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Merge pull request #36743 from foldl:make-speech-command-v2-compat

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PiperOrigin-RevId: 296056103
Change-Id: Ibee2e8731ff7e8ebf19e5fa4e53f62d95634e4b8
This commit is contained in:
TensorFlower Gardener 2020-02-19 14:23:08 -08:00
commit 23b4405ace
2 changed files with 24 additions and 26 deletions
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12
tensorflow/examples/speech_commands/accuracy_utils.py
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@ -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))

38
tensorflow/examples/speech_commands/test_streaming_accuracy.py
View File

@ -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)