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Running
on
Zero
// EMD approximation module (based on auction algorithm) | |
// author: Minghua Liu | |
int emd_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist, at::Tensor assignment, at::Tensor price, | |
at::Tensor assignment_inv, at::Tensor bid, at::Tensor bid_increments, at::Tensor max_increments, | |
at::Tensor unass_idx, at::Tensor unass_cnt, at::Tensor unass_cnt_sum, at::Tensor cnt_tmp, at::Tensor max_idx, float eps, int iters); | |
int emd_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz, at::Tensor graddist, at::Tensor idx); | |
int emd_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist, at::Tensor assignment, at::Tensor price, | |
at::Tensor assignment_inv, at::Tensor bid, at::Tensor bid_increments, at::Tensor max_increments, | |
at::Tensor unass_idx, at::Tensor unass_cnt, at::Tensor unass_cnt_sum, at::Tensor cnt_tmp, at::Tensor max_idx, float eps, int iters) { | |
return emd_cuda_forward(xyz1, xyz2, dist, assignment, price, assignment_inv, bid, bid_increments, max_increments, unass_idx, unass_cnt, unass_cnt_sum, cnt_tmp, max_idx, eps, iters); | |
} | |
int emd_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz, at::Tensor graddist, at::Tensor idx) { | |
return emd_cuda_backward(xyz1, xyz2, gradxyz, graddist, idx); | |
} | |
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { | |
m.def("forward", &emd_forward, "emd forward (CUDA)"); | |
m.def("backward", &emd_backward, "emd backward (CUDA)"); | |
} |