/
det.py
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/
det.py
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import chainer
from chainer.backends import cuda
from chainer import function_node
import chainer.functions
from chainer.utils import precision
from chainer.utils import type_check
class BatchDet(function_node.FunctionNode):
@property
def label(self):
return 'det'
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x',))
a_type, = in_types
type_check.expect(a_type.dtype.kind == 'f')
# Only a minibatch of 2D array shapes allowed.
type_check.expect(a_type.ndim == 3)
# Matrix inversion only allowed for square matrices
# so assert the last two dimensions are equal.
type_check.expect(a_type.shape[-1] == a_type.shape[-2])
@precision._fp16_mixed_precision_helper
def forward(self, inputs):
self.retain_inputs((0,))
self.retain_outputs((0,))
x, = inputs
xp = cuda.get_array_module(x)
detx = xp.linalg.det(x)
return detx,
def backward(self, indexes, gy):
x, = self.get_retained_inputs()
detx, = self.get_retained_outputs()
gy, = gy
inv_x = chainer.functions.batch_inv(
chainer.functions.transpose(x, (0, 2, 1)))
gy = chainer.functions.broadcast_to(gy[:, None, None], inv_x.shape)
detx = chainer.functions.broadcast_to(detx[:, None, None], inv_x.shape)
grad = gy * detx * inv_x
return grad,
def batch_det(a):
"""Computes the determinant of a batch of square matrices.
Args:
a (:class:`~chainer.Variable` or :ref:`ndarray`):
Input array to compute the determinant for.
The first dimension should iterate over each matrix and be
of the batchsize.
Returns:
~chainer.Variable: vector of determinants for every matrix
in the batch.
"""
return BatchDet().apply((a,))[0]
def det(a):
"""Computes the determinant of a single square matrix.
Args:
a (:class:`~chainer.Variable` or :ref:`ndarray`):
Input array to compute the determinant for.
Returns:
~chainer.Variable: Scalar determinant of the matrix a.
"""
shape = (1, a.shape[0], a.shape[1])
batched_a = chainer.functions.reshape(a, shape)
batched_det = BatchDet().apply((batched_a,))[0]
return chainer.functions.reshape(batched_det, ())