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"""
Fast image interpolation using a pyramid.
"""
import halide as hl
def _func_list(name, size):
"""Return a list containing `size` Funcs, named `name_n` for n in 0..size-1."""
return [hl.Func(f"{name}_{i}") for i in range(size)]
@hl.alias(
interpolate_Mullapudi2016={"autoscheduler": "Mullapudi2016"},
)
@hl.generator()
class interpolate:
levels = hl.GeneratorParam(10)
input_buf = hl.InputBuffer(hl.Float(32), 3)
output_buf = hl.OutputBuffer(hl.Float(32), 3)
def generate(self):
g = self
x, y, c = hl.vars("x y c")
# Input must have four color channels - rgba
g.input_buf.dim(2).set_bounds(0, 4)
downsampled = _func_list("downsampled", g.levels)
downx = _func_list("downx", g.levels)
interpolated = _func_list("interpolated", g.levels)
upsampled = _func_list("upsampled", g.levels)
upsampledx = _func_list("upsampledx", g.levels)
clamped = hl.BoundaryConditions.repeat_edge(g.input_buf)
downsampled[0][x, y, c] = hl.select(
c < 3,
clamped[x, y, c] * clamped[x, y, 3],
clamped[x, y, 3],
)
for lv in range(1, g.levels):
prev = downsampled[lv - 1]
if lv == 4:
# Also add a boundary condition at a middle pyramid level
# to prevent the footprint of the downsamplings to extend
# too far off the base image. Otherwise we look 512
# pixels off each edge.
w = g.input_buf.width() / (1 << (lv - 1))
h = g.input_buf.height() / (1 << (lv - 1))
prev = hl.lambda_func(
x, y, c, prev[hl.clamp(x, 0, w), hl.clamp(y, 0, h), c]
)
downx[lv][x, y, c] = (
prev[x * 2 - 1, y, c] + 2 * prev[x * 2, y, c] + prev[x * 2 + 1, y, c]
) * 0.25
downsampled[lv][x, y, c] = (
downx[lv][x, y * 2 - 1, c]
+ 2 * downx[lv][x, y * 2, c]
+ downx[lv][x, y * 2 + 1, c]
) * 0.25
interpolated[g.levels - 1][x, y, c] = downsampled[g.levels - 1][x, y, c]
for lv in range(g.levels - 2, -1, -1):
upsampledx[lv][x, y, c] = (
interpolated[lv + 1][x / 2, y, c]
+ interpolated[lv + 1][(x + 1) / 2, y, c]
) / 2
upsampled[lv][x, y, c] = (
upsampledx[lv][x, y / 2, c] + upsampledx[lv][x, (y + 1) / 2, c]
) / 2
alpha = 1.0 - downsampled[lv][x, y, 3]
interpolated[lv][x, y, c] = (
downsampled[lv][x, y, c] + alpha * upsampled[lv][x, y, c]
)
g.output_buf[x, y, c] = interpolated[0][x, y, c] / interpolated[0][x, y, 3]
# Schedule
if g.using_autoscheduler():
# nothing
pass
elif g.target().has_gpu_feature():
# 0.86ms on a 2060 RTX
yo, yi, xo, xi, xii, yii = hl.vars("yo yi xo xi xii yii")
(
g.output_buf.bound(x, 0, g.input_buf.width())
.bound(y, 0, g.input_buf.height())
.bound(c, 0, 3)
.reorder(c, x, y)
.tile(x, y, xi, yi, 32, 32, hl.TailStrategy.RoundUp)
.tile(xi, yi, xii, yii, 2, 2)
.gpu_blocks(x, y)
.gpu_threads(xi, yi)
.unroll(xii)
.unroll(yii)
.unroll(c)
)
for lv in range(1, g.levels):
(
downsampled[lv]
.compute_root()
.reorder(c, x, y)
.unroll(c)
.gpu_tile(x, y, xi, yi, 16, 16)
)
for lv in range(3, g.levels, 2):
(
interpolated[lv]
.compute_root()
.reorder(c, x, y)
.tile(x, y, xi, yi, 32, 32, hl.TailStrategy.RoundUp)
.tile(xi, yi, xii, yii, 2, 2)
.gpu_blocks(x, y)
.gpu_threads(xi, yi)
.unroll(xii)
.unroll(yii)
.unroll(c)
)
(
upsampledx[1]
.compute_at(g.output_buf, x)
.reorder(c, x, y)
.tile(x, y, xi, yi, 2, 1)
.unroll(xi)
.unroll(yi)
.unroll(c)
.gpu_threads(x, y)
)
(
interpolated[1]
.compute_at(g.output_buf, x)
.reorder(c, x, y)
.tile(x, y, xi, yi, 2, 2)
.unroll(xi)
.unroll(yi)
.unroll(c)
.gpu_threads(x, y)
)
(
interpolated[2]
.compute_at(g.output_buf, x)
.reorder(c, x, y)
.unroll(c)
.gpu_threads(x, y)
)
else:
# 4.54ms on an Intel i9-9960X using 16 threads
xo, xi, yo, yi = hl.vars("xo xi yo yi")
vec = g.natural_vector_size(hl.Float(32))
for lv in range(1, g.levels - 1):
# We must refer to the downsampled stages in the
# upsampling later, so they must all be
# compute_root or redundantly recomputed, as in
# the local_laplacian app.
(
downsampled[lv]
.compute_root()
.reorder(x, c, y)
.split(y, yo, yi, 8)
.parallel(yo)
.vectorize(x, vec)
)
# downsampled[0] takes too long to compute_root, so
# we'll redundantly recompute it instead. Make a
# separate clone of it in the first downsampled stage
# so that we can schedule the two versions
# separately.
(
downsampled[0]
.clone_in(downx[1])
.store_at(downsampled[1], yo)
.compute_at(downsampled[1], yi)
.reorder(c, x, y)
.unroll(c)
.vectorize(x, vec)
)
(
g.output_buf.bound(x, 0, g.input_buf.width())
.bound(y, 0, g.input_buf.height())
.bound(c, 0, 3)
.split(x, xo, xi, vec)
.split(y, yo, yi, 32)
.reorder(xi, c, xo, yi, yo)
.unroll(c)
.vectorize(xi)
.parallel(yo)
)
for lv in range(1, g.levels):
(
interpolated[lv]
.store_at(g.output_buf, yo)
.compute_at(g.output_buf, yi)
.vectorize(x, vec)
)
# Estimates (for autoscheduler; ignored otherwise)
(
g.input_buf.dim(0)
.set_estimate(0, 1536)
.dim(1)
.set_estimate(0, 2560)
.dim(2)
.set_estimate(0, 4)
)
(
g.output_buf.output_buffer()
.dim(0)
.set_estimate(0, 1536)
.dim(1)
.set_estimate(0, 2560)
.dim(2)
.set_estimate(0, 3)
)
if __name__ == "__main__":
hl.main()
|
