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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Converts a trained checkpoint into a frozen model for mobile inference.
Once you've trained a model using the `train.py` script, you can use this tool
to convert it into a binary GraphDef file that can be loaded into the Android,
iOS, or Raspberry Pi example code. Here's an example of how to run it:
bazel run tensorflow/examples/speech_commands/freeze -- \
--sample_rate=16000 --dct_coefficient_count=40 --window_size_ms=20 \
--window_stride_ms=10 --clip_duration_ms=1000 \
--model_architecture=conv \
--start_checkpoint=/tmp/speech_commands_train/conv.ckpt-1300 \
--output_file=/tmp/my_frozen_graph.pb
One thing to watch out for is that you need to pass in the same arguments for
`sample_rate` and other command line variables here as you did for the training
script.
The resulting graph has an input for WAV-encoded data named 'wav_data', one for
raw PCM data (as floats in the range -1.0 to 1.0) called 'decoded_sample_data',
and the output is called 'labels_softmax'.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import sys
import tensorflow as tf
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
import input_data
import models
from tensorflow.python.framework import graph_util
FLAGS = None
def create_inference_graph(wanted_words, sample_rate, clip_duration_ms,
clip_stride_ms, window_size_ms, window_stride_ms,
feature_bin_count, model_architecture, preprocess):
"""Creates an audio model with the nodes needed for inference.
Uses the supplied arguments to create a model, and inserts the input and
output nodes that are needed to use the graph for inference.
Args:
wanted_words: Comma-separated list of the words we're trying to recognize.
sample_rate: How many samples per second are in the input audio files.
clip_duration_ms: How many samples to analyze for the audio pattern.
clip_stride_ms: How often to run recognition. Useful for models with cache.
window_size_ms: Time slice duration to estimate frequencies from.
window_stride_ms: How far apart time slices should be.
feature_bin_count: Number of frequency bands to analyze.
model_architecture: Name of the kind of model to generate.
preprocess: How the spectrogram is processed to produce features, for
example 'mfcc' or 'average'.
Raises:
Exception: If the preprocessing mode isn't recognized.
"""
words_list = input_data.prepare_words_list(wanted_words.split(','))
model_settings = models.prepare_model_settings(
len(words_list), sample_rate, clip_duration_ms, window_size_ms,
window_stride_ms, feature_bin_count, preprocess)
runtime_settings = {'clip_stride_ms': clip_stride_ms}
wav_data_placeholder = tf.placeholder(tf.string, [], name='wav_data')
decoded_sample_data = contrib_audio.decode_wav(
wav_data_placeholder,
desired_channels=1,
desired_samples=model_settings['desired_samples'],
name='decoded_sample_data')
spectrogram = contrib_audio.audio_spectrogram(
decoded_sample_data.audio,
window_size=model_settings['window_size_samples'],
stride=model_settings['window_stride_samples'],
magnitude_squared=True)
if preprocess == 'average':
fingerprint_input = tf.nn.pool(
tf.expand_dims(spectrogram, -1),
window_shape=[1, model_settings['average_window_width']],
strides=[1, model_settings['average_window_width']],
pooling_type='AVG',
padding='SAME')
elif preprocess == 'mfcc':
fingerprint_input = contrib_audio.mfcc(
spectrogram,
sample_rate,
dct_coefficient_count=model_settings['fingerprint_width'])
else:
raise Exception('Unknown preprocess mode "%s" (should be "mfcc" or'
' "average")' % (preprocess))
fingerprint_size = model_settings['fingerprint_size']
reshaped_input = tf.reshape(fingerprint_input, [-1, fingerprint_size])
logits = models.create_model(
reshaped_input, model_settings, model_architecture, is_training=False,
runtime_settings=runtime_settings)
# Create an output to use for inference.
tf.nn.softmax(logits, name='labels_softmax')
def main(_):
# Create the model and load its weights.
sess = tf.InteractiveSession()
create_inference_graph(
FLAGS.wanted_words, FLAGS.sample_rate, FLAGS.clip_duration_ms,
FLAGS.clip_stride_ms, FLAGS.window_size_ms, FLAGS.window_stride_ms,
FLAGS.feature_bin_count, FLAGS.model_architecture, FLAGS.preprocess)
if FLAGS.quantize:
tf.contrib.quantize.create_eval_graph()
models.load_variables_from_checkpoint(sess, FLAGS.start_checkpoint)
# Turn all the variables into inline constants inside the graph and save it.
frozen_graph_def = graph_util.convert_variables_to_constants(
sess, sess.graph_def, ['labels_softmax'])
tf.train.write_graph(
frozen_graph_def,
os.path.dirname(FLAGS.output_file),
os.path.basename(FLAGS.output_file),
as_text=False)
tf.logging.info('Saved frozen graph to %s', FLAGS.output_file)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--sample_rate',
type=int,
default=16000,
help='Expected sample rate of the wavs',)
parser.add_argument(
'--clip_duration_ms',
type=int,
default=1000,
help='Expected duration in milliseconds of the wavs',)
parser.add_argument(
'--clip_stride_ms',
type=int,
default=30,
help='How often to run recognition. Useful for models with cache.',)
parser.add_argument(
'--window_size_ms',
type=float,
default=30.0,
help='How long each spectrogram timeslice is',)
parser.add_argument(
'--window_stride_ms',
type=float,
default=10.0,
help='How long the stride is between spectrogram timeslices',)
parser.add_argument(
'--feature_bin_count',
type=int,
default=40,
help='How many bins to use for the MFCC fingerprint',
)
parser.add_argument(
'--start_checkpoint',
type=str,
default='',
help='If specified, restore this pretrained model before any training.')
parser.add_argument(
'--model_architecture',
type=str,
default='conv',
help='What model architecture to use')
parser.add_argument(
'--wanted_words',
type=str,
default='yes,no,up,down,left,right,on,off,stop,go',
help='Words to use (others will be added to an unknown label)',)
parser.add_argument(
'--output_file', type=str, help='Where to save the frozen graph.')
parser.add_argument(
'--quantize',
type=bool,
default=False,
help='Whether to train the model for eight-bit deployment')
parser.add_argument(
'--preprocess',
type=str,
default='mfcc',
help='Spectrogram processing mode. Can be "mfcc" or "average"')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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