2 ///////////////////////////////////////////////////////////////////////////
3 // This program is free software: you can redistribute it and/or modify //
4 // it under the terms of the version 3 of the GNU General Public License //
5 // as published by the Free Software Foundation. //
7 // This program is distributed in the hope that it will be useful, but //
8 // WITHOUT ANY WARRANTY; without even the implied warranty of //
9 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU //
10 // General Public License for more details. //
12 // You should have received a copy of the GNU General Public License //
13 // along with this program. If not, see <http://www.gnu.org/licenses/>. //
15 // Written by Francois Fleuret //
16 // (C) Idiap Research Institute //
18 // Contact <francois.fleuret@idiap.ch> for comments & bug reports //
19 ///////////////////////////////////////////////////////////////////////////
21 #include "parsing_pool.h"
24 ParsingPool::ParsingPool(LabelledImagePool *image_pool, PoseCellHierarchy *hierarchy, scalar_t proportion_negative_cells) {
25 _nb_images = image_pool->nb_images();
26 _parsings = new Parsing *[_nb_images];
29 _nb_positive_cells = 0;
30 _nb_negative_cells = 0;
31 for(int i = 0; i < _nb_images; i++) {
32 _parsings[i] = new Parsing(image_pool, hierarchy, proportion_negative_cells, i);
33 _nb_cells += _parsings[i]->nb_cells();
34 _nb_positive_cells += _parsings[i]->nb_positive_cells();
35 _nb_negative_cells += _parsings[i]->nb_negative_cells();
37 (*global.log_stream) << "ParsingPool initialized" << endl;
38 (*global.log_stream) << " _nb_cells = " << _nb_cells << endl;
39 (*global.log_stream) << " _nb_positive_cells = " << _nb_positive_cells << endl;
40 (*global.log_stream) << " _nb_negative_cells = " << _nb_negative_cells << endl;
43 ParsingPool::~ParsingPool() {
44 for(int i = 0; i < _nb_images; i++)
49 void ParsingPool::down_one_level(LossMachine *loss_machine, PoseCellHierarchy *hierarchy, int level) {
50 scalar_t *labels = new scalar_t[_nb_cells];
51 scalar_t *tmp_responses = new scalar_t[_nb_cells];
55 { ////////////////////////////////////////////////////////////////////
58 for(int i = 0; i < _nb_images; i++) {
59 for(int d = 0; d < _parsings[i]->nb_cells(); d++) {
60 if(_parsings[i]->label(d) != 0) {
61 l += exp( - _parsings[i]->label(d) * _parsings[i]->response(d));
65 (*global.log_stream) << "* INITIAL LOSS IS " << l << endl;
66 } ////////////////////////////////////////////////////////////////////
68 // Put the negative samples with their current responses, and all
72 for(int i = 0; i < _nb_images; i++) {
73 for(int d = 0; d < _parsings[i]->nb_cells(); d++) {
74 if(_parsings[i]->label(d) < 0) {
76 tmp_responses[c] = _parsings[i]->response(d);
85 // Sub-sample among the negative ones
87 int *sample_nb_occurences = new int[_nb_cells];
88 scalar_t *sample_responses = new scalar_t[_nb_cells];
90 loss_machine->subsample(_nb_cells, labels, tmp_responses,
91 _nb_negative_cells, sample_nb_occurences, sample_responses,
95 for(int i = 0; i < _nb_images; i++) {
96 for(int d = 0; d < _parsings[i]->nb_cells(); d++) {
97 if(_parsings[i]->label(d) > 0) {
98 sample_nb_occurences[c + d] = 1;
99 sample_responses[c + d] = _parsings[i]->response(d);
103 int d = c + _parsings[i]->nb_cells();
105 _parsings[i]->down_one_level(hierarchy, level, sample_nb_occurences + c, sample_responses + c);
110 { ////////////////////////////////////////////////////////////////////
113 for(int i = 0; i < _nb_images; i++) {
114 for(int d = 0; d < _parsings[i]->nb_cells(); d++) {
115 if(_parsings[i]->label(d) != 0) {
116 l += exp( - _parsings[i]->label(d) * _parsings[i]->response(d));
120 (*global.log_stream) << "* FINAL LOSS IS " << l << endl;
121 } ////////////////////////////////////////////////////////////////////
123 delete[] sample_responses;
124 delete[] sample_nb_occurences;
127 delete[] tmp_responses;
130 void ParsingPool::update_cell_responses(PiFeatureFamily *pi_feature_family,
131 Classifier *classifier) {
132 for(int i = 0; i < _nb_images; i++) {
133 _parsings[i]->update_cell_responses(pi_feature_family, classifier);
137 void ParsingPool::weighted_sampling(LossMachine *loss_machine,
138 PiFeatureFamily *pi_feature_family,
139 SampleSet *sample_set,
140 scalar_t *responses) {
142 int nb_negatives_to_sample = sample_set->nb_samples() - _nb_positive_cells;
144 ASSERT(nb_negatives_to_sample > 0);
146 scalar_t *labels = new scalar_t[_nb_cells];
147 scalar_t *tmp_responses = new scalar_t[_nb_cells];
151 // Put the negative samples with their current responses, and all
155 for(int i = 0; i < _nb_images; i++) {
156 for(int d = 0; d < _parsings[i]->nb_cells(); d++) {
157 if(_parsings[i]->label(d) < 0) {
159 tmp_responses[c] = _parsings[i]->response(d);
162 tmp_responses[c] = 0;
168 // Sub-sample among the negative ones
170 int *sample_nb_occurences = new int[_nb_cells];
171 scalar_t *sample_responses = new scalar_t[_nb_cells];
173 loss_machine->subsample(_nb_cells, labels, tmp_responses,
174 nb_negatives_to_sample, sample_nb_occurences, sample_responses,
177 for(int k = 0; k < _nb_cells; k++) {
178 if(sample_nb_occurences[k] > 0) {
179 ASSERT(sample_nb_occurences[k] == 1);
181 tmp_responses[k] = sample_responses[k];
187 delete[] sample_responses;
188 delete[] sample_nb_occurences;
190 // Put the positive ones
193 for(int i = 0; i < _nb_images; i++) {
194 for(int d = 0; d < _parsings[i]->nb_cells(); d++) {
195 if(_parsings[i]->label(d) > 0) {
197 tmp_responses[c] = _parsings[i]->response(d);
203 // Here we have the responses for the sub-sampled in tmp_responses,
204 // and we have labels[n] set to zero for non-sampled samples
209 for(int i = 0; i < _nb_images; i++) {
211 int *to_collect = new int[_parsings[i]->nb_cells()];
213 for(int d = 0; d < _parsings[i]->nb_cells(); d++) {
214 to_collect[d] = (labels[c + d] != 0);
217 _parsings[i]->collect_samples(sample_set, pi_feature_family, s, to_collect);
219 for(int d = 0; d < _parsings[i]->nb_cells(); d++) {
221 responses[s++] = tmp_responses[c + d];
227 c += _parsings[i]->nb_cells();
230 delete[] tmp_responses;
234 void ParsingPool::write_roc(ofstream *out) {
235 int nb_negatives = nb_negative_cells();
236 int nb_positives = nb_positive_cells();
238 scalar_t *pos_responses = new scalar_t[nb_positives];
239 scalar_t *neg_responses = new scalar_t[nb_negatives];
241 for(int i = 0; i < _nb_images; i++) {
242 for(int c = 0; c < _parsings[i]->nb_cells(); c++) {
243 if(_parsings[i]->label(c) > 0)
244 pos_responses[np++] = _parsings[i]->response(c);
245 else if(_parsings[i]->label(c) < 0)
246 neg_responses[nn++] = _parsings[i]->response(c);
250 ASSERT(nn == nb_negatives && np == nb_positives);
252 print_roc_small_pos(out,
253 nb_positives, pos_responses,
254 nb_negatives, neg_responses,
257 delete[] pos_responses;
258 delete[] neg_responses;