Python gfile.FastGFile方法代码示例

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def load_graph(graph_path,tensorboard=False,**kwargs):
    '''
    :param graph_filename: the path of the pb file
    :return: tensorflow graph
    '''
    with gfile.FastGFile(graph_path,'rb') as f:
        graph_def = graph_pb2.GraphDef()
        graph_def.ParseFromString(f.read())

    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def,name="")

    if tensorboard:
        writer = tf.summary.FileWriter("log/")
        writer.add_graph(graph)

    return graph 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def create_inception_graph():
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Graph().as_default() as graph:
    model_filename = os.path.join(
        FLAGS.model_dir, 'classify_image_graph_def.pb')
    with gfile.FastGFile(model_filename, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
          tf.import_graph_def(graph_def, name='', return_elements=[
              BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,
              RESIZED_INPUT_TENSOR_NAME]))
  return graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
                           image_dir, category, sess, jpeg_data_tensor,
                           bottleneck_tensor):
  """Create a single bottleneck file."""
  print('Creating bottleneck at ' + bottleneck_path)
  image_path = get_image_path(image_lists, label_name, index,
                              image_dir, category)
  if not gfile.Exists(image_path):
    tf.logging.fatal('File does not exist %s', image_path)
  image_data = gfile.FastGFile(image_path, 'rb').read()
  try:
    bottleneck_values = run_bottleneck_on_image(
        sess, image_data, jpeg_data_tensor, bottleneck_tensor)
  except:
    raise RuntimeError('Error during processing file %s' % image_path)

  bottleneck_string = ','.join(str(x) for x in bottleneck_values)
  with open(bottleneck_path, 'w') as bottleneck_file:
    bottleneck_file.write(bottleneck_string) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def create_inception_graph():
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Session() as sess:
    model_filename = os.path.join(
        FLAGS.model_dir, 'classify_image_graph_def.pb')
    with gfile.FastGFile(model_filename, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
          tf.import_graph_def(graph_def, name='', return_elements=[
              BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,
              RESIZED_INPUT_TENSOR_NAME]))
  return sess.graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def load_model(model, input_map=None):
    # Check if the model is a model directory (containing a metagraph and a checkpoint file)
    #  or if it is a protobuf file with a frozen graph
    model_exp = os.path.expanduser(model)
    if (os.path.isfile(model_exp)):
        print('Model filename: %s' % model_exp)
        with gfile.FastGFile(model_exp,'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            tf.import_graph_def(graph_def, input_map=input_map, name='')
    else:
        print('Model directory: %s' % model_exp)
        meta_file, ckpt_file = get_model_filenames(model_exp)
        
        print('Metagraph file: %s' % meta_file)
        print('Checkpoint file: %s' % ckpt_file)
      
        saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map)
        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file)) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def create_model_graph(model_info):
    """"Creates a graph from saved GraphDef file and returns a Graph object.

    Args:
      model_info: Dictionary containing information about the model architecture.

    Returns:
      Graph holding the trained Inception network, and various tensors we'll be
      manipulating.
    """
    with tf.Graph().as_default() as graph:
        model_path = os.path.join(FLAGS.model_dir, model_info['model_file_name'])
        with gfile.FastGFile(model_path, 'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            bottleneck_tensor, resized_input_tensor = (tf.import_graph_def(
                graph_def,
                name='',
                return_elements=[
                    model_info['bottleneck_tensor_name'],
                    model_info['resized_input_tensor_name'],
                ]))
    return graph, bottleneck_tensor, resized_input_tensor 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def create_model_graph_bis(model_info, model_dir):
    """"Creates a graph from saved GraphDef file and returns a Graph object.

    Args:
      model_info: Dictionary containing information about the model architecture.
      model_dir: String containing where the model is saved.

    Returns:
      Graph holding the trained Inception network, and various tensors we'll be
      manipulating.
    """
    with tf.Graph().as_default() as graph:
        model_path = os.path.join(model_dir, model_info['model_file_name'])
        with gfile.FastGFile(model_path, 'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            bottleneck_tensor, resized_input_tensor = (tf.import_graph_def(
                graph_def,
                name='',
                return_elements=[
                    model_info['bottleneck_tensor_name'],
                    model_info['resized_input_tensor_name'],
                ]))
    return graph, bottleneck_tensor, resized_input_tensor 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def load_model(model):
    """to load the deep learning model

    Args:
        model: model path
    Returns:
        tensorflow graph

    """

    logger.info(msg="load_model called")
    model_exp = os.path.expanduser(model)
    if os.path.isfile(model_exp):
        with gfile.FastGFile(model_exp, 'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            graph = tf.import_graph_def(graph_def, name='')
            return graph
    else:
        meta_file, ckpt_file = get_model_filenames(model_exp)
        saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file))
        graph = saver.restore(tf.get_default_session(),
                              os.path.join(model_exp, ckpt_file))
        return graph 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def load_model(model, input_map=None):

    logger.info(msg="load_model called")
    # Check if the model is a model directory (containing a metagraph and
    # a checkpoint file) or if it is a protobuf file with a frozen graph
    model_exp = os.path.expanduser(model)
    if (os.path.isfile(model_exp)):
        print('Model filename: %s' % model_exp)
        with gfile.FastGFile(model_exp, 'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            tf.import_graph_def(graph_def, input_map=input_map, name='')
    else:
        print('Model directory: %s' % model_exp)
        meta_file, ckpt_file = get_model_filenames(model_exp)

        print('Metagraph file: %s' % meta_file)
        print('Checkpoint file: %s' % ckpt_file)

        saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file),
                                           input_map=input_map)
        saver.restore(tf.get_default_session(),
                      os.path.join(model_exp, ckpt_file)) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def create_model_graph(model_info):
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Args:
    model_info: Dictionary containing information about the model architecture.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Graph().as_default() as graph:
    model_path = os.path.join(FLAGS.model_dir, model_info['model_file_name'])
    with gfile.FastGFile(model_path, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, resized_input_tensor = (tf.import_graph_def(
          graph_def,
          name='',
          return_elements=[
              model_info['bottleneck_tensor_name'],
              model_info['resized_input_tensor_name'],
          ]))
  return graph, bottleneck_tensor, resized_input_tensor 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
                           image_dir, category, sess, jpeg_data_tensor,
                           decoded_image_tensor, resized_input_tensor,
                           bottleneck_tensor):
  """Create a single bottleneck file."""
  tf.logging.info('Creating bottleneck at ' + bottleneck_path)
  image_path = get_image_path(image_lists, label_name, index,
                              image_dir, category)
  if not gfile.Exists(image_path):
    tf.logging.fatal('File does not exist %s', image_path)
  image_data = gfile.FastGFile(image_path, 'rb').read()
  try:
    bottleneck_values = run_bottleneck_on_image(
        sess, image_data, jpeg_data_tensor, decoded_image_tensor,
        resized_input_tensor, bottleneck_tensor)
  except Exception as e:
    raise RuntimeError('Error during processing file %s (%s)' % (image_path,
                                                                 str(e)))
  bottleneck_string = ','.join(str(x) for x in bottleneck_values)
  with open(bottleneck_path, 'w') as bottleneck_file:
    bottleneck_file.write(bottleneck_string) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def load_model(model):
    # Check if the model is a model directory (containing a metagraph and a checkpoint file)
    #  or if it is a protobuf file with a frozen graph
    model_exp = os.path.expanduser(model)
    if (os.path.isfile(model_exp)):
        print('Model filename: %s' % model_exp)
        with gfile.FastGFile(model_exp,'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            tf.import_graph_def(graph_def, name='')
    else:
        print('Model directory: %s' % model_exp)
        meta_file, ckpt_file = get_model_filenames(model_exp)
        
        print('Metagraph file: %s' % meta_file)
        print('Checkpoint file: %s' % ckpt_file)
      
        saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file))
        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file)) 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def create_tfevent_from_pb(model,optimized=False):
    print("> creating tfevent of model: {}".format(model))

    if optimized:
        model_path=ROOT_DIR+'/models/{}/optimized_inference_graph.pb'.format(model)
        log_dir=ROOT_DIR+'/models/{}/log_opt/'.format(model)
    else:
        model_path=ROOT_DIR+'/models/{}/frozen_inference_graph.pb'.format(model)
        log_dir=ROOT_DIR+'/models/{}/log/'.format(model)

    with session.Session(graph=ops.Graph()) as sess:
        with gfile.FastGFile(model_path, "rb") as f:
          graph_def = graph_pb2.GraphDef()
          graph_def.ParseFromString(f.read())
          importer.import_graph_def(graph_def)
        pb_visual_writer = summary.FileWriter(log_dir)
        pb_visual_writer.add_graph(sess.graph)
    print("> Model {} Imported. \nVisualize by running: \
    tensorboard --logdir={}".format(model_path, log_dir))

# Gather all Model Names in models/ 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def create_model_graph(model_info):
  """"Creates a graph from saved GraphDef file and returns a Graph object.

  Args:
    model_info: Dictionary containing information about the model architecture.

  Returns:
    Graph holding the trained Inception network, and various tensors we'll be
    manipulating.
  """
  with tf.Graph().as_default() as graph:
    model_path = os.path.join(FLAGS.model_dir, model_info['model_file_name'])
    print('Model path: ', model_path)
    with gfile.FastGFile(model_path, 'rb') as f:
      graph_def = tf.compat.v1.GraphDef()
      graph_def.ParseFromString(f.read())
      bottleneck_tensor, resized_input_tensor = (tf.import_graph_def(
          graph_def,
          name='',
          return_elements=[
              model_info['bottleneck_tensor_name'],
              model_info['resized_input_tensor_name'],
          ]))
  return graph, bottleneck_tensor, resized_input_tensor 

# 需要导入模块: from tensorflow.python.platform import gfile [as 别名]
# 或者: from tensorflow.python.platform.gfile import FastGFile [as 别名]
def RewriteContext():
  context = task_spec_pb2.TaskSpec()
  with gfile.FastGFile(FLAGS.task_context, 'rb') as fin:
    text_format.Merge(fin.read(), context)
  for resource in context.input:
    if resource.creator == StageName():
      del resource.part[:]
      part = resource.part.add()
      part.file_pattern = os.path.join(OutputPath(resource.name))
  with gfile.FastGFile(OutputPath('context'), 'w') as fout:
    fout.write(str(context))