def codegen(context, builder, signature, args):
# check that the return type is now defined
arrty = signature.return_type
assert arrty.is_precise()
shapes = unpack_tuple(builder, args[0])
# redirect implementation to np.empty
res = _empty_nd_impl(context, builder, arrty, shapes)
return impl_ret_new_ref(context, builder, arrty, res._getvalue())
def _image_to_array(context, builder, shapes_array, arrtype, data, img):
# allocate array
shapes = cgutils.unpack_tuple(builder, builder.load(shapes_array))
ary = _empty_nd_impl(context, builder, arrtype, shapes)
cgutils.raw_memcpy(builder,
ary.data,
builder.load(data),
ary.nitems,
ary.itemsize,
align=1)
# clean up cv::Mat image
fnty = lir.FunctionType(lir.VoidType(), [lir.IntType(8).as_pointer()])
fn_release = builder.module.get_or_insert_function(fnty,
name="cv_mat_release")
builder.call(fn_release, [img])
return impl_ret_new_ref(context, builder, arrtype, ary._getvalue())
def random_arr(context, builder, sig, args, typing_key=typing_key):
arrty = sig.return_type
dtype = arrty.dtype
scalar_sig = signature(dtype, *sig.args[:-1])
scalar_args = args[:-1]
# Allocate array...
shapes = arrayobj._parse_shape(context, builder, sig.args[-1],
args[-1])
arr = arrayobj._empty_nd_impl(context, builder, arrty, shapes)
# ... and populate it in natural order
scalar_impl = context.get_function(typing_key, scalar_sig)
with cgutils.for_range(builder, arr.nitems) as loop:
val = scalar_impl(builder, scalar_args)
ptr = cgutils.gep(builder, arr.data, loop.index)
arrayobj.store_item(context, builder, arrty, val, ptr)
return impl_ret_new_ref(context, builder, sig.return_type,
arr._getvalue())
def codegen(context, builder, sig, args):
assert (len(args) == 2)
data = args[0]
shape = args[1]
# XXX: unnecessary allocation and copy, reuse data pointer
shape_list = cgutils.unpack_tuple(builder, shape, shape.type.count)
ary = _empty_nd_impl(context, builder, arr_typ, shape_list)
cgutils.raw_memcpy(builder,
ary.data,
data,
ary.nitems,
ary.itemsize,
align=1)
# clean up image buffer
fnty = lir.FunctionType(lir.VoidType(), [lir.IntType(8).as_pointer()])
fn_release = builder.module.get_or_insert_function(
fnty, name="cv_delete_buf")
builder.call(fn_release, [data])
return impl_ret_new_ref(context, builder, sig.return_type,
ary._getvalue())
def _build_array(context, builder, array_ty, input_types, inputs):
"""Utility function to handle allocation of an implicit output array
given the target context, builder, output array type, and a list of
_ArrayHelper instances.
"""
# First, strip optional types, ufunc loops are typed on concrete types
input_types = [
x.type if isinstance(x, types.Optional) else x for x in input_types
]
intp_ty = context.get_value_type(types.intp)
def make_intp_const(val):
return context.get_constant(types.intp, val)
ZERO = make_intp_const(0)
ONE = make_intp_const(1)
src_shape = cgutils.alloca_once(builder, intp_ty, array_ty.ndim,
"src_shape")
dest_ndim = make_intp_const(array_ty.ndim)
dest_shape = cgutils.alloca_once(builder, intp_ty, array_ty.ndim,
"dest_shape")
dest_shape_addrs = tuple(
cgutils.gep_inbounds(builder, dest_shape, index)
for index in range(array_ty.ndim))
# Initialize the destination shape with all ones.
for dest_shape_addr in dest_shape_addrs:
builder.store(ONE, dest_shape_addr)
# For each argument, try to broadcast onto the destination shape,
# mutating along any axis where the argument shape is not one and
# the destination shape is one.
for arg_number, arg in enumerate(inputs):
if not hasattr(arg, "ndim"): # Skip scalar arguments
continue
arg_ndim = make_intp_const(arg.ndim)
for index in range(arg.ndim):
builder.store(arg.shape[index],
cgutils.gep_inbounds(builder, src_shape, index))
arg_result = context.compile_internal(
builder, _broadcast_onto, _broadcast_onto_sig,
[arg_ndim, src_shape, dest_ndim, dest_shape])
with cgutils.if_unlikely(builder,
builder.icmp(lc.ICMP_SLT, arg_result, ONE)):
msg = "unable to broadcast argument %d to output array" % (
arg_number, )
loc = errors.loc_info.get('loc', None)
if loc is not None:
msg += '\nFile "%s", line %d, ' % (loc.filename, loc.line)
context.call_conv.return_user_exc(builder, ValueError, (msg, ))
real_array_ty = array_ty.as_array
dest_shape_tup = tuple(
builder.load(dest_shape_addr) for dest_shape_addr in dest_shape_addrs)
array_val = arrayobj._empty_nd_impl(context, builder, real_array_ty,
dest_shape_tup)
# Get the best argument to call __array_wrap__ on
array_wrapper_index = select_array_wrapper(input_types)
array_wrapper_ty = input_types[array_wrapper_index]
try:
# __array_wrap__(source wrapped array, out array) -> out wrapped array
array_wrap = context.get_function(
'__array_wrap__', array_ty(array_wrapper_ty, real_array_ty))
except NotImplementedError:
# If it's the same priority as a regular array, assume we
# should use the allocated array unchanged.
if array_wrapper_ty.array_priority != types.Array.array_priority:
raise
out_val = array_val._getvalue()
else:
wrap_args = (inputs[array_wrapper_index].return_val,
array_val._getvalue())
out_val = array_wrap(builder, wrap_args)
ndim = array_ty.ndim
shape = cgutils.unpack_tuple(builder, array_val.shape, ndim)
strides = cgutils.unpack_tuple(builder, array_val.strides, ndim)
return _ArrayHelper(context, builder, shape, strides, array_val.data,
array_ty.layout, array_ty.dtype, ndim, out_val)