convert numpy input params to dtype float and order 'F' automatically ...
using numpy.astype(...). No copy if the params are already in the correct dtype and storage order.
For a function
f(Matrix A, Matrix B),
simply wrapping it to pyx as
f(A.astype(float, order='F', copy=False), B.astype(float, order='F', copy=False))
won't work.
It produces a strange side-effect that the content of A is overwritten by B and the two inputs are the same (data address) inside the function!
This is because Cython decreases the ref count for the temporary variable resulted from A.astype(...) before generates the wrap for B.astype(...).
Hence, the A.astype temp var is probably reused for B.astype, and they were pointing to the same data address.
For that reason, we have to go a longer route and wrap it as:
A = A.astype(float, order='F', copy=False)
B = B.astype(float, order='F', copy=False)
f(A, B)
For future ref., here is a sample of the wrongly generated code that wraps the JacobianFactor constructor:
Jacobian(Key i1, Matrix A1, Key i2, Matrix A2, Vector b, noiseModel::Diagonal model)
Wrongly wrapped pyx code:
self.shared_CJacobianFactor_ = shared_ptr[CJacobianFactor](new CJacobianFactor(i1, <MatrixXd>(Map[MatrixXd](A1.astype(float, order='F',copy=False)), i2, <MatrixXd>(Map[MatrixXd](A2.astype(float, order='F', copy=False)), <VectorXd>(Map[VectorXd](b.astype(float, order='F', copy=False))), model.shared_CnoiseModel_Diagonal_))
The problematic Cython generated CPP code with a comment on the problematic line:
/////////////////////////////////////////
// WRONG VERSION
/////////////////////////////////////////
__pyx_t_12 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_A1), __pyx_n_s_astype); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_12);
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__Pyx_GOTREF(__pyx_t_4);
__Pyx_INCREF(((PyObject *)(&PyFloat_Type)));
__Pyx_GIVEREF(((PyObject *)(&PyFloat_Type)));
PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)(&PyFloat_Type)));
__pyx_t_5 = PyDict_New(); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_5);
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__Pyx_GOTREF(__pyx_t_13);
__Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
if (!(likely(((__pyx_t_13) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_13, __pyx_ptype_5numpy_ndarray))))) __PYX_ERR(0, 2107, __pyx_L1_error)
try {
__pyx_t_14 = eigency::Map<Eigen::MatrixXd> (((PyArrayObject *)__pyx_t_13));
} catch(...) {
__Pyx_CppExn2PyErr();
__PYX_ERR(0, 2107, __pyx_L1_error)
}
///////////////////////////////////////////////
__Pyx_DECREF(__pyx_t_13); __pyx_t_13 = 0; //<------- Problematic line!!! Killing this will result in the correct result!
///////////////////////////////////////////////
__pyx_t_13 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_A2), __pyx_n_s_astype); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_13);
__pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_5);
__Pyx_INCREF(((PyObject *)(&PyFloat_Type)));
__Pyx_GIVEREF(((PyObject *)(&PyFloat_Type)));
PyTuple_SET_ITEM(__pyx_t_5, 0, ((PyObject *)(&PyFloat_Type)));
__pyx_t_4 = PyDict_New(); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_4);
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if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_copy, Py_False) < 0) __PYX_ERR(0, 2107, __pyx_L1_error)
__pyx_t_12 = __Pyx_PyObject_Call(__pyx_t_13, __pyx_t_5, __pyx_t_4); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_12);
__Pyx_DECREF(__pyx_t_13); __pyx_t_13 = 0;
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
if (!(likely(((__pyx_t_12) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_12, __pyx_ptype_5numpy_ndarray))))) __PYX_ERR(0, 2107, __pyx_L1_error)
try {
__pyx_t_15 = eigency::Map<Eigen::MatrixXd> (((PyArrayObject *)__pyx_t_12));
} catch(...) {
__Pyx_CppExn2PyErr();
__PYX_ERR(0, 2107, __pyx_L1_error)
}
__Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
__pyx_t_12 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_b), __pyx_n_s_astype); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_12);
__pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_4);
__Pyx_INCREF(((PyObject *)(&PyFloat_Type)));
__Pyx_GIVEREF(((PyObject *)(&PyFloat_Type)));
PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)(&PyFloat_Type)));
__pyx_t_5 = PyDict_New(); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_5);
if (PyDict_SetItem(__pyx_t_5, __pyx_n_s_order, __pyx_n_s_F) < 0) __PYX_ERR(0, 2107, __pyx_L1_error)
if (PyDict_SetItem(__pyx_t_5, __pyx_n_s_copy, Py_False) < 0) __PYX_ERR(0, 2107, __pyx_L1_error)
__pyx_t_13 = __Pyx_PyObject_Call(__pyx_t_12, __pyx_t_4, __pyx_t_5); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 2107, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_13);
__Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
if (!(likely(((__pyx_t_13) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_13, __pyx_ptype_5numpy_ndarray))))) __PYX_ERR(0, 2107, __pyx_L1_error)
try {
__pyx_t_16 = eigency::Map<Eigen::VectorXd> (((PyArrayObject *)__pyx_t_13));
} catch(...) {
__Pyx_CppExn2PyErr();
__PYX_ERR(0, 2107, __pyx_L1_error)
}
__Pyx_DECREF(__pyx_t_13); __pyx_t_13 = 0;
try {
__pyx_t_17 = new gtsam::JacobianFactor(__pyx_v_i1, ((Eigen::MatrixXd)__pyx_t_14), __pyx_v_i2, ((Eigen::MatrixXd)__pyx_t_15), ((Eigen::VectorXd)__pyx_t_16), __pyx_v_model->shared_CnoiseModel_Diagonal_);
} catch(...) {
__Pyx_CppExn2PyErr();
__PYX_ERR(0, 2107, __pyx_L1_error)
}
__pyx_v_self->shared_CJacobianFactor_ = boost::shared_ptr<gtsam::JacobianFactor> (__pyx_t_17);
6bf7ea23cf