Fixes #8 Algorithm.read_toscore_object should not exist

bob/pad/base/algorithm/Algorithm.py

@@ -85,7 +85,9 @@ class Algorithm(object):
         **Parameters:**
 
         toscore : object
-          The object to compute the score for.
+          The object to compute the score for. This will be the output of
+          extractor if performs_projection is False, otherwise this will be the
+          output of project method of the algorithm.
 
         **Returns:**
 
@@ -159,27 +161,6 @@ class Algorithm(object):
         """
         return utils.load(feature_file)
 
-    def read_toscore_object(self, toscore_object_file):
-        """read_toscore_object(toscore_object_file) -> toscore_object
-
-        Reads the toscore_object feature from a file.
-        By default, the toscore_object feature is identical to the projected feature.
-        Hence, this base class implementation simply calls :py:meth:`read_feature`.
-
-        If your algorithm requires different behavior, please overwrite this function.
-
-        **Parameters:**
-
-        toscore_object_file : str or :py:class:`bob.io.base.HDF5File`
-          The file open for reading, or the file name to read from.
-
-        **Returns:**
-
-        toscore_object : object
-          The toscore_object that was read from file.
-        """
-        return self.read_feature(toscore_object_file)
-
     def train_projector(self, training_features, projector_file):
         """This function can be overwritten to train the feature projector.
         If you do this, please also register the function by calling this base class constructor

bob/pad/base/algorithm/PadLDA.py

@@ -6,12 +6,12 @@ from bob.bio.base.algorithm import LDA
 
 class PadLDA(LDA):
     """Wrapper for bob.bio.base.algorithm.LDA,
-    
+
     Here, LDA is used in a PAD context. This means that the feature
     will be projected on a single dimension subspace, which acts as a score
 
 
-    For more details, you may want to have a look at 
+    For more details, you may want to have a look at
     `bob.learn.linear Documentation`_
 
     .. _bob.learn.linear Documentation:
@@ -58,26 +58,5 @@ class PadLDA(LDA):
             **kwargs
           )
 
-    def read_toscore_object(self, toscore_object_file):
-        """Reads the toscore_object feature from a file.
-      
-        By default, the toscore_object feature is identical to the projected feature.
-        Hence, this base class implementation simply calls :py:meth:`bob.pad.base.algorithm.Algorithm.read_feature`.
-
-        If your algorithm requires different behavior, please overwrite this function.
-
-        Parameters
-        ----------
-        toscore_object_file : str or :py:class:`bob.io.base.HDF5File`
-          The file open for reading, or the file name to read from.
-
-        Returns
-        -------
-        object:
-          The toscore_object that was read from file.
-        
-        """
-        return self.read_feature(toscore_object_file)
-  
     def score(self, toscore):
         return [toscore[0]]

bob/pad/base/script/spoof.py

@@ -202,6 +202,7 @@ def execute(args):
     elif args.sub_task == 'compute-scores':
         tools.compute_scores(
             args.algorithm,
+            args.extractor,
             groups=[args.group],
             allow_missing_files=args.allow_missing_files,
             force=args.force,

bob/pad/base/tools/scoring.py

@@ -23,7 +23,7 @@ from .FileSelector import FileSelector
 from bob.bio.base import utils
 
 
-def _compute_scores(algorithm, toscore_objects, allow_missing_files):
+def _compute_scores(algorithm, extractor, toscore_objects, allow_missing_files):
     """Compute scores for the given list of objects using provided algorithm.
     """
     # the scores to be computed
@@ -37,7 +37,10 @@ def _compute_scores(algorithm, toscore_objects, allow_missing_files):
             scores.insert(i, [numpy.nan])
             continue
         # read toscore
-        toscore = algorithm.read_toscore_object(toscore_element)
+        if algorithm.performs_projection:
+            toscore = algorithm.read_feature(toscore_element)
+        else:
+            toscore = extractor.read_feature(toscore_element)
         # compute score
         if isinstance(toscore, list) or isinstance(toscore[0], numpy.ndarray):
             scores.insert(i, algorithm.score_for_multiple_projections(toscore))
@@ -120,7 +123,7 @@ def _save_scores(score_file, scores, toscore_objects, write_compressed=False):
     _close_written(score_file, f, write_compressed)
 
 
-def _scores_all(algorithm, group, force, allow_missing_files=False, write_compressed=False):
+def _scores_all(algorithm, extractor, group, force, allow_missing_files=False, write_compressed=False):
     """Computes scores for all (real, attack) files in a given group using the provided algorithm."""
     # the file selector object
     fs = FileSelector.instance()
@@ -148,7 +151,7 @@ def _scores_all(algorithm, group, force, allow_missing_files=False, write_compre
             # get the attack files
             current_files = fs.get_paths(current_objects, 'projected' if algorithm.performs_projection else 'extracted')
             # compute scores for the list of File objects
-            cur_scores = _compute_scores(algorithm, current_files, allow_missing_files)
+            cur_scores = _compute_scores(algorithm, extractor, current_files, allow_missing_files)
             total_scores += cur_scores
             # Save scores to text file
             _save_scores(score_file, cur_scores, current_objects, write_compressed)
@@ -164,7 +167,7 @@ def _scores_all(algorithm, group, force, allow_missing_files=False, write_compre
                          current_toscore_objects[0]+current_toscore_objects[1], write_compressed)
 
 
-def compute_scores(algorithm, force=False, groups=['dev', 'eval'], allow_missing_files=False, write_compressed=False):
+def compute_scores(algorithm, extractor, force=False, groups=['dev', 'eval'], allow_missing_files=False, write_compressed=False):
     """Computes the scores for the given groups.
 
     This function computes all scores for the experiment and writes them to score files.
@@ -175,6 +178,8 @@ def compute_scores(algorithm, force=False, groups=['dev', 'eval'], allow_missing
     algorithm : py:class:`bob.bio.base.algorithm.Algorithm` or derived
       The algorithm, used for enrolling model and writing them to file.
 
+    extractor : py:class:`bob.bio.base.extractor.Extractor` or derived
+
     force : bool
       If given, files are regenerated, even if they already exist.
 
@@ -192,4 +197,4 @@ def compute_scores(algorithm, force=False, groups=['dev', 'eval'], allow_missing
         algorithm.load_projector(fs.projector_file)
 
     for group in groups:
-        _scores_all(algorithm, group, force, allow_missing_files, write_compressed)
+        _scores_all(algorithm, extractor, group, force, allow_missing_files, write_compressed)