Skip to content

T
tile_processor_gpu
Commit 406eb708 authored by Andrey Filippov's avatar Andrey Filippov
Browse files
Options

preparing for separate compilation in java2

parent 3c3ebfa9
Hide whitespace changes
Inline Side-by-side
Showing with 1206 additions and 0 deletions
src/dtt8x8.h 0 → 100644
View file @ 406eb708
/**
**
** dtt8x8.h
**
** Copyright (C) 2018 Elphel, Inc.
**
** -----------------------------------------------------------------------------**
**
** dtt8x8.cuh is free software: you can redistribute it and/or modify
** it under the terms of the GNU General Public License as published by
** the Free Software Foundation, either version 3 of the License, or
** (at your option) any later version.
**
** This program is distributed in the hope that it will be useful,
** but WITHOUT ANY WARRANTY; without even the implied warranty of
** MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
** GNU General Public License for more details.
**
** You should have received a copy of the GNU General Public License
** along with this program. If not, see <http://www.gnu.org/licenses/>.
**
** Additional permission under GNU GPL version 3 section 7
**
** If you modify this Program, or any covered work, by linking or
** combining it with NVIDIA Corporation's CUDA libraries from the
** NVIDIA CUDA Toolkit (or a modified version of those libraries),
** containing parts covered by the terms of NVIDIA CUDA Toolkit
** EULA, the licensors of this Program grant you additional
** permission to convey the resulting work.
** -----------------------------------------------------------------------------**
*/
/**
**************************************************************************
* \file dtt8x8.h
* \brief DCT-II, DST-II, DCT-IV and DST-IV for Complex Lapped Transform of 16x16 (stride 8)
* in GPU
* This file contains building blocks for the 16x16 stride 8 COmplex Lapped Transform (CLT)
* implementation. DTT-IV are used for forward and inverse 2D CLT, DTT-II - to convert correlation
* results from the frequency to pixel domain. DTT-III (inverse of DTT-II) is not implemented
* here it is used to convert convolution kernels and LPF to the frequency domain - done in
* software.
*
* This file is cpompatible with both runtime and driver API, runtime is used for development
* with Nvidia Nsight, driver API when calling these kernels from Java
*/
#ifndef JCUDA
#define DTT_SIZE_LOG2 3
#endif
#pragma once
#define DTT_SIZE (1 << DTT_SIZE_LOG2)
#define DTT_SIZE1 (DTT_SIZE + 1)
#define DTT_SIZE2 (2 * DTT_SIZE)
#define DTT_SIZE21 (DTT_SIZE2 + 1)
#define DTT_SIZE4 (4 * DTT_SIZE)
#define DTT_SIZE2M1 (DTT_SIZE2 - 1)
#define BAYER_RED 0
#define BAYER_BLUE 1
#define BAYER_GREEN 2
// assuming GR/BG as now
#define BAYER_RED_ROW 0
#define BAYER_RED_COL 1
#define DTTTEST_BLOCK_WIDTH 32
#define DTTTEST_BLOCK_HEIGHT 16
#define DTTTEST_BLK_STRIDE (DTTTEST_BLOCK_WIDTH+1)
extern __constant__ float idct_signs[4][4][4];
extern __constant__ int imclt_indx9[16];
extern __constant__ float HWINDOW2[];
inline __device__ void dttii_shared_mem_nonortho(float * x0, int inc, int dst_not_dct); // does not scale by y[0] (y[7]) by 1/sqrt[0]
inline __device__ void dttii_shared_mem(float * x0, int inc, int dst_not_dct); // used in GPU_DTT24_DRV
inline __device__ void dttiv_shared_mem(float * x0, int inc, int dst_not_dct); // used in GPU_DTT24_DRV
inline __device__ void dttiv_nodiverg (float * x, int inc, int dst_not_dct); // not used
inline __device__ void dctiv_nodiverg (float * x0, int inc); // used in TP
inline __device__ void dstiv_nodiverg (float * x0, int inc); // used in TP
inline __device__ void dct_ii8 ( float x[8], float y[8]); // x,y point to 8-element arrays each // not used
inline __device__ void dct_iv8 ( float x[8], float y[8]); // x,y point to 8-element arrays each // not used
inline __device__ void dst_iv8 ( float x[8], float y[8]); // x,y point to 8-element arrays each // not used
inline __device__ void _dctii_nrecurs8 ( float x[8], float y[8]); // x,y point to 8-element arrays each // not used
inline __device__ void _dctiv_nrecurs8 ( float x[8], float y[8]); // x,y point to 8-element arrays each // not used
// kernels (not used so far)
extern "C" __global__ void GPU_DTT24_DRV(float *dst, float *src, int src_stride, int dtt_mode);
//=========================== 2D functions ===============
extern __device__ void corrUnfoldTile(
int corr_radius,
float* qdata0, // [4][DTT_SIZE][DTT_SIZE1], // 4 quadrants of the clt data, rows extended to optimize shared ports
float* rslt); // [DTT_SIZE2M1][DTT_SIZE2M1]) // 15x15
extern __device__ void dttii_2d(
float * clt_corr); // shared memory, [4][DTT_SIZE1][DTT_SIZE]
extern __device__ void dttiv_color_2d(
float * clt_tile,
int color);
extern __device__ void imclt(
float * clt_tile, // [4][DTT_SIZE][DTT_SIZE1], // +1 to alternate column ports [4][8][9]
float * mclt_tile );
extern __device__ void imclt8threads(
int do_acc, // 1 - add to previous value, 0 - overwrite
float * clt_tile, // [4][DTT_SIZE][DTT_SIZE1], // +1 to alternate column ports [4][8][9]
float * mclt_tile, // [2* DTT_SIZE][DTT_SIZE1+ DTT_SIZE], // +1 to alternate column ports[16][17]
int debug);
src/test_tp.cu 0 → 100644
View file @ 406eb708
/**
**
** dtt8x8.cu - CPU test code to run GPU tile processor
**
** Copyright (C) 2018 Elphel, Inc.
**
** -----------------------------------------------------------------------------**
**
** dtt8x8.cu is free software: you can redistribute it and/or modify
** it under the terms of the GNU General Public License as published by
** the Free Software Foundation, either version 3 of the License, or
** (at your option) any later version.
**
** This program is distributed in the hope that it will be useful,
** but WITHOUT ANY WARRANTY; without even the implied warranty of
** MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
** GNU General Public License for more details.
**
** You should have received a copy of the GNU General Public License
** along with this program. If not, see <http://www.gnu.org/licenses/>.
**
** Additional permission under GNU GPL version 3 section 7
**
** If you modify this Program, or any covered work, by linking or
** combining it with NVIDIA Corporation's CUDA libraries from the
** NVIDIA CUDA Toolkit (or a modified version of those libraries),
** containing parts covered by the terms of NVIDIA CUDA Toolkit
** EULA, the licensors of this Program grant you additional
** permission to convey the resulting work.
** -----------------------------------------------------------------------------**
*/
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <cuda_runtime.h>
#include <helper_cuda.h>
#include <helper_functions.h>
// for reading binary files
#include <fstream>
#include <iterator>
#include <vector>
//#include "dtt8x8.cuh"
#include "dtt8x8.h"
#include "TileProcessor.cuh"
///#include "cuda_profiler_api.h"
//#include "cudaProfiler.h"
float * copyalloc_kernel_gpu(float * kernel_host,
int size, // size in floats
int full_size)
{
float *kernel_gpu;
checkCudaErrors(cudaMalloc((void **)&kernel_gpu, full_size * sizeof(float)));
checkCudaErrors(cudaMemcpy( // segfault
kernel_gpu,
kernel_host,
size * sizeof(float),
cudaMemcpyHostToDevice));
return kernel_gpu;
}
float * copyalloc_kernel_gpu(float * kernel_host,
int size)
{
return copyalloc_kernel_gpu(kernel_host,
size, // size in floats
size);
}
float * alloccopy_from_gpu(
float * gpu_data,
float * cpu_data, // if null, will allocate
int size)
{
if (!cpu_data) {
cpu_data = (float *)malloc(size*sizeof(float));
}
checkCudaErrors(cudaMemcpy( // segfault
cpu_data,
gpu_data,
size * sizeof(float),
cudaMemcpyDeviceToHost));
return cpu_data;
}
float * alloc_kernel_gpu(int size) // size in floats
{
float *kernel_gpu;
checkCudaErrors(cudaMalloc((void **)&kernel_gpu, size * sizeof(float)));
return kernel_gpu;
}
float ** copyalloc_pointers_gpu(float ** gpu_pointer,
int size) // number of entries (cameras)
{
float ** gpu_pointer_to_gpu_pointers;
checkCudaErrors(cudaMalloc((void **)&gpu_pointer_to_gpu_pointers, size * sizeof(float*)));
checkCudaErrors(cudaMemcpy(
gpu_pointer_to_gpu_pointers,
gpu_pointer,
size * sizeof(float*),
cudaMemcpyHostToDevice));
return gpu_pointer_to_gpu_pointers;
}
float * copyalloc_image_gpu(float * image_host,
size_t* dstride, // in floats !
int width,
int height)
{
float *image_gpu;
checkCudaErrors(cudaMallocPitch((void **)&image_gpu, dstride, width * sizeof(float), height));
checkCudaErrors(cudaMemcpy2D(
image_gpu,
*dstride, // * sizeof(float),
image_host,
width * sizeof(float), // make in 16*n?
width * sizeof(float),
height,
cudaMemcpyHostToDevice));
return image_gpu;
}
float * alloc_image_gpu(size_t* dstride, // in bytes!!
int width,
int height)
{
float *image_gpu;
checkCudaErrors(cudaMallocPitch((void **)&image_gpu, dstride, width * sizeof(float), height));
return image_gpu;
}
int readFloatsFromFile(float * data, // allocated array
const char * path) // file path
{
std::ifstream input(path, std::ios::binary );
// copies all data into buffer
std::vector<char> buffer((
std::istreambuf_iterator<char>(input)),
(std::istreambuf_iterator<char>()));
std::copy( buffer.begin(), buffer.end(), (char *) data);
return 0;
}
int writeFloatsToFile(float * data, // allocated array
int size, // length in elements
const char * path) // file path
{
// std::ifstream input(path, std::ios::binary );
std::ofstream ofile(path, std::ios::binary);
ofile.write((char *) data, size * sizeof(float));
return 0;
}
// Prepare low pass filter (64 long) to be applied to each quadrant of the CLT data
void set_clt_lpf(
float * lpf, // size*size array to be filled out
float sigma,
const int dct_size)
{
int dct_len = dct_size * dct_size;
if (sigma == 0.0f) {
lpf[0] = 1.0f;
for (int i = 1; i < dct_len; i++){
lpf[i] = 0.0;
}
} else {
for (int i = 0; i < dct_size; i++){
for (int j = 0; j < dct_size; j++){
lpf[i*dct_size+j] = exp(-(i*i+j*j)/(2*sigma));
}
}
// normalize
double sum = 0;
for (int i = 0; i < dct_size; i++){
for (int j = 0; j < dct_size; j++){
double d = lpf[i*dct_size+j];
d*=cos(M_PI*i/(2*dct_size))*cos(M_PI*j/(2*dct_size));
if (i > 0) d*= 2.0;
if (j > 0) d*= 2.0;
sum +=d;
}
}
for (int i = 0; i< dct_len; i++){
lpf[i] /= sum;
}
}
}
/**
**************************************************************************
* Program entry point
*
* \param argc [IN] - Number of command-line arguments
* \param argv [IN] - Array of command-line arguments
*
* \return Status code
*/
int main(int argc, char **argv)
{
//
// Sample initialization
//
printf("%s Starting...\n\n", argv[0]);
printf("sizeof(float*)=%d\n",(int)sizeof(float*));
//initialize CUDA
findCudaDevice(argc, (const char **)argv);
// CLT testing
const char* kernel_file[] = {
"/data_ssd/git/tile_processor_gpu/clt/main_chn0_transposed.kernel",
"/data_ssd/git/tile_processor_gpu/clt/main_chn1_transposed.kernel",
"/data_ssd/git/tile_processor_gpu/clt/main_chn2_transposed.kernel",
"/data_ssd/git/tile_processor_gpu/clt/main_chn3_transposed.kernel"};
const char* kernel_offs_file[] = {
"/data_ssd/git/tile_processor_gpu/clt/main_chn0_transposed.kernel_offsets",
"/data_ssd/git/tile_processor_gpu/clt/main_chn1_transposed.kernel_offsets",
"/data_ssd/git/tile_processor_gpu/clt/main_chn2_transposed.kernel_offsets",
"/data_ssd/git/tile_processor_gpu/clt/main_chn3_transposed.kernel_offsets"};
const char* image_files[] = {
"/data_ssd/git/tile_processor_gpu/clt/main_chn0.bayer",
"/data_ssd/git/tile_processor_gpu/clt/main_chn1.bayer",
"/data_ssd/git/tile_processor_gpu/clt/main_chn2.bayer",
"/data_ssd/git/tile_processor_gpu/clt/main_chn3.bayer"};
const char* ports_offs_xy_file[] = {
"/data_ssd/git/tile_processor_gpu/clt/main_chn0.portsxy",
"/data_ssd/git/tile_processor_gpu/clt/main_chn1.portsxy",
"/data_ssd/git/tile_processor_gpu/clt/main_chn2.portsxy",
"/data_ssd/git/tile_processor_gpu/clt/main_chn3.portsxy"};
const char* ports_clt_file[] = { // never referenced
"/data_ssd/git/tile_processor_gpu/clt/main_chn0.clt",
"/data_ssd/git/tile_processor_gpu/clt/main_chn1.clt",
"/data_ssd/git/tile_processor_gpu/clt/main_chn2.clt",
"/data_ssd/git/tile_processor_gpu/clt/main_chn3.clt"};
const char* result_rbg_file[] = {
"/data_ssd/git/tile_processor_gpu/clt/main_chn0.rbg",
"/data_ssd/git/tile_processor_gpu/clt/main_chn1.rbg",
"/data_ssd/git/tile_processor_gpu/clt/main_chn2.rbg",
"/data_ssd/git/tile_processor_gpu/clt/main_chn3.rbg"};
const char* result_corr_file = "/data_ssd/git/tile_processor_gpu/clt/main_corr.corr";
const char* result_textures_file = "/data_ssd/git/tile_processor_gpu/clt/texture.rgba";
const char* result_textures_rgba_file = "/data_ssd/git/tile_processor_gpu/clt/texture_rgba.rgba";
// not yet used
float lpf_sigmas[3] = {0.9f, 0.9f, 0.9f}; // G, B, G
float port_offsets[NUM_CAMS][2] = {// used only in textures to scale differences
{-0.5, -0.5},
{ 0.5, -0.5},
{-0.5, 0.5},
{ 0.5, 0.5}};
int keep_texture_weights = 1; // try with 0 also
int texture_colors = 3; // result will be 3+1 RGBA (for mono - 2)
/*
#define IMG_WIDTH 2592
#define IMG_HEIGHT 1936
#define NUM_CAMS 4
#define NUM_COLORS 3
#define KERNELS_STEP 16
#define KERNELS_HOR 164
#define KERNELS_VERT 123
#define KERNEL_OFFSETS 8
#define TILESX 324
#define TILESY 242
*/
/*
struct tp_task {
long task;
short ty;
short tx;
float xy[NUM_CAMS][2];
} ;
*/
int KERN_TILES = KERNELS_HOR * KERNELS_VERT * NUM_COLORS;
int KERN_SIZE = KERN_TILES * 4 * 64;
// int CORR_SIZE = (2 * DTT_SIZE -1) * (2 * DTT_SIZE -1);
int CORR_SIZE = (2 * CORR_OUT_RAD + 1) * (2 * CORR_OUT_RAD + 1);
float * host_kern_buf = (float *)malloc(KERN_SIZE * sizeof(float));
struct tp_task task_data [TILESX*TILESY]; // maximal length - each tile
int corr_indices [NUM_PAIRS*TILESX*TILESY];
// int texture_indices [TILESX*TILESY];
int texture_indices [TILESX*TILESYA];
int cpu_woi [4];
// host array of pointers to GPU memory
float * gpu_kernels_h [NUM_CAMS];
struct CltExtra * gpu_kernel_offsets_h [NUM_CAMS];
float * gpu_images_h [NUM_CAMS];
float tile_coords_h [NUM_CAMS][TILESX * TILESY][2];
float * gpu_clt_h [NUM_CAMS];
float * gpu_lpf_h [NUM_COLORS]; // never used
#ifndef NOICLT
float * gpu_corr_images_h [NUM_CAMS];
#endif
float * gpu_corrs;
int * gpu_corr_indices;
float * gpu_textures;
float * gpu_textures_rbga;
int * gpu_texture_indices;
int * gpu_woi;
int * gpu_num_texture_tiles;
float * gpu_port_offsets;
int num_corrs;
int num_textures;
int num_ports = NUM_CAMS;
// GPU pointers to GPU pointers to memory
float ** gpu_kernels; // [NUM_CAMS];
struct CltExtra ** gpu_kernel_offsets; // [NUM_CAMS];
float ** gpu_images; // [NUM_CAMS];
float ** gpu_clt; // [NUM_CAMS];
float ** gpu_lpf; // [NUM_CAMS]; // never referenced
// GPU pointers to GPU memory
// float * gpu_tasks;
struct tp_task * gpu_tasks;
size_t dstride; // in bytes !
size_t dstride_rslt; // in bytes !
size_t dstride_corr; // in bytes ! for one 2d phase correlation (padded 15x15x4 bytes)
size_t dstride_textures; // in bytes ! for one rgba/ya 16x16 tile
size_t dstride_textures_rbga; // in bytes ! for one rgba/ya 16x16 tile
float lpf_rbg[3][64]; // not used
for (int ncol = 0; ncol < 3; ncol++) {
if (lpf_sigmas[ncol] > 0.0) {
set_clt_lpf (
lpf_rbg[ncol], // float * lpf, // size*size array to be filled out
lpf_sigmas[ncol], // float sigma,
8); // int dct_size)
gpu_lpf_h[ncol] = copyalloc_kernel_gpu(lpf_rbg[ncol], 64);
} else {
gpu_lpf_h[ncol] = NULL;
}
}
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
readFloatsFromFile(
host_kern_buf, // float * data, // allocated array
kernel_file[ncam]); // char * path) // file path
gpu_kernels_h[ncam] = copyalloc_kernel_gpu(host_kern_buf, KERN_SIZE);
readFloatsFromFile(
host_kern_buf, // float * data, // allocated array
kernel_offs_file[ncam]); // char * path) // file path
gpu_kernel_offsets_h[ncam] = (struct CltExtra *) copyalloc_kernel_gpu(
host_kern_buf,
KERN_TILES * (sizeof( struct CltExtra)/sizeof(float)));
// will get results back
gpu_clt_h[ncam] = alloc_kernel_gpu(TILESY * TILESX * NUM_COLORS * 4 * DTT_SIZE * DTT_SIZE);
printf("Allocating GPU memory, 0x%x floats\n", (TILESY * TILESX * NUM_COLORS * 4 * DTT_SIZE * DTT_SIZE)) ;
// allocate result images (3x height to accommodate 3 colors
// Image is extended by 4 pixels each side to avoid checking (mclt tiles extend by 4)
//host array of pointers to GPU arrays
#ifndef NOICLT
gpu_corr_images_h[ncam] = alloc_image_gpu(
&dstride_rslt, // size_t* dstride, // in bytes!!
IMG_WIDTH + DTT_SIZE, // int width,
3*(IMG_HEIGHT + DTT_SIZE)); // int height);
#endif
}
// allocates one correlation kernel per line (15x15 floats), number of rows - number of tiles * number of pairs
gpu_corrs = alloc_image_gpu(
&dstride_corr, // in bytes ! for one 2d phase correlation (padded 15x15x4 bytes)
CORR_SIZE, // int width,
NUM_PAIRS * TILESX * TILESY); // int height);
// read channel images (assuming host_kern_buf size > image size, reusing it)
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
readFloatsFromFile(
host_kern_buf, // float * data, // allocated array
image_files[ncam]); // char * path) // file path
gpu_images_h[ncam] = copyalloc_image_gpu(
host_kern_buf, // float * image_host,
&dstride, // size_t* dstride,
IMG_WIDTH, // int width,
IMG_HEIGHT); // int height);
}
//#define DBG_TILE (174*324 +118)
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
readFloatsFromFile(
(float *) &tile_coords_h[ncam],
ports_offs_xy_file[ncam]); // char * path) // file path
}
// build TP task that processes all tiles in linescan order
for (int ty = 0; ty < TILESY; ty++){
for (int tx = 0; tx < TILESX; tx++){
int nt = ty * TILESX + tx;
task_data[nt].task = 0xf | (((1 << NUM_PAIRS)-1) << TASK_CORR_BITS);
task_data[nt].txy = tx + (ty << 16);
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
task_data[nt].xy[ncam][0] = tile_coords_h[ncam][nt][0];
task_data[nt].xy[ncam][1] = tile_coords_h[ncam][nt][1];
}
}
}
int tp_task_size = sizeof(task_data)/sizeof(struct tp_task);
#ifdef DBG0
//#define NUM_TEST_TILES 128
#define NUM_TEST_TILES 1
for (int t = 0; t < NUM_TEST_TILES; t++) {
task_data[t].task = 1;
task_data[t].txy = ((DBG_TILE + t) - 324* ((DBG_TILE + t) / 324)) + (((DBG_TILE + t) / 324)) << 16;
int nt = task_data[t].ty * TILESX + task_data[t].tx;
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
task_data[t].xy[ncam][0] = tile_coords_h[ncam][nt][0];
task_data[t].xy[ncam][1] = tile_coords_h[ncam][nt][1];
}
}
tp_task_size = NUM_TEST_TILES; // sizeof(task_data)/sizeof(float);
#endif
// segfault in the next
gpu_tasks = (struct tp_task *) copyalloc_kernel_gpu((float * ) &task_data, tp_task_size * (sizeof(struct tp_task)/sizeof(float)));
// build corr_indices
num_corrs = 0;
for (int ty = 0; ty < TILESY; ty++){
for (int tx = 0; tx < TILESX; tx++){
int nt = ty * TILESX + tx;
int cm = (task_data[nt].task >> TASK_CORR_BITS) & ((1 << NUM_PAIRS)-1);
if (cm){
for (int b = 0; b < NUM_PAIRS; b++) if ((cm & (1 << b)) != 0) {
corr_indices[num_corrs++] = (nt << CORR_NTILE_SHIFT) | b;
}
}
}
}
// num_corrs now has the total number of correlations
// copy corr_indices to gpu
// gpu_corr_indices = (int *) copyalloc_kernel_gpu((float * ) corr_indices, num_corrs);
gpu_corr_indices = (int *) copyalloc_kernel_gpu(
(float * ) corr_indices,
num_corrs,
NUM_PAIRS * TILESX * TILESY);
// build texture_indices
num_textures = 0;
for (int ty = 0; ty < TILESY; ty++){
for (int tx = 0; tx < TILESX; tx++){
int nt = ty * TILESX + tx;
// int cm = (task_data[nt].task >> TASK_TEXTURE_BIT) & 1;
int cm = task_data[nt].task & TASK_TEXTURE_BITS;
if (cm){
texture_indices[num_textures++] = (nt << CORR_NTILE_SHIFT) | (1 << LIST_TEXTURE_BIT);
}
}
}
// num_textures now has the total number of textures
// copy corr_indices to gpu
// gpu_texture_indices = (int *) copyalloc_kernel_gpu((float * ) texture_indices, num_textures);
gpu_texture_indices = (int *) copyalloc_kernel_gpu(
(float * ) texture_indices,
num_textures,
TILESX * TILESYA); // number of rows - multiple of 4
// just allocate
checkCudaErrors(cudaMalloc((void **)&gpu_woi, 4 * sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&gpu_num_texture_tiles, 8 * sizeof(float))); // for each subsequence - number of non-border,
// number of border tiles
// copy port indices to gpu
gpu_port_offsets = (float *) copyalloc_kernel_gpu((float * ) port_offsets, num_ports * 2);
// int keep_texture_weights = 1; // try with 0 also
// int texture_colors = 3; // result will be 3+1 RGBA (for mono - 2)
// double [][] rgba = new double[numcol + 1 + (keep_weights?(ports + numcol + 1):0)][];
int tile_texture_size = (texture_colors + 1 + (keep_texture_weights? (NUM_CAMS + texture_colors + 1): 0)) *256;
gpu_textures = alloc_image_gpu(
&dstride_textures, // in bytes ! for one rgba/ya 16x16 tile
tile_texture_size, // int width (floats),
TILESX * TILESY); // int height);
int rgba_width = (TILESX+1) * DTT_SIZE;
int rgba_height = (TILESY+1) * DTT_SIZE;
int rbga_slices = texture_colors + 1; // 4/1
gpu_textures_rbga = alloc_image_gpu(
&dstride_textures_rbga, // in bytes ! for one rgba/ya 16x16 tile
rgba_width, // int width (floats),
rgba_height * rbga_slices); // int height);
// Now copy arrays of per-camera pointers to GPU memory to GPU itself
gpu_kernels = copyalloc_pointers_gpu (gpu_kernels_h, NUM_CAMS);
gpu_kernel_offsets = (struct CltExtra **) copyalloc_pointers_gpu ((float **) gpu_kernel_offsets_h, NUM_CAMS);
gpu_images = copyalloc_pointers_gpu (gpu_images_h, NUM_CAMS);
gpu_clt = copyalloc_pointers_gpu (gpu_clt_h, NUM_CAMS);
// gpu_corr_images = copyalloc_pointers_gpu (gpu_corr_images_h, NUM_CAMS);
//create and start CUDA timer
StopWatchInterface *timerTP = 0;
sdkCreateTimer(&timerTP);
dim3 threads_tp(THREADSX, TILES_PER_BLOCK, 1);
dim3 grid_tp((tp_task_size + TILES_PER_BLOCK -1 )/TILES_PER_BLOCK, 1);
printf("threads_tp=(%d, %d, %d)\n",threads_tp.x,threads_tp.y,threads_tp.z);
printf("grid_tp= (%d, %d, %d)\n",grid_tp.x, grid_tp.y, grid_tp.z);
#ifdef DBG_TILE
const int numIterations = 1; //0;
const int i0 = 0; // -1;
#else
const int numIterations = 10; // 0; //0;
const int i0 = -1; // 0; // -1;
#endif
cudaFuncSetCacheConfig(convert_correct_tiles, cudaFuncCachePreferShared);
/// cudaProfilerStart();
float ** fgpu_kernel_offsets = (float **) gpu_kernel_offsets; // [NUM_CAMS];
for (int i = i0; i < numIterations; i++)
{
if (i == 0)
{
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&timerTP);
sdkStartTimer(&timerTP);
}
convert_correct_tiles<<<grid_tp,threads_tp>>>(
fgpu_kernel_offsets, // struct CltExtra ** gpu_kernel_offsets,
gpu_kernels, // float ** gpu_kernels,
gpu_images, // float ** gpu_images,
gpu_tasks, // struct tp_task * gpu_tasks,
gpu_clt, // float ** gpu_clt, // [NUM_CAMS][TILESY][TILESX][NUM_COLORS][DTT_SIZE*DTT_SIZE]
dstride/sizeof(float), // size_t dstride, // for gpu_images
tp_task_size, // int num_tiles) // number of tiles in task
0); // 7); // 0); // 7); // int lpf_mask) // apply lpf to colors : bit 0 - red, bit 1 - blue, bit2 - green
getLastCudaError("Kernel execution failed");
checkCudaErrors(cudaDeviceSynchronize());
printf("%d\n",i);
}
// checkCudaErrors(cudaDeviceSynchronize());
sdkStopTimer(&timerTP);
float avgTime = (float)sdkGetTimerValue(&timerTP) / (float)numIterations;
sdkDeleteTimer(&timerTP);
printf("Run time =%f ms\n", avgTime);
#ifdef SAVE_CLT
int rslt_size = (TILESY * TILESX * NUM_COLORS * 4 * DTT_SIZE * DTT_SIZE);
float * cpu_clt = (float *)malloc(rslt_size*sizeof(float));
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
checkCudaErrors(cudaMemcpy( // segfault
cpu_clt,
gpu_clt_h[ncam],
rslt_size * sizeof(float),
cudaMemcpyDeviceToHost));
#ifndef DBG_TILE
printf("Writing CLT data to %s\n", ports_clt_file[ncam]);
writeFloatsToFile(cpu_clt, // float * data, // allocated array
rslt_size, // int size, // length in elements
ports_clt_file[ncam]); // const char * path) // file path
#endif
}
#endif
#ifdef TEST_IMCLT
{
// testing imclt
dim3 threads_imclt(IMCLT_THREADS_PER_TILE, IMCLT_TILES_PER_BLOCK, 1);
dim3 grid_imclt(1,1,1);
printf("threads_imclt=(%d, %d, %d)\n",threads_imclt.x,threads_imclt.y,threads_imclt.z);
printf("grid_imclt= (%d, %d, %d)\n",grid_imclt.x, grid_imclt.y, grid_imclt.z);
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
test_imclt<<<grid_imclt,threads_imclt>>>(
gpu_clt_h[ncam], // ncam]); // // float ** gpu_clt, // [NUM_CAMS][TILESY][TILESX][NUM_COLORS][DTT_SIZE*DTT_SIZE]
ncam); // int ncam); // just for debug print
}
getLastCudaError("Kernel execution failed");
checkCudaErrors(cudaDeviceSynchronize());
printf("test_imclt() DONE\n");
}
#endif
#ifndef NOICLT
// testing imclt
dim3 threads_imclt(IMCLT_THREADS_PER_TILE, IMCLT_TILES_PER_BLOCK, 1);
printf("threads_imclt=(%d, %d, %d)\n",threads_imclt.x,threads_imclt.y,threads_imclt.z);
StopWatchInterface *timerIMCLT = 0;
sdkCreateTimer(&timerIMCLT);
for (int i = i0; i < numIterations; i++)
{
if (i == 0)
{
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&timerIMCLT);
sdkStartTimer(&timerIMCLT);
}
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
for (int color = 0; color < NUM_COLORS; color++) {
#ifdef IMCLT14
for (int v_offs = 0; v_offs < 1; v_offs++){ // temporarily for debugging
for (int h_offs = 0; h_offs < 1; h_offs++){ // temporarily for debugging
#else
for (int v_offs = 0; v_offs < 2; v_offs++){
for (int h_offs = 0; h_offs < 2; h_offs++){
#endif
int tilesy_half = (TILESY + (v_offs ^ 1)) >> 1;
int tilesx_half = (TILESX + (h_offs ^ 1)) >> 1;
int tiles_in_pass = tilesy_half * tilesx_half;
dim3 grid_imclt((tiles_in_pass + IMCLT_TILES_PER_BLOCK-1) / IMCLT_TILES_PER_BLOCK,1,1);
// printf("grid_imclt= (%d, %d, %d)\n",grid_imclt.x, grid_imclt.y, grid_imclt.z);
imclt_rbg<<<grid_imclt,threads_imclt>>>(
gpu_clt_h[ncam], // float * gpu_clt, // [TILESY][TILESX][NUM_COLORS][DTT_SIZE*DTT_SIZE]
gpu_corr_images_h[ncam], // float * gpu_rbg, // WIDTH, 3 * HEIGHT
color, // int color,
v_offs, // int v_offset,
h_offs, // int h_offset,
dstride_rslt/sizeof(float)); //const size_t dstride); // in floats (pixels)
}
}
}
}
getLastCudaError("Kernel failure");
checkCudaErrors(cudaDeviceSynchronize());
printf("test pass: %d\n",i);
}
sdkStopTimer(&timerIMCLT);
float avgTimeIMCLT = (float)sdkGetTimerValue(&timerIMCLT) / (float)numIterations;
sdkDeleteTimer(&timerIMCLT);
printf("Average IMCLT run time =%f ms\n", avgTimeIMCLT);
int rslt_img_size = NUM_COLORS * (IMG_HEIGHT + DTT_SIZE) * (IMG_WIDTH + DTT_SIZE);
float * cpu_corr_image = (float *)malloc(rslt_img_size * sizeof(float));
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
checkCudaErrors(cudaMemcpy2D( // segfault
cpu_corr_image,
(IMG_WIDTH + DTT_SIZE) * sizeof(float),
gpu_corr_images_h[ncam],
dstride_rslt,
(IMG_WIDTH + DTT_SIZE) * sizeof(float),
3* (IMG_HEIGHT + DTT_SIZE),
cudaMemcpyDeviceToHost));
#ifndef DBG_TILE
printf("Writing RBG data to %s\n", result_rbg_file[ncam]);
writeFloatsToFile( // will have margins
cpu_corr_image, // float * data, // allocated array
rslt_img_size, // int size, // length in elements
result_rbg_file[ncam]); // const char * path) // file path
#endif
}
free(cpu_corr_image);
#endif
#ifndef NOCORR
// cudaProfilerStart();
// testing corr
dim3 threads_corr(CORR_THREADS_PER_TILE, CORR_TILES_PER_BLOCK, 1);
printf("threads_corr=(%d, %d, %d)\n",threads_corr.x,threads_corr.y,threads_corr.z);
StopWatchInterface *timerCORR = 0;
sdkCreateTimer(&timerCORR);
for (int i = i0; i < numIterations; i++)
{
if (i == 0)
{
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&timerCORR);
sdkStartTimer(&timerCORR);
}
dim3 grid_corr((num_corrs + CORR_TILES_PER_BLOCK-1) / CORR_TILES_PER_BLOCK,1,1);
correlate2D<<<grid_corr,threads_corr>>>(
gpu_clt, // float ** gpu_clt, // [NUM_CAMS] ->[TILESY][TILESX][NUM_COLORS][DTT_SIZE*DTT_SIZE]
3, // int colors, // number of colors (3/1)
0.25, // float scale0, // scale for R
0.25, // float scale1, // scale for B
0.5, // float scale2, // scale for G
30.0, // float fat_zero, // here - absolute
num_corrs, // size_t num_corr_tiles, // number of correlation tiles to process
gpu_corr_indices, // int * gpu_corr_indices, // packed tile+pair
dstride_corr/sizeof(float), // const size_t corr_stride, // in floats
CORR_OUT_RAD, // int corr_radius, // radius of the output correlation (7 for 15x15)
gpu_corrs); // float * gpu_corrs); // correlation output data
getLastCudaError("Kernel failure");
checkCudaErrors(cudaDeviceSynchronize());
printf("test pass: %d\n",i);
}
sdkStopTimer(&timerCORR);
float avgTimeCORR = (float)sdkGetTimerValue(&timerCORR) / (float)numIterations;
sdkDeleteTimer(&timerCORR);
printf("Average CORR run time =%f ms\n", avgTimeCORR);
int corr_size = 2 * CORR_OUT_RAD + 1;
int rslt_corr_size = num_corrs * corr_size * corr_size;
float * cpu_corr = (float *)malloc(rslt_corr_size * sizeof(float));
checkCudaErrors(cudaMemcpy2D(
cpu_corr,
(corr_size * corr_size) * sizeof(float),
gpu_corrs,
dstride_corr,
(corr_size * corr_size) * sizeof(float),
num_corrs,
cudaMemcpyDeviceToHost));
#ifndef NSAVE_CORR
printf("Writing phase correlation data to %s\n", result_corr_file);
writeFloatsToFile(
cpu_corr, // float * data, // allocated array
rslt_corr_size, // int size, // length in elements
result_corr_file); // const char * path) // file path
#endif
free(cpu_corr);
#endif // ifndef NOCORR
// -----------------
#ifndef NOTEXTURES
// cudaProfilerStart();
// testing textures
dim3 threads_texture(TEXTURE_THREADS_PER_TILE, NUM_CAMS, 1); // TEXTURE_TILES_PER_BLOCK, 1);
dim3 grid_texture((num_textures + TEXTURE_TILES_PER_BLOCK-1) / TEXTURE_TILES_PER_BLOCK,1,1);
printf("threads_texture=(%d, %d, %d)\n",threads_texture.x,threads_texture.y,threads_texture.z);
printf("grid_texture=(%d, %d, %d)\n",grid_texture.x,grid_texture.y,grid_texture.z);
StopWatchInterface *timerTEXTURE = 0;
sdkCreateTimer(&timerTEXTURE);
for (int i = i0; i < numIterations; i++)
{
if (i == 0)
{
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&timerTEXTURE);
sdkStartTimer(&timerTEXTURE);
}
// Channel0 weight = 0.294118
// Channel1 weight = 0.117647
// Channel2 weight = 0.588235
textures_accumulate<<<grid_texture,threads_texture>>> (
// 0, // int border_tile, // if 1 - watch for border
(int *) 0, // int * woi, // x, y, width,height
gpu_clt , // float ** gpu_clt, // [NUM_CAMS] ->[TILESY][TILESX][NUM_COLORS][DTT_SIZE*DTT_SIZE]
num_textures, // size_t num_texture_tiles, // number of texture tiles to process
gpu_texture_indices, // int * gpu_texture_indices,// packed tile + bits (now only (1 << 7)
gpu_port_offsets, // float * port_offsets, // relative ports x,y offsets - just to scale differences, may be approximate
texture_colors, // int colors, // number of colors (3/1)
(texture_colors == 1), // int is_lwir, // do not perform shot correction
10.0, // float min_shot, // 10.0
3.0, // float scale_shot, // 3.0
1.5f, // float diff_sigma, // pixel value/pixel change
10.0f, // float diff_threshold, // pixel value/pixel change
3.0, // float min_agree, // minimal number of channels to agree on a point (real number to work with fuzzy averages)
0.294118, // float weight0, // scale for R
0.117647, // float weight1, // scale for B
0.588235, // float weight2, // scale for G
1, // int dust_remove, // Do not reduce average weight when only one image differes much from the average
keep_texture_weights, // int keep_weights, // return channel weights after A in RGBA
// combining both non-overlap and overlap (each calculated if pointer is not null )
0, // const size_t texture_rbg_stride, // in floats
(float *) 0, // float * gpu_texture_rbg, // (number of colors +1 + ?)*16*16 rgba texture tiles
dstride_textures/sizeof(float), // const size_t texture_stride, // in floats (now 256*4 = 1024)
gpu_textures); // float * gpu_texture_tiles); // 4*16*16 rgba texture tiles
getLastCudaError("Kernel failure");
checkCudaErrors(cudaDeviceSynchronize());
printf("test pass: %d\n",i);
}
/// cudaProfilerStop();
sdkStopTimer(&timerTEXTURE);
float avgTimeTEXTURES = (float)sdkGetTimerValue(&timerTEXTURE) / (float)numIterations;
sdkDeleteTimer(&timerTEXTURE);
printf("Average Texture run time =%f ms\n", avgTimeTEXTURES);
int rslt_texture_size = num_textures * tile_texture_size;
float * cpu_textures = (float *)malloc(rslt_texture_size * sizeof(float));
checkCudaErrors(cudaMemcpy2D(
cpu_textures,
tile_texture_size * sizeof(float),
gpu_textures,
dstride_textures,
tile_texture_size * sizeof(float),
num_textures,
cudaMemcpyDeviceToHost));
#ifndef NSAVE_TEXTURES
printf("Writing phase texture data to %s\n", result_textures_file);
writeFloatsToFile(
cpu_textures, // float * data, // allocated array
rslt_texture_size, // int size, // length in elements
result_textures_file); // const char * path) // file path
//DBG_TILE
#ifdef DEBUG10
int texture_offset = DBG_TILE * tile_texture_size;
int chn = 0;
for (int i = 0; i < tile_texture_size; i++){
if ((i % 256) == 0){
printf("\nchn = %d\n", chn++);
}
printf("%10.4f", *(cpu_textures + texture_offset + i));
if (((i + 1) % 16) == 0){
printf("\n");
} else {
printf(" ");
}
}
// int tile_texture_size = (texture_colors + 1 + (keep_texture_weights? (NUM_CAMS + texture_colors + 1): 0)) *256;
#endif // DEBUG9
#endif
free(cpu_textures);
#endif // ifndef NOTEXTURES
#define GEN_TEXTURE_LIST
#ifdef GEN_TEXTURE_LIST
dim3 threads_list(1,1, 1); // TEXTURE_TILES_PER_BLOCK, 1);
dim3 grid_list (1,1,1);
printf("threads_list=(%d, %d, %d)\n",threads_list.x,threads_list.y,threads_list.z);
printf("grid_list=(%d, %d, %d)\n",grid_list.x,grid_list.y,grid_list.z);
StopWatchInterface *timerTEXTURELIST = 0;
sdkCreateTimer(&timerTEXTURELIST);
for (int i = i0; i < numIterations; i++)
{
if (i == 0)
{
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&timerTEXTURELIST);
sdkStartTimer(&timerTEXTURELIST);
}
prepare_texture_list<<<grid_list,threads_list>>> (
gpu_tasks, // struct tp_task * gpu_tasks,
tp_task_size, // int num_tiles, // number of tiles in task list
gpu_texture_indices, // int * gpu_texture_indices,// packed tile + bits (now only (1 << 7)
gpu_num_texture_tiles, // int * num_texture_tiles, // number of texture tiles to process (8 elements)
gpu_woi, // int * woi, // x,y,width,height of the woi
TILESX, // int width, // <= TILESX, use for faster processing of LWIR images (should be actual + 1)
TILESY); // int height); // <= TILESY, use for faster processing of LWIR images
getLastCudaError("Kernel failure");
checkCudaErrors(cudaDeviceSynchronize());
printf("test pass: %d\n",i);
}
/// cudaProfilerStop();
sdkStopTimer(&timerTEXTURELIST);
float avgTimeTEXTURESLIST = (float)sdkGetTimerValue(&timerTEXTURELIST) / (float)numIterations;
sdkDeleteTimer(&timerTEXTURELIST);
printf("Average TextureList run time =%f ms\n", avgTimeTEXTURESLIST);
int cpu_num_texture_tiles[8];
checkCudaErrors(cudaMemcpy(
cpu_woi,
gpu_woi,
4 * sizeof(float),
cudaMemcpyDeviceToHost));
printf("WOI x=%d, y=%d, width=%d, height=%d\n", cpu_woi[0], cpu_woi[1], cpu_woi[2], cpu_woi[3]);
checkCudaErrors(cudaMemcpy(
cpu_num_texture_tiles,
gpu_num_texture_tiles,
8 * sizeof(float), // 8 sequences (0,2,4,6 - non-border, growing up;
//1,3,5,7 - border, growing down from the end of the corresponding non-border buffers
cudaMemcpyDeviceToHost));
printf("cpu_num_texture_tiles=(%d(%d), %d(%d), %d(%d), %d(%d) -> %d tp_task_size=%d)\n",
cpu_num_texture_tiles[0], cpu_num_texture_tiles[1],
cpu_num_texture_tiles[2], cpu_num_texture_tiles[3],
cpu_num_texture_tiles[4], cpu_num_texture_tiles[5],
cpu_num_texture_tiles[6], cpu_num_texture_tiles[7],
cpu_num_texture_tiles[0] + cpu_num_texture_tiles[1] +
cpu_num_texture_tiles[2] + cpu_num_texture_tiles[3] +
cpu_num_texture_tiles[4] + cpu_num_texture_tiles[5] +
cpu_num_texture_tiles[6] + cpu_num_texture_tiles[7],
tp_task_size
);
for (int q = 0; q < 4; q++) {
checkCudaErrors(cudaMemcpy(
texture_indices + q * TILESX * (TILESYA >> 2),
gpu_texture_indices + q * TILESX * (TILESYA >> 2),
cpu_num_texture_tiles[q] * sizeof(float), // change to cpu_num_texture_tiles when ready
cudaMemcpyDeviceToHost));
}
for (int q = 0; q < 4; q++) {
printf("%d: %3x:%3x %3x:%3x %3x:%3x %3x:%3x %3x:%3x %3x:%3x %3x:%3x %3x:%3x \n",q,
(texture_indices[q * TILESX * (TILESYA >> 2) + 0] >> 8) / TILESX, (texture_indices[q * TILESX * (TILESYA >> 2) + 0] >> 8) % TILESX,
(texture_indices[q * TILESX * (TILESYA >> 2) + 1] >> 8) / TILESX, (texture_indices[q * TILESX * (TILESYA >> 2) + 1] >> 8) % TILESX,
(texture_indices[q * TILESX * (TILESYA >> 2) + 2] >> 8) / TILESX, (texture_indices[q * TILESX * (TILESYA >> 2) + 2] >> 8) % TILESX,
(texture_indices[q * TILESX * (TILESYA >> 2) + 3] >> 8) / TILESX, (texture_indices[q * TILESX * (TILESYA >> 2) + 3] >> 8) % TILESX,
(texture_indices[q * TILESX * (TILESYA >> 2) + 4] >> 8) / TILESX, (texture_indices[q * TILESX * (TILESYA >> 2) + 4] >> 8) % TILESX,
(texture_indices[q * TILESX * (TILESYA >> 2) + 5] >> 8) / TILESX, (texture_indices[q * TILESX * (TILESYA >> 2) + 5] >> 8) % TILESX,
(texture_indices[q * TILESX * (TILESYA >> 2) + 6] >> 8) / TILESX, (texture_indices[q * TILESX * (TILESYA >> 2) + 6] >> 8) % TILESX,
(texture_indices[q * TILESX * (TILESYA >> 2) + 7] >> 8) / TILESX, (texture_indices[q * TILESX * (TILESYA >> 2) + 7] >> 8) % TILESX);
}
#endif //GEN_TEXTURE_LIST
#ifndef NOTEXTURE_RGBA
dim3 threads_rgba(1, 1, 1);
dim3 grid_rgba(1,1,1);
printf("threads_rgba=(%d, %d, %d)\n", threads_rgba.x,threads_rgba.y,threads_rgba.z);
printf("grid_rgba=(%d, %d, %d)\n", grid_rgba.x,grid_rgba.y,grid_rgba.z);
StopWatchInterface *timerRGBA = 0;
sdkCreateTimer(&timerRGBA);
for (int i = i0; i < numIterations; i++)
{
if (i == 0)
{
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&timerRGBA);
sdkStartTimer(&timerRGBA);
}
generate_RBGA<<<grid_rgba,threads_rgba>>> (
// Parameters to generate texture tasks
gpu_tasks, // struct tp_task * gpu_tasks,
tp_task_size, // int num_tiles, // number of tiles in task list
// declare arrays in device code?
gpu_texture_indices, // int * gpu_texture_indices,// packed tile + bits (now only (1 << 7)
gpu_num_texture_tiles, // int * num_texture_tiles, // number of texture tiles to process (8 elements)
gpu_woi, // int * woi, // x,y,width,height of the woi
TILESX, // int width, // <= TILESX, use for faster processing of LWIR images (should be actual + 1)
TILESY, // int height); // <= TILESY, use for faster processing of LWIR images
// Parameters for the texture generation
gpu_clt , // float ** gpu_clt, // [NUM_CAMS] ->[TILESY][TILESX][NUM_COLORS][DTT_SIZE*DTT_SIZE]
gpu_port_offsets, // float * port_offsets, // relative ports x,y offsets - just to scale differences, may be approximate
texture_colors, // int colors, // number of colors (3/1)
(texture_colors == 1), // int is_lwir, // do not perform shot correction
10.0, // float min_shot, // 10.0
3.0, // float scale_shot, // 3.0
1.5f, // float diff_sigma, // pixel value/pixel change
10.0f, // float diff_threshold, // pixel value/pixel change
3.0, // float min_agree, // minimal number of channels to agree on a point (real number to work with fuzzy averages)
0.294118, // float weight0, // scale for R
0.117647, // float weight1, // scale for B
0.588235, // float weight2, // scale for G
1, // int dust_remove, // Do not reduce average weight when only one image differes much from the average
0, // int keep_weights, // return channel weights after A in RGBA
dstride_textures_rbga/sizeof(float), // const size_t texture_rbga_stride, // in floats
gpu_textures_rbga); // float * gpu_texture_tiles) // (number of colors +1 + ?)*16*16 rgba texture tiles
getLastCudaError("Kernel failure");
checkCudaErrors(cudaDeviceSynchronize());
printf("test pass: %d\n",i);
}
sdkStopTimer(&timerRGBA);
float avgTimeRGBA = (float)sdkGetTimerValue(&timerRGBA) / (float)numIterations;
sdkDeleteTimer(&timerRGBA);
printf("Average Texture run time =%f ms\n", avgTimeRGBA);
checkCudaErrors(cudaMemcpy(
cpu_woi,
gpu_woi,
4 * sizeof(float),
cudaMemcpyDeviceToHost));
printf("WOI x=%d, y=%d, width=%d, height=%d\n", cpu_woi[0], cpu_woi[1], cpu_woi[2], cpu_woi[3]);
// temporarily use larger array (4 pixels each size, switch to cudaMemcpy2DFromArray()
int rgba_woi_width = (cpu_woi[2] + 1) * DTT_SIZE;
int rgba_woi_height = (cpu_woi[3] + 1)* DTT_SIZE;
int rslt_rgba_size = rgba_woi_width * rgba_woi_height * rbga_slices;
float * cpu_textures_rgba = (float *)malloc(rslt_rgba_size * sizeof(float));
checkCudaErrors(cudaMemcpy2D(
cpu_textures_rgba,
rgba_width * sizeof(float),
gpu_textures_rbga,
dstride_textures_rbga,
rgba_width * sizeof(float),
rgba_height * rbga_slices,
cudaMemcpyDeviceToHost));
#ifndef NSAVE_TEXTURES
printf("Writing RBGA texture slices to %s\n", result_textures_rgba_file);
writeFloatsToFile(
cpu_textures_rgba, // float * data, // allocated array
rslt_rgba_size, // int size, // length in elements
result_textures_rgba_file); // const char * path) // file path
#endif
#ifdef DEBUG11
int rgba_offset = (DBG_TILE_Y - cpu_woi[1]) * DTT_SIZE * rgba_woi_width + (DBG_TILE_X - cpu_woi[0]);
for (int chn = 0; chn < rbga_slices; chn++){
printf("\nchn = %d\n", chn);
int rgba_offset_chn = rgba_offset + chn * rgba_woi_width * rgba_woi_height;
for (int i = 0; i < 8; i++){
for (int j = 0; j < 8; j++){
printf("%10.4f ", *(cpu_textures_rgba + rgba_offset_chn + i * rgba_woi_width + j));
}
printf("\n");
}
}
#endif // DEBUG11
free(cpu_textures_rgba);
#endif // ifndef NOTEXTURES
#ifdef SAVE_CLT
free(cpu_clt);
#endif
free (host_kern_buf);
// TODO: move somewhere when all is done
for (int ncam = 0; ncam < NUM_CAMS; ncam++) {
checkCudaErrors(cudaFree(gpu_kernels_h[ncam]));
checkCudaErrors(cudaFree(gpu_kernel_offsets_h[ncam]));
checkCudaErrors(cudaFree(gpu_images_h[ncam]));
checkCudaErrors(cudaFree(gpu_clt_h[ncam]));
#ifndef NOICLT
checkCudaErrors(cudaFree(gpu_corr_images_h[ncam]));
#endif
}
checkCudaErrors(cudaFree(gpu_tasks));
checkCudaErrors(cudaFree(gpu_kernels));
checkCudaErrors(cudaFree(gpu_kernel_offsets));
checkCudaErrors(cudaFree(gpu_images));
checkCudaErrors(cudaFree(gpu_clt));
// checkCudaErrors(cudaFree(gpu_corr_images));
checkCudaErrors(cudaFree(gpu_corrs));
checkCudaErrors(cudaFree(gpu_corr_indices));
checkCudaErrors(cudaFree(gpu_texture_indices));
checkCudaErrors(cudaFree(gpu_port_offsets));
checkCudaErrors(cudaFree(gpu_textures));
checkCudaErrors(cudaFree(gpu_textures_rbga));
checkCudaErrors(cudaFree(gpu_woi));
checkCudaErrors(cudaFree(gpu_num_texture_tiles));
exit(0);
}
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment