/
minmax.py
210 lines (158 loc) · 6.25 KB
/
minmax.py
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import numpy
import six
from chainer import backend
from chainer import function_node
import chainer.functions
import chainer.utils
from chainer.utils import type_check
import chainerx
class SelectorBase(function_node.FunctionNode):
"""Select an array element from a given axis or set of axes."""
def __init__(self, axis=None, keepdims=False):
self.keepdims = keepdims
if axis is None:
self.axis = None
elif isinstance(axis, six.integer_types):
self.axis = (axis,)
elif isinstance(axis, tuple) and all(
isinstance(a, six.integer_types) for a in axis):
if len(set(axis)) != len(axis):
raise ValueError('duplicate value in axis: ({})'.format(
', '.join(map(str, axis))))
self.axis = axis
else:
raise TypeError('None, int or tuple of int are required')
def _fwd(self, x, xp):
raise NotImplementedError('_fwd should be implemented in sub-class.')
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x',))
type_check.expect(in_types[0].dtype.kind == 'f')
if self.axis is not None:
for axis in self.axis:
if axis >= 0:
type_check.expect(
axis < in_types[0].ndim,
)
else:
type_check.expect(
-axis - 1 < in_types[0].ndim,
)
def forward(self, x):
self.retain_inputs((0,))
self.retain_outputs((0,))
xp = backend.get_array_module(*x)
return xp.asarray(self._fwd(x[0], xp)),
def backward(self, indexes, gy):
x = self.get_retained_inputs()[0]
y = self.get_retained_outputs()[0]
if self.axis is None:
axis = range(x.ndim)
else:
axis = [ax % x.ndim for ax in self.axis]
# Add broadcastable dimensions to y and gy
# for each one that was reduced in the forward operation
shape = [s if ax not in axis else 1 for ax, s in enumerate(x.shape)]
gy = gy[0].reshape(shape)
y = y.reshape(shape)
# Compute the gradient
cond = (x.data == y.data)
gy = chainer.functions.broadcast_to(gy, cond.shape)
return gy * cond,
class Max(SelectorBase):
def forward_chainerx(self, x):
return chainerx.amax(x[0], axis=self.axis, keepdims=self.keepdims),
def _fwd(self, x, xp):
return xp.amax(x, axis=self.axis, keepdims=self.keepdims)
class Min(SelectorBase):
def forward_chainerx(self, x):
return chainerx.amin(x[0], axis=self.axis, keepdims=self.keepdims),
def _fwd(self, x, xp):
return xp.amin(x, axis=self.axis, keepdims=self.keepdims)
class IndexSelectorBase(function_node.FunctionNode):
"""Select index of an array element from a given axis."""
def __init__(self, axis=None):
if axis is None:
self.axis = None
elif isinstance(axis, six.integer_types):
self.axis = axis
else:
raise TypeError('None or int are required')
def _fwd(self, x, xp):
raise NotImplementedError('_fwd should be implemented in sub-class.')
def check_type_forward(self, in_types):
type_check.expect(
in_types.size() == 1,
in_types[0].dtype.kind == 'f'
)
if self.axis is not None:
if self.axis >= 0:
type_check.expect(
self.axis < in_types[0].ndim,
)
else:
type_check.expect(
-self.axis - 1 < in_types[0].ndim,
)
def forward(self, x):
xp = backend.get_array_module(*x)
return xp.asarray(self._fwd(x[0], xp)),
def backward(self, indexes, grad_outputs):
return None,
class ArgMin(IndexSelectorBase):
def forward_chainerx(self, x):
return chainerx.argmin(x[0], axis=self.axis).astype(numpy.int32),
def _fwd(self, x, xp):
return xp.argmin(x, axis=self.axis).astype(numpy.int32)
class ArgMax(IndexSelectorBase):
def forward_chainerx(self, x):
return chainerx.argmax(x[0], axis=self.axis).astype(numpy.int32),
def _fwd(self, x, xp):
return xp.argmax(x, axis=self.axis).astype(numpy.int32)
def max(x, axis=None, keepdims=False):
"""Maximum of array elements over a given axis.
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`):
Array to be maximized.
axis (None, int, or tuple of int): Axis over which a max is performed.
The default (axis = None) is perform a max over all the dimensions
of the input array.
Returns:
~chainer.Variable: Output variable.
"""
return Max(axis, keepdims).apply((x,))[0]
def min(x, axis=None, keepdims=False):
"""Minimum of array elements over a given axis.
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`):
Array to be minimized.
axis (None, int, or tuple of int): Axis over which a min is performed.
The default (axis = None) is perform a min over all the dimensions
of the input array.
Returns:
~chainer.Variable: Output variable.
"""
return Min(axis, keepdims).apply((x,))[0]
def argmax(x, axis=None):
"""Returns index which holds maximum of array elements over a given axis.
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`):
Array to find maximum elements.
axis (None or int): Axis over which a max is performed.
The default (axis = None) is perform a max over all the dimensions
of the input array.
Returns:
~chainer.Variable: Output variable.
"""
return ArgMax(axis).apply((x,))[0]
def argmin(x, axis=None):
"""Returns index which holds minimum of array elements over a given axis.
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`):
Array to find minimum elements.
axis (None or int): Axis over which a min is performed.
The default (axis = None) is perform a min over all the dimensions
of the input array.
Returns:
~chainer.Variable: Output variable.
"""
return ArgMin(axis).apply((x,))[0]