C++ Func::realize方法代码示例

int main(int argc, char **argv) {
		int i, j;
		Halide::Func black;
		Halide::Func white;
    Halide::Var x, y;
		
    black(x, y) = 0;
		white(x, y) = 254;
    
		Halide::Image<int32_t> output1 = black.realize(800, 600);
		Halide::Image<int32_t> output2 = white.realize(800, 600);

    // Save the output for inspection. It should look like a bright parrot.
    save(output1, "input1.png");
		save(output2, "input2.png");
		//Check to see everything is copacetic
		for( i = 0; i < 800; i ++ )
		{
			for( j = 0; j < 600; j ++ )
				if (output2(i, j) != 254 || output1(i, j) != 0)
				{
					printf("Failure! Failed at (%d, %d)\n", i, j);
					return 1;
				}	
		}
    printf("Success!\n");
    return 0;
}

void prog01()
{
  Halide::Var x;
  Halide::Func init_cond;
  init_cond(x) = 1.0f*x;
  Halide::Image<float_t> input, output;
  input=init_cond.realize(NX);

  Halide::ImageParam inPar(Halide::Float(32), 1, "inPar");
  Halide::Func cell;
  cell(x)=inPar(x)+1;
  
  {
    std::vector<Halide::Argument> arg_vect;
    arg_vect.push_back(Halide::Argument("inPar", true, Halide::Int(32)));
    cell.compile_to_bitcode("stencil-fusion-01.bc", arg_vect, "blur");
  }


  for (int t =0; t < 100000; ++t) {
    inPar.set(input);
    output = cell.realize(NX);
    swap(output,input);
  }

  for (int i = 0; i < NX; ++i) 
    cout << input(i) << " ";
  cout << endl;
}

inline void _autotune_timing_stub(Halide::Func& func) {
    func.compile_jit();

    // TODO: this assumes scalar/non-Tuple outputs - should generalize to a Realization
    Halide::Type out_type = func.output_types()[0];
    buffer_t out_size_buf;
    {
        // Use the Buffer constructor as a helper to set up the buffer_t,
        // but then throw away its allocation which we don't really want.
        Halide::Buffer bufinit(out_type, AUTOTUNE_N);
        out_size_buf = *bufinit.raw_buffer();
        out_size_buf.host = NULL;
    }
    Halide::Buffer out_size(out_type, &out_size_buf);
    assert(out_size.host_ptr() == NULL); // make sure we don't have an allocation

    func.infer_input_bounds(out_size);

    // allocate the real output using the inferred mins + extents
    Halide::Buffer output(  out_type,
                            out_size.extent(0),
                            out_size.extent(1),
                            out_size.extent(2),
                            out_size.extent(3),
                            NULL,
                            "output" );
    output.set_min( out_size.min(0),
                    out_size.min(1),
                    out_size.min(2),
                    out_size.min(3) );

    // re-run input inference on enlarged output buffer
    func.unbind_image_params(); // TODO: iterate to convergence
    func.infer_input_bounds(output);

    timeval t1, t2;
    double rv = 0;
    const unsigned int timeout = AUTOTUNE_LIMIT;
    alarm(timeout);
    for (int i = 0; i < AUTOTUNE_TRIALS; i++) {
      gettimeofday(&t1, NULL);
      func.realize(output);
      gettimeofday(&t2, NULL);
      alarm(0); // disable alarm
      double t = (t2.tv_sec - t1.tv_sec) + (t2.tv_usec - t1.tv_usec)/1000000.0;
      if(i == 0 || t < rv)
        rv = t;
    }
    printf("{\"time\": %.10f}\n", rv);
    exit(0);
}

inline void _autotune_timing_stub(Halide::Func& func) {
    func.compile_jit();
    func.infer_input_bounds(1024,1024);
    timeval t1, t2;
    double rv = 0;
    for (int i = 0; i < 3; i++) {
      gettimeofday(&t1, NULL);
      func.realize(1024,1024);
      gettimeofday(&t2, NULL);
      double t = (t2.tv_sec - t1.tv_sec) + (t2.tv_usec - t1.tv_usec)/1000000.0;
      if(i == 0 || t < rv)
        rv = t;
    }
    printf("{\"time\": %.10f}\n", rv);
    exit(0);
}

void NamedWindow::showImage2D(Halide::Image<uint8_t> im)
{
	static Halide::Func convert("convertToMat2D");
	static Halide::ImageParam ip(Halide::UInt(8), 2);
	static Halide::Var x, y;

	if (!convert.defined())
	{
		convert(x, y) = ip(x, y);
		convert.vectorize(x, 4).parallel(y, 4);
	}

	ip.set(im);
	cv::Mat mat(im.height(), im.width(), CV_8UC1, cv::Scalar(0));
	convert.realize(Halide::Buffer(Halide::UInt(8), im.width(), im.height(), 0, 0, mat.data));
	cv::imshow(name, mat);
}

inline void _autotune_timing_stub(Halide::Func& func) {
    func.compile_jit();
    func.infer_input_bounds(AUTOTUNE_N);
    timeval t1, t2;
    double rv = 0;
    const unsigned int timeout = AUTOTUNE_LIMIT;
    alarm(timeout);
    for (int i = 0; i < AUTOTUNE_TRIALS; i++) {
      gettimeofday(&t1, NULL);
      func.realize(AUTOTUNE_N);
      gettimeofday(&t2, NULL);
      alarm(0); // disable alarm
      double t = (t2.tv_sec - t1.tv_sec) + (t2.tv_usec - t1.tv_usec)/1000000.0;
      if(i == 0 || t < rv)
        rv = t;
    }
    printf("{\"time\": %.10f}\n", rv);
    exit(0);
}

int main(int argc, char **argv) {

    // This program defines a single-stage imaging pipeline that
    // brightens an image.

    // First we'll load the input image we wish to brighten.
    Halide::Image<uint8_t> input = load<uint8_t>("../apps/images/rgb.png");

    // Next we define our Func object that represents our one pipeline
    // stage.
    Halide::Func brighter;

    // Our 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.
    Halide::Var x, y, 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.
    Halide::Expr value = input(x, y, c);

    // Cast it to a floating point value.
    value = Halide::cast<float>(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 * 1.5f;

    // Clamp it to be less than 255, so we don't get overflow when we
    // cast it back to an 8-bit unsigned int.
    value = Halide::min(value, 255.0f);

    // Cast it back to an 8-bit unsigned integer.
    value = Halide::cast<uint8_t>(value);

    // Define the function.
    brighter(x, y, c) = value;

    // The equivalent one-liner to all of the above is:
    //
    // brighter(x, y, c) = Halide::cast<uint8_t>(min(input(x, y, c) * 1.5f, 255));
    //
    // In the shorter version:
    // - I skipped the cast to float, because multiplying by 1.5f does
    //   that automatically.
    // - I also used integer constants in clamp, because they get cast
    //   to match the type of the first argument.
    // - I left the Halide:: off 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 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.
    Halide::Image<uint8_t> output = brighter.realize(input.width(), input.height(), input.channels());

    // Save the output for inspection. It should look like a bright parrot.
    save(output, "brighter.png");

    printf("Success!\n");
    return 0;
}

void func_realize3(h::Func &that, h::Buffer dst, const h::Target &target = h::Target())
{
    that.realize(dst, target);
    return;
}

void func_realize2(h::Func &that, h::Realization dst, const h::Target &target = h::Target())
{
    that.realize(dst, target);
    return;
}

h::Realization func_realize1(h::Func &that, int x_size=0, int y_size=0, int z_size=0, int w_size=0,
                             const h::Target &target = h::Target())
{
    return that.realize(x_size, y_size, z_size, w_size, target);
}

int main(int argc, char **argv) {

    // This program defines a single-stage imaging pipeline that
    // outputs a grayscale diagonal gradient.

    // A 'Func' object represents a pipeline stage. It's a pure
    // function that defines what value each pixel should have. You
    // can think of it as a computed image.
    Halide::Func gradient;

    // Var objects are names to use as variables in the definition of
    // a Func. They have no meaning by themselves.
    Halide::Var x, y;

    // We typically use Vars named 'x' and 'y' to correspond to the x
    // and y axes of an image, and we write them in that order. If
    // you're used to thinking of images as having rows and columns,
    // then x is the column index, and y is the row index.

    // Funcs are defined at any integer coordinate of its variables as
    // an Expr in terms of those variables and other functions.
    // Here, we'll define an Expr which has the value x + y. Vars have
    // appropriate operator overloading so that expressions like
    // 'x + y' become 'Expr' objects.
    Halide::Expr e = x + y;

    // Now we'll add a definition for the Func object. At pixel x, y,
    // the image will have the value of the Expr e. On the left hand
    // side we have the Func we're defining and some Vars. On the right
    // hand side we have some Expr object that uses those same Vars.
    gradient(x, y) = e;

    // This is the same as writing:
    //
    //   gradient(x, y) = x + y;
    //
    // which is the more common form, but we are showing the
    // intermediate Expr here for completeness.

    // That line of code defined the Func, but it didn't actually
    // compute the output image yet. At this stage it's just Funcs,
    // Exprs, and Vars in memory, representing the structure of our
    // imaging pipeline. We're meta-programming. This C++ program is
    // constructing a Halide program in memory. Actually computing
    // pixel data comes next.

    // Now we 'realize' the Func, which JIT compiles some code that
    // implements the pipeline we've defined, and then runs it.  We
    // also need to tell Halide the domain over which to evaluate the
    // Func, which determines the range of x and y above, and the
    // resolution of the output image. Halide.h also provides a basic
    // templatized Image type we can use. We'll make an 800 x 600
    // image.
    Halide::Image<int32_t> output = gradient.realize(800, 600);

    // Halide does type inference for you. Var objects represent
    // 32-bit integers, so the Expr object 'x + y' also represents a
    // 32-bit integer, and so 'gradient' defines a 32-bit image, and
    // so we got a 32-bit signed integer image out when we call
    // 'realize'. Halide types and type-casting rules are equivalent
    // to C.

    // Let's check everything worked, and we got the output we were
    // expecting:
    for (int j = 0; j < output.height(); j++) {
        for (int i = 0; i < output.width(); i++) {
            // We can access a pixel of an Image object using similar
            // syntax to defining and using functions.
            if (output(i, j) != i + j) {
                printf("Something went wrong!\n"
                       "Pixel %d, %d was supposed to be %d, but instead it's %d\n",
                       i, j, i+j, output(i, j));
                return -1;
            }
        }
    }

    // Everything worked! We defined a Func, then called 'realize' on
    // it to generate and run machine code that produced an Image.
    printf("Success!\n");

    return 0;
}