源碼分析——遷移學習Inception V3網絡重訓練實現圖片分類

時間 2019-12-09

標籤源碼分析遷移學習 inception v3 網絡訓練實現圖片分類欄目系統網絡简体版

原文原文鏈接

1. 前言

近些年來，隨着以卷積神經網絡（CNN）爲表明的深度學習在圖像識別領域的突破，愈來愈多的圖像識別算法不斷涌現。在去年，咱們初步成功嘗試了圖像識別在測試領域的應用：將網站樣式錯亂問題、無線領域機型適配問題轉換爲「特定場景下的正常圖片和異常圖片的二分類問題」，並藉助Goolge開源的Inception V3網絡進行遷移學習，重訓練出對應場景下的圖片分類模型，問題圖片的準確率達到95%以上。node

過去一年，咱們在圖片智能識別作的主要工做包括：git

模型的落地和參數調優
模型的服務化
模型服務的優化（包括數據庫鏈接池的引入、gunicorn容器的引入、docker化等）

本篇文章主要是對模型重訓練的源碼進行學習和分析，加深對模型訓練過程的理解，以便後續在對模型訓練過程進行調整時有的放矢。github

這邊對遷移學習作個簡單解釋：圖像識別每每包含數以百萬計的參數，從頭訓練須要大量打好標籤的圖片，還須要大量的計算力（每每數百小時的GPU時間）。對此，遷移學習是一個捷徑，它能夠在已經訓練好的類似工做模型基礎上，繼續訓練新的模型。算法

2. retrain.py源碼分析

目前咱們使用的圖像智能服務，對於遷移學習的代碼，是參考的開源代碼 github: tensorflow/hub/image_retraining/retrain.py。docker

下面是對源碼的學習和解讀：數據庫

2.1 執行主入口main：

if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--image_dir',
      type=str,
      default='',
      help='Path to folders of labeled images.'
  )
  parser.add_argument(
      '--output_graph',
      type=str,
      default='/tmp/output_graph.pb',
      help='Where to save the trained graph.'
  )
  ......省略......
  parser.add_argument(
      '--logging_verbosity',
      type=str,
      default='INFO',
      choices=['DEBUG', 'INFO', 'WARN', 'ERROR', 'FATAL'],
      help='How much logging output should be produced.')
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

能夠看到，程序main入口主要是對輸入參數的聲明和解析，實際執行時傳入的參數會存入到FLAGS變量中，而後執行tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)開始正式訓練。緩存

2.2 main(_)方法

def main(_):
  # Needed to make sure the logging output is visible.
  # See https://github.com/tensorflow/tensorflow/issues/3047
  
  ## 設置log級別
  logging_verbosity = logging_level_verbosity(FLAGS.logging_verbosity)
  tf.logging.set_verbosity(logging_verbosity)

  ## 判斷image_dir參數是否傳入，該參數表示用於訓練的圖片集路徑
  if not FLAGS.image_dir:
    tf.logging.error('Must set flag --image_dir.')
    return -1

  # Prepare necessary directories that can be used during training
  ## 重建summaries_dir，並確保intermediate_output_graphs_dir存在
  prepare_file_system()

  # Look at the folder structure, and create lists of all the images.
  ## 根據輸入的圖片集路徑、測試圖片佔比、驗證圖片佔比來劃分輸入的圖集，將圖集劃分爲訓練集、測試集、驗證集
  image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage,
                                   FLAGS.validation_percentage)
                   
  ## 根據image_dir下的子目錄個數，判斷要分類的數量。每一個子目錄爲一個類別，每一個類別會各自分爲訓練集、測試集、驗證集。若是類別數爲0或1，則返回錯誤，由於分類問題至少要有2個類。
  class_count = len(image_lists.keys())
  if class_count == 0:
    tf.logging.error('No valid folders of images found at ' + FLAGS.image_dir)
    return -1
  if class_count == 1:
    tf.logging.error('Only one valid folder of images found at ' +
                     FLAGS.image_dir +
                     ' - multiple classes are needed for classification.')
    return -1

  # See if the command-line flags mean we're applying any distortions.
  ## 根據傳入的參數判斷是否要對圖片進行一些調整
  do_distort_images = should_distort_images(
      FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale,
      FLAGS.random_brightness)

  # Set up the pre-trained graph.
  ## 載入module，默認使用inception v3，能夠用參數--tfhub_module調整爲使用其餘已訓練的模型
  module_spec = hub.load_module_spec(FLAGS.tfhub_module)
  ## 建立模型圖graph
  graph, bottleneck_tensor, resized_image_tensor, wants_quantization = (
      create_module_graph(module_spec))

  # Add the new layer that we'll be training.
  ## 調用add_final_retrain_ops方法得到訓練步驟、交叉熵、瓶頸輸入、真實的輸入、最終的tensor
  with graph.as_default():
    (train_step, cross_entropy, bottleneck_input,
     ground_truth_input, final_tensor) = add_final_retrain_ops(
         class_count, FLAGS.final_tensor_name, bottleneck_tensor,
         wants_quantization, is_training=True)

  with tf.Session(graph=graph) as sess:
    # Initialize all weights: for the module to their pretrained values,
    # and for the newly added retraining layer to random initial values.
    ## 初始化變量
    init = tf.global_variables_initializer()
    sess.run(init)

    # Set up the image decoding sub-graph.
    ## 調用圖片解碼操做的函數得到輸入的圖片tensor和解碼後的圖片tensor
    jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding(module_spec)
  
    if do_distort_images:
      # We will be applying distortions, so set up the operations we'll need.
      (distorted_jpeg_data_tensor,
       distorted_image_tensor) = add_input_distortions(
           FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale,
           FLAGS.random_brightness, module_spec)
    else:
      # We'll make sure we've calculated the 'bottleneck' image summaries and
      # cached them on disk.
      ## 建立各個image的bottlenecks，並緩存到磁盤disk
      cache_bottlenecks(sess, image_lists, FLAGS.image_dir,
                        FLAGS.bottleneck_dir, jpeg_data_tensor,
                        decoded_image_tensor, resized_image_tensor,
                        bottleneck_tensor, FLAGS.tfhub_module)

    # Create the operations we need to evaluate the accuracy of our new layer.
    ## 建立評估的operation
    evaluation_step, _ = add_evaluation_step(final_tensor, ground_truth_input)

    # Merge all the summaries and write them out to the summaries_dir
    ## 將summary merge並寫到summaries_dir目錄下
    merged = tf.summary.merge_all()
    train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
                                         sess.graph)

    validation_writer = tf.summary.FileWriter(
        FLAGS.summaries_dir + '/validation')

    # Create a train saver that is used to restore values into an eval graph
    # when exporting models.
    train_saver = tf.train.Saver()

    # Run the training for as many cycles as requested on the command line.
    ## 根據傳入的迭代次數，開始訓練
    for i in range(FLAGS.how_many_training_steps):
      # Get a batch of input bottleneck values, either calculated fresh every
      # time with distortions applied, or from the cache stored on disk.
      if do_distort_images:
        (train_bottlenecks,
         train_ground_truth) = get_random_distorted_bottlenecks(
             sess, image_lists, FLAGS.train_batch_size, 'training',
             FLAGS.image_dir, distorted_jpeg_data_tensor,
             distorted_image_tensor, resized_image_tensor, bottleneck_tensor)
      else:
        ## 獲取用於training的圖片bottlenecks值，默認train_batch_size=100，即每次迭代會批量取100張圖片進行訓練
        (train_bottlenecks,
         train_ground_truth, _) = get_random_cached_bottlenecks(
             sess, image_lists, FLAGS.train_batch_size, 'training',
             FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
             decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
             FLAGS.tfhub_module)
      # Feed the bottlenecks and ground truth into the graph, and run a training
      # step. Capture training summaries for TensorBoard with the `merged` op.
      ## 執行merge操做，並用feed_dict的內容填充placeholder
      train_summary, _ = sess.run(
          [merged, train_step],
          feed_dict={bottleneck_input: train_bottlenecks,
                     ground_truth_input: train_ground_truth})
      train_writer.add_summary(train_summary, i)

      # Every so often, print out how well the graph is training.
      ## 判斷是否最後一步訓練
      is_last_step = (i + 1 == FLAGS.how_many_training_steps)
    
      ## 默認eval_step_interval=10，即每訓練10次或訓練所有完成，打印一下當前的訓練結果
      if (i % FLAGS.eval_step_interval) == 0 or is_last_step:
      ## 打印訓練精確度和交叉熵
        train_accuracy, cross_entropy_value = sess.run(
            [evaluation_step, cross_entropy],
            feed_dict={bottleneck_input: train_bottlenecks,
                       ground_truth_input: train_ground_truth})
        tf.logging.info('%s: Step %d: Train accuracy = %.1f%%' %
                        (datetime.now(), i, train_accuracy * 100))
        tf.logging.info('%s: Step %d: Cross entropy = %f' %
                        (datetime.now(), i, cross_entropy_value))
        # TODO: Make this use an eval graph, to avoid quantization
        # moving averages being updated by the validation set, though in
        # practice this makes a negligable difference.
        ## 獲取驗證集的圖片的bottleneck值，也是每批次取100
        validation_bottlenecks, validation_ground_truth, _ = (
            get_random_cached_bottlenecks(
                sess, image_lists, FLAGS.validation_batch_size, 'validation',
                FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
                decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
                FLAGS.tfhub_module))
        # Run a validation step and capture training summaries for TensorBoard
        # with the `merged` op.
        validation_summary, validation_accuracy = sess.run(
            [merged, evaluation_step],
            feed_dict={bottleneck_input: validation_bottlenecks,
                       ground_truth_input: validation_ground_truth})
        validation_writer.add_summary(validation_summary, i)
     
        ## 打印驗證集的測試精確度和測試的圖片數
        tf.logging.info('%s: Step %d: Validation accuracy = %.1f%% (N=%d)' %
                        (datetime.now(), i, validation_accuracy * 100,
                         len(validation_bottlenecks)))

      # Store intermediate results
      ## 存儲瞬時結果
      intermediate_frequency = FLAGS.intermediate_store_frequency

      if (intermediate_frequency > 0 and (i % intermediate_frequency == 0)
          and i > 0):
        # If we want to do an intermediate save, save a checkpoint of the train
        # graph, to restore into the eval graph.
        train_saver.save(sess, CHECKPOINT_NAME)
        intermediate_file_name = (FLAGS.intermediate_output_graphs_dir +
                                  'intermediate_' + str(i) + '.pb')
        tf.logging.info('Save intermediate result to : ' +
                        intermediate_file_name)
        save_graph_to_file(intermediate_file_name, module_spec,
                           class_count)

    # After training is complete, force one last save of the train checkpoint.
    train_saver.save(sess, CHECKPOINT_NAME)

    # We've completed all our training, so run a final test evaluation on
    # some new images we haven't used before.
    ## 執行最終的評估
    run_final_eval(sess, module_spec, class_count, image_lists,
                   jpeg_data_tensor, decoded_image_tensor, resized_image_tensor,
                   bottleneck_tensor)

    # Write out the trained graph and labels with the weights stored as
    # constants.
    tf.logging.info('Save final result to : ' + FLAGS.output_graph)
    if wants_quantization:
      tf.logging.info('The model is instrumented for quantization with TF-Lite')
    save_graph_to_file(FLAGS.output_graph, module_spec, class_count)
    with tf.gfile.GFile(FLAGS.output_labels, 'w') as f:
      f.write('\n'.join(image_lists.keys()) + '\n')
   
    ## 保存訓練的graph
    if FLAGS.saved_model_dir:
      export_model(module_spec, class_count, FLAGS.saved_model_dir)

main方法中的一些細節解釋已經用中文備註在上述代碼（使用「##」開頭）中，它的主要步驟是：網絡

設置log級別
準備workspace
從image_dir載入輸入圖片集，並建立image_lists，該image_lists是一個字段，key爲各個類別，value爲對應類別的圖集（包含訓練集、測試集、驗證集，劃分比例默認爲0.八、0.一、0.1）
載入在ImageNet上已經訓練好的Inception V3網絡的特徵張量
針對每一個圖片，調用圖片解碼操做得到圖片的原始張量和解碼後張量
針對每一個圖片的jpeg_data_tensor和decoded_image_tensor，建立其對應的bottlenects（其實是1*2048維的張量），並緩存到磁盤
獲取訓練步驟、交叉熵
開始迭代訓練
每迭代10次，打印訓練的精度和交叉熵，打印驗證集的測試結果。默認狀況下訓練集和測試集都是取100張圖
訓練完成後，使用測試集進行最後的評估
結果的打印和保存

2.3 其它方法

分析完代碼的主要執行路徑，下面解讀下其它方法。由於總的代碼很是的長，篇幅有限，下面按照順序簡單介紹下其它方法的內容。app

2.3.1 create_image_lists

def create_image_lists(image_dir, testing_percentage, validation_percentage):
    ...... 省略......
    result[label_name] = {
        'dir': dir_name,
        'training': training_images,
        'testing': testing_images,
        'validation': validation_images,
    }
  return result

根據image_dir的地址，testing_percentage和testing_percentage的比例劃分圖集，返回的格式相似以下：dom

{
    'correct': {
        'dir': correct_image_dir,
        'training': correct_training_images,
        'testing': correct_testing_images,
        'validation': correct_validation_images
    },
    'error': {
        'dir': error_image_dir,
        'training': error_training_images,
        'testing': error_testing_images,
        'validation': error_validation_images
    }
}

每一個training/testing/validation對應的value爲image的file_name list。

2.3.2 get_image_path

獲取圖片的全路徑

2.3.3 get_bottleneck_path

得到不一樣類別（training、testing、validation）的bottleneck路徑

2.3.4 create_module_graph

根據給定的已訓練好的模型Hub Module，建立模型的圖

2.3.5 run_bottleneck_on_image

def run_bottleneck_on_image(sess, image_data, image_data_tensor,
                            decoded_image_tensor, resized_input_tensor,
                            bottleneck_tensor):
  """Runs inference on an image to extract the 'bottleneck' summary layer.
  Args:
    sess: Current active TensorFlow Session.
    image_data: String of raw JPEG data.
    image_data_tensor: Input data layer in the graph.
    decoded_image_tensor: Output of initial image resizing and preprocessing.
    resized_input_tensor: The input node of the recognition graph.
    bottleneck_tensor: Layer before the final softmax.
  Returns:
    Numpy array of bottleneck values.
  """
  # First decode the JPEG image, resize it, and rescale the pixel values.
  resized_input_values = sess.run(decoded_image_tensor,
                                  {image_data_tensor: image_data})
  # Then run it through the recognition network.
  bottleneck_values = sess.run(bottleneck_tensor,
                               {resized_input_tensor: resized_input_values})
  bottleneck_values = np.squeeze(bottleneck_values)
  return bottleneck_values

根據給定的輸入圖片解碼後的tensor，計算bottleneck_values，並執行squeeze操做（刪除單維度條目，把shape中爲1的維度去掉）

2.3.6 ensure_dir_exists

2.3.7 create_bottleneck_file

調run_bottleneck_on_image方法計算bottleneck值，並緩存到磁盤文件

2.3.8 get_or_create_bottleneck

批量獲取一組圖片的bottleneck值

2.3.9 cache_bottlenecks

批量緩存bottleneck

2.3.10 get_random_cached_bottlenecks

隨機獲取一批緩存的bottlenecks，以及其對應的真實標ground_truths和文件名filenames

2.3.11 add_final_retrain_ops

def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
                          quantize_layer, is_training):
                          
  batch_size, bottleneck_tensor_size = bottleneck_tensor.get_shape().as_list()
  assert batch_size is None, 'We want to work with arbitrary batch size.'
  with tf.name_scope('input'):
    bottleneck_input = tf.placeholder_with_default(
        bottleneck_tensor,
        shape=[batch_size, bottleneck_tensor_size],
        name='BottleneckInputPlaceholder')

    ground_truth_input = tf.placeholder(
        tf.int64, [batch_size], name='GroundTruthInput')

  # Organizing the following ops so they are easier to see in TensorBoard.
  layer_name = 'final_retrain_ops'
  with tf.name_scope(layer_name):
    with tf.name_scope('weights'):
      initial_value = tf.truncated_normal(
          [bottleneck_tensor_size, class_count], stddev=0.001)
      layer_weights = tf.Variable(initial_value, name='final_weights')
      variable_summaries(layer_weights)

    with tf.name_scope('biases'):
      layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
      variable_summaries(layer_biases)

    with tf.name_scope('Wx_plus_b'):
      logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
      tf.summary.histogram('pre_activations', logits)

  final_tensor = tf.nn.softmax(logits, name=final_tensor_name)

  # The tf.contrib.quantize functions rewrite the graph in place for
  # quantization. The imported model graph has already been rewritten, so upon
  # calling these rewrites, only the newly added final layer will be
  # transformed.
  if quantize_layer:
    if is_training:
      tf.contrib.quantize.create_training_graph()
    else:
      tf.contrib.quantize.create_eval_graph()

  tf.summary.histogram('activations', final_tensor)

  # If this is an eval graph, we don't need to add loss ops or an optimizer.
  if not is_training:
    return None, None, bottleneck_input, ground_truth_input, final_tensor

  with tf.name_scope('cross_entropy'):
    cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
        labels=ground_truth_input, logits=logits)

  tf.summary.scalar('cross_entropy', cross_entropy_mean)

  with tf.name_scope('train'):
    optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate)
    train_step = optimizer.minimize(cross_entropy_mean)

  return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input,
          final_tensor)

在結尾處添加一個新的softmax層和全鏈接層（y=WX+b），用於訓練和評估。此處與logistic模型是同樣的，採用梯度降低的方式來最小化交叉熵進行迭代訓練。

2.3.12 add_evaluation_step

def add_evaluation_step(result_tensor, ground_truth_tensor):
  with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
      ## 對每組向量按列找到最大值的index
      prediction = tf.argmax(result_tensor, 1)
      ## 將每組張量比較預測的結果和實際的結果的一致性，一致則爲True，不然爲False
      correct_prediction = tf.equal(prediction, ground_truth_tensor)
    with tf.name_scope('accuracy'):
      ## 將True或False轉爲float格式，並計算平均值
      evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  tf.summary.scalar('accuracy', evaluation_step)
  return evaluation_step, prediction

註解見上述代碼，返回最終的accuracy和預測的值list。