rocm-systems/runtime/hsa-ext-aql-profile/test/simple_convolution/simple_convolution.cpp

/******************************************************************************

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#include "helper_funcs.hpp"
#include "simple_convolution.h"

SimpleConvolution::SimpleConvolution() {
  width_ = 64;
  height_ = 64;
  mask_width_ = 3;
  mask_height_ = mask_width_;

  if (!isPowerOf2(width_)) {
    width_ = roundToPowerOf2(width_);
  }

  if (!isPowerOf2(height_)) {
    height_ = roundToPowerOf2(height_);
  }

  if (!(mask_width_ % 2)) {
    mask_width_++;
  }

  if (!(mask_height_ % 2)) {
    mask_height_++;
  }

  if (width_ * height_ < 256) {
    width_ = 64;
    height_ = 64;
  }

  const uint32_t input_size_bytes = width_ * height_ * sizeof(uint32_t);
  const uint32_t mask_size_bytes = mask_width_ * mask_height_ * sizeof(float);

  set_sys_descr(KERNARG_DES_ID, sizeof(kernel_args_t));
  set_sys_descr(INPUT_DES_ID, input_size_bytes);
  set_sys_descr(OUTPUT_DES_ID, input_size_bytes);
  set_local_descr(LOCAL_DES_ID, input_size_bytes);
  set_sys_descr(MASK_DES_ID, mask_size_bytes);
  set_sys_descr(REFOUT_DES_ID, input_size_bytes);
}

void SimpleConvolution::init() {
  std::cout << "SimpleConvolution::init :" << std::endl;

  mem_descr_t input_des = get_descr(INPUT_DES_ID);
  mem_descr_t local_des = get_descr(LOCAL_DES_ID);
  mem_descr_t mask_des = get_descr(MASK_DES_ID);
  mem_descr_t refout_des = get_descr(REFOUT_DES_ID);
  mem_descr_t kernarg_des = get_descr(KERNARG_DES_ID);

  uint32_t* input = (uint32_t*)input_des.ptr;
  uint32_t* output_local = (uint32_t*)local_des.ptr;
  float* mask = (float*)mask_des.ptr;
  kernel_args_t* kernel_args = (kernel_args_t*)kernarg_des.ptr;

  // random initialisation of input
  fillRandom<uint32_t>(input, width_, height_, 0, 255);

  // Fill a blurr filter or some other filter of your choice
  const float val = 1.0f / (mask_width_ * 2.0f - 1.0f);
  for (uint32_t i = 0; i < (mask_width_ * mask_height_); i++) {
    mask[i] = 0;
  }
  for (uint32_t i = 0; i < mask_width_; i++) {
    uint32_t y = mask_height_ / 2;
    mask[y * mask_width_ + i] = val;
  }
  for (uint32_t i = 0; i < mask_height_; i++) {
    uint32_t x = mask_width_ / 2;
    mask[i * mask_width_ + x] = val;
  }

  // Print the INPUT array.
  printArray<uint32_t>("> Input[0]", input, width_, 1);
  printArray<float>("> Mask", mask, mask_width_, mask_height_);

  // Fill the kernel args
  kernel_args->arg1 = output_local;
  kernel_args->arg2 = input;
  kernel_args->arg3 = mask;
  kernel_args->arg4 = width_;
  kernel_args->arg41 = height_;
  kernel_args->arg5 = mask_width_;
  kernel_args->arg51 = mask_height_;

  // Calculate the reference output
  memset(refout_des.ptr, 0, refout_des.size);
  reference_impl((uint32_t*)refout_des.ptr, input, mask, width_, height_, mask_width_,
                 mask_height_);
}

void SimpleConvolution::print_output() const {
  printArray<uint32_t>("> Output[0]", (uint32_t*)get_output_ptr(), width_, 1);
}

bool SimpleConvolution::reference_impl(uint32_t* output, const uint32_t* input, const float* mask,
                                       const uint32_t width, const uint32_t height,
                                       const uint32_t mask_width, const uint32_t mask_height) {
  const uint32_t vstep = (mask_width - 1) / 2;
  const uint32_t hstep = (mask_height - 1) / 2;

  // for each pixel in the input
  for (uint32_t x = 0; x < width; x++) {
    for (uint32_t y = 0; y < height; y++) {
      // find the left, right, top and bottom indices such that
      // the indices do not go beyond image boundaires
      const uint32_t left = (x < vstep) ? 0 : (x - vstep);
      const uint32_t right = ((x + vstep) >= width) ? width - 1 : (x + vstep);
      const uint32_t top = (y < hstep) ? 0 : (y - hstep);
      const uint32_t bottom = ((y + hstep) >= height) ? height - 1 : (y + hstep);

      // initializing wighted sum value
      float sum_fx = 0;
      for (uint32_t i = left; i <= right; ++i) {
        for (uint32_t j = top; j <= bottom; ++j) {
          // performing wighted sum within the mask boundaries
          uint32_t mask_idx = (j - (y - hstep)) * mask_width + (i - (x - vstep));
          uint32_t index = j * width + i;

          // to round to the nearest integer
          sum_fx += ((float)input[index] * mask[mask_idx]);
        }
      }
      sum_fx += 0.5f;
      output[y * width + x] = uint32_t(sum_fx);
    }
  }

  return true;
}