def __init__(self, shape, dtype, allocator=drv.mem_alloc,
base=None, gpudata=None, strides=None, order="C"):
dtype = np.dtype(dtype)
try:
s = 1
for dim in shape:
s *= dim
except TypeError:
# handle dim-0 ndarrays:
if isinstance(shape, np.ndarray):
shape = np.asscalar(shape)
assert isinstance(shape, numbers.Integral)
s = shape
shape = (shape,)
else:
# handle shapes that are ndarrays
shape = tuple(shape)
if isinstance(s, np.integer):
# bombs if s is a Python integer
s = np.asscalar(s)
if strides is None:
if order == "F":
strides = _f_contiguous_strides(
dtype.itemsize, shape)
elif order == "C":
strides = _c_contiguous_strides(
dtype.itemsize, shape)
else:
raise ValueError("invalid order: %s" % order)
else:
# FIXME: We should possibly perform some plausibility
# checking on 'strides' here.
strides = tuple(strides)
self.shape = tuple(shape)
self.dtype = dtype
self.strides = strides
self.mem_size = self.size = s
self.nbytes = self.dtype.itemsize * self.size
self.allocator = allocator
if gpudata is None:
if self.size:
self.gpudata = self.allocator(self.size * self.dtype.itemsize)
else:
self.gpudata = None
assert base is None
else:
self.gpudata = gpudata
self.base = base
self._grid, self._block = splay(self.mem_size)
def __init__(self, shape, dtype, allocator=drv.mem_alloc,
base=None, gpudata=None, strides=None, order="C"):
dtype = np.dtype(dtype)
try:
s = 1
for dim in shape:
s *= dim
except TypeError:
# handle dim-0 ndarrays:
if isinstance(shape, np.ndarray):
shape = np.asscalar(shape)
assert isinstance(shape, numbers.Integral)
s = shape
shape = (shape,)
else:
# handle shapes that are ndarrays
shape = tuple(shape)
if isinstance(s, np.integer):
# bombs if s is a Python integer
s = np.asscalar(s)
if strides is None:
if order == "F":
strides = _f_contiguous_strides(
dtype.itemsize, shape)
elif order == "C":
strides = _c_contiguous_strides(
dtype.itemsize, shape)
else:
raise ValueError("invalid order: %s" % order)
else:
# FIXME: We should possibly perform some plausibility
# checking on 'strides' here.
strides = tuple(strides)
self.shape = tuple(shape)
self.dtype = dtype
self.strides = strides
self.mem_size = self.size = s
self.nbytes = self.dtype.itemsize * self.size
self.allocator = allocator
if gpudata is None:
if self.size:
self.gpudata = self.allocator(self.size * self.dtype.itemsize)
else:
self.gpudata = None
assert base is None
else:
self.gpudata = gpudata
self.base = base
self._grid, self._block = splay(self.mem_size)
def __init__(self,
backend,
shape,
iwl,
allocator=drv.mem_alloc,
base=None,
gpudata=None,
strides=None,
is_trans=False,
order="C"):
dtype = np.dtype(np.int16)
try:
size = 1
for dim in shape:
size *= dim
except TypeError:
assert isinstance(shape, (int, long, np.integer))
size = shape
shape = (shape, )
if isinstance(size, np.integer):
size = np.asscalar(size)
if strides is None:
if order == "F":
strides = _f_contiguous_strides(dtype.itemsize, shape)
elif order == "C":
strides = _c_contiguous_strides(dtype.itemsize, shape)
else:
raise ValueError("invalid order: %s" % order)
else:
strides = tuple(strides)
self.backend = backend
self.base = base
self.shape = shape
self.iwl = iwl
self.strides = strides
self.size = size
self.dtype = dtype
self.nbytes = dtype.itemsize * size
self.allocator = allocator
self.is_trans = is_trans
if gpudata is None:
if size:
self.gpudata = allocator(self.nbytes)
else:
self.gpudata = None
assert base is None
else:
self.gpudata = gpudata
def __init__(self,
shape,
dtype,
allocator=drv.mem_alloc,
base=None,
gpudata=None,
strides=None,
order="C"):
dtype = np.dtype(dtype)
try:
s = 1
for dim in shape:
s *= dim
except TypeError:
assert isinstance(shape, (int, long, np.integer))
s = shape
shape = (shape, )
if strides is None:
if order == "F":
strides = _f_contiguous_strides(dtype.itemsize, shape)
elif order == "C":
strides = _c_contiguous_strides(dtype.itemsize, shape)
else:
raise ValueError("invalid order: %s" % order)
else:
# FIXME: We should possibly perform some plausibility
# checking on 'strides' here.
strides = tuple(strides)
self.shape = shape
self.dtype = dtype
self.strides = strides
self.mem_size = self.size = s
self.nbytes = self.dtype.itemsize * self.size
self.allocator = allocator
if gpudata is None:
if self.size:
self.gpudata = self.allocator(self.size * self.dtype.itemsize)
else:
self.gpudata = None
assert base is None
else:
self.gpudata = gpudata
self.base = base
self._grid, self._block = splay(self.mem_size)
def __init__(self, shape, dtype, allocator=drv.mem_alloc,
base=None, gpudata=None, strides=None, order="C"):
dtype = np.dtype(dtype)
try:
s = 1
for dim in shape:
s *= dim
except TypeError:
assert isinstance(shape, (int, long, np.integer))
s = shape
shape = (shape,)
if strides is None:
if order == "F":
strides = _f_contiguous_strides(
dtype.itemsize, shape)
elif order == "C":
strides = _c_contiguous_strides(
dtype.itemsize, shape)
else:
raise ValueError("invalid order: %s" % order)
else:
# FIXME: We should possibly perform some plausibility
# checking on 'strides' here.
strides = tuple(strides)
self.shape = shape
self.dtype = dtype
self.strides = strides
self.mem_size = self.size = s
self.nbytes = self.dtype.itemsize * self.size
self.allocator = allocator
if gpudata is None:
if self.size:
self.gpudata = self.allocator(self.size * self.dtype.itemsize)
else:
self.gpudata = None
assert base is None
else:
self.gpudata = gpudata
self.base = base
self._grid, self._block = splay(self.mem_size)
def __init__(self, backend, shape, iwl, allocator=drv.mem_alloc,
base=None, gpudata=None, strides=None, is_trans=False, order="C"):
dtype = np.dtype(np.int16)
try:
size = 1
for dim in shape:
size *= dim
except TypeError:
assert isinstance(shape, (int, long, np.integer))
size = shape
shape = (shape,)
if isinstance(size, np.integer):
size = np.asscalar(size)
if strides is None:
if order == "F":
strides = _f_contiguous_strides(dtype.itemsize, shape)
elif order == "C":
strides = _c_contiguous_strides(dtype.itemsize, shape)
else:
raise ValueError("invalid order: %s" % order)
else:
strides = tuple(strides)
self.backend = backend
self.base = base
self.shape = shape
self.iwl = iwl
self.strides = strides
self.size = size
self.dtype = dtype
self.nbytes = dtype.itemsize * size
self.allocator = allocator
self.is_trans = is_trans
if gpudata is None:
if size:
self.gpudata = allocator(self.nbytes)
else:
self.gpudata = None
assert base is None
else:
self.gpudata = gpudata
def reshape(self, *shape):
if isinstance(shape[0], tuple) or isinstance(shape[0], list):
shape = tuple(shape[0])
if shape == self.shape:
return self
size = reduce(lambda x, y: x * y, shape, 1)
if size != self.size:
raise ValueError("total size of new array must be unchanged")
if not self.flags.forc:
raise TypeError("reshaping of non-contigous arrays is not yet supported")
return self.__class__(
backend = self.backend,
shape = shape,
iwl = self.iwl,
allocator = self.allocator,
base = self,
gpudata = self.gpudata,
strides = _c_contiguous_strides(self.dtype.itemsize, shape))
