Fix and enable tests

bob/learn/tensorflow/script/db_to_tfrecords.py

@@ -36,7 +36,7 @@ The configuration files should have the following objects totally::
     samples : a list of all samples that you want to write in the tfrecords
               file. Whatever is inside this list is passed to the reader.
     reader  : a function with the signature of
-              `data, label, key = reader(sample)` which takes a sample and
+              ``data, label, key = reader(sample)`` which takes a sample and
               returns the loaded data, the label of the data, and a key which
               is unique for every sample.
 
@@ -91,7 +91,6 @@ from __future__ import print_function
 import random
 # import pkg_resources so that bob imports work properly:
 import pkg_resources
-import six
 import tensorflow as tf
 from bob.io.base import create_directories_safe
 from bob.bio.base.utils import read_config_file

bob/learn/tensorflow/script/predict_bio.py

@@ -205,7 +205,7 @@ def main(argv=None):
     try:
         pred_buffer = defaultdict(list)
         for i, pred in enumerate(predictions):
-            key = pred['keys']
+            key = pred['key']
             prob = pred.get('probabilities', pred.get('embeddings'))
             pred_buffer[key].append(prob)
             if i == 0:

bob/learn/tensorflow/script/predict_generic.py

@@ -105,7 +105,7 @@ def main(argv=None):
     try:
         pred_buffer = defaultdict(list)
         for i, pred in enumerate(predictions):
-            key = pred['keys']
+            key = pred['key']
             prob = pred.get('probabilities', pred.get('embeddings'))
             pred_buffer[key].append(prob)
             if i == 0:

bob/learn/tensorflow/test/data/dummy_verify_config.py

+import os
 from bob.bio.base.test.dummy.database import database
-from bob.bio.base.test.dummy.preprocessor import preprocessor
+from bob.bio.base.utils import read_original_data
 
-groups = 'dev'
+groups = ['dev']
 
-files = database.all_files(groups=groups)
+samples = database.all_files(groups=groups)
+
+output = os.path.join('TEST_DIR', 'dev.tfrecords')
 
 CLIENT_IDS = (str(f.client_id) for f in database.all_files(groups=groups))
 CLIENT_IDS = list(set(CLIENT_IDS))
@@ -15,8 +18,8 @@ def file_to_label(f):
 
 
 def reader(biofile):
-    data = preprocessor.read_original_data(
+    data = read_original_data(
         biofile, database.original_directory, database.original_extension)
     label = file_to_label(biofile)
-    key = biofile.path
+    key = str(biofile.path)
     return (data, label, key)

bob/learn/tensorflow/test/test_db_to_tfrecords.py

@@ -4,7 +4,6 @@ import pkg_resources
 import tempfile
 
 from bob.learn.tensorflow.script.db_to_tfrecords import main as tfrecords
-from bob.bio.base.script.verify import main as verify
 
 regenerate_reference = False
 
@@ -21,9 +20,7 @@ def test_verify_and_tfrecords():
 
   parameters = [config_path]
   try:
-    #verify(parameters)
-    #tfrecords(parameters)
-    pass
+    tfrecords(parameters)
 
     # TODO: test if tfrecords are equal
     # tfrecords_path = os.path.join(test_dir, 'sub_directory', 'dev.tfrecords')

bob/learn/tensorflow/test/test_estimator_scripts.py

@@ -7,7 +7,6 @@ logging.getLogger("tensorflow").setLevel(logging.WARNING)
 from bob.io.base.test_utils import datafile
 
 from bob.learn.tensorflow.script.db_to_tfrecords import main as tfrecords
-from bob.bio.base.script.verify import main as verify
 from bob.learn.tensorflow.script.train_generic import main as train_generic
 from bob.learn.tensorflow.script.eval_generic import main as eval_generic
 
@@ -44,6 +43,9 @@ def architecture(images):
 
 
 def model_fn(features, labels, mode, params, config):
+    key = features['key']
+    features = features['data']
+
     logits = architecture(features)
 
     predictions = {
@@ -51,7 +53,8 @@ def model_fn(features, labels, mode, params, config):
         "classes": tf.argmax(input=logits, axis=1),
         # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
         # `logging_hook`.
-        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
+        "probabilities": tf.nn.softmax(logits, name="softmax_tensor"),
+        "key": key,
     }
     if mode == tf.estimator.ModeKeys.PREDICT:
         return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
@@ -82,9 +85,8 @@ def _create_tfrecord(test_dir):
     config_path = os.path.join(test_dir, 'tfrecordconfig.py')
     with open(dummy_tfrecord_config) as f, open(config_path, 'w') as f2:
         f2.write(f.read().replace('TEST_DIR', test_dir))
-    #verify([config_path])
     tfrecords([config_path])
-    return os.path.join(test_dir, 'sub_directory', 'dev.tfrecords')
+    return os.path.join(test_dir, 'dev.tfrecords')
 
 
 def _create_checkpoint(tmpdir, model_dir, dummy_tfrecord):
@@ -112,21 +114,21 @@ def test_eval_once():
         eval_dir = os.path.join(model_dir, 'eval')
 
         print('\nCreating a dummy tfrecord')
-        #dummy_tfrecord = _create_tfrecord(tmpdir)
+        dummy_tfrecord = _create_tfrecord(tmpdir)
 
         print('Training a dummy network')
-        #_create_checkpoint(tmpdir, model_dir, dummy_tfrecord)
+        _create_checkpoint(tmpdir, model_dir, dummy_tfrecord)
 
         print('Evaluating a dummy network')
-        #_eval(tmpdir, model_dir, dummy_tfrecord)
+        _eval(tmpdir, model_dir, dummy_tfrecord)
 
-        #evaluated_path = os.path.join(eval_dir, 'evaluated')
-        #assert os.path.exists(evaluated_path), evaluated_path
-        #with open(evaluated_path) as f:
-        #    doc = f.read()
+        evaluated_path = os.path.join(eval_dir, 'evaluated')
+        assert os.path.exists(evaluated_path), evaluated_path
+        with open(evaluated_path) as f:
+           doc = f.read()
 
-       # assert '1' in doc, doc
-       # assert '100' in doc, doc
+        assert '1' in doc, doc
+        assert '100' in doc, doc
     finally:
         try:
             shutil.rmtree(tmpdir)