automatic commit

diff --git a/README.txt b/README.txt
index c322ce7..cbfa44a 100644 (file)
--- a/README.txt
+++ b/README.txt
@@ -1,8 +1,9 @@
 
 I. INTRODUCTION
 
 I. INTRODUCTION

+---------------

 
 

-  This is the open-source C++ implementation of the folded hierarchy
-  of classifiers for cat detection described in
+  This is the documentation for the open-source C++ implementation of
+  the folded hierarchy of classifiers for cat detection described in

 
      F. Fleuret and D. Geman, "Stationary Features and Cat Detection",
      Journal of Machine Learning Research (JMLR), 2008, to appear.
 
      F. Fleuret and D. Geman, "Stationary Features and Cat Detection",
      Journal of Machine Learning Research (JMLR), 2008, to appear.


@@ -13,6 +14,7 @@ I. INTRODUCTION
   reports.
 
 II. INSTALLATION
   reports.
 
 II. INSTALLATION

+----------------

 
   If you have installed the RateMyKitten images provided on
 
 
   If you have installed the RateMyKitten images provided on
 


@@ -23,13 +25,13 @@ II. INSTALLATION
 
   It will
 
 
   It will
 

-   * Compile the source code entirely
+  * Compile the source code entirely

 
 

-   * Generate the "pool file" containing the uncompressed images
-     converted to gray levels, labeled with the ground truth.
+  * Generate the "pool file" containing the uncompressed images
+    converted to gray levels, labeled with the ground truth.

 
 

-   * Run 20 rounds of training / test (ten rounds for each of HB and
-     H+B detectors with different random seeds)
+  * Run 20 rounds of training / test (ten rounds for each of HB and
+    H+B detectors with different random seeds)

 
   You can run the full thing with the following commands if you have
   wget installed
 
   You can run the full thing with the following commands if you have
   wget installed


@@ -54,20 +56,32 @@ II. INSTALLATION
   This program was developed on Debian GNU/Linux computers with the
   following main tool versions
 
   This program was developed on Debian GNU/Linux computers with the
   following main tool versions
 

-   * GNU bash, version 3.2.39
-   * g++ 4.3.2
-   * gnuplot 4.2 patchlevel 4
+  * GNU bash, version 3.2.39
+  * g++ 4.3.2
+  * gnuplot 4.2 patchlevel 4

 
 

-   Due to approximations in the optimized arithmetic operations with
-   g++, results may vary with different versions of the compiler
-   and/or different levels of optimization.
+  Due to approximations in the optimized arithmetic operations with
+  g++, results may vary with different versions of the compiler and/or
+  different levels of optimization.

 
 

-III. PARAMETERS
+III. EXECUTING THE PROGRAM
+--------------------------
+
+  The main command has to be invoked with a list of parameter values,
+  followed by commands to execute.

 
   To set the value of a parameter, just add an argument of the form
   --parameter-name=value before the commands that should take it into
   account.
 
 
   To set the value of a parameter, just add an argument of the form
   --parameter-name=value before the commands that should take it into
   account.
 

+  For instance, to open a scene pool ./something.pool, train a
+  detector and save it, you would do
+
+    ./folding --pool-name=./something.pool open-pool train-detector write-detector
+
+IV. PARAMETERS
+--------------
+

   For every parameter below, the default value is given between
   parenthesis.
 
   For every parameter below, the default value is given between
   parenthesis.
 


@@ -83,11 +97,11 @@ III. PARAMETERS
 
     Should the pictures be generated for printing in black and white.
 
 
     Should the pictures be generated for printing in black and white.
 

-  * pool-name (no default)
+  * pool-name (none)

 
     The scene pool file name.
 
 
     The scene pool file name.
 

-  * test-pool-name (no default)
+  * test-pool-name (none)

 
     Should we use a separate test pool file. If none is given, then
     the test scenes are taken at random from the main pool file
 
     Should we use a separate test pool file. If none is given, then
     the test scenes are taken at random from the main pool file


@@ -121,7 +135,7 @@ III. PARAMETERS
   * proportion-negative-cells-for-training (0.025)
 
     Overall proportion of negative cells to use during learning (we
   * proportion-negative-cells-for-training (0.025)
 
     Overall proportion of negative cells to use during learning (we

-    sample among for boosting).
+    sample among them for boosting).

 
   * nb-negative-samples-per-positive (10)
 
 
   * nb-negative-samples-per-positive (10)
 


@@ -217,48 +231,49 @@ III. PARAMETERS
 
     Should we display a progress bar during long computations.
 
 
     Should we display a progress bar during long computations.
 

-IV. COMMANDS
+V. COMMANDS
+-----------

 
 

-   * open-pool
+  * open-pool

 
 

-     Open the pool of scenes.
+    Open the pool of scenes.

 
 

-   * train-detector
+  * train-detector

 
 

-     Create a new detector from the training scenes.
+    Create a new detector from the training scenes.

 
 

-   * compute-thresholds
+  * compute-thresholds

 
 

-     Compute the thresholds of the detector classifiers from the
-     validation set to obtain the required wanted-true-positive-rate.
+    Compute the thresholds of the detector classifiers from the
+    validation set to obtain the required wanted-true-positive-rate.

 
 

-   * test-detector
+  * test-detector

 
 

-     Run the detector on the test scenes.
+    Run the detector on the test scenes.

 
 

-   * sequence-test-detector
+  * sequence-test-detector

 
 

-     Visit nb-wanted-true-positive-rates rates between 0 and
-     wanted-true-positive-rate, for each compute the detector
-     thresholds on the validation set and estimate the error rate on
-     the test set.
+    Visit nb-wanted-true-positive-rates rates between 0 and
+    wanted-true-positive-rate, for each compute the detector
+    thresholds on the validation set and estimate the error rate on
+    the test set.

 
 

-   * write-detector
+  * write-detector

 
 

-     Write the current detector to the file detector-name
+    Write the current detector to the file detector-name

 
 

-   * read-detector
+  * read-detector

 
 

-     Read a detector from the file detector-name
+    Read a detector from the file detector-name

 
 

-   * write-pool-images
+  * write-pool-images

 
 

-     For every of the first nb-images of the pool, save one PNG image
-     with the ground truth, one with the corresponding referential at
-     the reference scale, and one with the feature material-feature-nb
-     from the detector. This last image is not saved if either no
-     detector has been read/trained or if no feature number has been
-     specified.
+    For every of the first nb-images of the pool, save one PNG image
+    with the ground truth, one with the corresponding referential at
+    the reference scale, and one with the feature material-feature-nb
+    from the detector. This last image is not saved if either no
+    detector has been read/trained or if no feature number has been
+    specified.

 
 --
 Francois Fleuret
 
 --
 Francois Fleuret

diff --git a/detector.cc b/detector.cc
index 358c66b..5eb0867 100644 (file)
--- a/detector.cc
+++ b/detector.cc
@@ -72,15 +72,11 @@ void Detector::train_classifier(int level,
 
   scalar_t *responses = new scalar_t[nb_positives + nb_negatives_to_sample];
 
 
   scalar_t *responses = new scalar_t[nb_positives + nb_negatives_to_sample];
 

-  (*global.log_stream) << "Collecting the sampled training set." << endl;
-

   parsing_pool->weighted_sampling(loss_machine,
                                   &full_pi_feature_family,
                                   sample_set,
                                   responses);
 
   parsing_pool->weighted_sampling(loss_machine,
                                   &full_pi_feature_family,
                                   sample_set,
                                   responses);
 

-  (*global.log_stream) << "Training the classifier." << endl;
-

   (*global.log_stream) << "Initial train_loss "
                        << loss_machine->loss(sample_set, responses)
                        << endl;
   (*global.log_stream) << "Initial train_loss "
                        << loss_machine->loss(sample_set, responses)
                        << endl;


@@ -167,8 +163,6 @@ void Detector::train(LabelledImagePool *train_pool,
     for(int c = 0; c < _nb_classifiers_per_level; c++) {
       int q = l * _nb_classifiers_per_level + c;
 
     for(int c = 0; c < _nb_classifiers_per_level; c++) {
       int q = l * _nb_classifiers_per_level + c;
 

-      (*global.log_stream) << "Building classifier " << q << " (level " << l << ")" << endl;
-

       // Train the classifier
 
       train_classifier(l,
       // Train the classifier
 
       train_classifier(l,


@@ -178,8 +172,6 @@ void Detector::train(LabelledImagePool *train_pool,
 
       // Update the cell responses on the training set
 
 
       // Update the cell responses on the training set
 

-      (*global.log_stream) << "Updating training cell responses." << endl;
-

       train_parsing->update_cell_responses(_pi_feature_families[q],
                                            _classifiers[q]);
 
       train_parsing->update_cell_responses(_pi_feature_families[q],
                                            _classifiers[q]);
 


@@ -197,8 +189,6 @@ void Detector::train(LabelledImagePool *train_pool,
 
         // Update the cell responses on the validation set
 
 
         // Update the cell responses on the validation set
 

-        (*global.log_stream) << "Updating validation cell responses." << endl;
-

         validation_parsing->update_cell_responses(_pi_feature_families[q],
                                                   _classifiers[q]);
 
         validation_parsing->update_cell_responses(_pi_feature_families[q],
                                                   _classifiers[q]);
 


@@ -316,11 +306,6 @@ void Detector::compute_thresholds(LabelledImagePool *validation_pool, scalar_t w
     int wanted_nb_fn_at_this_classifier
       = int(nb_targets_total * (1 - wanted_tp_at_this_classifier));
 
     int wanted_nb_fn_at_this_classifier
       = int(nb_targets_total * (1 - wanted_tp_at_this_classifier));
 

-    (*global.log_stream) << "q = " << q
-                         << " wanted_tp_at_this_classifier = " << wanted_tp_at_this_classifier
-                         << " wanted_nb_fn_at_this_classifier = " << wanted_nb_fn_at_this_classifier
-                         << endl;
-

     indexed_fusion_sort(nb_targets_total, indexes, sorted_indexes,
                         responses + q * nb_targets_total);
 
     indexed_fusion_sort(nb_targets_total, indexes, sorted_indexes,
                         responses + q * nb_targets_total);
 


@@ -338,35 +323,6 @@ void Detector::compute_thresholds(LabelledImagePool *validation_pool, scalar_t w
   delete[] still_detected;
   delete[] indexes;
   delete[] sorted_indexes;
   delete[] still_detected;
   delete[] indexes;
   delete[] sorted_indexes;

-
-  { ////////////////////////////////////////////////////////////////////
-    // Sanity check
-
-    int nb_positives = 0;
-
-    for(int t = 0; t < nb_targets_total; t++) {
-      int positive = 1;
-      for(int q = 0; q < _nb_classifiers; q++) {
-        if(responses[t + nb_targets_total * q] < _thresholds[q]) positive = 0;
-      }
-      if(positive) nb_positives++;
-    }
-
-    scalar_t actual_tp = scalar_t(nb_positives) / scalar_t(nb_targets_total);
-
-    (*global.log_stream) << "Overall detection rate " << nb_positives << "/" << nb_targets_total
-                         << " "
-                         << "actual_tp = " << actual_tp
-                         << " "
-                         << "wanted_tp = " << wanted_tp
-                         << endl;
-
-    if(actual_tp < wanted_tp) {
-      cerr << "INCONSISTENCY" << endl;
-      abort();
-    }
-  } ////////////////////////////////////////////////////////////////////
-

   delete[] responses;
 }
 
   delete[] responses;
 }
 


@@ -453,10 +409,6 @@ void Detector::read(istream *is) {
   }
 
   _hierarchy = read_hierarchy(is);
   }
 
   _hierarchy = read_hierarchy(is);

-
-  (*global.log_stream) << "Read Detector" << endl
-                       << "  _nb_levels " << _nb_levels << endl
-                       << "  _nb_classifiers_per_level " << _nb_classifiers_per_level << endl;

 }
 
 void Detector::write(ostream *os) {
 }
 
 void Detector::write(ostream *os) {

diff --git a/graph.sh b/graph.sh
index b54fcf4..c1beaee 100755 (executable)
--- a/graph.sh
+++ b/graph.sh
@@ -20,7 +20,7 @@
 echo "Parsing the log files"
 
 for p in hb h+b; do
 echo "Parsing the log files"
 
 for p in hb h+b; do

-    grep ^INFO results/${p}-*/log  | grep "FALSE_NEGATIVE_RATE\|PER_VGA" | \
+    grep ^INFO results/${p}-*/log  | grep "DECIMATED_FALSE_NEGATIVE_RATE\|DECIMATED_NB_FALSE_POSITIVES_PER_VGA" | \

         sed -e "s/[^0-9A-Z_ .]//g" | \
         awk '{
                if($2 == "DECIMATED_FALSE_NEGATIVE_RATE") {
         sed -e "s/[^0-9A-Z_ .]//g" | \
         awk '{
                if($2 == "DECIMATED_FALSE_NEGATIVE_RATE") {

diff --git a/run.sh b/run.sh
index 42dc58b..f718d11 100755 (executable)
--- a/run.sh
+++ b/run.sh
@@ -19,7 +19,7 @@
 
 MAIN_URL="http://www.idiap.ch/folded-ctf"
 
 
 MAIN_URL="http://www.idiap.ch/folded-ctf"
 

-######################################################################
+#########################################################################

 # Compiling
 
 make -j -k
 # Compiling
 
 make -j -k


@@ -31,7 +31,7 @@ fi
 
 echo
 
 
 echo
 

-######################################################################
+#########################################################################

 # Generating the pool file
 
 DATA_PATH=./rmk-data
 # Generating the pool file
 
 DATA_PATH=./rmk-data


@@ -77,13 +77,13 @@ else
 
 fi
 
 
 fi
 

-######################################################################
-# Running the computation per se
-

 RESULT_DIR=./results
 
 case $1 in
 
 RESULT_DIR=./results
 
 case $1 in
 

+    #####################################################################
+    ## Generate the pictures from the paper
+

     pics)
 
         SEED=0
     pics)
 
         SEED=0


@@ -92,14 +92,14 @@ case $1 in
 
         if [[ -d "${EXPERIMENT_RESULT_DIR}" ]]; then
 
 
         if [[ -d "${EXPERIMENT_RESULT_DIR}" ]]; then
 

-            for n in -1 0 2501 2504; do
+            for npf in -1 0 2497; do

 
                 ./folding --random-seed=${SEED} \
                     --pool-name=${POOL_NAME} \
 
                 ./folding --random-seed=${SEED} \
                     --pool-name=${POOL_NAME} \

-                    --result-path=${EXPERIMENT_RESULT_DIR} \
+                    --result-path=/tmp/ \

                     --detector-name=${EXPERIMENT_RESULT_DIR}/default.det \
                     --detector-name=${EXPERIMENT_RESULT_DIR}/default.det \

-                    --nb-images=1 \
-                    --material-feature-nb=${n} \
+                    --nb-images=63 \
+                    --material-feature-nb=${npf} \

                     open-pool \
                     read-detector \
                     write-pool-images
                     open-pool \
                     read-detector \
                     write-pool-images


@@ -114,6 +114,9 @@ case $1 in
 
         ;;
 
 
         ;;
 

+    #####################################################################
+    ## Run the full computation
+

     valgrind|"")
 
         if [[ ! -d ${RESULT_DIR} ]]; then
     valgrind|"")
 
         if [[ ! -d ${RESULT_DIR} ]]; then