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#include "Halide.h"
#include <iostream>
#include <stdio.h>
using namespace Halide;
using namespace Halide::ConciseCasts;
using namespace Halide::Internal;
int main(int arch, char **argv) {
Halide::Target target = get_jit_target_from_environment();
if (target.has_feature(Target::Vulkan) && ((target.os == Target::IOS) || target.os == Target::OSX)) {
printf("[SKIP] Skipping test for Vulkan on iOS/OSX (MoltenVK fails to convert max/min intrinsics correctly)!\n");
return 0;
}
const int W = 256, H = 256;
Buffer<uint8_t> in(W, H);
// Set up the input.
for (int y = 0; y < H; y++) {
for (int x = 0; x < W; x++) {
in(x, y) = rand() & 0xff;
}
}
// Define a convolution kernel, and its sum.
Buffer<int8_t> kernel(3, 3);
kernel.set_min(-1, -1);
for (int y = -1; y <= 1; y++) {
for (int x = -1; x <= 1; x++) {
kernel(x, y) = rand() % 8 - 4;
}
}
Var x("x"), y("y"), xi("xi"), yi("yi");
RDom r(-1, 3, -1, 3);
// Boundary condition.
Func input = BoundaryConditions::repeat_edge(in);
input.compute_root();
// Test a widening reduction, followed by a narrowing.
{
Func f;
f(x, y) = u8_sat(sum(i16(input(x + r.x, y + r.y)) * kernel(r.x, r.y)) / 16);
// Schedule.
if (target.has_gpu_feature()) {
f.gpu_tile(x, y, xi, yi, 16, 16);
} else if (target.has_feature(Target::HVX)) {
f.hexagon().vectorize(x, 128);
} else {
f.vectorize(x, target.natural_vector_size<uint8_t>());
}
// Run the pipeline and verify the results are correct.
Buffer<uint8_t> out = f.realize({W, H}, target);
for (int y = 1; y < H - 1; y++) {
for (int x = 1; x < W - 1; x++) {
int16_t correct = 0;
for (int ry = -1; ry <= 1; ry++) {
for (int rx = -1; rx <= 1; rx++) {
correct += static_cast<int16_t>(in(x + rx, y + ry)) * kernel(rx, ry);
}
}
correct = std::min(std::max(correct / 16, 0), 255);
if (correct != out(x, y)) {
std::cout << "out(" << x << ", " << y << ") = " << (int)out(x, y) << " instead of " << correct << "\n";
return 1;
}
}
}
}
// Test a tuple reduction with widening, followed by narrowing the result.
{
Func f;
f(x, y) = {i16(0), i8(0)};
f(x, y) = {
f(x, y)[0] + i16(input(x + r.x, y + r.y)) * kernel(r.x, r.y),
f(x, y)[1] + kernel(r.x, r.y),
};
Func g;
g(x, y) = u8_sat((f(x, y)[0] + f(x, y)[1]) / 16);
// Schedule.
if (target.has_gpu_feature()) {
g.gpu_tile(x, y, xi, yi, 16, 16);
} else if (target.has_feature(Target::HVX)) {
g.hexagon().vectorize(x, 128);
} else {
g.vectorize(x, target.natural_vector_size<uint8_t>());
}
// Run the pipeline and verify the results are correct.
Buffer<uint8_t> out = g.realize({W, H}, target);
for (int y = 1; y < H - 1; y++) {
for (int x = 1; x < W - 1; x++) {
int16_t correct = 0;
for (int ry = -1; ry <= 1; ry++) {
for (int rx = -1; rx <= 1; rx++) {
correct += static_cast<int16_t>(in(x + rx, y + ry)) * kernel(rx, ry);
correct += kernel(rx, ry);
}
}
correct = std::min(std::max(correct / 16, 0), 255);
if (correct != out(x, y)) {
std::cout << "out(" << x << ", " << y << ") = " << (int)out(x, y) << " instead of " << correct << "\n";
return 1;
}
}
}
}
// Test a widening, followed by a narrowing reduction with an
// unaligned output. This triggered a bug in EliminateInterleaves
// on Hexagon.
{
Func f;
f(x, y) = i16(input(x, y));
Func g;
g(x, y) = u8_sat((f(x, y) + f(x + 1, y)) / 2);
// Schedule.
if (target.has_gpu_feature()) {
g.gpu_tile(x, y, xi, yi, 16, 16);
} else if (target.has_feature(Target::HVX)) {
g.hexagon().vectorize(x, 128);
f.compute_at(g, y).vectorize(x, 128, TailStrategy::RoundUp);
} else {
g.vectorize(x, target.natural_vector_size<uint8_t>());
}
g.output_buffer().dim(0).set_min(0).set_extent(W - 2);
g.output_buffer().dim(1).set_min(0).set_extent(H);
// Run the pipeline and verify the results are correct.
Buffer<uint8_t> out = g.realize({W - 2, H}, target);
for (int y = 1; y < H - 1; y++) {
for (int x = 0; x < W - 3; x++) {
uint8_t correct = (static_cast<int16_t>(in(x, y)) + in(x + 1, y)) / 2;
if (correct != out(x, y)) {
std::cout << "out(" << x << ", " << y << ") = " << (int)out(x, y) << " instead of " << (int)correct << "\n";
return 1;
}
}
}
}
std::cout << "Success!\n";
return 0;
}
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