def arr_vulkan_layout_to_arr_normal_layout(vk_arr: ndarray_type.ndarray(),
normal_arr: ndarray_type.ndarray()):
static_assert(len(normal_arr.shape) == 2)
w = normal_arr.shape[0]
h = normal_arr.shape[1]
for i, j in ndrange(w, h):
normal_arr[i, j] = vk_arr[(h - 1 - j) * w + i]
def ndarray_matrix_to_ext_arr(ndarray: ndarray_type.ndarray(),
arr: ndarray_type.ndarray(),
layout_is_aos: template(),
as_vector: template()):
for I in grouped(ndarray):
for p in static(range(ndarray[I].n)):
for q in static(range(ndarray[I].m)):
if static(as_vector):
if static(layout_is_aos):
arr[I, p] = ndarray[I][p]
else:
arr[p, I] = ndarray[I][p]
else:
if static(layout_is_aos):
arr[I, p, q] = ndarray[I][p, q]
else:
arr[p, q, I] = ndarray[I][p, q]
def ext_arr_to_matrix(arr: ndarray_type.ndarray(), mat: template(),
as_vector: template()):
for I in grouped(mat):
for p in static(range(mat.n)):
for q in static(range(mat.m)):
if static(as_vector):
mat[I][p] = arr[I, p]
else:
mat[I][p, q] = arr[I, p, q]
def vector_to_fast_image(img: template(), out: ndarray_type.ndarray()):
# FIXME: Why is ``for i, j in img:`` slower than:
for i, j in ndrange(*img.shape):
r, g, b = 0, 0, 0
color = img[i, img.shape[1] - 1 - j]
if static(img.dtype in [f16, f32, f64]):
r, g, b = min(255, max(0, int(color * 255)))
else:
static_assert(img.dtype == u8)
r, g, b = color
idx = j * img.shape[0] + i
# We use i32 for |out| since OpenGL and Metal doesn't support u8 types
if static(get_os_name() != 'osx'):
out[idx] = (r << 16) + (g << 8) + b
else:
# What's -16777216?
#
# On Mac, we need to set the alpha channel to 0xff. Since Mac's GUI
# is big-endian, the color is stored in ABGR order, and we need to
# add 0xff000000, which is -16777216 in I32's legit range. (Albeit
# the clarity, adding 0xff000000 doesn't work.)
alpha = -16777216
out[idx] = (b << 16) + (g << 8) + r + alpha
def tensor_to_image(tensor: template(), arr: ndarray_type.ndarray()):
for I in grouped(tensor):
t = ops.cast(tensor[I], f32)
arr[I, 0] = t
arr[I, 1] = t
arr[I, 2] = t
def ndarray_to_ext_arr(ndarray: ndarray_type.ndarray(),
arr: ndarray_type.ndarray()):
for I in grouped(ndarray):
arr[I] = ndarray[I]
def tensor_to_ext_arr(tensor: template(), arr: ndarray_type.ndarray()):
for I in grouped(tensor):
arr[I] = tensor[I]
def fill_ndarray_matrix(ndarray: ndarray_type.ndarray(), val: template()):
for I in grouped(ndarray):
ndarray[I].fill(val)
def fill_ndarray(ndarray: ndarray_type.ndarray(), val: template()):
for I in grouped(ndarray):
ndarray[I] = val
def ext_arr_to_ndarray(arr: ndarray_type.ndarray(),
ndarray: ndarray_type.ndarray()):
for I in grouped(ndarray):
ndarray[I] = arr[I]
def ndarray_to_ndarray(ndarray: ndarray_type.ndarray(),
other: ndarray_type.ndarray()):
for I in grouped(ndarray):
ndarray[I] = other[I]
def ext_arr_to_tensor(arr: ndarray_type.ndarray(), tensor: template()):
for I in grouped(tensor):
tensor[I] = arr[I]
def vector_to_image(mat: template(), arr: ndarray_type.ndarray()):
for I in grouped(mat):
for p in static(range(mat.n)):
arr[I, p] = ops.cast(mat[I][p], f32)
if static(mat.n <= 2):
arr[I, 2] = 0
