🎨 autoformatting cpp/cu files
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Laurent FAINSIN
parent
2375a23ca5
commit
892b79f5c5
11
cuda/emd.cpp
11
cuda/emd.cpp
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@ -4,7 +4,7 @@
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#include <vector>
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#include <torch/extension.h>
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//CUDA declarations
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// CUDA declarations
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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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@ -20,10 +20,11 @@ std::vector<at::Tensor> MatchCostBackward(
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const at::Tensor xyz2,
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const at::Tensor match);
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("approxmatch_forward", &ApproxMatchForward,"ApproxMatch forward (CUDA)");
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m.def("matchcost_forward", &MatchCostForward,"MatchCost forward (CUDA)");
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m.def("matchcost_backward", &MatchCostBackward,"MatchCost backward (CUDA)");
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
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{
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m.def("approxmatch_forward", &ApproxMatchForward, "ApproxMatch forward (CUDA)");
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m.def("matchcost_forward", &MatchCostForward, "MatchCost forward (CUDA)");
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m.def("matchcost_backward", &MatchCostBackward, "MatchCost backward (CUDA)");
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}
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#endif
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@ -14,152 +14,177 @@
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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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#define CHECK_INPUT(x) \
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CHECK_CUDA(x); \
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CHECK_CONTIGUOUS(x)
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/********************************
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* Forward kernel for approxmatch
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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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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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{
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scalar_t *remainL = temp + blockIdx.x * (n + m) * 2;
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scalar_t *remainR = temp + blockIdx.x * (n + m) * 2 + n;
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scalar_t *ratioL = temp + blockIdx.x * (n + m) * 2 + n + m;
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scalar_t *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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{
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multiL = 1;
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multiR = n / m;
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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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else
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{
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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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{
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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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for (int j = 7; j >= -2; j--)
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{
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scalar_t level = -powf(4.0f, j);
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if (j == -2)
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{
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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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for (int k0 = 0; k0 < n; k0 += blockDim.x)
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{
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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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{
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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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scalar_t suml = 1e-9f;
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for (int l0 = 0; l0 < m; l0 += Block)
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{
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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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{
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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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for (int l = 0; l < lend; l++)
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{
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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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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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for (int l0 = 0; l0 < m; l0 += blockDim.x)
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{
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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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{
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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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scalar_t sumr = 0;
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for (int k0 = 0; k0 < n; k0 += Block)
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{
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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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{
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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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for (int k = 0; k < kend; k++)
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{
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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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if (l < m)
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{
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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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for (int k0 = 0; k0 < n; k0 += blockDim.x)
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{
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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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{
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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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scalar_t suml = 0;
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for (int l0 = 0; l0 < m; l0 += Block)
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{
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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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{
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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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scalar_t rl = ratioL[k];
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if (k < n)
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{
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for (int l = 0; l < lend; l++)
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{
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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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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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@ -169,82 +194,86 @@ Output:
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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 at::Tensor xyz2)
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{
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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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TORCH_CHECK_EQ (xyz2.size(0), b);
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TORCH_CHECK_EQ (xyz1.size(2), 3);
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TORCH_CHECK_EQ (xyz2.size(2), 3);
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TORCH_CHECK_EQ(xyz2.size(0), b);
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TORCH_CHECK_EQ(xyz1.size(2), 3);
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TORCH_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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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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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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C10_CUDA_CHECK(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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* 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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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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{
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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];
|
||||
for (int i=blockIdx.x;i<b;i+=gridDim.x){
|
||||
scalar_t subsum=0;
|
||||
for (int k0=0;k0<n;k0+=blockDim.x){
|
||||
int k=k0+threadIdx.x;
|
||||
scalar_t x1=0,y1=0,z1=0;
|
||||
if (k<n){
|
||||
x1=xyz1[i*n*3+k*3+0];
|
||||
y1=xyz1[i*n*3+k*3+1];
|
||||
z1=xyz1[i*n*3+k*3+2];
|
||||
const int Block = 1024;
|
||||
__shared__ scalar_t buf[Block * 3];
|
||||
for (int i = blockIdx.x; i < b; i += gridDim.x)
|
||||
{
|
||||
scalar_t subsum = 0;
|
||||
for (int k0 = 0; k0 < n; k0 += blockDim.x)
|
||||
{
|
||||
int k = k0 + threadIdx.x;
|
||||
scalar_t x1 = 0, y1 = 0, z1 = 0;
|
||||
if (k < n)
|
||||
{
|
||||
x1 = xyz1[i * n * 3 + k * 3 + 0];
|
||||
y1 = xyz1[i * n * 3 + k * 3 + 1];
|
||||
z1 = xyz1[i * n * 3 + k * 3 + 2];
|
||||
}
|
||||
for (int l0=0;l0<m;l0+=Block){
|
||||
int lend=min(m,l0+Block)-l0;
|
||||
for (int l=threadIdx.x;l<lend*3;l+=blockDim.x)
|
||||
buf[l]=xyz2[i*m*3+l0*3+l];
|
||||
for (int l0 = 0; l0 < m; l0 += Block)
|
||||
{
|
||||
int lend = min(m, l0 + Block) - l0;
|
||||
for (int l = threadIdx.x; l < lend * 3; l += blockDim.x)
|
||||
buf[l] = xyz2[i * m * 3 + l0 * 3 + l];
|
||||
__syncthreads();
|
||||
if (k<n){
|
||||
for (int l=0;l<lend;l++){
|
||||
scalar_t x2=buf[l*3+0];
|
||||
scalar_t y2=buf[l*3+1];
|
||||
scalar_t z2=buf[l*3+2];
|
||||
scalar_t d=(x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1);
|
||||
subsum+=d*match[i*n*m+(l0+l)*n+k];
|
||||
if (k < n)
|
||||
{
|
||||
for (int l = 0; l < lend; l++)
|
||||
{
|
||||
scalar_t x2 = buf[l * 3 + 0];
|
||||
scalar_t y2 = buf[l * 3 + 1];
|
||||
scalar_t z2 = buf[l * 3 + 2];
|
||||
scalar_t d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
|
||||
subsum += d * match[i * n * m + (l0 + l) * n + k];
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
}
|
||||
}
|
||||
allsum[threadIdx.x]=subsum;
|
||||
for (int j=1;j<blockDim.x;j<<=1){
|
||||
allsum[threadIdx.x] = subsum;
|
||||
for (int j = 1; j < blockDim.x; j <<= 1)
|
||||
{
|
||||
__syncthreads();
|
||||
if ((threadIdx.x&j)==0 && threadIdx.x+j<blockDim.x){
|
||||
allsum[threadIdx.x]+=allsum[threadIdx.x+j];
|
||||
if ((threadIdx.x & j) == 0 && threadIdx.x + j < blockDim.x)
|
||||
{
|
||||
allsum[threadIdx.x] += allsum[threadIdx.x + j];
|
||||
}
|
||||
}
|
||||
if (threadIdx.x==0)
|
||||
out[i]=allsum[0];
|
||||
if (threadIdx.x == 0)
|
||||
out[i] = allsum[0];
|
||||
__syncthreads();
|
||||
}
|
||||
}
|
||||
|
||||
//void matchcostLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,const scalar_t * match,scalar_t * out){
|
||||
// matchcost<<<32,512>>>(b,n,m,xyz1,xyz2,match,out);
|
||||
//}
|
||||
|
||||
/* MatchCost forward interface
|
||||
Input:
|
||||
xyz1: (B, N1, 3) # dataset_points
|
||||
|
|
@ -256,69 +285,75 @@ Output:
|
|||
at::Tensor MatchCostForward(
|
||||
const at::Tensor xyz1,
|
||||
const at::Tensor xyz2,
|
||||
const at::Tensor match){
|
||||
const at::Tensor match)
|
||||
{
|
||||
const auto b = xyz1.size(0);
|
||||
const auto n = xyz1.size(1);
|
||||
const auto m = xyz2.size(1);
|
||||
|
||||
TORCH_CHECK_EQ (xyz2.size(0), b);
|
||||
TORCH_CHECK_EQ (xyz1.size(2), 3);
|
||||
TORCH_CHECK_EQ (xyz2.size(2), 3);
|
||||
TORCH_CHECK_EQ(xyz2.size(0), b);
|
||||
TORCH_CHECK_EQ(xyz1.size(2), 3);
|
||||
TORCH_CHECK_EQ(xyz2.size(2), 3);
|
||||
CHECK_INPUT(xyz1);
|
||||
CHECK_INPUT(xyz2);
|
||||
|
||||
auto cost = at::zeros({b}, xyz1.type());
|
||||
|
||||
AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostForward", ([&] {
|
||||
matchcost<scalar_t><<<32,512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), cost.data<scalar_t>());
|
||||
}));
|
||||
AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostForward", ([&]
|
||||
{ matchcost<scalar_t><<<32, 512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), cost.data<scalar_t>()); }));
|
||||
C10_CUDA_CHECK(cudaGetLastError());
|
||||
|
||||
return cost;
|
||||
}
|
||||
|
||||
|
||||
/********************************
|
||||
* matchcostgrad2 kernel
|
||||
*********************************/
|
||||
* matchcostgrad2 kernel
|
||||
*********************************/
|
||||
|
||||
template<typename scalar_t>
|
||||
__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){
|
||||
__shared__ scalar_t sum_grad[256*3];
|
||||
for (int i=blockIdx.x;i<b;i+=gridDim.x){
|
||||
int kbeg=m*blockIdx.y/gridDim.y;
|
||||
int kend=m*(blockIdx.y+1)/gridDim.y;
|
||||
for (int k=kbeg;k<kend;k++){
|
||||
scalar_t x2=xyz2[(i*m+k)*3+0];
|
||||
scalar_t y2=xyz2[(i*m+k)*3+1];
|
||||
scalar_t z2=xyz2[(i*m+k)*3+2];
|
||||
scalar_t subsumx=0,subsumy=0,subsumz=0;
|
||||
for (int j=threadIdx.x;j<n;j+=blockDim.x){
|
||||
scalar_t x1=x2-xyz1[(i*n+j)*3+0];
|
||||
scalar_t y1=y2-xyz1[(i*n+j)*3+1];
|
||||
scalar_t z1=z2-xyz1[(i*n+j)*3+2];
|
||||
scalar_t d=match[i*n*m+k*n+j]*2;
|
||||
subsumx+=x1*d;
|
||||
subsumy+=y1*d;
|
||||
subsumz+=z1*d;
|
||||
template <typename scalar_t>
|
||||
__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)
|
||||
{
|
||||
__shared__ scalar_t sum_grad[256 * 3];
|
||||
for (int i = blockIdx.x; i < b; i += gridDim.x)
|
||||
{
|
||||
int kbeg = m * blockIdx.y / gridDim.y;
|
||||
int kend = m * (blockIdx.y + 1) / gridDim.y;
|
||||
for (int k = kbeg; k < kend; k++)
|
||||
{
|
||||
scalar_t x2 = xyz2[(i * m + k) * 3 + 0];
|
||||
scalar_t y2 = xyz2[(i * m + k) * 3 + 1];
|
||||
scalar_t z2 = xyz2[(i * m + k) * 3 + 2];
|
||||
scalar_t subsumx = 0, subsumy = 0, subsumz = 0;
|
||||
for (int j = threadIdx.x; j < n; j += blockDim.x)
|
||||
{
|
||||
scalar_t x1 = x2 - xyz1[(i * n + j) * 3 + 0];
|
||||
scalar_t y1 = y2 - xyz1[(i * n + j) * 3 + 1];
|
||||
scalar_t z1 = z2 - xyz1[(i * n + j) * 3 + 2];
|
||||
scalar_t d = match[i * n * m + k * n + j] * 2;
|
||||
subsumx += x1 * d;
|
||||
subsumy += y1 * d;
|
||||
subsumz += z1 * d;
|
||||
}
|
||||
sum_grad[threadIdx.x*3+0]=subsumx;
|
||||
sum_grad[threadIdx.x*3+1]=subsumy;
|
||||
sum_grad[threadIdx.x*3+2]=subsumz;
|
||||
for (int j=1;j<blockDim.x;j<<=1){
|
||||
sum_grad[threadIdx.x * 3 + 0] = subsumx;
|
||||
sum_grad[threadIdx.x * 3 + 1] = subsumy;
|
||||
sum_grad[threadIdx.x * 3 + 2] = subsumz;
|
||||
for (int j = 1; j < blockDim.x; j <<= 1)
|
||||
{
|
||||
__syncthreads();
|
||||
int j1=threadIdx.x;
|
||||
int j2=threadIdx.x+j;
|
||||
if ((j1&j)==0 && j2<blockDim.x){
|
||||
sum_grad[j1*3+0]+=sum_grad[j2*3+0];
|
||||
sum_grad[j1*3+1]+=sum_grad[j2*3+1];
|
||||
sum_grad[j1*3+2]+=sum_grad[j2*3+2];
|
||||
int j1 = threadIdx.x;
|
||||
int j2 = threadIdx.x + j;
|
||||
if ((j1 & j) == 0 && j2 < blockDim.x)
|
||||
{
|
||||
sum_grad[j1 * 3 + 0] += sum_grad[j2 * 3 + 0];
|
||||
sum_grad[j1 * 3 + 1] += sum_grad[j2 * 3 + 1];
|
||||
sum_grad[j1 * 3 + 2] += sum_grad[j2 * 3 + 2];
|
||||
}
|
||||
}
|
||||
if (threadIdx.x==0){
|
||||
grad2[(i*m+k)*3+0]=sum_grad[0]*grad_cost[i];
|
||||
grad2[(i*m+k)*3+1]=sum_grad[1]*grad_cost[i];
|
||||
grad2[(i*m+k)*3+2]=sum_grad[2]*grad_cost[i];
|
||||
if (threadIdx.x == 0)
|
||||
{
|
||||
grad2[(i * m + k) * 3 + 0] = sum_grad[0] * grad_cost[i];
|
||||
grad2[(i * m + k) * 3 + 1] = sum_grad[1] * grad_cost[i];
|
||||
grad2[(i * m + k) * 3 + 2] = sum_grad[2] * grad_cost[i];
|
||||
}
|
||||
__syncthreads();
|
||||
}
|
||||
|
|
@ -326,39 +361,37 @@ __global__ void matchcostgrad2(int b,int n,int m,const scalar_t * __restrict__ g
|
|||
}
|
||||
|
||||
/********************************
|
||||
* matchcostgrad1 kernel
|
||||
*********************************/
|
||||
* 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;
|
||||
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];
|
||||
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
|
||||
|
|
@ -373,24 +406,25 @@ std::vector<at::Tensor> MatchCostBackward(
|
|||
const at::Tensor grad_cost,
|
||||
const at::Tensor xyz1,
|
||||
const at::Tensor xyz2,
|
||||
const at::Tensor match){
|
||||
const at::Tensor match)
|
||||
{
|
||||
const auto b = xyz1.size(0);
|
||||
const auto n = xyz1.size(1);
|
||||
const auto m = xyz2.size(1);
|
||||
|
||||
TORCH_CHECK_EQ (xyz2.size(0), b);
|
||||
TORCH_CHECK_EQ (xyz1.size(2), 3);
|
||||
TORCH_CHECK_EQ (xyz2.size(2), 3);
|
||||
TORCH_CHECK_EQ(xyz2.size(0), b);
|
||||
TORCH_CHECK_EQ(xyz1.size(2), 3);
|
||||
TORCH_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", ([&] {
|
||||
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>());
|
||||
}));
|
||||
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>()); }));
|
||||
C10_CUDA_CHECK(cudaGetLastError());
|
||||
|
||||
return std::vector<at::Tensor>({grad1, grad2});
|
||||
|
|
|
|||
Loading…
Reference in a new issue