halide - 使用增强型生成器的双边网格生成器类

Question

我正在尝试使用增强的生成器类重新实现双边网格示例(例如使用 schedule() 和 generate()。 但我在尝试编译代码时遇到错误。

g++ -std=c++11 -I ../../include/ -I ../../tools/ -I ../../apps/support/ -g -  fno-rtti bilateral_grid_generator.cpp ../../lib/libHalide.a ../../tools/GenGen.cpp -o bin/bilateral_grid_exec  -ldl -lpthread -lz
bin/bilateral_grid_exec -o ./bin  target=host 
Generator bilateral_grid has base_path ./bin/bilateral_grid
Internal error at /home/xxx/Projects/Halide/src/Generator.cpp:966 triggered by user code at /usr/include/c++/4.8/functional:2057:
Condition failed: generator
make: *** [bin/bilateral_grid.a] Aborted (core dumped)

看来我没有将RDom和GeneratorParam的定义放在正确的位置。由于 r.x 和 r.y 在 schedule() 和 generate() 中都使用，我想我应该把它作为类(class)成员。应该采取什么措施来解决这个问题？

这是我编写的代码。

class BilateralGrid : public Halide::Generator<BilateralGrid> {
public:
GeneratorParam<int>   s_sigma{"s_sigma", 8};

//ImageParam            input{Float(32), 2, "input"};
//Param<float>          r_sigma{"r_sigma"};

Input<Buffer<float>>  input{"input", 2};
Input<float>          r_sigma{"r_sigma"};

Output<Buffer<float>> output{"output", 2};

// Algorithm Description
void generate() {
    //int s_sigma = 8;
    // Add a boundary condition
    clamped(x,y) = BoundaryConditions::repeat_edge(input)(x,y);

    // Construct the bilateral grid
    Expr val = clamped(x * s_sigma + r.x - s_sigma/2, y * s_sigma + r.y - s_sigma/2);
    val = clamp(val, 0.0f, 1.0f);

    Expr zi = cast<int>(val * (1.0f/r_sigma) + 0.5f);

    // Histogram
    histogram(x, y, z, c) = 0.0f;
    histogram(x, y, zi, c) += select(c == 0, val, 1.0f);

    // Blur the grid using a five-tap filter
    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     = clamp(input(x, y), 0.0f, 1.0f);
    Expr zv = val * (1.0f/r_sigma);
    zi      = cast<int>(zv);
    Expr zf = zv - zi;
    Expr xf = cast<float>(x % s_sigma) / s_sigma;
    Expr yf = cast<float>(y % s_sigma) / s_sigma;
    Expr xi = x/s_sigma;
    Expr yi = y/s_sigma;

    interpolated(x, y, c) =
        lerp(lerp(lerp(blury(xi, yi, zi, c), blury(xi+1, yi, zi, c), xf),
                  lerp(blury(xi, yi+1, zi, c), blury(xi+1, yi+1, zi, c), xf), yf),
             lerp(lerp(blury(xi, yi, zi+1, c), blury(xi+1, yi, zi+1, c), xf),
                  lerp(blury(xi, yi+1, zi+1, c), blury(xi+1, yi+1, zi+1, c), xf), yf), zf);

    // Normalize and return the output.
    bilateral_grid(x, y) = interpolated(x, y, 0)/interpolated(x, y, 1);
    output(x,y)          = bilateral_grid(x,y);

}

// Scheduling
void schedule() { 
    // int s_sigma = 8;
    if (get_target().has_gpu_feature()) {
        // The GPU schedule
        Var xi{"xi"}, yi{"yi"}, zi{"zi"};

        // Schedule blurz in 8x8 tiles. This is a tile in
        // grid-space, which means it represents something like
        // 64x64 pixels in the input (if s_sigma is 8).
        blurz.compute_root().reorder(c, z, x, y).gpu_tile(x, y, xi, yi, 8, 8);

        // Schedule histogram to happen per-tile of blurz, with
        // intermediate results in shared memory. This means histogram
        // and blurz makes a three-stage kernel:
        // 1) Zero out the 8x8 set of histograms
        // 2) Compute those histogram by iterating over lots of the input image
        // 3) Blur the set of histograms in z
        histogram.reorder(c, z, x, y).compute_at(blurz, x).gpu_threads(x, y);
        histogram.update().reorder(c, r.x, r.y, x, y).gpu_threads(x, y).unroll(c);

        // An alternative schedule for histogram that doesn't use shared memory:
        // histogram.compute_root().reorder(c, z, x, y).gpu_tile(x, y, xi, yi, 8, 8);
        // histogram.update().reorder(c, r.x, r.y, x, y).gpu_tile(x, y, xi, yi, 8, 8).unroll(c);

        // Schedule the remaining blurs and the sampling at the end similarly.
        blurx.compute_root().gpu_tile(x, y, z, xi, yi, zi, 8, 8, 1);
        blury.compute_root().gpu_tile(x, y, z, xi, yi, zi, 8, 8, 1);
        bilateral_grid.compute_root().gpu_tile(x, y, xi, yi, s_sigma, s_sigma);
    } else {
        // The CPU schedule.
        blurz.compute_root().reorder(c, z, x, y).parallel(y).vectorize(x, 8).unroll(c);
        histogram.compute_at(blurz, y);
        histogram.update().reorder(c, r.x, r.y, x, y).unroll(c);
        blurx.compute_root().reorder(c, x, y, z).parallel(z).vectorize(x, 8).unroll(c);
        blury.compute_root().reorder(c, x, y, z).parallel(z).vectorize(x, 8).unroll(c);
        bilateral_grid.compute_root().parallel(y).vectorize(x, 8);
    }
}

Func clamped{"clamped"}, histogram{"histogram"};
Func bilateral_grid{"bilateral_grid"};
Func blurx{"blurx"}, blury{"blury"}, blurz{"blurz"}, interpolated{"interpolated"};
Var x{"x"}, y{"y"}, z{"z"}, c{"c"};
RDom r{0, s_sigma, 0, s_sigma};

};

//Halide::RegisterGenerator<BilateralGrid> register_me{"bilateral_grid"};
HALIDE_REGISTER_GENERATOR(BilateralGrid, "bilateral_grid");

}  // namespace

Answer 1

这里的错误很微妙，令人遗憾的是，当前的断言失败消息没有帮助。

这里的问题是这段代码使用 GeneratorParam (s_sigma) 初始化成员变量-RDom (r)，但是 GeneratorParam此时可能尚未设置其最终值。一般来说，访问GeneratorParam (或 ScheduleParam )在 generate() 之前调用方法会产生这样的断言。

这是为什么呢？让我们看看典型构建系统中生成器的创建和初始化方式:

1. GenGen.cpp 创建 Generator 的 C++ 类的实例；自然地，这会按照声明的顺序执行其 C++ 构造函数以及所有成员变量的 C++ 构造函数。
2. GenGen.cpp 使用命令行上提供的参数来覆盖 GeneratorParams 的默认值。例如，如果您使用 bin/bilateral_grid_exec -o ./bin target=host s_sigma=7 调用了生成器，默认值(8)存储在s_sigma中将替换为 7。
3. GenGen.cpp 调用 generate() ，然后schedule() ，然后将结果编译为 .o(或 .a 等)。

那么你为什么会看到这个断言呢？这段代码中发生的情况是，在上面的步骤 1 中， r 的 ctor正在步骤 1 中运行...但 r 的 ctor 的参数读取 s_sigma 的当前值，它有一个默认值 (8)，但不一定是构建文件指定的值。如果我们允许在没有断言的情况下进行此读取，则 s_sigma 可能会得到不一致的值。在生成器的不同部分。

您可以通过将 RDom 的初始化推迟到 generate() 来解决此问题方法:

class BilateralGrid : public Halide::Generator<BilateralGrid> { public: GeneratorParam<int> s_sigma{"s_sigma", 8}; ... void generate() { r = RDom(0, s_sigma, 0, s_sigma); ... } ... private: RDom r; };

(显然，断言失败需要更有用的错误消息；我将修改代码来做到这一点。)