freeze.py

# 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

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.
# bazel run tensorflow/examples/speech_commands:freeze_graph
try:
  from tensorflow.lite.experimental.microfrontend.python.ops import audio_microfrontend_op as frontend_op  # pylint:disable=g-import-not-at-top
except ImportError:
  frontend_op = None

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', 'average', or 'micro'.

  Returns:
    Input and output tensor objects.

  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.compat.v1.placeholder(tf.string, [],
                                                  name='wav_data')
  decoded_sample_data = tf.audio.decode_wav(
      wav_data_placeholder,
      desired_channels=1,
      desired_samples=model_settings['desired_samples'],
      name='decoded_sample_data')
  spectrogram = audio_ops.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(
        input=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 = audio_ops.mfcc(
        spectrogram,
        sample_rate,
        dct_coefficient_count=model_settings['fingerprint_width'])
  elif preprocess == 'micro':
    if not frontend_op:
      raise Exception(
          'Micro frontend op is currently not available when running TensorFlow'
          ' directly from Python, you need to build and run through Bazel, for'
          ' example'
          ' `bazel run tensorflow/examples/speech_commands:freeze_graph`')
    sample_rate = model_settings['sample_rate']
    window_size_ms = (model_settings['window_size_samples'] *
                      1000) / sample_rate
    window_step_ms = (model_settings['window_stride_samples'] *
                      1000) / sample_rate
    int16_input = tf.cast(
        tf.multiply(decoded_sample_data.audio, 32767), tf.int16)
    micro_frontend = frontend_op.audio_microfrontend(
        int16_input,
        sample_rate=sample_rate,
        window_size=window_size_ms,
        window_step=window_step_ms,
        num_channels=model_settings['fingerprint_width'],
        out_scale=1,
        out_type=tf.float32)
    fingerprint_input = tf.multiply(micro_frontend, (10.0 / 256.0))
  else:
    raise Exception('Unknown preprocess mode "%s" (should be "mfcc",'
                    ' "average", or "micro")' % (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.
  softmax = tf.nn.softmax(logits, name='labels_softmax')

  return reshaped_input, softmax


def save_graph_def(file_name, frozen_graph_def):
  """Writes a graph def file out to disk.

  Args:
    file_name: Where to save the file.
    frozen_graph_def: GraphDef proto object to save.
  """
  tf.io.write_graph(
      frozen_graph_def,
      os.path.dirname(file_name),
      os.path.basename(file_name),
      as_text=False)
  tf.compat.v1.logging.info('Saved frozen graph to %s', file_name)


def save_saved_model(file_name, sess, input_tensor, output_tensor):
  """Writes a SavedModel out to disk.

  Args:
    file_name: Where to save the file.
    sess: TensorFlow session containing the graph.
    input_tensor: Tensor object defining the input's properties.
    output_tensor: Tensor object defining the output's properties.
  """
  # Store the frozen graph as a SavedModel for v2 compatibility.
  builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(file_name)
  tensor_info_inputs = {
      'input': tf.compat.v1.saved_model.utils.build_tensor_info(input_tensor)
  }
  tensor_info_outputs = {
      'output': tf.compat.v1.saved_model.utils.build_tensor_info(output_tensor)
  }
  signature = (
      tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
          inputs=tensor_info_inputs,
          outputs=tensor_info_outputs,
          method_name=tf.compat.v1.saved_model.signature_constants
          .PREDICT_METHOD_NAME))
  builder.add_meta_graph_and_variables(
      sess,
      [tf.compat.v1.saved_model.tag_constants.SERVING],
      signature_def_map={
          tf.compat.v1.saved_model.signature_constants
          .DEFAULT_SERVING_SIGNATURE_DEF_KEY:
              signature,
      },
  )
  builder.save()


def main(_):
  if FLAGS.quantize:
    try:
      _ = tf.contrib
    except AttributeError as e:
      msg = e.args[0]
      msg += ('\n\n The --quantize option still requires contrib, which is not '
              'part of TensorFlow 2.0. Please install a previous version:'
              '\n    `pip install tensorflow<=1.15`')
      e.args = (msg,)
      raise e

  # Create the model and load its weights.
  sess = tf.compat.v1.InteractiveSession()
  input_tensor, output_tensor = 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'])

  if FLAGS.save_format == 'graph_def':
    save_graph_def(FLAGS.output_file, frozen_graph_def)
  elif FLAGS.save_format == 'saved_model':
    save_saved_model(FLAGS.output_file, sess, input_tensor, output_tensor)
  else:
    raise Exception('Unknown save format "%s" (should be "graph_def" or'
                    ' "saved_model")' % (FLAGS.save_format))


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"')
  parser.add_argument(
      '--save_format',
      type=str,
      default='graph_def',
      help='How to save the result. Can be "graph_def" or "saved_model"')
  FLAGS, unparsed = parser.parse_known_args()
  tf.compat.v1.app.run(main=main, argv=[sys.argv[0]] + unparsed)