Merging upstream version 1.44.3.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 151 insertions, 0 deletions

diff --git a/ml/dlib/tools/python/src/svm_struct.cpp b/ml/dlib/tools/python/src/svm_struct.cpp
new file mode 100644
index 000000000..d8ebad957
--- /dev/null
+++ b/ml/dlib/tools/python/src/svm_struct.cpp
@@ -0,0 +1,151 @@
+// Copyright (C) 2013  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+
+#include "opaque_types.h"
+#include <dlib/python.h>
+#include <dlib/matrix.h>
+#include <dlib/svm.h>
+
+using namespace dlib;
+using namespace std;
+namespace py = pybind11;
+
+template <typename psi_type>
+class svm_struct_prob : public structural_svm_problem<matrix<double,0,1>, psi_type>
+{
+    typedef structural_svm_problem<matrix<double,0,1>, psi_type> base;
+    typedef typename base::feature_vector_type feature_vector_type;
+    typedef typename base::matrix_type matrix_type;
+    typedef typename base::scalar_type scalar_type;
+public:
+    svm_struct_prob (
+        py::object& problem_,
+        long num_dimensions_,
+        long num_samples_
+    ) : 
+        num_dimensions(num_dimensions_),
+        num_samples(num_samples_),
+        problem(problem_)
+    {} 
+
+    virtual long get_num_dimensions (
+    ) const { return num_dimensions; }
+
+    virtual long get_num_samples (
+    ) const { return num_samples; }
+
+    virtual void get_truth_joint_feature_vector (
+        long idx,
+        feature_vector_type& psi 
+    ) const 
+    {
+        psi = problem.attr("get_truth_joint_feature_vector")(idx).template cast<feature_vector_type&>();
+    }
+
+    virtual void separation_oracle (
+        const long idx,
+        const matrix_type& current_solution,
+        scalar_type& loss,
+        feature_vector_type& psi
+    ) const 
+    {
+        py::object res = problem.attr("separation_oracle")(idx,std::ref(current_solution));
+        pyassert(len(res) == 2, "separation_oracle() must return two objects, the loss and the psi vector");
+        py::tuple t = res.cast<py::tuple>();
+        // let the user supply the output arguments in any order.
+        try {
+            loss = t[0].cast<scalar_type>();
+            psi = t[1].cast<feature_vector_type&>();
+        } catch(py::cast_error &e) {
+            psi = t[0].cast<feature_vector_type&>();
+            loss = t[1].cast<scalar_type>();
+       }
+    }
+
+private:
+
+    const long num_dimensions;
+    const long num_samples;
+    py::object& problem;
+};
+
+// ----------------------------------------------------------------------------------------
+
+template <typename psi_type>
+matrix<double,0,1> solve_structural_svm_problem_impl(
+    py::object problem
+)
+{
+  const double C = problem.attr("C").cast<double>();
+    const bool be_verbose = py::hasattr(problem,"be_verbose") && problem.attr("be_verbose").cast<bool>();
+    const bool use_sparse_feature_vectors = py::hasattr(problem,"use_sparse_feature_vectors") && 
+                                            problem.attr("use_sparse_feature_vectors").cast<bool>();
+    const bool learns_nonnegative_weights = py::hasattr(problem,"learns_nonnegative_weights") && 
+                                            problem.attr("learns_nonnegative_weights").cast<bool>();
+
+    double eps = 0.001;
+    unsigned long max_cache_size = 10;
+    if (py::hasattr(problem, "epsilon"))
+        eps = problem.attr("epsilon").cast<double>();
+    if (py::hasattr(problem, "max_cache_size"))
+        max_cache_size = problem.attr("max_cache_size").cast<double>();
+
+    const long num_samples = problem.attr("num_samples").cast<long>();
+    const long num_dimensions = problem.attr("num_dimensions").cast<long>();
+
+    pyassert(num_samples > 0, "You can't train a Structural-SVM if you don't have any training samples.");
+
+    if (be_verbose)
+    {
+        cout << "C:              " << C << endl;
+        cout << "epsilon:        " << eps << endl;
+        cout << "max_cache_size: " << max_cache_size << endl;
+        cout << "num_samples:    " << num_samples << endl;
+        cout << "num_dimensions: " << num_dimensions << endl;
+        cout << "use_sparse_feature_vectors: " << std::boolalpha << use_sparse_feature_vectors << endl;
+        cout << "learns_nonnegative_weights: " << std::boolalpha << learns_nonnegative_weights << endl;
+        cout << endl;
+    }
+
+    svm_struct_prob<psi_type> prob(problem, num_dimensions, num_samples);
+    prob.set_c(C);
+    prob.set_epsilon(eps);
+    prob.set_max_cache_size(max_cache_size);
+    if (be_verbose)
+        prob.be_verbose();
+
+    oca solver;
+    matrix<double,0,1> w;
+    if (learns_nonnegative_weights)
+        solver(prob, w, prob.get_num_dimensions());
+    else
+        solver(prob, w);
+    return w;
+}
+
+// ----------------------------------------------------------------------------------------
+
+matrix<double,0,1> solve_structural_svm_problem(
+    py::object problem
+)
+{
+    // Check if the python code is using sparse or dense vectors to represent PSI()
+    if (py::isinstance<matrix<double,0,1>>(problem.attr("get_truth_joint_feature_vector")(0)))
+        return solve_structural_svm_problem_impl<matrix<double,0,1> >(problem);
+    else
+        return solve_structural_svm_problem_impl<std::vector<std::pair<unsigned long,double> > >(problem);
+}
+
+// ----------------------------------------------------------------------------------------
+
+void bind_svm_struct(py::module& m)
+{
+    m.def("solve_structural_svm_problem",solve_structural_svm_problem, py::arg("problem"),
+"This function solves a structural SVM problem and returns the weight vector    \n\
+that defines the solution.  See the example program python_examples/svm_struct.py    \n\
+for documentation about how to create a proper problem object.   " 
+        );
+}
+
+// ----------------------------------------------------------------------------------------
+