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 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.i32(1), y * s_sigma * hl.i32(1)]
val22 = clamped[x * s_sigma - hl.i32(s_sigma // 2),
y * s_sigma - hl.i32(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 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.i32((val / r_sigma) + 0.5)
histogram = hl.Func('histogram')
histogram[x, y, z, c] = 0.0
ss = hl.select(c == 0, val, 1.0)
left = histogram[x, y, zi, c]
left += 5
left += ss
def test_image_to_ndarray():
if "image_to_ndarray" not in globals():
print("Skipping test_image_to_ndarray")
return
import numpy
i0 = Image(hl.Float(32), 50, 50)
assert i0.type() == hl.Float(32)
a0 = image_to_ndarray(i0)
print("a0.shape", a0.shape)
print("a0.dtype", a0.dtype)
assert a0.dtype == numpy.float32
i1 = Image(hl.Int(16), 50, 50)
assert i1.type() == hl.Int(16)
i1[24, 24] = 42
assert i1(24, 24) == 42
a1 = image_to_ndarray(i1)
print("a1.shape", a1.shape)
print("a1.dtype", a1.dtype)
assert a1.dtype == numpy.int16
assert a1[24, 24] == 42
return
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 test_multipass_constraints():
input = hl.ImageParam(hl.Float(32), 2, "input")
f = hl.Func("f")
x = hl.Var("x")
y = hl.Var("y")
f[x, y] = input[x + 1, y + 1] + input[x - 1, y - 1]
f[x, y] += 3.0
f.update().vectorize(x, 4)
o = f.output_buffer()
# Now make some hard-to-resolve constraints
input.dim(0).set_bounds(min=input.dim(1).min() - 5,
extent=input.dim(1).extent() + o.dim(0).extent())
o.dim(0).set_bounds(min=0,
extent=hl.select(
o.dim(0).extent() < 22,
o.dim(0).extent() + 1,
o.dim(0).extent()))
# Make a bounds query buffer
query_buf = hl.Buffer.make_bounds_query(type=hl.Float(32), sizes=[7, 8])
query_buf.set_min([2, 2])
f.infer_input_bounds(query_buf)
if input.get().dim(0).min() != -4 or \
input.get().dim(0).extent() != 34 or \
input.get().dim(1).min() != 1 or \
input.get().dim(1).extent() != 10 or \
query_buf.dim(0).min() != 0 or \
query_buf.dim(0).extent() != 24 or \
query_buf.dim(1).min() != 2 or \
query_buf.dim(1).extent() != 8:
print("Constraints not correctly satisfied:\n", "in:",
input.get().dim(0).min(),
input.get().dim(0).extent(),
input.get().dim(1).min(),
input.get().dim(1).extent(), "out:",
query_buf.dim(0).min(),
query_buf.dim(0).extent(),
query_buf.dim(1).min(),
query_buf.dim(1).extent())
assert False
def gauss_15x15(input, name):
print(' gauss_15x15')
k = hl.Buffer(hl.Float(32), [15], "gauss_15x15")
k.translate([-7])
rdom = hl.RDom([(-7, 15)])
k.fill(0)
k[-7] = 0.004961
k[-6] = 0.012246
k[-5] = 0.026304
k[-4] = 0.049165
k[-3] = 0.079968
k[-2] = 0.113193
k[-1] = 0.139431
k[0] = 0.149464
k[7] = 0.004961
k[6] = 0.012246
k[5] = 0.026304
k[4] = 0.049165
k[3] = 0.079968
k[2] = 0.113193
k[1] = 0.139431
return gauss(input, k, rdom, name)
def test_for_each_element():
buf = hl.Buffer(hl.Float(32), [3, 4])
for x in range(3):
for y in range(4):
buf[x, y] = x + y
# Can't use 'assert' in a lambda, but can call a fn that uses it.
buf.for_each_element(lambda pos, buf=buf: _assert_fn(buf[pos[0], pos[1]] == pos[0] + pos[1]))
def main():
input = hl.ImageParam(hl.Float(32), 2, '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
bilateral_grid = get_bilateral_grid(input, r_sigma, s_sigma)
# Set `generate` to False to run the jit immediately and get instant gratification.
#generate = True
generate = False
if generate:
generate_compiled_file(bilateral_grid)
else:
filter_test_image(bilateral_grid, input)
print("\nEnd of game. Have a nice day!")
return
def _realize_and_check(f, offset=0):
b = hl.Buffer(hl.Float(32), [2, 2])
f.realize(b)
assert b[0, 0] == 3.5 + offset + 123
assert b[0, 1] == 4.5 + offset + 123
assert b[1, 0] == 4.5 + offset + 123
assert b[1, 1] == 5.5 + offset + 123
def test_division():
f32 = hl.Param(hl.Float(32), 'f32', -32.0)
f64 = hl.Param(hl.Float(64), 'f64', 64.0)
i16 = hl.Param(hl.Int(16), 'i16', -16)
i32 = hl.Param(hl.Int(32), 'i32', 32)
u16 = hl.Param(hl.UInt(16), 'u16', 16)
u32 = hl.Param(hl.UInt(32), 'u32', 32)
# Verify that the types match the rules in match_types()
assert (f32 / f64).type() == hl.Float(64)
assert (f32 // f64).type() == hl.Float(64)
assert (i16 / i32).type() == hl.Int(32)
assert (i16 // i32).type() == hl.Int(32)
assert (u16 / u32).type() == hl.UInt(32)
assert (u16 // u32).type() == hl.UInt(32)
# int / uint -> int
assert (u16 / i32).type() == hl.Int(32)
assert (i32 // u16).type() == hl.Int(32)
# any / float -> float
# float / any -> float
assert (u16 / f32).type() == hl.Float(32)
assert (u16 // f32).type() == hl.Float(32)
assert (i16 / f64).type() == hl.Float(64)
assert (i16 // f64).type() == hl.Float(64)
# Verify that division semantics match those for Halide
# (rather than python); this differs for int/int which
# defaults to float (rather than floordiv) in Python3.
# Also test that // always floors the result, even for float.
assert _evaluate(f32 / f64) == -0.5
assert _evaluate(f32 // f64) == -1.0
assert _evaluate(i16 / i32) == -1
assert _evaluate(i16 // i32) == -1
assert _evaluate(i32 / i16) == -2
assert _evaluate(u16 / u32) == 0
assert _evaluate(u16 // u32) == 0
assert _evaluate(u16 / i32) == 0
assert _evaluate(i32 // u16) == 2
assert _evaluate(u16 / f32) == -0.5
assert _evaluate(u16 // f32) == -1.0
assert _evaluate(i16 / f64) == -0.25
assert _evaluate(i16 // f64) == -1.0
def generate_compiled_file(bilateral_grid):
target = hl.get_target_from_environment()
# Need to copy the filter executable from the C++ apps/bilateral_grid folder to run this.
# (after making it of course)
arguments = ArgumentsVector()
arguments.append(Argument('r_sigma', InputScalar, hl.Float(32), 0))
arguments.append(Argument('input', InputBuffer, hl.UInt(16), 2))
bilateral_grid.compile_to_file("bilateral_grid", arguments,
"bilateral_grid", target)
print("Generated compiled file for bilateral_grid function.")
def test_scalar_buffers():
buf = hl.Buffer.make_scalar(hl.Float(32))
assert buf.dimensions() == 0
buf.fill(0)
buf[()] = 2.5
assert buf[()] == 2.5
buf.fill(32)
assert buf[()] == 32
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 test_nobuildmethod():
x, y, c = hl.Var(), hl.Var(), hl.Var()
target = hl.get_jit_target_from_environment()
b_in = hl.Buffer(hl.Float(32), [2, 2])
b_in.fill(123)
b_out = hl.Buffer(hl.Int(32), [2, 2])
f = nobuildmethod.generate(target, b_in, 1.0)
f.realize(b_out)
assert b_out.all_equal(123)
def test_partialbuildmethod():
x, y, c = hl.Var(), hl.Var(), hl.Var()
target = hl.get_jit_target_from_environment()
b_in = hl.Buffer(hl.Float(32), [2, 2])
b_in.fill(123)
b_out = hl.Buffer(hl.Int(32), [2, 2])
try:
f = partialbuildmethod.generate(target, b_in, 1)
except RuntimeError as e:
assert "Generators that use build() (instead of generate()+Output<>) are not supported in the Python bindings." in str(e)
else:
assert False, 'Did not see expected exception!'
def gauss_7x7(input, name):
k = hl.Buffer(hl.Float(32), [7], "gauss_7x7_kernel")
k.translate([-3])
rdom = hl.RDom([(-3, 7)])
k.fill(0)
k[-3] = 0.026267
k[-2] = 0.100742
k[-1] = 0.225511
k[0] = 0.29496
k[1] = 0.225511
k[2] = 0.100742
k[3] = 0.026267
return gauss(input, k, rdom, name)
def test_float_or_int():
x = hl.Var('x')
i32, f32 = hl.Int(32), hl.Float(32)
assert hl.Expr(x).type() == i32
assert (x * 2).type() == i32
assert (x / 2).type() == i32
assert ((x // 2) - 1 + 2 * (x % 2)).type() == i32
assert ((x / 2) - 1 + 2 * (x % 2)).type() == i32
assert ((x / 2)).type() == i32
assert ((x / 2.0)).type() == f32
assert ((x // 2)).type() == i32
assert ((x // 2) - 1).type() == i32
assert ((x % 2)).type() == i32
assert (2 * (x % 2)).type() == i32
assert ((x // 2) - 1 + 2 * (x % 2)).type() == i32
assert type(x) == hl.Var
assert (hl.Expr(x)).type() == i32
assert (hl.Expr(2.0)).type() == f32
assert (hl.Expr(2)).type() == i32
assert (x + 2).type() == i32
assert (2 + x).type() == i32
assert (hl.Expr(2) + hl.Expr(3)).type() == i32
assert (hl.Expr(2.0) + hl.Expr(3)).type() == f32
assert (hl.Expr(2) + 3.0).type() == f32
assert (hl.Expr(2) + 3).type() == i32
assert (hl.Expr(x) + 2).type() == i32
assert (2 + hl.Expr(x)).type() == i32
assert (2 * (x + 2)).type() == i32
assert (x + 0).type() == i32
assert (x % 2).type() == i32
assert (2 * x).type() == i32
assert (x * 2).type() == i32
assert (x * 2).type() == i32
assert ((x % 2)).type() == i32
assert ((x % 2) * 2).type() == i32
assert (2 * (x % 2)).type() == i32
assert ((x + 2) * 2).type() == i32
def test_float_or_int():
x = hl.Var('x')
i, f = hl.Int(32), hl.Float(32)
assert ((x//2) - 1 + 2*(x%2)).type() == i
assert ((x/2) - 1 + 2*(x%2)).type() == i
assert ((x/2)).type() == i
assert ((x/2.0)).type() == f
assert ((x//2)).type() == i
assert ((x//2) - 1).type() == i
assert ((x%2)).type() == i
assert (2*(x%2)).type() == i
assert ((x//2) - 1 + 2*(x%2)).type() == i
assert type(x) == hl.Var
assert (x.as_expr()).type() == i
assert (hl.Expr(2.0)).type() == f
assert (hl.Expr(2)).type() == i
assert (x + 2).type() == i
assert (2 + x).type() == i
assert (hl.Expr(2) + hl.Expr(3)).type() == i
assert (hl.Expr(2.0) + hl.Expr(3)).type() == f
assert (hl.Expr(2) + 3.0).type() == f
assert (hl.Expr(2) + 3).type() == i
assert (x.as_expr() + 2).type() == i # yes this failed at some point
assert (2 + x.as_expr()).type() == i
assert (2 * (x + 2)).type() == i # yes this failed at some point
assert (x + 0).type() == i
assert (x % 2).type() == i
assert (2 * x).type() == i
assert (x * 2).type() == i
assert (x * 2).type() == i
assert ((x % 2)).type() == i
assert ((x % 2) * 2).type() == i
#assert (2 * (x % 2)).type() == i # yes this failed at some point
assert ((x + 2) * 2).type() == i
return
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
# TODO: This allows you to use "true" div (vs floordiv) in Python2 for the / operator;
# unfortunately it appears to also replace the overloads we've carefully added for Halide.
# Figure out if it's possible to allow this to leave our Halide stuff unaffected.
#
# from __future__ import division
import time, sys
import halide as hl
from datetime import datetime
from scipy.misc import imread, imsave
import numpy as np
import os.path
int_t = hl.Int(32)
float_t = hl.Float(32)
def get_interpolate(input, levels):
"""
Build function, schedules it, and invokes jit compiler
:return: halide.hl.Func
"""
# THE ALGORITHM
downsampled = [hl.Func('downsampled%d' % i) for i in range(levels)]
downx = [hl.Func('downx%d' % l) for l in range(levels)]
interpolated = [hl.Func('interpolated%d' % i) for i in range(levels)]
# level_widths = [hl.Param(int_t,'level_widths%d'%i) for i in range(levels)]
# level_heights = [hl.Param(int_t,'level_heights%d'%i) for i in range(levels)]
def call_twoel(zone_name,
seed=2,
datasize=15,
itercount=10,
target_name="host-disable_llvm_loop_opt",
**kwargs):
N = datasize
seed = 2
inputs = [
{
"name": "delo2",
"d": 0,
"value": 0.001
},
{
"name": "delta",
"d": 0,
"value": 0.001
},
{
"name": "rdelta",
"d": 0,
"value": 0.001
},
{
"name": "expnt",
"d": 1,
"value": 0.00001
},
{
"name": "rnorm",
"d": 1
},
{
"name": "x",
"d": 1
},
{
"name": "y",
"d": 1
},
{
"name": "z",
"d": 1
},
{
"name": "fm",
"d": 2,
"shape": [1002, 5]
},
{
"name": "g_fock",
"d": 2
},
{
"name": "g_dens",
"d": 2
},
{
"name": "g_trace",
"d": 4,
"value": 0.0
},
]
outputs = [
{
"name": "rv",
"d": 1,
"shape": [1]
},
{
"name": "g_fock",
"d": 2
},
]
inputs = {x["name"]: x for x in inputs}
outputs = {x["name"]: x for x in outputs}
# generate input data
print("input/output size is", N, "^2")
buffers = []
buffers_by_name = {}
np.random.seed(seed)
for key in inputs:
param = inputs[key]
if param['d'] == 0:
thing = 0.2
else:
shape = [N] * param['d']
if 'shape' in param:
shape = param['shape']
thing = hl.Buffer(hl.Float(64), shape, name=key)
if 'value' in param:
if param['value'] != 0.0:
for pos in np.ndindex(*shape):
thing[pos] = param['value']
else:
values = np.random.rand(*shape) - 0.5
for pos in np.ndindex(*shape):
thing[pos] = values[pos]
buffers.append(thing)
buffers_by_name[key] = thing
# get JIT pipeline
zones = twoel_gen.define_original_twoel_zone().split_recursive()
zone_names = zone_name.split(",")
myzones = []
for zone in zones.loops:
if zone_name == 'all' or zone['name'] in zone_names:
myzones.append(zone)
if len(myzones) == 0:
if zone_name == 'list':
print([z.name for z in zones])
else:
print("no zone %s found" % zone_name)
exit(1)
zones.loops = myzones
gen = twoel_gen.Generate_twoel(loopnests=zones, **kwargs)
gen.generate_twoel()
p = gen.pipeline
target = hl.Target(target_name)
zone_names = [z.name for z in myzones]
print("compiling zones", zone_names, "for target", target)
p.compile_jit(target)
# plug in the parameter values
for param in gen.inputs.values():
name = param.name()
if name in buffers_by_name:
thing = buffers_by_name[name]
elif name.endswith("_in") and name[:-3] in buffers_by_name:
name = name[:-3]
thing = buffers_by_name[name]
else:
raise KeyError(name)
param.set(thing)
# dry-run
p.realize(N, N)
print(itercount, "timed runs")
if itercount == 0:
# when generating trace output, just doing the dry-run is enough.
return 0.0, 0.0
# benchmark it
walltime = 0.0
cputime = 0.0
for _ in range(itercount):
cpu_before = time.process_time()
wall_before = time.time()
rv, g_fock_out = p.realize(N, N)
cpu_after = time.process_time()
wall_after = time.time()
walltime += wall_after - wall_before
cputime += cpu_after - cpu_before
print("walltime: %.3f" % walltime)
print("cputime: %.3f" % cputime)
walltime /= itercount
cputime /= itercount
print("walltime per iter: %.3e" % walltime)
print("cputime per iter: %.3e" % cputime)
throughput = N * N * N * N / walltime
print("throughput: %.3e g() calls per second (roughly)" % throughput)
rv = rv[0]
g_fock_out = np.array(g_fock_out)
return walltime, cputime
def test_complexstub():
constant_image = _make_constant_image()
input = hl.ImageParam(hl.UInt(8), 3, 'input')
input.set(constant_image)
x, y, c = hl.Var(), hl.Var(), hl.Var()
target = hl.get_jit_target_from_environment()
float_arg = 1.25
int_arg = 33
r = complexstub(target,
typed_buffer_input=constant_image,
untyped_buffer_input=constant_image,
simple_input=input,
array_input=[input, input],
float_arg=float_arg,
int_arg=[int_arg, int_arg],
untyped_buffer_output_type="uint8",
vectorize=True)
# return value is a tuple; unpack separately to avoid
# making the callsite above unreadable
(simple_output, tuple_output, array_output, typed_buffer_output,
untyped_buffer_output, static_compiled_buffer_output) = r
b = simple_output.realize(32, 32, 3, target)
assert b.type() == hl.Float(32)
for x in range(32):
for y in range(32):
for c in range(3):
expected = constant_image[x, y, c]
actual = b[x, y, c]
assert expected == actual, "Expected %s Actual %s" % (expected,
actual)
b = tuple_output.realize(32, 32, 3, target)
assert b[0].type() == hl.Float(32)
assert b[1].type() == hl.Float(32)
assert len(b) == 2
for x in range(32):
for y in range(32):
for c in range(3):
expected1 = constant_image[x, y, c] * float_arg
expected2 = expected1 + int_arg
actual1, actual2 = b[0][x, y, c], b[1][x, y, c]
assert expected1 == actual1, "Expected1 %s Actual1 %s" % (
expected1, actual1)
assert expected2 == actual2, "Expected2 %s Actual1 %s" % (
expected2, actual2)
assert len(array_output) == 2
for a in array_output:
b = a.realize(32, 32, target)
assert b.type() == hl.Int(16)
for x in range(32):
for y in range(32):
expected = constant_image[x, y, 0] + int_arg
actual = b[x, y]
assert expected == actual, "Expected %s Actual %s" % (expected,
actual)
# TODO: Output<Buffer<>> has additional behaviors useful when a Stub
# is used within another Generator; this isn't yet implemented since there
# isn't yet Python bindings for Generator authoring. This section
# of the test may need revision at that point.
b = typed_buffer_output.realize(32, 32, 3, target)
assert b.type() == hl.Float(32)
for x in range(32):
for y in range(32):
for c in range(3):
expected = constant_image[x, y, c]
actual = b[x, y, c]
assert expected == actual, "Expected %s Actual %s" % (expected,
actual)
b = untyped_buffer_output.realize(32, 32, 3, target)
assert b.type() == hl.UInt(8)
for x in range(32):
for y in range(32):
for c in range(3):
expected = constant_image[x, y, c]
actual = b[x, y, c]
assert expected == actual, "Expected %s Actual %s" % (expected,
actual)
b = static_compiled_buffer_output.realize(4, 4, 1, target)
assert b.type() == hl.UInt(8)
for x in range(4):
for y in range(4):
for c in range(1):
expected = constant_image[x, y, c] + 42
actual = b[x, y, c]
assert expected == actual, "Expected %s Actual %s" % (expected,
actual)
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(imageio.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.
# python3-imageio versions <2.5 expect a numpy array
imageio.imsave("brighter.png", np.asanyarray(output_image))
print("Created brighter.png result file.")
print("Success!")
return 0
def f32(e):
return hl.cast(hl.Float(32), e)
def main():
# So far Funcs (such as the one below) have evaluated to a single
# scalar value for each point in their domain.
single_valued = hl.Func()
x, y = hl.Var("x"), hl.Var("y")
single_valued[x, y] = x + y
# One way to write a hl.Func that returns a collection of values is
# to add an additional dimension which indexes that
# collection. This is how we typically deal with color. For
# example, the hl.Func below represents a collection of three values
# for every x, y coordinate indexed by c.
color_image = hl.Func()
c = hl.Var("c")
color_image[x, y, c] = hl.select(
c == 0,
245, # Red value
c == 1,
42, # Green value
132) # Blue value
# Since this pattern appears quite often, Halide provides a
# syntatic sugar to write the code above as the following,
# using the "mux" function.
# color_image[x, y, c] = hl.mux(c, [245, 42, 132]);
# This method is often convenient because it makes it easy to
# operate on this hl.Func in a way that treats each item in the
# collection equally:
brighter = hl.Func()
brighter[x, y, c] = color_image[x, y, c] + 10
# However this method is also inconvenient for three reasons.
#
# 1) Funcs are defined over an infinite domain, so users of this
# hl.Func can for example access color_image(x, y, -17), which is
# not a meaningful value and is probably indicative of a bug.
#
# 2) It requires a hl.select, which can impact performance if not
# bounded and unrolled:
# brighter.bound(c, 0, 3).unroll(c)
#
# 3) With this method, all values in the collection must have the
# same type. While the above two issues are merely inconvenient,
# this one is a hard limitation that makes it impossible to
# express certain things in this way.
# It is also possible to represent a collection of values as a
# collection of Funcs:
func_array = [hl.Func() for i in range(3)]
func_array[0][x, y] = x + y
func_array[1][x, y] = hl.sin(x)
func_array[2][x, y] = hl.cos(y)
# This method avoids the three problems above, but introduces a
# new annoyance. Because these are separate Funcs, it is
# difficult to schedule them so that they are all computed
# together inside a single loop over x, y.
# A third alternative is to define a hl.Func as evaluating to a
# Tuple instead of an hl.Expr. A Tuple is a fixed-size collection of
# Exprs which may have different type. The following function
# evaluates to an integer value (x+y), and a floating point value
# (hl.sin(x*y)).
multi_valued = hl.Func("multi_valued")
multi_valued[x, y] = (x + y, hl.sin(x * y))
# Realizing a tuple-valued hl.Func returns a collection of
# Buffers. We call this a Realization. It's equivalent to a
# std::vector of hl.Buffer/Image objects:
if True:
im1, im2 = multi_valued.realize([80, 60])
assert im1.type() == hl.Int(32)
assert im2.type() == hl.Float(32)
assert im1[30, 40] == 30 + 40
assert np.isclose(im2[30, 40], math.sin(30 * 40))
# You can also pass a tuple of pre-allocated buffers to realize()
# rather than having new ones created. (The Buffers must have the correct
# types and have identical sizes.)
if True:
im1, im2 = hl.Buffer(hl.Int(32),
[80, 60]), hl.Buffer(hl.Float(32), [80, 60])
multi_valued.realize((im1, im2))
assert im1[30, 40] == 30 + 40
assert np.isclose(im2[30, 40], math.sin(30 * 40))
# All Tuple elements are evaluated together over the same domain
# in the same loop nest, but stored in distinct allocations. The
# equivalent C++ code to the above is:
if True:
multi_valued_0 = np.empty((80 * 60), dtype=np.int32)
multi_valued_1 = np.empty((80 * 60), dtype=np.int32)
for yy in range(80):
for xx in range(60):
multi_valued_0[xx + 60 * yy] = xx + yy
multi_valued_1[xx + 60 * yy] = math.sin(xx * yy)
# When compiling ahead-of-time, a Tuple-valued hl.Func evaluates
# into multiple distinct output halide_buffer_t structs. These appear in
# order at the end of the function signature:
# int multi_valued(...input buffers and params..., halide_buffer_t
# *output_1, halide_buffer_t *output_2)
# You can construct a Tuple by passing multiple Exprs to the
# Tuple constructor as we did above. Perhaps more elegantly, you
# can also take advantage of initializer lists and just
# enclose your Exprs in braces:
multi_valued_2 = hl.Func("multi_valued_2")
multi_valued_2[x, y] = (x + y, hl.sin(x * y))
# Calls to a multi-valued hl.Func cannot be treated as Exprs. The
# following is a syntax error:
# hl.Func consumer
# consumer[x, y] = multi_valued_2[x, y] + 10
# Instead you must index the returned object with square brackets
# to retrieve the individual Exprs:
integer_part = multi_valued_2[x, y][0]
floating_part = multi_valued_2[x, y][1]
assert type(integer_part) is hl.FuncTupleElementRef
assert type(floating_part) is hl.FuncTupleElementRef
consumer = hl.Func()
consumer[x, y] = (integer_part + 10, floating_part + 10.0)
# Tuple reductions.
if True:
# Tuples are particularly useful in reductions, as they allow
# the reduction to maintain complex state as it walks along
# its domain. The simplest example is an argmax.
# First we create an Image to take the argmax over.
input_func = hl.Func()
input_func[x] = hl.sin(x)
input = input_func.realize([100])
assert input.type() == hl.Float(32)
# Then we defined a 2-valued Tuple which tracks the maximum value
# its index.
arg_max = hl.Func()
# Pure definition.
# (using [()] for zero-dimensional Funcs is a convention of this python interface)
arg_max[()] = (0, input[0])
# Update definition.
r = hl.RDom([(1, 99)])
old_index = arg_max[()][0]
old_max = arg_max[()][1]
new_index = hl.select(old_max > input[r], r, old_index)
new_max = hl.max(input[r], old_max)
arg_max[()] = (new_index, new_max)
# The equivalent C++ is:
arg_max_0 = 0
arg_max_1 = float(input[0])
for r in range(1, 100):
old_index = arg_max_0
old_max = arg_max_1
new_index = r if (old_max > input[r]) else old_index
new_max = max(input[r], old_max)
# In a tuple update definition, all loads and computation
# are done before any stores, so that all Tuple elements
# are updated atomically with respect to recursive calls
# to the same hl.Func.
arg_max_0 = new_index
arg_max_1 = new_max
# Let's verify that the Halide and C++ found the same maximum
# value and index.
if True:
r0, r1 = arg_max.realize()
assert r0.type() == hl.Int(32)
assert r1.type() == hl.Float(32)
assert arg_max_0 == r0[()]
assert np.isclose(arg_max_1, r1[()])
# Halide provides argmax and hl.argmin as built-in reductions
# similar to sum, product, maximum, and minimum. They return
# a Tuple consisting of the point in the reduction domain
# corresponding to that value, and the value itself. In the
# case of ties they return the first value found. We'll use
# one of these in the following section.
# Tuples for user-defined types.
if True:
# Tuples can also be a convenient way to represent compound
# objects such as complex numbers. Defining an object that
# can be converted to and from a Tuple is one way to extend
# Halide's type system with user-defined types.
class Complex:
def __init__(self, r, i=None):
if type(r) is float and type(i) is float:
self.real = hl.Expr(r)
self.imag = hl.Expr(i)
elif i is not None:
self.real = r
self.imag = i
else:
self.real = r[0]
self.imag = r[1]
def as_tuple(self):
"Convert to a Tuple"
return (self.real, self.imag)
def __add__(self, other):
"Complex addition"
return Complex(self.real + other.real, self.imag + other.imag)
def __mul__(self, other):
"Complex multiplication"
return Complex(self.real * other.real - self.imag * other.imag,
self.real * other.imag + self.imag * other.real)
def __getitem__(self, idx):
return (self.real, self.imag)[idx]
def __len__(self):
return 2
def magnitude(self):
"Complex magnitude"
return (self.real * self.real) + (self.imag * self.imag)
# Other complex operators would go here. The above are
# sufficient for this example.
# Let's use the Complex struct to compute a Mandelbrot set.
mandelbrot = hl.Func()
# The initial complex value corresponding to an x, y coordinate
# in our hl.Func.
initial = Complex(x / 15.0 - 2.5, y / 6.0 - 2.0)
# Pure definition.
t = hl.Var("t")
mandelbrot[x, y, t] = Complex(0.0, 0.0)
# We'll use an update definition to take 12 steps.
r = hl.RDom([(1, 12)])
current = Complex(mandelbrot[x, y, r - 1])
# The following line uses the complex multiplication and
# addition we defined above.
mandelbrot[x, y, r] = (Complex(current * current) + initial)
# We'll use another tuple reduction to compute the iteration
# number where the value first escapes a circle of radius 4.
# This can be expressed as an hl.argmin of a boolean - we want
# the index of the first time the given boolean expression is
# false (we consider false to be less than true). The argmax
# would return the index of the first time the expression is
# true.
escape_condition = Complex(mandelbrot[x, y, r]).magnitude() < 16.0
first_escape = hl.argmin(escape_condition)
assert type(first_escape) is tuple
# We only want the index, not the value, but hl.argmin returns
# both, so we'll index the hl.argmin Tuple expression using
# square brackets to get the hl.Expr representing the index.
escape = hl.Func()
escape[x, y] = first_escape[0]
# Realize the pipeline and print the result as ascii art.
result = escape.realize([61, 25])
assert result.type() == hl.Int(32)
code = " .:-~*={&%#@"
for yy in range(result.height()):
for xx in range(result.width()):
index = result[xx, yy]
if index < len(code):
print("%c" % code[index], end="")
else:
pass # is lesson 13 cpp version buggy ?
print("")
print("Success!")
return 0
def test_target():
# Target("") should be exactly like get_host_target().
t1 = hl.get_host_target()
t2 = hl.Target("")
assert t1 == t2, "Default ctor failure"
assert t1.supported()
# to_string roundtripping
t1 = hl.Target()
ts = t1.to_string()
assert ts == "arch_unknown-0-os_unknown"
# Note, this should *not* validate, since validate_target_string
# now returns false if any of arch-bits-os are undefined
assert not hl.Target.validate_target_string(ts)
# Don't attempt to roundtrip this: trying to create
# a Target with unknown portions will now assert-fail.
#
# t2 = hl.Target(ts)
# assert t2 == t1
# repr() and str()
assert str(t1) == "arch_unknown-0-os_unknown"
assert repr(t1) == "<halide.Target arch_unknown-0-os_unknown>"
assert t1.os == hl.TargetOS.OSUnknown
assert t1.arch == hl.TargetArch.ArchUnknown
assert t1.bits == 0
# Full specification round-trip:
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32,
[hl.TargetFeature.SSE41])
ts = t1.to_string()
assert ts == "x86-32-linux-sse41"
assert hl.Target.validate_target_string(ts)
# Full specification (without features) round-trip:
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32)
ts = t1.to_string()
assert ts == "x86-32-linux"
assert hl.Target.validate_target_string(ts)
# Full specification round-trip, crazy features
t1 = hl.Target(hl.TargetOS.Android, hl.TargetArch.ARM, 32, [
hl.TargetFeature.JIT, hl.TargetFeature.SSE41, hl.TargetFeature.AVX,
hl.TargetFeature.AVX2, hl.TargetFeature.CUDA, hl.TargetFeature.OpenCL,
hl.TargetFeature.OpenGL, hl.TargetFeature.OpenGLCompute,
hl.TargetFeature.Debug
])
ts = t1.to_string()
assert ts == "arm-32-android-avx-avx2-cuda-debug-jit-opencl-opengl-openglcompute-sse41"
assert hl.Target.validate_target_string(ts)
# Expected failures:
ts = "host-unknowntoken"
assert not hl.Target.validate_target_string(ts)
ts = "x86-23"
assert not hl.Target.validate_target_string(ts)
# bits == 0 is allowed only if arch_unknown and os_unknown are specified,
# and no features are set
ts = "x86-0"
assert not hl.Target.validate_target_string(ts)
ts = "0-arch_unknown-os_unknown-sse41"
assert not hl.Target.validate_target_string(ts)
# "host" is only supported as the first token
ts = "opencl-host"
assert not hl.Target.validate_target_string(ts)
# set_feature
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32,
[hl.TargetFeature.SSE41])
assert t1.has_feature(hl.TargetFeature.SSE41)
assert not t1.has_feature(hl.TargetFeature.AVX)
t1.set_feature(hl.TargetFeature.AVX)
t1.set_feature(hl.TargetFeature.SSE41, False)
assert t1.has_feature(hl.TargetFeature.AVX)
assert not t1.has_feature(hl.TargetFeature.SSE41)
# set_features
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32,
[hl.TargetFeature.SSE41])
assert t1.has_feature(hl.TargetFeature.SSE41)
assert not t1.has_feature(hl.TargetFeature.AVX)
t1.set_features([hl.TargetFeature.SSE41], False)
t1.set_features([hl.TargetFeature.AVX, hl.TargetFeature.AVX2], True)
assert t1.has_feature(hl.TargetFeature.AVX)
assert t1.has_feature(hl.TargetFeature.AVX2)
assert not t1.has_feature(hl.TargetFeature.SSE41)
# with_feature
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32,
[hl.TargetFeature.SSE41])
t2 = t1.with_feature(hl.TargetFeature.NoAsserts).with_feature(
hl.TargetFeature.NoBoundsQuery)
ts = t2.to_string()
assert ts == "x86-32-linux-no_asserts-no_bounds_query-sse41"
# without_feature
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32,
[hl.TargetFeature.SSE41, hl.TargetFeature.NoAsserts])
# Note that NoBoundsQuery wasn't set here, so 'without' is a no-op
t2 = t1.without_feature(hl.TargetFeature.NoAsserts).without_feature(
hl.TargetFeature.NoBoundsQuery)
ts = t2.to_string()
assert ts == "x86-32-linux-sse41"
# natural_vector_size
# SSE4.1 is 16 bytes wide
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32,
[hl.TargetFeature.SSE41])
assert t1.natural_vector_size(hl.UInt(8)) == 16
assert t1.natural_vector_size(hl.Int(16)) == 8
assert t1.natural_vector_size(hl.UInt(32)) == 4
assert t1.natural_vector_size(hl.Float(32)) == 4
# has_gpu_feature
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32,
[hl.TargetFeature.OpenCL])
t2 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32, [])
assert t1.has_gpu_feature()
assert not t2.has_gpu_feature()
# has_large_buffers & maximum_buffer_size
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 64,
[hl.TargetFeature.LargeBuffers])
t2 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 64, [])
assert t1.has_large_buffers()
assert t1.maximum_buffer_size() == 9223372036854775807
assert not t2.has_large_buffers()
assert t2.maximum_buffer_size() == 2147483647
# supports_device_api
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 64,
[hl.TargetFeature.CUDA])
t2 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 64)
assert t1.supports_device_api(hl.DeviceAPI.CUDA)
assert not t2.supports_device_api(hl.DeviceAPI.CUDA)
# supports_type (deprecated version)
t1 = hl.Target(hl.TargetOS.OSX, hl.TargetArch.X86, 64,
[hl.TargetFeature.Metal])
t2 = hl.Target(hl.TargetOS.OSX, hl.TargetArch.X86, 64)
assert not t1.supports_type(hl.Float(64))
assert t2.supports_type(hl.Float(64))
# supports_type (preferred version)
t1 = hl.Target(hl.TargetOS.OSX, hl.TargetArch.X86, 64,
[hl.TargetFeature.Metal])
t2 = hl.Target(hl.TargetOS.OSX, hl.TargetArch.X86, 64)
assert not t1.supports_type(hl.Float(64), hl.DeviceAPI.Metal)
assert not t2.supports_type(hl.Float(64), hl.DeviceAPI.Metal)
# target_feature_for_device_api
assert hl.target_feature_for_device_api(
hl.DeviceAPI.OpenCL) == hl.TargetFeature.OpenCL
# with_feature with non-convertible lists
try:
t1 = hl.Target(hl.TargetOS.Linux, hl.TargetArch.X86, 32,
["this is a string"])
except TypeError as e:
assert "incompatible constructor arguments" in str(e)
else:
assert False, 'Did not see expected exception!'
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 bilateral_filter(input, width, height):
print(' bilateral_filter')
k = hl.Buffer(hl.Float(32), [7, 7], "gauss_kernel")
k.translate([-3, -3])
weights = hl.Func("bilateral_weights")
total_weights = hl.Func("bilateral_total_weights")
bilateral = hl.Func("bilateral")
output = hl.Func("bilateral_filter_output")
x, y, dx, dy, c = hl.Var("x"), hl.Var("y"), hl.Var("dx"), hl.Var("dy"), hl.Var("c")
rdom = hl.RDom([(-3, 7), (-3, 7)])
k.fill(0)
k[-3, -3] = 0.000690
k[-2, -3] = 0.002646
k[-1, -3] = 0.005923
k[0, -3] = 0.007748
k[1, -3] = 0.005923
k[2, -3] = 0.002646
k[3, -3] = 0.000690
k[-3, -2] = 0.002646
k[-2, -2] = 0.010149
k[-1, -2] = 0.022718
k[0, -2] = 0.029715
k[1, -2] = 0.022718
k[2, -2] = 0.010149
k[3, -2] = 0.002646
k[-3, -1] = 0.005923
k[-2, -1] = 0.022718
k[-1, -1] = 0.050855
k[0, -1] = 0.066517
k[1, -1] = 0.050855
k[2, -1] = 0.022718
k[3, -1] = 0.005923
k[-3, 0] = 0.007748
k[-2, 0] = 0.029715
k[-1, 0] = 0.066517
k[0, 0] = 0.087001
k[1, 0] = 0.066517
k[2, 0] = 0.029715
k[3, 0] = 0.007748
k[-3, 1] = 0.005923
k[-2, 1] = 0.022718
k[-1, 1] = 0.050855
k[0, 1] = 0.066517
k[1, 1] = 0.050855
k[2, 1] = 0.022718
k[3, 1] = 0.005923
k[-3, 2] = 0.002646
k[-2, 2] = 0.010149
k[-1, 2] = 0.022718
k[0, 2] = 0.029715
k[1, 2] = 0.022718
k[2, 2] = 0.010149
k[3, 2] = 0.002646
k[-3, 3] = 0.000690
k[-2, 3] = 0.002646
k[-1, 3] = 0.005923
k[0, 3] = 0.007748
k[1, 3] = 0.005923
k[2, 3] = 0.002646
k[3, 3] = 0.000690
input_mirror = hl.BoundaryConditions.mirror_interior(input, [(0, width), (0, height)])
dist = hl.cast(hl.Float(32),
hl.cast(hl.Int(32), input_mirror[x, y, c]) - hl.cast(hl.Int(32), input_mirror[x + dx, y + dy, c]))
sig2 = 100
threshold = 25000
score = hl.select(hl.abs(input_mirror[x + dx, y + dy, c]) > threshold, 0, hl.exp(-dist * dist / sig2))
weights[dx, dy, x, y, c] = k[dx, dy] * score
total_weights[x, y, c] = hl.sum(weights[rdom.x, rdom.y, x, y, c])
bilateral[x, y, c] = hl.sum(input_mirror[x + rdom.x, y + rdom.y, c] * weights[rdom.x, rdom.y, x, y, c]) / \
total_weights[x, y, c]
output[x, y, c] = hl.cast(hl.Float(32), input[x, y, c])
output[x, y, 1] = bilateral[x, y, 1]
output[x, y, 2] = bilateral[x, y, 2]
weights.compute_at(output, y).vectorize(x, 16)
output.compute_root().parallel(y).vectorize(x, 16)
output.update(0).parallel(y).vectorize(x, 16)
output.update(1).parallel(y).vectorize(x, 16)
return output
