old_contents: # Copyright 2019 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.
  # ==============================================================================
  
  """Inference demo for YAMNet."""
  from __future__ import division, print_function
  
  import sys
  
  import numpy as np
  import resampy
  import soundfile as sf
  
  import params
  import yamnet as yamnet_model
  
  
  def main(argv):
    assert argv
  
    yamnet = yamnet_model.yamnet_frames_model(params)
    yamnet.load_weights('yamnet.h5')
    yamnet_classes = yamnet_model.class_names('yamnet_class_map.csv')
  
    for file_name in argv:
      # Decode the WAV file.
      wav_data, sr = sf.read(file_name, dtype=np.int16)
      assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
      waveform = wav_data / 32768.0  # Convert to [-1.0, +1.0]
  
      # Convert to mono and the sample rate expected by YAMNet.
      if len(waveform.shape) > 1:
        waveform = np.mean(waveform, axis=1)
      if sr != params.SAMPLE_RATE:
        waveform = resampy.resample(waveform, sr, params.SAMPLE_RATE)
  
      # Predict YAMNet classes.
      # Second output is log-mel-spectrogram array (used for visualizations).
      # (steps=1 is a work around for Keras batching limitations.)
      scores, _ = yamnet.predict(np.reshape(waveform, [1, -1]), steps=1)
      # Scores is a matrix of (time_frames, num_classes) classifier scores.
      # Average them along time to get an overall classifier output for the clip.
      prediction = np.mean(scores, axis=0)
      # Report the highest-scoring classes and their scores.
      top5_i = np.argsort(prediction)[::-1][:5]
      print(file_name, ':\n' + 
            '\n'.join('  {:12s}: {:.3f}'.format(yamnet_classes[i], prediction[i])
                      for i in top5_i))
  
  
  if __name__ == '__main__':
    main(sys.argv[1:])
  
new_contents: # Copyright 2019 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.
  # ==============================================================================
  
  """Inference demo for YAMNet."""
  from __future__ import division, print_function
  
  import sys
  
  import numpy as np
  import resampy
  import soundfile as sf
  import tensorflow as tf
  
  import params
  import yamnet as yamnet_model
  
  
  def main(argv):
    assert argv
  
    graph = tf.Graph()
    with graph.as_default():
      yamnet = yamnet_model.yamnet_frames_model(params)
      yamnet.load_weights('yamnet.h5')
    yamnet_classes = yamnet_model.class_names('yamnet_class_map.csv')
  
    for file_name in argv:
      # Decode the WAV file.
      wav_data, sr = sf.read(file_name, dtype=np.int16)
      assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
      waveform = wav_data / 32768.0  # Convert to [-1.0, +1.0]
  
      # Convert to mono and the sample rate expected by YAMNet.
      if len(waveform.shape) > 1:
        waveform = np.mean(waveform, axis=1)
      if sr != params.SAMPLE_RATE:
        waveform = resampy.resample(waveform, sr, params.SAMPLE_RATE)
  
      # Predict YAMNet classes.
      # Second output is log-mel-spectrogram array (used for visualizations).
      # (steps=1 is a work around for Keras batching limitations.)
      with graph.as_default():
        scores, _ = yamnet.predict(np.reshape(waveform, [1, -1]), steps=1)
      # Scores is a matrix of (time_frames, num_classes) classifier scores.
      # Average them along time to get an overall classifier output for the clip.
      prediction = np.mean(scores, axis=0)
      # Report the highest-scoring classes and their scores.
      top5_i = np.argsort(prediction)[::-1][:5]
      print(file_name, ':\n' + 
            '\n'.join('  {:12s}: {:.3f}'.format(yamnet_classes[i], prediction[i])
                      for i in top5_i))
  
  
  if __name__ == '__main__':
    main(sys.argv[1:])
  
subject: Add the tf2-compatible graph wrapping idiom to inference.py. (#8203)