Cleanup.

python/tests/transformers/named_image_test.py

@@ -32,27 +32,22 @@
 
 class NamedImageTransformerImagenetTest(SparkDLTestCase):
 
-    def test_inceptionV3_prediction(self):
-        """
-        Test inceptionV3 using keras, tensorflow and sparkDL
-
-        We run the sparkDL test with and without resizing beforehand
-        """
-
     @classmethod
     def setUpClass(cls):
         super(NamedImageTransformerImagenetTest, cls).setUpClass()
+
+        # Compute values used by multiple tests.
         imgFiles, images = getSampleImageList()
         imageArray = np.empty((len(images), 299, 299, 3), 'uint8')
         for i, img in enumerate(images):
             assert img is not None and img.mode == "RGB"
             imageArray[i] = np.array(img.resize((299, 299)))
 
-        # Basic keras flow
-        # We predict the class probabilities for the images in our test library using keras API.
+        # Predict the class probabilities for the images in our test library using keras API.
         prepedImaged = inception_v3.preprocess_input(imageArray.astype('float32'))
         model = inception_v3.InceptionV3()
         kerasPredict = model.predict(prepedImaged)
+        # These values are used by multiple tests so cache them on class setup.
         cls.imageArray = imageArray
         cls.kerasPredict = kerasPredict
 
@@ -75,6 +70,10 @@ def test_buildtfgraphforname(self):
         np.testing.assert_array_almost_equal(kerasPredict, tfPredict)
 
     def test_DeepImagePredictorNoReshape(self):
+        """
+        Run sparkDL inceptionV3 transformer on resized images and compare result to cached keras
+        result.
+        """
         imageArray = self.imageArray
         kerasPredict = self.kerasPredict
         def rowWithImage(img):
@@ -84,8 +83,6 @@ def rowWithImage(img):
             return [[getattr(row, field.name) for field in imageIO.imageSchema]]
 
         # test: predictor vs keras on resized images
-        # Run sparkDL inceptionV3 transformer on resized images and compare result to above keras
-        # result.
         rdd = self.sc.parallelize([rowWithImage(img) for img in imageArray])
         dfType = StructType([StructField("image", imageIO.imageSchema)])
         imageDf = rdd.toDF(dfType)