Python config_pb2.ConfigProto方法代码示例

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def __init__(self, target='', graph=None, config=None):
    """Creates a new TensorFlow session.

    If no `graph` argument is specified when constructing the session,
    the default graph will be launched in the session. If you are
    using more than one graph (created with `tf.Graph()` in the same
    process, you will have to use different sessions for each graph,
    but each graph can be used in multiple sessions. In this case, it
    is often clearer to pass the graph to be launched explicitly to
    the session constructor.

    Args:
      target: (Optional.) The execution engine to connect to.
        Defaults to using an in-process engine. See
        @{$distributed$Distributed TensorFlow}
        for more examples.
      graph: (Optional.) The `Graph` to be launched (described above).
      config: (Optional.) A [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)
        protocol buffer with configuration options for the session.

    """
    super(Session, self).__init__(target, graph, config=config)
    # NOTE(mrry): Create these on first `__enter__` to avoid a reference cycle.
    self._default_graph_context_manager = None
    self._default_session_context_manager = None 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def testClearDevices(self):
    export_dir = os.path.join(test.get_temp_dir(), "test_clear_devices")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Specify a device and save a variable.
    ops.reset_default_graph()
    with session.Session(
        target="",
        config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
      with sess.graph.device("/cpu:0"):
        self._init_and_validate_variable(sess, "v", 42)
        builder.add_meta_graph_and_variables(
            sess, [tag_constants.TRAINING], clear_devices=True)

    # Save the SavedModel to disk.
    builder.save()

    # Restore the graph with a single predefined tag whose variables were saved
    # without any device information.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, [tag_constants.TRAINING], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def __init__(self, target='', graph=None, config=None):
    """Creates a new TensorFlow session.

    If no `graph` argument is specified when constructing the session,
    the default graph will be launched in the session. If you are
    using more than one graph (created with `tf.Graph()` in the same
    process, you will have to use different sessions for each graph,
    but each graph can be used in multiple sessions. In this case, it
    is often clearer to pass the graph to be launched explicitly to
    the session constructor.

    Args:
      target: (Optional.) The execution engine to connect to.
        Defaults to using an in-process engine. See
        [Distributed Tensorflow](https://www.tensorflow.org/how_tos/distributed/index.html)
        for more examples.
      graph: (Optional.) The `Graph` to be launched (described above).
      config: (Optional.) A [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)
        protocol buffer with configuration options for the session.

    """
    super(Session, self).__init__(target, graph, config=config)
    # NOTE(mrry): Create these on first `__enter__` to avoid a reference cycle.
    self._default_graph_context_manager = None
    self._default_session_context_manager = None 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def testLegacyBasic(self):
    base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
    ops.reset_default_graph()
    sess, meta_graph_def = (
        bundle_shim.load_session_bundle_or_saved_model_bundle_from_path(
            base_path,
            tags=[""],
            target="",
            config=config_pb2.ConfigProto(device_count={"CPU": 2})))

    self.assertTrue(sess)
    asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
    with sess.as_default():
      path1, path2 = sess.run(["filename1:0", "filename2:0"])
      self.assertEqual(
          compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
      self.assertEqual(
          compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)

      collection_def = meta_graph_def.collection_def

      signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value
      self.assertEqual(len(signatures_any), 1) 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def testSavedModelBasic(self):
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    ops.reset_default_graph()
    sess, meta_graph_def = (
        bundle_shim.load_session_bundle_or_saved_model_bundle_from_path(
            base_path,
            tags=[tag_constants.SERVING],
            target="",
            config=config_pb2.ConfigProto(device_count={"CPU": 2})))

    self.assertTrue(sess)

    # Check basic signature def property.
    signature_def = meta_graph_def.signature_def
    self.assertEqual(len(signature_def), 2)
    self.assertEqual(
        signature_def[signature_constants.REGRESS_METHOD_NAME].method_name,
        signature_constants.REGRESS_METHOD_NAME)
    signature = signature_def["tensorflow/serving/regress"]
    asset_path = os.path.join(base_path, saved_model_constants.ASSETS_DIRECTORY)
    with sess.as_default():
      output1 = sess.run(["filename_tensor:0"])
      self.assertEqual(["foo.txt"], output1) 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def testClearDevices(self):
    export_dir = os.path.join(tf.test.get_temp_dir(), "test_clear_devices")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Specify a device and save a variable.
    tf.reset_default_graph()
    with tf.Session(
        target="",
        config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
      with sess.graph.device("/cpu:0"):
        self._init_and_validate_variable(sess, "v", 42)
        builder.add_meta_graph_and_variables(
            sess, [tag_constants.TRAINING], clear_devices=True)

    # Save the SavedModel to disk.
    builder.save()

    # Restore the graph with a single predefined tag whose variables were saved
    # without any device information.
    with self.test_session(graph=tf.Graph()) as sess:
      loader.load(sess, [tag_constants.TRAINING], export_dir)
      self.assertEqual(
          42, tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)[0].eval()) 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def testBuildCostModel(self):
    run_options = config_pb2.RunOptions()
    config = config_pb2.ConfigProto(
        allow_soft_placement=True,
        graph_options=config_pb2.GraphOptions(build_cost_model=100))
    with session.Session(config=config) as sess:
      with ops.device('/gpu:0'):
        a = array_ops.placeholder(dtypes.float32, shape=[])
        b = math_ops.add(a, a)
        c = array_ops.identity(b)
        d = math_ops.mul(c, c)
      for step in xrange(120):
        run_metadata = config_pb2.RunMetadata()
        sess.run(d, feed_dict={a: 1.0},
                 options=run_options, run_metadata=run_metadata)
        if step == 99:
          self.assertTrue(run_metadata.HasField('cost_graph'))
        else:
          self.assertFalse(run_metadata.HasField('cost_graph')) 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def optimize_graph(graph: tf.Graph, level=None) -> GraphDef:
    """Optimise a tensorflow graph for inference after modification

    This function optimises the given graph for inference after the graph
    may have been modified to replace known, but unsupported operations.
    Optimisation might use multiple passes and aim at CPUs or GPUs.

    Args:
        graph: Tensorflow v1 graph (or wrapped v2 function) to be optimised
        level: optional optimisation level; currently unsupported

    Returns:
        Optimised ``GraphDef`` message for inference or format conversion
    """
    inputs = get_input_nodes(graph)
    outputs = get_output_nodes(graph)
    signature_def = _build_signature_def(graph, inputs, outputs)
    _mark_outputs_as_train_op(graph, signature_def)
    config = ConfigProto()
    _set_optimization_options(config, [
        'debug_stripper', 'remap', 'constfold', 'arithmetic', 'dependency'
    ])
    optimised_graph = _run_tf_optimizer(config, graph, signature_def)
    optimised_graph = _remove_unused_control_flow_inputs(optimised_graph)
    return optimised_graph 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def test_replace_with_allowed_properties(self):
    session_config = config_pb2.ConfigProto(allow_soft_placement=True)
    device_fn = lambda op: '/cpu:0'

    config = run_config_lib.RunConfig().replace(
        tf_random_seed=11,
        save_summary_steps=12,
        save_checkpoints_secs=14,
        session_config=session_config,
        keep_checkpoint_max=16,
        keep_checkpoint_every_n_hours=17,
        device_fn=device_fn,
        session_creation_timeout_secs=18)
    self.assertEqual(11, config.tf_random_seed)
    self.assertEqual(12, config.save_summary_steps)
    self.assertEqual(14, config.save_checkpoints_secs)
    self.assertEqual(session_config, config.session_config)
    self.assertEqual(16, config.keep_checkpoint_max)
    self.assertEqual(17, config.keep_checkpoint_every_n_hours)
    self.assertEqual(device_fn, config.device_fn)
    self.assertEqual(18, config.session_creation_timeout_secs) 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def test_init_with_allowed_properties(self):
    session_config = config_pb2.ConfigProto(allow_soft_placement=True)
    device_fn = lambda op: '/cpu:0'

    config = run_config_lib.RunConfig(
        tf_random_seed=11,
        save_summary_steps=12,
        save_checkpoints_secs=14,
        session_config=session_config,
        keep_checkpoint_max=16,
        keep_checkpoint_every_n_hours=17,
        device_fn=device_fn,
        experimental_max_worker_delay_secs=10)
    self.assertEqual(11, config.tf_random_seed)
    self.assertEqual(12, config.save_summary_steps)
    self.assertEqual(14, config.save_checkpoints_secs)
    self.assertEqual(session_config, config.session_config)
    self.assertEqual(16, config.keep_checkpoint_max)
    self.assertEqual(17, config.keep_checkpoint_every_n_hours)
    self.assertEqual(device_fn, config.device_fn)
    self.assertEqual(10, config.experimental_max_worker_delay_secs) 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def test_linear_model_mismatched_dense_values(self):
    column = fc.weighted_categorical_column(
        categorical_column=fc.categorical_column_with_identity(
            key='ids', num_buckets=3),
        weight_feature_key='values')
    with ops.Graph().as_default():
      model = linear.LinearModel((column,), sparse_combiner='mean')
      predictions = model({
          'ids':
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=(0, 2, 1),
                  dense_shape=(2, 2)),
          'values': ((.5,), (1.,))
      })
      # Disabling the constant folding optimizer here since it changes the
      # error message differently on CPU and GPU.
      config = config_pb2.ConfigProto()
      config.graph_options.rewrite_options.constant_folding = (
          rewriter_config_pb2.RewriterConfig.OFF)
      with _initialized_session(config):
        with self.assertRaisesRegexp(errors.OpError, 'Incompatible shapes'):
          self.evaluate(predictions) 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def test_gpu_config(self):
    with tf.Graph().as_default():
      keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model()
      keras_model.compile(
          loss='categorical_crossentropy',
          optimizer='rmsprop',
          metrics=['mse', keras.metrics.CategoricalAccuracy()])

      gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.3)
      sess_config = config_pb2.ConfigProto(gpu_options=gpu_options)
      self._config._session_config = sess_config
      keras_lib.model_to_estimator(keras_model=keras_model, config=self._config)
      self.assertEqual(
          keras.backend.get_session(
          )._config.gpu_options.per_process_gpu_memory_fraction,
          gpu_options.per_process_gpu_memory_fraction) 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def _validate_properties(run_config):
  """Validates the properties."""
  def _validate(property_name, cond, message):
    property_value = getattr(run_config, property_name)
    if property_value is not None and not cond(property_value):
      raise ValueError(message)

  _validate('model_dir', lambda dir: dir,
            message='model_dir should be non-empty')

  _validate('save_summary_steps', lambda steps: steps >= 0,
            message='save_summary_steps should be >= 0')

  _validate('save_checkpoints_steps', lambda steps: steps >= 0,
            message='save_checkpoints_steps should be >= 0')
  _validate('save_checkpoints_secs', lambda secs: secs >= 0,
            message='save_checkpoints_secs should be >= 0')

  _validate('session_config',
            lambda sc: isinstance(sc, config_pb2.ConfigProto),
            message='session_config must be instance of ConfigProto')

  _validate('keep_checkpoint_max', lambda keep_max: keep_max >= 0,
            message='keep_checkpoint_max should be >= 0')
  _validate('keep_checkpoint_every_n_hours', lambda keep_hours: keep_hours > 0,
            message='keep_checkpoint_every_n_hours should be > 0')

  _validate('tf_random_seed', lambda seed: isinstance(seed, six.integer_types),
            message='tf_random_seed must be integer.') 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def get_session():
  """Returns the TF session to be used by the backend.

  If a default TensorFlow session is available, we will return it.

  Else, we will return the global Keras session.

  If no global Keras session exists at this point:
  we will create a new global session.

  Note that you can manually set the global session
  via `K.set_session(sess)`.

  Returns:
      A TensorFlow session.
  """
  global _SESSION
  if ops.get_default_session() is not None:
    session = ops.get_default_session()
  else:
    if _SESSION is None:
      if not os.environ.get('OMP_NUM_THREADS'):
        config = config_pb2.ConfigProto(allow_soft_placement=True)
      else:
        num_thread = int(os.environ.get('OMP_NUM_THREADS'))
        config = config_pb2.ConfigProto(
            intra_op_parallelism_threads=num_thread, allow_soft_placement=True)
      _SESSION = session_module.Session(config=config)
    session = _SESSION
  if not _MANUAL_VAR_INIT:
    with session.graph.as_default():
      _initialize_variables()
  return session 

# 需要导入模块: from tensorflow.core.protobuf import config_pb2 [as 别名]
# 或者: from tensorflow.core.protobuf.config_pb2 import ConfigProto [as 别名]
def testBasic(self):
    base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
    ops.reset_default_graph()
    sess, meta_graph_def = session_bundle.load_session_bundle_from_path(
        base_path,
        target="",
        config=config_pb2.ConfigProto(device_count={"CPU": 2}))

    self.assertTrue(sess)
    asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
    with sess.as_default():
      path1, path2 = sess.run(["filename1:0", "filename2:0"])
      self.assertEqual(
          compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
      self.assertEqual(
          compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)

      collection_def = meta_graph_def.collection_def

      signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value
      self.assertEquals(len(signatures_any), 1)

      signatures = manifest_pb2.Signatures()
      signatures_any[0].Unpack(signatures)
      self._checkRegressionSignature(signatures, sess)
      self._checkNamedSignatures(signatures, sess)