import numpy
from chainer import cuda
from chainer import function
from chainer.utils import array
from chainer.utils import type_check
class BatchL2NormSquared(function.Function):
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1)
x_type, = in_types
type_check.expect(
x_type.dtype == numpy.float32,
x_type.ndim >= 2,
)
def forward_cpu(self, inputs):
x = array.as_mat(inputs[0])
return (x * x).sum(axis=1),
def forward_gpu(self, inputs):
x = array.as_mat(inputs[0])
l2normsquared_kernel = cuda.cupy.ReductionKernel(
'T x', 'T y', 'x * x', 'a + b', 'y = a', '0', 'l2normsquared'
)
return l2normsquared_kernel(x, axis=1),
def backward(self, inputs, gy):
x = inputs[0]
xp = cuda.get_array_module(x)
gy0 = gy[0].reshape(-1, *((1,) * (x.ndim - 1)))
if xp is numpy:
gx = 2 * x * gy0
else:
kernel = cuda.elementwise(
'T x, T gy', 'T gx', 'gx = 2 * x * gy',
'l2normsquared_bwd')
gx = kernel(x, gy0)
return gx,
[docs]def batch_l2_norm_squared(x):
"""L2 norm (a.k.a. Euclidean norm) squared.
This function implements the square of L2 norm on a vector. No reduction
along batch axis is done.
Args:
x (~chainer.Variable): Input variable. The first dimension is assumed
to be the *minibatch dimension*. If ``x`` has more than two
dimensions all but the first dimension are flattened to one
dimension.
Returns:
~chainer.Variable: Two dimensional output variable.
"""
return BatchL2NormSquared()(x)