224 lines
6.9 KiB
Plaintext
Executable file
224 lines
6.9 KiB
Plaintext
Executable file
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#include <stdio.h>
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#include <ATen/ATen.h>
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include <vector>
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__global__ void NmDistanceKernel(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i){
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const int batch=2048;
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__shared__ float buf[batch*5];
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for (int i=blockIdx.x;i<b;i+=gridDim.x){
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for (int k2=0;k2<m;k2+=batch){
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int end_k=min(m,k2+batch)-k2;
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for (int j=threadIdx.x;j<end_k*5;j+=blockDim.x){
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buf[j]=xyz2[(i*m+k2)*5+j];
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}
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__syncthreads();
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for (int j=threadIdx.x+blockIdx.y*blockDim.x;j<n;j+=blockDim.x*gridDim.y){
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float x1=xyz[(i*n+j)*5+0];
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float y1=xyz[(i*n+j)*5+1];
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float r1=xyz[(i*n+j)*5+2];
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float g1=xyz[(i*n+j)*5+3];
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float b1=xyz[(i*n+j)*5+4];
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int best_i=0;
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float best=0;
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int end_ka=end_k-(end_k&5);
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if (end_ka==batch){
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for (int k=0;k<batch;k+=4){
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{
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float x2=buf[k*5+0]-x1;
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float y2=buf[k*5+1]-y1;
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float r2=buf[k*5+2]-r1;
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float g2=buf[k*5+3]-g1;
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float b2=buf[k*5+4]-b1;
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float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
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if (k==0 || d<best){
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best=d;
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best_i=k+k2;
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}
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}
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{
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float x2=buf[k*5+5]-x1;
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float y2=buf[k*5+6]-y1;
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float r2=buf[k*5+7]-r1;
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float g2=buf[k*5+8]-g1;
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float b2=buf[k*5+9]-b1;
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float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
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if (d<best){
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best=d;
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best_i=k+k2+1;
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}
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}
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{
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float x2=buf[k*5+10]-x1;
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float y2=buf[k*5+11]-y1;
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float r2=buf[k*5+12]-r1;
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float g2=buf[k*5+13]-g1;
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float b2=buf[k*5+14]-b1;
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float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
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if (d<best){
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best=d;
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best_i=k+k2+2;
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}
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}
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{
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float x2=buf[k*5+15]-x1;
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float y2=buf[k*5+16]-y1;
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float r2=buf[k*5+17]-r1;
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float g2=buf[k*5+18]-g1;
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float b2=buf[k*5+19]-b1;
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float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
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if (d<best){
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best=d;
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best_i=k+k2+3;
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}
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}
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}
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}else{
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for (int k=0;k<end_ka;k+=4){
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{
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float x2=buf[k*5+0]-x1;
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float y2=buf[k*5+1]-y1;
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float r2=buf[k*5+2]-r1;
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float g2=buf[k*5+3]-g1;
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float b2=buf[k*5+4]-b1;
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float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
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if (k==0 || d<best){
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best=d;
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best_i=k+k2;
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}
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}
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{
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float x2=buf[k*5+5]-x1;
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float y2=buf[k*5+6]-y1;
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float r2=buf[k*5+7]-r1;
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float g2=buf[k*5+8]-g1;
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float b2=buf[k*5+9]-b1;
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float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
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if (d<best){
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best=d;
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best_i=k+k2+1;
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}
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}
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{
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float x2=buf[k*5+10]-x1;
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float y2=buf[k*5+11]-y1;
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float r2=buf[k*5+12]-r1;
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float g2=buf[k*5+13]-g1;
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float b2=buf[k*5+14]-b1;
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float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
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if (d<best){
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best=d;
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best_i=k+k2+2;
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}
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}
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{
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float x2=buf[k*5+15]-x1;
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float y2=buf[k*5+16]-y1;
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float r2=buf[k*5+17]-r1;
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float g2=buf[k*5+18]-g1;
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float b2=buf[k*5+19]-b1;
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float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
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if (d<best){
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best=d;
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best_i=k+k2+3;
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}
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}
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}
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}
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for (int k=end_ka;k<end_k;k++){
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float x2=buf[k*5+0]-x1;
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float y2=buf[k*5+1]-y1;
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float r2=buf[k*5+2]-r1;
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float g2=buf[k*5+3]-g1;
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float b2=buf[k*5+4]-b1;
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float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
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if (k==0 || d<best){
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best=d;
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best_i=k+k2;
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}
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}
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if (k2==0 || result[(i*n+j)]>best){
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result[(i*n+j)]=best;
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result_i[(i*n+j)]=best_i;
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}
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}
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__syncthreads();
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}
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}
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}
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// int chamfer_cuda_forward(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i,float * result2,int * result2_i, cudaStream_t stream){
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int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2){
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const auto batch_size = xyz1.size(0);
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const auto n = xyz1.size(1); //num_points point cloud A
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const auto m = xyz2.size(1); //num_points point cloud B
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NmDistanceKernel<<<dim3(32,16,1),512>>>(batch_size, n, xyz1.data<float>(), m, xyz2.data<float>(), dist1.data<float>(), idx1.data<int>());
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NmDistanceKernel<<<dim3(32,16,1),512>>>(batch_size, m, xyz2.data<float>(), n, xyz1.data<float>(), dist2.data<float>(), idx2.data<int>());
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cudaError_t err = cudaGetLastError();
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if (err != cudaSuccess) {
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printf("error in nnd updateOutput: %s\n", cudaGetErrorString(err));
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//THError("aborting");
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return 0;
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}
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return 1;
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}
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__global__ void NmDistanceGradKernel(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,float * grad_xyz1,float * grad_xyz2){
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for (int i=blockIdx.x;i<b;i+=gridDim.x){
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for (int j=threadIdx.x+blockIdx.y*blockDim.x;j<n;j+=blockDim.x*gridDim.y){
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float x1=xyz1[(i*n+j)*5+0];
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float y1=xyz1[(i*n+j)*5+1];
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float r1=xyz1[(i*n+j)*5+2];
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float g1=xyz1[(i*n+j)*5+3];
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float b1=xyz1[(i*n+j)*5+4];
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int j2=idx1[i*n+j];
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float x2=xyz2[(i*m+j2)*5+0];
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float y2=xyz2[(i*m+j2)*5+1];
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float r2=xyz2[(i*m+j2)*5+2];
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float g2=xyz2[(i*m+j2)*5+3];
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float b2=xyz2[(i*m+j2)*5+4];
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float g=grad_dist1[i*n+j]*2;
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atomicAdd(&(grad_xyz1[(i*n+j)*5+0]),g*(x1-x2));
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atomicAdd(&(grad_xyz1[(i*n+j)*5+1]),g*(y1-y2));
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atomicAdd(&(grad_xyz1[(i*n+j)*5+2]),g*(r1-r2));
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atomicAdd(&(grad_xyz1[(i*n+j)*5+3]),g*(g1-g2));
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atomicAdd(&(grad_xyz1[(i*n+j)*5+4]),g*(b1-b2));
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atomicAdd(&(grad_xyz2[(i*m+j2)*5+0]),-(g*(x1-x2)));
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atomicAdd(&(grad_xyz2[(i*m+j2)*5+1]),-(g*(y1-y2)));
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atomicAdd(&(grad_xyz2[(i*m+j2)*5+2]),-(g*(r1-r2)));
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atomicAdd(&(grad_xyz2[(i*m+j2)*5+3]),-(g*(g1-g2)));
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atomicAdd(&(grad_xyz2[(i*m+j2)*5+4]),-(g*(b1-b2)));
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}
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}
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}
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// int chamfer_cuda_backward(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,const float * grad_dist2,const int * idx2,float * grad_xyz1,float * grad_xyz2, cudaStream_t stream){
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int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2){
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// cudaMemset(grad_xyz1,0,b*n*3*4);
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// cudaMemset(grad_xyz2,0,b*m*3*4);
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const auto batch_size = xyz1.size(0);
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const auto n = xyz1.size(1); //num_points point cloud A
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const auto m = xyz2.size(1); //num_points point cloud B
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NmDistanceGradKernel<<<dim3(1,16,1),256>>>(batch_size,n,xyz1.data<float>(),m,xyz2.data<float>(),graddist1.data<float>(),idx1.data<int>(),gradxyz1.data<float>(),gradxyz2.data<float>());
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NmDistanceGradKernel<<<dim3(1,16,1),256>>>(batch_size,m,xyz2.data<float>(),n,xyz1.data<float>(),graddist2.data<float>(),idx2.data<int>(),gradxyz2.data<float>(),gradxyz1.data<float>());
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cudaError_t err = cudaGetLastError();
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if (err != cudaSuccess) {
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printf("error in nnd get grad: %s\n", cudaGetErrorString(err));
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//THError("aborting");
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return 0;
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}
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return 1;
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}
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