Python text_format.Parse方法代码示例

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def RunTraining(self, hyperparam_config):
    master_spec = self.LoadSpec('master_spec_link.textproto')

    self.assertTrue(isinstance(hyperparam_config, spec_pb2.GridPoint))
    gold_doc = sentence_pb2.Sentence()
    text_format.Parse(_DUMMY_GOLD_SENTENCE, gold_doc)
    gold_doc_2 = sentence_pb2.Sentence()
    text_format.Parse(_DUMMY_GOLD_SENTENCE_2, gold_doc_2)
    reader_strings = [
        gold_doc.SerializeToString(), gold_doc_2.SerializeToString()
    ]
    tf.logging.info('Generating graph with config: %s', hyperparam_config)
    with tf.Graph().as_default():
      builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)

      target = spec_pb2.TrainTarget()
      target.name = 'testTraining-all'
      train = builder.add_training_from_config(target)
      with self.test_session() as sess:
        logging.info('Initializing')
        sess.run(tf.global_variables_initializer())

        # Run one iteration of training and verify nothing crashes.
        logging.info('Training')
        sess.run(train['run'], feed_dict={train['input_batch']: reader_strings}) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testFailsOnFixedFeature(self):
    component_spec = spec_pb2.ComponentSpec()
    text_format.Parse("""
        name: "annotate"
        network_unit {
          registered_name: "IdentityNetwork"
        }
        fixed_feature {
          name: "fixed" embedding_dim: 32 size: 1
        }
        """, component_spec)
    with tf.Graph().as_default():
      comp = bulk_component.BulkAnnotatorComponentBuilder(
          self.master, component_spec)

      # Expect feature extraction to generate a runtime error due to the
      # fixed feature.
      with self.assertRaises(RuntimeError):
        comp.build_greedy_training(self.master_state, self.network_states) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testBulkFeatureIdExtractorOkWithOneFixedFeature(self):
    component_spec = spec_pb2.ComponentSpec()
    text_format.Parse("""
        name: "test"
        network_unit {
          registered_name: "IdentityNetwork"
        }
        fixed_feature {
          name: "fixed" embedding_dim: -1 size: 1
        }
        """, component_spec)
    with tf.Graph().as_default():
      comp = bulk_component.BulkFeatureIdExtractorComponentBuilder(
          self.master, component_spec)

      # Should not raise errors.
      self.network_states[component_spec.name] = component.NetworkState()
      comp.build_greedy_training(self.master_state, self.network_states)
      self.network_states[component_spec.name] = component.NetworkState()
      comp.build_greedy_inference(self.master_state, self.network_states) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testBulkFeatureIdExtractorFailsOnLinkedFeature(self):
    component_spec = spec_pb2.ComponentSpec()
    text_format.Parse("""
        name: "test"
        network_unit {
          registered_name: "IdentityNetwork"
        }
        fixed_feature {
          name: "fixed" embedding_dim: -1 size: 1
        }
        linked_feature {
          name: "linked" embedding_dim: -1 size: 1
          source_translator: "identity"
          source_component: "mock"
        }
        """, component_spec)
    with tf.Graph().as_default():
      with self.assertRaises(ValueError):
        unused_comp = bulk_component.BulkFeatureIdExtractorComponentBuilder(
            self.master, component_spec) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def main(unused_argv):
  request = inference_flags.request_from_flags()

  if not gfile.Exists(request.segmentation_output_dir):
    gfile.MakeDirs(request.segmentation_output_dir)

  bbox = bounding_box_pb2.BoundingBox()
  text_format.Parse(FLAGS.bounding_box, bbox)

  runner = inference.Runner()
  runner.start(request)
  runner.run((bbox.start.z, bbox.start.y, bbox.start.x),
             (bbox.size.z, bbox.size.y, bbox.size.x))

  counter_path = os.path.join(request.segmentation_output_dir, 'counters.txt')
  if not gfile.Exists(counter_path):
    runner.counters.dump(counter_path) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def LoadInferenceGraph(path, clear_device_placement=False):
  """Parse the given path as an InferenceGraph proto.

  Args:
    path: The path to the file to load.
    clear_device_placement: If true, clears device field from nodes in graph.

  Returns:
    An InferenceGraph object.
  """
  inference_graph = inference_graph_pb2.InferenceGraph()
  with tf.io.gfile.GFile(path, "r") as f:
    text_format.Parse(f.read(), inference_graph)
  if clear_device_placement:
    for node in inference_graph.graph_def.node:
      node.ClearField("device")
    for function in inference_graph.graph_def.library.function:
      for node_def in function.node_def:
        node_def.ClearField("device")
  return inference_graph 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def get_proto(push_list, output_dir):
    bench_factory = aibench_pb2.BenchFactory()
    model_factory = aibench_pb2.ModelFactory()
    try:
        with open("aibench/proto/benchmark.meta", "rb") as fin:
            file_content = fin.read()
            text_format.Parse(file_content, bench_factory)
            filepath = output_dir + "/benchmark.pb"
            with open(filepath, "wb") as fout:
                fout.write(bench_factory.SerializeToString())
                push_list.append(filepath)
        with open("aibench/proto/model.meta", "rb") as fin:
            file_content = fin.read()
            text_format.Parse(file_content, model_factory)
            filepath = output_dir + "/model.pb"
            with open(filepath, "wb") as fout:
                fout.write(model_factory.SerializeToString())
                push_list.append(filepath)
    except text_format.ParseError as e:
        raise IOError("Cannot parse file.", e)

    return bench_factory, model_factory 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testParseExotic(self, message_module):
    message = message_module.TestAllTypes()
    text = ('repeated_int64: -9223372036854775808\n'
            'repeated_uint64: 18446744073709551615\n'
            'repeated_double: 123.456\n'
            'repeated_double: 1.23e+22\n'
            'repeated_double: 1.23e-18\n'
            'repeated_string: \n'
            '"\\000\\001\\007\\010\\014\\n\\r\\t\\013\\\\\\\'\\""\n'
            'repeated_string: "foo" \'corge\' "grault"\n'
            'repeated_string: "\\303\\274\\352\\234\\237"\n'
            'repeated_string: "\\xc3\\xbc"\n'
            'repeated_string: "\xc3\xbc"\n')
    text_format.Parse(text, message)

    self.assertEqual(-9223372036854775808, message.repeated_int64[0])
    self.assertEqual(18446744073709551615, message.repeated_uint64[0])
    self.assertEqual(123.456, message.repeated_double[0])
    self.assertEqual(1.23e22, message.repeated_double[1])
    self.assertEqual(1.23e-18, message.repeated_double[2])
    self.assertEqual('\000\001\a\b\f\n\r\t\v\\\'"', message.repeated_string[0])
    self.assertEqual('foocorgegrault', message.repeated_string[1])
    self.assertEqual('\u00fc\ua71f', message.repeated_string[2])
    self.assertEqual('\u00fc', message.repeated_string[3]) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testParseStringFieldUnescape(self, message_module):
    message = message_module.TestAllTypes()
    text = r'''repeated_string: "\xf\x62"
               repeated_string: "\\xf\\x62"
               repeated_string: "\\\xf\\\x62"
               repeated_string: "\\\\xf\\\\x62"
               repeated_string: "\\\\\xf\\\\\x62"
               repeated_string: "\x5cx20"'''

    text_format.Parse(text, message)

    SLASH = '\\'
    self.assertEqual('\x0fb', message.repeated_string[0])
    self.assertEqual(SLASH + 'xf' + SLASH + 'x62', message.repeated_string[1])
    self.assertEqual(SLASH + '\x0f' + SLASH + 'b', message.repeated_string[2])
    self.assertEqual(SLASH + SLASH + 'xf' + SLASH + SLASH + 'x62',
                     message.repeated_string[3])
    self.assertEqual(SLASH + SLASH + '\x0f' + SLASH + SLASH + 'b',
                     message.repeated_string[4])
    self.assertEqual(SLASH + 'x20', message.repeated_string[5]) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testParseMessageSet(self):
    message = unittest_pb2.TestAllTypes()
    text = ('repeated_uint64: 1\n' 'repeated_uint64: 2\n')
    text_format.Parse(text, message)
    self.assertEqual(1, message.repeated_uint64[0])
    self.assertEqual(2, message.repeated_uint64[1])

    message = unittest_mset_pb2.TestMessageSetContainer()
    text = ('message_set {\n'
            '  [protobuf_unittest.TestMessageSetExtension1] {\n'
            '    i: 23\n'
            '  }\n'
            '  [protobuf_unittest.TestMessageSetExtension2] {\n'
            '    str: \"foo\"\n'
            '  }\n'
            '}\n')
    text_format.Parse(text, message)
    ext1 = unittest_mset_pb2.TestMessageSetExtension1.message_set_extension
    ext2 = unittest_mset_pb2.TestMessageSetExtension2.message_set_extension
    self.assertEqual(23, message.message_set.Extensions[ext1].i)
    self.assertEqual('foo', message.message_set.Extensions[ext2].str) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testMergeExpandedAny(self):
    message = any_test_pb2.TestAny()
    text = ('any_value {\n'
            '  [type.googleapis.com/protobuf_unittest.OneString] {\n'
            '    data: "string"\n'
            '  }\n'
            '}\n')
    text_format.Merge(text, message)
    packed_message = unittest_pb2.OneString()
    message.any_value.Unpack(packed_message)
    self.assertEqual('string', packed_message.data)
    message.Clear()
    text_format.Parse(text, message)
    packed_message = unittest_pb2.OneString()
    message.any_value.Unpack(packed_message)
    self.assertEqual('string', packed_message.data) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testCreateLexiconContext(self):
    expected_context = task_spec_pb2.TaskSpec()
    text_format.Parse(_EXPECTED_CONTEXT, expected_context)
    self.assertProtoEquals(
        lexicon.create_lexicon_context('/tmp'), expected_context) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def LoadSpec(self, spec_path):
    master_spec = spec_pb2.MasterSpec()
    testdata = os.path.join(FLAGS.test_srcdir,
                            'dragnn/core/testdata')
    with file(os.path.join(testdata, spec_path), 'r') as fin:
      text_format.Parse(fin.read().replace('TESTDATA', testdata), master_spec)
      return master_spec 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testFailsOnNonIdentityTranslator(self):
    component_spec = spec_pb2.ComponentSpec()
    text_format.Parse("""
        name: "test"
        network_unit {
          registered_name: "IdentityNetwork"
        }
        linked_feature {
          name: "features" embedding_dim: -1 size: 1
          source_translator: "history"
          source_component: "mock"
        }
        """, component_spec)

    # For feature extraction:
    with tf.Graph().as_default():
      comp = bulk_component.BulkFeatureExtractorComponentBuilder(
          self.master, component_spec)

      # Expect feature extraction to generate a error due to the "history"
      # translator.
      with self.assertRaises(NotImplementedError):
        comp.build_greedy_training(self.master_state, self.network_states)

    # As well as annotation:
    with tf.Graph().as_default():
      comp = bulk_component.BulkAnnotatorComponentBuilder(
          self.master, component_spec)

      with self.assertRaises(NotImplementedError):
        comp.build_greedy_training(self.master_state, self.network_states) 

# 需要导入模块: from google.protobuf import text_format [as 别名]
# 或者: from google.protobuf.text_format import Parse [as 别名]
def testFailsOnRecurrentLinkedFeature(self):
    component_spec = spec_pb2.ComponentSpec()
    text_format.Parse("""
        name: "test"
        network_unit {
          registered_name: "FeedForwardNetwork"
          parameters {
            key: 'hidden_layer_sizes' value: '64'
          }
        }
        linked_feature {
          name: "features" embedding_dim: -1 size: 1
          source_translator: "identity"
          source_component: "test"
          source_layer: "layer_0"
        }
        """, component_spec)

    # For feature extraction:
    with tf.Graph().as_default():
      comp = bulk_component.BulkFeatureExtractorComponentBuilder(
          self.master, component_spec)

      # Expect feature extraction to generate a error due to the "history"
      # translator.
      with self.assertRaises(RuntimeError):
        comp.build_greedy_training(self.master_state, self.network_states)

    # As well as annotation:
    with tf.Graph().as_default():
      comp = bulk_component.BulkAnnotatorComponentBuilder(
          self.master, component_spec)

      with self.assertRaises(RuntimeError):
        comp.build_greedy_training(self.master_state, self.network_states)