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#include <torch/torch.h>
// CUDA forward declarations
int ChamferDistanceKernelLauncher(
const int b, const int n,
const float* xyz,
const int m,
const float* xyz2,
float* result,
int* result_i,
float* result2,
int* result2_i);
int ChamferDistanceGradKernelLauncher(
const int b, const int n,
const float* xyz1,
const int m,
const float* xyz2,
const float* grad_dist1,
const int* idx1,
const float* grad_dist2,
const int* idx2,
float* grad_xyz1,
float* grad_xyz2);
void chamfer_distance_forward_cuda(
const at::Tensor xyz1,
const at::Tensor xyz2,
const at::Tensor dist1,
const at::Tensor dist2,
const at::Tensor idx1,
const at::Tensor idx2)
{
ChamferDistanceKernelLauncher(xyz1.size(0), xyz1.size(1), xyz1.data<float>(),
xyz2.size(1), xyz2.data<float>(),
dist1.data<float>(), idx1.data<int>(),
dist2.data<float>(), idx2.data<int>());
}
void chamfer_distance_backward_cuda(
const at::Tensor xyz1,
const at::Tensor xyz2,
at::Tensor gradxyz1,
at::Tensor gradxyz2,
at::Tensor graddist1,
at::Tensor graddist2,
at::Tensor idx1,
at::Tensor idx2)
{
ChamferDistanceGradKernelLauncher(xyz1.size(0), xyz1.size(1), xyz1.data<float>(),
xyz2.size(1), xyz2.data<float>(),
graddist1.data<float>(), idx1.data<int>(),
graddist2.data<float>(), idx2.data<int>(),
gradxyz1.data<float>(), gradxyz2.data<float>());
}
void nnsearch(
const int b, const int n, const int m,
const float* xyz1,
const float* xyz2,
float* dist,
int* idx)
{
for (int i = 0; i < b; i++) {
for (int j = 0; j < n; j++) {
const float x1 = xyz1[(i*n+j)*3+0];
const float y1 = xyz1[(i*n+j)*3+1];
const float z1 = xyz1[(i*n+j)*3+2];
double best = 0;
int besti = 0;
for (int k = 0; k < m; k++) {
const float x2 = xyz2[(i*m+k)*3+0] - x1;
const float y2 = xyz2[(i*m+k)*3+1] - y1;
const float z2 = xyz2[(i*m+k)*3+2] - z1;
const double d=x2*x2+y2*y2+z2*z2;
if (k==0 || d < best){
best = d;
besti = k;
}
}
dist[i*n+j] = best;
idx[i*n+j] = besti;
}
}
}
void chamfer_distance_forward(
const at::Tensor xyz1,
const at::Tensor xyz2,
const at::Tensor dist1,
const at::Tensor dist2,
const at::Tensor idx1,
const at::Tensor idx2)
{
const int batchsize = xyz1.size(0);
const int n = xyz1.size(1);
const int m = xyz2.size(1);
const float* xyz1_data = xyz1.data<float>();
const float* xyz2_data = xyz2.data<float>();
float* dist1_data = dist1.data<float>();
float* dist2_data = dist2.data<float>();
int* idx1_data = idx1.data<int>();
int* idx2_data = idx2.data<int>();
nnsearch(batchsize, n, m, xyz1_data, xyz2_data, dist1_data, idx1_data);
nnsearch(batchsize, m, n, xyz2_data, xyz1_data, dist2_data, idx2_data);
}
void chamfer_distance_backward(
const at::Tensor xyz1,
const at::Tensor xyz2,
at::Tensor gradxyz1,
at::Tensor gradxyz2,
at::Tensor graddist1,
at::Tensor graddist2,
at::Tensor idx1,
at::Tensor idx2)
{
const int b = xyz1.size(0);
const int n = xyz1.size(1);
const int m = xyz2.size(1);
const float* xyz1_data = xyz1.data<float>();
const float* xyz2_data = xyz2.data<float>();
float* gradxyz1_data = gradxyz1.data<float>();
float* gradxyz2_data = gradxyz2.data<float>();
float* graddist1_data = graddist1.data<float>();
float* graddist2_data = graddist2.data<float>();
const int* idx1_data = idx1.data<int>();
const int* idx2_data = idx2.data<int>();
for (int i = 0; i < b*n*3; i++)
gradxyz1_data[i] = 0;
for (int i = 0; i < b*m*3; i++)
gradxyz2_data[i] = 0;
for (int i = 0;i < b; i++) {
for (int j = 0; j < n; j++) {
const float x1 = xyz1_data[(i*n+j)*3+0];
const float y1 = xyz1_data[(i*n+j)*3+1];
const float z1 = xyz1_data[(i*n+j)*3+2];
const int j2 = idx1_data[i*n+j];
const float x2 = xyz2_data[(i*m+j2)*3+0];
const float y2 = xyz2_data[(i*m+j2)*3+1];
const float z2 = xyz2_data[(i*m+j2)*3+2];
const float g = graddist1_data[i*n+j]*2;
gradxyz1_data[(i*n+j)*3+0] += g*(x1-x2);
gradxyz1_data[(i*n+j)*3+1] += g*(y1-y2);
gradxyz1_data[(i*n+j)*3+2] += g*(z1-z2);
gradxyz2_data[(i*m+j2)*3+0] -= (g*(x1-x2));
gradxyz2_data[(i*m+j2)*3+1] -= (g*(y1-y2));
gradxyz2_data[(i*m+j2)*3+2] -= (g*(z1-z2));
}
for (int j = 0; j < m; j++) {
const float x1 = xyz2_data[(i*m+j)*3+0];
const float y1 = xyz2_data[(i*m+j)*3+1];
const float z1 = xyz2_data[(i*m+j)*3+2];
const int j2 = idx2_data[i*m+j];
const float x2 = xyz1_data[(i*n+j2)*3+0];
const float y2 = xyz1_data[(i*n+j2)*3+1];
const float z2 = xyz1_data[(i*n+j2)*3+2];
const float g = graddist2_data[i*m+j]*2;
gradxyz2_data[(i*m+j)*3+0] += g*(x1-x2);
gradxyz2_data[(i*m+j)*3+1] += g*(y1-y2);
gradxyz2_data[(i*m+j)*3+2] += g*(z1-z2);
gradxyz1_data[(i*n+j2)*3+0] -= (g*(x1-x2));
gradxyz1_data[(i*n+j2)*3+1] -= (g*(y1-y2));
gradxyz1_data[(i*n+j2)*3+2] -= (g*(z1-z2));
}
}
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &chamfer_distance_forward, "ChamferDistance forward");
m.def("forward_cuda", &chamfer_distance_forward_cuda, "ChamferDistance forward (CUDA)");
m.def("backward", &chamfer_distance_backward, "ChamferDistance backward");
m.def("backward_cuda", &chamfer_distance_backward_cuda, "ChamferDistance backward (CUDA)");
}
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