From ff98216b7ae80efd698bf7144a1f1c8f33351aa0 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 21 May 2021 13:25:48 -0700 Subject: [PATCH 01/47] Create a templated method of RPC_process_related_viewgrams_gradient --- ...ihoodWithLinearModelForMeanAndProjData.cxx | 52 ++++++++++++------- 1 file changed, 32 insertions(+), 20 deletions(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index 84bef7219f..5145a0da3e 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -628,11 +628,9 @@ compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, this->max_segment_num_to_process, this->zero_seg0_end_planes!=0, NULL, - this->additive_proj_data_sptr - , caching_info_ptr - ); - - + this->additive_proj_data_sptr, + caching_info_ptr, + false); } @@ -1077,7 +1075,7 @@ RPC_process_related_viewgrams_type RPC_process_related_viewgrams_sensitivity_com #else //! Call-back function for compute_gradient -static RPC_process_related_viewgrams_type RPC_process_related_viewgrams_gradient; +template static RPC_process_related_viewgrams_type RPC_process_related_viewgrams_gradient; //! Call-back function for accumulate_loglikelihood static RPC_process_related_viewgrams_type RPC_process_related_viewgrams_accumulate_loglikelihood; @@ -1097,10 +1095,28 @@ void distributable_compute_gradient(const shared_ptr& for bool zero_seg0_end_planes, double* log_likelihood_ptr, shared_ptr const& additive_binwise_correction, - DistributedCachingInformation* caching_info_ptr + DistributedCachingInformation* caching_info_ptr, + const bool do_subtraction ) { - + if (do_subtraction){ + // Within the RPC process, subtract ones before to back projection ( backproj[ y/ybar - 1] ) + distributable_computation(forward_projector_sptr, + back_projector_sptr, + symmetries_sptr, + &output_image, &input_image, + proj_dat, true, //i.e. do read projection data + subset_num, num_subsets, + min_segment, max_segment, + zero_seg0_end_planes, + log_likelihood_ptr, + additive_binwise_correction, + /* normalisation info to be ignored */ shared_ptr(), 0., 0., + &RPC_process_related_viewgrams_gradient, + caching_info_ptr + ); + } else { + // Within the RPC process, only do div/truncate ( backproj[ y/ybar ] ) distributable_computation(forward_projector_sptr, back_projector_sptr, symmetries_sptr, @@ -1112,9 +1128,10 @@ void distributable_compute_gradient(const shared_ptr& for log_likelihood_ptr, additive_binwise_correction, /* normalisation info to be ignored */ shared_ptr(), 0., 0., - &RPC_process_related_viewgrams_gradient, + &RPC_process_related_viewgrams_gradient, caching_info_ptr - ); + ); + } } @@ -1195,7 +1212,7 @@ void distributable_sensitivity_computation( //////////// RPC functions - +template void RPC_process_related_viewgrams_gradient( const shared_ptr& forward_projector_sptr, const shared_ptr& back_projector_sptr, @@ -1233,19 +1250,14 @@ void RPC_process_related_viewgrams_gradient( if (additive_binwise_correction_ptr != NULL) - { estimated_viewgrams += (*additive_binwise_correction_ptr); - } - - - - - // for sinogram division - divide_and_truncate(*measured_viewgrams_ptr, estimated_viewgrams, rim_truncation_sino, count, count2, log_likelihood_ptr); - + if (do_subtraction){ + // subtract ones from estimated data [y/ybar - 1] + estimated_viewgrams -= 1; + } back_projector_sptr->back_project(*measured_viewgrams_ptr); }; From bca4ba6d2580069e447b538085f46b1d24d2534f Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 21 May 2021 15:47:11 -0700 Subject: [PATCH 02/47] Update the standard Likelihood classes to use actual_compute_sub_gradient_without_penalty --- ...issonLogLikelihoodWithLinearModelForMean.h | 20 +++++------- ...elihoodWithLinearModelForMeanAndProjData.h | 6 ++++ ...sonLogLikelihoodWithLinearModelForMean.cxx | 22 +------------ ...ihoodWithLinearModelForMeanAndProjData.cxx | 31 +++++++++++++------ 4 files changed, 36 insertions(+), 43 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h index 108127c6e8..35f18d3b04 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h @@ -120,18 +120,7 @@ public GeneralisedObjectiveFunction //PoissonLogLikelihoodWithLinearModelForMean(); - //! Implementation in terms of compute_sub_gradient_without_penalty_plus_sensitivity() - /*! \warning If separate subsensitivities are not used, we just subtract the total - sensitivity divided by the number of subsets. - This is fine for some algorithms as the sum over all the subsets is - equal to gradient of the objective function (without prior). - Other algorithms do not behave very stable under this approximation - however. So, currently setup() will return an error if - !subsets_are_approximately_balanced() and subset sensitivities - are not used. - - \see get_use_subset_sensitivities() - */ + //! Computes the gradient of the data fit term virtual void compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, @@ -264,6 +253,13 @@ public GeneralisedObjectiveFunction */ void compute_sensitivities(); + + virtual void + actual_compute_sub_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool do_subtraction) = 0; + //! Sets defaults for parsing /*! Resets \c sensitivity_filename, \c subset_sensitivity_filenames to empty, \c recompute_sensitivity to \c false, and \c use_subset_sensitivities to false. diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h index 23ff58c916..64cd25af32 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h @@ -217,6 +217,12 @@ public RegisteredParsingObject get_exam_info_uptr_for_target() const; #if 0 diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx index d63701a535..60166815cb 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx @@ -358,27 +358,7 @@ compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num) { - if (subset_num<0 || subset_num>=this->get_num_subsets()) - error("compute_sub_gradient_without_penalty subset_num out-of-range error"); - - this-> - compute_sub_gradient_without_penalty_plus_sensitivity(gradient, - current_estimate, - subset_num); - // compute gradient -= sub_sensitivity - { - typename TargetT::full_iterator gradient_iter = - gradient.begin_all(); - const typename TargetT::full_iterator gradient_end = - gradient.end_all(); - typename TargetT::const_full_iterator sensitivity_iter = - this->get_subset_sensitivity(subset_num).begin_all_const(); - while (gradient_iter != gradient_end) - { - *gradient_iter -= (*sensitivity_iter); - ++gradient_iter; ++sensitivity_iter; - } - } + this->actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, true); } diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index 5145a0da3e..f37140d01b 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -613,24 +613,35 @@ PoissonLogLikelihoodWithLinearModelForMeanAndProjData:: compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num) +{ + actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); +} + +template +void +PoissonLogLikelihoodWithLinearModelForMeanAndProjData:: +actual_compute_sub_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool do_subtraction) { assert(subset_num>=0); assert(subset_numnum_subsets); - distributable_compute_gradient(this->projector_pair_ptr->get_forward_projector_sptr(), - this->projector_pair_ptr->get_back_projector_sptr(), + distributable_compute_gradient(this->projector_pair_ptr->get_forward_projector_sptr(), + this->projector_pair_ptr->get_back_projector_sptr(), this->symmetries_sptr, gradient, - current_estimate, - this->proj_data_sptr, - subset_num, - this->num_subsets, + current_estimate, + this->proj_data_sptr, + subset_num, + this->num_subsets, -this->max_segment_num_to_process, - this->max_segment_num_to_process, - this->zero_seg0_end_planes!=0, - NULL, + this->max_segment_num_to_process, + this->zero_seg0_end_planes!=0, + NULL, this->additive_proj_data_sptr, caching_info_ptr, - false); + do_subtraction); } From 817c02fb0fc61cd0fb7d6f50cfd44989415ab81d Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 21 May 2021 15:47:31 -0700 Subject: [PATCH 03/47] Dynamic data actual_compute_sub_gradient_without_penalty --- ...odWithLinearKineticModelAndDynamicProjectionData.h | 7 +++++++ ...WithLinearKineticModelAndDynamicProjectionData.txx | 11 +++++++++++ 2 files changed, 18 insertions(+) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h index b523a51f46..f1200bc75d 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h @@ -170,6 +170,13 @@ public RegisteredParsingObject:: compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num) +{ + this->actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); +} + +template +void +PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData:: +actual_compute_sub_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool do_subtraction) { if (subset_num<0 || subset_num>=this->get_num_subsets()) error("compute_sub_gradient_without_penalty subset_num out-of-range error"); From c75b0e507ea6aa7beef30586f19f5d0bd9d1baf5 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 21 May 2021 15:47:41 -0700 Subject: [PATCH 04/47] Gated data actual_compute_sub_gradient_without_penalty --- ...WithLinearModelForMeanAndGatedProjDataWithMotion.h | 8 +++++++- ...thLinearModelForMeanAndGatedProjDataWithMotion.txx | 11 +++++++++++ 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h index 5b2664cb0c..54e1c9ef67 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h @@ -82,7 +82,13 @@ public RegisteredParsingObject:: compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num) +{ + this->actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); +} + +template +void +PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion:: +actual_compute_sub_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool do_subtraction) { assert(subset_num>=0); assert(subset_numnum_subsets); From 59a69562d46245a26fdbfa96108eedbe944f7f2c Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 21 May 2021 15:47:57 -0700 Subject: [PATCH 05/47] List mode actual_compute_sub_gradient_without_penalty will error if do_subtraction is true --- ...odelForMeanAndListModeDataWithProjMatrixByBin.h | 9 ++++++++- ...elForMeanAndListModeDataWithProjMatrixByBin.cxx | 14 +++++++++++++- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h index 013b9bdca0..3c63e54515 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h @@ -70,7 +70,14 @@ typedef RegisteredParsingObjectactual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); +} +template +void +PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin:: +actual_compute_sub_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool do_subtraction) +{ + if (do_subtraction) + error("List mode cannot currently do subtraction on the gradient computation."); assert(subset_num>=0); assert(subset_numnum_subsets); From 8cba366e84833a643c3aa3a998cec02a223bf1e5 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Sat, 22 May 2021 09:28:41 -0700 Subject: [PATCH 06/47] Remove overrides [ci skip] --- ...ogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h | 2 +- ...LikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h | 2 +- .../PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h index f1200bc75d..3ab6f50f24 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h @@ -175,7 +175,7 @@ public RegisteredParsingObject get_exam_info_uptr_for_target() const; From f5252358ce2640b9e305f7ce600ab5b72b759147 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Sun, 23 May 2021 15:24:56 -0700 Subject: [PATCH 07/47] Update documentation --- ...issonLogLikelihoodWithLinearModelForMean.h | 39 ++++++++++++------- 1 file changed, 25 insertions(+), 14 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h index 35f18d3b04..2b3915f40f 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h @@ -66,14 +66,12 @@ START_NAMESPACE_STIR the sensitivity because (if \f$r=0\f$) it is the total probability of detecting a count (in any bin) originating from \f$v\f$. - This class computes the gradient as a sum of these two terms. The - sensitivity has to be computed by the virtual function - \c add_subset_sensitivity(). The sum is computed by - \c compute_sub_gradient_without_penalty_plus_sensitivity(). - - The reason for this is that the sensitivity is data-independent, and - can be computed only once. See also - PoissonLogLikelihoodWithLinearModelForMeanAndListModeData. + This class computes the gradient directly, via \c compute_sub_gradient_without_penalty(). + However, an additional method (\c compute_sub_gradient_without_penalty_plus_sensitivity()) + is provided that computes the sum of the subset gradient and the sensitivity. + This method is used in STIR algorithms such as \c OSMAPOSL. + + See also \c PoissonLogLikelihoodWithLinearModelForMeanAndListModeData. \par Relation with Kullback-Leibler distance @@ -121,6 +119,10 @@ public GeneralisedObjectiveFunction //PoissonLogLikelihoodWithLinearModelForMean(); //! Computes the gradient of the data fit term + /*! + This function is implemented in terms of \c actual_compute_sub_gradient_without_penalty() + by setting do_subtraction = true + */ virtual void compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, @@ -130,12 +132,8 @@ public GeneralisedObjectiveFunction /*! This function is used for instance by OSMAPOSL. - This computes - \f[ {\partial L \over \partial \lambda_v} + P_v = - \sum_b P_{bv} {y_b \over Y_b} - \f] - (see the class general documentation). - The sum will however be restricted to a subset. + This function is implemented in terms of \c actual_compute_sub_gradient_without_penalty() + by setting do_subtraction = false */ virtual void compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, @@ -253,7 +251,20 @@ public GeneralisedObjectiveFunction */ void compute_sensitivities(); + //! computes the objective function subset gradient without the penalty + /*! + If do_subtraction is false, this computes + \f[ {\partial L \over \partial \lambda_v} + P_v = + \sum_b P_{bv} {y_b \over Y_b} + \f] + (see the class general documentation). + The sum will however be restricted to a subset. + However, if do_subtraction is true, this function will instead compute + \f[ {\partial L \over \partial \lambda_v} = + \sum_b P_{bv} ({y_b \over Y_b} - 1) + \f] + */ virtual void actual_compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, From 6b1af734902aeffee07ce4af7e0fc67fdd345e1f Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Mon, 24 May 2021 15:32:42 -0700 Subject: [PATCH 08/47] Gradient computation will subtract normalised ones (mult_viewgrams) --- ...ihoodWithLinearModelForMeanAndProjData.cxx | 21 ++++++++++++------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index f37140d01b..c30599ba85 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -640,6 +640,7 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, this->zero_seg0_end_planes!=0, NULL, this->additive_proj_data_sptr, + this->normalisation_sptr, caching_info_ptr, do_subtraction); } @@ -1106,6 +1107,7 @@ void distributable_compute_gradient(const shared_ptr& for bool zero_seg0_end_planes, double* log_likelihood_ptr, shared_ptr const& additive_binwise_correction, + shared_ptr const& normalisation_sptr, DistributedCachingInformation* caching_info_ptr, const bool do_subtraction ) @@ -1122,7 +1124,8 @@ void distributable_compute_gradient(const shared_ptr& for zero_seg0_end_planes, log_likelihood_ptr, additive_binwise_correction, - /* normalisation info to be ignored */ shared_ptr(), 0., 0., + normalisation_sptr, + 0., 0., &RPC_process_related_viewgrams_gradient, caching_info_ptr ); @@ -1233,8 +1236,6 @@ void RPC_process_related_viewgrams_gradient( const RelatedViewgrams* mult_viewgrams_ptr) { assert(measured_viewgrams_ptr != NULL); - if (!is_null_ptr(mult_viewgrams_ptr)) - error("Internal error: mult_viewgrams_ptr should be zero when computing gradient"); RelatedViewgrams estimated_viewgrams = measured_viewgrams_ptr->get_empty_copy(); @@ -1257,17 +1258,21 @@ void RPC_process_related_viewgrams_gradient( } */ forward_projector_sptr->forward_project(estimated_viewgrams); - - - + if (additive_binwise_correction_ptr != NULL) estimated_viewgrams += (*additive_binwise_correction_ptr); // for sinogram division divide_and_truncate(*measured_viewgrams_ptr, estimated_viewgrams, rim_truncation_sino, count, count2, log_likelihood_ptr); if (do_subtraction){ - // subtract ones from estimated data [y/ybar - 1] - estimated_viewgrams -= 1; + if (mult_viewgrams_ptr) + { + // subtract normalised ones from the data [y/ybar - 1/N] + *measured_viewgrams_ptr -= *mult_viewgrams_ptr; + } else { + // subtract ones from the data [y/ybar - 1] + *measured_viewgrams_ptr -= 1; + } } back_projector_sptr->back_project(*measured_viewgrams_ptr); }; From b9c596c3c3fdf8350296edc4bc5a6e0460914bc6 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Wed, 26 May 2021 10:01:46 -0700 Subject: [PATCH 09/47] [ci skip] Minor commit --- .../PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index c30599ba85..cd0d115ffa 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -652,7 +652,7 @@ PoissonLogLikelihoodWithLinearModelForMeanAndProjData:: actual_compute_objective_function_without_penalty(const TargetT& current_estimate, const int subset_num) { - double accum=0.; + double accum=0.; distributable_accumulate_loglikelihood(this->projector_pair_ptr->get_forward_projector_sptr(), this->projector_pair_ptr->get_back_projector_sptr(), From 69264e10f80dfa6b69ec9761455fb4189a52432f Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 08:55:43 -0700 Subject: [PATCH 10/47] If zero_seg0_end_planes and no normalisation, create mult_viewgrams so the endplanes can be zeroed --- ...issonLogLikelihoodWithLinearModelForMeanAndProjData.cxx | 5 ++++- src/recon_buildblock/distributable.cxx | 7 +++++++ src/recon_buildblock/distributableMPICacheEnabled.cxx | 7 +++++++ 3 files changed, 18 insertions(+), 1 deletion(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index cd0d115ffa..e9df5ea062 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -1264,13 +1264,16 @@ void RPC_process_related_viewgrams_gradient( // for sinogram division divide_and_truncate(*measured_viewgrams_ptr, estimated_viewgrams, rim_truncation_sino, count, count2, log_likelihood_ptr); + + if (do_subtraction){ + // do_subtraction that will compute [y/ybar - 1] if (mult_viewgrams_ptr) { // subtract normalised ones from the data [y/ybar - 1/N] *measured_viewgrams_ptr -= *mult_viewgrams_ptr; } else { - // subtract ones from the data [y/ybar - 1] + // No mult_viewgrams_ptr, subtract ones [y/ybar - 1] *measured_viewgrams_ptr -= 1; } } diff --git a/src/recon_buildblock/distributable.cxx b/src/recon_buildblock/distributable.cxx index a7f034737e..129ce51d99 100644 --- a/src/recon_buildblock/distributable.cxx +++ b/src/recon_buildblock/distributable.cxx @@ -216,6 +216,13 @@ void get_viewgrams(shared_ptr >& y, #endif normalisation_sptr->undo(*mult_viewgrams_sptr); } + else if (zero_seg0_end_planes) + { + // No normalisation provided but zero_seg0_end_planes, create a mult_viewgrams + mult_viewgrams_sptr.reset( + new RelatedViewgrams(proj_dat_ptr->get_empty_related_viewgrams(view_segment_num, symmetries_ptr))); + mult_viewgrams_sptr->fill(1.F); + } if (view_segment_num.segment_num()==0 && zero_seg0_end_planes) { diff --git a/src/recon_buildblock/distributableMPICacheEnabled.cxx b/src/recon_buildblock/distributableMPICacheEnabled.cxx index 0bea637b5e..cd65441907 100644 --- a/src/recon_buildblock/distributableMPICacheEnabled.cxx +++ b/src/recon_buildblock/distributableMPICacheEnabled.cxx @@ -126,6 +126,13 @@ void get_viewgrams(shared_ptr >& y, mult_viewgrams_sptr->fill(1.F); normalisation_sptr->undo(*mult_viewgrams_sptr); } + else if (zero_seg0_end_planes) + { + // No normalisation provided but zero_seg0_end_planes, create a mult_viewgrams + mult_viewgrams_sptr.reset( + new RelatedViewgrams(proj_dat_ptr->get_empty_related_viewgrams(view_segment_num, symmetries_ptr))); + mult_viewgrams_sptr->fill(1.F); + } if (view_segment_num.segment_num()==0 && zero_seg0_end_planes) { From 70de1a9587d074c45af8a9cc2d5fc703213811aa Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 09:14:09 -0700 Subject: [PATCH 11/47] remove compute_sub_gradient_without_penalty_plus_sensitivity from child Poisson classes --- ...oodWithLinearKineticModelAndDynamicProjectionData.h | 4 ---- ...dWithLinearKineticModelAndDynamicProjectionData.txx | 10 ---------- .../PoissonLogLikelihoodWithLinearModelForMean.h | 2 +- ...dWithLinearModelForMeanAndGatedProjDataWithMotion.h | 5 ----- ...ithLinearModelForMeanAndGatedProjDataWithMotion.txx | 10 ---------- ...ogLikelihoodWithLinearModelForMeanAndListModeData.h | 4 ---- ...earModelForMeanAndListModeDataWithProjMatrixByBin.h | 8 ++------ ...sonLogLikelihoodWithLinearModelForMeanAndProjData.h | 5 ----- .../PoissonLogLikelihoodWithLinearModelForMean.cxx | 9 +++++++++ ...rModelForMeanAndListModeDataWithProjMatrixByBin.cxx | 10 ---------- ...nLogLikelihoodWithLinearModelForMeanAndProjData.cxx | 10 ---------- 11 files changed, 12 insertions(+), 65 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h index 3ab6f50f24..e635c14907 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h @@ -70,10 +70,6 @@ public RegisteredParsingObject get_exam_info_uptr_for_target() const; diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.txx b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.txx index f216d94e87..374b8a1baf 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.txx +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.txx @@ -423,16 +423,6 @@ set_normalisation_sptr(const shared_ptr& arg) functions that compute the value/gradient of the objective function etc *************************************************************************/ -template -void -PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData:: -compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num) -{ - this->actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); -} - template void PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData:: diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h index 2b3915f40f..928032e499 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h @@ -138,7 +138,7 @@ public GeneralisedObjectiveFunction virtual void compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, const TargetT ¤t_estimate, - const int subset_num) =0; + const int subset_num); //! set-up sensitivity etc if possible /*! If \c recompute_sensitivity is \c false, we will try to diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h index b561327f74..ff6e84977d 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h @@ -79,11 +79,6 @@ public RegisteredParsingObject const& target_sptr) functions that compute the value/gradient of the objective function etc *************************************************************************/ -template -void -PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion:: -compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num) -{ - this->actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); -} - template void PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion:: diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeData.h index c886bc2f94..1ab752b665 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeData.h @@ -78,10 +78,6 @@ public PoissonLogLikelihoodWithLinearModelForMean virtual Succeeded set_up(shared_ptr const& target_sptr); - virtual - void compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num)=0; //! time frame definitions /*! \todo This is currently used to be able to compute the gradient for one time frame. However, it probably does not belong here. diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h index 3c63e54515..a20483abd8 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h @@ -66,12 +66,8 @@ typedef RegisteredParsingObject(); - //! This should compute the gradient of the objective function at the \a current_estimate overwriting \a gradient - virtual - void compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num); - + //! This should compute the gradient of the objective function at the \a current_estimate overwriting \a gradient, + /*! do_subtraction needs to be false for list mode computation */ virtual void actual_compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h index 43c4d629e0..72da37e836 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h @@ -212,11 +212,6 @@ public RegisteredParsingObjectactual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, true); } +template +void +PoissonLogLikelihoodWithLinearModelForMean:: +compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num) +{ + actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); +} template void diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx index febfe43bb1..85e3a30db6 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx @@ -421,16 +421,6 @@ construct_target_ptr() const this->target_parameter_parser.create(this->get_input_data()); } -template -void -PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin:: -compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num) -{ - this->actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); -} - template void PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin:: diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index e9df5ea062..c2b5109f6a 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -607,16 +607,6 @@ set_up_before_sensitivity(shared_ptr const& target_sptr) functions that compute the value/gradient of the objective function etc ***************************************************************/ -template -void -PoissonLogLikelihoodWithLinearModelForMeanAndProjData:: -compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num) -{ - actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); -} - template void PoissonLogLikelihoodWithLinearModelForMeanAndProjData:: From 9bb14be3f83d832ef7bfa653cc68036412190f06 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 09:46:45 -0700 Subject: [PATCH 12/47] Fix documentation formatting and code whitespace --- .../PoissonLogLikelihoodWithLinearModelForMean.h | 8 ++++---- ...LikelihoodWithLinearModelForMeanAndProjData.cxx | 14 +++++++------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h index 928032e499..0215f46a12 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h @@ -121,7 +121,7 @@ public GeneralisedObjectiveFunction //! Computes the gradient of the data fit term /*! This function is implemented in terms of \c actual_compute_sub_gradient_without_penalty() - by setting do_subtraction = true + by setting do_subtraction = true */ virtual void compute_sub_gradient_without_penalty(TargetT& gradient, @@ -133,7 +133,7 @@ public GeneralisedObjectiveFunction This function is used for instance by OSMAPOSL. This function is implemented in terms of \c actual_compute_sub_gradient_without_penalty() - by setting do_subtraction = false + by setting do_subtraction = false */ virtual void compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, @@ -253,14 +253,14 @@ public GeneralisedObjectiveFunction //! computes the objective function subset gradient without the penalty /*! - If do_subtraction is false, this computes + If \c do_subtraction is \c false, this computes \f[ {\partial L \over \partial \lambda_v} + P_v = \sum_b P_{bv} {y_b \over Y_b} \f] (see the class general documentation). The sum will however be restricted to a subset. - However, if do_subtraction is true, this function will instead compute + However, if \c do_subtraction is \c true, this function will instead compute \f[ {\partial L \over \partial \lambda_v} = \sum_b P_{bv} ({y_b \over Y_b} - 1) \f] diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index c2b5109f6a..2f1f50eb04 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -617,14 +617,14 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, { assert(subset_num>=0); assert(subset_numnum_subsets); - distributable_compute_gradient(this->projector_pair_ptr->get_forward_projector_sptr(), - this->projector_pair_ptr->get_back_projector_sptr(), + distributable_compute_gradient(this->projector_pair_ptr->get_forward_projector_sptr(), + this->projector_pair_ptr->get_back_projector_sptr(), this->symmetries_sptr, gradient, - current_estimate, - this->proj_data_sptr, - subset_num, - this->num_subsets, + current_estimate, + this->proj_data_sptr, + subset_num, + this->num_subsets, -this->max_segment_num_to_process, this->max_segment_num_to_process, this->zero_seg0_end_planes!=0, @@ -642,7 +642,7 @@ PoissonLogLikelihoodWithLinearModelForMeanAndProjData:: actual_compute_objective_function_without_penalty(const TargetT& current_estimate, const int subset_num) { - double accum=0.; + double accum=0.; distributable_accumulate_loglikelihood(this->projector_pair_ptr->get_forward_projector_sptr(), this->projector_pair_ptr->get_back_projector_sptr(), From 1aa957c5ff3904fdf80f81197131aace3cdb83f4 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 09:55:26 -0700 Subject: [PATCH 13/47] Gated and Dynamic actual's should call the objective function actual --- ...elihoodWithLinearKineticModelAndDynamicProjectionData.txx | 5 +++-- ...ihoodWithLinearModelForMeanAndGatedProjDataWithMotion.txx | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.txx b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.txx index 374b8a1baf..12f8eff5d7 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.txx +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.txx @@ -453,9 +453,10 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, this->_single_frame_obj_funcs[frame_num]. - compute_sub_gradient_without_penalty_plus_sensitivity(dyn_gradient[frame_num], + actual_compute_sub_gradient_without_penalty(dyn_gradient[frame_num], dyn_image_estimate[frame_num], - subset_num); + subset_num, + do_subtraction); } this->_patlak_plot_sptr->multiply_dynamic_image_with_model_gradient(gradient, diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.txx b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.txx index 843a47471e..403e2e9763 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.txx +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.txx @@ -436,9 +436,10 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, gated_gradient[gate_num].end_all(), 0.F); this->_single_gate_obj_funcs[gate_num]. - compute_sub_gradient_without_penalty_plus_sensitivity(gated_gradient[gate_num], + actual_compute_sub_gradient_without_penalty(gated_gradient[gate_num], gated_image_estimate[gate_num], - subset_num); + subset_num, + do_subtraction); } // if(this->_motion_correction_type==-1) this->_reverse_motion_vectors.warp_image(gradient,gated_gradient) ; From 471cab3d2b2d576f3c3fcd1e6ef136ffcaf4cbd1 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 10:06:34 -0700 Subject: [PATCH 14/47] reformat do_subtraction to add_sensitivity (involves inverting cases) --- ...nearKineticModelAndDynamicProjectionData.h | 2 +- ...arKineticModelAndDynamicProjectionData.txx | 4 ++-- ...issonLogLikelihoodWithLinearModelForMean.h | 10 ++++----- ...arModelForMeanAndGatedProjDataWithMotion.h | 2 +- ...ModelForMeanAndGatedProjDataWithMotion.txx | 4 ++-- ...orMeanAndListModeDataWithProjMatrixByBin.h | 4 ++-- ...elihoodWithLinearModelForMeanAndProjData.h | 2 +- ...sonLogLikelihoodWithLinearModelForMean.cxx | 2 +- ...MeanAndListModeDataWithProjMatrixByBin.cxx | 6 ++--- ...ihoodWithLinearModelForMeanAndProjData.cxx | 22 +++++++++---------- 10 files changed, 29 insertions(+), 29 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h index e635c14907..1dab716840 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h @@ -171,7 +171,7 @@ public RegisteredParsingObject:: actual_compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num, - const bool do_subtraction) + const bool add_sensitivity) { if (subset_num<0 || subset_num>=this->get_num_subsets()) error("compute_sub_gradient_without_penalty subset_num out-of-range error"); @@ -456,7 +456,7 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, actual_compute_sub_gradient_without_penalty(dyn_gradient[frame_num], dyn_image_estimate[frame_num], subset_num, - do_subtraction); + add_sensitivity); } this->_patlak_plot_sptr->multiply_dynamic_image_with_model_gradient(gradient, diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h index 0215f46a12..f090ae2cbb 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h @@ -121,7 +121,7 @@ public GeneralisedObjectiveFunction //! Computes the gradient of the data fit term /*! This function is implemented in terms of \c actual_compute_sub_gradient_without_penalty() - by setting do_subtraction = true + by setting add_sensitivity = false */ virtual void compute_sub_gradient_without_penalty(TargetT& gradient, @@ -133,7 +133,7 @@ public GeneralisedObjectiveFunction This function is used for instance by OSMAPOSL. This function is implemented in terms of \c actual_compute_sub_gradient_without_penalty() - by setting do_subtraction = false + by setting add_sensitivity = true */ virtual void compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, @@ -253,14 +253,14 @@ public GeneralisedObjectiveFunction //! computes the objective function subset gradient without the penalty /*! - If \c do_subtraction is \c false, this computes + If \c add_sensitivity is \c true, this computes \f[ {\partial L \over \partial \lambda_v} + P_v = \sum_b P_{bv} {y_b \over Y_b} \f] (see the class general documentation). The sum will however be restricted to a subset. - However, if \c do_subtraction is \c true, this function will instead compute + However, if \c add_sensitivity is \c false, this function will instead compute \f[ {\partial L \over \partial \lambda_v} = \sum_b P_{bv} ({y_b \over Y_b} - 1) \f] @@ -269,7 +269,7 @@ public GeneralisedObjectiveFunction actual_compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num, - const bool do_subtraction) = 0; + const bool add_sensitivity) = 0; //! Sets defaults for parsing /*! Resets \c sensitivity_filename, \c subset_sensitivity_filenames to empty, diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h index ff6e84977d..7174f77dca 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h @@ -83,7 +83,7 @@ public RegisteredParsingObject:: actual_compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num, - const bool do_subtraction) + const bool add_sensitivity) { assert(subset_num>=0); assert(subset_numnum_subsets); @@ -439,7 +439,7 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, actual_compute_sub_gradient_without_penalty(gated_gradient[gate_num], gated_image_estimate[gate_num], subset_num, - do_subtraction); + add_sensitivity); } // if(this->_motion_correction_type==-1) this->_reverse_motion_vectors.warp_image(gradient,gated_gradient) ; diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h index a20483abd8..ef94fc4a4f 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h @@ -67,12 +67,12 @@ typedef RegisteredParsingObject(); //! This should compute the gradient of the objective function at the \a current_estimate overwriting \a gradient, - /*! do_subtraction needs to be false for list mode computation */ + /*! add_sensitivity needs to be true for list mode computation */ virtual void actual_compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num, - const bool do_subtraction); + const bool add_sensitivity); virtual TargetT * construct_target_ptr() const; diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h index 72da37e836..267f785d4d 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h @@ -216,7 +216,7 @@ public RegisteredParsingObject get_exam_info_uptr_for_target() const; diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx index 680c28a24c..60ea5b714c 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx @@ -368,7 +368,7 @@ compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num) { - actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); + actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, true); } template diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx index 85e3a30db6..a617d6f28b 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx @@ -427,10 +427,10 @@ PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin=0); assert(subset_numnum_subsets); diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index 2f1f50eb04..260e86ddb4 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -613,7 +613,7 @@ PoissonLogLikelihoodWithLinearModelForMeanAndProjData:: actual_compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num, - const bool do_subtraction) + const bool add_sensitivity) { assert(subset_num>=0); assert(subset_numnum_subsets); @@ -632,7 +632,7 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, this->additive_proj_data_sptr, this->normalisation_sptr, caching_info_ptr, - do_subtraction); + add_sensitivity); } @@ -1077,7 +1077,7 @@ RPC_process_related_viewgrams_type RPC_process_related_viewgrams_sensitivity_com #else //! Call-back function for compute_gradient -template static RPC_process_related_viewgrams_type RPC_process_related_viewgrams_gradient; +template static RPC_process_related_viewgrams_type RPC_process_related_viewgrams_gradient; //! Call-back function for accumulate_loglikelihood static RPC_process_related_viewgrams_type RPC_process_related_viewgrams_accumulate_loglikelihood; @@ -1099,10 +1099,10 @@ void distributable_compute_gradient(const shared_ptr& for shared_ptr const& additive_binwise_correction, shared_ptr const& normalisation_sptr, DistributedCachingInformation* caching_info_ptr, - const bool do_subtraction + const bool add_sensitivity ) { - if (do_subtraction){ + if (add_sensitivity){ // Within the RPC process, subtract ones before to back projection ( backproj[ y/ybar - 1] ) distributable_computation(forward_projector_sptr, back_projector_sptr, @@ -1116,10 +1116,10 @@ void distributable_compute_gradient(const shared_ptr& for additive_binwise_correction, normalisation_sptr, 0., 0., - &RPC_process_related_viewgrams_gradient, + &RPC_process_related_viewgrams_gradient, caching_info_ptr ); - } else { + } else if (!add_sensitivity){ // Within the RPC process, only do div/truncate ( backproj[ y/ybar ] ) distributable_computation(forward_projector_sptr, back_projector_sptr, @@ -1132,7 +1132,7 @@ void distributable_compute_gradient(const shared_ptr& for log_likelihood_ptr, additive_binwise_correction, /* normalisation info to be ignored */ shared_ptr(), 0., 0., - &RPC_process_related_viewgrams_gradient, + &RPC_process_related_viewgrams_gradient, caching_info_ptr ); } @@ -1216,7 +1216,7 @@ void distributable_sensitivity_computation( //////////// RPC functions -template +template void RPC_process_related_viewgrams_gradient( const shared_ptr& forward_projector_sptr, const shared_ptr& back_projector_sptr, @@ -1256,8 +1256,8 @@ void RPC_process_related_viewgrams_gradient( divide_and_truncate(*measured_viewgrams_ptr, estimated_viewgrams, rim_truncation_sino, count, count2, log_likelihood_ptr); - if (do_subtraction){ - // do_subtraction that will compute [y/ybar - 1] + if (!add_sensitivity){ + // not adding the sensitivity computes the gradient [y/ybar - 1] if (mult_viewgrams_ptr) { // subtract normalised ones from the data [y/ybar - 1/N] From 953b0dfbe183fe6da4714301e2ff9770f80bb508 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 10:31:19 -0700 Subject: [PATCH 15/47] Revert one too many inverts --- .../PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index 260e86ddb4..d3502043dd 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -1116,7 +1116,7 @@ void distributable_compute_gradient(const shared_ptr& for additive_binwise_correction, normalisation_sptr, 0., 0., - &RPC_process_related_viewgrams_gradient, + &RPC_process_related_viewgrams_gradient, caching_info_ptr ); } else if (!add_sensitivity){ @@ -1132,7 +1132,7 @@ void distributable_compute_gradient(const shared_ptr& for log_likelihood_ptr, additive_binwise_correction, /* normalisation info to be ignored */ shared_ptr(), 0., 0., - &RPC_process_related_viewgrams_gradient, + &RPC_process_related_viewgrams_gradient, caching_info_ptr ); } From 7dcf393597751a41770e9fc85b608311515ee3f2 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 10:31:29 -0700 Subject: [PATCH 16/47] Improve comments --- ...issonLogLikelihoodWithLinearModelForMeanAndProjData.cxx | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index d3502043dd..19405d7cd3 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -1255,9 +1255,10 @@ void RPC_process_related_viewgrams_gradient( // for sinogram division divide_and_truncate(*measured_viewgrams_ptr, estimated_viewgrams, rim_truncation_sino, count, count2, log_likelihood_ptr); - + // adding the sensitivity: backproj[y/ybar] * + // not adding the sensitivity computes the gradient: backproj[y/ybar - 1] * + // * ignoring normalisation * if (!add_sensitivity){ - // not adding the sensitivity computes the gradient [y/ybar - 1] if (mult_viewgrams_ptr) { // subtract normalised ones from the data [y/ybar - 1/N] @@ -1267,6 +1268,8 @@ void RPC_process_related_viewgrams_gradient( *measured_viewgrams_ptr -= 1; } } + + // back project back_projector_sptr->back_project(*measured_viewgrams_ptr); }; From a370355d44c072b8ee31ce37b51e9cee0d279a3a Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 10:41:02 -0700 Subject: [PATCH 17/47] List mode recon: !add_sensitivity and error if not using subset sensitivity --- ...MeanAndListModeDataWithProjMatrixByBin.cxx | 24 +++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx index a617d6f28b..f23a35297d 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx @@ -429,8 +429,6 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, const int subset_num, const bool add_sensitivity) { - if (add_sensitivity) - error("List mode cannot currently add sensitivity on the gradient computation."); assert(subset_num>=0); assert(subset_numnum_subsets); @@ -534,6 +532,28 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, } } info(boost::format("Number of used events: %1%") % num_used_events); + + if (!add_sensitivity) + { + if (!this->get_use_subset_sensitivities()) + error("PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin::" + "actual_compute_sub_gradient_without_penalty(): cannot subtract subset sensitivity because " + "use_subset_sensitivities is false."); + + // subtract the subset sensitivity + // compute gradient -= sub_sensitivity + typename TargetT::full_iterator gradient_iter = + gradient.begin_all(); + const typename TargetT::full_iterator gradient_end = + gradient.end_all(); + typename TargetT::const_full_iterator sensitivity_iter = + this->get_subset_sensitivity(subset_num).begin_all_const(); + while (gradient_iter != gradient_end) + { + *gradient_iter -= (*sensitivity_iter); + ++gradient_iter; ++sensitivity_iter; + } + } } # ifdef _MSC_VER From 516d75c821d60b9f38ef4c43527b4ed83de2efe0 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 13:51:48 -0700 Subject: [PATCH 18/47] turn off add_sensitivity on compute_sub_gradient_without_penalty --- .../PoissonLogLikelihoodWithLinearModelForMean.cxx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx index 60ea5b714c..bd3bd3cd25 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx @@ -358,7 +358,7 @@ compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num) { - this->actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, true); + this->actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); } template From d2c1487826328b315f7630b20ea3427ebf51c444 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 13:53:08 -0700 Subject: [PATCH 19/47] distributable_compute_gradient: swap normalisation_sptr parsing case --- .../PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index 19405d7cd3..5860c754dc 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -1114,7 +1114,7 @@ void distributable_compute_gradient(const shared_ptr& for zero_seg0_end_planes, log_likelihood_ptr, additive_binwise_correction, - normalisation_sptr, + /* normalisation info to be ignored */ shared_ptr(), 0., 0., &RPC_process_related_viewgrams_gradient, caching_info_ptr @@ -1131,7 +1131,7 @@ void distributable_compute_gradient(const shared_ptr& for zero_seg0_end_planes, log_likelihood_ptr, additive_binwise_correction, - /* normalisation info to be ignored */ shared_ptr(), 0., 0., + normalisation_sptr, 0., 0., &RPC_process_related_viewgrams_gradient, caching_info_ptr ); From 6cc4a4caf14d7fc2631fd03ef77349ab5b4d953d Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 14:13:26 -0700 Subject: [PATCH 20/47] Refactor actual_compute_sub_grad... to actual_compute_subset_grad... --- ...hLinearKineticModelAndDynamicProjectionData.h | 8 ++++---- ...inearKineticModelAndDynamicProjectionData.txx | 16 ++++++++-------- .../PoissonLogLikelihoodWithLinearModelForMean.h | 12 ++++++------ ...inearModelForMeanAndGatedProjDataWithMotion.h | 8 ++++---- ...earModelForMeanAndGatedProjDataWithMotion.txx | 16 ++++++++-------- ...elForMeanAndListModeDataWithProjMatrixByBin.h | 8 ++++---- ...LikelihoodWithLinearModelForMeanAndProjData.h | 8 ++++---- ...oissonLogLikelihoodWithLinearModelForMean.cxx | 4 ++-- ...ForMeanAndListModeDataWithProjMatrixByBin.cxx | 10 +++++----- ...kelihoodWithLinearModelForMeanAndProjData.cxx | 8 ++++---- 10 files changed, 49 insertions(+), 49 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h index 1dab716840..f93cd58190 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h @@ -168,10 +168,10 @@ public RegisteredParsingObject& arg) template void PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData:: -actual_compute_sub_gradient_without_penalty(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num, - const bool add_sensitivity) +actual_compute_subset_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool add_sensitivity) { if (subset_num<0 || subset_num>=this->get_num_subsets()) error("compute_sub_gradient_without_penalty subset_num out-of-range error"); @@ -453,10 +453,10 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, this->_single_frame_obj_funcs[frame_num]. - actual_compute_sub_gradient_without_penalty(dyn_gradient[frame_num], - dyn_image_estimate[frame_num], - subset_num, - add_sensitivity); + actual_compute_subset_gradient_without_penalty(dyn_gradient[frame_num], + dyn_image_estimate[frame_num], + subset_num, + add_sensitivity); } this->_patlak_plot_sptr->multiply_dynamic_image_with_model_gradient(gradient, diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h index f090ae2cbb..91cc03f1f9 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h @@ -120,7 +120,7 @@ public GeneralisedObjectiveFunction //! Computes the gradient of the data fit term /*! - This function is implemented in terms of \c actual_compute_sub_gradient_without_penalty() + This function is implemented in terms of \c actual_compute_subset_gradient_without_penalty() by setting add_sensitivity = false */ virtual void @@ -132,7 +132,7 @@ public GeneralisedObjectiveFunction /*! This function is used for instance by OSMAPOSL. - This function is implemented in terms of \c actual_compute_sub_gradient_without_penalty() + This function is implemented in terms of \c actual_compute_subset_gradient_without_penalty() by setting add_sensitivity = true */ virtual void @@ -266,10 +266,10 @@ public GeneralisedObjectiveFunction \f] */ virtual void - actual_compute_sub_gradient_without_penalty(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num, - const bool add_sensitivity) = 0; + actual_compute_subset_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool add_sensitivity) = 0; //! Sets defaults for parsing /*! Resets \c sensitivity_filename, \c subset_sensitivity_filenames to empty, diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h index 7174f77dca..27ae39bef6 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h @@ -80,10 +80,10 @@ public RegisteredParsingObject const& target_sptr) template void PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion:: -actual_compute_sub_gradient_without_penalty(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num, - const bool add_sensitivity) +actual_compute_subset_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool add_sensitivity) { assert(subset_num>=0); assert(subset_numnum_subsets); @@ -436,10 +436,10 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, gated_gradient[gate_num].end_all(), 0.F); this->_single_gate_obj_funcs[gate_num]. - actual_compute_sub_gradient_without_penalty(gated_gradient[gate_num], - gated_image_estimate[gate_num], - subset_num, - add_sensitivity); + actual_compute_subset_gradient_without_penalty(gated_gradient[gate_num], + gated_image_estimate[gate_num], + subset_num, + add_sensitivity); } // if(this->_motion_correction_type==-1) this->_reverse_motion_vectors.warp_image(gradient,gated_gradient) ; diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h index ef94fc4a4f..013818e576 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h @@ -69,10 +69,10 @@ typedef RegisteredParsingObjectadd_sensitivity needs to be true for list mode computation */ virtual - void actual_compute_sub_gradient_without_penalty(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num, - const bool add_sensitivity); + void actual_compute_subset_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool add_sensitivity); virtual TargetT * construct_target_ptr() const; diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h index 267f785d4d..e105c746da 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h @@ -213,10 +213,10 @@ public RegisteredParsingObject get_exam_info_uptr_for_target() const; diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx index bd3bd3cd25..3851a50938 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.cxx @@ -358,7 +358,7 @@ compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num) { - this->actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, false); + this->actual_compute_subset_gradient_without_penalty(gradient, current_estimate, subset_num, false); } template @@ -368,7 +368,7 @@ compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num) { - actual_compute_sub_gradient_without_penalty(gradient, current_estimate, subset_num, true); + actual_compute_subset_gradient_without_penalty(gradient, current_estimate, subset_num, true); } template diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx index f23a35297d..2576a06553 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx @@ -424,10 +424,10 @@ construct_target_ptr() const template void PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin:: -actual_compute_sub_gradient_without_penalty(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num, - const bool add_sensitivity) +actual_compute_subset_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool add_sensitivity) { assert(subset_num>=0); assert(subset_numnum_subsets); @@ -537,7 +537,7 @@ actual_compute_sub_gradient_without_penalty(TargetT& gradient, { if (!this->get_use_subset_sensitivities()) error("PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin::" - "actual_compute_sub_gradient_without_penalty(): cannot subtract subset sensitivity because " + "actual_compute_subset_gradient_without_penalty(): cannot subtract subset sensitivity because " "use_subset_sensitivities is false."); // subtract the subset sensitivity diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index 5860c754dc..7df693010a 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -610,10 +610,10 @@ set_up_before_sensitivity(shared_ptr const& target_sptr) template void PoissonLogLikelihoodWithLinearModelForMeanAndProjData:: -actual_compute_sub_gradient_without_penalty(TargetT& gradient, - const TargetT ¤t_estimate, - const int subset_num, - const bool add_sensitivity) +actual_compute_subset_gradient_without_penalty(TargetT& gradient, + const TargetT ¤t_estimate, + const int subset_num, + const bool add_sensitivity) { assert(subset_num>=0); assert(subset_numnum_subsets); From de0fb828554e73c89700dcbd27f1be80ed578176 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 14:46:30 -0700 Subject: [PATCH 21/47] Update OSSPS test image correct OSSPS (with actual subset sensitivity subtraction) --- recon_test_pack/test_image_OSSPS_PM_QP_8.v | Bin 446400 -> 446400 bytes 1 file changed, 0 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From 3f57548c783c38cb1b4cf8a9dee1dbde06cc8204 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 15:40:09 -0700 Subject: [PATCH 22/47] Add OSSPS subset sensitivity test. Results should be identical --- recon_test_pack/OSSPS_test_PM_QP_subsens1.par | 73 +++++++++++++++++++ recon_test_pack/run_tests.sh | 15 ++++ 2 files changed, 88 insertions(+) create mode 100644 recon_test_pack/OSSPS_test_PM_QP_subsens1.par diff --git a/recon_test_pack/OSSPS_test_PM_QP_subsens1.par b/recon_test_pack/OSSPS_test_PM_QP_subsens1.par new file mode 100644 index 0000000000..c9bde1a966 --- /dev/null +++ b/recon_test_pack/OSSPS_test_PM_QP_subsens1.par @@ -0,0 +1,73 @@ +OSSPSParameters := +; sample file for OSSPS +; parameters used here are for illustrative purposes only +; i.e. they are not recommended values + +objective function type:= PoissonLogLikelihoodWithLinearModelForMeanAndProjData +PoissonLogLikelihoodWithLinearModelForMeanAndProjData Parameters:= + +input file := Utahscat600k_ca_seg4.hs +zero end planes of segment 0:= 1 +; if disabled, defaults to maximum segment number in the file +maximum absolute segment number to process := 3 + +; change to STIR 2.x default for compatibility +use subset sensitivities:=1 +sensitivity filename:= RPTsens_seg3_PM.hv + +projector pair type := Matrix + Projector Pair Using Matrix Parameters := + Matrix type := Ray Tracing + Ray tracing matrix parameters := + number of rays in tangential direction to trace for each bin := 2 +; restrict to cylindrical fov := 0 + End Ray tracing matrix parameters := + End Projector Pair Using Matrix Parameters := + +; additive sinogram:=my_fake_randoms.hs +prior type := quadratic + Quadratic Prior Parameters:= + penalisation factor := 0.5 + ; next defaults to 0, set to 1 for 2D inverse Euclidean weights, 0 for 3D + only 2D:= 0 + END Quadratic Prior Parameters:= + +End PoissonLogLikelihoodWithLinearModelForMeanAndProjData Parameters:= + +output filename prefix := my_test_image_OSSPS_PM_QP_subsens +; iteration scheme + +number of subsets:= 4 +;start at subset:= 0 +;start at subiteration number := 1 +number of subiterations:= 8 +Save estimates at subiteration intervals:= 8 +;write update image := 0 +;report objective function values interval := 1 + +; if next is disabled, defaults to image full of 1s (but that's not good for OSSPS) +; in particular, make sure it has the correct scale +initial estimate:= test_image_PM_QP_6.hv +enforce initial positivity condition := 1 + +; here start OSSPS specific values + +; values to use for the 'precomputed denominator' +; specify either procomputed denomiator or normalisation type + ; use the following if you have it already (e.g. from previous run) + ; note: setting the value to 1 will use an images full of ones. + ; precomputed denominator := my_precomputed_denominator.hv + +; specify relaxation scheme +; lambda = relaxation_parameter/ (1+relaxation_gamma*(subiteration_num/num_subsets) +relaxation parameter := 2 +relaxation gamma:=.1 + + +END := + + + + + + diff --git a/recon_test_pack/run_tests.sh b/recon_test_pack/run_tests.sh index 9ea59ef442..f123bf52e0 100755 --- a/recon_test_pack/run_tests.sh +++ b/recon_test_pack/run_tests.sh @@ -245,6 +245,21 @@ echo There were problems here!; ThereWereErrors=1; fi +${MPIRUN} ${INSTALL_DIR}OSSPS OSSPS_test_PM_QP_subsens1.par 1> OSSPS_PM_QP.log 2> OSSPS_PM_QP_stderr.log + +echo '---- Comparing output of OSSPS subiter 8 using subset sensitivity (should be identical up to tolerance)' +echo Running ${INSTALL_DIR}compare_image +# relax test for the outer-rim voxels as these turn out to be more unstable than the internal ones +if ${INSTALL_DIR}compare_image -t 0.002 test_image_OSSPS_PM_QP_8.hv my_test_image_OSSPS_PM_QP_subsens_8.hv -a + ${INSTALL_DIR}compare_image -r 1 test_image_OSSPS_PM_QP_8.hv my_test_image_OSSPS_PM_QP_subsens_8.hv +then + echo ---- This test seems to be ok !; + else + echo There were problems here!; + ThereWereErrors=1; +fi + + echo echo ------------- tests on stir_math and correct_projdata --------- echo "first make up some randoms (just a projdata full of 1)" From ed16ef717d126de1f9f5c20d785bccf1ffd6e7dd Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 28 May 2021 16:42:40 -0700 Subject: [PATCH 23/47] [ci skip] Add compute_sub_gradient_without_penalty documentation --- ...issonLogLikelihoodWithLinearModelForMean.h | 47 ++++++++++++------- 1 file changed, 30 insertions(+), 17 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h index 91cc03f1f9..82a3b7a235 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h @@ -67,9 +67,10 @@ START_NAMESPACE_STIR probability of detecting a count (in any bin) originating from \f$v\f$. This class computes the gradient directly, via \c compute_sub_gradient_without_penalty(). + This method is utilised by the \c OSSPS algorithm in STIR. However, an additional method (\c compute_sub_gradient_without_penalty_plus_sensitivity()) - is provided that computes the sum of the subset gradient and the sensitivity. - This method is used in STIR algorithms such as \c OSMAPOSL. + is provided that computes the sum of the subset gradient (without penalty) and the sensitivity. + This method is utilised by the \c OSMAPOSL algorithm. See also \c PoissonLogLikelihoodWithLinearModelForMeanAndListModeData. @@ -118,23 +119,35 @@ public GeneralisedObjectiveFunction //PoissonLogLikelihoodWithLinearModelForMean(); - //! Computes the gradient of the data fit term - /*! - This function is implemented in terms of \c actual_compute_subset_gradient_without_penalty() - by setting add_sensitivity = false - */ + //! Compute the subset gradient of the (unregularised) objective function + /*! + Implementation in terms of actual_compute_sub_gradient_without_penalty(). + This function is used for instance by OSSPS + + This computes + \f[ {\partial L \over \partial \lambda_v}= + \sum_b P_{bv} {y_b \over Y_b - 1} + \f] + (see the class general documentation). + The sum will however be restricted to a subset. + */ virtual void compute_sub_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, const int subset_num); - //! This should compute the gradient of the (unregularised) objective function plus the (sub)sensitivity - /*! - This function is used for instance by OSMAPOSL. - - This function is implemented in terms of \c actual_compute_subset_gradient_without_penalty() - by setting add_sensitivity = true - */ + //! This should compute the subset gradient of the (unregularised) objective function plus the subset sensitivity + /*! + Implementation in terms of actual_compute_sub_gradient_without_penalty(). + This function is used for instance by OSMAPOSL. + + This computes + \f[ {\partial L \over \partial \lambda_v} + P_v = + \sum_b P_{bv} {y_b \over Y_b} + \f] + (see the class general documentation). + The sum will however be restricted to a subset. + */ virtual void compute_sub_gradient_without_penalty_plus_sensitivity(TargetT& gradient, const TargetT ¤t_estimate, @@ -251,7 +264,7 @@ public GeneralisedObjectiveFunction */ void compute_sensitivities(); - //! computes the objective function subset gradient without the penalty + //! computes the subset gradient of the objective function without the penalty (optional: add subset sensitivity) /*! If \c add_sensitivity is \c true, this computes \f[ {\partial L \over \partial \lambda_v} + P_v = @@ -260,9 +273,9 @@ public GeneralisedObjectiveFunction (see the class general documentation). The sum will however be restricted to a subset. - However, if \c add_sensitivity is \c false, this function will instead compute + However, if \c add_sensitivity is \c false, this function will instead compute only the gradient \f[ {\partial L \over \partial \lambda_v} = - \sum_b P_{bv} ({y_b \over Y_b} - 1) + \sum_b P_{bv} ({y_b \over Y_b - 1}) \f] */ virtual void From 0a497aacf4e8c6d940dd58bc9ed25f1f4786b762 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Sun, 30 May 2021 09:45:44 -0700 Subject: [PATCH 24/47] [ci skip] Documentation cleanup --- .../PoissonLogLikelihoodWithLinearModelForMean.h | 16 +++++++++------- ...elForMeanAndListModeDataWithProjMatrixByBin.h | 7 +++++-- ...ForMeanAndListModeDataWithProjMatrixByBin.cxx | 9 ++++----- 3 files changed, 18 insertions(+), 14 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h index 82a3b7a235..5e8cc4596b 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMean.h @@ -121,13 +121,14 @@ public GeneralisedObjectiveFunction //! Compute the subset gradient of the (unregularised) objective function /*! - Implementation in terms of actual_compute_sub_gradient_without_penalty(). - This function is used for instance by OSSPS + Implementation in terms of actual_compute_sub_gradient_without_penalty() + This function is used by OSSPS may be used by other gradient ascent/descent algorithms This computes - \f[ {\partial L \over \partial \lambda_v}= - \sum_b P_{bv} {y_b \over Y_b - 1} - \f] + \f[ + {\partial L \over \partial \lambda_v} = + \sum_b P_{bv} ({y_b \over Y_b} - 1) + \f] (see the class general documentation). The sum will however be restricted to a subset. */ @@ -274,8 +275,9 @@ public GeneralisedObjectiveFunction The sum will however be restricted to a subset. However, if \c add_sensitivity is \c false, this function will instead compute only the gradient - \f[ {\partial L \over \partial \lambda_v} = - \sum_b P_{bv} ({y_b \over Y_b - 1}) + \f[ + {\partial L \over \partial \lambda_v} = + \sum_b P_{bv} ({y_b \over Y_b} - 1) \f] */ virtual void diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h index 013818e576..2eaeabe0ac 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h @@ -66,8 +66,11 @@ typedef RegisteredParsingObject(); - //! This should compute the gradient of the objective function at the \a current_estimate overwriting \a gradient, - /*! add_sensitivity needs to be true for list mode computation */ + //! Computes the gradient of the objective function at the \a current_estimate overwriting \a gradient. + /*! + \warning If add_sensitivity = false and use_subset_sensitivities = false will return an error + because the gradient will not be correct. Try use_subset_sensitivities = true. + */ virtual void actual_compute_subset_gradient_without_penalty(TargetT& gradient, const TargetT ¤t_estimate, diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx index 2576a06553..d585cfb8d0 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.cxx @@ -431,6 +431,10 @@ actual_compute_subset_gradient_without_penalty(TargetT& gradient, { assert(subset_num>=0); assert(subset_numnum_subsets); + if (!add_sensitivity && !this->get_use_subset_sensitivities()) + error("PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin::" + "actual_compute_subset_gradient_without_penalty(): cannot subtract subset sensitivity because " + "use_subset_sensitivities is false. This will result in an error in the gradient computation."); const double start_time = this->frame_defs.get_start_time(this->current_frame_num); const double end_time = this->frame_defs.get_end_time(this->current_frame_num); @@ -535,11 +539,6 @@ actual_compute_subset_gradient_without_penalty(TargetT& gradient, if (!add_sensitivity) { - if (!this->get_use_subset_sensitivities()) - error("PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin::" - "actual_compute_subset_gradient_without_penalty(): cannot subtract subset sensitivity because " - "use_subset_sensitivities is false."); - // subtract the subset sensitivity // compute gradient -= sub_sensitivity typename TargetT::full_iterator gradient_iter = From a8743f704a19597900942fffd2438037882156e6 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 4 Jun 2021 12:26:50 -0700 Subject: [PATCH 25/47] [ci skip] Add to release notes --- documentation/release_5.0.htm | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/documentation/release_5.0.htm b/documentation/release_5.0.htm index 3818f7ca0a..edd390777b 100644 --- a/documentation/release_5.0.htm +++ b/documentation/release_5.0.htm @@ -143,6 +143,12 @@

    Changed functionality

  • ROOT file I/O improvements. An entire entry's tree is no longer loaded by default and instead individual branches are loaded as needed. ROOT file event processing is now up to 4x faster. In addition, there is now an option to check energy window information (defaulting to on). Futhermore, added functionality to exclude true and unscattered event types from list mode processing.
    • +
  • + Poisson log-likelihood gradient methods now use actual_compute_subset_gradient_without_penalty + to properly handle the objective function gradient computation at the distributable level. + Removes the subtraction of the subset sensitivity, which lead to a inconsistency between log-likelihood function + and gradient when use_subset_sensitivities was false. +
    • From 62a55213a1fba672152bc38c25f552bf28919496 Mon Sep 17 00:00:00 2001 From: robbietuk Date: Sun, 6 Jun 2021 17:30:01 -0700 Subject: [PATCH 26/47] RDP/accumulate_Hessian_times_input * RDP accumulate_Hessian_times_input * Added RDP Hessian documentation * Add div by 0 safety * Add epsilon to Hessian function --- .../RelativeDifferencePrior.h | 26 +++++++ .../RelativeDifferencePrior.cxx | 75 +++++++++++++++++++ 2 files changed, 101 insertions(+) diff --git a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h index b4eaf4c74e..c8e4361ba0 100644 --- a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h +++ b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h @@ -130,6 +130,11 @@ class RelativeDifferencePrior: public add_multiplication_with_approximate_Hessian(DiscretisedDensity<3,elemT>& output, const DiscretisedDensity<3,elemT>& input) const; + //! Compute the multiplication of the hessian of the prior multiplied by the input. + virtual Succeeded accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, + const DiscretisedDensity<3,elemT>& current_estimate, + const DiscretisedDensity<3,elemT>& input) const; + //! get the gamma value used in RDP float get_gamma() const; //! set the gamma value used in the RDP @@ -193,6 +198,27 @@ class RelativeDifferencePrior: public virtual bool post_processing(); private: shared_ptr > kappa_ptr; + + //! The Hessian of the Relative Difference Prior + /*! + This function returns the hessian (second derivative) of the RDP. + It is assumed that the diagonal elements of the Hessian are 0, or the weighing is, and thus only compute the partial + derivative w.r.t \c x and \c y. + * @param x is the target voxel. + * @param y is the neighbourhood voxel. + * @param gamma is the edge preservation value controlling the transition between the quadratic and linear behaviour + * @param eps is a small non-negative value included to prevent division by zero, see epsilon. + * @return the second derivative of the Relative Difference Prior + + */ + static inline float Hessian(const float x, const float y, const float gamma, const float eps) + { + if (x > 0.0 && y > 0.0 && eps > 0.0) + return 2 * (2 * x + eps)*(2 * y + eps) / + pow(x + y + gamma * abs(x - y) + eps, 3); + else + return 0.0; + } }; diff --git a/src/recon_buildblock/RelativeDifferencePrior.cxx b/src/recon_buildblock/RelativeDifferencePrior.cxx index e7fce65ea5..5eb64f81a1 100644 --- a/src/recon_buildblock/RelativeDifferencePrior.cxx +++ b/src/recon_buildblock/RelativeDifferencePrior.cxx @@ -430,6 +430,81 @@ add_multiplication_with_approximate_Hessian(DiscretisedDensity<3,elemT>& output, return Succeeded::no; } +template +Succeeded +RelativeDifferencePrior:: +accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, + const DiscretisedDensity<3,elemT>& current_estimate, + const DiscretisedDensity<3,elemT>& input) const +{ + // TODO this function overlaps enormously with parabolic_surrogate_curvature + // the only difference is that parabolic_surrogate_curvature uses input==1 + + assert( output.has_same_characteristics(input)); + if (this->penalisation_factor==0) + { + return Succeeded::yes; + } + + DiscretisedDensityOnCartesianGrid<3,elemT>& output_cast = + dynamic_cast &>(output); + + if (weights.get_length() ==0) + { + compute_weights(weights, output_cast.get_grid_spacing(), this->only_2D); + } + + const bool do_kappa = !is_null_ptr(kappa_ptr); + + if (do_kappa && !kappa_ptr->has_same_characteristics(input)) + error("LogcoshPrior: kappa image has not the same index range as the reconstructed image\n"); + + const int min_z = output.get_min_index(); + const int max_z = output.get_max_index(); + for (int z=min_z; z<=max_z; z++) + { + const int min_dz = max(weights.get_min_index(), min_z-z); + const int max_dz = min(weights.get_max_index(), max_z-z); + + const int min_y = output[z].get_min_index(); + const int max_y = output[z].get_max_index(); + + for (int y=min_y;y<= max_y;y++) + { + const int min_dy = max(weights[0].get_min_index(), min_y-y); + const int max_dy = min(weights[0].get_max_index(), max_y-y); + + const int min_x = output[z][y].get_min_index(); + const int max_x = output[z][y].get_max_index(); + + for (int x=min_x;x<= max_x;x++) + { + const int min_dx = max(weights[0][0].get_min_index(), min_x-x); + const int max_dx = min(weights[0][0].get_max_index(), max_x-x); + + elemT result = 0; + for (int dz=min_dz;dz<=max_dz;++dz) + for (int dy=min_dy;dy<=max_dy;++dy) + for (int dx=min_dx;dx<=max_dx;++dx) + { + elemT current = weights[dz][dy][dx] * + Hessian(current_estimate[z][y][x], current_estimate[z+dz][y+dy][x+dx], + this->get_gamma(), this->get_epsilon()) * + input[z+dz][y+dy][x+dx]; + + if (do_kappa) + current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+dz][y+dy][x+dx]; + + result += current; + } + + output[z][y][x] += result * this->penalisation_factor; + } + } + } + return Succeeded::yes; +} + # ifdef _MSC_VER // prevent warning message on reinstantiation, // note that we get a linking error if we don't have the explicit instantiation below From eb077909f43e2fb3134a7b1604d1fbf6ee7ca74f Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Sun, 6 Jun 2021 18:10:20 -0700 Subject: [PATCH 27/47] Fix issue with logic --- src/include/stir/recon_buildblock/RelativeDifferencePrior.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h index c8e4361ba0..b027be725b 100644 --- a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h +++ b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h @@ -213,7 +213,7 @@ class RelativeDifferencePrior: public */ static inline float Hessian(const float x, const float y, const float gamma, const float eps) { - if (x > 0.0 && y > 0.0 && eps > 0.0) + if (x > 0.0 || y > 0.0 || eps > 0.0) return 2 * (2 * x + eps)*(2 * y + eps) / pow(x + y + gamma * abs(x - y) + eps, 3); else From b7007b4be6c62d324db2ea29869650cd35c74729 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Tue, 8 Jun 2021 14:26:07 -0700 Subject: [PATCH 28/47] Make prior test method `test_gradient` --- src/recon_test/test_priors.cxx | 48 ++++++++++++++++++++++------------ 1 file changed, 31 insertions(+), 17 deletions(-) diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index 961afdf0a8..5a9169e840 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -79,6 +79,10 @@ class GeneralisedPriorTests : public RunTests void run_tests_for_objective_function(const std::string& test_name, GeneralisedPrior& objective_function, shared_ptr target_sptr); + + void test_gradient(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr); }; GeneralisedPriorTests:: @@ -96,6 +100,16 @@ run_tests_for_objective_function(const std::string& test_name, if (!check(objective_function.set_up(target_sptr)==Succeeded::yes, "set-up of objective function")) return; + test_gradient(test_name, objective_function, target_sptr); +} + + +void +GeneralisedPriorTests:: +test_gradient(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr) +{ // setup images target_type& target(*target_sptr); shared_ptr gradient_sptr(target.get_empty_copy()); @@ -109,32 +123,32 @@ run_tests_for_objective_function(const std::string& test_name, const double value_at_target = objective_function.compute_value(target); target_type::full_iterator target_iter=target.begin_all(); target_type::full_iterator gradient_iter=gradient_sptr->begin_all(); - target_type::full_iterator gradient_2_iter=gradient_2_sptr->begin_all(); + target_type::full_iterator gradient_2_iter=gradient_2_sptr->begin_all(); // setup perturbation response const float eps = 1e-3F; bool testOK = true; info("Computing gradient of objective function by numerical differences (this will take a while)",3); while(target_iter!=target.end_all())// && testOK) + { + const float org_image_value = *target_iter; + *target_iter += eps; // perturb current voxel + const double value_at_inc = objective_function.compute_value(target); + *target_iter = org_image_value; // restore + const float ngradient_at_iter = static_cast((value_at_inc - value_at_target)/eps); + *gradient_2_iter = ngradient_at_iter; + testOK = testOK && this->check_if_equal(ngradient_at_iter, *gradient_iter, "gradient"); + //for (int i=0; i<5 && target_iter!=target.end_all(); ++i) { - const float org_image_value = *target_iter; - *target_iter += eps; // perturb current voxel - const double value_at_inc = objective_function.compute_value(target); - *target_iter = org_image_value; // restore - const float ngradient_at_iter = static_cast((value_at_inc - value_at_target)/eps); - *gradient_2_iter = ngradient_at_iter; - testOK = testOK && this->check_if_equal(ngradient_at_iter, *gradient_iter, "gradient"); - //for (int i=0; i<5 && target_iter!=target.end_all(); ++i) - { - ++gradient_2_iter; ++target_iter; ++ gradient_iter; - } + ++gradient_2_iter; ++target_iter; ++ gradient_iter; } + } if (!testOK) - { - info("Writing diagnostic files gradient" + test_name + ".hv, numerical_gradient" + test_name + ".hv"); - write_to_file("gradient" + test_name + ".hv", *gradient_sptr); - write_to_file("numerical_gradient" + test_name + ".hv", *gradient_2_sptr); - } + { + info("Writing diagnostic files gradient" + test_name + ".hv, numerical_gradient" + test_name + ".hv"); + write_to_file("gradient" + test_name + ".hv", *gradient_sptr); + write_to_file("numerical_gradient" + test_name + ".hv", *gradient_2_sptr); + } } From 96b6ec095e7660b71ac62f8489220dce76c3a1e6 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Tue, 8 Jun 2021 15:16:23 -0700 Subject: [PATCH 29/47] Adds test_Hessian method --- src/recon_test/test_priors.cxx | 49 ++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index 5a9169e840..e1b852d3e8 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -83,6 +83,10 @@ class GeneralisedPriorTests : public RunTests void test_gradient(const std::string& test_name, GeneralisedPrior& objective_function, shared_ptr target_sptr); + + void test_Hessian(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr); }; GeneralisedPriorTests:: @@ -101,6 +105,7 @@ run_tests_for_objective_function(const std::string& test_name, return; test_gradient(test_name, objective_function, target_sptr); + test_Hessian(test_name, objective_function, target_sptr); } @@ -149,7 +154,51 @@ test_gradient(const std::string& test_name, write_to_file("gradient" + test_name + ".hv", *gradient_sptr); write_to_file("numerical_gradient" + test_name + ".hv", *gradient_2_sptr); } +} + +void +GeneralisedPriorTests:: +test_Hessian(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr) +{ + + // setup images + target_type& target(*target_sptr); + shared_ptr output(target.get_empty_copy()); + shared_ptr input(target.get_empty_copy()); + +// std::cout << ". max = " << input->find_max() << ". min = " << input->find_min() << "\n"; + + { + /// Construct an input with negative values by subtracting 0.5 from the input target + target_type::full_iterator input_iter=input->begin_all(); + target_type::full_iterator target_iter=target.begin_all(); + while(input_iter!=input->end_all())// && testOK) + { + *input_iter = *target_iter - 0.5; + ++input_iter; ++target_iter; + } + } + objective_function.accumulate_Hessian_times_input(*output, target, *input); +// std::cout << "accumulate_Hessian_times_input output -> max = " << output->find_max() << ". min = " << output->find_min() << "\n"; + + { + /// Construct an input with negative values by subtracting 0.5 from the input target + target_type::full_iterator input_iter=input->begin_all(); + target_type::full_iterator output_iter=output->begin_all(); + float my_sum = 0.0; + while(input_iter!=input->end_all())// && testOK) + { + my_sum += *output_iter * *input_iter; + ++input_iter; ++output_iter; + } +// std::cout << "my_sum = " << my_sum << "\n"; + if (this->check_if_less(0, my_sum)) + info("Computation of (x^T H x < 0) " + test_name); + } +// std::cout << ". max = " << input->find_max() << ". min = " << input->find_min() << "\n"; } void From 2f418bcf02fb46c5ed6f2fa410ba3b476b06b78e Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Wed, 9 Jun 2021 14:33:23 -0700 Subject: [PATCH 30/47] Test an array of different cases for the Hessian condition --- src/recon_test/test_priors.cxx | 122 ++++++++++++++++++++++++++------- 1 file changed, 97 insertions(+), 25 deletions(-) diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index e1b852d3e8..d567e30d4d 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -53,6 +53,8 @@ START_NAMESPACE_STIR This test compares the result of GeneralisedPrior::compute_gradient() with a numerical gradient computed by using the GeneralisedPrior::compute_value() function. + Additionally, the Hessian is tested, via GeneralisedPrior::accumulate_Hessian_times_input(), + by evaluating the x^T Hx > 0 constraint. */ class GeneralisedPriorTests : public RunTests @@ -84,9 +86,26 @@ class GeneralisedPriorTests : public RunTests GeneralisedPrior& objective_function, shared_ptr target_sptr); - void test_Hessian(const std::string& test_name, - GeneralisedPrior& objective_function, - shared_ptr target_sptr); + //! Test various configurations of the prior's Hessian via accumulate_Hessian_times_input() + /*! + Tests the concave function condition + \f[ x^T \cdot H_{\lambda}x >= 0 \f] + for all non-negative \c x and non-zero \c \lambda (Relative Difference Penalty conditions). + This function constructs an array of configurations to test this condition and calls \c test_Hessian_configuration(). + */ + void test_Hessian(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr); + +private: + //! Hessian test for a particular configuration of the Hessian concave condition + void + test_Hessian_configuration(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr, + float beta, + float input_multiplication, float input_addition, + float current_image_multiplication, float current_image_addition); }; GeneralisedPriorTests:: @@ -104,7 +123,9 @@ run_tests_for_objective_function(const std::string& test_name, if (!check(objective_function.set_up(target_sptr)==Succeeded::yes, "set-up of objective function")) return; + std::cerr << "----- test " << test_name << " --> Gradient\n"; test_gradient(test_name, objective_function, target_sptr); + std::cerr << "----- test " << test_name << " --> Hessian-vector product (accumulate_Hessian_times_input)\n"; test_Hessian(test_name, objective_function, target_sptr); } @@ -162,43 +183,94 @@ test_Hessian(const std::string& test_name, GeneralisedPrior& objective_function, shared_ptr target_sptr) { + /// Construct configurations + float beta_array[] = {0.01, 1, 100}; // Penalty strength should only affect scale + // Modifications to the input image + float input_multiplication_array[] = {-100, -1, 0.01, 1, 100}; // Test negative, small and large values + float input_addition_array[] = {-10, -1, -0.5, 0.0, 1, 10}; + // Modifications to the current image (Hessian computation) + float current_image_multiplication_array[] = {0.01, 1, 100}; + float current_image_addition_array[] = {0.0, 0.5, 1, 10}; // RDP has constraint that current_image is non-negative + + float initial_beta = objective_function.get_penalisation_factor(); + for (float beta : beta_array) { + for (float input_multiplication : input_multiplication_array) { + for (float input_addition : input_addition_array) { + for (float current_image_multiplication : current_image_multiplication_array) { + for (float current_image_addition : current_image_addition_array) { + test_Hessian_configuration(test_name, objective_function, target_sptr, + beta, + input_multiplication, input_addition, + current_image_multiplication, current_image_addition); + } + } + } + } + } + /// Reset beta to original value + objective_function.set_penalisation_factor(initial_beta); +} - // setup images +void +GeneralisedPriorTests:: +test_Hessian_configuration(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr, + const float beta, + const float input_multiplication, const float input_addition, + const float current_image_multiplication, const float current_image_addition) +{ + /// setup images target_type& target(*target_sptr); shared_ptr output(target.get_empty_copy()); + shared_ptr current_image(target.get_empty_copy()); shared_ptr input(target.get_empty_copy()); - -// std::cout << ". max = " << input->find_max() << ". min = " << input->find_min() << "\n"; - + objective_function.set_penalisation_factor(beta); { - /// Construct an input with negative values by subtracting 0.5 from the input target - target_type::full_iterator input_iter=input->begin_all(); - target_type::full_iterator target_iter=target.begin_all(); - while(input_iter!=input->end_all())// && testOK) + /// Construct an current_image & input with various values that are variations of the target + target_type::full_iterator input_iter = input->begin_all(); + target_type::full_iterator current_image_iter = current_image->begin_all(); + target_type::full_iterator target_iter = target.begin_all(); + while (input_iter != input->end_all())// && testOK) { - *input_iter = *target_iter - 0.5; - ++input_iter; ++target_iter; + *input_iter = input_multiplication * *target_iter + input_addition; + *current_image_iter = current_image_multiplication * *target_iter + current_image_addition; + ++input_iter; ++target_iter; ++current_image_iter; } } - objective_function.accumulate_Hessian_times_input(*output, target, *input); -// std::cout << "accumulate_Hessian_times_input output -> max = " << output->find_max() << ". min = " << output->find_min() << "\n"; + /// Compute H x + objective_function.accumulate_Hessian_times_input(*output, *current_image, *input); + /// Compute x \cdot (H x) + float my_sum = 0.0; { - /// Construct an input with negative values by subtracting 0.5 from the input target - target_type::full_iterator input_iter=input->begin_all(); - target_type::full_iterator output_iter=output->begin_all(); - float my_sum = 0.0; - while(input_iter!=input->end_all())// && testOK) + target_type::full_iterator input_iter = input->begin_all(); + target_type::full_iterator output_iter = output->begin_all(); + while (input_iter != input->end_all())// && testOK) { my_sum += *output_iter * *input_iter; - ++input_iter; ++output_iter; + ++input_iter; + ++output_iter; } -// std::cout << "my_sum = " << my_sum << "\n"; - if (this->check_if_less(0, my_sum)) - info("Computation of (x^T H x < 0) " + test_name); } -// std::cout << ". max = " << input->find_max() << ". min = " << input->find_min() << "\n"; + + if (this->check_if_less(0, my_sum)) { +// info("PASS: Computation of x^T H x = " + std::to_string(my_sum) + " > 0"); + } else { + // print to console the FAILED configuration + info("FAIL: Computation of x^T H x = " + std::to_string(my_sum) + " < 0" + + "\ntest_name=" + test_name + + "\nbeta=" + std::to_string(beta) + + "\ninput_multiplication=" + std::to_string(input_multiplication) + + "\ninput_addition=" + std::to_string(input_addition) + + "\ncurrent_image_multiplication=" + std::to_string(current_image_multiplication) + + "\ncurrent_image_addition=" + std::to_string(current_image_addition) + + "\n >input image max=" + std::to_string(input->find_max()) + + "\n >input image min=" + std::to_string(input->find_min()) + + "\n >target image max=" + std::to_string(target.find_max()) + + "\n >target image min=" + std::to_string(target.find_min())); + } } void From 8356a7eec1eb77c701f610f36d02be45bb24dcab Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Wed, 9 Jun 2021 15:30:21 -0700 Subject: [PATCH 31/47] Test PoissonLogLikelihoodWithLinearModelForMeanAndProjData Hessian --- ...ihoodWithLinearModelForMeanAndProjData.cxx | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) diff --git a/src/recon_test/test_PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_test/test_PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index 7d43a549f9..2d5fa5a543 100644 --- a/src/recon_test/test_PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_test/test_PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -107,6 +107,14 @@ class PoissonLogLikelihoodWithLinearModelForMeanAndProjDataTests : public RunTes /*! Note that this function is not specific to PoissonLogLikelihoodWithLinearModelForMeanAndProjData */ void run_tests_for_objective_function(GeneralisedObjectiveFunction& objective_function, target_type& target); + + //! Test the gradient of the objective function by comparing to the numerical gradient via perturbation + void test_objective_function_gradient(GeneralisedObjectiveFunction& objective_function, + target_type& target); + + //! Test the Hessian of the objective function by testing the (x^T Hx > 0) condition + void test_objective_function_Hessian(GeneralisedObjectiveFunction& objective_function, + target_type& target); }; PoissonLogLikelihoodWithLinearModelForMeanAndProjDataTests:: @@ -118,6 +126,20 @@ void PoissonLogLikelihoodWithLinearModelForMeanAndProjDataTests:: run_tests_for_objective_function(GeneralisedObjectiveFunction& objective_function, PoissonLogLikelihoodWithLinearModelForMeanAndProjDataTests::target_type& target) { + std::cerr << "----- testing Gradient\n"; + test_objective_function_gradient(objective_function, + target); + + std::cerr << "----- testing Hessian-vector product (accumulate_Hessian_times_input)\n"; + test_objective_function_Hessian(objective_function, + target); +} + +void +PoissonLogLikelihoodWithLinearModelForMeanAndProjDataTests:: +test_objective_function_gradient(GeneralisedObjectiveFunction &objective_function, + target_type &target) +{ shared_ptr gradient_sptr(target.get_empty_copy()); shared_ptr gradient_2_sptr(target.get_empty_copy()); const int subset_num = 0; @@ -166,6 +188,38 @@ run_tests_for_objective_function(GeneralisedObjectiveFunction &objective_function, + target_type &target){ + /// setup images + shared_ptr output(target.get_empty_copy()); + + /// Compute H x + objective_function.accumulate_Hessian_times_input(*output, target, target); + + /// Compute dot(x,(H x)) + float my_sum = 0.0; + { + target_type::full_iterator target_iter = target.begin_all(); + target_type::full_iterator output_iter = output->begin_all(); + while (target_iter != target.end_all())// && testOK) + { + my_sum += *target_iter * *output_iter; + ++target_iter; ++output_iter; + } + } + // test for a CONCAVE function + if (this->check_if_less( my_sum, 0)) { +// info("PASS: Computation of x^T H x = " + std::to_string(my_sum) + " > 0"); + } else { + // print to console the FAILED configuration + info("FAIL: Computation of x^T H x = " + std::to_string(my_sum) + " < 0" + + "\n >target image max=" + std::to_string(target.find_max()) + + "\n >target image min=" + std::to_string(target.find_min())); + } +} + void PoissonLogLikelihoodWithLinearModelForMeanAndProjDataTests:: From 1b6d55bbf6a249437fe66de21224bad2ca84b002 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Wed, 9 Jun 2021 15:31:19 -0700 Subject: [PATCH 32/47] Do subtraction in accumulate_sub_Hessian_times_input for Poisson --- .../PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index 84bef7219f..df89848f0c 100644 --- a/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -1052,10 +1052,10 @@ actual_accumulate_sub_Hessian_times_input_without_penalty(TargetT& output, shared_ptr tmp(output.get_empty_copy()); this->get_projector_pair().get_back_projector_sptr()->get_output(*tmp); - // output += tmp; + // output -= tmp; std::transform(output.begin_all(), output.end_all(), tmp->begin_all(), output.begin_all(), - std::plus()); + std::minus()); return Succeeded::yes; } From 9ca2422ee25a4bf2778f1114247b6018c0a90d50 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Wed, 9 Jun 2021 15:52:41 -0700 Subject: [PATCH 33/47] Update Hessian_vector_product documentation and test error message --- .../PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h | 4 ++-- ..._PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h index 23ff58c916..59585770c2 100644 --- a/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h +++ b/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndProjData.h @@ -272,11 +272,11 @@ public RegisteredParsingObject &objec } // test for a CONCAVE function if (this->check_if_less( my_sum, 0)) { -// info("PASS: Computation of x^T H x = " + std::to_string(my_sum) + " > 0"); +// info("PASS: Computation of x^T H x = " + std::to_string(my_sum) + " < 0" and is therefore concave); } else { // print to console the FAILED configuration - info("FAIL: Computation of x^T H x = " + std::to_string(my_sum) + " < 0" + + info("FAIL: Computation of x^T H x = " + std::to_string(my_sum) + " > 0 and is therefore NOT concave" + "\n >target image max=" + std::to_string(target.find_max()) + "\n >target image min=" + std::to_string(target.find_min())); } From fa65422ff838bb8b8ea3d18a9f3d367d8e824bb9 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Wed, 9 Jun 2021 16:00:43 -0700 Subject: [PATCH 34/47] improve Hessian documentation and info --- src/recon_test/test_priors.cxx | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index d567e30d4d..7d231ace1d 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -53,7 +53,7 @@ START_NAMESPACE_STIR This test compares the result of GeneralisedPrior::compute_gradient() with a numerical gradient computed by using the GeneralisedPrior::compute_value() function. - Additionally, the Hessian is tested, via GeneralisedPrior::accumulate_Hessian_times_input(), + Additionally, the Hessian's convexity is tested, via GeneralisedPrior::accumulate_Hessian_times_input(), by evaluating the x^T Hx > 0 constraint. */ @@ -99,13 +99,12 @@ class GeneralisedPriorTests : public RunTests private: //! Hessian test for a particular configuration of the Hessian concave condition - void - test_Hessian_configuration(const std::string& test_name, - GeneralisedPrior& objective_function, - shared_ptr target_sptr, - float beta, - float input_multiplication, float input_addition, - float current_image_multiplication, float current_image_addition); + void test_Hessian_configuration(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr, + float beta, + float input_multiplication, float input_addition, + float current_image_multiplication, float current_image_addition); }; GeneralisedPriorTests:: @@ -255,11 +254,12 @@ test_Hessian_configuration(const std::string& test_name, } } + // test for a CONVEX function if (this->check_if_less(0, my_sum)) { -// info("PASS: Computation of x^T H x = " + std::to_string(my_sum) + " > 0"); +// info("PASS: Computation of x^T H x = " + std::to_string(my_sum) + " > 0 and is therefore convex"); } else { // print to console the FAILED configuration - info("FAIL: Computation of x^T H x = " + std::to_string(my_sum) + " < 0" + + info("FAIL: Computation of x^T H x = " + std::to_string(my_sum) + " < 0 and is therefore NOT convex" + "\ntest_name=" + test_name + "\nbeta=" + std::to_string(beta) + "\ninput_multiplication=" + std::to_string(input_multiplication) + From 3e4e5f44ffe917c64bb06f1563f30f894cd78bdb Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 11 Jun 2021 13:43:51 -0700 Subject: [PATCH 35/47] Improve dot product --- src/recon_test/test_priors.cxx | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index 7d231ace1d..3b8bebbf90 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -243,16 +243,7 @@ test_Hessian_configuration(const std::string& test_name, /// Compute x \cdot (H x) float my_sum = 0.0; - { - target_type::full_iterator input_iter = input->begin_all(); - target_type::full_iterator output_iter = output->begin_all(); - while (input_iter != input->end_all())// && testOK) - { - my_sum += *output_iter * *input_iter; - ++input_iter; - ++output_iter; - } - } + my_sum = std::inner_product(input->begin_all(), input->end_all(), output->begin_all(), my_sum); // test for a CONVEX function if (this->check_if_less(0, my_sum)) { From 101c4fc10f4d4888119c38bcec3de237ec5365f0 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 11 Jun 2021 13:51:33 -0700 Subject: [PATCH 36/47] Adds _is_convex variable to priors. default is false --- src/include/stir/recon_buildblock/GeneralisedPrior.h | 3 +++ src/recon_buildblock/GeneralisedPrior.cxx | 1 + src/recon_buildblock/LogcoshPrior.cxx | 1 + src/recon_buildblock/PLSPrior.cxx | 1 + src/recon_buildblock/QuadraticPrior.cxx | 1 + src/recon_buildblock/RelativeDifferencePrior.cxx | 1 + 6 files changed, 8 insertions(+) diff --git a/src/include/stir/recon_buildblock/GeneralisedPrior.h b/src/include/stir/recon_buildblock/GeneralisedPrior.h index caa4bf4dab..86135e5b8d 100644 --- a/src/include/stir/recon_buildblock/GeneralisedPrior.h +++ b/src/include/stir/recon_buildblock/GeneralisedPrior.h @@ -102,6 +102,9 @@ class GeneralisedPrior: virtual void check(DataT const& current_estimate) const; bool _already_set_up; + + //! Variable to indicate that the prior is a convex function + bool _is_convex; }; END_NAMESPACE_STIR diff --git a/src/recon_buildblock/GeneralisedPrior.cxx b/src/recon_buildblock/GeneralisedPrior.cxx index 5751c0a6b5..d62c520824 100644 --- a/src/recon_buildblock/GeneralisedPrior.cxx +++ b/src/recon_buildblock/GeneralisedPrior.cxx @@ -40,6 +40,7 @@ GeneralisedPrior::set_defaults() { _already_set_up = false; this->penalisation_factor = 0; + this->_is_convex = false; } template diff --git a/src/recon_buildblock/LogcoshPrior.cxx b/src/recon_buildblock/LogcoshPrior.cxx index f14742d7b1..d03498f6e9 100644 --- a/src/recon_buildblock/LogcoshPrior.cxx +++ b/src/recon_buildblock/LogcoshPrior.cxx @@ -108,6 +108,7 @@ void LogcoshPrior::set_defaults() { base_type::set_defaults(); + this->_is_convex = true; this->only_2D = false; this->scalar = 1.0; this->kappa_ptr.reset(); diff --git a/src/recon_buildblock/PLSPrior.cxx b/src/recon_buildblock/PLSPrior.cxx index 0b782546fa..daa09ca30c 100644 --- a/src/recon_buildblock/PLSPrior.cxx +++ b/src/recon_buildblock/PLSPrior.cxx @@ -127,6 +127,7 @@ void PLSPrior::set_defaults() { base_type::set_defaults(); + this->_is_convex = true; this->only_2D = false; this->alpha=1; this->eta=1; diff --git a/src/recon_buildblock/QuadraticPrior.cxx b/src/recon_buildblock/QuadraticPrior.cxx index 904b4b0d1b..3e247ce89f 100644 --- a/src/recon_buildblock/QuadraticPrior.cxx +++ b/src/recon_buildblock/QuadraticPrior.cxx @@ -123,6 +123,7 @@ void QuadraticPrior::set_defaults() { base_type::set_defaults(); + this->_is_convex = true; this->only_2D = false; this->kappa_ptr.reset(); this->weights.recycle(); diff --git a/src/recon_buildblock/RelativeDifferencePrior.cxx b/src/recon_buildblock/RelativeDifferencePrior.cxx index 5eb64f81a1..c3aa15ce41 100644 --- a/src/recon_buildblock/RelativeDifferencePrior.cxx +++ b/src/recon_buildblock/RelativeDifferencePrior.cxx @@ -127,6 +127,7 @@ void RelativeDifferencePrior::set_defaults() { base_type::set_defaults(); + this->_is_convex = true; this->only_2D = false; this->kappa_ptr.reset(); this->weights.recycle(); From 8187715bdea7498b09829e587fa81787525dacd4 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 11 Jun 2021 14:21:56 -0700 Subject: [PATCH 37/47] Reimplementation add_multiplication_with_approximate_Hessian --- src/recon_buildblock/QuadraticPrior.cxx | 67 ++++++++++++++++++++++++- 1 file changed, 66 insertions(+), 1 deletion(-) diff --git a/src/recon_buildblock/QuadraticPrior.cxx b/src/recon_buildblock/QuadraticPrior.cxx index 3e247ce89f..a0899491d3 100644 --- a/src/recon_buildblock/QuadraticPrior.cxx +++ b/src/recon_buildblock/QuadraticPrior.cxx @@ -582,7 +582,72 @@ QuadraticPrior:: add_multiplication_with_approximate_Hessian(DiscretisedDensity<3,elemT>& output, const DiscretisedDensity<3,elemT>& input) const { - return accumulate_Hessian_times_input(output, input, input); + // TODO this function overlaps enormously with parabolic_surrogate_curvature + // the only difference is that parabolic_surrogate_curvature uses input==1 + + assert( output.has_same_characteristics(input)); + if (this->penalisation_factor==0) + { + return Succeeded::yes; + } + + this->check(input); + + DiscretisedDensityOnCartesianGrid<3,elemT>& output_cast = + dynamic_cast &>(output); + + if (weights.get_length() ==0) + { + compute_weights(weights, output_cast.get_grid_spacing(), this->only_2D); + } + + const bool do_kappa = !is_null_ptr(kappa_ptr); + + if (do_kappa && !kappa_ptr->has_same_characteristics(input)) + error("QuadraticPrior: kappa image has not the same index range as the reconstructed image\n"); + + const int min_z = output.get_min_index(); + const int max_z = output.get_max_index(); + for (int z=min_z; z<=max_z; z++) + { + const int min_dz = max(weights.get_min_index(), min_z-z); + const int max_dz = min(weights.get_max_index(), max_z-z); + + const int min_y = output[z].get_min_index(); + const int max_y = output[z].get_max_index(); + + for (int y=min_y;y<= max_y;y++) + { + const int min_dy = max(weights[0].get_min_index(), min_y-y); + const int max_dy = min(weights[0].get_max_index(), max_y-y); + + const int min_x = output[z][y].get_min_index(); + const int max_x = output[z][y].get_max_index(); + + for (int x=min_x;x<= max_x;x++) + { + const int min_dx = max(weights[0][0].get_min_index(), min_x-x); + const int max_dx = min(weights[0][0].get_max_index(), max_x-x); + + elemT result = 0; + for (int dz=min_dz;dz<=max_dz;++dz) + for (int dy=min_dy;dy<=max_dy;++dy) + for (int dx=min_dx;dx<=max_dx;++dx) + { + elemT current = + weights[dz][dy][dx] * input[z+dz][y+dy][x+dx]; + + if (do_kappa) + current *= + (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+dz][y+dy][x+dx]; + result += current; + } + + output[z][y][x] += result * this->penalisation_factor; + } + } + } + return Succeeded::yes; } template From 410943e34c333480dc988bb1d7ab3eb70147c8a7 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 11 Jun 2021 14:22:21 -0700 Subject: [PATCH 38/47] Correct math in QP accumulate_Hessian_times_input --- src/recon_buildblock/QuadraticPrior.cxx | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/src/recon_buildblock/QuadraticPrior.cxx b/src/recon_buildblock/QuadraticPrior.cxx index a0899491d3..d2309d26a7 100644 --- a/src/recon_buildblock/QuadraticPrior.cxx +++ b/src/recon_buildblock/QuadraticPrior.cxx @@ -704,13 +704,27 @@ accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, const int min_dx = max(weights[0][0].get_min_index(), min_x-x); const int max_dx = min(weights[0][0].get_max_index(), max_x-x); + /// At this point, we have j = [z][y][x] + // The next for loops will have k = [z+dz][y+dy][x+dx] + // The following computes + //(H_{wf} y)_j = + // \sum_{k\in N_j} w_{(j,k)} f''_{d}(x_j,x_k) y_j + + // \sum_{(i \in N_j) \ne j} w_{(j,i)} f''_{od}(x_j, x_i) y_i + elemT result = 0; for (int dz=min_dz;dz<=max_dz;++dz) for (int dy=min_dy;dy<=max_dy;++dy) for (int dx=min_dx;dx<=max_dx;++dx) { - elemT current = - weights[dz][dy][dx] * input[z+dz][y+dy][x+dx]; + elemT current = 0.0; + if (dz != 0 || dy != 0 || dz != 0) + { + current = weights[dz][dy][dx] * + ( (1.0) * input[z][y][x] + (-1.0) * input[z+dz][y+dy][x+dx]) ; + } else { + // The j == k case + current = weights[dz][dy][dx] * (1.0) * input[z][y][x]; + } if (do_kappa) current *= From 948397f6aa8e5d3d5ec09841b401b79ef719a510 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 11 Jun 2021 14:24:29 -0700 Subject: [PATCH 39/47] Improve QP comments --- src/recon_buildblock/QuadraticPrior.cxx | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/recon_buildblock/QuadraticPrior.cxx b/src/recon_buildblock/QuadraticPrior.cxx index d2309d26a7..6c6d27b6c4 100644 --- a/src/recon_buildblock/QuadraticPrior.cxx +++ b/src/recon_buildblock/QuadraticPrior.cxx @@ -720,7 +720,8 @@ accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, if (dz != 0 || dy != 0 || dz != 0) { current = weights[dz][dy][dx] * - ( (1.0) * input[z][y][x] + (-1.0) * input[z+dz][y+dy][x+dx]) ; + ( (1.0) * input[z][y][x] + // diagonal Hessian terms + (-1.0) * input[z+dz][y+dy][x+dx] ); // off-diagonal Hessian terms } else { // The j == k case current = weights[dz][dy][dx] * (1.0) * input[z][y][x]; From a339ac05d1d2a63ab1d15a1eeea03c4c5c99c8d8 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Tue, 15 Jun 2021 09:42:24 -0700 Subject: [PATCH 40/47] [ci skip] Add two methods to RDP class :(off_)diagonal_second_derivative Updates the math for the RDP --- .../RelativeDifferencePrior.h | 10 +--- .../RelativeDifferencePrior.cxx | 47 +++++++++++++++++-- 2 files changed, 45 insertions(+), 12 deletions(-) diff --git a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h index b027be725b..adff6673ff 100644 --- a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h +++ b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h @@ -211,14 +211,8 @@ class RelativeDifferencePrior: public * @return the second derivative of the Relative Difference Prior */ - static inline float Hessian(const float x, const float y, const float gamma, const float eps) - { - if (x > 0.0 || y > 0.0 || eps > 0.0) - return 2 * (2 * x + eps)*(2 * y + eps) / - pow(x + y + gamma * abs(x - y) + eps, 3); - else - return 0.0; - } + float diagonal_second_derivative(const float x_j, const float x_k) const; + float off_diagonal_second_derivative(const float x_j, const float x_k) const; }; diff --git a/src/recon_buildblock/RelativeDifferencePrior.cxx b/src/recon_buildblock/RelativeDifferencePrior.cxx index c3aa15ce41..57c742ad39 100644 --- a/src/recon_buildblock/RelativeDifferencePrior.cxx +++ b/src/recon_buildblock/RelativeDifferencePrior.cxx @@ -483,15 +483,28 @@ accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, const int min_dx = max(weights[0][0].get_min_index(), min_x-x); const int max_dx = min(weights[0][0].get_max_index(), max_x-x); + /// At this point, we have j = [z][y][x] + // The next for loops will have k = [z+dz][y+dy][x+dx] + // The following computes + //(H_{wf} y)_j = + // \sum_{k\in N_j} w_{(j,k)} f''_{d}(x_j,x_k) y_j + + // \sum_{(i \in N_j) \ne j} w_{(j,i)} f''_{od}(x_j, x_i) y_i + elemT result = 0; for (int dz=min_dz;dz<=max_dz;++dz) for (int dy=min_dy;dy<=max_dy;++dy) for (int dx=min_dx;dx<=max_dx;++dx) { - elemT current = weights[dz][dy][dx] * - Hessian(current_estimate[z][y][x], current_estimate[z+dz][y+dy][x+dx], - this->get_gamma(), this->get_epsilon()) * - input[z+dz][y+dy][x+dx]; + elemT current = 0.0; + if (dz != 0 || dy != 0 || dz != 0) + { + current = weights[dz][dy][dx] * + (diagonal_second_derivative(current_estimate[z][y][x], current_estimate[z+dz][y+dy][x+dx]) * input[z][y][x] + + off_diagonal_second_derivative(current_estimate[z][y][x], current_estimate[z+dz][y+dy][x+dx])* input[z+dz][y+dy][x+dx] ); + } else { + // The j == k case + current = weights[dz][dy][dx] * (1.0) * input[z][y][x]; + } if (do_kappa) current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+dz][y+dy][x+dx]; @@ -506,6 +519,32 @@ accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, return Succeeded::yes; } +template +float +RelativeDifferencePrior:: +diagonal_second_derivative(const float x_j, const float x_k) const +{ + // THIS MATH IS WRONG!!! + if (x_j > 0.0 || x_k > 0.0 || this->epsilon > 0.0) + return 2 * (2 * x_j + this->epsilon)*(2 * x_k + this->epsilon) / + pow(x_j + x_k + this->gamma * abs(x_j - x_k) + this->epsilon, 3); + else + return 0.0; +} + +template +float +RelativeDifferencePrior:: +off_diagonal_second_derivative(const float x_j, const float x_k) const +{ + // Not sure if math is correct + if (x_j > 0.0 || x_k > 0.0 || this->epsilon > 0.0) + return 2 * (2 * x_j + this->epsilon)*(2 * x_k + this->epsilon) / + pow(x_j + x_k + this->gamma * abs(x_j - x_k) + this->epsilon, 3); + else + return 0.0; +} + # ifdef _MSC_VER // prevent warning message on reinstantiation, // note that we get a linking error if we don't have the explicit instantiation below From b6e6b54d54ab154e61856381cbde843f672c2afc Mon Sep 17 00:00:00 2001 From: robbietuk Date: Tue, 15 Jun 2021 14:42:29 -0700 Subject: [PATCH 41/47] Prior Hessian methods use second partial derivative functions (#14) * RDP Documententation * QP use (off_)diagonal_second_derivative methods and documentation * Restructure RDP and QP Hessian logic * Improve RDP and QP documentation * Implement log cosh accumulate_Hessian_times_input in terms of derivatives * Documentation --- .../stir/recon_buildblock/LogcoshPrior.h | 14 ++++++++ .../stir/recon_buildblock/QuadraticPrior.h | 14 ++++++++ .../RelativeDifferencePrior.h | 18 +++++----- src/recon_buildblock/LogcoshPrior.cxx | 30 +++++++++++++++-- src/recon_buildblock/QuadraticPrior.cxx | 33 ++++++++++++++----- .../RelativeDifferencePrior.cxx | 19 ++++++----- 6 files changed, 99 insertions(+), 29 deletions(-) diff --git a/src/include/stir/recon_buildblock/LogcoshPrior.h b/src/include/stir/recon_buildblock/LogcoshPrior.h index a3bed89478..574436300f 100644 --- a/src/include/stir/recon_buildblock/LogcoshPrior.h +++ b/src/include/stir/recon_buildblock/LogcoshPrior.h @@ -236,6 +236,20 @@ class LogcoshPrior: public const float x = d * scalar; return square((1/ cosh(x))); } + + //! The second partial derivatives of the LogCosh Prior + /*! + Diagonal refers to the second derivative w.r.t. x_j only (i.e. diagonal of the Hessian) + Off-diagonal refers to the second derivative w.r.t. x_j and x_k (i.e. off-diagonal of the Hessian) + For LogCosh, the off diagonal is the negative of the diagonal. + * @param x_j is the target voxel. + * @param x_k is the voxel in the neighbourhood. + * @return the second partial derivative of the LogCosh Prior + */ + //@{ + float diagonal_second_derivative(const float x_j, const float x_k) const; + float off_diagonal_second_derivative(const float x_j, const float x_k) const; + //@} }; diff --git a/src/include/stir/recon_buildblock/QuadraticPrior.h b/src/include/stir/recon_buildblock/QuadraticPrior.h index 4028169276..ca09af6f22 100644 --- a/src/include/stir/recon_buildblock/QuadraticPrior.h +++ b/src/include/stir/recon_buildblock/QuadraticPrior.h @@ -179,6 +179,20 @@ class QuadraticPrior: public virtual bool post_processing(); private: shared_ptr > kappa_ptr; + + //! The second partial derivatives of the Quadratic Prior + /*! + Diagonal refers to the second derivative w.r.t. x_j only (i.e. diagonal of the Hessian) + Off-diagonal refers to the second derivative w.r.t. x_j and x_k (i.e. off-diagonal of the Hessian) + For the Quadratic Prior, the off diagonal is the negative of the diagonal. + * @param x_j is the target voxel. + * @param x_k is the voxel in the neighbourhood. + * @return the second partial derivative of the Quadratic Prior + */ + //@{ + float diagonal_second_derivative(const float x_j, const float x_k) const; + float off_diagonal_second_derivative(const float x_j, const float x_k) const; + //@} }; diff --git a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h index adff6673ff..2e93b57784 100644 --- a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h +++ b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h @@ -199,20 +199,18 @@ class RelativeDifferencePrior: public private: shared_ptr > kappa_ptr; - //! The Hessian of the Relative Difference Prior + //! The second partial derivatives of the Relative Difference Prior /*! - This function returns the hessian (second derivative) of the RDP. - It is assumed that the diagonal elements of the Hessian are 0, or the weighing is, and thus only compute the partial - derivative w.r.t \c x and \c y. - * @param x is the target voxel. - * @param y is the neighbourhood voxel. - * @param gamma is the edge preservation value controlling the transition between the quadratic and linear behaviour - * @param eps is a small non-negative value included to prevent division by zero, see epsilon. - * @return the second derivative of the Relative Difference Prior - + Diagonal refers to the second derivative w.r.t. x_j only (i.e. diagonal of the Hessian) + Off-diagonal refers to the second derivative w.r.t. x_j and x_k (i.e. off-diagonal of the Hessian) + * @param x_j is the target voxel. + * @param x_k is the voxel in the neighbourhood. + * @return the second partial derivative of the Relative Difference Prior */ + //@{ float diagonal_second_derivative(const float x_j, const float x_k) const; float off_diagonal_second_derivative(const float x_j, const float x_k) const; + //@} }; diff --git a/src/recon_buildblock/LogcoshPrior.cxx b/src/recon_buildblock/LogcoshPrior.cxx index d03498f6e9..43134b84e4 100644 --- a/src/recon_buildblock/LogcoshPrior.cxx +++ b/src/recon_buildblock/LogcoshPrior.cxx @@ -562,8 +562,18 @@ accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, for (int dy=min_dy;dy<=max_dy;++dy) for (int dx=min_dx;dx<=max_dx;++dx) { - elemT voxel_diff= current_estimate[z][y][x] - current_estimate[z+dz][y+dy][x+dx]; - elemT current = weights[dz][dy][dx] * Hessian( voxel_diff, this->scalar) * input[z+dz][y+dy][x+dx]; + elemT current = weights[dz][dy][dx]; + if (dz == dy == dz == 0) { + // The j == k case + current *= diagonal_second_derivative(current_estimate[z][y][x], + current_estimate[z + dz][y + dy][x + dx]) * input[z][y][x]; + } else { + current *= (diagonal_second_derivative(current_estimate[z][y][x], + current_estimate[z + dz][y + dy][x + dx]) * input[z][y][x] + + off_diagonal_second_derivative(current_estimate[z][y][x], + current_estimate[z + dz][y + dy][x + dx]) * + input[z + dz][y + dy][x + dx]); + } if (do_kappa) current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+dz][y+dy][x+dx]; @@ -578,6 +588,22 @@ accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, return Succeeded::yes; } +template +float +LogcoshPrior:: +diagonal_second_derivative(const float x_j, const float x_k) const +{ + return square((1/ cosh((x_j - x_k) * scalar))); +} + +template +float +LogcoshPrior:: +off_diagonal_second_derivative(const float x_j, const float x_k) const +{ + return - diagonal_second_derivative(x_j, x_k); +} + # ifdef _MSC_VER // prevent warning message on reinstantiation, // note that we get a linking error if we don't have the explicit instantiation below diff --git a/src/recon_buildblock/QuadraticPrior.cxx b/src/recon_buildblock/QuadraticPrior.cxx index 6c6d27b6c4..657b8a7be6 100644 --- a/src/recon_buildblock/QuadraticPrior.cxx +++ b/src/recon_buildblock/QuadraticPrior.cxx @@ -654,7 +654,7 @@ template Succeeded QuadraticPrior:: accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, - const DiscretisedDensity<3,elemT>& /*current_estimate*/, + const DiscretisedDensity<3,elemT>& current_estimate, const DiscretisedDensity<3,elemT>& input) const { // TODO this function overlaps enormously with parabolic_surrogate_curvature @@ -710,21 +710,24 @@ accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, //(H_{wf} y)_j = // \sum_{k\in N_j} w_{(j,k)} f''_{d}(x_j,x_k) y_j + // \sum_{(i \in N_j) \ne j} w_{(j,i)} f''_{od}(x_j, x_i) y_i + // Note the condition in the second sum that i is not equal to j elemT result = 0; for (int dz=min_dz;dz<=max_dz;++dz) for (int dy=min_dy;dy<=max_dy;++dy) for (int dx=min_dx;dx<=max_dx;++dx) { - elemT current = 0.0; - if (dz != 0 || dy != 0 || dz != 0) - { - current = weights[dz][dy][dx] * - ( (1.0) * input[z][y][x] + // diagonal Hessian terms - (-1.0) * input[z+dz][y+dy][x+dx] ); // off-diagonal Hessian terms - } else { + elemT current = weights[dz][dy][dx]; + if (dz == dy == dz == 0) { // The j == k case - current = weights[dz][dy][dx] * (1.0) * input[z][y][x]; + current *= diagonal_second_derivative(current_estimate[z][y][x], + current_estimate[z + dz][y + dy][x + dx]) * input[z][y][x]; + } else { + current *= (diagonal_second_derivative(current_estimate[z][y][x], + current_estimate[z + dz][y + dy][x + dx]) * input[z][y][x] + + off_diagonal_second_derivative(current_estimate[z][y][x], + current_estimate[z + dz][y + dy][x + dx]) * + input[z + dz][y + dy][x + dx]); } if (do_kappa) @@ -740,6 +743,18 @@ accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, return Succeeded::yes; } +template +float +QuadraticPrior:: +diagonal_second_derivative(const float x_j, const float x_k) const +{ return 1.0;} + +template +float +QuadraticPrior:: +off_diagonal_second_derivative(const float x_j, const float x_k) const +{ return -1.0;} + # ifdef _MSC_VER // prevent warning message on reinstantiation, // note that we get a linking error if we don't have the explicit instantiation below diff --git a/src/recon_buildblock/RelativeDifferencePrior.cxx b/src/recon_buildblock/RelativeDifferencePrior.cxx index 57c742ad39..beb9557e7c 100644 --- a/src/recon_buildblock/RelativeDifferencePrior.cxx +++ b/src/recon_buildblock/RelativeDifferencePrior.cxx @@ -489,21 +489,24 @@ accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, //(H_{wf} y)_j = // \sum_{k\in N_j} w_{(j,k)} f''_{d}(x_j,x_k) y_j + // \sum_{(i \in N_j) \ne j} w_{(j,i)} f''_{od}(x_j, x_i) y_i + // Note the condition in the second sum that i is not equal to j elemT result = 0; for (int dz=min_dz;dz<=max_dz;++dz) for (int dy=min_dy;dy<=max_dy;++dy) for (int dx=min_dx;dx<=max_dx;++dx) { - elemT current = 0.0; - if (dz != 0 || dy != 0 || dz != 0) - { - current = weights[dz][dy][dx] * - (diagonal_second_derivative(current_estimate[z][y][x], current_estimate[z+dz][y+dy][x+dx]) * input[z][y][x] + - off_diagonal_second_derivative(current_estimate[z][y][x], current_estimate[z+dz][y+dy][x+dx])* input[z+dz][y+dy][x+dx] ); - } else { + elemT current = weights[dz][dy][dx]; + if (dz == dy == dz == 0) { // The j == k case - current = weights[dz][dy][dx] * (1.0) * input[z][y][x]; + current *= diagonal_second_derivative(current_estimate[z][y][x], + current_estimate[z + dz][y + dy][x + dx]) * input[z][y][x]; + } else { + current *= (diagonal_second_derivative(current_estimate[z][y][x], + current_estimate[z + dz][y + dy][x + dx]) * input[z][y][x] + + off_diagonal_second_derivative(current_estimate[z][y][x], + current_estimate[z + dz][y + dy][x + dx]) * + input[z + dz][y + dy][x + dx]); } if (do_kappa) From 8becfc67e723e40e2a4dde46a14e64cf396f46b4 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Fri, 18 Jun 2021 17:12:21 +0100 Subject: [PATCH 42/47] Rename test_objective_function_Hessian to test_objective_function_Hessian_concavity This is a change to keep consistency with changes in #901 --- ...ogLikelihoodWithLinearModelForMeanAndProjData.cxx | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/recon_test/test_PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx b/src/recon_test/test_PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx index c25de56db8..4dea600568 100644 --- a/src/recon_test/test_PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx +++ b/src/recon_test/test_PoissonLogLikelihoodWithLinearModelForMeanAndProjData.cxx @@ -113,8 +113,8 @@ class PoissonLogLikelihoodWithLinearModelForMeanAndProjDataTests : public RunTes target_type& target); //! Test the Hessian of the objective function by testing the (x^T Hx > 0) condition - void test_objective_function_Hessian(GeneralisedObjectiveFunction& objective_function, - target_type& target); + void test_objective_function_Hessian_concavity(GeneralisedObjectiveFunction& objective_function, + target_type& target); }; PoissonLogLikelihoodWithLinearModelForMeanAndProjDataTests:: @@ -131,8 +131,8 @@ run_tests_for_objective_function(GeneralisedObjectiveFunction &obje } void PoissonLogLikelihoodWithLinearModelForMeanAndProjDataTests:: -test_objective_function_Hessian(GeneralisedObjectiveFunction &objective_function, - target_type &target){ +test_objective_function_Hessian_concavity(GeneralisedObjectiveFunction &objective_function, + target_type &target){ /// setup images shared_ptr output(target.get_empty_copy()); From b3c8d6f425c051d9c15964ad2e3b2fd6d98af04d Mon Sep 17 00:00:00 2001 From: robbietuk Date: Sat, 19 Jun 2021 12:41:56 -0700 Subject: [PATCH 43/47] compute_Hessian method to all priors and test improvements in general * Add method get_is_convex(), which accesses prior is_convex parameter and only test convexity of priors if convex function * compute_Hessian method added to generalised prior and errors by default but with different messages depending on is_convex * Test rename and create test_Hessian_convexity and test_Hessian_methods * Add test for compute_Hessian * RelativeDifferencePrior initialisation call set_defaults * Modernise `compute_Hessian` for QP and LogCosh. Add `compute_Hessian` for RDP * Correct RDP second derivative functions * Major changes to test_Hessian_against_numerical, which loops over each voxel for perturbation response * Add verbosity suppression to suppress gradient info calls --- .../stir/recon_buildblock/GeneralisedPrior.h | 16 +- .../stir/recon_buildblock/LogcoshPrior.h | 22 +- .../stir/recon_buildblock/QuadraticPrior.h | 9 +- .../RelativeDifferencePrior.h | 7 +- src/recon_buildblock/GeneralisedPrior.cxx | 19 ++ src/recon_buildblock/LogcoshPrior.cxx | 46 ++-- src/recon_buildblock/QuadraticPrior.cxx | 43 ++-- .../RelativeDifferencePrior.cxx | 87 ++++++- src/recon_test/test_priors.cxx | 213 ++++++++++++++---- 9 files changed, 363 insertions(+), 99 deletions(-) diff --git a/src/include/stir/recon_buildblock/GeneralisedPrior.h b/src/include/stir/recon_buildblock/GeneralisedPrior.h index 86135e5b8d..31a3249967 100644 --- a/src/include/stir/recon_buildblock/GeneralisedPrior.h +++ b/src/include/stir/recon_buildblock/GeneralisedPrior.h @@ -60,6 +60,17 @@ class GeneralisedPrior: virtual void compute_gradient(DataT& prior_gradient, const DataT ¤t_estimate) =0; + //! This should computes a single row of the Hessian + /*! Default implementation just call error(). This function needs to be overridden by the + derived class. + This method computes the Hessian of a particular voxel, indicated by \c coords, of the current image estimate. + The default method should overwrite the values in \c prior_Hessian_for_single_densel. + */ + virtual Succeeded + compute_Hessian(DataT& prior_Hessian_for_single_densel, + const BasicCoordinate<3,int>& coords, + const DataT& current_image_estimate) const; + //! This should compute the multiplication of the Hessian with a vector and add it to \a output /*! Default implementation just call error(). This function needs to be overridden by the derived class. @@ -89,6 +100,9 @@ class GeneralisedPrior: virtual Succeeded set_up(shared_ptr const& target_sptr); + //! Returns the status of the _is_convex variable + bool get_is_convex() const; + protected: float penalisation_factor; //! sets value for penalisation factor @@ -103,7 +117,7 @@ class GeneralisedPrior: bool _already_set_up; - //! Variable to indicate that the prior is a convex function + //! Variable to indicate that the prior is a convex function, should be set in defaults by the derived class bool _is_convex; }; diff --git a/src/include/stir/recon_buildblock/LogcoshPrior.h b/src/include/stir/recon_buildblock/LogcoshPrior.h index 574436300f..127e63288f 100644 --- a/src/include/stir/recon_buildblock/LogcoshPrior.h +++ b/src/include/stir/recon_buildblock/LogcoshPrior.h @@ -125,9 +125,10 @@ class LogcoshPrior: public const DiscretisedDensity<3,elemT> ¤t_image_estimate); //! compute Hessian - void compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, - const BasicCoordinate<3,int>& coords, - const DiscretisedDensity<3,elemT> ¤t_image_estimate); + virtual Succeeded + compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, + const BasicCoordinate<3,int>& coords, + const DiscretisedDensity<3,elemT> ¤t_image_estimate) const; //! Compute the multiplication of the hessian of the prior multiplied by the input. virtual Succeeded accumulate_Hessian_times_input(DiscretisedDensity<3,elemT>& output, @@ -224,19 +225,6 @@ class LogcoshPrior: public { return tanh(x)/x; } } - //! The Hessian of log(cosh()) is sech^2(x) = (1/cosh(x))^2 - /*! - This function returns the hessian of the logcosh function - * @param d the difference between the ith and jth voxel. - * @param scalar is the logcosh scalar value controlling the priors transition between the quadratic and linear behaviour - * @return the second derivative of the log-cosh function - */ - static inline float Hessian(const float d, const float scalar) - { - const float x = d * scalar; - return square((1/ cosh(x))); - } - //! The second partial derivatives of the LogCosh Prior /*! Diagonal refers to the second derivative w.r.t. x_j only (i.e. diagonal of the Hessian) @@ -244,7 +232,7 @@ class LogcoshPrior: public For LogCosh, the off diagonal is the negative of the diagonal. * @param x_j is the target voxel. * @param x_k is the voxel in the neighbourhood. - * @return the second partial derivative of the LogCosh Prior + * @return the second order partial derivatives of the LogCosh Prior */ //@{ float diagonal_second_derivative(const float x_j, const float x_k) const; diff --git a/src/include/stir/recon_buildblock/QuadraticPrior.h b/src/include/stir/recon_buildblock/QuadraticPrior.h index ca09af6f22..611358de7f 100644 --- a/src/include/stir/recon_buildblock/QuadraticPrior.h +++ b/src/include/stir/recon_buildblock/QuadraticPrior.h @@ -117,9 +117,10 @@ class QuadraticPrior: public const DiscretisedDensity<3,elemT> ¤t_image_estimate); //! compute Hessian - void compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, - const BasicCoordinate<3,int>& coords, - const DiscretisedDensity<3,elemT> ¤t_image_estimate); + virtual Succeeded + compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, + const BasicCoordinate<3,int>& coords, + const DiscretisedDensity<3,elemT> ¤t_image_estimate) const; //! Call accumulate_Hessian_times_input virtual Succeeded @@ -187,7 +188,7 @@ class QuadraticPrior: public For the Quadratic Prior, the off diagonal is the negative of the diagonal. * @param x_j is the target voxel. * @param x_k is the voxel in the neighbourhood. - * @return the second partial derivative of the Quadratic Prior + * @return the second order partial derivatives of the Quadratic Prior */ //@{ float diagonal_second_derivative(const float x_j, const float x_k) const; diff --git a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h index 2e93b57784..d8a0707c33 100644 --- a/src/include/stir/recon_buildblock/RelativeDifferencePrior.h +++ b/src/include/stir/recon_buildblock/RelativeDifferencePrior.h @@ -125,6 +125,10 @@ class RelativeDifferencePrior: public void compute_gradient(DiscretisedDensity<3,elemT>& prior_gradient, const DiscretisedDensity<3,elemT> ¤t_image_estimate); + //! compute Hessian + virtual Succeeded compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, + const BasicCoordinate<3,int>& coords, + const DiscretisedDensity<3,elemT> ¤t_image_estimate) const; virtual Succeeded add_multiplication_with_approximate_Hessian(DiscretisedDensity<3,elemT>& output, @@ -203,9 +207,10 @@ class RelativeDifferencePrior: public /*! Diagonal refers to the second derivative w.r.t. x_j only (i.e. diagonal of the Hessian) Off-diagonal refers to the second derivative w.r.t. x_j and x_k (i.e. off-diagonal of the Hessian) + See J. Nuyts, et al., 2002, Equation 7. * @param x_j is the target voxel. * @param x_k is the voxel in the neighbourhood. - * @return the second partial derivative of the Relative Difference Prior + * @return the second order partial derivatives of the Relative Difference Prior */ //@{ float diagonal_second_derivative(const float x_j, const float x_k) const; diff --git a/src/recon_buildblock/GeneralisedPrior.cxx b/src/recon_buildblock/GeneralisedPrior.cxx index d62c520824..5ad4d81222 100644 --- a/src/recon_buildblock/GeneralisedPrior.cxx +++ b/src/recon_buildblock/GeneralisedPrior.cxx @@ -52,6 +52,20 @@ set_up(shared_ptr const&) return Succeeded::yes; } +template +Succeeded +GeneralisedPrior:: +compute_Hessian(TargetT& output, + const BasicCoordinate<3,int>& coords, + const TargetT& current_image_estimate) const +{ + if (this->get_is_convex()) + error("GeneralisedPrior:\n compute_Hessian implementation is not overloaded by your convex prior."); + else + error("GeneralisedPrior:\n compute_Hessian is not implemented because the prior is not convex."); + return Succeeded::no; +} + template Succeeded GeneralisedPrior:: @@ -82,6 +96,11 @@ void GeneralisedPrior::check(TargetT const& current_estimate) const error("The prior should already be set-up, but it's not."); } +template +bool GeneralisedPrior::get_is_convex() const { + return this->_is_convex; +} + # ifdef _MSC_VER // prevent warning message on instantiation of abstract class # pragma warning(disable:4661) diff --git a/src/recon_buildblock/LogcoshPrior.cxx b/src/recon_buildblock/LogcoshPrior.cxx index 43134b84e4..78fe8036d3 100644 --- a/src/recon_buildblock/LogcoshPrior.cxx +++ b/src/recon_buildblock/LogcoshPrior.cxx @@ -377,18 +377,21 @@ compute_gradient(DiscretisedDensity<3,elemT>& prior_gradient, } template -void +Succeeded LogcoshPrior:: compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, const BasicCoordinate<3,int>& coords, - const DiscretisedDensity<3,elemT> ¤t_image_estimate) + const DiscretisedDensity<3,elemT> ¤t_image_estimate) const { assert( prior_Hessian_for_single_densel.has_same_characteristics(current_image_estimate)); prior_Hessian_for_single_densel.fill(0); if (this->penalisation_factor==0) { - return; + return Succeeded::yes; } + + this->check(current_image_estimate); + const DiscretisedDensityOnCartesianGrid<3,elemT>& current_image_cast = dynamic_cast< const DiscretisedDensityOnCartesianGrid<3,elemT> &>(current_image_estimate); @@ -422,20 +425,37 @@ compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, for (int dy=min_dy;dy<=max_dy;++dy) for (int dx=min_dx;dx<=max_dx;++dx) { - // sech^2(x * scalar); sech(x) = 1/cosh(x) - elemT voxel_diff= current_image_estimate[z][y][x] - current_image_estimate[z+dz][y+dy][x+dx]; - elemT current = weights[dz][dy][dx] * Hessian(voxel_diff, this->scalar); - - if (do_kappa) - current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+dz][y+dy][x+dx]; - - diagonal += current; - prior_Hessian_for_single_densel_cast[z+dz][y+dy][x+dx] = -current*this->penalisation_factor; + elemT current = 0.0; + if (dz == 0 && dy == 0 && dx == 0) + { + // The j == k case (diagonal Hessian element), which is a sum over the neighbourhood. + for (int ddz=min_dz;ddz<=max_dz;++ddz) + for (int ddy=min_dy;ddy<=max_dy;++ddy) + for (int ddx=min_dx;ddx<=max_dx;++ddx) + { + elemT diagonal_current = weights[ddz][ddy][ddx] * + diagonal_second_derivative(current_image_estimate[z][y][x], + current_image_estimate[z+ddz][y+ddy][x+ddx]); + if (do_kappa) + diagonal_current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+ddz][y+ddy][x+ddx]; + current += diagonal_current; + } + } + else + { + // The j != k vases (off-diagonal Hessian elements) + current = weights[dz][dy][dx] * off_diagonal_second_derivative(current_image_estimate[z][y][x], + current_image_estimate[z+dz][y+dy][x+dx]); + if (do_kappa) + current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+dz][y+dy][x+dx]; + } + prior_Hessian_for_single_densel_cast[z+dz][y+dy][x+dx] = + current*this->penalisation_factor; } - prior_Hessian_for_single_densel[z][y][x]= diagonal * this->penalisation_factor; + return Succeeded::yes; } + template void LogcoshPrior::parabolic_surrogate_curvature(DiscretisedDensity<3,elemT>& parabolic_surrogate_curvature, diff --git a/src/recon_buildblock/QuadraticPrior.cxx b/src/recon_buildblock/QuadraticPrior.cxx index 657b8a7be6..d206f02071 100644 --- a/src/recon_buildblock/QuadraticPrior.cxx +++ b/src/recon_buildblock/QuadraticPrior.cxx @@ -431,17 +431,17 @@ compute_gradient(DiscretisedDensity<3,elemT>& prior_gradient, } template -void +Succeeded QuadraticPrior:: compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, const BasicCoordinate<3,int>& coords, - const DiscretisedDensity<3,elemT> ¤t_image_estimate) + const DiscretisedDensity<3,elemT> ¤t_image_estimate) const { assert( prior_Hessian_for_single_densel.has_same_characteristics(current_image_estimate)); prior_Hessian_for_single_densel.fill(0); if (this->penalisation_factor==0) { - return; + return Succeeded::yes; } this->check(current_image_estimate); @@ -480,19 +480,34 @@ compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, for (int dy=min_dy;dy<=max_dy;++dy) for (int dx=min_dx;dx<=max_dx;++dx) { - // dz==0,dy==0,dx==0 will have weight 0, so we can just include it in the loop - elemT current = - weights[dz][dy][dx]; - - if (do_kappa) - current *= - (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+dz][y+dy][x+dx]; - - diagonal += current; - prior_Hessian_for_single_densel_cast[z+dz][y+dy][x+dx] = -current*this->penalisation_factor; + elemT current = 0.0; + if (dz == 0 && dy == 0 && dx == 0) + { + // The j == k case (diagonal Hessian element), which is a sum over the neighbourhood. + for (int ddz=min_dz;ddz<=max_dz;++ddz) + for (int ddy=min_dy;ddy<=max_dy;++ddy) + for (int ddx=min_dx;ddx<=max_dx;++ddx) + { + elemT diagonal_current = weights[ddz][ddy][ddx] * + diagonal_second_derivative(current_image_estimate[z][y][x], + current_image_estimate[z+ddz][y+ddy][x+ddx]); + if (do_kappa) + diagonal_current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+ddz][y+ddy][x+ddx]; + current += diagonal_current; + } + } + else + { + // The j != k vases (off-diagonal Hessian elements) + current = weights[dz][dy][dx] * off_diagonal_second_derivative(current_image_estimate[z][y][x], + current_image_estimate[z+dz][y+dy][x+dx]); + if (do_kappa) + current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+dz][y+dy][x+dx]; + } + prior_Hessian_for_single_densel_cast[z+dz][y+dy][x+dx] = + current*this->penalisation_factor; } - prior_Hessian_for_single_densel[z][y][x]= diagonal * this->penalisation_factor; + return Succeeded::yes; } template diff --git a/src/recon_buildblock/RelativeDifferencePrior.cxx b/src/recon_buildblock/RelativeDifferencePrior.cxx index beb9557e7c..1fd446156b 100644 --- a/src/recon_buildblock/RelativeDifferencePrior.cxx +++ b/src/recon_buildblock/RelativeDifferencePrior.cxx @@ -179,6 +179,7 @@ template RelativeDifferencePrior::RelativeDifferencePrior(const bool only_2D_v, float penalisation_factor_v, float gamma_v, float epsilon_v) : only_2D(only_2D_v) { + set_defaults(); this->penalisation_factor = penalisation_factor_v; this->gamma = gamma_v; this->epsilon = epsilon_v; @@ -421,6 +422,86 @@ compute_gradient(DiscretisedDensity<3,elemT>& prior_gradient, } } +template +Succeeded +RelativeDifferencePrior:: +compute_Hessian(DiscretisedDensity<3,elemT>& prior_Hessian_for_single_densel, + const BasicCoordinate<3,int>& coords, + const DiscretisedDensity<3,elemT> ¤t_image_estimate) const +{ + assert( prior_Hessian_for_single_densel.has_same_characteristics(current_image_estimate)); + prior_Hessian_for_single_densel.fill(0); + if (this->penalisation_factor==0) + { + return Succeeded::yes; + } + + this->check(current_image_estimate); + + const DiscretisedDensityOnCartesianGrid<3,elemT>& current_image_cast = + dynamic_cast< const DiscretisedDensityOnCartesianGrid<3,elemT> &>(current_image_estimate); + + DiscretisedDensityOnCartesianGrid<3,elemT>& prior_Hessian_for_single_densel_cast = + dynamic_cast &>(prior_Hessian_for_single_densel); + + if (weights.get_length() ==0) + { + compute_weights(weights, current_image_cast.get_grid_spacing(), this->only_2D); + } + + + const bool do_kappa = !is_null_ptr(kappa_ptr); + + if (do_kappa && kappa_ptr->has_same_characteristics(current_image_estimate)) + error("RelativeDifferencePrior: kappa image has not the same index range as the reconstructed image\n"); + + const int z = coords[1]; + const int y = coords[2]; + const int x = coords[3]; + const int min_dz = max(weights.get_min_index(), prior_Hessian_for_single_densel.get_min_index()-z); + const int max_dz = min(weights.get_max_index(), prior_Hessian_for_single_densel.get_max_index()-z); + + const int min_dy = max(weights[0].get_min_index(), prior_Hessian_for_single_densel[z].get_min_index()-y); + const int max_dy = min(weights[0].get_max_index(), prior_Hessian_for_single_densel[z].get_max_index()-y); + + const int min_dx = max(weights[0][0].get_min_index(), prior_Hessian_for_single_densel[z][y].get_min_index()-x); + const int max_dx = min(weights[0][0].get_max_index(), prior_Hessian_for_single_densel[z][y].get_max_index()-x); + + elemT diagonal = 0; + for (int dz=min_dz;dz<=max_dz;++dz) + for (int dy=min_dy;dy<=max_dy;++dy) + for (int dx=min_dx;dx<=max_dx;++dx) + { + elemT current = 0.0; + if (dz == 0 && dy == 0 && dx == 0) + { + // The j == k case (diagonal Hessian element), which is a sum over the neighbourhood. + for (int ddz=min_dz;ddz<=max_dz;++ddz) + for (int ddy=min_dy;ddy<=max_dy;++ddy) + for (int ddx=min_dx;ddx<=max_dx;++ddx) + { + elemT diagonal_current = weights[ddz][ddy][ddx] * + diagonal_second_derivative(current_image_estimate[z][y][x], + current_image_estimate[z+ddz][y+ddy][x+ddx]); + if (do_kappa) + diagonal_current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+ddz][y+ddy][x+ddx]; + current += diagonal_current; + } + } + else + { + // The j != k vases (off-diagonal Hessian elements) + current = weights[dz][dy][dx] * off_diagonal_second_derivative(current_image_estimate[z][y][x], + current_image_estimate[z+dz][y+dy][x+dx]); + if (do_kappa) + current *= (*kappa_ptr)[z][y][x] * (*kappa_ptr)[z+dz][y+dy][x+dx]; + } + prior_Hessian_for_single_densel_cast[z+dz][y+dy][x+dx] = + current*this->penalisation_factor; + } + + return Succeeded::yes; +} + template Succeeded RelativeDifferencePrior:: @@ -527,9 +608,8 @@ float RelativeDifferencePrior:: diagonal_second_derivative(const float x_j, const float x_k) const { - // THIS MATH IS WRONG!!! if (x_j > 0.0 || x_k > 0.0 || this->epsilon > 0.0) - return 2 * (2 * x_j + this->epsilon)*(2 * x_k + this->epsilon) / + return 2 * pow(2 * x_k + this->epsilon, 2) / pow(x_j + x_k + this->gamma * abs(x_j - x_k) + this->epsilon, 3); else return 0.0; @@ -540,9 +620,8 @@ float RelativeDifferencePrior:: off_diagonal_second_derivative(const float x_j, const float x_k) const { - // Not sure if math is correct if (x_j > 0.0 || x_k > 0.0 || this->epsilon > 0.0) - return 2 * (2 * x_j + this->epsilon)*(2 * x_k + this->epsilon) / + return - 2 * (2 * x_j + this->epsilon)*(2 * x_k + this->epsilon) / pow(x_j + x_k + this->gamma * abs(x_j - x_k) + this->epsilon, 3); else return 0.0; diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index 3b8bebbf90..44ebb2366b 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -34,6 +34,7 @@ #include "stir/IO/read_from_file.h" #include "stir/IO/write_to_file.h" #include "stir/info.h" +#include "stir/Verbosity.h" #include "stir/Succeeded.h" #include "stir/num_threads.h" #include @@ -86,25 +87,34 @@ class GeneralisedPriorTests : public RunTests GeneralisedPrior& objective_function, shared_ptr target_sptr); - //! Test various configurations of the prior's Hessian via accumulate_Hessian_times_input() + //! Test various configurations of the Hessian of the prior via accumulate_Hessian_times_input() for convexity /*! - Tests the concave function condition + Tests the convexity condition: \f[ x^T \cdot H_{\lambda}x >= 0 \f] - for all non-negative \c x and non-zero \c \lambda (Relative Difference Penalty conditions). - This function constructs an array of configurations to test this condition and calls \c test_Hessian_configuration(). + for all non-negative \c x and non-zero \c \lambda (Relative Difference Prior conditions). + This function constructs an array of configurations to test this condition and calls + \c test_Hessian_convexity_configuration(). */ - void test_Hessian(const std::string& test_name, - GeneralisedPrior& objective_function, - shared_ptr target_sptr); + void test_Hessian_convexity(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr); + + //! Tests the compute_Hessian method implemented into convex priors + /*! Performs a perturbation response using compute_gradient to determine if the compute_Hessian (for a single densel) + is within tolerance. + */ + void test_Hessian_against_numerical(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr); private: //! Hessian test for a particular configuration of the Hessian concave condition - void test_Hessian_configuration(const std::string& test_name, - GeneralisedPrior& objective_function, - shared_ptr target_sptr, - float beta, - float input_multiplication, float input_addition, - float current_image_multiplication, float current_image_addition); + bool test_Hessian_convexity_configuration(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr, + const float beta, + const float input_multiplication, const float input_addition, + const float current_image_multiplication, const float current_image_addition); }; GeneralisedPriorTests:: @@ -122,10 +132,19 @@ run_tests_for_objective_function(const std::string& test_name, if (!check(objective_function.set_up(target_sptr)==Succeeded::yes, "set-up of objective function")) return; + // Gradient tests std::cerr << "----- test " << test_name << " --> Gradient\n"; test_gradient(test_name, objective_function, target_sptr); - std::cerr << "----- test " << test_name << " --> Hessian-vector product (accumulate_Hessian_times_input)\n"; - test_Hessian(test_name, objective_function, target_sptr); + + if (objective_function.get_is_convex()) + { + // The Hessian tests can only be performed on convex priors + std::cerr << "----- test " << test_name << " --> Hessian-vector product for convexity\n"; + test_Hessian_convexity(test_name, objective_function, target_sptr); + + std::cerr << "----- test " << test_name << " --> Hessian against numerical\n"; + test_Hessian_against_numerical(test_name, objective_function, target_sptr); + } } @@ -141,7 +160,10 @@ test_gradient(const std::string& test_name, shared_ptr gradient_2_sptr(target.get_empty_copy()); info("Computing gradient",3); + const int verbosity_default = Verbosity::get(); + Verbosity::set(0); objective_function.compute_gradient(*gradient_sptr, target); + Verbosity::set(verbosity_default); this->set_tolerance(std::max(fabs(double(gradient_sptr->find_min())), fabs(double(gradient_sptr->find_max()))) /1000); info("Computing objective function at target",3); @@ -170,6 +192,7 @@ test_gradient(const std::string& test_name, } if (!testOK) { + std::cerr << "Numerical gradient test failed with for " + test_name + " prior\n"; info("Writing diagnostic files gradient" + test_name + ".hv, numerical_gradient" + test_name + ".hv"); write_to_file("gradient" + test_name + ".hv", *gradient_sptr); write_to_file("numerical_gradient" + test_name + ".hv", *gradient_2_sptr); @@ -178,10 +201,12 @@ test_gradient(const std::string& test_name, void GeneralisedPriorTests:: -test_Hessian(const std::string& test_name, - GeneralisedPrior& objective_function, - shared_ptr target_sptr) +test_Hessian_convexity(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr) { + if (!objective_function.get_is_convex()) + return; /// Construct configurations float beta_array[] = {0.01, 1, 100}; // Penalty strength should only affect scale // Modifications to the input image @@ -191,35 +216,33 @@ test_Hessian(const std::string& test_name, float current_image_multiplication_array[] = {0.01, 1, 100}; float current_image_addition_array[] = {0.0, 0.5, 1, 10}; // RDP has constraint that current_image is non-negative + bool testOK = true; float initial_beta = objective_function.get_penalisation_factor(); - for (float beta : beta_array) { - for (float input_multiplication : input_multiplication_array) { - for (float input_addition : input_addition_array) { - for (float current_image_multiplication : current_image_multiplication_array) { + for (float beta : beta_array) + for (float input_multiplication : input_multiplication_array) + for (float input_addition : input_addition_array) + for (float current_image_multiplication : current_image_multiplication_array) for (float current_image_addition : current_image_addition_array) { - test_Hessian_configuration(test_name, objective_function, target_sptr, - beta, - input_multiplication, input_addition, - current_image_multiplication, current_image_addition); + if (testOK) // only compute configuration if testOK from previous tests + testOK = test_Hessian_convexity_configuration(test_name, objective_function, target_sptr, + beta, + input_multiplication, input_addition, + current_image_multiplication, current_image_addition); } - } - } - } - } /// Reset beta to original value objective_function.set_penalisation_factor(initial_beta); } -void +bool GeneralisedPriorTests:: -test_Hessian_configuration(const std::string& test_name, - GeneralisedPrior& objective_function, - shared_ptr target_sptr, - const float beta, - const float input_multiplication, const float input_addition, - const float current_image_multiplication, const float current_image_addition) +test_Hessian_convexity_configuration(const std::string& test_name, + GeneralisedPrior& objective_function, + shared_ptr target_sptr, + const float beta, + const float input_multiplication, const float input_addition, + const float current_image_multiplication, const float current_image_addition) { - /// setup images + /// setup targets target_type& target(*target_sptr); shared_ptr output(target.get_empty_copy()); shared_ptr current_image(target.get_empty_copy()); @@ -230,7 +253,7 @@ test_Hessian_configuration(const std::string& test_name, target_type::full_iterator input_iter = input->begin_all(); target_type::full_iterator current_image_iter = current_image->begin_all(); target_type::full_iterator target_iter = target.begin_all(); - while (input_iter != input->end_all())// && testOK) + while (input_iter != input->end_all()) { *input_iter = input_multiplication * *target_iter + input_addition; *current_image_iter = current_image_multiplication * *target_iter + current_image_addition; @@ -248,6 +271,7 @@ test_Hessian_configuration(const std::string& test_name, // test for a CONVEX function if (this->check_if_less(0, my_sum)) { // info("PASS: Computation of x^T H x = " + std::to_string(my_sum) + " > 0 and is therefore convex"); + return true; } else { // print to console the FAILED configuration info("FAIL: Computation of x^T H x = " + std::to_string(my_sum) + " < 0 and is therefore NOT convex" + @@ -261,9 +285,102 @@ test_Hessian_configuration(const std::string& test_name, "\n >input image min=" + std::to_string(input->find_min()) + "\n >target image max=" + std::to_string(target.find_max()) + "\n >target image min=" + std::to_string(target.find_min())); + return false; } } + +void +GeneralisedPriorTests:: +test_Hessian_against_numerical(const std::string &test_name, + GeneralisedPrior &objective_function, + shared_ptr target_sptr) +{ + if (!objective_function.get_is_convex()) + return; + + /// Setup + const float eps = 1e-3F; + bool testOK = true; + const int verbosity_default = Verbosity::get(); + + // setup images + target_type& input(*target_sptr->get_empty_copy()); + input += *target_sptr; // make input have same values as target_sptr + shared_ptr gradient_sptr(target_sptr->get_empty_copy()); + shared_ptr pert_grad_and_numerical_Hessian_sptr(target_sptr->get_empty_copy()); + shared_ptr Hessian_sptr(target_sptr->get_empty_copy()); + + Verbosity::set(0); + objective_function.compute_gradient(*gradient_sptr, input); + Verbosity::set(verbosity_default); +// this->set_tolerance(std::max(fabs(double(gradient_sptr->find_min())), fabs(double(gradient_sptr->find_max()))) /10); + + // Setup coordinates (z,y,x) for perturbation test (Hessian will also be computed w.r.t this voxel, j) + BasicCoordinate<3, int> perturbation_coords; + + // Get min/max indices + const int min_z = input.get_min_index(); + const int max_z = input.get_max_index(); + const int min_y = input[min_z].get_min_index(); + const int max_y = input[min_z].get_max_index(); + const int min_x = input[min_z][min_y].get_min_index(); + const int max_x = input[min_z][min_y].get_max_index(); + + // Loop over each voxel j in the input and check perturbation response. + for (int z=min_z;z<= max_z;z++) + for (int y=min_y;y<= max_y;y++) + for (int x=min_x;x<= max_x;x++) + if (testOK) + { + perturbation_coords[1] = z; perturbation_coords[2] = y; perturbation_coords[3] = x; + + // Compute H(x)_j (row of the Hessian at the jth voxel) + objective_function.compute_Hessian(*Hessian_sptr, perturbation_coords, input); + this->set_tolerance(std::max(fabs(double(Hessian_sptr->find_min())), fabs(double(Hessian_sptr->find_max()))) /500); + + // Compute g(x + eps) + Verbosity::set(0); + // Perturb target at jth voxel, compute perturbed gradient, and reset voxel to original value + float perturbed_voxels_original_value = input[perturbation_coords[1]][perturbation_coords[2]][perturbation_coords[3]]; + input[perturbation_coords[1]][perturbation_coords[2]][perturbation_coords[3]] += eps; + objective_function.compute_gradient(*pert_grad_and_numerical_Hessian_sptr, input); + input[perturbation_coords[1]][perturbation_coords[2]][perturbation_coords[3]] = perturbed_voxels_original_value; + + // Now compute the numerical-Hessian = (g(x+eps) - g(x))/eps + *pert_grad_and_numerical_Hessian_sptr -= *gradient_sptr; + *pert_grad_and_numerical_Hessian_sptr /= eps; + + Verbosity::set(verbosity_default); + // Test if pert_grad_and_numerical_Hessian_sptr is all zeros. + // This can happen if the eps is too small. This is a quick test that allows for easier debugging. + if (pert_grad_and_numerical_Hessian_sptr->sum_positive() == 0.0) + { + this->everything_ok = false; + testOK = false; + info("test_Hessian_against_numerical: failed because all values are 0 in numerical Hessian"); + } + + // Loop over each of the voxels and compare the numerical-Hessian with Hessian + target_type::full_iterator numerical_Hessian_iter = pert_grad_and_numerical_Hessian_sptr->begin_all(); + target_type::full_iterator Hessian_iter = Hessian_sptr->begin_all(); + while(numerical_Hessian_iter != pert_grad_and_numerical_Hessian_sptr->end_all() && testOK) + { + testOK = testOK && this->check_if_equal(*Hessian_iter, *numerical_Hessian_iter, "Hessian"); + ++numerical_Hessian_iter; ++ Hessian_iter; + } + + if (!testOK) + { + std::cerr << "Numerical-Hessian test failed with for " + test_name + " prior\n"; + info("Writing diagnostic files `Hessian_" + test_name + ".hv` and `numerical_Hessian_" + test_name + ".hv`"); + write_to_file("Hessian_" + test_name + ".hv", *Hessian_sptr); + write_to_file("numerical_Hessian_" + test_name + ".hv", *pert_grad_and_numerical_Hessian_sptr); + write_to_file("input_" + test_name + ".hv", input); + } + } +} + void GeneralisedPriorTests:: construct_input_data(shared_ptr& density_sptr) @@ -305,19 +422,25 @@ run_tests() shared_ptr density_sptr; construct_input_data(density_sptr); - std::cerr << "Tests for QuadraticPrior\n"; + std::cerr << "\n\nTests for QuadraticPrior\n"; { QuadraticPrior objective_function(false, 1.F); this->run_tests_for_objective_function("Quadratic_no_kappa", objective_function, density_sptr); } - std::cerr << "Tests for Relative Difference Prior\n"; + std::cerr << "\n\nTests for Relative Difference Prior with epsilon = 0\n"; { - // gamma is default and epsilon is off + // gamma is default and epsilon is 0.0 RelativeDifferencePrior objective_function(false, 1.F, 2.F, 0.F); - this->run_tests_for_objective_function("RDP_no_kappa", objective_function, density_sptr); + this->run_tests_for_objective_function("RDP_no_kappa_no_eps", objective_function, density_sptr); + } + std::cerr << "\n\nTests for Relative Difference Prior with epsilon = 0.001\n"; + { + // gamma is default and epsilon is "small" + RelativeDifferencePrior objective_function(false, 1.F, 2.F, 0.0001F); + this->run_tests_for_objective_function("RDP_no_kappa_with_eps", objective_function, density_sptr); } // Disabled PLS due to known issue -// std::cerr << "Tests for PLSPrior\n"; +// std::cerr << "\n\nTests for PLSPrior\n"; // { // PLSPrior objective_function(false, 1.F); // shared_ptr > anatomical_image_sptr(density_sptr->get_empty_copy()); @@ -325,7 +448,7 @@ run_tests() // objective_function.set_anatomical_image_sptr(anatomical_image_sptr); // this->run_tests_for_objective_function("PLS_no_kappa_flat_anatomical", objective_function, density_sptr); // } - std::cerr << "Tests for Logcosh Prior\n"; + std::cerr << "\n\nTests for Logcosh Prior\n"; { // scalar is off LogcoshPrior objective_function(false, 1.F, 1.F); From d767044156b7069918e9bb104e858214362fb235 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Sat, 19 Jun 2021 21:10:49 +0100 Subject: [PATCH 44/47] [ci skip] Add documentation to `test_gradient` --- src/recon_test/test_priors.cxx | 1 + 1 file changed, 1 insertion(+) diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index 44ebb2366b..0c39fe77d6 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -83,6 +83,7 @@ class GeneralisedPriorTests : public RunTests GeneralisedPrior& objective_function, shared_ptr target_sptr); + //! Tests the prior's gradient by comparing to the numerical gradient computed using perturbation response. void test_gradient(const std::string& test_name, GeneralisedPrior& objective_function, shared_ptr target_sptr); From c2ccf06e9eff836ce850226f222d838ad3e1ab77 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Thu, 24 Jun 2021 23:23:51 +0100 Subject: [PATCH 45/47] Increase RDP epsilon to be 0.1, increase so well above test `eps` value --- src/recon_test/test_priors.cxx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index 0c39fe77d6..6433acfe5e 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -434,10 +434,10 @@ run_tests() RelativeDifferencePrior objective_function(false, 1.F, 2.F, 0.F); this->run_tests_for_objective_function("RDP_no_kappa_no_eps", objective_function, density_sptr); } - std::cerr << "\n\nTests for Relative Difference Prior with epsilon = 0.001\n"; + std::cerr << "\n\nTests for Relative Difference Prior with epsilon = 0.1\n"; { // gamma is default and epsilon is "small" - RelativeDifferencePrior objective_function(false, 1.F, 2.F, 0.0001F); + RelativeDifferencePrior objective_function(false, 1.F, 2.F, 0.1F); this->run_tests_for_objective_function("RDP_no_kappa_with_eps", objective_function, density_sptr); } // Disabled PLS due to known issue From f60d97cae55366200572985a00d33a5dd0eb695b Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Thu, 24 Jun 2021 23:24:37 +0100 Subject: [PATCH 46/47] Add variables and set methods for toggling tests Reenable PLS, but disable all numerical tests - just run setup. --- src/recon_test/test_priors.cxx | 86 ++++++++++++++++++++++++++++------ 1 file changed, 71 insertions(+), 15 deletions(-) diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index 6433acfe5e..d56f012b5c 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -29,7 +29,7 @@ #include "stir/recon_buildblock/QuadraticPrior.h" #include "stir/recon_buildblock/RelativeDifferencePrior.h" #include "stir/recon_buildblock/LogcoshPrior.h" -//#include "stir/recon_buildblock/PLSPrior.h" +#include "stir/recon_buildblock/PLSPrior.h" #include "stir/RunTests.h" #include "stir/IO/read_from_file.h" #include "stir/IO/write_to_file.h" @@ -73,6 +73,14 @@ class GeneralisedPriorTests : public RunTests void construct_input_data(shared_ptr& density_sptr); void run_tests(); + + //! Set methods that control which tests are run. + //@{ + void set_do_test_gradient(bool d); + void set_do_test_Hessian_convexity(bool d); + void set_do_test_Hessian_against_numerical(bool d); + //@} + protected: char const * density_filename; shared_ptr > objective_function_sptr; @@ -116,6 +124,13 @@ class GeneralisedPriorTests : public RunTests const float beta, const float input_multiplication, const float input_addition, const float current_image_multiplication, const float current_image_addition); + + //! Variables to control which tests are run, see the set methods + //@{ + bool do_test_gradient = false; + bool do_test_Hessian_convexity = false; + bool do_test_Hessian_against_numerical = false; + //@} }; GeneralisedPriorTests:: @@ -123,6 +138,27 @@ GeneralisedPriorTests(char const * const density_filename) : density_filename(density_filename) {} +void +GeneralisedPriorTests:: +set_do_test_gradient(const bool d) +{ + do_test_gradient = d; +} + +void +GeneralisedPriorTests:: +set_do_test_Hessian_convexity(const bool d) +{ + do_test_Hessian_convexity = d; +} + +void +GeneralisedPriorTests:: +set_do_test_Hessian_against_numerical(const bool d) +{ + do_test_Hessian_against_numerical = d; +} + void GeneralisedPriorTests:: run_tests_for_objective_function(const std::string& test_name, @@ -133,16 +169,20 @@ run_tests_for_objective_function(const std::string& test_name, if (!check(objective_function.set_up(target_sptr)==Succeeded::yes, "set-up of objective function")) return; - // Gradient tests - std::cerr << "----- test " << test_name << " --> Gradient\n"; - test_gradient(test_name, objective_function, target_sptr); + if (do_test_gradient) + { + std::cerr << "----- test " << test_name << " --> Gradient\n"; + test_gradient(test_name, objective_function, target_sptr); + } - if (objective_function.get_is_convex()) + if (do_test_Hessian_convexity) { - // The Hessian tests can only be performed on convex priors std::cerr << "----- test " << test_name << " --> Hessian-vector product for convexity\n"; test_Hessian_convexity(test_name, objective_function, target_sptr); + } + if (do_test_Hessian_against_numerical) + { std::cerr << "----- test " << test_name << " --> Hessian against numerical\n"; test_Hessian_against_numerical(test_name, objective_function, target_sptr); } @@ -426,33 +466,49 @@ run_tests() std::cerr << "\n\nTests for QuadraticPrior\n"; { QuadraticPrior objective_function(false, 1.F); + set_do_test_gradient(true); + set_do_test_Hessian_convexity(true); + set_do_test_Hessian_against_numerical(true); this->run_tests_for_objective_function("Quadratic_no_kappa", objective_function, density_sptr); } std::cerr << "\n\nTests for Relative Difference Prior with epsilon = 0\n"; { // gamma is default and epsilon is 0.0 RelativeDifferencePrior objective_function(false, 1.F, 2.F, 0.F); + set_do_test_gradient(true); + set_do_test_Hessian_convexity(true); + set_do_test_Hessian_against_numerical(false); // RDP, with epsilon = 0.0, will fail the numerical Hessian test this->run_tests_for_objective_function("RDP_no_kappa_no_eps", objective_function, density_sptr); } std::cerr << "\n\nTests for Relative Difference Prior with epsilon = 0.1\n"; { // gamma is default and epsilon is "small" RelativeDifferencePrior objective_function(false, 1.F, 2.F, 0.1F); + set_do_test_gradient(true); + set_do_test_Hessian_convexity(true); + set_do_test_Hessian_against_numerical(true); // With a large enough epsilon the RDP numerical test will pass this->run_tests_for_objective_function("RDP_no_kappa_with_eps", objective_function, density_sptr); } - // Disabled PLS due to known issue -// std::cerr << "\n\nTests for PLSPrior\n"; -// { -// PLSPrior objective_function(false, 1.F); -// shared_ptr > anatomical_image_sptr(density_sptr->get_empty_copy()); -// anatomical_image_sptr->fill(1.F); -// objective_function.set_anatomical_image_sptr(anatomical_image_sptr); -// this->run_tests_for_objective_function("PLS_no_kappa_flat_anatomical", objective_function, density_sptr); -// } + + std::cerr << "\n\nTests for PLSPrior\n"; + { + PLSPrior objective_function(false, 1.F); + shared_ptr > anatomical_image_sptr(density_sptr->get_empty_copy()); + anatomical_image_sptr->fill(1.F); + objective_function.set_anatomical_image_sptr(anatomical_image_sptr); + // Disabled PLS due to known issue + set_do_test_gradient(false); + set_do_test_Hessian_convexity(false); + set_do_test_Hessian_against_numerical(false); + this->run_tests_for_objective_function("PLS_no_kappa_flat_anatomical", objective_function, density_sptr); + } std::cerr << "\n\nTests for Logcosh Prior\n"; { // scalar is off LogcoshPrior objective_function(false, 1.F, 1.F); + set_do_test_gradient(true); + set_do_test_Hessian_convexity(true); + set_do_test_Hessian_against_numerical(true); this->run_tests_for_objective_function("Logcosh_no_kappa", objective_function, density_sptr); } } From 6669186592d986110fbdd7f599363f35aa1344d9 Mon Sep 17 00:00:00 2001 From: Robert Twyman Date: Thu, 24 Jun 2021 23:25:32 +0100 Subject: [PATCH 47/47] Cleanup and improve debugging checks --- src/recon_test/test_priors.cxx | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/recon_test/test_priors.cxx b/src/recon_test/test_priors.cxx index d56f012b5c..b3f7d0a1e7 100644 --- a/src/recon_test/test_priors.cxx +++ b/src/recon_test/test_priors.cxx @@ -395,7 +395,7 @@ test_Hessian_against_numerical(const std::string &test_name, Verbosity::set(verbosity_default); // Test if pert_grad_and_numerical_Hessian_sptr is all zeros. // This can happen if the eps is too small. This is a quick test that allows for easier debugging. - if (pert_grad_and_numerical_Hessian_sptr->sum_positive() == 0.0) + if (pert_grad_and_numerical_Hessian_sptr->sum_positive() == 0.0 && Hessian_sptr->sum_positive() > 0.0) { this->everything_ok = false; testOK = false; @@ -413,6 +413,7 @@ test_Hessian_against_numerical(const std::string &test_name, if (!testOK) { + // Output volumes for debug std::cerr << "Numerical-Hessian test failed with for " + test_name + " prior\n"; info("Writing diagnostic files `Hessian_" + test_name + ".hv` and `numerical_Hessian_" + test_name + ".hv`"); write_to_file("Hessian_" + test_name + ".hv", *Hessian_sptr);

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