self, cond, reduce_axes, extended_shape):
self.cond = cond
self.reduce_axes = reduce_axes
self.extended_shape = extended_shape
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x', 'W', 'gy'))
x_type, W_type, gy_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
W_type.dtype == x_type.dtype,
gy_type.dtype == x_type.dtype,
x_type.ndim >= W_type.ndim + 1,
x_type.shape[1:1 + type_check.eval(W_type.ndim)] == W_type.shape,
gy_type.shape == x_type.shape
)
def forward_cpu(self, inputs):
x, W, gy = inputs
mask = self.cond >= 0
masked = numpy.where(mask, 0, x * gy)
if self.reduce_axes is None:
# Reached from backward() of PReLUFunctionGrad i.e. this class, to
# compute higher order derivatives
gW = masked
else:
# Reached from backward() of PReLUFunction, to compute first
# derivatives
gW = masked.sum(axis=self.reduce_axes)
if numpy.isscalar(gW):
gW = numpy.array(gW)
gx = gy.copy()
masked = numpy.ma.array(gx, mask=mask)
masked *= W.reshape(self.extended_shape)
self.retain_inputs((0, 1, 2))
return gx, gW
def forward_gpu(self, inputs):
x, W, gy = inputs
masked = cuda.elementwise(
'T x, T cond, T gy', 'T masked',
'masked = cond >= 0 ? (T)0 : (T)(x * gy)',
'prelu_masked')(x, self.cond, gy)
if self.reduce_axes is None:
gW = masked.copy()
else:
gW = masked.sum(axis=self.reduce_axes)
gx = masked # reuse buffer
_fwd_kern()(gy, self.cond, W.reshape(self.extended_shape), gx)
self.retain_inputs((0, 1, 2))
return gx, gW
def backward(self, indexes, grad_outputs):
x, W, gy = self.get_retained_inputs()
ggx, ggW = grad_outputs
ggW = chainer.functions.broadcast_to(
chainer.functions.reshape(ggW, self.extended_shape), x.shape)
ggW *= self.cond < 0
gxgy, gxW = (
PReLUFunctionGrad(self.cond, None, self.extended_shape)
.apply((gy, W, ggx))
)
ret = []
if 0 in indexes:
ret.append(gy * ggW)
if 1 in indexes:
ret.append(chainer.functions.sum(gxW, axis=self.reduce_axes))
if 2 in indexes:
ret.append(x * ggW + gxgy)
return ret
def prelu(x, W):
"""Parametric ReLU function.
It accepts two arguments: an input ``x`` and a weight array ``W``
and computes the output as
.. math::
PReLU(x_i) = \\begin{cases}
x_i & (x_i>0) \\\\ W_i * x_i & (otherwise)\\end{cases}
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
Its first axis is assumed to be the minibatch dimension.
W (:class:`~chainer.Variable` or :ref:`ndarray`): Weight variable.
Returns:
~chainer.Variable: Output variable
.. admonition:: Example
>>> x = np.arange(-3, 3, dtype=np.float32).reshape((2, 3))
>>> x
array([[-3., -2., -1.],
[ 0., 1., 2.]], dtype=float32)
>>> W = np.array([0.01, 0.1, 1], dtype=np.float32)
>>> W
array([0.01, 0.1 , 1. ], dtype=float32)
>>> F.prelu(x, W)
variable([[-0.03, -0.2 , -1. ],
[ 0. , 1. , 2. ]])
.. note::
When the PReLU function is combined with two-dimensional convolution,
the elements of parameter :math:`W` are typically shared across the
same filter of different pixels. In order to support such usage,
this function supports the shape of parameter array that indicates
leading dimensions of input arrays except the batch dimension.
For example, if :math:`W` has the shape of :math:`(2, 3, 4)`,
:math:`x` must have the shape of :math:`(B, 2, 3, 4, S_1, ..., S_N)`
where :math:`B` is the batch size and the number of trailing
:math:`S`'s :math:`N` is an arbitrary non-negative integer.
.. warning::
:math:`W` is a trainable parameter in the original paper
(https://arxiv.org/abs/1502.01852). To train :math:`W`, use
:class:`chainer.links.PReLU` instead.
.. seealso::
:class:`chainer.links.PReLU` to manage the model parameter ``W``.
"""
return PReLUFunction().apply((x, W))[0]