def inputs_all_ready(inputs, supported_ndim=(2, 4)):
"""Checks if input arrays are supported for ideep optimization.
Information to be checked includes array types, dimesions and data types.
The function checks ``inputs`` info and ``supported_ndim``.
Args:
inputs (sequence of arrays or variables``):
Inputs to be checked.
supported_ndim (tuple of ints):
Supported ndim values.
iDeep supports array dimension in either 2 or 4 only.
Returns:
bool: ``True`` if all conditions meet.
"""
if _ideep_version is None:
return False
inputs = [
x.data if isinstance(x, chainer.variable.Variable) else x
for x in inputs
]
return (ideep.check_ndim(inputs, supported_ndim)
and (all([isinstance(a, ideep.mdarray)
for a in inputs]) or ideep.check_type(inputs)))
def all_ready(inputs, supported_ndim=(2, 4)):
"""Check inputs and configuration supported for ideep optimization.
The function checks :func:`chainer.ia.should_use_ideep`, ``inputs``
info and ``supported_ndim``.
Args:
inputs (numpy.ndarray, cupy.ndarray, ideep.mdarray):
``inputs`` to be checked including array type, dimension
and data type.
supported_ndim: A tuple of ndim. ideep supports array dimension
in either 2 or 4 only.
Returns:
bool: ``True`` if all conditions meet.
"""
if not ideep_enabled:
return False
_inputs = [
x.data if isinstance(x, chainer.variable.Variable) else x
for x in inputs
]
if ideep4py.check_ndim(_inputs, supported_ndim) is False:
return False
elif isinstance(_inputs[0], mdarray):
return True
else:
return ideep4py.check_type(_inputs) and \
should_use_ideep('>=auto')
def _is_supported_array_type(a):
return isinstance(a, ideep.mdarray) or ideep.check_type([a])
def _is_supported_array_type(a):
return isinstance(a, ideep.mdarray) or ideep.check_type([a])
