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I have a non-linear system I'm using Ceres to solve. It's a sparse system with a sparse block structure. Since I'm also working on image data, I've based my code off of the 'denoising.cc' example.

The issue I'm encountering is that my code fails with "Terminating: Residual and Jacobian evaluation failed.". I'm able to fix the issue by hard-coding the variable 'num_weights' in Evaluate.

The issue persists when I call this function on one or on many pixels. For each pixel, my weights are different.

Any insight as to why this is will help.

Thanks!

Cost::Cost(const std::vector<double> &weights) : _weights(weights)
{
    set_num_residuals(1);
    mutable_parameter_block_sizes()->push_back(1); //has more parameters than weights

    for (int i = 0; i < _weights.size(); ++i)
        mutable_parameter_block_sizes()->push_back(1);
}

bool Cost::Evaluate(double const* const* parameters,
                             double *residuals,
                             double **jacobians) const
{
    int num_weights = (int)_weights.size();

    float d0 = parameters[0][0];
    residuals[0] = d0;

    for (int i = 0; i < num_weights; ++i)
    {
        residuals[0] += parameters[i+1][0];
    }

    if (jacobians != NULL)
    {
        for (int i = 0; i < num_weights+1; ++i)
        {
            if (jacobians[i] != NULL)
            {
                jacobians[i][0] = 0;
            }
        }
    }

    return true;
}
Jan-Michael Tressler
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1 Answers1

0

Just figured it out.

Basically, my vector "std::vector weights" went out of scope. The ceres cost function does not retain ownership of the vector, so "num_weights" evaluated to 0.

Jan-Michael Tressler
  • 629
  • 5
  • 17