from chainer import cuda
from chainer import function
from chainer import utils
from chainer.utils import type_check
class LinearInterpolate(function.Function):
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 3)
p_type, x_type, y_type = in_types
type_check.expect(
p_type.dtype.kind == 'f',
x_type.dtype == p_type.dtype,
y_type.dtype == p_type.dtype,
p_type.shape == x_type.shape,
p_type.shape == y_type.shape,
)
def forward_cpu(self, inputs):
p, x, y = inputs
one = p.dtype.type(1)
return utils.force_array(p * x + (one - p) * y),
def forward_gpu(self, inputs):
p, x, y = inputs
return cuda.elementwise(
'T p, T x, T y', 'T z',
'z = p * x + (1 - p) * y',
'linear_interpolate_fwd',
)(p, x, y),
def backward_cpu(self, inputs, grads):
p, x, y = inputs
g = grads[0]
pg = p * g
return (utils.force_array((x - y) * g),
utils.force_array(pg),
utils.force_array(g - pg))
def backward_gpu(self, inputs, grads):
p, x, y = inputs
g = grads[0]
return cuda.elementwise(
'T p, T x, T y, T g', 'T gp, T gx, T gy',
'''
gp = (x - y) * g;
gx = g * p;
gy = g * (1 - p);
''',
'linear_interpolate_bwd'
)(p, x, y, g)
[docs]def linear_interpolate(p, x, y):
"""Elementwise linear-interpolation function.
This function is defined as
.. math::
f(p, x, y) = p x + (1 - p) y.
Args:
p (~chainer.Variable): Input variable.
x (~chainer.Variable): Input variable.
y (~chainer.Variable): Input variable.
Returns:
~chainer.Variable: Output variable.
"""
return LinearInterpolate()(p, x, y)