0.0.1

Author: kshitijgoel007

CMakeLists.txt

@@ -5,54 +5,24 @@ project(sogmm_open3d
 	"SOGMM Open3D"
 	LANGUAGES CXX C)
 
+# configuration
 set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 set(CMAKE_CXX_FLAGS "-std=c++17 -march=native -O3 -fopenmp -lstdc++fs")
 set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
 
+# find dependencies
 find_package(pybind11 REQUIRED)
 find_package(Open3D REQUIRED)
 find_package(Eigen3 3.4 REQUIRED)
 find_package(self_organizing_gmm REQUIRED)
+find_package(nanoflann REQUIRED)
 find_package(GSL REQUIRED)
 find_package(CUDA REQUIRED)
 
-add_executable(sogmm_test src/tests/sogmm_test.cpp)
-target_include_directories(sogmm_test
-  PRIVATE 
-  include
-  ${sogmm_INCLUDE_DIR}
-  ${GSL_INCLUDE_DIRS}
-  ${CUDA_INCLUDE_DIRS}
-)
-target_link_libraries(
-  sogmm_test
-  PRIVATE stdc++fs
-  PRIVATE ${GSL_LIBRARIES}
-  PRIVATE Eigen3::Eigen
-  PRIVATE Open3D::Open3D
-)
-set_target_properties(sogmm_test PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_INSTALL_PREFIX})
-
-pybind11_add_module(sogmm_open3d_py src/sogmm_open3d_py.cpp)
-target_include_directories(sogmm_open3d_py
-  PRIVATE
-  include
-  ${sogmm_INCLUDE_DIR}
-  ${GSL_INCLUDE_DIRS}
-  ${CUDA_INCLUDE_DIRS}
-)
-target_link_libraries(
-  sogmm_open3d_py
-  PRIVATE stdc++fs
-  PRIVATE ${GSL_LIBRARIES}
-  PRIVATE Eigen3::Eigen
-  PRIVATE Open3D::Open3D
-)
-set_target_properties(sogmm_open3d_py PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_INSTALL_PREFIX})
-
-pybind11_add_module(gmm_open3d_py src/gmm_open3d_py.cpp)
+# Setup pybind modules
+pybind11_add_module(gmm_open3d_py src/pybind/gmm_open3d_py.cpp)
 target_include_directories(gmm_open3d_py
   PRIVATE
   include
@@ -69,34 +39,25 @@ target_link_libraries(
 )
 set_target_properties(gmm_open3d_py PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_INSTALL_PREFIX})
 
-pybind11_add_module(time_profiler_py src/time_profiler_py.cpp)
-target_include_directories(time_profiler_py
-  PRIVATE
-  include
-  ${sogmm_INCLUDE_DIR}
-  ${CUDA_INCLUDE_DIRS}
-)
-target_link_libraries(
-  time_profiler_py
-  PRIVATE stdc++fs
-)
-set_target_properties(time_profiler_py PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_INSTALL_PREFIX})
-
-pybind11_add_module(kinit_open3d_py src/kinit_open3d_py.cpp)
-target_include_directories(kinit_open3d_py
+pybind11_add_module(sogmm_gpu src/pybind/sogmm_gpu.cpp)
+target_include_directories(sogmm_gpu
   PRIVATE
   include
   ${sogmm_INCLUDE_DIR}
+  ${GSL_INCLUDE_DIRS}
   ${CUDA_INCLUDE_DIRS}
 )
 target_link_libraries(
-  kinit_open3d_py
+  sogmm_gpu
   PRIVATE stdc++fs
+  PRIVATE ${GSL_LIBRARIES}
   PRIVATE Eigen3::Eigen
   PRIVATE Open3D::Open3D
+  PRIVATE nanoflann::nanoflann
 )
-set_target_properties(kinit_open3d_py PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_INSTALL_PREFIX})
+set_target_properties(sogmm_gpu PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_INSTALL_PREFIX})
 
+# Install
 install(TARGETS DESTINATION ${CMAKE_INSTALL_PREFIX})
 
 install(DIRECTORY include/${PROJECT_NAME}/

README.md

@@ -1,3 +1,11 @@
 # Self-Organizing Gaussian Mixture Models
 
 Please see the meta-package https://github.com/gira3d/gira3d-reconstruction for detailed documentation.
+
+## Changelog
+
+### New in 0.0.1
+- Updates to memory management on the GPU for incremental mapping
+- Conditional color inference on the GPU
+- Likelihood calculation on the GPU
+- On-GPU merging of mixture components

include/sogmm_open3d/EM.h

@@ -0,0 +1,279 @@
+#pragma once
+
+#define BUILD_CUDA_MODULE
+
+#include <iostream>
+#include <memory>
+#include <random>
+
+#include <Eigen/Dense>
+#include <gsl/gsl_randist.h>
+
+#include <sogmm_open3d/SOGMMGPU.h>
+
+#define LOG_2_M_PI 1.83788
+
+namespace o3d = open3d;
+
+namespace sogmm
+{
+  namespace gpu
+  {
+    template <typename T, uint32_t D>
+    class EM
+    {
+    public:
+      static constexpr uint32_t C = D * D;
+
+      using Ptr = std::shared_ptr<EM<T, D>>;
+      using ConstPtr = std::shared_ptr<const EM<T, D>>;
+
+      using Container = SOGMM<T, D>;
+
+      using Tensor = typename Container::Tensor;
+      using SizeVector = typename Container::SizeVector;
+      using Device = typename Container::Device;
+      using Dtype = typename Container::Dtype;
+
+      EM()
+      {
+        tol_ = 1e-3;
+        reg_covar_ = 1e-6;
+        max_iter_ = 100;
+
+        device_ = Device("CUDA:0");
+        dtype_ = Dtype::template FromType<T>();
+      }
+
+      ~EM()
+      {
+      }
+
+      void initialize(const unsigned int &n_samples, const unsigned int &n_components)
+      {
+        unsigned int N = n_samples;
+        unsigned int K = n_components;
+
+        amax__ = Tensor::Zeros({N, 1, 1}, dtype_, device_);
+        Log_Det_Cholesky__ = Tensor::Zeros({K, 1}, dtype_, device_);
+        Log_Det_Cholesky_Tmp__ = Tensor::Zeros({K, D}, dtype_, device_);
+        eDiff__ = Tensor::Zeros({N, K, D, 1}, dtype_, device_);
+        Log_Prob_Norm__ = Tensor::Zeros({N, 1, 1}, dtype_, device_);
+
+        Nk__ = Tensor::Zeros({1, K}, dtype_, device_);
+        mDiff__ = Tensor::Zeros({K, D, N}, dtype_, device_);
+        Log_Resp__ = Tensor::Zeros({N, K, 1}, dtype_, device_);
+      }
+
+      void logSumExp(const Tensor &in, const unsigned int dim, Tensor &out)
+      {
+        amax__ = in.Max({dim}, true);
+        ((in - amax__).Exp()).Sum_({dim}, true, out);
+        out.Log_();
+        out.Add_(amax__);
+      }
+
+      void estimateWeightedLogProb(const Tensor &Xt, const Container &sogmm,
+                                   Tensor &Weighted_Log_Prob)
+      {
+        // Term 2 of Equation (3.14)
+        // Log_Det_Cholesky__ = ((GetDiagonal(Precs_Chol_[0]).Log()).Sum({ 1 }, true))
+        //                         .Reshape({ 1, n_components_, 1 });
+
+        SizeVector Xt_shape = Xt.GetShape();
+        unsigned int N = Xt_shape[0];
+        unsigned int K = sogmm.n_components_;
+
+        Log_Det_Cholesky__.template Fill<float>(0.0);
+        Tensor Log_Det_Cholesky_View = (GetDiagonal(sogmm.precisions_cholesky_[0]));
+        Log_Det_Cholesky_Tmp__.CopyFrom(Log_Det_Cholesky_View);
+        Log_Det_Cholesky_Tmp__.Log_();
+        Log_Det_Cholesky_Tmp__.Sum_({1}, true, Log_Det_Cholesky__);
+
+        // Diff, PDiff, Log_Gaussian_Prob are all terms for the first
+        // term in Equation (3.14)
+        eDiff__ = (Xt.Reshape({N, 1, D}) - sogmm.means_).Reshape({N, K, D, 1});
+
+        eDiff__ = (sogmm.precisions_cholesky_.Mul(eDiff__)).Sum({2}, true).Reshape({N, K, D, 1});
+
+        // Equation (3.14), output of estimateLogGaussianProb
+        (eDiff__.Mul(eDiff__)).Sum_({2}, false, Weighted_Log_Prob);
+        Weighted_Log_Prob.Add_(D * LOG_2_M_PI);
+        Weighted_Log_Prob.Mul_(-0.5);
+        Weighted_Log_Prob.Add_(Log_Det_Cholesky__.Reshape({1, K, 1}));
+
+        // This is the first two terms in Equation (3.7)
+        Weighted_Log_Prob.Add_(sogmm.weights_.Log());
+      }
+
+      void eStep(const Tensor &Xt, const Container &sogmm)
+      {
+        unsigned int K = sogmm.n_components_;
+
+        estimateWeightedLogProb(Xt, sogmm, Log_Resp__);
+
+        Log_Prob_Norm__.template Fill<float>(0.0);
+        logSumExp(Log_Resp__, 1, Log_Prob_Norm__);
+
+        Log_Resp__.Sub_(Log_Prob_Norm__);
+
+        likelihood_ = Log_Prob_Norm__.Mean({0, 1, 2}, false).template Item<T>();
+      }
+
+      void mStep(const Tensor &Xt, const Tensor &Respt, Container &sogmm)
+      {
+        SizeVector Xt_shape = Xt.GetShape();
+        unsigned int N = Xt_shape[0];
+        unsigned int K = sogmm.n_components_;
+
+        // initialize tensor for weights
+        Nk__.template Fill<float>(0.0);
+        Respt.Sum_({0}, true, Nk__);
+        sogmm.weights_ = Nk__.T();
+        sogmm.weights_.Add_(Tensor::Ones({K, 1}, dtype_, device_) * 10 *
+                            std::numeric_limits<T>::epsilon());
+
+        // update means
+        sogmm.means_ = (Respt.T().Matmul(Xt)).Div(sogmm.weights_).Reshape(sogmm.means_.GetShape());
+
+        // update covariances
+        mDiff__ = (Xt.Reshape({N, 1, D}) - sogmm.means_).AsStrided({K, D, N}, {D, 1, D * K});
+        sogmm.covariances_ = mDiff__.MatmulBatched(mDiff__.Transpose(1, 2) *
+                                                   Respt.AsStrided({K, N, 1}, {1, K, 1}))
+                                 .Reshape(sogmm.covariances_.GetShape());
+        sogmm.covariances_.Div_(sogmm.weights_.Reshape({1, K, 1, 1}));
+
+        // add reg_covar_ along the diagonal for Covariances_
+        sogmm.covariances_[0].AsStrided({K, D}, {C, D + 1}).Add_(reg_covar_);
+
+        // update weights
+        sogmm.weights_.Div_(N);
+        sogmm.weights_.Div_(sogmm.weights_.Sum({0}, false));
+
+        // update precision and covariance cholesky
+        sogmm.updateCholesky();
+      }
+
+      bool fit(const Tensor &Xt, const Tensor &Respt, Container &sogmm)
+      {
+        unsigned int K = sogmm.n_components_;
+
+        SizeVector Xt_shape = Xt.GetShape();
+        unsigned int N = Xt_shape[0];
+
+        if (N <= 1)
+        {
+          throw std::runtime_error("fit: number of samples should be greater than 1.");
+        }
+
+        if (K <= 0)
+        {
+          throw std::runtime_error("fit: number of components should be greater than 0.");
+        }
+
+        if (N < K)
+        {
+          throw std::runtime_error("fit: number of components is " +
+                                   std::to_string(K) +
+                                   ". It should be strictly smaller than the "
+                                   "number of points: " +
+                                   std::to_string(N));
+        }
+
+        initialize(N, K);
+
+        mStep(Xt, Respt, sogmm);
+
+        T lower_bound = -std::numeric_limits<T>::infinity();
+        for (unsigned int n_iter = 0; n_iter <= max_iter_; n_iter++)
+        {
+          T prev_lower_bound = lower_bound;
+
+          // E step
+          eStep(Xt, sogmm);
+
+          // M step
+          mStep(Xt, Log_Resp__.Exp().Reshape({N, K}), sogmm);
+
+          // convergence check
+          lower_bound = likelihood_;
+          T change = lower_bound - prev_lower_bound;
+          if (!std::isinf(change) && std::abs(change) < tol_)
+          {
+            converged_ = true;
+            break;
+          }
+        }
+
+        // o3d::core::MemoryManagerCached::ReleaseCache(device_);
+
+        if (converged_)
+        {
+          return true;
+        }
+        else
+        {
+          return false;
+        }
+      }
+
+      static void scoreSamples(const Tensor &Xt, const Container &sogmm,
+                               Tensor &Per_Sample_Log_Likelihood)
+      {
+        SizeVector Xt_shape = Xt.GetShape();
+        unsigned int N = Xt_shape[0];
+        unsigned int K = sogmm.n_components_;
+
+        Tensor Weighted_Log_Prob = Tensor::Zeros({N, K, 1}, sogmm.dtype_, sogmm.device_);
+        Tensor eDiff = Tensor::Zeros({N, K, D, 1}, sogmm.dtype_, sogmm.device_);
+        Tensor Log_Det_Cholesky = Tensor::Zeros({K, 1}, sogmm.dtype_, sogmm.device_);
+        Tensor Log_Det_Cholesky_Tmp = Tensor::Zeros({K, D}, sogmm.dtype_, sogmm.device_);
+
+        Tensor Log_Det_Cholesky_View = (GetDiagonal(sogmm.precisions_cholesky_[0]));
+
+        Log_Det_Cholesky.template Fill<float>(0.0);
+        Log_Det_Cholesky_Tmp.CopyFrom(Log_Det_Cholesky_View);
+        Log_Det_Cholesky_Tmp.Log_();
+        Log_Det_Cholesky_Tmp.Sum_({1}, true, Log_Det_Cholesky);
+
+        eDiff = (Xt.Reshape({N, 1, D}) - sogmm.means_).Reshape({N, K, D, 1});
+        eDiff = (sogmm.precisions_cholesky_.Mul(eDiff)).Sum({2}, true).Reshape({N, K, D, 1});
+
+        (eDiff.Mul(eDiff)).Sum_({2}, false, Weighted_Log_Prob);
+        Weighted_Log_Prob.Add_(D * LOG_2_M_PI);
+        Weighted_Log_Prob.Mul_(-0.5);
+        Weighted_Log_Prob.Add_(Log_Det_Cholesky.Reshape({1, K, 1}));
+
+        Weighted_Log_Prob.Add_(sogmm.weights_.Log());
+
+        Tensor amax = Tensor::Zeros({N, 1, 1}, sogmm.dtype_, sogmm.device_);
+
+        amax = Weighted_Log_Prob.Max({1}, true);
+        ((Weighted_Log_Prob - amax).Exp()).Sum_({1}, true, Per_Sample_Log_Likelihood);
+        Per_Sample_Log_Likelihood.Log_();
+        Per_Sample_Log_Likelihood.Add_(amax);
+      }
+
+      bool converged_ = false;
+
+      Device device_;
+      Dtype dtype_;
+
+      T tol_;
+      T reg_covar_;
+      unsigned int max_iter_;
+      T likelihood_;
+
+    private:
+      Tensor amax__;
+      Tensor Log_Det_Cholesky__;
+      Tensor Log_Det_Cholesky_Tmp__;
+      Tensor eDiff__;
+      Tensor Log_Prob_Norm__;
+
+      Tensor Nk__;
+      Tensor mDiff__;
+      Tensor Log_Resp__;
+    };
+  }
+}