def test_basics2():
input = hl.ImageParam(hl.Float(32), 3, 'input')
r_sigma = hl.Param(hl.Float(32), 'r_sigma', 0.1) # Value needed if not generating an executable
s_sigma = 8 # This is passed during code generation in the C++ version
x = hl.Var('x')
y = hl.Var('y')
z = hl.Var('z')
c = hl.Var('c')
# Add a boundary condition
clamped = hl.Func('clamped')
clamped[x, y] = input[hl.clamp(x, 0, input.width()-1),
hl.clamp(y, 0, input.height()-1),0]
# Construct the bilateral grid
r = hl.RDom(0, s_sigma, 0, s_sigma, 'r')
val0 = clamped[x * s_sigma, y * s_sigma]
val00 = clamped[x * s_sigma * hl.cast(hl.Int(32), 1), y * s_sigma * hl.cast(hl.Int(32), 1)]
#val1 = clamped[x * s_sigma - s_sigma/2, y * s_sigma - s_sigma/2] # should fail
val22 = clamped[x * s_sigma - hl.cast(hl.Int(32), s_sigma//2),
y * s_sigma - hl.cast(hl.Int(32), s_sigma//2)]
val2 = clamped[x * s_sigma - s_sigma//2, y * s_sigma - s_sigma//2]
val3 = clamped[x * s_sigma + r.x - s_sigma//2, y * s_sigma + r.y - s_sigma//2]
return
def __init__(self, input):
assert type(input) == hl.Buffer_uint8
self.lut = hl.Func("lut")
self.padded = hl.Func("padded")
self.padded16 = hl.Func("padded16")
self.sharpen = hl.Func("sharpen")
self.curved = hl.Func("curved")
self.input = input
# For this lesson, we'll use a two-stage pipeline that sharpens
# and then applies a look-up-table (LUT).
# First we'll define the LUT. It will be a gamma curve.
self.lut[i] = hl.cast(hl.UInt(8), hl.clamp(pow(i / 255.0, 1.2) * 255.0, 0, 255))
# Augment the input with a boundary condition.
self.padded[x, y, c] = input[hl.clamp(x, 0, input.width()-1),
hl.clamp(y, 0, input.height()-1), c]
# Cast it to 16-bit to do the math.
self.padded16[x, y, c] = hl.cast(hl.UInt(16), self.padded[x, y, c])
# Next we sharpen it with a five-tap filter.
self.sharpen[x, y, c] = (self.padded16[x, y, c] * 2-
(self.padded16[x - 1, y, c] +
self.padded16[x, y - 1, c] +
self.padded16[x + 1, y, c] +
self.padded16[x, y + 1, c]) / 4)
# Then apply the LUT.
self.curved[x, y, c] = self.lut[self.sharpen[x, y, c]]
def get_blur(input):
assert type(input) == hl.ImageParam
assert input.dimensions() == 2
x, y = hl.Var("x"), hl.Var("y")
clamped_input = hl.repeat_edge(input)
input_uint16 = hl.Func("input_uint16")
input_uint16[x,y] = hl.cast(hl.UInt(16), clamped_input[x,y])
ci = input_uint16
blur_x = hl.Func("blur_x")
blur_y = hl.Func("blur_y")
blur_x[x,y] = (ci[x,y]+ci[x+1,y]+ci[x+2,y])/3
blur_y[x,y] = hl.cast(hl.UInt(8), (blur_x[x,y]+blur_x[x,y+1]+blur_x[x,y+2])/3)
# schedule
xi, yi = hl.Var("xi"), hl.Var("yi")
blur_y.tile(x, y, xi, yi, 8, 4).parallel(y).vectorize(xi, 8)
blur_x.compute_at(blur_y, x).vectorize(x, 8)
return blur_y
def u8(x, y, c, img):
out = mkfunc("u8", img)
if img.dimensions() == 2:
out[x, y] = hl.cast(hl.UInt(8), img[x, y])
else:
out[x, y, c] = hl.cast(hl.UInt(8), img[x, y, c])
return out
def __init__(self, input):
assert input.type() == hl.UInt(8)
self.lut = hl.Func("lut")
self.padded = hl.Func("padded")
self.padded16 = hl.Func("padded16")
self.sharpen = hl.Func("sharpen")
self.curved = hl.Func("curved")
self.input = input
# For this lesson, we'll use a two-stage pipeline that sharpens
# and then applies a look-up-table (LUT).
# First we'll define the LUT. It will be a gamma curve.
self.lut[i] = hl.cast(hl.UInt(8),
hl.clamp(pow(i / 255.0, 1.2) * 255.0, 0, 255))
# Augment the input with a boundary condition.
self.padded[x, y, c] = input[hl.clamp(x, 0,
input.width() - 1),
hl.clamp(y, 0,
input.height() - 1), c]
# Cast it to 16-bit to do the math.
self.padded16[x, y, c] = hl.cast(hl.UInt(16), self.padded[x, y, c])
# Next we sharpen it with a five-tap filter.
self.sharpen[x, y, c] = (
self.padded16[x, y, c] * 2 -
(self.padded16[x - 1, y, c] + self.padded16[x, y - 1, c] +
self.padded16[x + 1, y, c] + self.padded16[x, y + 1, c]) / 4)
# Then apply the LUT.
self.curved[x, y, c] = self.lut[self.sharpen[x, y, c]]
def get_blur(input):
assert type(input) == hl.ImageParam
assert input.dimensions() == 2
x, y = hl.Var("x"), hl.Var("y")
clamped_input = hl.BoundaryConditions.repeat_edge(input)
input_uint16 = hl.Func("input_uint16")
input_uint16[x, y] = hl.cast(hl.UInt(16), clamped_input[x, y])
ci = input_uint16
blur_x = hl.Func("blur_x")
blur_y = hl.Func("blur_y")
blur_x[x, y] = (ci[x, y] + ci[x + 1, y] + ci[x + 2, y]) / 3
blur_y[x, y] = hl.cast(
hl.UInt(8), (blur_x[x, y] + blur_x[x, y + 1] + blur_x[x, y + 2]) / 3)
# schedule
xi, yi = hl.Var("xi"), hl.Var("yi")
blur_y.tile(x, y, xi, yi, 8, 4).parallel(y).vectorize(xi, 8)
blur_x.compute_at(blur_y, x).vectorize(x, 8)
return blur_y
def test_basics2():
input = hl.ImageParam(hl.Float(32), 3, 'input')
r_sigma = hl.Param(hl.Float(32), 'r_sigma', 0.1)
s_sigma = 8
x = hl.Var('x')
y = hl.Var('y')
z = hl.Var('z')
c = hl.Var('c')
# Add a boundary condition
clamped = hl.Func('clamped')
clamped[x, y] = input[hl.clamp(x, 0,
input.width() - 1),
hl.clamp(y, 0,
input.height() - 1), 0]
# Construct the bilateral grid
r = hl.RDom([(0, s_sigma), (0, s_sigma)], 'r')
val0 = clamped[x * s_sigma, y * s_sigma]
val00 = clamped[x * s_sigma * hl.cast(hl.Int(32), 1),
y * s_sigma * hl.cast(hl.Int(32), 1)]
val22 = clamped[x * s_sigma - hl.cast(hl.Int(32), s_sigma // 2),
y * s_sigma - hl.cast(hl.Int(32), s_sigma // 2)]
val2 = clamped[x * s_sigma - s_sigma // 2, y * s_sigma - s_sigma // 2]
val3 = clamped[x * s_sigma + r.x - s_sigma // 2,
y * s_sigma + r.y - s_sigma // 2]
try:
val1 = clamped[x * s_sigma - s_sigma / 2, y * s_sigma - s_sigma / 2]
except RuntimeError as e:
assert 'Implicit cast from float32 to int' in str(e)
else:
assert False, 'Did not see expected exception!'
def average(a, b):
if type(a) is not hl.Expr:
a = hl.Expr(a)
if type(b) is not hl.Expr:
b = hl.Expr(b)
"hl.Expr average(hl.Expr a, hl.Expr b)"
# Types must match.
assert a.type() == b.type()
# For floating point types:
if (a.type().is_float()):
# The '2' will be promoted to the floating point type due to
# rule 3 above.
return (a + b)/2
# For integer types, we must compute the intermediate value in a
# wider type to avoid overflow.
narrow = a.type()
wider = narrow.with_bits(narrow.bits() * 2)
a = hl.cast(wider, a)
b = hl.cast(wider, b)
return hl.cast(narrow, (a + b)/2)
def test_basics2():
input = hl.ImageParam(hl.Float(32), 3, 'input')
r_sigma = hl.Param(hl.Float(32), 'r_sigma',
0.1) # Value needed if not generating an executable
s_sigma = 8 # This is passed during code generation in the C++ version
x = hl.Var('x')
y = hl.Var('y')
z = hl.Var('z')
c = hl.Var('c')
# Add a boundary condition
clamped = hl.Func('clamped')
clamped[x, y] = input[hl.clamp(x, 0,
input.width() - 1),
hl.clamp(y, 0,
input.height() - 1), 0]
if True:
print("s_sigma", s_sigma)
print("s_sigma/2", s_sigma / 2)
print("s_sigma//2", s_sigma // 2)
print()
print("x * s_sigma", x * s_sigma)
print("x * 8", x * 8)
print("x * 8 + 4", x * 8 + 4)
print("x * 8 * 4", x * 8 * 4)
print()
print("x", x)
print("(x * s_sigma).type()", )
print("(x * 8).type()", (x * 8).type())
print("(x * 8 + 4).type()", (x * 8 + 4).type())
print("(x * 8 * 4).type()", (x * 8 * 4).type())
print("(x * 8 / 4).type()", (x * 8 / 4).type())
print("((x * 8) * 4).type()", ((x * 8) * 4).type())
print("(x * (8 * 4)).type()", (x * (8 * 4)).type())
assert (x * 8).type() == hl.Int(32)
assert (x * 8 * 4).type() == hl.Int(32) # yes this did fail at some point
assert ((x * 8) / 4).type() == hl.Int(32)
assert (x * (8 / 4)).type() == hl.Float(32) # under python3 division rules
assert (x * (8 // 4)).type() == hl.Int(32)
#assert (x * 8 // 4).type() == hl.Int(32) # not yet implemented
# Construct the bilateral grid
r = hl.RDom(0, s_sigma, 0, s_sigma, 'r')
val0 = clamped[x * s_sigma, y * s_sigma]
val00 = clamped[x * s_sigma * hl.cast(hl.Int(32), 1),
y * s_sigma * hl.cast(hl.Int(32), 1)]
#val1 = clamped[x * s_sigma - s_sigma/2, y * s_sigma - s_sigma/2] # should fail
val22 = clamped[x * s_sigma - hl.cast(hl.Int(32), s_sigma // 2),
y * s_sigma - hl.cast(hl.Int(32), s_sigma // 2)]
val2 = clamped[x * s_sigma - s_sigma // 2, y * s_sigma - s_sigma // 2]
val3 = clamped[x * s_sigma + r.x - s_sigma // 2,
y * s_sigma + r.y - s_sigma // 2]
return
def f32(x, y, c, img):
out = mkfunc("f32", img)
if img.dimensions() == 2:
out[x, y] = hl.cast(hl.Float(32), img[x, y])
else:
out[x, y, c] = hl.cast(hl.Float(32), img[x, y, c])
return out
def merge_temporal(images, alignment):
weight = hl.Func("merge_temporal_weights")
total_weight = hl.Func("merge_temporal_total_weights")
output = hl.Func("merge_temporal_output")
ix, iy, tx, ty, n = hl.Var('ix'), hl.Var('iy'), hl.Var('tx'), hl.Var('ty'), hl.Var('n')
rdom0 = hl.RDom([(0, 16), (0, 16)])
rdom1 = hl.RDom([(1, images.dim(2).extent() - 1)])
imgs_mirror = hl.BoundaryConditions.mirror_interior(images, [(0, images.width()), (0, images.height())])
layer = box_down2(imgs_mirror, "merge_layer")
offset = Point(alignment[tx, ty, n]).clamp(Point(MINIMUM_OFFSET, MINIMUM_OFFSET),
Point(MAXIMUM_OFFSET, MAXIMUM_OFFSET))
al_x = idx_layer(tx, rdom0.x) + offset.x / 2
al_y = idx_layer(ty, rdom0.y) + offset.y / 2
ref_val = layer[idx_layer(tx, rdom0.x), idx_layer(ty, rdom0.y), 0]
alt_val = layer[al_x, al_y, n]
factor = 8.0
min_distance = 10
max_distance = 300 # max L1 distance, otherwise the value is not used
distance = hl.sum(hl.abs(hl.cast(hl.Int(32), ref_val) - hl.cast(hl.Int(32), alt_val))) / 256
normal_distance = hl.max(1, hl.cast(hl.Int(32), distance) / factor - min_distance / factor)
# Weight for the alternate frame
weight[tx, ty, n] = hl.select(normal_distance > (max_distance - min_distance), 0.0,
1.0 / normal_distance)
total_weight[tx, ty] = hl.sum(weight[tx, ty, rdom1]) + 1
offset = Point(alignment[tx, ty, rdom1])
al_x = idx_im(tx, ix) + offset.x
al_y = idx_im(ty, iy) + offset.y
ref_val = imgs_mirror[idx_im(tx, ix), idx_im(ty, iy), 0]
alt_val = imgs_mirror[al_x, al_y, rdom1]
# Sum all values according to their weight, and divide by total weight to obtain average
output[ix, iy, tx, ty] = hl.sum(weight[tx, ty, rdom1] * alt_val / total_weight[tx, ty]) + ref_val / total_weight[
tx, ty]
weight.compute_root().parallel(ty).vectorize(tx, 16)
total_weight.compute_root().parallel(ty).vectorize(tx, 16)
output.compute_root().parallel(ty).vectorize(ix, 32)
return output
def mult(input, scale):
brighter = hl.Func("mult")
x, y, c = hl.Var("x"), hl.Var("y"), hl.Var("c")
value = input[x, y, c]
value = hl.cast(hl.Float(32), value)
value = value * scale
value = hl.min(value, 255.0)
value = hl.cast(hl.UInt(8), value)
brighter[x, y, c] = value
return brighter
def box_down2(input, name):
output = hl.Func(name)
x, y, n = hl.Var("x"), hl.Var("y"), hl.Var('n')
rdom = hl.RDom([(0, 2), (0, 2)])
output[x, y, n] = hl.cast(
hl.UInt(16),
hl.sum(hl.cast(hl.UInt(32), input[2 * x + rdom.x, 2 * y + rdom.y, n]))
/ 4)
output.compute_root().parallel(y).vectorize(x, 16)
return output
def resize_scale(input, fx, fy):
shr = hl.Func('resize')
x, y, c = hl.Var("x"), hl.Var("y"), hl.Var("c")
index_x = hl.Func("index_x")
index_y = hl.Func("index_y")
index_x.trace_stores()
index_y.trace_stores()
index_x[x] = hl.cast(hl.Int(32), x / fx)
index_y[y] = hl.cast(hl.Int(32), y / fy)
final = hl.Func("final")
final[x, y, c] = input[index_x[x], index_y[y], c]
return final
def test_basics3():
input = hl.ImageParam(hl.Float(32), 3, 'input')
r_sigma = hl.Param(hl.Float(32), 'r_sigma', 0.1) # Value needed if not generating an executable
s_sigma = 8 # This is passed during code generation in the C++ version
x = hl.Var('x')
y = hl.Var('y')
z = hl.Var('z')
c = hl.Var('c')
# Add a boundary condition
clamped = hl.Func('clamped')
clamped[x, y] = input[hl.clamp(x, 0, input.width()-1),
hl.clamp(y, 0, input.height()-1),0]
# Construct the bilateral grid
r = hl.RDom(0, s_sigma, 0, s_sigma, 'r')
val = clamped[x * s_sigma + r.x - s_sigma//2, y * s_sigma + r.y - s_sigma//2]
val = hl.clamp(val, 0.0, 1.0)
#zi = hl.cast(hl.Int(32), val * (1.0/r_sigma) + 0.5)
zi = hl.cast(hl.Int(32), (val / r_sigma) + 0.5)
histogram = hl.Func('histogram')
histogram[x, y, z, c] = 0.0
ss = hl.select(c == 0, val, 1.0)
print("hl.select(c == 0, val, 1.0)", ss)
left = histogram[x, y, zi, c]
print("histogram[x, y, zi, c]", histogram[x, y, zi, c])
print("histogram[x, y, zi, c]", left)
left += 5
print("histogram[x, y, zi, c] after += 5", left)
left += ss
return
def merge_spatial(input):
weight = hl.Func("raised_cosine_weights")
output = hl.Func("merge_spatial_output")
v, x, y = hl.Var('v'), hl.Var('x'), hl.Var('y')
# modified raised cosine window
weight[v] = 0.5 - 0.5 * hl.cos(2 * math.pi * (v + 0.5) / TILE_SIZE)
weight_00 = weight[idx_0(x)] * weight[idx_0(y)]
weight_10 = weight[idx_1(x)] * weight[idx_0(y)]
weight_01 = weight[idx_0(x)] * weight[idx_1(y)]
weight_11 = weight[idx_1(x)] * weight[idx_1(y)]
val_00 = input[idx_0(x), idx_0(y), tile_0(x), tile_0(y)]
val_10 = input[idx_1(x), idx_0(y), tile_1(x), tile_0(y)]
val_01 = input[idx_0(x), idx_1(y), tile_0(x), tile_1(y)]
val_11 = input[idx_1(x), idx_1(y), tile_1(x), tile_1(y)]
output[x, y] = hl.cast(hl.UInt(16), weight_00 * val_00
+ weight_10 * val_10
+ weight_01 * val_01
+ weight_11 * val_11)
weight.compute_root().vectorize(v, 32)
output.compute_root().parallel(y).vectorize(x, 32)
return output
def test_basics():
input = hl.ImageParam(hl.UInt(16), 2, 'input')
x, y = hl.Var('x'), hl.Var('y')
blur_x = hl.Func('blur_x')
blur_xx = hl.Func('blur_xx')
blur_y = hl.Func('blur_y')
yy = hl.cast(hl.Int(32), 1)
assert yy.type() == hl.Int(32)
z = x + 1
input[x, y]
input[0, 0]
input[z, y]
input[x + 1, y]
input[x, y] + input[x + 1, y]
if False:
aa = blur_x[x, y]
bb = blur_x[x, y + 1]
aa + bb
blur_x[x, y] + blur_x[x, y + 1]
(input[x, y] + input[x + 1, y]) / 2
blur_x[x, y]
blur_xx[x, y] = input[x, y]
blur_x[x, y] = (input[x, y] + input[x + 1, y] + input[x + 2, y]) / 3
blur_y[x, y] = (blur_x[x, y] + blur_x[x, y + 1] + blur_x[x, y + 2]) / 3
xi, yi = hl.Var('xi'), hl.Var('yi')
blur_y.tile(x, y, xi, yi, 8, 4).parallel(y).vectorize(xi, 8)
blur_x.compute_at(blur_y, x).vectorize(x, 8)
blur_y.compile_jit()
def gaussian_down4(input, name):
output = hl.Func(name)
k = hl.Func(name + "_filter")
x, y, n = hl.Var("x"), hl.Var("y"), hl.Var('n')
rdom = hl.RDom([(-2, 5), (-2, 5)])
k[x, y] = 0
k[-2, -2] = 2
k[-1, -2] = 4
k[0, -2] = 5
k[1, -2] = 4
k[2, -2] = 2
k[-2, -1] = 4
k[-1, -1] = 9
k[0, -1] = 12
k[1, -1] = 9
k[2, -1] = 4
k[-2, 0] = 5
k[-1, 0] = 12
k[0, 0] = 15
k[1, 0] = 12
k[2, 0] = 5
k[-2, 1] = 4
k[-1, 1] = 9
k[0, 1] = 12
k[1, 1] = 9
k[2, 1] = 4
k[-2, 2] = 2
k[-1, 2] = 4
k[0, 2] = 5
k[1, 2] = 4
k[2, 2] = 2
output[x, y, n] = hl.cast(
hl.UInt(16),
hl.sum(
hl.cast(
hl.UInt(32), input[4 * x + rdom.x, 4 * y + rdom.y, n] *
k[rdom.x, rdom.y])) / 159)
k.compute_root().parallel(y).parallel(x)
output.compute_root().parallel(y).vectorize(x, 16)
return output
def test_print_when():
x = hl.Var('x')
f = hl.Func('f')
f[x] = hl.print_when(x == 3, hl.cast(hl.UInt(8), x * x), 'is result at', x)
buf = hl.Buffer(hl.UInt(8), [10])
output = StringIO()
with _redirect_stdout(output):
f.realize(buf)
expected = '9 is result at 3\n'
actual = output.getvalue()
assert expected == actual, "Expected: %s, Actual: %s" % (expected,
actual)
def test_print_expr():
x = hl.Var('x')
f = hl.Func('f')
f[x] = hl.print(hl.cast(hl.UInt(8), x), 'is what', 'the', 1, 'and', 3.1415, 'saw')
buf = hl.Buffer(hl.UInt(8), 1)
output = StringIO()
with _redirect_stdout(output):
f.realize(buf)
expected = '0 is what the 1 and 3.141500 saw\n'
actual = output.getvalue()
assert expected == actual, "Expected: %s, Actual: %s" % (expected, actual)
return
def test_compiletime_error():
x = hl.Var('x')
y = hl.Var('y')
f = hl.Func('f')
f[x, y] = hl.cast(hl.UInt(16), x + y)
# Deliberate type-mismatch error
buf = hl.Buffer(hl.UInt(8), [2, 2])
try:
f.realize(buf)
except RuntimeError as e:
assert 'Buffer has type uint8, but Func "f" has type uint16.' in str(e)
else:
assert False, 'Did not see expected exception!'
def test_runtime_error():
x = hl.Var('x')
f = hl.Func('f')
f[x] = hl.cast(hl.UInt(8), x)
f.bound(x, 0, 1)
# Deliberate runtime error
buf = hl.Buffer(hl.UInt(8), [10])
try:
f.realize(buf)
except RuntimeError as e:
assert 'do not cover required region' in str(e)
else:
assert False, 'Did not see expected exception!'
def test_basics():
input = hl.ImageParam(hl.UInt(16), 2, 'input')
x, y = hl.Var('x'), hl.Var('y')
blur_x = hl.Func('blur_x')
blur_xx = hl.Func('blur_xx')
blur_y = hl.Func('blur_y')
yy = hl.cast(hl.Int(32), 1)
assert yy.type() == hl.Int(32)
print("yy type:", yy.type())
z = x + 1
input[x,y]
input[0,0]
input[z,y]
input[x+1,y]
print("ping 0.2")
input[x,y]+input[x+1,y]
if False:
aa = blur_x[x,y]
bb = blur_x[x,y+1]
aa + bb
blur_x[x,y]+blur_x[x,y+1]
print("ping 0.3")
(input[x,y]+input[x+1,y]) / 2
print("ping 0.4")
blur_x[x,y]
print("ping 0.4.1")
blur_xx[x,y] = input[x,y]
print("ping 0.5")
blur_x[x,y] = (input[x,y]+input[x+1,y]+input[x+2,y])/3
print("ping 1")
blur_y[x,y] = (blur_x[x,y]+blur_x[x,y+1]+blur_x[x,y+2])/3
xi, yi = hl.Var('xi'), hl.Var('yi')
print("ping 2")
blur_y.tile(x, y, xi, yi, 8, 4).parallel(y).vectorize(xi, 8)
blur_x.compute_at(blur_y, x).vectorize(x, 8)
blur_y.compile_jit()
print("Compiled to jit")
return
def __init__(self, x=None, y=None):
if x is None and y is None:
self.x = hl.cast(hl.Int(16), 0)
self.y = hl.cast(hl.Int(16), 0)
elif x is not None and y is None:
if type(x) is hl.FuncRef:
hl.Tuple(x)
self.x = hl.cast(hl.Int(16), x[0])
self.y = hl.cast(hl.Int(16), x[1])
elif type(x) is tuple:
self.x = hl.cast(hl.Int(16), x[0])
self.y = hl.cast(hl.Int(16), x[1])
else:
self.x = hl.cast(hl.Int(16), x)
self.y = hl.cast(hl.Int(16), y)
def get_erode(input):
"""
Erode on 5x5 stencil, first erode x then erode y.
"""
x = hl.Var("x")
y = hl.Var("y")
c = hl.Var("c")
input_clamped = hl.Func("input_clamped")
erode_x = hl.Func("erode_x")
erode_y = hl.Func("erode_y")
input_clamped[x, y, c] = input[
hl.clamp(x, hl.cast(hl.Int(32), 0
), hl.cast(hl.Int(32),
input.width() - 1)),
hl.clamp(y, hl.cast(hl.Int(32), 0
), hl.cast(hl.Int(32),
input.height() - 1)), c]
erode_x[x, y, c] = hl.min(
hl.min(
hl.min(
hl.min(input_clamped[x - 2, y, c], input_clamped[x - 1, y, c]),
input_clamped[x, y, c]), input_clamped[x + 1, y, c]),
input_clamped[x + 2, y, c])
erode_y[x, y, c] = hl.min(
hl.min(
hl.min(hl.min(erode_x[x, y - 2, c], erode_x[x, y - 1, c]),
erode_x[x, y, c]), erode_x[x, y + 1, c]), erode_x[x, y + 2,
c])
yi = hl.Var("yi")
# CPU Schedule
erode_x.compute_root().split(y, y, yi, 8).parallel(y)
erode_y.compute_root().split(y, y, yi, 8).parallel(y)
return erode_y
def test_compiletime_error():
x = hl.Var('x')
y = hl.Var('y')
f = hl.Func('f')
f[x, y] = hl.cast(hl.UInt(16), x + y)
# Deliberate type-mismatch error
buf = hl.Buffer(hl.UInt(8), 2, 2)
try:
f.realize(buf)
except RuntimeError as e:
print('Saw expected exception (%s)' % str(e))
else:
assert False, 'Did not see expected exception!'
def test_print_when():
x = hl.Var('x')
f = hl.Func('f')
f[x] = hl.print_when(x == 3, hl.cast(hl.UInt(8), x*x), 'is result at', x)
buf = hl.Buffer(hl.UInt(8), 10)
output = StringIO()
with _redirect_stdout(output):
f.realize(buf)
expected = '9 is result at 3\n'
actual = output.getvalue()
assert expected == actual, "Expected: %s, Actual: %s" % (expected, actual)
return
def prefilterXSobel(image, W, H):
x, y = Var("x"), Var("y")
clamped, gray = Func("clamped"), Func("gray")
gray[x, y] = 0.2989*image[x, y, 0] + 0.5870*image[x, y, 1] + 0.1140*image[x, y, 2]
clamped[x, y] = gray[h.clamp(x, 0, W-1), h.clamp(y, 0, H-1)]
temp, xSobel = Func("temp"), Func("xSobel")
temp[x, y] = clamped[x+1, y] - clamped[x-1, y]
xSobel[x, y] = h.cast(Int(8), h.clamp(temp[x, y-1] + 2 * temp[x, y] + temp[x, y+1], -31, 31))
xi, xo, yi, yo = Var("xi"), Var("xo"), Var("yi"), Var("yo")
xSobel.compute_root().tile(x, y, xo, yo, xi, yi, 64, 32).parallel(yo).parallel(xo)
temp.compute_at(xSobel, yi).vectorize(x, 8)
return xSobel
def contrast(input, strength, black_point):
output = hl.Func("contrast_output")
x, y, c = hl.Var("x"), hl.Var("y"), hl.Var("c")
scale = strength
inner_constant = math.pi / (2 * scale)
sin_constant = hl.sin(inner_constant)
slope = 65535 / (2 * sin_constant)
constant = slope * sin_constant
factor = math.pi / (scale * 65535)
val = factor * hl.cast(hl.Float(32), input[x, y, c])
output[x, y, c] = hl.u16_sat(slope * hl.sin(val - inner_constant) + constant)
white_scale = 65535 / (65535 - black_point)
output[x, y, c] = hl.u16_sat((hl.cast(hl.Int(32), output[x, y, c]) - black_point) * white_scale)
output.compute_root().parallel(y).vectorize(x, 16)
return output
def get_erode(input):
"""
Erode on 5x5 stencil, first erode x then erode y.
"""
x = hl.Var("x")
y = hl.Var("y")
c = hl.Var("c")
input_clamped = hl.Func("input_clamped")
erode_x = hl.Func("erode_x")
erode_y = hl.Func("erode_y")
input_clamped[x,y,c] = input[hl.clamp(x,hl.cast(hl.Int(32),0),hl.cast(hl.Int(32),input.width()-1)),
hl.clamp(y,hl.cast(hl.Int(32),0),hl.cast(hl.Int(32),input.height()-1)), c]
erode_x[x,y,c] = hl.min(hl.min(hl.min(hl.min(input_clamped[x-2,y,c],input_clamped[x-1,y,c]),input_clamped[x,y,c]),input_clamped[x+1,y,c]),input_clamped[x+2,y,c])
erode_y[x,y,c] = hl.min(hl.min(hl.min(hl.min(erode_x[x,y-2,c],erode_x[x,y-1,c]),erode_x[x,y,c]),erode_x[x,y+1,c]),erode_x[x,y+2,c])
yi = hl.Var("yi")
# CPU Schedule
erode_x.compute_root().split(y, y, yi, 8).parallel(y)
erode_y.compute_root().split(y, y, yi, 8).parallel(y)
return erode_y
def align_layer(layer, prev_alignment, prev_min, prev_max):
scores = hl.Func(layer.name() + "_scores")
alignment = hl.Func(layer.name() + "_alignment")
xi, yi, tx, ty, n = hl.Var("xi"), hl.Var("yi"), hl.Var('tx'), hl.Var(
'ty'), hl.Var('n')
rdom0 = hl.RDom([(0, 16), (0, 16)])
rdom1 = hl.RDom([(-4, 8), (-4, 8)])
# Alignment of the previous (more coarse) layer scaled to this (finer) layer
prev_offset = DOWNSAMPLE_RATE * Point(
prev_alignment[prev_tile(tx), prev_tile(ty), n]).clamp(
prev_min, prev_max)
x0 = idx_layer(tx, rdom0.x)
y0 = idx_layer(ty, rdom0.y)
# (x,y) coordinates in the search region relative to the offset obtained from the alignment of the previous layer
x = x0 + prev_offset.x + xi
y = y0 + prev_offset.y + yi
ref_val = layer[x0, y0, 0] # Value of reference frame (the first frame)
alt_val = layer[x, y, n] # alternate frame value
# L1 distance between reference frame and alternate frame
d = hl.abs(hl.cast(hl.Int(32), ref_val) - hl.cast(hl.Int(32), alt_val))
scores[xi, yi, tx, ty, n] = hl.sum(d)
# Alignment for each tile, where L1 distances are minimum
alignment[tx, ty, n] = Point(hl.argmin(scores[rdom1.x, rdom1.y, tx, ty,
n])) + prev_offset
scores.compute_at(alignment, tx).vectorize(xi, 8)
alignment.compute_root().parallel(ty).vectorize(tx, 16)
return alignment
def test_runtime_error():
x = hl.Var('x')
f = hl.Func('f')
f[x] = hl.cast(hl.UInt(8), x)
f.bound(x, 0, 1)
# Deliberate runtime error
buf = hl.Buffer(hl.UInt(8), 10)
try:
f.realize(buf)
except RuntimeError as e:
print('Saw expected exception (%s)' % str(e))
else:
assert False, 'Did not see expected exception!'
return
def prefilterXSobel(image, W, H):
x, y = Var("x"), Var("y")
clamped, gray = Func("clamped"), Func("gray")
gray[x, y] = 0.2989 * image[x, y, 0] + 0.5870 * image[
x, y, 1] + 0.1140 * image[x, y, 2]
clamped[x, y] = gray[h.clamp(x, 0, W - 1), h.clamp(y, 0, H - 1)]
temp, xSobel = Func("temp"), Func("xSobel")
temp[x, y] = clamped[x + 1, y] - clamped[x - 1, y]
xSobel[x, y] = h.cast(
Int(8),
h.clamp(temp[x, y - 1] + 2 * temp[x, y] + temp[x, y + 1], -31, 31))
xi, xo, yi, yo = Var("xi"), Var("xo"), Var("yi"), Var("yo")
xSobel.compute_root().tile(x, y, xo, yo, xi, yi, 64,
32).parallel(yo).parallel(xo)
temp.compute_at(xSobel, yi).vectorize(x, 8)
return xSobel
def yuv_to_rgb(input):
print(' yuv_to_rgb')
output = hl.Func("yuv_to_rgb_output")
x, y, c = hl.Var("x"), hl.Var("y"), hl.Var("c")
Y = input[x, y, 0]
U = input[x, y, 1]
V = input[x, y, 2]
output[x, y, c] = hl.cast(hl.UInt(16), 0)
output[x, y, 0] = hl.u16_sat(Y + 1.403 * V)
output[x, y, 1] = hl.u16_sat(Y - 0.344 * U - 0.714 * V)
output[x, y, 2] = hl.u16_sat(Y + 1.77 * U)
output.compute_root().parallel(y).vectorize(x, 16)
output.update(0).parallel(y).vectorize(x, 16)
output.update(1).parallel(y).vectorize(x, 16)
output.update(2).parallel(y).vectorize(x, 16)
return output
def test_schedules(verbose=False, test_random=False):
#random_module.seed(int(sys.argv[1]) if len(sys.argv)>1 else 0)
halide.exit_on_signal()
f = halide.Func('f')
x = halide.Var('x')
y = halide.Var('y')
c = halide.Var('c')
g = halide.Func('g')
v = halide.Var('v')
input = halide.UniformImage(halide.UInt(16), 3)
int_t = halide.Int(32)
f[x, y, c] = input[
halide.clamp(x, halide.cast(int_t, 0
), halide.cast(int_t,
input.width() - 1)),
halide.clamp(y, halide.cast(int_t, 0
), halide.cast(int_t,
input.height() - 1)),
halide.clamp(c, halide.cast(int_t, 0), halide.cast(int_t, 2))]
#g[v] = f[v,v]
g[x, y, c] = f[x, y, c] + 1
assert sorted(halide.all_vars(g).keys()) == sorted(['x', 'y',
'c']) #, 'v'])
if verbose:
print halide.func_varlist(f)
print 'caller_vars(f) =', caller_vars(g, f)
print 'caller_vars(g) =', caller_vars(g, g)
# validL = list(valid_schedules(g, f, 4))
# validL = [repr(_x) for _x in validL]
#
# for L in sorted(validL):
# print repr(L)
T0 = time.time()
if not test_random:
random = True #False
nvalid_determ = 0
for L in schedules_func(g, f, 0, 3):
nvalid_determ += 1
if verbose:
print L
nvalid_random = 0
for i in range(100):
for L in schedules_func(
g, f, 0, DEFAULT_MAX_DEPTH, random=True
): #sorted([repr(_x) for _x in valid_schedules(g, f, 3)]):
if verbose and 0:
print L #repr(L)
nvalid_random += 1
s = []
for i in range(400):
d = random_schedule(g, 0, DEFAULT_MAX_DEPTH)
si = str(d)
s.append(si)
if verbose:
print 'Schedule:', si
d.apply()
evaluate = d.test((36, 36, 3), input)
print 'evaluate'
evaluate()
if test_random:
print 'Success'
sys.exit()
T1 = time.time()
s = '\n'.join(s)
assert 'f.chunk(_c0)' in s
assert 'f.root().vectorize' in s
assert 'f.root().unroll' in s
assert 'f.root().split' in s
assert 'f.root().tile' in s
assert 'f.root().parallel' in s
assert 'f.root().transpose' in s
assert nvalid_random == 100
if verbose:
print 'generated in %.3f secs' % (T1 - T0)
print 'random_schedule: OK'
def get_local_laplacian(input, levels, alpha, beta, J=8):
downsample_counter=[0]
upsample_counter=[0]
x = hl.Var('x')
y = hl.Var('y')
def downsample(f):
downx, downy = hl.Func('downx%d'%downsample_counter[0]), hl.Func('downy%d'%downsample_counter[0])
downsample_counter[0] += 1
downx[x,y,c] = (f[2*x-1,y,c] + 3.0*(f[2*x,y,c]+f[2*x+1,y,c]) + f[2*x+2,y,c])/8.0
downy[x,y,c] = (downx[x,2*y-1,c] + 3.0*(downx[x,2*y,c]+downx[x,2*y+1,c]) + downx[x,2*y+2,c])/8.0
return downy
def upsample(f):
upx, upy = hl.Func('upx%d'%upsample_counter[0]), hl.Func('upy%d'%upsample_counter[0])
upsample_counter[0] += 1
upx[x,y,c] = 0.25 * f[(x//2) - 1 + 2*(x%2),y,c] + 0.75 * f[x//2,y,c]
upy[x,y,c] = 0.25 * upx[x, (y//2) - 1 + 2*(y%2),c] + 0.75 * upx[x,y//2,c]
return upy
def downsample2D(f):
downx, downy = hl.Func('downx%d'%downsample_counter[0]), hl.Func('downy%d'%downsample_counter[0])
downsample_counter[0] += 1
downx[x,y] = (f[2*x-1,y] + 3.0*(f[2*x,y]+f[2*x+1,y]) + f[2*x+2,y])/8.0
downy[x,y] = (downx[x,2*y-1] + 3.0*(downx[x,2*y]+downx[x,2*y+1]) + downx[x,2*y+2])/8.0
return downy
def upsample2D(f):
upx, upy = hl.Func('upx%d'%upsample_counter[0]), hl.Func('upy%d'%upsample_counter[0])
upsample_counter[0] += 1
upx[x,y] = 0.25 * f[(x//2) - 1 + 2*(x%2),y] + 0.75 * f[x//2,y]
upy[x,y] = 0.25 * upx[x, (y//2) - 1 + 2*(y%2)] + 0.75 * upx[x,y//2]
return upy
# THE ALGORITHM
# loop variables
c = hl.Var('c')
k = hl.Var('k')
# Make the remapping function as a lookup table.
remap = hl.Func('remap')
fx = hl.cast(float_t, x/256.0)
#remap[x] = alpha*fx*exp(-fx*fx/2.0)
remap[x] = alpha*fx*hl.exp(-fx*fx/2.0)
# Convert to floating point
floating = hl.Func('floating')
floating[x,y,c] = hl.cast(float_t, input[x,y,c]) / 65535.0
# Set a boundary condition
clamped = hl.Func('clamped')
clamped[x,y,c] = floating[hl.clamp(x, 0, input.width()-1), hl.clamp(y, 0, input.height()-1), c]
# Get the luminance channel
gray = hl.Func('gray')
gray[x,y] = 0.299*clamped[x,y,0] + 0.587*clamped[x,y,1] + 0.114*clamped[x,y,2]
# Make the processed Gaussian pyramid.
gPyramid = [hl.Func('gPyramid%d'%i) for i in range(J)]
# Do a lookup into a lut with 256 entires per intensity level
level = k / (levels - 1)
idx = gray[x,y]*hl.cast(float_t, levels-1)*256.0
idx = hl.clamp(hl.cast(int_t, idx), 0, (levels-1)*256)
gPyramid[0][x,y,k] = beta*(gray[x, y] - level) + level + remap[idx - 256*k]
for j in range(1,J):
gPyramid[j][x,y,k] = downsample(gPyramid[j-1])[x,y,k]
# Get its laplacian pyramid
lPyramid = [hl.Func('lPyramid%d'%i) for i in range(J)]
lPyramid[J-1] = gPyramid[J-1]
for j in range(J-1)[::-1]:
lPyramid[j][x,y,k] = gPyramid[j][x,y,k] - upsample(gPyramid[j+1])[x,y,k]
# Make the Gaussian pyramid of the input
inGPyramid = [hl.Func('inGPyramid%d'%i) for i in range(J)]
inGPyramid[0] = gray
for j in range(1,J):
inGPyramid[j][x,y] = downsample2D(inGPyramid[j-1])[x,y]
# Make the laplacian pyramid of the output
outLPyramid = [hl.Func('outLPyramid%d'%i) for i in range(J)]
for j in range(J):
# Split input pyramid value into integer and floating parts
level = inGPyramid[j][x,y]*hl.cast(float_t, levels-1)
li = hl.clamp(hl.cast(int_t, level), 0, levels-2)
lf = level - hl.cast(float_t, li)
# Linearly interpolate between the nearest processed pyramid levels
outLPyramid[j][x,y] = (1.0-lf)*lPyramid[j][x,y,li] + lf*lPyramid[j][x,y,li+1]
# Make the Gaussian pyramid of the output
outGPyramid = [hl.Func('outGPyramid%d'%i) for i in range(J)]
outGPyramid[J-1] = outLPyramid[J-1]
for j in range(J-1)[::-1]:
outGPyramid[j][x,y] = upsample2D(outGPyramid[j+1])[x,y] + outLPyramid[j][x,y]
# Reintroduce color (Connelly: use eps to avoid scaling up noise w/ apollo3.png input)
color = hl.Func('color')
eps = 0.01
color[x,y,c] = outGPyramid[0][x,y] * (clamped[x,y,c] + eps) / (gray[x,y] + eps)
output = hl.Func('local_laplacian')
# Convert back to 16-bit
output[x,y,c] = hl.cast(hl.UInt(16), hl.clamp(color[x,y,c], 0.0, 1.0) * 65535.0)
# THE SCHEDULE
remap.compute_root()
target = hl.get_target_from_environment()
if target.has_gpu_feature():
# GPU Schedule
print ("Compiling for GPU")
xi, yi = hl.Var("xi"), hl.Var("yi")
output.compute_root().gpu_tile(x, y, 32, 32, GPU_Default)
for j in range(J):
blockw = 32
blockh = 16
if j > 3:
blockw = 2
blockh = 2
if j > 0:
inGPyramid[j].compute_root().gpu_tile(x, y, xi, yi, blockw, blockh, GPU_Default)
if j > 0:
gPyramid[j].compute_root().reorder(k, x, y).gpu_tile(x, y, xi, yi, blockw, blockh, GPU_Default)
outGPyramid[j].compute_root().gpu_tile(x, y, xi, yi, blockw, blockh, GPU_Default)
else:
# CPU schedule
print ("Compiling for CPU")
output.parallel(y, 4).vectorize(x, 4);
gray.compute_root().parallel(y, 4).vectorize(x, 4);
for j in range(4):
if j > 0:
inGPyramid[j].compute_root().parallel(y, 4).vectorize(x, 4)
if j > 0:
gPyramid[j].compute_root().parallel(y, 4).vectorize(x, 4)
outGPyramid[j].compute_root().parallel(y).vectorize(x, 4)
for j in range(4,J):
inGPyramid[j].compute_root().parallel(y)
gPyramid[j].compute_root().parallel(k)
outGPyramid[j].compute_root().parallel(y)
return output
def main():
# Declare some Vars to use below.
x, y = hl.Var ("x"), hl.Var ("y")
# Load a grayscale image to use as an input.
image_path = os.path.join(os.path.dirname(__file__), "../../tutorial/images/gray.png")
input_data = imread(image_path)
if True:
# making the image smaller to go faster
input_data = input_data[:160, :150]
assert input_data.dtype == np.uint8
input = hl.Buffer(input_data)
# You can define a hl.Func in multiple passes. Let's see a toy
# example first.
if True:
# The first definition must be one like we have seen already
# - a mapping from Vars to an hl.Expr:
f = hl.Func("f")
f[x, y] = x + y
# We call this first definition the "pure" definition.
# But the later definitions can include computed expressions on
# both sides. The simplest example is modifying a single point:
f[3, 7] = 42
# We call these extra definitions "update" definitions, or
# "reduction" definitions. A reduction definition is an
# update definition that recursively refers back to the
# function's current value at the same site:
if False:
e = f[x, y] + 17
print("f[x, y] + 17", e)
print("(f[x, y] + 17).type()", e.type())
print("(f[x, y]).type()", f[x,y].type())
f[x, y] = f[x, y] + 17
# If we confine our update to a single row, we can
# recursively refer to values in the same column:
f[x, 3] = f[x, 0] * f[x, 10]
# Similarly, if we confine our update to a single column, we
# can recursively refer to other values in the same row.
f[0, y] = f[0, y] / f[3, y]
# The general rule is: Each hl.Var used in an update definition
# must appear unadorned in the same position as in the pure
# definition in all references to the function on the left-
# and right-hand sides. So the following definitions are
# legal updates:
f[x, 17] = x + 8 # x is used, so all uses of f must have x as the first argument.
f[0, y] = y * 8 # y is used, so all uses of f must have y as the second argument.
f[x, x + 1] = x + 8
f[y/2, y] = f[0, y] * 17
# But these ones would cause an error:
# f[x, 0) = f[x + 1, 0) <- First argument to f on the right-hand-side must be 'x', not 'x + 1'.
# f[y, y + 1) = y + 8 <- Second argument to f on the left-hand-side must be 'y', not 'y + 1'.
# f[y, x) = y - x <- Arguments to f on the left-hand-side are in the wrong places.
# f[3, 4) = x + y <- Free variables appear on the right-hand-side but not the left-hand-side.
# We'll realize this one just to make sure it compiles. The
# second-to-last definition forces us to realize over a
# domain that is taller than it is wide.
f.realize(100, 101)
# For each realization of f, each step runs in its entirety
# before the next one begins. Let's trace the loads and
# stores for a simpler example:
g = hl.Func("g")
g[x, y] = x + y # Pure definition
g[2, 1] = 42 # First update definition
g[x, 0] = g[x, 1] # Second update definition
g.trace_loads()
g.trace_stores()
g.realize(4, 4)
# Reading the log, we see that each pass is applied in turn. The equivalent C is:
result = np.empty( (4,4), dtype=np.int)
# Pure definition
for yy in range(4):
for xx in range(4):
result[yy][xx] = xx + yy
# First update definition
result[1][2] = 42
# Second update definition
for xx in range(4):
result[0][xx] = result[1][xx]
# end of section
# Putting update passes inside loops.
if True:
# Starting with this pure definition:
f = hl.Func("f")
f[x, y] = x + y
# Say we want an update that squares the first fifty rows. We
# could do this by adding 50 update definitions:
# f[x, 0) = f[x, 0) * f[x, 0)
# f[x, 1) = f[x, 1) * f[x, 1)
# f[x, 2) = f[x, 2) * f[x, 2)
# ...
# f[x, 49) = f[x, 49) * f[x, 49)
# Or equivalently using a compile-time loop in our C++:
# for (int i = 0 i < 50 i++) {
# f[x, i) = f[x, i) * f[x, i)
#
# But it's more manageable and more flexible to put the loop
# in the generated code. We do this by defining a "reduction
# domain" and using it inside an update definition:
r = hl.RDom([(0, 50)])
f[x, r] = f[x, r] * f[x, r]
halide_result = f.realize(100, 100)
# The equivalent C is:
c_result = np.empty((100, 100), dtype=np.int)
for yy in range(100):
for xx in range(100):
c_result[yy][xx] = xx + yy
for xx in range(100):
for rr in range(50):
# The loop over the reduction domain occurs inside of
# the loop over any pure variables used in the update
# step:
c_result[rr][xx] = c_result[rr][xx] * c_result[rr][xx]
# Check the results match:
for yy in range(100):
for xx in range(100):
if halide_result[xx, yy] != c_result[yy][xx]:
raise Exception("halide_result(%d, %d) = %d instead of %d" % (
xx, yy, halide_result[xx, yy], c_result[yy][xx]))
return -1
# Now we'll examine a real-world use for an update definition:
# computing a histogram.
if True:
# Some operations on images can't be cleanly expressed as a pure
# function from the output coordinates to the value stored
# there. The classic example is computing a histogram. The
# natural way to do it is to iterate over the input image,
# updating histogram buckets. Here's how you do that in Halide:
histogram = hl.Func("histogram")
# Histogram buckets start as zero.
histogram[x] = 0
# Define a multi-dimensional reduction domain over the input image:
r = hl.RDom([(0, input.width()), (0, input.height())])
# For every point in the reduction domain, increment the
# histogram bucket corresponding to the intensity of the
# input image at that point.
histogram[input[r.x, r.y]] += 1
halide_result = histogram.realize(256)
# The equivalent C is:
c_result = np.empty((256), dtype=np.int)
for xx in range(256):
c_result[xx] = 0
for r_y in range(input.height()):
for r_x in range(input.width()):
c_result[input_data[r_x, r_y]] += 1
# Check the answers agree:
for xx in range(256):
if c_result[xx] != halide_result[xx]:
raise Exception("halide_result(%d) = %d instead of %d" % (
xx, halide_result[xx], c_result[xx]))
return -1
# Scheduling update steps
if True:
# The pure variables in an update step and can be
# parallelized, vectorized, split, etc as usual.
# Vectorizing, splitting, or parallelize the variables that
# are part of the reduction domain is trickier. We'll cover
# that in a later lesson.
# Consider the definition:
f = hl.Func("x")
f[x, y] = x*y
# Set the second row to equal the first row.
f[x, 1] = f[x, 0]
# Set the second column to equal the first column plus 2.
f[1, y] = f[0, y] + 2
# The pure variables in each stage can be scheduled
# independently. To control the pure definition, we schedule
# as we have done in the past. The following code vectorizes
# and parallelizes the pure definition only.
f.vectorize(x, 4).parallel(y)
# We use hl.Func::update(int) to get a handle to an update step
# for the purposes of scheduling. The following line
# vectorizes the first update step across x. We can't do
# anything with y for this update step, because it doesn't
# use y.
f.update(0).vectorize(x, 4)
# Now we parallelize the second update step in chunks of size
# 4.
yo, yi = hl.Var("yo"), hl.Var("yi")
f.update(1).split(y, yo, yi, 4).parallel(yo)
halide_result = f.realize(16, 16)
# Here's the equivalent (serial) C:
c_result = np.empty((16, 16), dtype=np.int)
# Pure step. Vectorized in x and parallelized in y.
for yy in range( 16): # Should be a parallel for loop
for x_vec in range(4):
xx = [x_vec*4, x_vec*4+1, x_vec*4+2, x_vec*4+3]
c_result[yy][xx[0]] = xx[0] * yy
c_result[yy][xx[1]] = xx[1] * yy
c_result[yy][xx[2]] = xx[2] * yy
c_result[yy][xx[3]] = xx[3] * yy
# First update. Vectorized in x.
for x_vec in range(4):
xx = [x_vec*4, x_vec*4+1, x_vec*4+2, x_vec*4+3]
c_result[1][xx[0]] = c_result[0][xx[0]]
c_result[1][xx[1]] = c_result[0][xx[1]]
c_result[1][xx[2]] = c_result[0][xx[2]]
c_result[1][xx[3]] = c_result[0][xx[3]]
# Second update. Parallelized in chunks of size 4 in y.
for yo in range(4): # Should be a parallel for loop
for yi in range(4):
yy = yo*4 + yi
c_result[yy][1] = c_result[yy][0] + 2
# Check the C and Halide results match:
for yy in range( 16):
for xx in range( 16 ):
if halide_result[xx, yy] != c_result[yy][xx]:
raise Exception("halide_result(%d, %d) = %d instead of %d" % (
xx, yy, halide_result[xx, yy], c_result[yy][xx]))
return -1
# That covers how to schedule the variables within a hl.Func that
# uses update steps, but what about producer-consumer
# relationships that involve compute_at and store_at? Let's
# examine a reduction as a producer, in a producer-consumer pair.
if True:
# Because an update does multiple passes over a stored array,
# it's not meaningful to inline them. So the default schedule
# for them does the closest thing possible. It computes them
# in the innermost loop of their consumer. Consider this
# trivial example:
producer, consumer = hl.Func("producer"), hl.Func("consumer")
producer[x] = x*17
producer[x] += 1
consumer[x] = 2 * producer[x]
halide_result = consumer.realize(10)
# The equivalent C is:
c_result = np.empty((10), dtype=np.int)
for xx in range(10):
producer_storage = np.empty((1), dtype=np.int)
# Pure step for producer
producer_storage[0] = xx * 17
# Update step for producer
producer_storage[0] = producer_storage[0] + 1
# Pure step for consumer
c_result[xx] = 2 * producer_storage[0]
# Check the results match
for xx in range( 10 ):
if halide_result[xx] != c_result[xx]:
raise Exception("halide_result(%d) = %d instead of %d" % (
xx, halide_result[xx], c_result[xx]))
return -1
# For all other compute_at/store_at options, the reduction
# gets placed where you would expect, somewhere in the loop
# nest of the consumer.
# Now let's consider a reduction as a consumer in a
# producer-consumer pair. This is a little more involved.
if True:
if True:
# Case 1: The consumer references the producer in the pure step only.
producer, consumer = hl.Func("producer"), hl.Func("consumer")
# The producer is pure.
producer[x] = x*17
consumer[x] = 2 * producer[x]
consumer[x] += 1
# The valid schedules for the producer in this case are
# the default schedule - inlined, and also:
#
# 1) producer.compute_at(x), which places the computation of
# the producer inside the loop over x in the pure step of the
# consumer.
#
# 2) producer.compute_root(), which computes all of the
# producer ahead of time.
#
# 3) producer.store_root().compute_at(x), which allocates
# space for the consumer outside the loop over x, but fills
# it in as needed inside the loop.
#
# Let's use option 1.
producer.compute_at(consumer, x)
halide_result = consumer.realize(10)
# The equivalent C is:
c_result = np.empty((10), dtype=np.int)
# Pure step for the consumer
for xx in range( 10 ):
# Pure step for producer
producer_storage = np.empty((1), dtype=np.int)
producer_storage[0] = xx * 17
c_result[xx] = 2 * producer_storage[0]
# Update step for the consumer
for xx in range( 10 ):
c_result[xx] += 1
# All of the pure step is evaluated before any of the
# update step, so there are two separate loops over x.
# Check the results match
for xx in range( 10 ):
if halide_result[xx] != c_result[xx]:
raise Exception("halide_result(%d) = %d instead of %d" % (
xx, halide_result[xx], c_result[xx]))
return -1
if True:
# Case 2: The consumer references the producer in the update step only
producer, consumer = hl.Func("producer"), hl.Func("consumer")
producer[x] = x * 17
consumer[x] = x
consumer[x] += producer[x]
# Again we compute the producer per x coordinate of the
# consumer. This places producer code inside the update
# step of the producer, because that's the only step that
# uses the producer.
producer.compute_at(consumer, x)
# Note however, that we didn't say:
#
# producer.compute_at(consumer.update(0), x).
#
# Scheduling is done with respect to Vars of a hl.Func, and
# the Vars of a hl.Func are shared across the pure and
# update steps.
halide_result = consumer.realize(10)
# The equivalent C is:
c_result = np.empty((10), dtype=np.int)
# Pure step for the consumer
for xx in range( 10 ):
c_result[xx] = xx
# Update step for the consumer
for xx in range( 10 ):
# Pure step for producer
producer_storage = np.empty((1), dtype=np.int)
producer_storage[0] = xx * 17
c_result[xx] += producer_storage[0]
# Check the results match
for xx in range( 10 ):
if halide_result[xx] != c_result[xx]:
raise Exception("halide_result(%d) = %d instead of %d" % (
xx, halide_result[xx], c_result[xx]))
return -1
if True:
# Case 3: The consumer references the producer in
# multiple steps that share common variables
producer, consumer = hl.Func("producer"), hl.Func("consumer")
producer[x] = x * 17
consumer[x] = producer[x] * x
consumer[x] += producer[x]
# Again we compute the producer per x coordinate of the
# consumer. This places producer code inside both the
# pure and the update step of the producer. So there ends
# up being two separate realizations of the producer, and
# redundant work occurs.
producer.compute_at(consumer, x)
halide_result = consumer.realize(10)
# The equivalent C is:
c_result = np.empty((10), dtype=np.int)
# Pure step for the consumer
for xx in range( 10 ):
# Pure step for producer
producer_storage = np.empty((1), dtype=np.int)
producer_storage[0] = xx * 17
c_result[xx] = producer_storage[0] * xx
# Update step for the consumer
for xx in range( 10 ):
# Another copy of the pure step for producer
producer_storage = np.empty((1), dtype=np.int)
producer_storage[0] = xx * 17
c_result[xx] += producer_storage[0]
# Check the results match
for xx in range( 10 ):
if halide_result[xx] != c_result[xx]:
raise Exception("halide_result(%d) = %d instead of %d" % (
xx, halide_result[xx], c_result[xx]))
return -1
if True:
# Case 4: The consumer references the producer in
# multiple steps that do not share common variables
producer, consumer = hl.Func("producer"), hl.Func("consumer")
producer[x, y] = x*y
consumer[x, y] = x + y
consumer[x, 0] = producer[x, x-1]
consumer[0, y] = producer[y, y-1]
# In this case neither producer.compute_at(consumer, x)
# nor producer.compute_at(consumer, y) will work, because
# either one fails to cover one of the uses of the
# producer. So we'd have to inline producer, or use
# producer.compute_root().
# Let's say we really really want producer to be
# compute_at the inner loops of both consumer update
# steps. Halide doesn't allow multiple different
# schedules for a single hl.Func, but we can work around it
# by making two wrappers around producer, and scheduling
# those instead:
# Attempt 2:
producer_wrapper_1, producer_wrapper_2, consumer_2 = hl.Func(), hl.Func(), hl.Func()
producer_wrapper_1[x, y] = producer[x, y]
producer_wrapper_2[x, y] = producer[x, y]
consumer_2[x, y] = x + y
consumer_2[x, 0] += producer_wrapper_1[x, x-1]
consumer_2[0, y] += producer_wrapper_2[y, y-1]
# The wrapper functions give us two separate handles on
# the producer, so we can schedule them differently.
producer_wrapper_1.compute_at(consumer_2, x)
producer_wrapper_2.compute_at(consumer_2, y)
halide_result = consumer_2.realize(10, 10)
# The equivalent C is:
c_result = np.empty((10, 10), dtype=np.int)
# Pure step for the consumer
for yy in range( 10):
for xx in range( 10 ):
c_result[yy][xx] = xx + yy
# First update step for consumer
for xx in range( 10 ):
producer_wrapper_1_storage = np.empty((1), dtype=np.int)
producer_wrapper_1_storage[0] = xx * (xx-1)
c_result[0][xx] += producer_wrapper_1_storage[0]
# Second update step for consumer
for yy in range( 10):
producer_wrapper_2_storage = np.empty((1), dtype=np.int)
producer_wrapper_2_storage[0] = yy * (yy-1)
c_result[yy][0] += producer_wrapper_2_storage[0]
# Check the results match
for yy in range( 10):
for xx in range( 10 ):
if halide_result[xx, yy] != c_result[yy][xx]:
print("halide_result(%d, %d) = %d instead of %d",
xx, yy, halide_result[xx, yy], c_result[yy][xx])
return -1
if True:
# Case 5: Scheduling a producer under a reduction domain
# variable of the consumer.
# We are not just restricted to scheduling producers at
# the loops over the pure variables of the consumer. If a
# producer is only used within a loop over a reduction
# domain (hl.RDom) variable, we can also schedule the
# producer there.
producer, consumer = hl.Func("producer"), hl.Func("consumer")
r = hl.RDom([(0, 5)])
producer[x] = x * 17
consumer[x] = x + 10
consumer[x] += r + producer[x + r]
producer.compute_at(consumer, r)
halide_result = consumer.realize(10)
# The equivalent C is:
c_result = np.empty((10), dtype=np.int)
# Pure step for the consumer.
for xx in range(10):
c_result[xx] = xx + 10
# Update step for the consumer.
for xx in range( 10 ):
for rr in range(5): # The loop over the reduction domain is always the inner loop.
# We've schedule the storage and computation of
# the producer here. We just need a single value.
producer_storage = np.empty((1), dtype=np.int)
# Pure step of the producer.
producer_storage[0] = (xx + rr) * 17
# Now use it in the update step of the consumer.
c_result[xx] += rr + producer_storage[0]
# Check the results match
for xx in range( 10 ):
if halide_result[xx] != c_result[xx]:
raise Exception("halide_result(%d) = %d instead of %d" % (
xx, halide_result[xx], c_result[xx]))
return -1
# A real-world example of a reduction inside a producer-consumer chain.
if True:
# The default schedule for a reduction is a good one for
# convolution-like operations. For example, the following
# computes a 5x5 box-blur of our grayscale test image with a
# hl.clamp-to-edge boundary condition:
# First add the boundary condition.
clamped = hl.BoundaryConditions.repeat_edge(input)
# Define a 5x5 box that starts at (-2, -2)
r = hl.RDom([(-2, 5), (-2, 5)])
# Compute the 5x5 sum around each pixel.
local_sum = hl.Func("local_sum")
local_sum[x, y] = 0 # Compute the sum as a 32-bit integer
local_sum[x, y] += clamped[x + r.x, y + r.y]
# Divide the sum by 25 to make it an average
blurry = hl.Func("blurry")
blurry[x, y] = hl.cast(hl.UInt(8), local_sum[x, y] / 25)
halide_result = blurry.realize(input.width(), input.height())
# The default schedule will inline 'clamped' into the update
# step of 'local_sum', because clamped only has a pure
# definition, and so its default schedule is fully-inlined.
# We will then compute local_sum per x coordinate of blurry,
# because the default schedule for reductions is
# compute-innermost. Here's the equivalent C:
#cast_to_uint8 = lambda x_: np.array([x_], dtype=np.uint8)[0]
local_sum = np.empty((1), dtype=np.int32)
c_result = hl.Buffer(hl.UInt(8), [input.width(), input.height()])
for yy in range(input.height()):
for xx in range(input.width()):
# FIXME this loop is quite slow
# Pure step of local_sum
local_sum[0] = 0
# Update step of local_sum
for r_y in range(-2, 2+1):
for r_x in range(-2, 2+1):
# The clamping has been inlined into the update step.
clamped_x = min(max(xx + r_x, 0), input.width()-1)
clamped_y = min(max(yy + r_y, 0), input.height()-1)
local_sum[0] += input[clamped_x, clamped_y]
# Pure step of blurry
#c_result(x, y) = (uint8_t)(local_sum[0] / 25)
#c_result[xx, yy] = cast_to_uint8(local_sum[0] / 25)
c_result[xx, yy] = int(local_sum[0] / 25) # hl.cast done internally
# Check the results match
for yy in range(input.height()):
for xx in range(input.width()):
if halide_result[xx, yy] != c_result[xx, yy]:
raise Exception("halide_result(%d, %d) = %d instead of %d"
% (xx, yy,
halide_result[xx, yy], c_result[xx, yy]))
return -1
# Reduction helpers.
if True:
# There are several reduction helper functions provided in
# Halide.h, which compute small reductions and schedule them
# innermost into their consumer. The most useful one is
# "sum".
f1 = hl.Func ("f1")
r = hl.RDom([(0, 100)])
f1[x] = hl.sum(r + x) * 7
# Sum creates a small anonymous hl.Func to do the reduction. It's equivalent to:
f2, anon = hl.Func("f2"), hl.Func("anon")
anon[x] = 0
anon[x] += r + x
f2[x] = anon[x] * 7
# So even though f1 references a reduction domain, it is a
# pure function. The reduction domain has been swallowed to
# define the inner anonymous reduction.
halide_result_1 = f1.realize(10)
halide_result_2 = f2.realize(10)
# The equivalent C is:
c_result = np.empty((10), dtype=np.int)
for xx in range( 10 ):
anon = np.empty((1), dtype=np.int)
anon[0] = 0
for rr in range(100):
anon[0] += rr + xx
c_result[xx] = anon[0] * 7
# Check they all match.
for xx in range( 10 ):
if halide_result_1[xx] != c_result[xx]:
print("halide_result_1(%d) = %d instead of %d",
xx, halide_result_1[xx], c_result[xx])
return -1
if halide_result_2[xx] != c_result[xx]:
print("halide_result_2(%d) = %d instead of %d",
xx, halide_result_2[xx], c_result[xx])
return -1
# A complex example that uses reduction helpers.
if False: # non-sense to port SSE code to python, skipping this test
# Other reduction helpers include "product", "minimum",
# "maximum", "hl.argmin", and "argmax". Using hl.argmin and argmax
# requires understanding tuples, which come in a later
# lesson. Let's use minimum and maximum to compute the local
# spread of our grayscale image.
# First, add a boundary condition to the input.
clamped = hl.Func("clamped")
x_clamped = hl.clamp(x, 0, input.width()-1)
y_clamped = hl.clamp(y, 0, input.height()-1)
clamped[x, y] = input[x_clamped, y_clamped]
box = hl.RDom([(-2, 5), (-2, 5)])
# Compute the local maximum minus the local minimum:
spread = hl.Func("spread")
spread[x, y] = (maximum(clamped(x + box.x, y + box.y)) -
minimum(clamped(x + box.x, y + box.y)))
# Compute the result in strips of 32 scanlines
yo, yi = hl.Var("yo"), hl.Var("yi")
spread.split(y, yo, yi, 32).parallel(yo)
# Vectorize across x within the strips. This implicitly
# vectorizes stuff that is computed within the loop over x in
# spread, which includes our minimum and maximum helpers, so
# they get vectorized too.
spread.vectorize(x, 16)
# We'll apply the boundary condition by padding each scanline
# as we need it in a circular buffer (see lesson 08).
clamped.store_at(spread, yo).compute_at(spread, yi)
halide_result = spread.realize(input.width(), input.height())
# The C equivalent is almost too horrible to contemplate (and
# took me a long time to debug). This time I want to time
# both the Halide version and the C version, so I'll use sse
# intrinsics for the vectorization, and openmp to do the
# parallel for loop (you'll need to compile with -fopenmp or
# similar to get correct timing).
#ifdef __SSE2__
# Don't include the time required to allocate the output buffer.
c_result = hl.Buffer(hl.UInt(8), input.width(), input.height())
#ifdef _OPENMP
t1 = datetime.now()
#endif
# Run this one hundred times so we can average the timing results.
for iters in range(100):
pass
# #pragma omp parallel for
# for yo in range((input.height() + 31)/32):
# y_base = hl.min(yo * 32, input.height() - 32)
#
# # Compute clamped in a circular buffer of size 8
# # (smallest power of two greater than 5). Each thread
# # needs its own allocation, so it must occur here.
#
# clamped_width = input.width() + 4
# clamped_storage = np.empty((clamped_width * 8), dtype=np.uint8)
#
# for yi in range(32):
# y = y_base + yi
#
# uint8_t *output_row = &c_result(0, y)
#
# # Compute clamped for this scanline, skipping rows
# # already computed within this slice.
# int min_y_clamped = (yi == 0) ? (y - 2) : (y + 2)
# int max_y_clamped = (y + 2)
# for (int cy = min_y_clamped cy <= max_y_clamped cy++) {
# # Figure out which row of the circular buffer
# # we're filling in using bitmasking:
# uint8_t *clamped_row = clamped_storage + (cy & 7) * clamped_width
#
# # Figure out which row of the input we're reading
# # from by clamping the y coordinate:
# int clamped_y = std::hl.min(std::hl.max(cy, 0), input.height()-1)
# uint8_t *input_row = &input(0, clamped_y)
#
# # Fill it in with the padding.
# for (int x = -2 x < input.width() + 2 ):
# int clamped_x = std::hl.min(std::hl.max(x, 0), input.width()-1)
# *clamped_row++ = input_row[clamped_x]
#
#
#
# # Now iterate over vectors of x for the pure step of the output.
# for (int x_vec = 0 x_vec < (input.width() + 15)/16 x_vec++) {
# int x_base = std::hl.min(x_vec * 16, input.width() - 16)
#
# # Allocate storage for the minimum and maximum
# # helpers. One vector is enough.
# __m128i minimum_storage, maximum_storage
#
# # The pure step for the maximum is a vector of zeros
# maximum_storage = (__m128i)_mm_setzero_ps()
#
# # The update step for maximum
# for (int max_y = y - 2 max_y <= y + 2 max_y++) {
# uint8_t *clamped_row = clamped_storage + (max_y & 7) * clamped_width
# for (int max_x = x_base - 2 max_x <= x_base + 2 max_):
# __m128i v = _mm_loadu_si128((__m128i const *)(clamped_row + max_x + 2))
# maximum_storage = _mm_max_epu8(maximum_storage, v)
#
#
#
# # The pure step for the minimum is a vector of
# # ones. Create it by comparing something to
# # itself.
# minimum_storage = (__m128i)_mm_cmpeq_ps(_mm_setzero_ps(),
# _mm_setzero_ps())
#
# # The update step for minimum.
# for (int min_y = y - 2 min_y <= y + 2 min_y++) {
# uint8_t *clamped_row = clamped_storage + (min_y & 7) * clamped_width
# for (int min_x = x_base - 2 min_x <= x_base + 2 min_):
# __m128i v = _mm_loadu_si128((__m128i const *)(clamped_row + min_x + 2))
# minimum_storage = _mm_min_epu8(minimum_storage, v)
#
#
#
# # Now compute the spread.
# __m128i spread = _mm_sub_epi8(maximum_storage, minimum_storage)
#
# # Store it.
# _mm_storeu_si128((__m128i *)(output_row + x_base), spread)
#
#
#
# del clamped_storage
#
# end of hundred iterations
# Skip the timing comparison if we don't have openmp
# enabled. Otherwise it's unfair to C.
#ifdef _OPENMP
t2 = datetime.now()
# Now run the Halide version again without the
# jit-compilation overhead. Also run it one hundred times.
for iters in range(100):
spread.realize(halide_result)
t3 = datetime.now()
# Report the timings. On my machine they both take about 3ms
# for the 4-megapixel input (fast!), which makes sense,
# because they're using the same vectorization and
# parallelization strategy. However I find the Halide easier
# to read, write, debug, modify, and port.
print("Halide spread took %f ms. C equivalent took %f ms" % (
(t3 - t2).total_seconds() * 1000,
(t2 - t1).total_seconds() * 1000))
#endif # _OPENMP
# Check the results match:
for yy in range(input.height()):
for xx in range(input.width()):
if halide_result(xx, yy) != c_result(xx, yy):
raise Exception("halide_result(%d, %d) = %d instead of %d" % (
xx, yy, halide_result(xx, yy), c_result(xx, yy)))
return -1
#endif # __SSE2__
else:
print("(Skipped the SSE2 section of the code, "
"since non-sense in python world.)")
print("Success!")
return 0
def test_schedules(verbose=False, test_random=False):
#random_module.seed(int(sys.argv[1]) if len(sys.argv)>1 else 0)
halide.exit_on_signal()
f = halide.Func('f')
x = halide.Var('x')
y = halide.Var('y')
c = halide.Var('c')
g = halide.Func('g')
v = halide.Var('v')
input = halide.UniformImage(halide.UInt(16), 3)
int_t = halide.Int(32)
f[x,y,c] = input[halide.clamp(x,halide.cast(int_t,0),halide.cast(int_t,input.width()-1)),
halide.clamp(y,halide.cast(int_t,0),halide.cast(int_t,input.height()-1)),
halide.clamp(c,halide.cast(int_t,0),halide.cast(int_t,2))]
#g[v] = f[v,v]
g[x,y,c] = f[x,y,c]+1
assert sorted(halide.all_vars(g).keys()) == sorted(['x', 'y', 'c']) #, 'v'])
if verbose:
print halide.func_varlist(f)
print 'caller_vars(f) =', caller_vars(g, f)
print 'caller_vars(g) =', caller_vars(g, g)
# validL = list(valid_schedules(g, f, 4))
# validL = [repr(_x) for _x in validL]
#
# for L in sorted(validL):
# print repr(L)
T0 = time.time()
if not test_random:
random = True #False
nvalid_determ = 0
for L in schedules_func(g, f, 0, 3):
nvalid_determ += 1
if verbose:
print L
nvalid_random = 0
for i in range(100):
for L in schedules_func(g, f, 0, DEFAULT_MAX_DEPTH, random=True): #sorted([repr(_x) for _x in valid_schedules(g, f, 3)]):
if verbose and 0:
print L#repr(L)
nvalid_random += 1
s = []
for i in range(400):
d = random_schedule(g, 0, DEFAULT_MAX_DEPTH)
si = str(d)
s.append(si)
if verbose:
print 'Schedule:', si
d.apply()
evaluate = d.test((36, 36, 3), input)
print 'evaluate'
evaluate()
if test_random:
print 'Success'
sys.exit()
T1 = time.time()
s = '\n'.join(s)
assert 'f.chunk(_c0)' in s
assert 'f.root().vectorize' in s
assert 'f.root().unroll' in s
assert 'f.root().split' in s
assert 'f.root().tile' in s
assert 'f.root().parallel' in s
assert 'f.root().transpose' in s
assert nvalid_random == 100
if verbose:
print 'generated in %.3f secs' % (T1-T0)
print 'random_schedule: OK'
def main():
# This program defines a single-stage imaging pipeline that
# brightens an image.
# First we'll load the input image we wish to brighten.
image_path = os.path.join(os.path.dirname(__file__), "../../tutorial/images/rgb.png")
# We create a hl.Buffer object to wrap the numpy array
input = hl.Buffer(imread(image_path))
assert input.type() == hl.UInt(8)
# Next we define our hl.Func object that represents our one pipeline
# stage.
brighter = hl.Func("brighter")
# Our hl.Func will have three arguments, representing the position
# in the image and the color channel. Halide treats color
# channels as an extra dimension of the image.
x, y, c = hl.Var("x"), hl.Var("y"), hl.Var("c")
# Normally we'd probably write the whole function definition on
# one line. Here we'll break it apart so we can explain what
# we're doing at every step.
# For each pixel of the input image.
value = input[x, y, c]
assert type(value) == hl.Expr
# Cast it to a floating point value.
value = hl.cast(hl.Float(32), value)
# Multiply it by 1.5 to brighten it. Halide represents real
# numbers as floats, not doubles, so we stick an 'f' on the end
# of our constant.
value = value * 1.5
# Clamp it to be less than 255, so we don't get overflow when we
# hl.cast it back to an 8-bit unsigned int.
value = hl.min(value, 255.0)
# Cast it back to an 8-bit unsigned integer.
value = hl.cast(hl.UInt(8), value)
# Define the function.
brighter[x, y, c] = value
# The equivalent one-liner to all of the above is:
#
# brighter(x, y, c) = hl.cast<uint8_t>(hl.min(input(x, y, c) * 1.5f, 255))
# brighter[x, y, c] = hl.cast(hl.UInt(8), hl.min(input[x, y, c] * 1.5, 255))
#
# In the shorter version:
# - I skipped the hl.cast to float, because multiplying by 1.5f does
# that automatically.
# - I also used integer constants in hl.clamp, because they get hl.cast
# to match the type of the first argument.
# - I left the h. off hl.clamp. It's unnecessary due to Koenig
# lookup.
# Remember. All we've done so far is build a representation of a
# Halide program in memory. We haven't actually processed any
# pixels yet. We haven't even compiled that Halide program yet.
# So now we'll realize the hl.Func. The size of the output image
# should match the size of the input image. If we just wanted to
# brighten a portion of the input image we could request a
# smaller size. If we request a larger size Halide will throw an
# error at runtime telling us we're trying to read out of bounds
# on the input image.
output_image = brighter.realize(input.width(), input.height(), input.channels())
assert output_image.type() == hl.UInt(8)
# Save the output for inspection. It should look like a bright parrot.
imsave("brighter.png", output_image)
print("Created brighter.png result file.")
print("Success!")
return 0
def main():
# All Exprs have a scalar type, and all Funcs evaluate to one or
# more scalar types. The scalar types in Halide are unsigned
# integers of various bit widths, signed integers of the same set
# of bit widths, floating point numbers in single and double
# precision, and opaque handles (equivalent to void *). The
# following array contains all the legal types.
valid_halide_types = [
hl.UInt(8), hl.UInt(16), hl.UInt(32), hl.UInt(64),
hl.Int(8), hl.Int(16), hl.Int(32), hl.Int(64),
hl.Float(32), hl.Float(64), hl.Handle() ]
# Constructing and inspecting types.
if True:
# You can programmatically examine the properties of a Halide
# type. This is useful when you write a C++ function that has
# hl.Expr arguments and you wish to check their types:
assert hl.UInt(8).bits() == 8
assert hl.Int(8).is_int()
# You can also programmatically construct Types as a function of other Types.
t = hl.UInt(8)
t = t.with_bits(t.bits() * 2)
assert t == hl.UInt(16)
# Or construct a Type from a C++ scalar type
#assert type_of<float>() == hl.Float(32)
# The Type struct is also capable of representing vector types,
# but this is reserved for Halide's internal use. You should
# vectorize code by using hl.Func::vectorize, not by attempting to
# construct vector expressions directly. You may encounter vector
# types if you programmatically manipulate lowered Halide code,
# but this is an advanced topic (see hl.Func::add_custom_lowering_pass).
# You can query any Halide hl.Expr for its type. An hl.Expr
# representing a hl.Var has type hl.Int(32):
x = hl.Var("x")
assert hl.Expr(x).type() == hl.Int(32)
# Most transcendental functions in Halide hl.cast their inputs to a
# hl.Float(32) and return a hl.Float(32):
assert hl.sin(x).type() == hl.Float(32)
# You can hl.cast an hl.Expr from one Type to another using the hl.cast operator:
assert hl.cast(hl.UInt(8), x).type() == hl.UInt(8)
# This also comes in a template form that takes a C++ type.
#assert hl.cast<uint8_t>(x).type() == hl.UInt(8)
# You can also query any defined hl.Func for the types it produces.
f1 = hl.Func("f1")
f1[x] = hl.cast(hl.UInt(8), x)
assert f1.output_types()[0] == hl.UInt(8)
f2 = hl.Func("f2")
f2[x] = (x, hl.sin(x))
assert f2.output_types()[0] == hl.Int(32) and \
f2.output_types()[1] == hl.Float(32)
# Type promotion rules.
if True:
# When you combine Exprs of different types (e.g. using '+',
# '*', etc), Halide uses a system of type promotion
# rules. These differ to C's rules. To demonstrate these
# we'll make some Exprs of each type.
x = hl.Var("x")
u8 = hl.cast(hl.UInt(8), x)
u16 = hl.cast(hl.UInt(16), x)
u32 = hl.cast(hl.UInt(32), x)
u64 = hl.cast(hl.UInt(64), x)
s8 = hl.cast(hl.Int(8), x)
s16 = hl.cast(hl.Int(16), x)
s32 = hl.cast(hl.Int(32), x)
s64 = hl.cast(hl.Int(64), x)
f32 = hl.cast(hl.Float(32), x)
f64 = hl.cast(hl.Float(64), x)
# The rules are as follows, and are applied in the order they are
# written below.
# 1) It is an error to hl.cast or use arithmetic operators on Exprs of type hl.Handle().
# 2) If the types are the same, then no type conversions occur.
for t in valid_halide_types:
# Skip the handle type.
if t.is_handle():
continue
e = hl.cast(t, x)
assert (e + e).type() == e.type()
# 3) If one type is a float but the other is not, then the
# non-float argument is promoted to a float (possibly causing a
# loss of precision for large integers).
assert (u8 + f32).type() == hl.Float(32)
assert (f32 + s64).type() == hl.Float(32)
assert (u16 + f64).type() == hl.Float(64)
assert (f64 + s32).type() == hl.Float(64)
# 4) If both types are float, then the narrower argument is
# promoted to the wider bit-width.
assert (f64 + f32).type() == hl.Float(64)
# The rules above handle all the floating-point cases. The
# following three rules handle the integer cases.
# 5) If one of the expressions is an integer constant, then it is
# coerced to the type of the other expression.
assert (u32 + 3).type() == hl.UInt(32)
assert (3 + s16).type() == hl.Int(16)
# If this rule would cause the integer to overflow, then Halide
# will trigger an error, e.g. uncommenting the following line
# will cause this program to terminate with an error.
# hl.Expr bad = u8 + 257
# 6) If both types are unsigned integers, or both types are
# signed integers, then the narrower argument is promoted to
# wider type.
assert (u32 + u8).type() == hl.UInt(32)
assert (s16 + s64).type() == hl.Int(64)
# 7) If one type is signed and the other is unsigned, both
# arguments are promoted to a signed integer with the greater of
# the two bit widths.
assert (u8 + s32).type() == hl.Int(32)
assert (u32 + s8).type() == hl.Int(32)
# Note that this may silently overflow the unsigned type in the
# case where the bit widths are the same.
assert (u32 + s32).type() == hl.Int(32)
if False: # evaluate<X> not yet exposed to python
# When an unsigned hl.Expr is converted to a wider signed type in
# this way, it is first widened to a wider unsigned type
# (zero-extended), and then reinterpreted as a signed
# integer. I.e. casting the hl.UInt(8) value 255 to an hl.Int(32)
# produces 255, not -1.
#int32_t result32 = evaluate<int>(hl.cast<int32_t>(hl.cast<uint8_t>(255)))
assert result32 == 255
# When a signed type is explicitly converted to a wider unsigned
# type with the hl.cast operator (the type promotion rules will
# never do this automatically), it is first converted to the
# wider signed type (sign-extended), and then reinterpreted as
# an unsigned integer. I.e. casting the hl.Int(8) value -1 to a
# hl.UInt(16) produces 65535, not 255.
#uint16_t result16 = evaluate<uint16_t>(hl.cast<uint16_t>(hl.cast<int8_t>(-1)))
assert result16 == 65535
# The type hl.Handle().
if True:
# hl.Handle is used to represent opaque pointers. Applying
# type_of to any pointer type will return hl.Handle()
#assert type_of<void *>() == hl.Handle()
#assert type_of<const char * const **>() == hl.Handle()
# (not clear what the proper python version would be)
# Handles are always stored as 64-bit, regardless of the compilation
# target.
assert hl.Handle().bits() == 64
# The main use of an hl.Expr of type hl.Handle is to pass
# it through Halide to other external code.
# Generic code.
if True:
# The main explicit use of Type in Halide is to write Halide
# code parameterized by a Type. In C++ you'd do this with
# templates. In Halide there's no need - you can inspect and
# modify the types dynamically at C++ runtime instead. The
# function defined below averages two expressions of any
# equal numeric type.
x = hl.Var("x")
assert average(hl.cast(hl.Float(32), x), 3.0).type() == hl.Float(32)
assert average(x, 3).type() == hl.Int(32)
assert average(hl.cast(hl.UInt(8), x), hl.cast(hl.UInt(8), 3)).type() == hl.UInt(8)
print("Success!")
return 0
def get_bilateral_grid(input, r_sigma, s_sigma):
x = hl.Var('x')
y = hl.Var('y')
z = hl.Var('z')
c = hl.Var('c')
xi = hl.Var("xi")
yi = hl.Var("yi")
zi = hl.Var("zi")
# Add a boundary condition
clamped = hl.Func('clamped')
clamped[x, y] = input[hl.clamp(x, 0, input.width()-1),
hl.clamp(y, 0, input.height()-1)]
# Construct the bilateral grid
r = hl.RDom(0, s_sigma, 0, s_sigma, 'r')
val = clamped[x * s_sigma + r.x - s_sigma//2, y * s_sigma + r.y - s_sigma//2]
val = hl.clamp(val, 0.0, 1.0)
#zi = hl.cast(int_t, val * (1.0/r_sigma) + 0.5)
zi = hl.cast(int_t, (val / r_sigma) + 0.5)
histogram = hl.Func('histogram')
histogram[x, y, z, c] = 0.0
histogram[x, y, zi, c] += hl.select(c == 0, val, 1.0)
# Blur the histogram using a five-tap filter
blurx, blury, blurz = hl.Func('blurx'), hl.Func('blury'), hl.Func('blurz')
blurz[x, y, z, c] = histogram[x, y, z-2, c] + histogram[x, y, z-1, c]*4 + histogram[x, y, z, c]*6 + histogram[x, y, z+1, c]*4 + histogram[x, y, z+2, c]
blurx[x, y, z, c] = blurz[x-2, y, z, c] + blurz[x-1, y, z, c]*4 + blurz[x, y, z, c]*6 + blurz[x+1, y, z, c]*4 + blurz[x+2, y, z, c]
blury[x, y, z, c] = blurx[x, y-2, z, c] + blurx[x, y-1, z, c]*4 + blurx[x, y, z, c]*6 + blurx[x, y+1, z, c]*4 + blurx[x, y+2, z, c]
# Take trilinear samples to compute the output
val = hl.clamp(clamped[x, y], 0.0, 1.0)
zv = val / r_sigma
zi = hl.cast(int_t, zv)
zf = zv - zi
xf = hl.cast(float_t, x % s_sigma) / s_sigma
yf = hl.cast(float_t, y % s_sigma) / s_sigma
xi = x/s_sigma
yi = y/s_sigma
interpolated = hl.Func('interpolated')
interpolated[x, y, c] = hl.lerp(hl.lerp(hl.lerp(blury[xi, yi, zi, c], blury[xi+1, yi, zi, c], xf),
hl.lerp(blury[xi, yi+1, zi, c], blury[xi+1, yi+1, zi, c], xf), yf),
hl.lerp(hl.lerp(blury[xi, yi, zi+1, c], blury[xi+1, yi, zi+1, c], xf),
hl.lerp(blury[xi, yi+1, zi+1, c], blury[xi+1, yi+1, zi+1, c], xf), yf), zf)
# Normalize
bilateral_grid = hl.Func('bilateral_grid')
bilateral_grid[x, y] = interpolated[x, y, 0]/interpolated[x, y, 1]
target = hl.get_target_from_environment()
if target.has_gpu_feature():
#if True:
# GPU schedule
# Currently running this directly from the Python code is very slow.
# Probably because of the dispatch time because generated code
# is same speed as C++ generated code.
print ("Compiling for GPU.")
histogram.compute_root().reorder(c, z, x, y).gpu_tile(x, y, 8, 8);
histogram = histogram.update() # Because returns ScheduleHandle
histogram.reorder(c, r.x, r.y, x, y).gpu_tile(x, y, xi, yi, 8, 8).unroll(c)
blurx.compute_root().gpu_tile(x, y, z, xi, yi, zi, 16, 16, 1)
blury.compute_root().gpu_tile(x, y, z, xi, yi, zi, 16, 16, 1)
blurz.compute_root().gpu_tile(x, y, z, xi, yi, zi, 8, 8, 4)
bilateral_grid.compute_root().gpu_tile(x, y, xi, yi, s_sigma, s_sigma)
else:
# CPU schedule
print ("Compiling for CPU.")
histogram.compute_root().parallel(z)
histogram = histogram.update() # Because returns ScheduleHandle
histogram.reorder(c, r.x, r.y, x, y).unroll(c)
blurz.compute_root().reorder(c, z, x, y).parallel(y).vectorize(x, 4).unroll(c)
blurx.compute_root().reorder(c, x, y, z).parallel(z).vectorize(x, 4).unroll(c)
blury.compute_root().reorder(c, x, y, z).parallel(z).vectorize(x, 4).unroll(c)
bilateral_grid.compute_root().parallel(y).vectorize(x, 4)
return bilateral_grid
def main():
# First we'll declare some Vars to use below.
x, y, c = hl.Var("x"), hl.Var("y"), hl.Var("c")
image_path = os.path.join(os.path.dirname(__file__), "../../tutorial/images/rgb.png")
# Now we'll express a multi-stage pipeline that blurs an image
# first horizontally, and then vertically.
if True:
# Take a color 8-bit input
input = hl.Buffer(imread(image_path))
assert input.type() == hl.UInt(8)
# Upgrade it to 16-bit, so we can do math without it overflowing.
input_16 = hl.Func("input_16")
input_16[x, y, c] = hl.cast(hl.UInt(16), input[x, y, c])
# Blur it horizontally:
blur_x = hl.Func("blur_x")
blur_x[x, y, c] = (input_16[x - 1, y, c] + 2 * input_16[x, y, c] + input_16[x + 1, y, c]) / 4
# Blur it vertically:
blur_y = hl.Func("blur_y")
blur_y[x, y, c] = (blur_x[x, y - 1, c] + 2 * blur_x[x, y, c] + blur_x[x, y + 1, c]) / 4
# Convert back to 8-bit.
output = hl.Func("output")
output[x, y, c] = hl.cast(hl.UInt(8), blur_y[x, y, c])
# Each hl.Func in this pipeline calls a previous one using
# familiar function call syntax (we've overloaded operator()
# on hl.Func objects). A hl.Func may call any other hl.Func that has
# been given a definition. This restriction prevents
# pipelines with loops in them. Halide pipelines are always
# feed-forward graphs of Funcs.
# Now let's realize it...
# result = output.realize(input.width(), input.height(), 3)
# Except that the line above is not going to work. Uncomment
# it to see what happens.
# Realizing this pipeline over the same domain as the input
# image requires reading pixels out of bounds in the input,
# because the blur_x stage reaches outwards horizontally, and
# the blur_y stage reaches outwards vertically. Halide
# detects this by injecting a piece of code at the top of the
# pipeline that computes the region over which the input will
# be read. When it starts to run the pipeline it first runs
# this code, determines that the input will be read out of
# bounds, and refuses to continue. No actual bounds checks
# occur in the inner loop that would be slow.
#
# So what do we do? There are a few options. If we realize
# over a domain shifted inwards by one pixel, we won't be
# asking the Halide routine to read out of bounds. We saw how
# to do this in the previous lesson:
result = hl.Buffer(hl.UInt(8), [input.width() - 2, input.height() - 2, 3])
result.set_min([1, 1])
output.realize(result)
# Save the result. It should look like a slightly blurry
# parrot, and it should be two pixels narrower and two pixels
# shorter than the input image.
imsave("blurry_parrot_1.png", result)
print("Created blurry_parrot_1.png")
# This is usually the fastest way to deal with boundaries:
# don't write code that reads out of bounds :) The more
# general solution is our next example.
# The same pipeline, with a boundary condition on the input.
if True:
# Take a color 8-bit input
input = hl.Buffer(imread(image_path))
assert input.type() == hl.UInt(8)
# This time, we'll wrap the input in a hl.Func that prevents
# reading out of bounds:
clamped = hl.Func("clamped")
# Define an expression that clamps x to lie within the the
# range [0, input.width()-1].
clamped_x = hl.clamp(x, 0, input.width() - 1)
# Similarly hl.clamp y.
clamped_y = hl.clamp(y, 0, input.height() - 1)
# Load from input at the clamped coordinates. This means that
# no matter how we evaluated the hl.Func 'clamped', we'll never
# read out of bounds on the input. This is a hl.clamp-to-edge
# style boundary condition, and is the simplest boundary
# condition to express in Halide.
clamped[x, y, c] = input[clamped_x, clamped_y, c]
# Defining 'clamped' in that way can be done more concisely
# using a helper function from the BoundaryConditions
# namespace like so:
#
# clamped = hl.BoundaryConditions.repeat_edge(input)
#
# These are important to use for other boundary conditions,
# because they are expressed in the way that Halide can best
# understand and optimize.
# Upgrade it to 16-bit, so we can do math without it
# overflowing. This time we'll refer to our new hl.Func
# 'clamped', instead of referring to the input image
# directly.
input_16 = hl.Func("input_16")
input_16[x, y, c] = hl.cast(hl.UInt(16), clamped[x, y, c])
# The rest of the pipeline will be the same...
# Blur it horizontally:
blur_x = hl.Func("blur_x")
blur_x[x, y, c] = (input_16[x - 1, y, c] + 2 * input_16[x, y, c] + input_16[x + 1, y, c]) / 4
# Blur it vertically:
blur_y = hl.Func("blur_y")
blur_y[x, y, c] = (blur_x[x, y - 1, c] + 2 * blur_x[x, y, c] + blur_x[x, y + 1, c]) / 4
# Convert back to 8-bit.
output = hl.Func("output")
output[x, y, c] = hl.cast(hl.UInt(8), blur_y[x, y, c])
# This time it's safe to evaluate the output over the some
# domain as the input, because we have a boundary condition.
result = output.realize(input.width(), input.height(), 3)
# Save the result. It should look like a slightly blurry
# parrot, but this time it will be the same size as the
# input.
imsave("blurry_parrot_2.png", result)
print("Created blurry_parrot_2.png")
print("Success!")
return 0
def findStereoCorrespondence(left, right, SADWindowSize, minDisparity, numDisparities,
xmin, xmax, ymin, ymax,
x_tile_size=32, y_tile_size=32, test=False, uniquenessRatio=0.15, disp12MaxDiff=1):
""" Returns Func (left: Func, right: Func) """
x, y, c, d = Var("x"), Var("y"), Var("c"), Var("d")
diff = Func("diff")
diff[d, x, y] = h.cast(UInt(16), h.abs(left[x, y] - right[x-d, y]))
win2 = SADWindowSize/2
diff_T = Func("diff_T")
xi, xo, yi, yo = Var("xi"), Var("xo"), Var("yi"), Var("yo")
diff_T[d, xi, yi, xo, yo] = diff[d, xi + xo * x_tile_size + xmin, yi + yo * y_tile_size + ymin]
cSAD, vsum = Func("cSAD"), Func("vsum")
rk = RDom(-win2, SADWindowSize, "rk")
rxi, ryi = RDom(1, x_tile_size - 1, "rxi"), RDom(1, y_tile_size - 1, "ryi")
if test:
vsum[d, xi, yi, xo, yo] = h.sum(diff_T[d, xi, yi+rk, xo, yo])
cSAD[d, xi, yi, xo, yo] = h.sum(vsum[d, xi+rk, yi, xo, yo])
else:
vsum[d, xi, yi, xo, yo] = h.select(yi != 0, h.cast(UInt(16), 0), h.sum(diff_T[d, xi, rk, xo, yo]))
vsum[d, xi, ryi, xo, yo] = vsum[d, xi, ryi-1, xo, yo] + diff_T[d, xi, ryi+win2, xo, yo] - diff_T[d, xi, ryi-win2-1, xo, yo]
cSAD[d, xi, yi, xo, yo] = h.select(xi != 0, h.cast(UInt(16), 0), h.sum(vsum[d, rk, yi, xo, yo]))
cSAD[d, rxi, yi, xo, yo] = cSAD[d, rxi-1, yi, xo, yo] + vsum[d, rxi+win2, yi, xo, yo] - vsum[d, rxi-win2-1, yi, xo, yo]
rd = RDom(minDisparity, numDisparities)
disp_left = Func("disp_left")
disp_left[xi, yi, xo, yo] = h.Tuple(h.cast(UInt(16), minDisparity), h.cast(UInt(16), (2<<16)-1))
disp_left[xi, yi, xo, yo] = h.tuple_select(
cSAD[rd, xi, yi, xo, yo] < disp_left[xi, yi, xo, yo][1],
h.Tuple(h.cast(UInt(16), rd), cSAD[rd, xi, yi, xo, yo]),
h.Tuple(disp_left[xi, yi, xo, yo]))
FILTERED = -16
disp = Func("disp")
disp[x, y] = h.select(
# x > xmax-xmin or y > ymax-ymin,
x < xmax,
h.cast(UInt(16), disp_left[x % x_tile_size, y % y_tile_size, x / x_tile_size, y / y_tile_size][0]),
h.cast(UInt(16), FILTERED))
# Schedule
vector_width = 8
disp.compute_root() \
.tile(x, y, xo, yo, xi, yi, x_tile_size, y_tile_size).reorder(xi, yi, xo, yo) \
.vectorize(xi, vector_width).parallel(xo).parallel(yo)
# reorder storage
disp_left.reorder_storage(xi, yi, xo, yo)
diff_T .reorder_storage(xi, yi, xo, yo, d)
vsum .reorder_storage(xi, yi, xo, yo, d)
cSAD .reorder_storage(xi, yi, xo, yo, d)
disp_left.compute_at(disp, xo).reorder(xi, yi, xo, yo) \
.vectorize(xi, vector_width) \
.update() \
.reorder(xi, yi, rd, xo, yo).vectorize(xi, vector_width)
if test:
cSAD.compute_at(disp_left, rd).reorder(xi, yi, xo, yo, d).vectorize(xi, vector_width)
vsum.compute_at(disp_left, rd).reorder(xi, yi, xo, yo, d).vectorize(xi, vector_width)
else:
cSAD.compute_at(disp_left, rd).reorder(xi, yi, xo, yo, d).vectorize(xi, vector_width) \
.update() \
.reorder(yi, rxi, xo, yo, d).vectorize(yi, vector_width)
vsum.compute_at(disp_left, rd).reorder(xi, yi, xo, yo, d).vectorize(xi, vector_width) \
.update() \
.reorder(xi, ryi, xo, yo, d).vectorize(xi, vector_width)
return disp
