Python google.protobuf.text_format.Parse() Examples
The following are 30 code examples for showing how to use google.protobuf.text_format.Parse(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: DOTA_models Author: ringringyi File: graph_builder_test.py License: Apache License 2.0 | 6 votes |
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})
Example 2
Project: DOTA_models Author: ringringyi File: bulk_component_test.py License: Apache License 2.0 | 6 votes |
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)
Example 3
Project: DOTA_models Author: ringringyi File: bulk_component_test.py License: Apache License 2.0 | 6 votes |
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)
Example 4
Project: DOTA_models Author: ringringyi File: bulk_component_test.py License: Apache License 2.0 | 6 votes |
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)
Example 5
Project: ffn Author: google File: run_inference.py License: Apache License 2.0 | 6 votes |
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)
Example 6
Project: lingvo Author: tensorflow File: predictor.py License: Apache License 2.0 | 6 votes |
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
Example 7
Project: mobile-ai-bench Author: XiaoMi File: bench_engine.py License: Apache License 2.0 | 6 votes |
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
Example 8
Project: lambda-packs Author: ryfeus File: text_format_test.py License: MIT License | 6 votes |
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])
Example 9
Project: lambda-packs Author: ryfeus File: text_format_test.py License: MIT License | 6 votes |
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])
Example 10
Project: lambda-packs Author: ryfeus File: text_format_test.py License: MIT License | 6 votes |
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)
Example 11
Project: lambda-packs Author: ryfeus File: text_format_test.py License: MIT License | 6 votes |
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)
Example 12
Project: auto-alt-text-lambda-api Author: abhisuri97 File: text_format_test.py License: MIT License | 6 votes |
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])
Example 13
Project: auto-alt-text-lambda-api Author: abhisuri97 File: text_format_test.py License: MIT License | 6 votes |
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)
Example 14
Project: DOTA_models Author: ringringyi File: lexicon_test.py License: Apache License 2.0 | 5 votes |
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)
Example 15
Project: DOTA_models Author: ringringyi File: graph_builder_test.py License: Apache License 2.0 | 5 votes |
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
Example 16
Project: DOTA_models Author: ringringyi File: bulk_component_test.py License: Apache License 2.0 | 5 votes |
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)
Example 17
Project: DOTA_models Author: ringringyi File: bulk_component_test.py License: Apache License 2.0 | 5 votes |
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)
Example 18
Project: DOTA_models Author: ringringyi File: bulk_component_test.py License: Apache License 2.0 | 5 votes |
def testNormalFixedFeaturesAreDifferentiable(self): component_spec = spec_pb2.ComponentSpec() text_format.Parse(""" name: "test" network_unit { registered_name: "IdentityNetwork" } fixed_feature { name: "fixed" embedding_dim: 32 size: 1 pretrained_embedding_matrix { part {} } vocab { part {} } } component_builder { registered_name: "bulk_component.BulkFeatureExtractorComponentBuilder" } """, component_spec) with tf.Graph().as_default(): comp = bulk_component.BulkFeatureExtractorComponentBuilder( self.master, component_spec) # Get embedding matrix variables. with tf.variable_scope(comp.name, reuse=True): fixed_embedding_matrix = tf.get_variable( network_units.fixed_embeddings_name(0)) # Get output layer. comp.build_greedy_training(self.master_state, self.network_states) activations = self.network_states[comp.name].activations outputs = activations[comp.network.layers[0].name].bulk_tensor # Compute the gradient of the output layer w.r.t. the embedding matrix. # This should be well-defined for in the normal case. gradients = tf.gradients(outputs, fixed_embedding_matrix) self.assertEqual(len(gradients), 1) self.assertFalse(gradients[0] is None)
Example 19
Project: DOTA_models Author: ringringyi File: bulk_component_test.py License: Apache License 2.0 | 5 votes |
def testBulkFeatureIdExtractorFailsOnEmbeddedFixedFeature(self): component_spec = spec_pb2.ComponentSpec() text_format.Parse(""" name: "test" network_unit { registered_name: "IdentityNetwork" } fixed_feature { name: "fixed" embedding_dim: 2 size: 1 } """, component_spec) with tf.Graph().as_default(): with self.assertRaises(ValueError): unused_comp = bulk_component.BulkFeatureIdExtractorComponentBuilder( self.master, component_spec)
Example 20
Project: DOTA_models Author: ringringyi File: spec_builder_test.py License: Apache License 2.0 | 5 votes |
def assertSpecEqual(self, expected_spec_text, spec): expected_spec = spec_pb2.ComponentSpec() text_format.Parse(expected_spec_text, expected_spec) self.assertProtoEquals(expected_spec, spec)
Example 21
Project: DOTA_models Author: ringringyi File: model_trainer.py License: Apache License 2.0 | 5 votes |
def _read_text_proto(path, proto_type): """Reads a text-format instance of |proto_type| from the |path|.""" proto = proto_type() with tf.gfile.FastGFile(path) as proto_file: text_format.Parse(proto_file.read(), proto) return proto
Example 22
Project: recipes-py Author: luci File: definition.py License: Apache License 2.0 | 5 votes |
def parse_warning_definitions(file_path): """Parse the warning definition file at the given absolute path. The file content is expected to be in text proto format of warning.DefinitionCollection proto message. Duplicate warning names will be raised. Each warning definition will be validated. The conditions are documented in warning.proto. Args: * file_path (str) - Absolute path to warning definition file Returns a dict of warning name to warning.Definition proto message instance """ raw_text = '' try: with open(file_path, encoding='utf-8') as f: raw_text = f.read() except IOError as ex: if ex.errno == errno.ENOENT: # No warning defined return {} raise ex from PB.recipe_engine.warning import DefinitionCollection definition_collection = textpb.Parse(raw_text, DefinitionCollection()) definitions = list(definition_collection.warning) if definition_collection.HasField('monorail_bug_default'): _populate_monorail_bug_default_fields( definitions, definition_collection.monorail_bug_default) ret = {} for definition in definitions: if definition.name in ret: raise ValueError( 'Found warning definitions with duplicate name: %s' % definition.name) _validate(definition) ret[definition.name] = definition return ret
Example 23
Project: vehicle_counting_tensorflow Author: ahmetozlu File: exporter_test.py License: MIT License | 5 votes |
def _load_inference_graph(self, inference_graph_path, is_binary=True): od_graph = tf.Graph() with od_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(inference_graph_path) as fid: if is_binary: od_graph_def.ParseFromString(fid.read()) else: text_format.Parse(fid.read(), od_graph_def) tf.import_graph_def(od_graph_def, name='') return od_graph
Example 24
Project: turkish-morphology Author: google-research File: pretty_print_test.py License: Apache License 2.0 | 5 votes |
def _read_analysis(basename): path = os.path.join(_TESTDATA_DIR, f"{basename}.pbtxt") return text_format.Parse(_read_file(path), analysis_pb2.Analysis())
Example 25
Project: turkish-morphology Author: google-research File: decompose_test.py License: Apache License 2.0 | 5 votes |
def _read_analysis(basename): path = os.path.join(_TESTDATA_DIR, f"{basename}.pbtxt") return text_format.Parse(_read_file(path), analysis_pb2.Analysis())
Example 26
Project: turkish-morphology Author: google-research File: validate_test.py License: Apache License 2.0 | 5 votes |
def _read_analysis(basename): path = os.path.join(_TESTDATA_DIR, f"{basename}.pbtxt") return text_format.Parse(_read_file(path), analysis_pb2.Analysis())
Example 27
Project: turkish-morphology Author: google-research File: generate_test.py License: Apache License 2.0 | 5 votes |
def _read_analysis(basename): path = os.path.join(_TESTDATA_DIR, f"{basename}.pbtxt") return text_format.Parse(_read_file(path), analysis_pb2.Analysis())
Example 28
Project: turkish-morphology Author: google-research File: parser_test.py License: Apache License 2.0 | 5 votes |
def test_success(self, entries, expected_pbtxt): actual = parser.parse(entries) expected = rule_pb2.RewriteRuleSet() text_format.Parse(expected_pbtxt, expected) self.assertEqual(expected, actual)
Example 29
Project: ffn Author: google File: inference_flags.py License: Apache License 2.0 | 5 votes |
def options_from_flags(): options = inference_pb2.InferenceOptions() if FLAGS.inference_options: text_format.Parse(FLAGS.inference_options, options) return options
Example 30
Project: ffn Author: google File: inference_flags.py License: Apache License 2.0 | 5 votes |
def request_from_flags(): request = inference_pb2.InferenceRequest() if FLAGS.inference_request: text_format.Parse(FLAGS.inference_request, request) return request