Python saver.checkpoint_exists方法代碼示例

# 需要導入模塊: from tensorflow.python.training import saver [as 別名]
# 或者: from tensorflow.python.training.saver import checkpoint_exists [as 別名]
def _read_latest_config_files(self, run_path_pairs):
    """Reads and returns the projector config files in every run directory."""
    configs = {}
    config_fpaths = {}
    for run_name, assets_dir in run_path_pairs:
      config = projector_config_pb2.ProjectorConfig()
      config_fpath = os.path.join(assets_dir, PROJECTOR_FILENAME)
      if file_io.file_exists(config_fpath):
        file_content = file_io.read_file_to_string(config_fpath)
        text_format.Merge(file_content, config)
      has_tensor_files = False
      for embedding in config.embeddings:
        if embedding.tensor_path:
          if not embedding.tensor_name:
            embedding.tensor_name = os.path.basename(embedding.tensor_path)
          has_tensor_files = True
          break

      if not config.model_checkpoint_path:
        # See if you can find a checkpoint file in the logdir.
        logdir = _assets_dir_to_logdir(assets_dir)
        ckpt_path = _find_latest_checkpoint(logdir)
        if not ckpt_path and not has_tensor_files:
          continue
        if ckpt_path:
          config.model_checkpoint_path = ckpt_path

      # Sanity check for the checkpoint file.
      if (config.model_checkpoint_path and
          not checkpoint_exists(config.model_checkpoint_path)):
        logging.warning('Checkpoint file "%s" not found',
                        config.model_checkpoint_path)
        continue
      configs[run_name] = config
      config_fpaths[run_name] = config_fpath
    return configs, config_fpaths 

# 需要導入模塊: from tensorflow.python.training import saver [as 別名]
# 或者: from tensorflow.python.training.saver import checkpoint_exists [as 別名]
def _read_latest_config_files(self, run_path_pairs):
    """Reads and returns the projector config files in every run directory."""
    configs = {}
    config_fpaths = {}
    for run_name, logdir in run_path_pairs:
      config = ProjectorConfig()
      config_fpath = os.path.join(logdir, PROJECTOR_FILENAME)
      if file_io.file_exists(config_fpath):
        file_content = file_io.read_file_to_string(config_fpath).decode('utf-8')
        text_format.Merge(file_content, config)

      has_tensor_files = False
      for embedding in config.embeddings:
        if embedding.tensor_path:
          has_tensor_files = True
          break

      if not config.model_checkpoint_path:
        # See if you can find a checkpoint file in the logdir.
        ckpt_path = latest_checkpoint(logdir)
        if not ckpt_path:
          # Or in the parent of logdir.
          ckpt_path = latest_checkpoint(os.path.join(logdir, os.pardir))
          if not ckpt_path and not has_tensor_files:
            continue
        if ckpt_path:
          config.model_checkpoint_path = ckpt_path

      # Sanity check for the checkpoint file.
      if (config.model_checkpoint_path and
          not checkpoint_exists(config.model_checkpoint_path)):
        logging.warning('Checkpoint file %s not found',
                        config.model_checkpoint_path)
        continue
      configs[run_name] = config
      config_fpaths[run_name] = config_fpath
    return configs, config_fpaths 

# 需要導入模塊: from tensorflow.python.training import saver [as 別名]
# 或者: from tensorflow.python.training.saver import checkpoint_exists [as 別名]
def _read_latest_config_files(self, run_path_pairs):
    """Reads and returns the projector config files in every run directory."""
    configs = {}
    config_fpaths = {}
    for run_name, logdir in run_path_pairs:
      config = ProjectorConfig()
      config_fpath = os.path.join(logdir, PROJECTOR_FILENAME)
      if file_io.file_exists(config_fpath):
        file_content = file_io.read_file_to_string(config_fpath).decode('utf-8')
        text_format.Merge(file_content, config)

      has_tensor_files = False
      for embedding in config.embeddings:
        if embedding.tensor_path:
          has_tensor_files = True
          break

      if not config.model_checkpoint_path:
        # See if you can find a checkpoint file in the logdir.
        ckpt_path = latest_checkpoint(logdir)
        if not ckpt_path:
          # Or in the parent of logdir.
          ckpt_path = latest_checkpoint(os.path.join('../', logdir))
          if not ckpt_path and not has_tensor_files:
            continue
        if ckpt_path:
          config.model_checkpoint_path = ckpt_path

      # Sanity check for the checkpoint file.
      if (config.model_checkpoint_path and
          not checkpoint_exists(config.model_checkpoint_path)):
        logging.warning('Checkpoint file %s not found',
                        config.model_checkpoint_path)
        continue
      configs[run_name] = config
      config_fpaths[run_name] = config_fpath
    return configs, config_fpaths 

# 需要導入模塊: from tensorflow.python.training import saver [as 別名]
# 或者: from tensorflow.python.training.saver import checkpoint_exists [as 別名]
def freeze_graph_with_def_protos(
    input_graph_def,
    input_saver_def,
    input_checkpoint,
    output_node_names,
    restore_op_name,
    filename_tensor_name,
    clear_devices,
    initializer_nodes,
    variable_names_blacklist=''):
  """Converts all variables in a graph and checkpoint into constants."""
  del restore_op_name, filename_tensor_name  # Unused by updated loading code.

  # 'input_checkpoint' may be a prefix if we're using Saver V2 format
  if not saver_lib.checkpoint_exists(input_checkpoint):
    raise ValueError(
        'Input checkpoint "' + input_checkpoint + '" does not exist!')

  if not output_node_names:
    raise ValueError(
        'You must supply the name of a node to --output_node_names.')

  # Remove all the explicit device specifications for this node. This helps to
  # make the graph more portable.
  if clear_devices:
    for node in input_graph_def.node:
      node.device = ''

  _ = importer.import_graph_def(input_graph_def, name='')

  with session.Session() as sess:
    if input_saver_def:
      saver = saver_lib.Saver(saver_def=input_saver_def)
      saver.restore(sess, input_checkpoint)
    else:
      var_list = {}
      reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint)
      var_to_shape_map = reader.get_variable_to_shape_map()
      for key in var_to_shape_map:
        try:
          tensor = sess.graph.get_tensor_by_name(key + ':0')
        except KeyError:
          # This tensor doesn't exist in the graph (for example it's
          # 'global_step' or a similar housekeeping element) so skip it.
          continue
        var_list[key] = tensor
      saver = saver_lib.Saver(var_list=var_list)
      saver.restore(sess, input_checkpoint)
      if initializer_nodes:
        sess.run(initializer_nodes)

    variable_names_blacklist = (variable_names_blacklist.split(',') if
                                variable_names_blacklist else None)
    output_graph_def = graph_util.convert_variables_to_constants(
        sess,
        input_graph_def,
        output_node_names.split(','),
        variable_names_blacklist=variable_names_blacklist)

  return output_graph_def 

# 需要導入模塊: from tensorflow.python.training import saver [as 別名]
# 或者: from tensorflow.python.training.saver import checkpoint_exists [as 別名]
def freeze_graph_with_def_protos(
    input_graph_def,
    input_saver_def,
    input_checkpoint,
    output_node_names,
    restore_op_name,
    filename_tensor_name,
    clear_devices,
    initializer_nodes,
    variable_names_blacklist=''):
  """Converts all variables in a graph and checkpoint into constants."""
  del restore_op_name, filename_tensor_name  # Unused by updated loading code.

  # 'input_checkpoint' may be a prefix if we're using Saver V2 format
  if not saver_lib.checkpoint_exists(input_checkpoint):
    raise ValueError(
        'Input checkpoint "' + input_checkpoint + '" does not exist!')

  if not output_node_names:
    raise ValueError(
        'You must supply the name of a node to --output_node_names.')

  # Remove all the explicit device specifications for this node. This helps to
  # make the graph more portable.
  if clear_devices:
    for node in input_graph_def.node:
      node.device = ''

  with tf.Graph().as_default():
    tf.import_graph_def(input_graph_def, name='')
    config = tf.ConfigProto(graph_options=tf.GraphOptions())
    with session.Session(config=config) as sess:
      if input_saver_def:
        saver = saver_lib.Saver(saver_def=input_saver_def)
        saver.restore(sess, input_checkpoint)
      else:
        var_list = {}
        reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint)
        var_to_shape_map = reader.get_variable_to_shape_map()
        for key in var_to_shape_map:
          try:
            tensor = sess.graph.get_tensor_by_name(key + ':0')
          except KeyError:
            # This tensor doesn't exist in the graph (for example it's
            # 'global_step' or a similar housekeeping element) so skip it.
            continue
          var_list[key] = tensor
        saver = saver_lib.Saver(var_list=var_list)
        saver.restore(sess, input_checkpoint)
        if initializer_nodes:
          sess.run(initializer_nodes)

      variable_names_blacklist = (variable_names_blacklist.split(',') if
                                  variable_names_blacklist else None)
      output_graph_def = graph_util.convert_variables_to_constants(
          sess,
          input_graph_def,
          output_node_names.split(','),
          variable_names_blacklist=variable_names_blacklist)

  return output_graph_def