def to_intel64(self):
"""Copies the data and gradient arrays to intel64 specific mdarray.
If the array is not suited for intel64, it will be converted to
:class:`numpy.ndarray`.
"""
intel64.check_ideep_available()
data = self.data
if data is not None:
if isinstance(data, cuda.ndarray):
# cupy.ndarray to numpy.ndarray
data = data.get()
if (isinstance(data, numpy.ndarray) and data.ndim in (1, 2, 4)):
# TODO(kmaehashi): Remove ndim validation once iDeep has fixed.
# Currently iDeep only supports (1, 2, 4)-dim arrays.
# Note that array returned from `ideep.array` may not be an
# iDeep mdarray, e.g., when the dtype is not float32.
data = intel64.ideep.array(
data, itype=intel64.ideep.wgt_array)
self._data = [data]
if self._grad_var is not None:
self._grad_var.to_intel64()
# ensure that the node tracks the device migration
node = self._node
if node._data is not None:
node.retain_data()
def to_intel64(self):
"""Copies the data and gradient arrays to intel64 specific mdarray.
If the array is not suited for intel64, it will be converted to
:class:`numpy.ndarray`.
"""
intel64.check_ideep_available()
data = self.data
if data is not None:
if isinstance(data, numpy.ndarray):
# numpy.ndarray to ideep
self._data = [
intel64.ideep.array(data, itype=intel64.ideep.wgt_array)
]
elif isinstance(data, cuda.ndarray):
# cupy.ndarray to ideep
self._data = [
intel64.ideep.array(data.get(),
itype=intel64.ideep.wgt_array)
]
if self._grad_var is not None:
self._grad_var.to_intel64()
# ensure that the node tracks the device migration
node = self._node
if node._data is not None:
node.retain_data()
def to_intel64(self):
"""Copies the data and gradient arrays to intel64 specific mdarray.
If the array is not suited for intel64, it will be converted to
:class:`numpy.ndarray`.
"""
intel64.check_ideep_available()
data = self.data
if data is not None:
if isinstance(data, numpy.ndarray):
# numpy.ndarray to ideep
self._data = [
intel64.ideep.array(
data, itype=intel64.ideep.wgt_array)]
elif isinstance(data, cuda.ndarray):
# cupy.ndarray to ideep
self._data = [
intel64.ideep.array(
data.get(), itype=intel64.ideep.wgt_array)]
if self._grad_var is not None:
self._grad_var.to_intel64()
# ensure that the node tracks the device migration
node = self._node
if node._data is not None:
node.retain_data()
def to_intel64(self):
"""Copies the data and gradient arrays to intel64 specific mdarray.
If the array is not suited for intel64, it will be converted to
:class:`numpy.ndarray`.
"""
intel64.check_ideep_available()
array = self.array
if array is not None:
if isinstance(array, cuda.ndarray):
# cupy.ndarray to numpy.ndarray
array = array.get()
if (isinstance(array, numpy.ndarray) and array.ndim in (1, 2, 4)):
# TODO(kmaehashi): Remove ndim validation once iDeep has fixed.
# Currently iDeep only supports (1, 2, 4)-dim arrays.
# Note that array returned from `ideep.array` may not be an
# iDeep mdarray, e.g., when the dtype is not float32.
array = intel64.ideep.array(array,
itype=intel64.ideep.wgt_array)
self._data = [array]
if self._grad_var is not None:
self._grad_var.to_intel64()
# ensure that the node tracks the device migration
node = self._node
if node._data is not None:
node.retain_data()
def to_intel64(self):
# type: () -> 'DeviceResident'
"""Copies parameter variables and persistent values to CPU."""
intel64.check_ideep_available()
visitor = _ToDeviceVisitor(chainer.get_device(intel64.Intel64Device()),
entry_method_info=('to_intel64', {}))
self.__to_device(visitor)
return self
def to_intel64(self):
# type: () -> 'DeviceResident'
"""Copies parameter variables and persistent values to CPU."""
intel64.check_ideep_available()
visitor = _ToDeviceVisitor(
chainer.get_device(intel64.Intel64Device()),
entry_method_info=('to_intel64', {}))
self.__to_device(visitor)
return self
def to_intel64(self):
"""Copies parameter variables and persistent values to CPU."""
intel64.check_ideep_available()
d = self.__dict__
for name in self._params:
d[name].to_intel64()
for name in self._persistent:
value = d[name]
if isinstance(value, numpy.ndarray):
d[name] = intel64.ideep.array(
value, itype=intel64.ideep4.wgt_array)
self._cpu = True
return self
def to_intel64(self):
# type: () -> 'DeviceResident'
"""Copies parameter variables and persistent values to CPU.
.. deprecated:: v7.0.0
Use :meth:`to_device` instead.
"""
intel64.check_ideep_available()
visitor = _ToDeviceVisitor(chainer.get_device(intel64.Intel64Device()),
entry_method_info=('to_intel64', {}),
starting_device_resident=self)
self.__to_device(visitor)
return self
def to_intel64(self):
"""Copies parameter variables and persistent values to CPU."""
intel64.check_ideep_available()
d = self.__dict__
for name in self._params:
d[name].to_intel64()
for name in self._persistent:
value = d[name]
if isinstance(value, cuda.ndarray):
value = value.get() # to numpy.ndarray
if isinstance(value, numpy.ndarray):
d[name] = intel64.ideep.array(
value, itype=intel64.ideep.wgt_array)
self._cpu = True
self._device_id = None
return self
def to_intel64(self):
"""Copies parameter variables and persistent values to CPU."""
intel64.check_ideep_available()
d = self.__dict__
for name in self._params:
d[name].to_intel64()
for name in self._persistent:
value = d[name]
if isinstance(value, cuda.ndarray):
value = value.get() # to numpy.ndarray
if (isinstance(value, numpy.ndarray) and intel64.inputs_all_ready(
(value, ))):
value = intel64.ideep.array(value,
itype=intel64.ideep.wgt_array)
d[name] = value
self._cpu = True
self._device_id = None
return self
def to_intel64(self):
"""Copies parameter variables and persistent values to CPU."""
intel64.check_ideep_available()
d = self.__dict__
for name in self._params:
d[name].to_intel64()
for name in self._persistent:
value = d[name]
if isinstance(value, cuda.ndarray):
value = value.get() # to numpy.ndarray
if (isinstance(value, numpy.ndarray) and value.ndim in (1, 2, 4)):
# TODO(kmaehashi): Remove ndim validation once iDeep has fixed.
# Currently iDeep only supports (1, 2, 4)-dim arrays.
# Note that array returned from `ideep.array` may not be an
# iDeep mdarray, e.g., when the dtype is not float32.
value = intel64.ideep.array(value,
itype=intel64.ideep.wgt_array)
d[name] = value
self._cpu = True
self._device_id = None
return self
def to_intel64(self):
"""Copies parameter variables and persistent values to CPU."""
intel64.check_ideep_available()
d = self.__dict__
for name in self._params:
d[name].to_intel64()
for name in self._persistent:
value = d[name]
if isinstance(value, cuda.ndarray):
value = value.get() # to numpy.ndarray
if (isinstance(value, numpy.ndarray) and value.ndim in (1, 2, 4)):
# TODO(kmaehashi): Remove ndim validation once iDeep has fixed.
# Currently iDeep only supports (1, 2, 4)-dim arrays.
# Note that array returned from `ideep.array` may not be an
# iDeep mdarray, e.g., when the dtype is not float32.
value = intel64.ideep.array(
value, itype=intel64.ideep.wgt_array)
d[name] = value
self._cpu = True
self._device_id = None
return self
def to_intel64(self):
# type: () -> 'Link'
"""Copies parameter variables and persistent values to CPU."""
intel64.check_ideep_available()
return self.to_device(intel64)
def to_intel64(self):
"""Copies parameter variables and persistent values to CPU."""
intel64.check_ideep_available()
return self.to_device(intel64)
