Removed kwargs overhead for overloaded methods
dellaert
parent
8ae9d7c577
commit
81c15d950a
112
gtsam.h
112
gtsam.h
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@ -2,9 +2,13 @@
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* GTSAM Wrap Module Definition
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*
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* These are the current classes available through the matlab toolbox interface,
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* These are the current classes available through the matlab and python wrappers,
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* add more functions/classes as they are available.
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*
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* IMPORTANT: the python wrapper supports keyword arguments for functions/methods. Hence, the
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* argument names matter. An implementation restriction is that in overloaded methods
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* or functions, arguments of different types *have* to have different names.
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*
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* Requirements:
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* Classes must start with an uppercase letter
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* - Can wrap a typedef
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@ -16,7 +20,7 @@
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* - Any class with which be copied with boost::make_shared()
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* - boost::shared_ptr of any object type
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* Constructors
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* - Overloads are supported, but arguments *have* to have different names
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* - Overloads are supported, but arguments of different types *have* to have different names
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* - A class with no constructors can be returned from other functions but not allocated directly in MATLAB
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* Methods
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* - Constness has no effect
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@ -26,7 +30,7 @@
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* Static methods
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* - Must start with a letter (upper or lowercase) and use the "static" keyword
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* - The first letter will be made uppercase in the generated MATLAB interface
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* - Overloads are supported
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* - Overloads are supported, but arguments of different types *have* to have different names
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* Arguments to functions any of
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* - Eigen types: Matrix, Vector
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* - Eigen types and classes as an optionally const reference
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@ -601,23 +605,23 @@ class Pose3 {
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// Group
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static gtsam::Pose3 identity();
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gtsam::Pose3 inverse() const;
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gtsam::Pose3 compose(const gtsam::Pose3& p2) const;
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gtsam::Pose3 between(const gtsam::Pose3& p2) const;
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gtsam::Pose3 compose(const gtsam::Pose3& pose) const;
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gtsam::Pose3 between(const gtsam::Pose3& pose) const;
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// Manifold
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gtsam::Pose3 retract(Vector v) const;
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Vector localCoordinates(const gtsam::Pose3& T2) const;
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Vector localCoordinates(const gtsam::Pose3& pose) const;
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// Lie Group
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static gtsam::Pose3 Expmap(Vector v);
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static Vector Logmap(const gtsam::Pose3& p);
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static Vector Logmap(const gtsam::Pose3& pose);
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Matrix AdjointMap() const;
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Vector Adjoint(Vector xi) const;
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static Matrix wedge(double wx, double wy, double wz, double vx, double vy, double vz);
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// Group Action on Point3
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gtsam::Point3 transform_from(const gtsam::Point3& p) const;
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gtsam::Point3 transform_to(const gtsam::Point3& p) const;
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gtsam::Point3 transform_from(const gtsam::Point3& point) const;
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gtsam::Point3 transform_to(const gtsam::Point3& point) const;
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// Standard Interface
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gtsam::Rot3 rotation() const;
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@ -642,7 +646,7 @@ class Pose3Vector
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size_t size() const;
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bool empty() const;
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gtsam::Pose3 at(size_t n) const;
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void push_back(const gtsam::Pose3& x);
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void push_back(const gtsam::Pose3& pose);
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};
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#include <gtsam/geometry/Unit3.h>
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@ -915,7 +919,7 @@ class PinholeCamera {
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gtsam::Point2 project(const gtsam::Point3& point);
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gtsam::Point3 backproject(const gtsam::Point2& p, double depth) const;
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double range(const gtsam::Point3& point);
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double range(const gtsam::Pose3& point);
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double range(const gtsam::Pose3& pose);
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// enabling serialization functionality
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void serialize() const;
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@ -952,7 +956,7 @@ virtual class SimpleCamera {
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gtsam::Point2 project(const gtsam::Point3& point);
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gtsam::Point3 backproject(const gtsam::Point2& p, double depth) const;
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double range(const gtsam::Point3& point);
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double range(const gtsam::Pose3& point);
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double range(const gtsam::Pose3& pose);
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// enabling serialization functionality
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void serialize() const;
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@ -1066,13 +1070,13 @@ virtual class SymbolicFactorGraph {
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const gtsam::Ordering& ordering);
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pair<gtsam::SymbolicBayesTree*, gtsam::SymbolicFactorGraph*> eliminatePartialMultifrontal(
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const gtsam::KeyVector& keys);
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gtsam::SymbolicBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& variables);
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gtsam::SymbolicBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& variables);
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gtsam::SymbolicBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& variables,
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gtsam::SymbolicBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& ordering);
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gtsam::SymbolicBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& key_vector);
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gtsam::SymbolicBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& ordering,
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const gtsam::Ordering& marginalizedVariableOrdering);
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gtsam::SymbolicBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& variables,
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gtsam::SymbolicBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& key_vector,
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const gtsam::Ordering& marginalizedVariableOrdering);
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gtsam::SymbolicFactorGraph* marginal(const gtsam::KeyVector& variables);
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gtsam::SymbolicFactorGraph* marginal(const gtsam::KeyVector& key_vector);
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};
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#include <gtsam/symbolic/SymbolicConditional.h>
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@ -1466,7 +1470,7 @@ class GaussianFactorGraph {
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// Building the graph
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void push_back(const gtsam::GaussianFactor* factor);
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void push_back(const gtsam::GaussianConditional* factor);
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void push_back(const gtsam::GaussianConditional* conditional);
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void push_back(const gtsam::GaussianFactorGraph& graph);
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void push_back(const gtsam::GaussianBayesNet& bayesNet);
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void push_back(const gtsam::GaussianBayesTree& bayesTree);
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@ -1505,13 +1509,13 @@ class GaussianFactorGraph {
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const gtsam::Ordering& ordering);
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pair<gtsam::GaussianBayesTree*, gtsam::GaussianFactorGraph*> eliminatePartialMultifrontal(
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const gtsam::KeyVector& keys);
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gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& variables);
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gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& variables);
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gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& variables,
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gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& ordering);
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gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& key_vector);
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gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& ordering,
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const gtsam::Ordering& marginalizedVariableOrdering);
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gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& variables,
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gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& key_vector,
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const gtsam::Ordering& marginalizedVariableOrdering);
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gtsam::GaussianFactorGraph* marginal(const gtsam::KeyVector& variables);
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gtsam::GaussianFactorGraph* marginal(const gtsam::KeyVector& key_vector);
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// Conversion to matrices
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Matrix sparseJacobian_() const;
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@ -1865,34 +1869,34 @@ class Values {
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// void update(size_t j, const gtsam::Value& val);
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// gtsam::Value at(size_t j) const;
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void insert(size_t j, const gtsam::Point2& t);
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void insert(size_t j, const gtsam::Point3& t);
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void insert(size_t j, const gtsam::Rot2& t);
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void insert(size_t j, const gtsam::Pose2& t);
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void insert(size_t j, const gtsam::Rot3& t);
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void insert(size_t j, const gtsam::Pose3& t);
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void insert(size_t j, const gtsam::Cal3_S2& t);
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void insert(size_t j, const gtsam::Cal3DS2& t);
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void insert(size_t j, const gtsam::Cal3Bundler& t);
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void insert(size_t j, const gtsam::EssentialMatrix& t);
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void insert(size_t j, const gtsam::SimpleCamera& t);
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void insert(size_t j, const gtsam::imuBias::ConstantBias& t);
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void insert(size_t j, Vector t);
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void insert(size_t j, Matrix t);
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void insert(size_t j, const gtsam::Point2& point2);
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void insert(size_t j, const gtsam::Point3& point3);
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void insert(size_t j, const gtsam::Rot2& rot2);
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void insert(size_t j, const gtsam::Pose2& pose2);
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void insert(size_t j, const gtsam::Rot3& rot3);
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void insert(size_t j, const gtsam::Pose3& pose3);
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void insert(size_t j, const gtsam::Cal3_S2& cal3_s2);
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void insert(size_t j, const gtsam::Cal3DS2& cal3ds2);
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void insert(size_t j, const gtsam::Cal3Bundler& cal3bundler);
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void insert(size_t j, const gtsam::EssentialMatrix& essential_matrix);
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void insert(size_t j, const gtsam::SimpleCamera& simpel_camera);
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void insert(size_t j, const gtsam::imuBias::ConstantBias& constant_bias);
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void insert(size_t j, Vector vector);
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void insert(size_t j, Matrix matrix);
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void update(size_t j, const gtsam::Point2& t);
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void update(size_t j, const gtsam::Point3& t);
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void update(size_t j, const gtsam::Rot2& t);
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void update(size_t j, const gtsam::Pose2& t);
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void update(size_t j, const gtsam::Rot3& t);
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void update(size_t j, const gtsam::Pose3& t);
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void update(size_t j, const gtsam::Cal3_S2& t);
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void update(size_t j, const gtsam::Cal3DS2& t);
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void update(size_t j, const gtsam::Cal3Bundler& t);
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void update(size_t j, const gtsam::EssentialMatrix& t);
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void update(size_t j, const gtsam::imuBias::ConstantBias& t);
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void update(size_t j, Vector t);
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void update(size_t j, Matrix t);
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void update(size_t j, const gtsam::Point2& point2);
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void update(size_t j, const gtsam::Point3& point3);
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void update(size_t j, const gtsam::Rot2& rot2);
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void update(size_t j, const gtsam::Pose2& pose2);
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void update(size_t j, const gtsam::Rot3& rot3);
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void update(size_t j, const gtsam::Pose3& pose3);
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void update(size_t j, const gtsam::Cal3_S2& cal3_s2);
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void update(size_t j, const gtsam::Cal3DS2& cal3ds2);
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void update(size_t j, const gtsam::Cal3Bundler& cal3bundler);
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void update(size_t j, const gtsam::EssentialMatrix& essential_matrix);
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void update(size_t j, const gtsam::imuBias::ConstantBias& constant_bias);
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void update(size_t j, Vector vector);
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void update(size_t j, Matrix matrix);
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template<T = {gtsam::Point2, gtsam::Point3, gtsam::Rot2, gtsam::Pose2, gtsam::Rot3, gtsam::Pose3, gtsam::Cal3_S2, gtsam::Cal3DS2, gtsam::Cal3Bundler, gtsam::EssentialMatrix, gtsam::imuBias::ConstantBias, Vector, Matrix}>
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T at(size_t j);
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@ -2106,10 +2110,10 @@ class ISAM2Params {
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void print(string str) const;
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/** Getters and Setters for all properties */
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void setOptimizationParams(const gtsam::ISAM2GaussNewtonParams& params);
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void setOptimizationParams(const gtsam::ISAM2DoglegParams& params);
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void setRelinearizeThreshold(double relinearizeThreshold);
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void setRelinearizeThreshold(const gtsam::ISAM2ThresholdMap& relinearizeThreshold);
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void setOptimizationParams(const gtsam::ISAM2GaussNewtonParams& gauss_newton__params);
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void setOptimizationParams(const gtsam::ISAM2DoglegParams& dogleg_params);
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void setRelinearizeThreshold(double threshold);
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void setRelinearizeThreshold(const gtsam::ISAM2ThresholdMap& threshold_map);
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int getRelinearizeSkip() const;
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void setRelinearizeSkip(int relinearizeSkip);
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bool isEnableRelinearization() const;
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@ -141,7 +141,8 @@ void Method::emit_cython_pyx(FileWriter& file, const Class& cls) const {
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// doesn't allow overloads
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// e.g. template<T={A,B,C}> funcName(...) --> funcNameA, funcNameB, funcNameC
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string instantiatedName =
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(templateArgValue_) ? funcName + templateArgValue_->pyxClassName() : funcName;
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(templateArgValue_) ? funcName + templateArgValue_->pyxClassName() :
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funcName;
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size_t N = nrOverloads();
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// It's easy if there's no overload
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@ -152,38 +153,38 @@ void Method::emit_cython_pyx(FileWriter& file, const Class& cls) const {
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// Dealing with overloads..
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file.oss << " def " << instantiatedName << "(self, *args, **kwargs):\n";
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for (size_t i = 0; i < N; ++i) {
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file.oss << " success, results = self." << instantiatedName << "_" << i
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<< "(args, kwargs)\n";
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file.oss << " if success:\n return results\n";
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}
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file.oss << " raise TypeError('Could not find the correct overload')\n";
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file.oss << " cdef list __params\n";
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for (size_t i = 0; i < N; ++i) {
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ArgumentList args = argumentList(i);
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file.oss << " def " + instantiatedName + "_" + to_string(i) +
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"(self, args, kwargs):\n";
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file.oss << " cdef list __params\n";
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// Define return values for all possible overloads
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vector<string> return_value;
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for (size_t i = 0; i < nrOverloads(); ++i) {
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return_value.push_back("return_value_" + to_string(i));
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if (!returnVals_[i].isVoid()) {
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file.oss << " cdef " << returnVals_[i].pyx_returnType() << " return_value\n";
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file.oss << " cdef " << returnVals_[i].pyx_returnType() << " "
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<< return_value[i] << "\n";
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}
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}
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for (size_t i = 0; i < nrOverloads(); ++i) {
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ArgumentList args = argumentList(i);
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file.oss << " try:\n";
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file.oss << pyx_resolveOverloadParams(args, false, 3); // lazy: always return None even if it's a void function
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/// Call corresponding cython function
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file.oss << argumentList(i).pyx_convertEigenTypeAndStorageOrder(" ");
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file.oss << args.pyx_convertEigenTypeAndStorageOrder(" ");
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string caller = "self." + cls.shared_pxd_obj_in_pyx() + ".get()";
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string call = pyx_functionCall(caller, funcName, i);
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if (!returnVals_[i].isVoid()) {
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file.oss << " return_value = " << call << "\n";
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file.oss << " return True, " << returnVals_[i].pyx_casting("return_value") << "\n";
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file.oss << " " << return_value[i] << " = " << call << "\n";
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file.oss << " return "
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<< returnVals_[i].pyx_casting(return_value[i]) << "\n";
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} else {
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file.oss << " " << call << "\n";
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file.oss << " return True, None\n";
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file.oss << " return\n";
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}
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file.oss << " except:\n";
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file.oss << " return False, None\n\n";
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file.oss << " pass\n";
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}
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file.oss
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<< " raise TypeError('Incorrect arguments for method call.')\n\n";
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}
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/* ************************************************************************* */
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