我正在学习Halide，我正在努力调度部分。我试图让Halide做与算法的手动编码实现相同的事情。我不想对它进行并行化，而是对它进行矢量化，但首先我想了解如何使Halide做一个简单的滑动窗口。我尝试了不同的东西：
但我不能让它产生类似的东西。它归结为均值滤波器的简单变化。那么，我如何安排Halide代码实际执行像原始代码一样的滑动窗口？
这是代码：
void lmrCPU(const cv::Mat& image, std::vector<uint16_t>& vertSum, int xMin, int xMax, int yMin, int yMax, int lmrSize, cv::Mat& lmr ) {
int lmrWidth = 2*lmrSize + 1;
int area = lmrWidth*lmrWidth;
vertSum.resize(image.cols);
// Zero contents of vertSum
memset(vertSum.data(), 0, vertSum.size()*sizeof(uint16_t));
for (int yy = yMin; yy < yMin + lmrWidth; ++yy)
{
for (int x=0; x<image.cols; x++) vertSum[x] += unsigned(image.ptr<uint8_t>(yy)[x]);
}
for (int y = yMin; y <= yMax; y++)
{
if (y > yMin + lmrSize && y <= yMax - lmrSize)
{
for (int x = 0; x < image.cols; x++)
vertSum[x] += unsigned(image.at<uint8_t>(y + lmrSize, x)) - unsigned(image.at<uint8_t>(y - lmrSize - 1, x));
}
unsigned sum = 0;
int xx, x, xxBack;
for (xx = xMin; xx < xMin + lmrWidth; xx++) sum += vertSum[xx];
for (x = xMin; x < xMin + lmrSize; x++)
lmr.at<int>(y, x) = int(image.at<uint8_t>(y, x)) * area - sum;
sum -= vertSum[xMin + 2*lmrSize]; // take off ready for next loop
for (x = xMin + lmrSize, xxBack = x - lmrSize, xx = x + lmrSize; x < xMax - lmrSize; x++, xx++, xxBack++)
{
sum += vertSum[xx];
lmr.at<int>(y, x) = int(image.at<uint8_t>(y,x))*area - sum;
sum -= vertSum[xxBack];
}
sum += vertSum[xx];
for ( ; x <= xMax; x++)
lmr.at<int>(y, x) = int(image.at<uint8_t>(y,x))*area - sum;
}
}
#include "Halide.h"
namespace {
class LMR : public Halide::Generator<LMR> {
public:
ImageParam input{UInt(8), 2, "input"};
Param<int32_t> xMin{"xMin"}, xMax{"xMax"};
Param<int32_t> yMin{"yMin"}, yMax{"yMax"};
Param<int32_t> lmrSize{"lmrSize"};
Var x{"x"}, y{"y"};
Func build() {
auto lmrWidth = 2*lmrSize + 1;
auto area = lmrWidth*lmrWidth;
Halide::Func input_int32 ("input_int32");
input_int32(x, y) = Halide::cast<int32_t>(input(x, y));
Halide::Func input_uint16 ("input_uint16");
input_uint16(x, y) = Halide::cast<uint16_t>(input(x, y));
Halide::Expr clamped_x = Halide::clamp(x, xMin, xMax);
Halide::Expr clamped_y = Halide::clamp(y, yMin, yMax);
Halide::Expr lmr_x = Halide::clamp(x, xMin+lmrSize, xMax-lmrSize);
Halide::Expr lmr_y = Halide::clamp(y, yMin+lmrSize, yMax-lmrSize);
Halide::RDom box (-lmrSize, lmrWidth, "box");
Halide::Func vertSum ("vertSum");
vertSum(x, y) = Halide::undef<uint16_t>();
{
Halide::RDom ry (yMin+lmrSize+1, yMax-yMin-2*lmrSize, "ry");
vertSum(x, yMin+lmrSize) = Halide::cast<uint16_t>(0);//Halide::sum(input_uint16(x, yMin+lmrSize+box), "sum_y");
vertSum(x, yMin+lmrSize) += input(x, yMin+lmrSize+box);
vertSum(x, ry) = vertSum(x, ry-1) + input_uint16(x, ry+lmrSize) - input_uint16(x, ry-1-lmrSize);
}
Halide::Func sumLmr ("sumLmr");
sumLmr(x, y) = Halide::undef<uint16_t>();
{
Halide::RDom rx (xMin+lmrSize+1, xMax-xMin-2*lmrSize, "rx");
sumLmr(xMin+lmrSize, y) = Halide::cast<uint16_t>(0);//Halide::sum(vertSum(xMin+lmrSize+box, y), "sum_x");
sumLmr(xMin+lmrSize, y) += vertSum(xMin+lmrSize+box, y);
sumLmr(rx, y) = sumLmr(rx-1, y) + vertSum(rx+lmrSize, y) - vertSum(rx-1-lmrSize, y);
}
Halide::Func lmr ("lmr");
lmr(x, y) = input_int32(clamped_x, clamped_y)*area - Halide::cast<int32_t>(sumLmr(lmr_x, lmr_y));
vertSum
.fold_storage(y, 1)
.store_root()
.compute_at(lmr, y);
sumLmr
.fold_storage(x, 1)
.store_at(lmr, y)
.compute_at(lmr, x);
return lmr;
}
};
HALIDE_REGISTER_GENERATOR(LMR, "lmr")
}
这是lmr.print_loop_nest();
store vertSum:
produce lmr:
for y:
produce vertSum:
for y:
for x:
vertSum(...) = ...
for x:
vertSum(...) = ...
for x:
for box:
vertSum(...) = ...
for x:
for ry:
vertSum(...) = ...
consume vertSum:
store sumLmr:
for x:
produce sumLmr:
for y:
for x:
sumLmr(...) = ...
for y:
sumLmr(...) = ...
for y:
for box:
sumLmr(...) = ...
for y:
for rx:
sumLmr(...) = ...
consume sumLmr:
lmr(...) = ...
答案 0 :(得分：2)
简洁而基本的，如果有点简洁，回答你的问题是＆＃34;为了使用滑动窗口，计算的存储和计算必须安排在不同的循环级别。＆＃34; （这是在计划中使用compute_at然后store_at完成的。）实际上，将存储从计算中进一步移出，因此它将具有比一个计算单位更大的边界。然后，如果满足某组约束，例如两个大小窗口的幂，Halide将自动推断并生成滑动窗口。使用fold_storage指令可以使滑动窗口更大。
测试： Exporting Plots section of the User Guide 和： https://github.com/halide/Halide/blob/master/test/correctness/sliding_window.cpp
演示拆分存储/计算调度。
如果您使用sum内联缩减功能，我不确定您是否拥有执行此计划所需的所有可用名称。
答案 1 :(得分：0)
找出问题所在。 Halide不知道如何使用可变窗口大小优化代码。通过对lmrWidth进行硬编码，它会生成正确的代码。最终代码如下所示：
#include "Halide.h"
namespace {
class LMR : public Halide::Generator<LMR> {
public:
ImageParam input{UInt(8), 2, "input"};
Param<int32_t> xMin{"xMin"}, xMax{"xMax"};
Param<int32_t> yMin{"yMin"}, yMax{"yMax"};
Param<int32_t> lmrSize{"lmrSize"};
Var x{"x"}, y{"y"};
Func build() {
auto lmrWidth = 11;//2*lmrSize + 1;
auto area = lmrWidth*lmrWidth;
Halide::Expr clamped_x = Halide::clamp(x, xMin, xMax);
Halide::Expr clamped_y = Halide::clamp(y, yMin, yMax);
Halide::Expr lmr_x = Halide::clamp(x, xMin+lmrSize, xMax-lmrSize);
Halide::Expr lmr_y = Halide::clamp(y, yMin+lmrSize, yMax-lmrSize);
Halide::Func sum_y ("sum_y");
{
Halide::Expr sum = input(x, y-lmrSize);
for (int i=1;i<lmrWidth;i++) {
sum = sum + Halide::cast<uint16_t>(input(x, y-lmrSize+i));
}
sum_y(x, y) = sum;
}
Halide::Func sum_x ("sum_x");
{
Halide::Expr sum = sum_y(x-lmrSize, y);
for (int i=1;i<lmrWidth;i++) {
sum = sum + sum_y(x-lmrSize+i, y);
}
sum_x(x, y) = sum;
}
Halide::Func sumLmr("sumLmr");
sumLmr(x, y) = sum_x(x, y);
Halide::Func output("output");
output(x, y) = Halide::cast<int32_t>(input(clamped_x, clamped_y))*area - Halide::cast<int32_t>(sumLmr(lmr_x, lmr_y));
sum_x.compute_root();
sum_y.compute_at(sum_x, y);
output.vectorize(x, 16);
return output;
}
};
HALIDE_REGISTER_GENERATOR(LMR, "lmr")
}