implement some tests too

bob/bio/base/extractor/stacks.py

@@ -33,7 +33,7 @@ class MultipleExtractor(Extractor):
                 training_data = [e(d) for d in training_data]
         # if any of the extractors require splitting the data, the
         # split_training_data_by_client is True.
-        if e.split_training_data_by_client:
+        elif e.split_training_data_by_client:
             e.train(training_data, extractor_file)
             if not apply:
                 return
@@ -62,7 +62,8 @@ class MultipleExtractor(Extractor):
             groups = self.get_extractor_groups()
             for e, group in zip(self.processors, groups):
                 f.cd(group)
-                e.load(f)
+                if e.requires_training:
+                    e.load(f)
                 f.cd('..')
 
 
@@ -110,10 +111,15 @@ class SequentialExtractor(SequentialProcessor, MultipleExtractor):
     def train(self, training_data, extractor_file):
         with HDF5File(extractor_file, 'w') as f:
             groups = self.get_extractor_groups()
-            for e, group in zip(self.processors, groups):
+            for i, (e, group) in enumerate(zip(self.processors, groups)):
+                if i == len(self.processors) - 1:
+                    apply = False
+                else:
+                    apply = True
                 f.create_group(group)
                 f.cd(group)
-                training_data = self.train_one(e, training_data, f, apply=True)
+                training_data = self.train_one(e, training_data, f,
+                                               apply=apply)
                 f.cd('..')
 
     def read_feature(self, feature_file):

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

 import numpy
-import bob.io.base
+import bob.bio.base
 
 from bob.bio.base.extractor import Extractor
 
@@ -12,10 +12,10 @@ class DummyExtractor (Extractor):
 
   def train(self, train_data, extractor_file):
     assert isinstance(train_data, list)
-    bob.io.base.save(_data, extractor_file)
+    bob.bio.base.save(_data, extractor_file)
 
   def load(self, extractor_file):
-    data = bob.io.base.load(extractor_file)
+    data = bob.bio.base.load(extractor_file)
     assert (_data == data).all()
     self.model = True
 

bob/bio/base/test/test_stacks.py

 from functools import partial
 import numpy as np
+import tempfile
 from bob.bio.base.utils.processors import (
     SequentialProcessor, ParallelProcessor)
 from bob.bio.base.preprocessor import (
     SequentialPreprocessor, ParallelPreprocessor, CallablePreprocessor)
 from bob.bio.base.extractor import (
     SequentialExtractor, ParallelExtractor, CallableExtractor)
+from bob.bio.base.test.dummy.extractor import extractor as dummy_extractor
 
 DATA = [0, 1, 2, 3, 4]
 PROCESSORS = [partial(np.power, 2), np.mean]
@@ -43,3 +45,13 @@ def test_extractors():
   proc = ParallelExtractor(processors)
   data = proc(DATA)
   assert all(np.allclose(x1, x2) for x1, x2 in zip(data, PAR_DATA))
+
+
+def test_trainable_extractors():
+  processors = [CallableExtractor(p) for p in PROCESSORS] + [dummy_extractor]
+  proc = SequentialExtractor(processors)
+  with tempfile.NamedTemporaryFile(suffix='.hdf5') as f:
+    proc.train(DATA, f.name)
+    proc.load(f.name)
+  data = proc(DATA)
+  assert np.allclose(data, SEQ_DATA)