def __init__(self, *args, **kwargs):
if not self._cached():
self.dtype = kwargs.get('dtype', np.float32)
self._external = bool(kwargs.get('external', False))
self._onstack = bool(kwargs.get('onstack', False))
self._onheap = bool(kwargs.get('onheap', True))
# The memory scope of an Array must be well-defined
assert single_or([self._external, self._onstack, self._onheap])
def update(self, dtype=None, shape=None, dimensions=None, onstack=None,
onheap=None, external=None):
self.dtype = dtype or self.dtype
self.shape = shape or self.shape
self.indices = dimensions or self.indices
if any(i is not None for i in [external, onstack, onheap]):
self._external = bool(external)
self._onstack = bool(onstack)
self._onheap = bool(onheap)
assert single_or([self._external, self._onstack, self._onheap])
def __init__(self, *args, **kwargs):
if not self._cached():
self.dtype = kwargs.get('dtype', np.float32)
self._halo = kwargs.get('halo', tuple((0, 0) for i in range(self.ndim)))
self._padding = kwargs.get('padding', tuple((0, 0) for i in range(self.ndim)))
self._external = bool(kwargs.get('external', False))
self._onstack = bool(kwargs.get('onstack', False))
self._onheap = bool(kwargs.get('onheap', True))
# The memory scope of an Array must be well-defined
assert single_or([self._external, self._onstack, self._onheap])
def __init__(self, *args, **kwargs):
if not self._cached():
self.name = kwargs.get('name')
self.shape = kwargs.get('shape')
self.indices = kwargs.get('dimensions')
self.dtype = kwargs.get('dtype', np.float32)
self._external = bool(kwargs.get('external', False))
self._onstack = bool(kwargs.get('onstack', False))
self._onheap = bool(kwargs.get('onheap', True))
# The memory scope of a TensorFunction must be well-defined
assert single_or([self._external, self._onstack, self._onheap])