Update speech_commands example: Code cleanup by resolving warnings and errors.

PiperOrigin-RevId: 296559221
Change-Id: I07442f9296dd02f2a6ef46969a3efc475c2235e5

tensorflow/examples/speech_commands/BUILD

@@ -48,7 +48,6 @@ py_binary(
         ":recognize_commands_py",
         "//tensorflow:tensorflow_py",
         "//third_party/py/numpy",
-        "@six_archive//:six",
     ],
 )
 
@@ -172,8 +171,6 @@ py_library(
         ":input_data",
         ":models",
         "//tensorflow:tensorflow_py",
-        "//third_party/py/numpy",
-        "@six_archive//:six",
     ],
 )
 
@@ -216,8 +213,6 @@ py_library(
         ":input_data",
         ":models",
         "//tensorflow:tensorflow_py",
-        "//third_party/py/numpy",
-        "@six_archive//:six",
     ],
 )
 
@@ -261,7 +256,6 @@ py_library(
         ":models",
         "//tensorflow:tensorflow_py",
         "//third_party/py/numpy",
-        "@six_archive//:six",
     ],
 )
 

tensorflow/examples/speech_commands/freeze.py

@@ -44,10 +44,10 @@ import sys
 
 import tensorflow as tf
 
-from tensorflow.python.ops import gen_audio_ops as audio_ops
 import input_data
 import models
 from tensorflow.python.framework import graph_util
+from tensorflow.python.ops import gen_audio_ops as audio_ops
 
 # If it's available, load the specialized feature generator. If this doesn't
 # work, try building with bazel instead of running the Python script directly.

tensorflow/examples/speech_commands/input_data.py

@@ -233,15 +233,15 @@ class AudioProcessor(object):
         filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress)
       except:
         tf.compat.v1.logging.error(
-            'Failed to download URL: %s to folder: %s', data_url, filepath)
+            'Failed to download URL: {0} to folder: {1}. Please make sure you '
-        tf.compat.v1.logging.error(
+            'have enough free space and an internet connection'.format(
-            'Please make sure you have enough free space and'
+                data_url, filepath))
-            ' an internet connection')
         raise
       print()
       statinfo = os.stat(filepath)
-      tf.compat.v1.logging.info('Successfully downloaded %s (%d bytes)',
+      tf.compat.v1.logging.info(
-                                filename, statinfo.st_size)
+          'Successfully downloaded {0} ({1} bytes)'.format(
+              filename, statinfo.st_size))
       tarfile.open(filepath, 'r:gz').extractall(dest_directory)
 
   def prepare_data_index(self, silence_percentage, unknown_percentage,

tensorflow/examples/speech_commands/input_data_test.py

@@ -33,7 +33,7 @@ from tensorflow.python.platform import test
 class InputDataTest(test.TestCase):
 
   def _getWavData(self):
-    with self.cached_session() as sess:
+    with self.cached_session():
       sample_data = tf.zeros([32000, 2])
       wav_encoder = tf.audio.encode_wav(sample_data, 16000)
       wav_data = self.evaluate(wav_encoder)
@@ -105,10 +105,10 @@ class InputDataTest(test.TestCase):
                                                 ["a", "b"], 10, 10,
                                                 self._model_settings(), tmp_dir)
     self.assertLess(0, audio_processor.set_size("training"))
-    self.assertTrue("training" in audio_processor.data_index)
+    self.assertIn("training", audio_processor.data_index)
-    self.assertTrue("validation" in audio_processor.data_index)
+    self.assertIn("validation", audio_processor.data_index)
-    self.assertTrue("testing" in audio_processor.data_index)
+    self.assertIn("testing", audio_processor.data_index)
-    self.assertEquals(input_data.UNKNOWN_WORD_INDEX,
+    self.assertEqual(input_data.UNKNOWN_WORD_INDEX,
                      audio_processor.word_to_index["c"])
 
   def testPrepareDataIndexEmpty(self):
@@ -117,7 +117,7 @@ class InputDataTest(test.TestCase):
     with self.assertRaises(Exception) as e:
       _ = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], 10, 10,
                                     self._model_settings(), tmp_dir)
-    self.assertTrue("No .wavs found" in str(e.exception))
+    self.assertIn("No .wavs found", str(e.exception))
 
   def testPrepareDataIndexMissing(self):
     tmp_dir = self.get_temp_dir()
@@ -125,7 +125,7 @@ class InputDataTest(test.TestCase):
     with self.assertRaises(Exception) as e:
       _ = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b", "d"], 10,
                                     10, self._model_settings(), tmp_dir)
-    self.assertTrue("Expected to find" in str(e.exception))
+    self.assertIn("Expected to find", str(e.exception))
 
   @test_util.run_deprecated_v1
   def testPrepareBackgroundData(self):

tensorflow/examples/speech_commands/label_wav.py

@@ -77,13 +77,12 @@ def run_graph(wav_data, labels, input_layer_name, output_layer_name,
 def label_wav(wav, labels, graph, input_name, output_name, how_many_labels):
   """Loads the model and labels, and runs the inference to print predictions."""
   if not wav or not tf.io.gfile.exists(wav):
-    tf.compat.v1.logging.fatal('Audio file does not exist %s', wav)
+    raise ValueError('Audio file does not exist at {0}'.format(wav))
-
   if not labels or not tf.io.gfile.exists(labels):
-    tf.compat.v1.logging.fatal('Labels file does not exist %s', labels)
+    raise ValueError('Labels file does not exist at {0}'.format(labels))
 
   if not graph or not tf.io.gfile.exists(graph):
-    tf.compat.v1.logging.fatal('Graph file does not exist %s', graph)
+    raise ValueError('Graph file does not exist at {0}'.format(graph))
 
   labels_list = load_labels(labels)
 

tensorflow/examples/speech_commands/label_wav_dir.py

@@ -64,8 +64,7 @@ def run_graph(wav_dir, labels, input_layer_name, output_layer_name,
     #   predictions per class
     for wav_path in glob.glob(wav_dir + '/*.wav'):
       if not wav_path or not tf.io.gfile.exists(wav_path):
-        tf.compat.v1.logging.fatal('Audio file does not exist %s', wav_path)
+        raise ValueError('Audio file does not exist at {0}'.format(wav_path))
-
       with open(wav_path, 'rb') as wav_file:
         wav_data = wav_file.read()
 
@@ -86,10 +85,10 @@ def run_graph(wav_dir, labels, input_layer_name, output_layer_name,
 def label_wav(wav_dir, labels, graph, input_name, output_name, how_many_labels):
   """Loads the model and labels, and runs the inference to print predictions."""
   if not labels or not tf.io.gfile.exists(labels):
-    tf.compat.v1.logging.fatal('Labels file does not exist %s', labels)
+    raise ValueError('Labels file does not exist at {0}'.format(labels))
 
   if not graph or not tf.io.gfile.exists(graph):
-    tf.compat.v1.logging.fatal('Graph file does not exist %s', graph)
+    raise ValueError('Graph file does not exist at {0}'.format(graph))
 
   labels_list = load_labels(labels)
 

tensorflow/examples/speech_commands/label_wav_test.py

@@ -29,7 +29,7 @@ from tensorflow.python.platform import test
 class LabelWavTest(test.TestCase):
 
   def _getWavData(self):
-    with self.cached_session() as sess:
+    with self.cached_session():
       sample_data = tf.zeros([1000, 2])
       wav_encoder = tf.audio.encode_wav(sample_data, 16000)
       wav_data = self.evaluate(wav_encoder)

tensorflow/examples/speech_commands/train.py

@@ -251,12 +251,16 @@ def main(_):
             dropout_rate: 0.5
         })
     train_writer.add_summary(train_summary, training_step)
-    tf.compat.v1.logging.info(
+    tf.compat.v1.logging.debug(
         'Step #%d: rate %f, accuracy %.1f%%, cross entropy %f' %
         (training_step, learning_rate_value, train_accuracy * 100,
          cross_entropy_value))
     is_last_step = (training_step == training_steps_max)
     if (training_step % FLAGS.eval_step_interval) == 0 or is_last_step:
+      tf.compat.v1.logging.info(
+          'Step #%d: rate %f, accuracy %.1f%%, cross entropy %f' %
+          (training_step, learning_rate_value, train_accuracy * 100,
+           cross_entropy_value))
       set_size = audio_processor.set_size('validation')
       total_accuracy = 0
       total_conf_matrix = None

tensorflow/examples/speech_commands/wav_to_features_test.py

@@ -30,7 +30,7 @@ from tensorflow.python.platform import test
 class WavToFeaturesTest(test.TestCase):
 
   def _getWavData(self):
-    with self.cached_session() as sess:
+    with self.cached_session():
       sample_data = tf.zeros([32000, 2])
       wav_encoder = tf.audio.encode_wav(sample_data, 16000)
       wav_data = self.evaluate(wav_encoder)
@@ -63,7 +63,7 @@ class WavToFeaturesTest(test.TestCase):
                                     input_file_path, output_file_path)
     with open(output_file_path, "rb") as f:
       content = f.read()
-      self.assertTrue(b"const unsigned char g_input_data" in content)
+      self.assertIn(b"const unsigned char g_input_data", content)
 
   @test_util.run_deprecated_v1
   def testWavToFeaturesMicro(self):