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#include <torch/extension.h>
#include <vector>
#include "lietorch_gpu.h"
#include "lietorch_cpu.h"
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
/* Interface for cuda and c++ group operations
enum group_t { SO3=1, SE3=2, Sim3=3 };
X, Y, Z: (uppercase) Lie Group Elements
a, b, c: (lowercase) Lie Algebra Elements
*/
// Unary operations
torch::Tensor expm(int group_index, torch::Tensor a) {
CHECK_CONTIGUOUS(a);
if (a.device().type() == torch::DeviceType::CPU) {
return exp_forward_cpu(group_index, a);
} else if (a.device().type() == torch::DeviceType::CUDA) {
return exp_forward_gpu(group_index, a);
}
return a;
}
std::vector<torch::Tensor> expm_backward(int group_index, torch::Tensor grad, torch::Tensor a) {
CHECK_CONTIGUOUS(a);
CHECK_CONTIGUOUS(grad);
if (a.device().type() == torch::DeviceType::CPU) {
return exp_backward_cpu(group_index, grad, a);
} else if (a.device().type() == torch::DeviceType::CUDA) {
return exp_backward_gpu(group_index, grad, a);
}
return {};
}
torch::Tensor logm(int group_index, torch::Tensor X) {
CHECK_CONTIGUOUS(X);
if (X.device().type() == torch::DeviceType::CPU) {
return log_forward_cpu(group_index, X);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return log_forward_gpu(group_index, X);
}
return X;
}
std::vector<torch::Tensor> logm_backward(int group_index, torch::Tensor grad, torch::Tensor X) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(grad);
if (X.device().type() == torch::DeviceType::CPU) {
return log_backward_cpu(group_index, grad, X);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return log_backward_gpu(group_index, grad, X);
}
return {};
}
torch::Tensor inv(int group_index, torch::Tensor X) {
CHECK_CONTIGUOUS(X);
if (X.device().type() == torch::DeviceType::CPU) {
return inv_forward_cpu(group_index, X);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return inv_forward_gpu(group_index, X);
}
return X;
}
std::vector<torch::Tensor> inv_backward(int group_index, torch::Tensor grad, torch::Tensor X) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(grad);
if (X.device().type() == torch::DeviceType::CPU) {
return inv_backward_cpu(group_index, grad, X);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return inv_backward_gpu(group_index, grad, X);
}
return {};
}
// Binary operations
torch::Tensor mul(int group_index, torch::Tensor X, torch::Tensor Y) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(Y);
if (X.device().type() == torch::DeviceType::CPU) {
return mul_forward_cpu(group_index, X, Y);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return mul_forward_gpu(group_index, X, Y);
}
return X;
}
std::vector<torch::Tensor> mul_backward(int group_index, torch::Tensor grad, torch::Tensor X, torch::Tensor Y) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(Y);
CHECK_CONTIGUOUS(grad);
if (X.device().type() == torch::DeviceType::CPU) {
return mul_backward_cpu(group_index, grad, X, Y);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return mul_backward_gpu(group_index, grad, X, Y);
}
return {};
}
torch::Tensor adj(int group_index, torch::Tensor X, torch::Tensor a) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(a);
if (X.device().type() == torch::DeviceType::CPU) {
return adj_forward_cpu(group_index, X, a);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return adj_forward_gpu(group_index, X, a);
}
return X;
}
std::vector<torch::Tensor> adj_backward(int group_index, torch::Tensor grad, torch::Tensor X, torch::Tensor a) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(a);
CHECK_CONTIGUOUS(grad);
if (X.device().type() == torch::DeviceType::CPU) {
return adj_backward_cpu(group_index, grad, X, a);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return adj_backward_gpu(group_index, grad, X, a);
}
return {};
}
torch::Tensor adjT(int group_index, torch::Tensor X, torch::Tensor a) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(a);
if (X.device().type() == torch::DeviceType::CPU) {
return adjT_forward_cpu(group_index, X, a);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return adjT_forward_gpu(group_index, X, a);
}
return X;
}
std::vector<torch::Tensor> adjT_backward(int group_index, torch::Tensor grad, torch::Tensor X, torch::Tensor a) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(a);
CHECK_CONTIGUOUS(grad);
if (X.device().type() == torch::DeviceType::CPU) {
return adjT_backward_cpu(group_index, grad, X, a);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return adjT_backward_gpu(group_index, grad, X, a);
}
return {};
}
torch::Tensor act(int group_index, torch::Tensor X, torch::Tensor p) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(p);
if (X.device().type() == torch::DeviceType::CPU) {
return act_forward_cpu(group_index, X, p);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return act_forward_gpu(group_index, X, p);
}
return X;
}
std::vector<torch::Tensor> act_backward(int group_index, torch::Tensor grad, torch::Tensor X, torch::Tensor p) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(p);
CHECK_CONTIGUOUS(grad);
if (X.device().type() == torch::DeviceType::CPU) {
return act_backward_cpu(group_index, grad, X, p);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return act_backward_gpu(group_index, grad, X, p);
}
return {};
}
torch::Tensor act4(int group_index, torch::Tensor X, torch::Tensor p) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(p);
if (X.device().type() == torch::DeviceType::CPU) {
return act4_forward_cpu(group_index, X, p);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return act4_forward_gpu(group_index, X, p);
}
return X;
}
std::vector<torch::Tensor> act4_backward(int group_index, torch::Tensor grad, torch::Tensor X, torch::Tensor p) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(p);
CHECK_CONTIGUOUS(grad);
if (X.device().type() == torch::DeviceType::CPU) {
return act4_backward_cpu(group_index, grad, X, p);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return act4_backward_gpu(group_index, grad, X, p);
}
return {};
}
torch::Tensor projector(int group_index, torch::Tensor X) {
CHECK_CONTIGUOUS(X);
if (X.device().type() == torch::DeviceType::CPU) {
return orthogonal_projector_cpu(group_index, X);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return orthogonal_projector_gpu(group_index, X);
}
return X;
}
torch::Tensor as_matrix(int group_index, torch::Tensor X) {
CHECK_CONTIGUOUS(X);
if (X.device().type() == torch::DeviceType::CPU) {
return as_matrix_forward_cpu(group_index, X);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return as_matrix_forward_gpu(group_index, X);
}
return X;
}
torch::Tensor Jinv(int group_index, torch::Tensor X, torch::Tensor a) {
CHECK_CONTIGUOUS(X);
CHECK_CONTIGUOUS(a);
if (X.device().type() == torch::DeviceType::CPU) {
return jleft_forward_cpu(group_index, X, a);
} else if (X.device().type() == torch::DeviceType::CUDA) {
return jleft_forward_gpu(group_index, X, a);
}
return a;
}
// {exp, log, inv, mul, adj, adjT, act, act4} forward/backward bindings
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("expm", &expm, "exp map forward");
m.def("expm_backward", &expm_backward, "exp map backward");
m.def("logm", &logm, "log map forward");
m.def("logm_backward", &logm_backward, "log map backward");
m.def("inv", &inv, "inverse operator");
m.def("inv_backward", &inv_backward, "inverse operator backward");
m.def("mul", &mul, "group operator");
m.def("mul_backward", &mul_backward, "group operator backward");
m.def("adj", &adj, "adjoint operator");
m.def("adj_backward", &adj_backward, "adjoint operator backward");
m.def("adjT", &adjT, "transposed adjoint operator");
m.def("adjT_backward", &adjT_backward, "transposed adjoint operator backward");
m.def("act", &act, "action on point");
m.def("act_backward", &act_backward, "action on point backward");
m.def("act4", &act4, "action on homogeneous point");
m.def("act4_backward", &act4_backward, "action on homogeneous point backward");
// functions with no gradient
m.def("as_matrix", &as_matrix, "convert to matrix");
m.def("projector", &projector, "orthogonal projection matrix");
m.def("Jinv", &Jinv, "left inverse jacobian operator");
};
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