diff --git a/tensorflow/examples/speech_commands/accuracy_utils.py b/tensorflow/examples/speech_commands/accuracy_utils.py index dd5a12c2087..a1112e4abf5 100755 --- a/tensorflow/examples/speech_commands/accuracy_utils.py +++ b/tensorflow/examples/speech_commands/accuracy_utils.py @@ -137,14 +137,16 @@ class StreamingAccuracyStats(object): def print_accuracy_stats(self): """Write a human-readable description of the statistics to stdout.""" if self._how_many_gt == 0: - tf.logging.info('No ground truth yet, {}false positives'.format( + tf.compat.v1.logging.info('No ground truth yet, {}false positives'.format( self._how_many_fp)) else: any_match_percentage = self._how_many_gt_matched / self._how_many_gt * 100 correct_match_percentage = self._how_many_c / self._how_many_gt * 100 wrong_match_percentage = self._how_many_w / self._how_many_gt * 100 false_positive_percentage = self._how_many_fp / self._how_many_gt * 100 - tf.logging.info('{:.1f}% matched, {:.1f}% correct, {:.1f}% wrong, ' - '{:.1f}% false positive'.format( - any_match_percentage, correct_match_percentage, - wrong_match_percentage, false_positive_percentage)) + tf.compat.v1.logging.info( + '{:.1f}% matched, {:.1f}% correct, {:.1f}% wrong, ' + '{:.1f}% false positive'.format(any_match_percentage, + correct_match_percentage, + wrong_match_percentage, + false_positive_percentage)) diff --git a/tensorflow/examples/speech_commands/test_streaming_accuracy.py b/tensorflow/examples/speech_commands/test_streaming_accuracy.py index 4b7fa717348..1dfb7ac1bee 100755 --- a/tensorflow/examples/speech_commands/test_streaming_accuracy.py +++ b/tensorflow/examples/speech_commands/test_streaming_accuracy.py @@ -69,10 +69,9 @@ import sys import numpy import tensorflow as tf -from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio -from tensorflow.examples.speech_commands.accuracy_utils import StreamingAccuracyStats -from tensorflow.examples.speech_commands.recognize_commands import RecognizeCommands -from tensorflow.examples.speech_commands.recognize_commands import RecognizeResult +from accuracy_utils import StreamingAccuracyStats +from recognize_commands import RecognizeCommands +from recognize_commands import RecognizeResult from tensorflow.python.ops import io_ops FLAGS = None @@ -82,8 +81,8 @@ def load_graph(mode_file): """Read a tensorflow model, and creates a default graph object.""" graph = tf.Graph() with graph.as_default(): - od_graph_def = tf.GraphDef() - with tf.gfile.GFile(mode_file, 'rb') as fid: + od_graph_def = tf.compat.v1.GraphDef() + with tf.io.gfile.GFile(mode_file, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') @@ -101,10 +100,10 @@ def read_label_file(file_name): def read_wav_file(filename): """Load a wav file and return sample_rate and numpy data of float64 type.""" - with tf.Session(graph=tf.Graph()) as sess: - wav_filename_placeholder = tf.placeholder(tf.string, []) + with tf.compat.v1.Session(graph=tf.Graph()) as sess: + wav_filename_placeholder = tf.compat.v1.placeholder(tf.string, []) wav_loader = io_ops.read_file(wav_filename_placeholder) - wav_decoder = contrib_audio.decode_wav(wav_loader, desired_channels=1) + wav_decoder = tf.audio.decode_wav(wav_loader, desired_channels=1) res = sess.run(wav_decoder, feed_dict={wav_filename_placeholder: filename}) return res.sample_rate, res.audio.flatten() @@ -133,15 +132,12 @@ def main(_): # Load model and create a tf session to process audio pieces recognize_graph = load_graph(FLAGS.model) with recognize_graph.as_default(): - with tf.Session() as sess: + with tf.compat.v1.Session() as sess: # Get input and output tensor - data_tensor = tf.get_default_graph().get_tensor_by_name( - FLAGS.input_names[0]) - sample_rate_tensor = tf.get_default_graph().get_tensor_by_name( - FLAGS.input_names[1]) - output_softmax_tensor = tf.get_default_graph().get_tensor_by_name( - FLAGS.output_name) + data_tensor = sess.graph.get_tensor_by_name(FLAGS.input_names[0]) + sample_rate_tensor = sess.graph.get_tensor_by_name(FLAGS.input_names[1]) + output_softmax_tensor = sess.graph.get_tensor_by_name(FLAGS.output_name) # Inference along audio stream. for audio_data_offset in range(0, audio_data_end, clip_stride_samples): @@ -161,7 +157,7 @@ def main(_): recognize_commands.process_latest_result(outputs, current_time_ms, recognize_element) except ValueError as e: - tf.logging.error('Recognition processing failed: {}' % e) + tf.compat.v1.logging.error('Recognition processing failed: {}' % e) return if (recognize_element.is_new_command and recognize_element.founded_command != '_silence_'): @@ -173,10 +169,10 @@ def main(_): try: recognition_state = stats.delta() except ValueError as e: - tf.logging.error( + tf.compat.v1.logging.error( 'Statistics delta computing failed: {}'.format(e)) else: - tf.logging.info('{}ms {}:{}{}'.format( + tf.compat.v1.logging.info('{}ms {}:{}{}'.format( current_time_ms, recognize_element.founded_command, recognize_element.score, recognition_state)) stats.print_accuracy_stats() @@ -249,5 +245,5 @@ if __name__ == '__main__': help='Whether to print streaming accuracy on stdout.') FLAGS, unparsed = parser.parse_known_args() - tf.logging.set_verbosity(tf.logging.INFO) - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) + tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO) + tf.compat.v1.app.run(main=main, argv=[sys.argv[0]] + unparsed)