401 lines
12 KiB
Plaintext
401 lines
12 KiB
Plaintext
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/**********************************
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* Original Author: Haoqiang Fan
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* Modified by: Kaichun Mo
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*********************************/
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#ifndef _EMD_KERNEL
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#define _EMD_KERNEL
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#include <cmath>
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#include <vector>
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#include <ATen/ATen.h>
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#include <ATen/cuda/CUDAApplyUtils.cuh> // at::cuda::getApplyGrid
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#include <THC/THC.h>
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#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
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#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
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#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
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/********************************
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* Forward kernel for approxmatch
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*********************************/
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template<typename scalar_t>
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__global__ void approxmatch(int b,int n,int m,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,scalar_t * __restrict__ match,scalar_t * temp){
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scalar_t * remainL=temp+blockIdx.x*(n+m)*2, * remainR=temp+blockIdx.x*(n+m)*2+n,*ratioL=temp+blockIdx.x*(n+m)*2+n+m,*ratioR=temp+blockIdx.x*(n+m)*2+n+m+n;
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scalar_t multiL,multiR;
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if (n>=m){
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multiL=1;
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multiR=n/m;
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}else{
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multiL=m/n;
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multiR=1;
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}
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const int Block=1024;
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__shared__ scalar_t buf[Block*4];
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for (int i=blockIdx.x;i<b;i+=gridDim.x){
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for (int j=threadIdx.x;j<n*m;j+=blockDim.x)
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match[i*n*m+j]=0;
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for (int j=threadIdx.x;j<n;j+=blockDim.x)
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remainL[j]=multiL;
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for (int j=threadIdx.x;j<m;j+=blockDim.x)
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remainR[j]=multiR;
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__syncthreads();
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for (int j=7;j>=-2;j--){
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scalar_t level=-powf(4.0f,j);
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if (j==-2){
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level=0;
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}
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for (int k0=0;k0<n;k0+=blockDim.x){
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int k=k0+threadIdx.x;
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scalar_t x1=0,y1=0,z1=0;
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if (k<n){
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x1=xyz1[i*n*3+k*3+0];
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y1=xyz1[i*n*3+k*3+1];
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z1=xyz1[i*n*3+k*3+2];
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}
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scalar_t suml=1e-9f;
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for (int l0=0;l0<m;l0+=Block){
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int lend=min(m,l0+Block)-l0;
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for (int l=threadIdx.x;l<lend;l+=blockDim.x){
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scalar_t x2=xyz2[i*m*3+l0*3+l*3+0];
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scalar_t y2=xyz2[i*m*3+l0*3+l*3+1];
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scalar_t z2=xyz2[i*m*3+l0*3+l*3+2];
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buf[l*4+0]=x2;
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buf[l*4+1]=y2;
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buf[l*4+2]=z2;
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buf[l*4+3]=remainR[l0+l];
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}
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__syncthreads();
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for (int l=0;l<lend;l++){
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scalar_t x2=buf[l*4+0];
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scalar_t y2=buf[l*4+1];
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scalar_t z2=buf[l*4+2];
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scalar_t d=level*((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1));
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scalar_t w=__expf(d)*buf[l*4+3];
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suml+=w;
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}
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__syncthreads();
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}
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if (k<n)
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ratioL[k]=remainL[k]/suml;
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}
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__syncthreads();
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for (int l0=0;l0<m;l0+=blockDim.x){
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int l=l0+threadIdx.x;
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scalar_t x2=0,y2=0,z2=0;
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if (l<m){
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x2=xyz2[i*m*3+l*3+0];
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y2=xyz2[i*m*3+l*3+1];
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z2=xyz2[i*m*3+l*3+2];
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}
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scalar_t sumr=0;
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for (int k0=0;k0<n;k0+=Block){
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int kend=min(n,k0+Block)-k0;
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for (int k=threadIdx.x;k<kend;k+=blockDim.x){
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buf[k*4+0]=xyz1[i*n*3+k0*3+k*3+0];
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buf[k*4+1]=xyz1[i*n*3+k0*3+k*3+1];
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buf[k*4+2]=xyz1[i*n*3+k0*3+k*3+2];
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buf[k*4+3]=ratioL[k0+k];
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}
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__syncthreads();
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for (int k=0;k<kend;k++){
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scalar_t x1=buf[k*4+0];
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scalar_t y1=buf[k*4+1];
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scalar_t z1=buf[k*4+2];
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scalar_t w=__expf(level*((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1)))*buf[k*4+3];
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sumr+=w;
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}
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__syncthreads();
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}
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if (l<m){
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sumr*=remainR[l];
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scalar_t consumption=fminf(remainR[l]/(sumr+1e-9f),1.0f);
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ratioR[l]=consumption*remainR[l];
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remainR[l]=fmaxf(0.0f,remainR[l]-sumr);
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}
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}
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__syncthreads();
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for (int k0=0;k0<n;k0+=blockDim.x){
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int k=k0+threadIdx.x;
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scalar_t x1=0,y1=0,z1=0;
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if (k<n){
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x1=xyz1[i*n*3+k*3+0];
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y1=xyz1[i*n*3+k*3+1];
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z1=xyz1[i*n*3+k*3+2];
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}
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scalar_t suml=0;
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for (int l0=0;l0<m;l0+=Block){
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int lend=min(m,l0+Block)-l0;
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for (int l=threadIdx.x;l<lend;l+=blockDim.x){
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buf[l*4+0]=xyz2[i*m*3+l0*3+l*3+0];
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buf[l*4+1]=xyz2[i*m*3+l0*3+l*3+1];
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buf[l*4+2]=xyz2[i*m*3+l0*3+l*3+2];
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buf[l*4+3]=ratioR[l0+l];
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}
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__syncthreads();
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scalar_t rl=ratioL[k];
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if (k<n){
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for (int l=0;l<lend;l++){
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scalar_t x2=buf[l*4+0];
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scalar_t y2=buf[l*4+1];
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scalar_t z2=buf[l*4+2];
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scalar_t w=__expf(level*((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1)))*rl*buf[l*4+3];
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match[i*n*m+(l0+l)*n+k]+=w;
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suml+=w;
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}
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}
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__syncthreads();
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}
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if (k<n)
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remainL[k]=fmaxf(0.0f,remainL[k]-suml);
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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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//void approxmatchLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,scalar_t * match,scalar_t * temp){
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// approxmatch<<<32,512>>>(b,n,m,xyz1,xyz2,match,temp);
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//}
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/* ApproxMatch forward interface
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Input:
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xyz1: (B, N1, 3) # dataset_points
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xyz2: (B, N2, 3) # query_points
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Output:
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match: (B, N2, N1)
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*/
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at::Tensor ApproxMatchForward(
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const at::Tensor xyz1,
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const at::Tensor xyz2){
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const auto b = xyz1.size(0);
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const auto n = xyz1.size(1);
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const auto m = xyz2.size(1);
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CHECK_EQ(xyz2.size(0), b);
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CHECK_EQ(xyz1.size(2), 3);
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CHECK_EQ(xyz2.size(2), 3);
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CHECK_INPUT(xyz1);
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CHECK_INPUT(xyz2);
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auto match = at::zeros({b, m, n}, xyz1.type());
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auto temp = at::zeros({b, (n+m)*2}, xyz1.type());
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AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "ApproxMatchForward", ([&] {
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approxmatch<scalar_t><<<32,512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), temp.data<scalar_t>());
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}));
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THCudaCheck(cudaGetLastError());
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return match;
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}
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/********************************
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* Forward kernel for matchcost
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*********************************/
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template<typename scalar_t>
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__global__ void matchcost(int b,int n,int m,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,const scalar_t * __restrict__ match,scalar_t * __restrict__ out){
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__shared__ scalar_t allsum[512];
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const int Block=1024;
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__shared__ scalar_t buf[Block*3];
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for (int i=blockIdx.x;i<b;i+=gridDim.x){
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scalar_t subsum=0;
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for (int k0=0;k0<n;k0+=blockDim.x){
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int k=k0+threadIdx.x;
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scalar_t x1=0,y1=0,z1=0;
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if (k<n){
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x1=xyz1[i*n*3+k*3+0];
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y1=xyz1[i*n*3+k*3+1];
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z1=xyz1[i*n*3+k*3+2];
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}
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for (int l0=0;l0<m;l0+=Block){
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int lend=min(m,l0+Block)-l0;
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for (int l=threadIdx.x;l<lend*3;l+=blockDim.x)
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buf[l]=xyz2[i*m*3+l0*3+l];
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__syncthreads();
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if (k<n){
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for (int l=0;l<lend;l++){
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scalar_t x2=buf[l*3+0];
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scalar_t y2=buf[l*3+1];
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scalar_t z2=buf[l*3+2];
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scalar_t d=(x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1);
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subsum+=d*match[i*n*m+(l0+l)*n+k];
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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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allsum[threadIdx.x]=subsum;
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for (int j=1;j<blockDim.x;j<<=1){
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__syncthreads();
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if ((threadIdx.x&j)==0 && threadIdx.x+j<blockDim.x){
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allsum[threadIdx.x]+=allsum[threadIdx.x+j];
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}
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}
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if (threadIdx.x==0)
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out[i]=allsum[0];
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__syncthreads();
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}
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}
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//void matchcostLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,const scalar_t * match,scalar_t * out){
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// matchcost<<<32,512>>>(b,n,m,xyz1,xyz2,match,out);
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//}
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/* MatchCost forward interface
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Input:
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xyz1: (B, N1, 3) # dataset_points
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xyz2: (B, N2, 3) # query_points
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match: (B, N2, N1)
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Output:
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cost: (B)
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*/
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at::Tensor MatchCostForward(
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const at::Tensor xyz1,
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const at::Tensor xyz2,
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const at::Tensor match){
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const auto b = xyz1.size(0);
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const auto n = xyz1.size(1);
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const auto m = xyz2.size(1);
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CHECK_EQ(xyz2.size(0), b);
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CHECK_EQ(xyz1.size(2), 3);
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CHECK_EQ(xyz2.size(2), 3);
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CHECK_INPUT(xyz1);
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CHECK_INPUT(xyz2);
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auto cost = at::zeros({b}, xyz1.type());
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AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostForward", ([&] {
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matchcost<scalar_t><<<32,512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), cost.data<scalar_t>());
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}));
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THCudaCheck(cudaGetLastError());
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return cost;
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}
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/********************************
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* matchcostgrad2 kernel
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*********************************/
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template<typename scalar_t>
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__global__ void matchcostgrad2(int b,int n,int m,const scalar_t * __restrict__ grad_cost,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,const scalar_t * __restrict__ match,scalar_t * __restrict__ grad2){
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__shared__ scalar_t sum_grad[256*3];
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for (int i=blockIdx.x;i<b;i+=gridDim.x){
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int kbeg=m*blockIdx.y/gridDim.y;
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int kend=m*(blockIdx.y+1)/gridDim.y;
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for (int k=kbeg;k<kend;k++){
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scalar_t x2=xyz2[(i*m+k)*3+0];
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scalar_t y2=xyz2[(i*m+k)*3+1];
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scalar_t z2=xyz2[(i*m+k)*3+2];
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scalar_t subsumx=0,subsumy=0,subsumz=0;
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for (int j=threadIdx.x;j<n;j+=blockDim.x){
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scalar_t x1=x2-xyz1[(i*n+j)*3+0];
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scalar_t y1=y2-xyz1[(i*n+j)*3+1];
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scalar_t z1=z2-xyz1[(i*n+j)*3+2];
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scalar_t d=match[i*n*m+k*n+j]*2;
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subsumx+=x1*d;
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subsumy+=y1*d;
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subsumz+=z1*d;
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}
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sum_grad[threadIdx.x*3+0]=subsumx;
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sum_grad[threadIdx.x*3+1]=subsumy;
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sum_grad[threadIdx.x*3+2]=subsumz;
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for (int j=1;j<blockDim.x;j<<=1){
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__syncthreads();
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int j1=threadIdx.x;
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int j2=threadIdx.x+j;
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||
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|
if ((j1&j)==0 && j2<blockDim.x){
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||
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sum_grad[j1*3+0]+=sum_grad[j2*3+0];
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||
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sum_grad[j1*3+1]+=sum_grad[j2*3+1];
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||
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|
sum_grad[j1*3+2]+=sum_grad[j2*3+2];
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||
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|
}
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||
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|
}
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||
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|
if (threadIdx.x==0){
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||
|
|
grad2[(i*m+k)*3+0]=sum_grad[0]*grad_cost[i];
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||
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|
grad2[(i*m+k)*3+1]=sum_grad[1]*grad_cost[i];
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||
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|
grad2[(i*m+k)*3+2]=sum_grad[2]*grad_cost[i];
|
||
|
|
}
|
||
|
|
__syncthreads();
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/********************************
|
||
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|
* matchcostgrad1 kernel
|
||
|
|
*********************************/
|
||
|
|
|
||
|
|
template<typename scalar_t>
|
||
|
|
__global__ void matchcostgrad1(int b,int n,int m,const scalar_t * __restrict__ grad_cost,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,const scalar_t * __restrict__ match,scalar_t * __restrict__ grad1){
|
||
|
|
for (int i=blockIdx.x;i<b;i+=gridDim.x){
|
||
|
|
for (int l=threadIdx.x;l<n;l+=blockDim.x){
|
||
|
|
scalar_t x1=xyz1[i*n*3+l*3+0];
|
||
|
|
scalar_t y1=xyz1[i*n*3+l*3+1];
|
||
|
|
scalar_t z1=xyz1[i*n*3+l*3+2];
|
||
|
|
scalar_t dx=0,dy=0,dz=0;
|
||
|
|
for (int k=0;k<m;k++){
|
||
|
|
scalar_t x2=xyz2[i*m*3+k*3+0];
|
||
|
|
scalar_t y2=xyz2[i*m*3+k*3+1];
|
||
|
|
scalar_t z2=xyz2[i*m*3+k*3+2];
|
||
|
|
scalar_t d=match[i*n*m+k*n+l]*2;
|
||
|
|
dx+=(x1-x2)*d;
|
||
|
|
dy+=(y1-y2)*d;
|
||
|
|
dz+=(z1-z2)*d;
|
||
|
|
}
|
||
|
|
grad1[i*n*3+l*3+0]=dx*grad_cost[i];
|
||
|
|
grad1[i*n*3+l*3+1]=dy*grad_cost[i];
|
||
|
|
grad1[i*n*3+l*3+2]=dz*grad_cost[i];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
//void matchcostgradLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,const scalar_t * match,scalar_t * grad1,scalar_t * grad2){
|
||
|
|
// matchcostgrad1<<<32,512>>>(b,n,m,xyz1,xyz2,match,grad1);
|
||
|
|
// matchcostgrad2<<<dim3(32,32),256>>>(b,n,m,xyz1,xyz2,match,grad2);
|
||
|
|
//}
|
||
|
|
|
||
|
|
|
||
|
|
/* MatchCost backward interface
|
||
|
|
Input:
|
||
|
|
grad_cost: (B) # gradients on cost
|
||
|
|
xyz1: (B, N1, 3) # dataset_points
|
||
|
|
xyz2: (B, N2, 3) # query_points
|
||
|
|
match: (B, N2, N1)
|
||
|
|
Output:
|
||
|
|
grad1: (B, N1, 3)
|
||
|
|
grad2: (B, N2, 3)
|
||
|
|
*/
|
||
|
|
std::vector<at::Tensor> MatchCostBackward(
|
||
|
|
const at::Tensor grad_cost,
|
||
|
|
const at::Tensor xyz1,
|
||
|
|
const at::Tensor xyz2,
|
||
|
|
const at::Tensor match){
|
||
|
|
const auto b = xyz1.size(0);
|
||
|
|
const auto n = xyz1.size(1);
|
||
|
|
const auto m = xyz2.size(1);
|
||
|
|
|
||
|
|
CHECK_EQ(xyz2.size(0), b);
|
||
|
|
CHECK_EQ(xyz1.size(2), 3);
|
||
|
|
CHECK_EQ(xyz2.size(2), 3);
|
||
|
|
CHECK_INPUT(xyz1);
|
||
|
|
CHECK_INPUT(xyz2);
|
||
|
|
|
||
|
|
auto grad1 = at::zeros({b, n, 3}, xyz1.type());
|
||
|
|
auto grad2 = at::zeros({b, m, 3}, xyz1.type());
|
||
|
|
|
||
|
|
AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostBackward", ([&] {
|
||
|
|
matchcostgrad1<scalar_t><<<32,512>>>(b, n, m, grad_cost.data<scalar_t>(), xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), grad1.data<scalar_t>());
|
||
|
|
matchcostgrad2<scalar_t><<<dim3(32,32),256>>>(b, n, m, grad_cost.data<scalar_t>(), xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), grad2.data<scalar_t>());
|
||
|
|
}));
|
||
|
|
THCudaCheck(cudaGetLastError());
|
||
|
|
|
||
|
|
return std::vector<at::Tensor>({grad1, grad2});
|
||
|
|
}
|
||
|
|
|
||
|
|
#endif
|