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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "common.h"
#include <cooperative_groups.h>
namespace cg = cooperative_groups;
///////////////////////////////////////////////////////////////////////////////
/// \brief one iteration of classical Horn-Schunck method, CUDA kernel.
///
/// It is one iteration of Jacobi method for a corresponding linear system.
/// Template parameters are describe CTA size
/// \param[in] du0 current horizontal displacement approximation
/// \param[in] dv0 current vertical displacement approximation
/// \param[in] Ix image x derivative
/// \param[in] Iy image y derivative
/// \param[in] Iz temporal derivative
/// \param[in] w width
/// \param[in] h height
/// \param[in] s stride
/// \param[in] alpha degree of smoothness
/// \param[out] du1 new horizontal displacement approximation
/// \param[out] dv1 new vertical displacement approximation
///////////////////////////////////////////////////////////////////////////////
template <int bx, int by>
__global__ void JacobiIteration(const float *du0, const float *dv0,
const float *Ix, const float *Iy,
const float *Iz, int w, int h, int s,
float alpha, float *du1, float *dv1) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
volatile __shared__ float du[(bx + 2) * (by + 2)];
volatile __shared__ float dv[(bx + 2) * (by + 2)];
const int ix = threadIdx.x + blockIdx.x * blockDim.x;
const int iy = threadIdx.y + blockIdx.y * blockDim.y;
// position within global memory array
const int pos = min(ix, w - 1) + min(iy, h - 1) * s;
// position within shared memory array
const int shMemPos = threadIdx.x + 1 + (threadIdx.y + 1) * (bx + 2);
// Load data to shared memory.
// load tile being processed
du[shMemPos] = du0[pos];
dv[shMemPos] = dv0[pos];
// load necessary neighbouring elements
// We clamp out-of-range coordinates.
// It is equivalent to mirroring
// because we access data only one step away from borders.
if (threadIdx.y == 0) {
// beginning of the tile
const int bsx = blockIdx.x * blockDim.x;
const int bsy = blockIdx.y * blockDim.y;
// element position within matrix
int x, y;
// element position within linear array
// gm - global memory
// sm - shared memory
int gmPos, smPos;
x = min(bsx + threadIdx.x, w - 1);
// row just below the tile
y = max(bsy - 1, 0);
gmPos = y * s + x;
smPos = threadIdx.x + 1;
du[smPos] = du0[gmPos];
dv[smPos] = dv0[gmPos];
// row above the tile
y = min(bsy + by, h - 1);
smPos += (by + 1) * (bx + 2);
gmPos = y * s + x;
du[smPos] = du0[gmPos];
dv[smPos] = dv0[gmPos];
} else if (threadIdx.y == 1) {
// beginning of the tile
const int bsx = blockIdx.x * blockDim.x;
const int bsy = blockIdx.y * blockDim.y;
// element position within matrix
int x, y;
// element position within linear array
// gm - global memory
// sm - shared memory
int gmPos, smPos;
y = min(bsy + threadIdx.x, h - 1);
// column to the left
x = max(bsx - 1, 0);
smPos = bx + 2 + threadIdx.x * (bx + 2);
gmPos = x + y * s;
// check if we are within tile
if (threadIdx.x < by) {
du[smPos] = du0[gmPos];
dv[smPos] = dv0[gmPos];
// column to the right
x = min(bsx + bx, w - 1);
gmPos = y * s + x;
smPos += bx + 1;
du[smPos] = du0[gmPos];
dv[smPos] = dv0[gmPos];
}
}
cg::sync(cta);
if (ix >= w || iy >= h) return;
// now all necessary data are loaded to shared memory
int left, right, up, down;
left = shMemPos - 1;
right = shMemPos + 1;
up = shMemPos + bx + 2;
down = shMemPos - bx - 2;
float sumU = (du[left] + du[right] + du[up] + du[down]) * 0.25f;
float sumV = (dv[left] + dv[right] + dv[up] + dv[down]) * 0.25f;
float frac = (Ix[pos] * sumU + Iy[pos] * sumV + Iz[pos]) /
(Ix[pos] * Ix[pos] + Iy[pos] * Iy[pos] + alpha);
du1[pos] = sumU - Ix[pos] * frac;
dv1[pos] = sumV - Iy[pos] * frac;
}
///////////////////////////////////////////////////////////////////////////////
/// \brief one iteration of classical Horn-Schunck method, CUDA kernel wrapper.
///
/// It is one iteration of Jacobi method for a corresponding linear system.
/// \param[in] du0 current horizontal displacement approximation
/// \param[in] dv0 current vertical displacement approximation
/// \param[in] Ix image x derivative
/// \param[in] Iy image y derivative
/// \param[in] Iz temporal derivative
/// \param[in] w width
/// \param[in] h height
/// \param[in] s stride
/// \param[in] alpha degree of smoothness
/// \param[out] du1 new horizontal displacement approximation
/// \param[out] dv1 new vertical displacement approximation
///////////////////////////////////////////////////////////////////////////////
static void SolveForUpdate(const float *du0, const float *dv0, const float *Ix,
const float *Iy, const float *Iz, int w, int h,
int s, float alpha, float *du1, float *dv1) {
// CTA size
dim3 threads(32, 6);
// grid size
dim3 blocks(iDivUp(w, threads.x), iDivUp(h, threads.y));
JacobiIteration<32, 6><<<blocks, threads>>>(du0, dv0, Ix, Iy, Iz, w, h, s,
alpha, du1, dv1);
}
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