Method gradient

dependency/densecrf/src/densecrf.cpp:238–297

Gradient computations

// Gradient computations
double DenseCRF::gradient( int n_iterations, const ObjectiveFunction & objective, VectorXf * unary_grad, VectorXf * lbl_cmp_grad, VectorXf * kernel_grad) const {
	// Run inference
	std::vector< MatrixXf > Q(n_iterations+1);
	MatrixXf tmp1, unary( M_, N_ ), tmp2;
	unary.fill(0);
	if( unary_ )
		unary = unary_->get();
	expAndNormalize( Q[0], -unary );
	for( int it=0; it<n_iterations; it++ ) {
		tmp1 = -unary;
		for( unsigned int k=0; k<pairwise_.size(); k++ ) {
			pairwise_[k]->apply( tmp2, Q[it] );
			tmp1 -= tmp2;
		}
		expAndNormalize( Q[it+1], tmp1 );
	}
	
	// Compute the objective value
	MatrixXf b( M_, N_ );
	double r = objective.evaluate( b, Q[n_iterations] );
	sumAndNormalize( b, b, Q[n_iterations] );

	// Compute the gradient
	if(unary_grad && unary_)
		*unary_grad = unary_->gradient( b );
	if( lbl_cmp_grad )
		*lbl_cmp_grad = 0*labelCompatibilityParameters();
	if( kernel_grad )
		*kernel_grad = 0*kernelParameters();
	
	for( int it=n_iterations-1; it>=0; it-- ) {
		// Do the inverse message passing
		tmp1.fill(0);
		int ip = 0, ik = 0;
		// Add up all pairwise potentials
		for( unsigned int k=0; k<pairwise_.size(); k++ ) {
			// Compute the pairwise gradient expression
			if( lbl_cmp_grad ) {
				VectorXf pg = pairwise_[k]->gradient( b, Q[it] );
				lbl_cmp_grad->segment( ip, pg.rows() ) += pg;
				ip += pg.rows();
			}
			// Compute the kernel gradient expression
			if( kernel_grad ) {
				VectorXf pg = pairwise_[k]->kernelGradient( b, Q[it] );
				kernel_grad->segment( ik, pg.rows() ) += pg;
				ik += pg.rows();
			}
			// Compute the new b
			pairwise_[k]->applyTranspose( tmp2, b );
			tmp1 += tmp2;
		}
		sumAndNormalize( b, tmp1.array()*Q[it].array(), Q[it] );
		
		// Add the gradient
		if(unary_grad && unary_)
			*unary_grad += unary_->gradient( b );
	}