Created the method is_argument_available that checks if an argument is...

Created the method is_argument_available that checks if an argument is available for certain method. provided both python2 and python3 implementations

Used the new method is_argument_available

Used the new method is_argument_available

Improved the test cases

bob/bio/base/test/dummy/algorithm.py

@@ -2,6 +2,7 @@ import scipy.spatial
 import bob.io.base
 import numpy
 from bob.bio.base.algorithm import Algorithm
+from bob.bio.base.database import BioFile
 
 _data = [5., 6., 7., 8., 9.]
 
@@ -63,7 +64,7 @@ class DummyAlgorithmMetadata (DummyAlgorithm):
 
   def train_projector(self, train_files, projector_file, metadata=None):
     """Does nothing, simply converts the data type of the data, ignoring any annotation."""
-    assert metadata is not None
+    assert isinstance(metadata, list)
     return super(DummyAlgorithmMetadata, self).train_projector(train_files, projector_file)
 
   def enroll(self, enroll_features, metadata=None):
@@ -74,11 +75,11 @@ class DummyAlgorithmMetadata (DummyAlgorithm):
 
   def score(self, model, probe, metadata=None):
     """Returns the Euclidean distance between model and probe"""
-    assert metadata is not None
+    assert isinstance(metadata, BioFile)
     return super(DummyAlgorithmMetadata, self).score(model, probe)
 
   def project(self, feature, metadata=None):
-    assert metadata is not None
+    assert isinstance(metadata, BioFile)
     return super(DummyAlgorithmMetadata, self).project(feature)
 
 algorithm_metadata = DummyAlgorithmMetadata()

bob/bio/base/test/dummy/extractor.py

 import numpy
 import bob.bio.base
-
+from bob.bio.base.database import BioFile
 from bob.bio.base.extractor import Extractor
 
 _data = [0., 1., 2., 3., 4.]
@@ -31,8 +31,7 @@ class DummyExtractorMetadata (DummyExtractor):
 
   def __call__(self, data, metadata=None):
     """Does nothing, simply converts the data type of the data, ignoring any annotation."""
-    assert metadata is not None
-    assert self.model
-    return data.astype(numpy.float).flatten()
+    assert isinstance(metadata, BioFile)
+    return super(DummyExtractorMetadata, self).__call__(data)
 
 extractor_metadata = DummyExtractorMetadata()

bob/bio/base/test/dummy/preprocessor.py

 from bob.bio.base.preprocessor import Preprocessor
+from bob.bio.base.database import BioFile
 import numpy
 numpy.random.seed(10)
 
@@ -23,7 +24,7 @@ class DummyPreprocessorMetadata (DummyPreprocessor):
 
   def __call__(self, data, annotation, metadata=None):
     """Does nothing, simply converts the data type of the data, ignoring any annotation."""
-    assert metadata is not None    
+    assert isinstance(metadata, BioFile)
     return super(DummyPreprocessorMetadata, self).__call__(data, annotation)
 
 preprocessor_metadata = DummyPreprocessorMetadata()

bob/bio/base/tools/algorithm.py

@@ -55,7 +55,7 @@ def train_projector(algorithm, extractor, allow_missing_files = False, force = F
       logger.info("- Projection: training projector '%s' using %d training files: ", fs.projector_file, len(train_files))
 
     # perform training
-    if "metadata" in inspect.getargspec(algorithm.train_projector).args:
+    if utils.is_argument_available("metadata", algorithm.train_projector):
       metadata = fs.database.training_files('train_projector', algorithm.split_training_features_by_client)
       algorithm.train_projector(train_features, fs.projector_file, metadata=metadata)
     else:
@@ -138,7 +138,7 @@ def project(algorithm, extractor, groups = None, indices = None, allow_missing_f
 
       # project feature
       if "metadata" in inspect.getargspec(algorithm.project).args:
-        projected = algorithm.project(feature, metadata=metadata)
+        projected = algorithm.project(feature, metadata=metadata[i])
       else:
         projected = algorithm.project(feature)
 
@@ -257,6 +257,9 @@ def enroll(algorithm, extractor, compute_zt_norm, indices = None, groups = ['dev
   # which tool to use to read the features...
   reader = algorithm if algorithm.use_projected_features_for_enrollment else extractor
 
+  # Checking if we need to ship the metadata to the method enroll
+  has_metadata = utils.is_argument_available("metadata", algorithm.enroll)
+
   # Create Models
   if 'N' in types:
     for group in groups:
@@ -290,7 +293,7 @@ def enroll(algorithm, extractor, compute_zt_norm, indices = None, groups = ['dev
           # load all files into memory
           enroll_features = [reader.read_feature(enroll_file) for enroll_file in enroll_files]
 
-          if "metadata" in inspect.getargspec(algorithm.enroll).args:
+          if has_metadata:
             metadata = fs.database.enroll_files(group=group, model_id=model_id)
             model = algorithm.enroll(enroll_features, metadata=metadata)
           else:
@@ -341,7 +344,7 @@ def enroll(algorithm, extractor, compute_zt_norm, indices = None, groups = ['dev
           # load all files into memory
           t_enroll_features = [reader.read_feature(t_enroll_file) for t_enroll_file in t_enroll_files]
 
-          if "metadata" in inspect.getargspec(algorithm.enroll).args:
+          if has_metadata:
             metadata = fs.database.enroll_files(group=group, model_id=t_model_id)
             t_model = algorithm.enroll(t_enroll_features, metadata=metadata)
           else:

bob/bio/base/tools/extractor.py

@@ -91,7 +91,11 @@ def extract(extractor, preprocessor, groups=None, indices = None, allow_missing_
   extractor.load(fs.extractor_file)
   data_files = fs.preprocessed_data_list(groups=groups)
   feature_files = fs.feature_list(groups=groups)
-  metadata = fs.original_data_list(groups=groups)
+
+  if utils.is_argument_available("metadata", extractor.__call__):
+    metadata = fs.original_data_list(groups=groups)
+  else:
+    metadata = None
 
   # select a subset of indices to iterate
   if indices is not None:
@@ -120,11 +124,12 @@ def extract(extractor, preprocessor, groups=None, indices = None, allow_missing_
       bob.io.base.create_directories_safe(os.path.dirname(feature_file))
       # load data
       data = preprocessor.read_data(data_file)
+
       # extract feature
-      if "metadata" in inspect.getargspec(extractor.__call__).args:
-        feature = extractor(data, metadata=metadata[i])
-      else:
+      if metadata is None:
         feature = extractor(data)
+      else:
+        feature = extractor(data, metadata=metadata[i])
 
       if feature is None:
         if allow_missing_files:

bob/bio/base/tools/preprocessor.py

@@ -46,7 +46,11 @@ def preprocess(preprocessor, groups = None, indices = None, allow_missing_files
   data_files = fs.original_data_list(groups=groups)
   original_directory, original_extension = fs.original_directory_and_extension()
   preprocessed_data_files = fs.preprocessed_data_list(groups=groups)
-  metadata = fs.original_data_list(groups=groups)
+
+  if utils.is_argument_available("metadata", preprocessor.__call__):
+    metadata = fs.original_data_list(groups=groups)
+  else:
+    metadata = None
 
   # select a subset of keys to iterate
   if indices is not None:
@@ -80,10 +84,10 @@ def preprocess(preprocessor, groups = None, indices = None, allow_missing_files
       annotations = fs.get_annotations(annotation_list[i])
 
       # call the preprocessor
-      if "metadata" in inspect.getargspec(preprocessor.__call__).args:
-        preprocessed_data = preprocessor(data, annotations, metadata=metadata[i])
-      else:
+      if metadata is None:
         preprocessed_data = preprocessor(data, annotations)
+      else:
+        preprocessed_data = preprocessor(data, annotations, metadata=metadata[i])
 
       if preprocessed_data is None:
         if allow_missing_files:

bob/bio/base/tools/scoring.py

@@ -27,6 +27,9 @@ def _scores(algorithm, reader, model, probe_objects, allow_missing_files):
     # if we have no model, all scores are undefined
     return scores
 
+  # Checking if we need to ship the metadata in the scoring method
+  has_metadata = utils.is_argument_available("metadata", algorithm.score)
+
   # Loops over the probe sets
   for i, probe_element, probe_metadata in zip(range(len(probes)), probes, probe_objects):
     if fs.uses_probe_file_sets():
@@ -47,8 +50,9 @@ def _scores(algorithm, reader, model, probe_objects, allow_missing_files):
         continue
       # read probe
       probe = reader.read_feature(probe_element)
+
       # compute score
-      if "metadata" in inspect.getargspec(algorithm.score).args:
+      if has_metadata:
         scores[0, i] = algorithm.score(model, probe, metadata=probe_metadata)
       else:
         scores[0, i] = algorithm.score(model, probe)

bob/bio/base/utils/__init__.py

@@ -7,7 +7,8 @@ from .resources import *
 from .io import *
 from .singleton import *
 from . import processors
-
+import six
+import inspect
 import numpy
 
 def score_fusion_strategy(strategy_name = 'average'):
@@ -53,6 +54,27 @@ def selected_elements(list_of_elements, desired_number_of_elements = None):
   # sub-select
   return [list_of_elements[i] for i in selected_indices(total_number_of_elements, desired_number_of_elements)]
 
+
 def pretty_print(obj, kwargs):
   """Returns a pretty-print of the parameters to the constructor of a class, which should be able to copy-paste on the command line to create the object (with few exceptions)."""
   return "%s(%s)" % (str(obj.__class__), ", ".join(["%s='%s'" % (key,value) if isinstance(value, str) else "%s=%s" % (key, value) for key,value in kwargs.items() if value is not None]))
+
+
+def is_argument_available(argument, method):
+  """
+  Check if an argument (or keyword argument) is available in a method
+
+  Attributes
+  ----------
+    argument: str
+      The name of the argument (or keyword argument).
+
+    method:
+      Pointer to the method
+
+  """
+
+  if six.PY2:
+    return argument in inspect.getargspec(method).args
+  else:
+    return argument in inspect.signature(method).parameters.keys()