def __call__(self, key, value):
if self.group is None:
if not self.strict:
return value
else:
raise ValueError('Inexistent group is specified')
if not self.strict and key not in self.group:
return value
dataset = self.group[key]
if dataset.shape is None: # Empty
return None
if value is None:
return numpy.asarray(dataset)
if isinstance(value, chainerx.ndarray):
value[...] = _chainerx._array_to_chainerx(numpy.asarray(dataset),
value.device)
elif isinstance(value, numpy.ndarray):
dataset.read_direct(value)
elif isinstance(value, cuda.ndarray):
value.set(numpy.asarray(dataset, dtype=value.dtype))
elif isinstance(value, intel64.mdarray):
intel64.ideep.basic_copyto(value, numpy.asarray(dataset))
else:
value = type(value)(numpy.asarray(dataset))
return value
def copyto(dst, src):
"""Copies the elements of an ndarray to those of another one.
This function can copy the CPU/GPU arrays to the destination arrays on
another device.
Args:
dst (`numpy.ndarray`, `cupy.ndarray` or `ideep4py.mdarray`):
Destination array.
src (`numpy.ndarray`, `cupy.ndarray` or `ideep4py.mdarray`):
Source array.
"""
if isinstance(dst, chainerx.ndarray):
dst[...] = _chainerx._array_to_chainerx(src, dst.device)
elif isinstance(dst, numpy.ndarray):
numpy.copyto(dst, _cpu._to_cpu(src))
elif isinstance(dst, intel64.mdarray):
intel64.ideep.basic_copyto(dst, _cpu._to_cpu(src))
elif isinstance(dst, cuda.ndarray):
if isinstance(src, chainer.get_cpu_array_types()):
src = numpy.asarray(src)
if dst.flags.c_contiguous or dst.flags.f_contiguous:
dst.set(src)
else:
cuda.cupy.copyto(dst, cuda.to_gpu(src, device=dst.device))
elif isinstance(src, cuda.ndarray):
cuda.cupy.copyto(dst, src)
else:
raise TypeError(
'cannot copy from non-array object of type {}'.format(
type(src)))
else:
raise TypeError('cannot copy to non-array object of type {}'.format(
type(dst)))
def __call__(self, key, value):
key = self.path + key.lstrip('/')
if not self.strict and key not in self.npz:
return value
if isinstance(self.ignore_names, (tuple, list)):
ignore_names = self.ignore_names
else:
ignore_names = (self.ignore_names, )
for ignore_name in ignore_names:
if isinstance(ignore_name, str):
if key == ignore_name:
return value
elif callable(ignore_name):
if ignore_name(key):
return value
else:
raise ValueError(
'ignore_names needs to be a callable, string or '
'list of them.')
dataset = self.npz[key]
if dataset[()] is None:
return None
if value is None:
return dataset
if isinstance(value, chainerx.ndarray):
value[...] = _chainerx._array_to_chainerx(numpy.asarray(dataset),
value.device)
elif isinstance(value, numpy.ndarray):
numpy.copyto(value, dataset)
elif isinstance(value, cuda.ndarray):
value.set(numpy.asarray(dataset, dtype=value.dtype))
elif isinstance(value, intel64.mdarray):
intel64.ideep.basic_copyto(value, numpy.asarray(dataset))
else:
value_type = type(value)
dataset_arr = numpy.asarray(dataset)
if (issubclass(dataset_arr.dtype.type, numpy.number)
and not (issubclass(dataset_arr.dtype.type, numpy.integer)
and value_type in six.integer_types)
# Casting a `numpy.integer` scalar by `int()` case above is
# safe as `int()` gives unlimited precision integer (it's
# also true for `long()`/`int()` on Python 2). For such a
# case, the check below may be too strict. For example,
# `numpy.can_cast(numpy.int64, int)`, which checks cast-
# ability to `dtype(int)`, gives `False` on a platform
# whose `dtype(int)` is `numpy.int32` like Windows/x64.
and not numpy.can_cast(dataset_arr.dtype,
value_type,
casting='safe')):
raise TypeError(
'Cannot safely deserialize from numpy array with dtype={} '
'into a variable of type {}.'.format(
dataset.dtype, type(value)))
value = value_type(dataset_arr)
return value