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This commit is contained in:
Kaichun MO
parent
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5
.gitignore
vendored
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.gitignore
vendored
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@ -0,0 +1,5 @@
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__pycache__
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build
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dist
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emd_ext.egg-info
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*.so
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@ -23,7 +23,7 @@ Check `test_emd_loss.py` for example.
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## Author
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The cuda code is originally written by Haoqiang Fan. The PyTorch version is modified by Kaichun Mo.
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The cuda code is originally written by Haoqiang Fan. The PyTorch wrapper is written by Kaichun Mo. Also, Jiayuan Gu provided helps.
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## License
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0
__init__.py
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__init__.py
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29
cuda/emd.cpp
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cuda/emd.cpp
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#ifndef _EMD
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#define _EMD
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#include <vector>
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#include <torch/extension.h>
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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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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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std::vector<at::Tensor> MatchCostBackward(
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const at::Tensor grad_cost,
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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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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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}
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#endif
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400
cuda/emd_kernel.cu
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cuda/emd_kernel.cu
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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) AT_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
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#define CHECK_CONTIGUOUS(x) AT_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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if ((j1&j)==0 && j2<blockDim.x){
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sum_grad[j1*3+0]+=sum_grad[j2*3+0];
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sum_grad[j1*3+1]+=sum_grad[j2*3+1];
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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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if (threadIdx.x==0){
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grad2[(i*m+k)*3+0]=sum_grad[0]*grad_cost[i];
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grad2[(i*m+k)*3+1]=sum_grad[1]*grad_cost[i];
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grad2[(i*m+k)*3+2]=sum_grad[2]*grad_cost[i];
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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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/********************************
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* matchcostgrad1 kernel
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*********************************/
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template<typename scalar_t>
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__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){
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for (int i=blockIdx.x;i<b;i+=gridDim.x){
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for (int l=threadIdx.x;l<n;l+=blockDim.x){
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scalar_t x1=xyz1[i*n*3+l*3+0];
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scalar_t y1=xyz1[i*n*3+l*3+1];
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scalar_t z1=xyz1[i*n*3+l*3+2];
|
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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
|
||||
46
emd.py
Executable file
46
emd.py
Executable file
|
|
@ -0,0 +1,46 @@
|
|||
import torch
|
||||
import emd_cuda
|
||||
|
||||
|
||||
class EarthMoverDistanceFunction(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, xyz1, xyz2):
|
||||
xyz1 = xyz1.contiguous()
|
||||
xyz2 = xyz2.contiguous()
|
||||
assert xyz1.is_cuda and xyz2.is_cuda, "Only support cuda currently."
|
||||
match = emd_cuda.approxmatch_forward(xyz1, xyz2)
|
||||
cost = emd_cuda.matchcost_forward(xyz1, xyz2, match)
|
||||
ctx.save_for_backward(xyz1, xyz2, match)
|
||||
return cost
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad_cost):
|
||||
xyz1, xyz2, match = ctx.saved_tensors
|
||||
grad_cost = grad_cost.contiguous()
|
||||
grad_xyz1, grad_xyz2 = emd_cuda.matchcost_backward(grad_cost, xyz1, xyz2, match)
|
||||
return grad_xyz1, grad_xyz2
|
||||
|
||||
|
||||
def earth_mover_distance(xyz1, xyz2, transpose=True):
|
||||
"""Earth Mover Distance (Approx)
|
||||
|
||||
Args:
|
||||
xyz1 (torch.Tensor): (b, 3, n1)
|
||||
xyz2 (torch.Tensor): (b, 3, n1)
|
||||
transpose (bool): whether to transpose inputs as it might be BCN format.
|
||||
Extensions only support BNC format.
|
||||
|
||||
Returns:
|
||||
cost (torch.Tensor): (b)
|
||||
|
||||
"""
|
||||
if xyz1.dim() == 2:
|
||||
xyz1 = xyz1.unsqueeze(0)
|
||||
if xyz2.dim() == 2:
|
||||
xyz2 = xyz2.unsqueeze(0)
|
||||
if transpose:
|
||||
xyz1 = xyz1.transpose(1, 2)
|
||||
xyz2 = xyz2.transpose(1, 2)
|
||||
cost = EarthMoverDistanceFunction.apply(xyz1, xyz2)
|
||||
return cost
|
||||
|
||||
27
setup.py
Executable file
27
setup.py
Executable file
|
|
@ -0,0 +1,27 @@
|
|||
"""Setup extension
|
||||
|
||||
Notes:
|
||||
If extra_compile_args is provided, you need to provide different instances for different extensions.
|
||||
Refer to https://github.com/pytorch/pytorch/issues/20169
|
||||
|
||||
"""
|
||||
|
||||
from setuptools import setup
|
||||
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
|
||||
|
||||
|
||||
setup(
|
||||
name='emd_ext',
|
||||
ext_modules=[
|
||||
CUDAExtension(
|
||||
name='emd_cuda',
|
||||
sources=[
|
||||
'cuda/emd.cpp',
|
||||
'cuda/emd_kernel.cu',
|
||||
],
|
||||
extra_compile_args={'cxx': ['-g'], 'nvcc': ['-O2']}
|
||||
),
|
||||
],
|
||||
cmdclass={
|
||||
'build_ext': BuildExtension
|
||||
})
|
||||
44
test_emd_loss.py
Normal file
44
test_emd_loss.py
Normal file
|
|
@ -0,0 +1,44 @@
|
|||
import torch
|
||||
import numpy as np
|
||||
import time
|
||||
from emd import earth_mover_distance
|
||||
|
||||
# gt
|
||||
p1 = torch.from_numpy(np.array([[[1.7, -0.1, 0.1], [0.1, 1.2, 0.3]]], dtype=np.float32)).cuda()
|
||||
p1 = p1.repeat(3, 1, 1)
|
||||
p2 = torch.from_numpy(np.array([[[0.3, 1.8, 0.2], [1.2, -0.2, 0.3]]], dtype=np.float32)).cuda()
|
||||
p2 = p2.repeat(3, 1, 1)
|
||||
print(p1)
|
||||
print(p2)
|
||||
p1.requires_grad = True
|
||||
p2.requires_grad = True
|
||||
|
||||
gt_dist = (((p1[0, 0] - p2[0, 1])**2).sum() + ((p1[0, 1] - p2[0, 0])**2).sum()) / 2 + \
|
||||
(((p1[1, 0] - p2[1, 1])**2).sum() + ((p1[1, 1] - p2[1, 0])**2).sum()) * 2 + \
|
||||
(((p1[2, 0] - p2[2, 1])**2).sum() + ((p1[2, 1] - p2[2, 0])**2).sum()) / 3
|
||||
print('gt_dist: ', gt_dist)
|
||||
|
||||
gt_dist.backward()
|
||||
print(p1.grad)
|
||||
print(p2.grad)
|
||||
|
||||
# emd
|
||||
p1 = torch.from_numpy(np.array([[[1.7, -0.1, 0.1], [0.1, 1.2, 0.3]]], dtype=np.float32)).cuda()
|
||||
p1 = p1.repeat(3, 1, 1)
|
||||
p2 = torch.from_numpy(np.array([[[0.3, 1.8, 0.2], [1.2, -0.2, 0.3]]], dtype=np.float32)).cuda()
|
||||
p2 = p2.repeat(3, 1, 1)
|
||||
print(p1)
|
||||
print(p2)
|
||||
p1.requires_grad = True
|
||||
p2.requires_grad = True
|
||||
|
||||
d = earth_mover_distance(p1, p2, transpose=False)
|
||||
print(d)
|
||||
|
||||
loss = d[0] / 2 + d[1] * 2 + d[2] / 3
|
||||
print(loss)
|
||||
|
||||
loss.backward()
|
||||
print(p1.grad)
|
||||
print(p2.grad)
|
||||
|
||||
Loading…
Reference in a new issue