13fe3c84
 Laila Saadatifard
update the stimli...
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#include <stim/cuda/sharedmem.cuh>
namespace stim{
namespace cuda{
// this kernel calculates the vote value by adding up the gradient magnitudes of every voter that this pixel is located in their voting area
template<typename T>
__global__ void cuda_vote(T* gpuVote, cudaTextureObject_t in, T* gpuTable, T phi, unsigned int rmax, unsigned int x, unsigned int y){
//generate a pointer to shared memory (size will be specified as a kernel parameter)
extern __shared__ float2 s_grad[];
//calculate the start point for this block
int bxi = blockIdx.x * blockDim.x;
//calculate the width of the shared memory block
int swidth = 2 * rmax + blockDim.x;
// calculate the 2D coordinates for this current thread.
int xi = bxi + threadIdx.x;
int yi = blockIdx.y;
// convert 2D coordinates to 1D
int i = yi * x + xi;
// define a local variable to sum the votes from the voters
float sum = 0;
// compute the size of window which will be checked for finding the proper voters for this pixel
unsigned int x_table = 2*rmax +1;
unsigned int rmax_sq = rmax * rmax;
int r = (int)rmax;
int tx_rmax = threadIdx.x + rmax;
int bxs = bxi - rmax;
for(int yr = -r; yr <= r; yr++){
//copy the portion of the image necessary for this block to shared memory
__syncthreads();
stim::cuda::sharedMemcpy_tex2D<float2>(s_grad, in, bxs, yi + yr , swidth, 1, threadIdx, blockDim);
__syncthreads();
//if the current thread is outside of the image, it doesn't have to be computed
if(xi < x && yi < y){
for(int xr = -r; xr <= r; xr++){
//find the location of this voter in the atan2 table
unsigned int id_t = (yr + rmax) * x_table + xr + rmax;
// calculate the angle between the pixel and the current voter in x and y directions
float atan_angle = gpuTable[id_t];
// calculate the voting direction based on the grtadient direction
int idx_share = xr + tx_rmax ;
float2 g = s_grad[idx_share];
float theta = g.x;
// check if the current voter is located in the voting area of this pixel.
if (((xr * xr + yr *yr)< rmax_sq) && (abs(atan_angle - theta) <phi)){
sum += g.y;
}
}
}
}
gpuVote[i] = sum;
}
template<typename T>
void gpu_vote(T* gpuVote, T* gpuGrad, T* gpuTable, T phi, unsigned int rmax, unsigned int x, unsigned int y){
//get the number of pixels in the image
unsigned int pixels = x * y;
unsigned int bytes = sizeof(T) * pixels;
unsigned int max_threads = stim::maxThreadsPerBlock();
//unsigned int thread_dim = sqrt(max_threads);
dim3 threads(max_threads, 1);
dim3 blocks(x/threads.x + (x %threads.x == 0 ? 0:1) , y);
// Allocate CUDA array in device memory
//define a channel descriptor for a single 32-bit channel
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc(32, 32, 0, 0,
cudaChannelFormatKindFloat);
cudaArray* cuArray; //declare the cuda array
cudaMallocArray(&cuArray, &channelDesc, x, y); //allocate the cuda array
// Copy the image data from global memory to the array
cudaMemcpyToArray(cuArray, 0, 0, gpuGrad, bytes*2,
cudaMemcpyDeviceToDevice);
// Specify texture
struct cudaResourceDesc resDesc; //create a resource descriptor
memset(&resDesc, 0, sizeof(resDesc)); //set all values to zero
resDesc.resType = cudaResourceTypeArray; //specify the resource descriptor type
resDesc.res.array.array = cuArray; //add a pointer to the cuda array
// Specify texture object parameters
struct cudaTextureDesc texDesc; //create a texture descriptor
memset(&texDesc, 0, sizeof(texDesc)); //set all values in the texture descriptor to zero
texDesc.addressMode[0] = cudaAddressModeWrap; //use wrapping (around the edges)
texDesc.addressMode[1] = cudaAddressModeWrap;
texDesc.filterMode = cudaFilterModePoint; //use linear filtering
texDesc.readMode = cudaReadModeElementType; //reads data based on the element type (32-bit floats)
texDesc.normalizedCoords = 0; //not using normalized coordinates
// Create texture object
cudaTextureObject_t texObj = 0;
cudaCreateTextureObject(&texObj, &resDesc, &texDesc, NULL);
// specify share memory
unsigned int share_bytes = (2*rmax + threads.x)*(1)*2*4;
//call the kernel to do the voting
cuda_vote <<< blocks, threads,share_bytes >>>(gpuVote, texObj, gpuTable, phi, rmax, x , y);
}
template<typename T>
void cpu_vote(T* cpuVote, T* cpuGrad,T* cpuTable, T phi, unsigned int rmax, unsigned int x, unsigned int y){
//calculate the number of bytes in the array
unsigned int bytes = x * y * sizeof(T);
//calculate the number of bytes in the atan2 table
unsigned int bytes_table = (2*rmax+1) * (2*rmax+1) * sizeof(T);
//allocate space on the GPU for the Vote Image
T* gpuVote;
cudaMalloc(&gpuVote, bytes);
//allocate space on the GPU for the input Gradient image
T* gpuGrad;
HANDLE_ERROR(cudaMalloc(&gpuGrad, bytes*2));
//copy the Gradient Magnitude data to the GPU
HANDLE_ERROR(cudaMemcpy(gpuGrad, cpuGrad, bytes*2, cudaMemcpyHostToDevice));
//allocate space on the GPU for the atan2 table
T* gpuTable;
HANDLE_ERROR(cudaMalloc(&gpuTable, bytes_table));
//copy the atan2 values to the GPU
HANDLE_ERROR(cudaMemcpy(gpuTable, cpuTable, bytes_table, cudaMemcpyHostToDevice));
//cudaMemcpyToSymbol (cstTable, cpuTable, bytes_table );
//call the GPU version of the vote calculation function
gpu_vote<T>(gpuVote, gpuGrad, gpuTable, phi, rmax, x , y);
//copy the Vote Data back to the CPU
cudaMemcpy(cpuVote, gpuVote, bytes, cudaMemcpyDeviceToHost) ;
//free allocated memory
cudaFree(gpuTable);
cudaFree(gpuVote);
cudaFree(gpuGrad);
}
}
}
#endif
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