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added optimized belief propagation implementation (used short for messages)

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This commit is contained in:
Vladislav Vinogradov
2010-08-02 11:10:29 +00:00
parent d6bbaea28a
commit 788ac96f8b
4 changed files with 504 additions and 308 deletions
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364
modules/gpu/src/cuda/beliefpropagation.cu
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@@ -41,43 +41,57 @@
//M*/
#include "opencv2/gpu/devmem2d.hpp"
#include "saturate_cast.hpp"
#include "safe_call.hpp"
using namespace cv::gpu;
static inline int divUp(int a, int b) { return (a % b == 0) ? a/b : a/b + 1; }
#ifndef FLT_MAX
#define FLT_MAX 3.402823466e+38F
#endif
typedef unsigned char uchar;
namespace beliefpropagation_gpu
{
__constant__ int cndisp;
__constant__ float cdisc_cost;
__constant__ float cdata_cost;
__constant__ float clambda;
};
///////////////////////////////////////////////////////////////
////////////////// comp data /////////////////////////////////
/////////////////////// load constants ////////////////////////
///////////////////////////////////////////////////////////////
namespace beliefpropagation_gpu
{
__global__ void comp_data_kernel(uchar* l, uchar* r, size_t step, float* data, size_t data_step, int cols, int rows)
__constant__ int cndisp;
__constant__ float cmax_data_term;
__constant__ float cdata_weight;
__constant__ float cmax_disc_term;
__constant__ float cdisc_single_jump;
};
namespace cv { namespace gpu { namespace impl {
void load_constants(int ndisp, float max_data_term, float data_weight, float max_disc_term, float disc_single_jump)
{
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cndisp, &ndisp, sizeof(int )) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cmax_data_term, &max_data_term, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cdata_weight, &data_weight, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cmax_disc_term, &max_disc_term, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cdisc_single_jump, &disc_single_jump, sizeof(float)) );
}
}}}
///////////////////////////////////////////////////////////////
////////////////////////// comp data //////////////////////////
///////////////////////////////////////////////////////////////
namespace beliefpropagation_gpu
{
template <typename T>
__global__ void comp_data(uchar* l, uchar* r, size_t step, T* data, size_t data_step, int cols, int rows)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y > 0 && y < rows - 1 && x > 0 && x < cols - 1)
if (y < rows && x < cols)
{
uchar *ls = l + y * step + x;
uchar *rs = r + y * step + x;
uchar* ls = l + y * step + x;
uchar* rs = r + y * step + x;
float *ds = data + y * data_step + x;
T* ds = data + y * data_step + x;
size_t disp_step = data_step * rows;
for (int disp = 0; disp < cndisp; disp++)
@@ -88,11 +102,11 @@ namespace beliefpropagation_gpu
int re = rs[-disp];
float val = abs(le - re);
ds[disp * disp_step] = clambda * fmin(val, cdata_cost);
ds[disp * disp_step] = saturate_cast<T>(fmin(cdata_weight * val, cdata_weight * cmax_data_term));
}
else
{
ds[disp * disp_step] = cdata_cost;
ds[disp * disp_step] = saturate_cast<T>(cdata_weight * cmax_data_term);
}
}
}
@@ -100,41 +114,52 @@ namespace beliefpropagation_gpu
}
namespace cv { namespace gpu { namespace impl {
extern "C" void load_constants(int ndisp, float disc_cost, float data_cost, float lambda)
{
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cndisp, &ndisp, sizeof(ndisp)) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cdisc_cost, &disc_cost, sizeof(disc_cost)) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cdata_cost, &data_cost, sizeof(data_cost)) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::clambda, &lambda, sizeof(lambda)) );
}
extern "C" void comp_data_caller(const DevMem2D& l, const DevMem2D& r, DevMem2D_<float> mdata, const cudaStream_t& stream)
typedef void (*CompDataFunc)(const DevMem2D& l, const DevMem2D& r, DevMem2D mdata, const cudaStream_t& stream);
template<typename T>
void comp_data_(const DevMem2D& l, const DevMem2D& r, DevMem2D mdata, const cudaStream_t& stream)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(l.cols, threads.x);
grid.y = divUp(l.rows, threads.y);
beliefpropagation_gpu::comp_data<T><<<grid, threads, 0, stream>>>(l.ptr, r.ptr, l.step, (T*)mdata.ptr, mdata.step/sizeof(T), l.cols, l.rows);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
if (stream == 0)
{
beliefpropagation_gpu::comp_data_kernel<<<grid, threads>>>(l.ptr, r.ptr, l.step, mdata.ptr, mdata.step/sizeof(float), l.cols, l.rows);
//cudaSafeCall( cudaThreadSynchronize() );
}
else
{
beliefpropagation_gpu::comp_data_kernel<<<grid, threads, 0, stream>>>(l.ptr, r.ptr, l.step, mdata.ptr, mdata.step/sizeof(float), l.cols, l.rows);
}
void comp_data(int msgType, const DevMem2D& l, const DevMem2D& r, DevMem2D mdata, const cudaStream_t& stream)
{
static CompDataFunc tab[8] =
{
0, // uchar
0, // schar
0, // ushort
comp_data_<short>, // short
0, // int
comp_data_<float>, // float
0, // double
0 // user type
};
CompDataFunc func = tab[msgType];
if (func == 0)
cv::gpu::error("Unsupported message type", __FILE__, __LINE__);
func(l, r, mdata, stream);
}
}}}
///////////////////////////////////////////////////////////////
////////////////// data_step_down ////////////////////////////
//////////////////////// data step down ///////////////////////
///////////////////////////////////////////////////////////////
namespace beliefpropagation_gpu
{
__global__ void data_down_kernel(int dst_cols, int dst_rows, int src_rows, float *src, size_t src_step, float *dst, size_t dst_step)
{
template <typename T>
__global__ void data_step_down(int dst_cols, int dst_rows, int src_rows, const T* src, size_t src_step, T* dst, size_t dst_step)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
@@ -151,14 +176,17 @@ namespace beliefpropagation_gpu
dst_reg += src[d * src_disp_step + src_step * (2*y+0) + (2*x+1)];
dst_reg += src[d * src_disp_step + src_step * (2*y+1) + (2*x+1)];
dst[d * dst_disp_step + y * dst_step + x] = dst_reg;
dst[d * dst_disp_step + y * dst_step + x] = saturate_cast<T>(dst_reg);
}
}
}
}
namespace cv { namespace gpu { namespace impl {
extern "C" void data_down_kernel_caller(int dst_cols, int dst_rows, int src_rows, const DevMem2D_<float>& src, DevMem2D_<float> dst, const cudaStream_t& stream)
typedef void (*DataStepDownFunc)(int dst_cols, int dst_rows, int src_rows, const DevMem2D& src, DevMem2D dst, const cudaStream_t& stream);
template<typename T>
void data_step_down_(int dst_cols, int dst_rows, int src_rows, const DevMem2D& src, DevMem2D dst, const cudaStream_t& stream)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
@@ -166,26 +194,41 @@ namespace cv { namespace gpu { namespace impl {
grid.x = divUp(dst_cols, threads.x);
grid.y = divUp(dst_rows, threads.y);
if (stream == 0)
{
beliefpropagation_gpu::data_down_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, src.ptr, src.step/sizeof(float), dst.ptr, dst.step/sizeof(float));
//cudaSafeCall( cudaThreadSynchronize() );
}
else
{
beliefpropagation_gpu::data_down_kernel<<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, src.ptr, src.step/sizeof(float), dst.ptr, dst.step/sizeof(float));
}
beliefpropagation_gpu::data_step_down<T><<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, (const T*)src.ptr, src.step/sizeof(T), (T*)dst.ptr, dst.step/sizeof(T));
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
void data_step_down(int dst_cols, int dst_rows, int src_rows, int msgType, const DevMem2D& src, DevMem2D dst, const cudaStream_t& stream)
{
static DataStepDownFunc tab[8] =
{
0, // uchar
0, // schar
0, // ushort
data_step_down_<short>, // short
0, // int
data_step_down_<float>, // float
0, // double
0 // user type
};
DataStepDownFunc func = tab[msgType];
if (func == 0)
cv::gpu::error("Unsupported message type", __FILE__, __LINE__);
func(dst_cols, dst_rows, src_rows, src, dst, stream);
}
}}}
///////////////////////////////////////////////////////////////
////////////////// level up messages ////////////////////////
/////////////////// level up messages ////////////////////////
///////////////////////////////////////////////////////////////
namespace beliefpropagation_gpu
{
__global__ void level_up_kernel(int dst_cols, int dst_rows, int src_rows, float *src, size_t src_step, float *dst, size_t dst_step)
{
template <typename T>
__global__ void level_up_message(int dst_cols, int dst_rows, int src_rows, const T* src, size_t src_step, T* dst, size_t dst_step)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
@@ -195,8 +238,8 @@ namespace beliefpropagation_gpu
const size_t dst_disp_step = dst_step * dst_rows;
const size_t src_disp_step = src_step * src_rows;
float *dstr = dst + y * dst_step + x;
float *srcr = src + y/2 * src_step + x/2;
T* dstr = dst + y * dst_step + x;
const T* srcr = src + y/2 * src_step + x/2;
for (int d = 0; d < cndisp; ++d)
dstr[d * dst_disp_step] = srcr[d * src_disp_step];
@@ -205,7 +248,10 @@ namespace beliefpropagation_gpu
}
namespace cv { namespace gpu { namespace impl {
extern "C" void level_up(int dst_idx, int dst_cols, int dst_rows, int src_rows, DevMem2D_<float>* mu, DevMem2D_<float>* md, DevMem2D_<float>* ml, DevMem2D_<float>* mr, const cudaStream_t& stream)
typedef void (*LevelUpMessagesFunc)(int dst_idx, int dst_cols, int dst_rows, int src_rows, DevMem2D* mus, DevMem2D* mds, DevMem2D* mls, DevMem2D* mrs, const cudaStream_t& stream);
template<typename T>
void level_up_messages_(int dst_idx, int dst_cols, int dst_rows, int src_rows, DevMem2D* mus, DevMem2D* mds, DevMem2D* mls, DevMem2D* mrs, const cudaStream_t& stream)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
@@ -215,74 +261,94 @@ namespace cv { namespace gpu { namespace impl {
int src_idx = (dst_idx + 1) & 1;
if (stream == 0)
{
beliefpropagation_gpu::level_up_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, mu[src_idx].ptr, mu[src_idx].step/sizeof(float), mu[dst_idx].ptr, mu[dst_idx].step/sizeof(float));
beliefpropagation_gpu::level_up_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, md[src_idx].ptr, md[src_idx].step/sizeof(float), md[dst_idx].ptr, md[dst_idx].step/sizeof(float));
beliefpropagation_gpu::level_up_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, ml[src_idx].ptr, ml[src_idx].step/sizeof(float), ml[dst_idx].ptr, ml[dst_idx].step/sizeof(float));
beliefpropagation_gpu::level_up_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, mr[src_idx].ptr, mr[src_idx].step/sizeof(float), mr[dst_idx].ptr, mr[dst_idx].step/sizeof(float));
//cudaSafeCall( cudaThreadSynchronize() );
}
else
{
beliefpropagation_gpu::level_up_kernel<<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, mu[src_idx].ptr, mu[src_idx].step/sizeof(float), mu[dst_idx].ptr, mu[dst_idx].step/sizeof(float));
beliefpropagation_gpu::level_up_kernel<<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, md[src_idx].ptr, md[src_idx].step/sizeof(float), md[dst_idx].ptr, md[dst_idx].step/sizeof(float));
beliefpropagation_gpu::level_up_kernel<<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, ml[src_idx].ptr, ml[src_idx].step/sizeof(float), ml[dst_idx].ptr, ml[dst_idx].step/sizeof(float));
beliefpropagation_gpu::level_up_kernel<<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, mr[src_idx].ptr, mr[src_idx].step/sizeof(float), mr[dst_idx].ptr, mr[dst_idx].step/sizeof(float));
}
beliefpropagation_gpu::level_up_message<T><<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, (const T*)mus[src_idx].ptr, mus[src_idx].step/sizeof(T), (T*)mus[dst_idx].ptr, mus[dst_idx].step/sizeof(T));
beliefpropagation_gpu::level_up_message<T><<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, (const T*)mds[src_idx].ptr, mds[src_idx].step/sizeof(T), (T*)mds[dst_idx].ptr, mds[dst_idx].step/sizeof(T));
beliefpropagation_gpu::level_up_message<T><<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, (const T*)mls[src_idx].ptr, mls[src_idx].step/sizeof(T), (T*)mls[dst_idx].ptr, mls[dst_idx].step/sizeof(T));
beliefpropagation_gpu::level_up_message<T><<<grid, threads, 0, stream>>>(dst_cols, dst_rows, src_rows, (const T*)mrs[src_idx].ptr, mrs[src_idx].step/sizeof(T), (T*)mrs[dst_idx].ptr, mrs[dst_idx].step/sizeof(T));
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
void level_up_messages(int dst_idx, int dst_cols, int dst_rows, int src_rows, int msgType, DevMem2D* mus, DevMem2D* mds, DevMem2D* mls, DevMem2D* mrs, const cudaStream_t& stream)
{
static LevelUpMessagesFunc tab[8] =
{
0, // uchar
0, // schar
0, // ushort
level_up_messages_<short>, // short
0, // int
level_up_messages_<float>, // float
0, // double
0 // user type
};
LevelUpMessagesFunc func = tab[msgType];
if (func == 0)
cv::gpu::error("Unsupported message type", __FILE__, __LINE__);
func(dst_idx, dst_cols, dst_rows, src_rows, mus, mds, mls, mrs, stream);
}
}}}
///////////////////////////////////////////////////////////////
///////////////// Calcs all iterations ///////////////////////
//////////////////// calc all iterations /////////////////////
///////////////////////////////////////////////////////////////
namespace beliefpropagation_gpu
{
__device__ void calc_min_linear_penalty(float *dst, size_t step)
template <typename T>
__device__ void calc_min_linear_penalty(T* dst, size_t step)
{
float prev = dst[0];
float cur;
for (int disp = 1; disp < cndisp; ++disp)
{
prev += 1.0f;
prev += cdisc_single_jump;
cur = dst[step * disp];
if (prev < cur)
{
cur = prev;
dst[step * disp] = prev = cur;
dst[step * disp] = saturate_cast<T>(prev);
}
prev = cur;
}
prev = dst[(cndisp - 1) * step];
for (int disp = cndisp - 2; disp >= 0; disp--)
{
prev += 1.0f;
prev += cdisc_single_jump;
cur = dst[step * disp];
if (prev < cur)
{
cur = prev;
dst[step * disp] = prev = cur;
dst[step * disp] = saturate_cast<T>(prev);
}
prev = cur;
}
}
__device__ void message(float *msg1, float *msg2, float *msg3, float *data, float *dst, size_t msg_disp_step, size_t data_disp_step)
template <typename T>
__device__ void message(const T* msg1, const T* msg2, const T* msg3, const T* data, T* dst, size_t msg_disp_step, size_t data_disp_step)
{
float minimum = FLT_MAX;
for(int i = 0; i < cndisp; ++i)
{
float dst_reg = msg1[msg_disp_step * i] + msg2[msg_disp_step * i] + msg3[msg_disp_step * i] + data[data_disp_step * i];
float dst_reg = msg1[msg_disp_step * i];
dst_reg += msg2[msg_disp_step * i];
dst_reg += msg3[msg_disp_step * i];
dst_reg += data[data_disp_step * i];
if (dst_reg < minimum)
minimum = dst_reg;
dst[msg_disp_step * i] = dst_reg;
dst[msg_disp_step * i] = saturate_cast<T>(dst_reg);
}
calc_min_linear_penalty(dst, msg_disp_step);
minimum += cdisc_cost;
minimum += cmax_disc_term;
float sum = 0;
for(int i = 0; i < cndisp; ++i)
@@ -290,7 +356,8 @@ namespace beliefpropagation_gpu
float dst_reg = dst[msg_disp_step * i];
if (dst_reg > minimum)
{
dst[msg_disp_step * i] = dst_reg = minimum;
dst_reg = minimum;
dst[msg_disp_step * i] = saturate_cast<T>(minimum);
}
sum += dst_reg;
}
@@ -300,18 +367,20 @@ namespace beliefpropagation_gpu
dst[msg_disp_step * i] -= sum;
}
__global__ void one_iteration(int t, float* u, float *d, float *l, float *r, size_t msg_step, float *data, size_t data_step, int cols, int rows)
template <typename T>
__global__ void one_iteration(int t, T* u, T* d, T* l, T* r, size_t msg_step, const T* data, size_t data_step, int cols, int rows)
{
int y = blockIdx.y * blockDim.y + threadIdx.y;
int x = ((blockIdx.x * blockDim.x + threadIdx.x) << 1) + ((y + t) & 1);
if ( (y > 0) && (y < rows - 1) && (x > 0) && (x < cols - 1))
{
float *us = u + y * msg_step + x;
float *ds = d + y * msg_step + x;
float *ls = l + y * msg_step + x;
float *rs = r + y * msg_step + x;
float *dt = data + y * data_step + x;
T* us = u + y * msg_step + x;
T* ds = d + y * msg_step + x;
T* ls = l + y * msg_step + x;
T* rs = r + y * msg_step + x;
const T* dt = data + y * data_step + x;
size_t msg_disp_step = msg_step * rows;
size_t data_disp_step = data_step * rows;
@@ -324,7 +393,10 @@ namespace beliefpropagation_gpu
}
namespace cv { namespace gpu { namespace impl {
extern "C" void call_all_iterations(int cols, int rows, int iters, DevMem2D_<float>& u, DevMem2D_<float>& d, DevMem2D_<float>& l, DevMem2D_<float>& r, const DevMem2D_<float>& data, const cudaStream_t& stream)
typedef void (*CalcAllIterationFunc)(int cols, int rows, int iters, DevMem2D& u, DevMem2D& d, DevMem2D& l, DevMem2D& r, const DevMem2D& data, const cudaStream_t& stream);
template<typename T>
void calc_all_iterations_(int cols, int rows, int iters, DevMem2D& u, DevMem2D& d, DevMem2D& l, DevMem2D& r, const DevMem2D& data, const cudaStream_t& stream)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
@@ -332,39 +404,55 @@ namespace cv { namespace gpu { namespace impl {
grid.x = divUp(cols, threads.x << 1);
grid.y = divUp(rows, threads.y);
if (stream == 0)
for(int t = 0; t < iters; ++t)
{
for(int t = 0; t < iters; ++t)
beliefpropagation_gpu::one_iteration<<<grid, threads>>>(t, u.ptr, d.ptr, l.ptr, r.ptr, u.step/sizeof(float), data.ptr, data.step/sizeof(float), cols, rows);
//cudaSafeCall( cudaThreadSynchronize() );
}
else
{
for(int t = 0; t < iters; ++t)
beliefpropagation_gpu::one_iteration<<<grid, threads, 0, stream>>>(t, u.ptr, d.ptr, l.ptr, r.ptr, u.step/sizeof(float), data.ptr, data.step/sizeof(float), cols, rows);
}
beliefpropagation_gpu::one_iteration<T><<<grid, threads, 0, stream>>>(t, (T*)u.ptr, (T*)d.ptr, (T*)l.ptr, (T*)r.ptr, u.step/sizeof(T), (const T*)data.ptr, data.step/sizeof(T), cols, rows);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
}
void calc_all_iterations(int cols, int rows, int iters, int msgType, DevMem2D& u, DevMem2D& d, DevMem2D& l, DevMem2D& r, const DevMem2D& data, const cudaStream_t& stream)
{
static CalcAllIterationFunc tab[8] =
{
0, // uchar
0, // schar
0, // ushort
calc_all_iterations_<short>, // short
0, // int
calc_all_iterations_<float>, // float
0, // double
0 // user type
};
CalcAllIterationFunc func = tab[msgType];
if (func == 0)
cv::gpu::error("Unsupported message type", __FILE__, __LINE__);
func(cols, rows, iters, u, d, l, r, data, stream);
}
}}}
///////////////////////////////////////////////////////////////
////////////////// Output caller /////////////////////////////
/////////////////////////// output ////////////////////////////
///////////////////////////////////////////////////////////////
namespace beliefpropagation_gpu
{
__global__ void output(int cols, int rows, float *u, float *d, float *l, float *r, float* data, size_t step, int *disp, size_t res_step)
{
template <typename T>
__global__ void output(int cols, int rows, const T* u, const T* d, const T* l, const T* r, const T* data, size_t step, short* disp, size_t res_step)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y > 0 && y < rows - 1 && x > 0 && x < cols - 1)
{
float *us = u + (y + 1) * step + x;
float *ds = d + (y - 1) * step + x;
float *ls = l + y * step + (x + 1);
float *rs = r + y * step + (x - 1);
float *dt = data + y * step + x;
const T* us = u + (y + 1) * step + x;
const T* ds = d + (y - 1) * step + x;
const T* ls = l + y * step + (x + 1);
const T* rs = r + y * step + (x - 1);
const T* dt = data + y * step + x;
size_t disp_step = rows * step;
@@ -372,7 +460,11 @@ namespace beliefpropagation_gpu
float best_val = FLT_MAX;
for (int d = 0; d < cndisp; ++d)
{
float val = us[d * disp_step] + ds[d * disp_step] + ls[d * disp_step] + rs[d * disp_step] + dt[d * disp_step];
float val = us[d * disp_step];
val += ds[d * disp_step];
val += ls[d * disp_step];
val += rs[d * disp_step];
val += dt[d * disp_step];
if (val < best_val)
{
@@ -381,28 +473,46 @@ namespace beliefpropagation_gpu
}
}
disp[res_step * y + x] = best;
disp[res_step * y + x] = saturate_cast<short>(best);
}
}
}
namespace cv { namespace gpu { namespace impl {
extern "C" void output_caller(const DevMem2D_<float>& u, const DevMem2D_<float>& d, const DevMem2D_<float>& l, const DevMem2D_<float>& r, const DevMem2D_<float>& data, DevMem2D_<int> disp, const cudaStream_t& stream)
{
typedef void (*OutputFunc)(const DevMem2D& u, const DevMem2D& d, const DevMem2D& l, const DevMem2D& r, const DevMem2D& data, DevMem2D disp, const cudaStream_t& stream);
template<typename T>
void output_(const DevMem2D& u, const DevMem2D& d, const DevMem2D& l, const DevMem2D& r, const DevMem2D& data, DevMem2D disp, const cudaStream_t& stream)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(disp.cols, threads.x);
grid.y = divUp(disp.rows, threads.y);
if (stream == 0)
{
beliefpropagation_gpu::output<<<grid, threads>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step/sizeof(int));
cudaSafeCall( cudaThreadSynchronize() );
}
else
{
beliefpropagation_gpu::output<<<grid, threads, 0, stream>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step/sizeof(int));
}
beliefpropagation_gpu::output<T><<<grid, threads, 0, stream>>>(disp.cols, disp.rows, (const T*)u.ptr, (const T*)d.ptr, (const T*)l.ptr, (const T*)r.ptr, (const T*)data.ptr, u.step/sizeof(T), (short*)disp.ptr, disp.step/sizeof(short));
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
void output(int msgType, const DevMem2D& u, const DevMem2D& d, const DevMem2D& l, const DevMem2D& r, const DevMem2D& data, DevMem2D disp, const cudaStream_t& stream)
{
static OutputFunc tab[8] =
{
0, // uchar
0, // schar
0, // ushort
output_<short>, // short
0, // int
output_<float>, // float
0, // double
0 // user type
};
OutputFunc func = tab[msgType];
if (func == 0)
cv::gpu::error("Unsupported message type", __FILE__, __LINE__);
func(u, d, l, r, data, disp, stream);
}
}}}

216
modules/gpu/src/cuda/saturate_cast.hpp
View File

@@ -49,119 +49,123 @@ namespace cv
{
namespace gpu
{
template<typename _Tp> __device__ _Tp saturate_cast(uchar v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(schar v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(ushort v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(short v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(uint v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(int v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(float v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(double v) { return _Tp(v); }
template<> __device__ uchar saturate_cast<uchar>(schar v)
{ return (uchar)max((int)v, 0); }
template<> __device__ uchar saturate_cast<uchar>(ushort v)
{ return (uchar)min((uint)v, (uint)UCHAR_MAX); }
template<> __device__ uchar saturate_cast<uchar>(int v)
{ return (uchar)((uint)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
template<> __device__ uchar saturate_cast<uchar>(uint v)
{ return (uchar)min(v, (uint)UCHAR_MAX); }
template<> __device__ uchar saturate_cast<uchar>(short v)
{ return saturate_cast<uchar>((uint)v); }
template<> __device__ uchar saturate_cast<uchar>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<uchar>(iv); }
template<> __device__ uchar saturate_cast<uchar>(double v)
// To fix link error: this func already defined in other obj file
namespace
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<uchar>(iv);
#else
return saturate_cast<uchar>((float)v);
#endif
}
template<typename _Tp> __device__ _Tp saturate_cast(uchar v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(schar v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(ushort v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(short v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(uint v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(int v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(float v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(double v) { return _Tp(v); }
template<> __device__ schar saturate_cast<schar>(uchar v)
{ return (schar)min((int)v, SCHAR_MAX); }
template<> __device__ schar saturate_cast<schar>(ushort v)
{ return (schar)min((uint)v, (uint)SCHAR_MAX); }
template<> __device__ schar saturate_cast<schar>(int v)
{
return (schar)((uint)(v-SCHAR_MIN) <= (uint)UCHAR_MAX ?
v : v > 0 ? SCHAR_MAX : SCHAR_MIN);
}
template<> __device__ schar saturate_cast<schar>(short v)
{ return saturate_cast<schar>((int)v); }
template<> __device__ schar saturate_cast<schar>(uint v)
{ return (schar)min(v, (uint)SCHAR_MAX); }
template<> __device__ uchar saturate_cast<uchar>(schar v)
{ return (uchar)max((int)v, 0); }
template<> __device__ uchar saturate_cast<uchar>(ushort v)
{ return (uchar)min((uint)v, (uint)UCHAR_MAX); }
template<> __device__ uchar saturate_cast<uchar>(int v)
{ return (uchar)((uint)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
template<> __device__ uchar saturate_cast<uchar>(uint v)
{ return (uchar)min(v, (uint)UCHAR_MAX); }
template<> __device__ uchar saturate_cast<uchar>(short v)
{ return saturate_cast<uchar>((uint)v); }
template<> __device__ schar saturate_cast<schar>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<schar>(iv); }
template<> __device__ schar saturate_cast<schar>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<schar>(iv);
#else
return saturate_cast<schar>((float)v);
#endif
}
template<> __device__ uchar saturate_cast<uchar>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<uchar>(iv); }
template<> __device__ uchar saturate_cast<uchar>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<uchar>(iv);
#else
return saturate_cast<uchar>((float)v);
#endif
}
template<> __device__ ushort saturate_cast<ushort>(schar v)
{ return (ushort)max((int)v, 0); }
template<> __device__ ushort saturate_cast<ushort>(short v)
{ return (ushort)max((int)v, 0); }
template<> __device__ ushort saturate_cast<ushort>(int v)
{ return (ushort)((uint)v <= (uint)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
template<> __device__ ushort saturate_cast<ushort>(uint v)
{ return (ushort)min(v, (uint)USHRT_MAX); }
template<> __device__ ushort saturate_cast<ushort>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<ushort>(iv); }
template<> __device__ ushort saturate_cast<ushort>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<ushort>(iv);
#else
return saturate_cast<ushort>((float)v);
#endif
}
template<> __device__ schar saturate_cast<schar>(uchar v)
{ return (schar)min((int)v, SCHAR_MAX); }
template<> __device__ schar saturate_cast<schar>(ushort v)
{ return (schar)min((uint)v, (uint)SCHAR_MAX); }
template<> __device__ schar saturate_cast<schar>(int v)
{
return (schar)((uint)(v-SCHAR_MIN) <= (uint)UCHAR_MAX ?
v : v > 0 ? SCHAR_MAX : SCHAR_MIN);
}
template<> __device__ schar saturate_cast<schar>(short v)
{ return saturate_cast<schar>((int)v); }
template<> __device__ schar saturate_cast<schar>(uint v)
{ return (schar)min(v, (uint)SCHAR_MAX); }
template<> __device__ short saturate_cast<short>(ushort v)
{ return (short)min((int)v, SHRT_MAX); }
template<> __device__ short saturate_cast<short>(int v)
{
return (short)((uint)(v - SHRT_MIN) <= (uint)USHRT_MAX ?
v : v > 0 ? SHRT_MAX : SHRT_MIN);
}
template<> __device__ short saturate_cast<short>(uint v)
{ return (short)min(v, (uint)SHRT_MAX); }
template<> __device__ short saturate_cast<short>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<short>(iv); }
template<> __device__ short saturate_cast<short>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<short>(iv);
#else
return saturate_cast<short>((float)v);
#endif
}
template<> __device__ schar saturate_cast<schar>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<schar>(iv); }
template<> __device__ schar saturate_cast<schar>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<schar>(iv);
#else
return saturate_cast<schar>((float)v);
#endif
}
template<> __device__ int saturate_cast<int>(float v) { return __float2int_rn(v); }
template<> __device__ int saturate_cast<int>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
return __double2int_rn(v);
#else
return saturate_cast<int>((float)v);
#endif
}
template<> __device__ ushort saturate_cast<ushort>(schar v)
{ return (ushort)max((int)v, 0); }
template<> __device__ ushort saturate_cast<ushort>(short v)
{ return (ushort)max((int)v, 0); }
template<> __device__ ushort saturate_cast<ushort>(int v)
{ return (ushort)((uint)v <= (uint)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
template<> __device__ ushort saturate_cast<ushort>(uint v)
{ return (ushort)min(v, (uint)USHRT_MAX); }
template<> __device__ ushort saturate_cast<ushort>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<ushort>(iv); }
template<> __device__ ushort saturate_cast<ushort>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<ushort>(iv);
#else
return saturate_cast<ushort>((float)v);
#endif
}
template<> __device__ uint saturate_cast<uint>(float v){ return __float2uint_rn(v); }
template<> __device__ uint saturate_cast<uint>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
return __double2uint_rn(v);
#else
return saturate_cast<uint>((float)v);
#endif
template<> __device__ short saturate_cast<short>(ushort v)
{ return (short)min((int)v, SHRT_MAX); }
template<> __device__ short saturate_cast<short>(int v)
{
return (short)((uint)(v - SHRT_MIN) <= (uint)USHRT_MAX ?
v : v > 0 ? SHRT_MAX : SHRT_MIN);
}
template<> __device__ short saturate_cast<short>(uint v)
{ return (short)min(v, (uint)SHRT_MAX); }
template<> __device__ short saturate_cast<short>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<short>(iv); }
template<> __device__ short saturate_cast<short>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<short>(iv);
#else
return saturate_cast<short>((float)v);
#endif
}
template<> __device__ int saturate_cast<int>(float v) { return __float2int_rn(v); }
template<> __device__ int saturate_cast<int>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
return __double2int_rn(v);
#else
return saturate_cast<int>((float)v);
#endif
}
template<> __device__ uint saturate_cast<uint>(float v){ return __float2uint_rn(v); }
template<> __device__ uint saturate_cast<uint>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
return __double2uint_rn(v);
#else
return saturate_cast<uint>((float)v);
#endif
}
}
}
}