def _create_metadata()

in tensorflow_examples/lite/model_maker/core/task/metadata_writers/text_classifier/metadata_writer_for_text_classifier.py [0:0]

• 56 lines of code
• 1 McCabe index (conditional complexity)

  def _create_metadata(self):
    """Creates the metadata for a text classifier."""

    # Creates model info.
    model_meta = _metadata_fb.ModelMetadataT()
    model_meta.name = self.model_info.name
    model_meta.description = self.model_info.description
    model_meta.version = self.model_info.version
    model_meta.author = "TensorFlow Lite Model Maker"
    model_meta.license = ("Apache License. Version 2.0 "
                          "http://www.apache.org/licenses/LICENSE-2.0.")

    # Creates input info.
    input_meta = _metadata_fb.TensorMetadataT()
    input_meta.name = "input_text"
    input_meta.description = (
        "Embedding vectors representing the input text to be classified. The "
        "input need to be converted from raw text to embedding vectors using "
        "the attached dictionary file.")
    # Create the vocab file.
    vocab_file = _metadata_fb.AssociatedFileT()
    vocab_file.name = os.path.basename(self.associated_files[1])
    vocab_file.description = ("Vocabulary file to convert natural language "
                              "words to embedding vectors.")
    vocab_file.type = _metadata_fb.AssociatedFileType.VOCABULARY

    # Create the RegexTokenizer.
    tokenizer = _metadata_fb.ProcessUnitT()
    tokenizer.optionsType = (
        _metadata_fb.ProcessUnitOptions.RegexTokenizerOptions)
    tokenizer.options = _metadata_fb.RegexTokenizerOptionsT()
    tokenizer.options.delimRegexPattern = self.model_info.delim_regex_pattern
    tokenizer.options.vocabFile = [vocab_file]

    input_meta.content = _metadata_fb.ContentT()
    input_meta.content.contentPropertiesType = (
        _metadata_fb.ContentProperties.FeatureProperties)
    input_meta.content.contentProperties = _metadata_fb.FeaturePropertiesT()
    input_meta.processUnits = [tokenizer]

    # Creates output info.
    output_meta = _metadata_fb.TensorMetadataT()
    output_meta.name = "probability"
    output_meta.description = "Probabilities of the labels respectively."
    output_meta.content = _metadata_fb.ContentT()
    output_meta.content.contentProperties = _metadata_fb.FeaturePropertiesT()
    output_meta.content.contentPropertiesType = (
        _metadata_fb.ContentProperties.FeatureProperties)
    output_stats = _metadata_fb.StatsT()
    output_stats.max = [1.0]
    output_stats.min = [0.0]
    output_meta.stats = output_stats
    label_file = _metadata_fb.AssociatedFileT()
    label_file.name = os.path.basename(self.associated_files[0])
    label_file.description = ("Labels for the categories that the model can "
                              "classify.")
    label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
    output_meta.associatedFiles = [label_file]

    # Creates subgraph info.
    subgraph = _metadata_fb.SubGraphMetadataT()
    subgraph.inputTensorMetadata = [input_meta]
    subgraph.outputTensorMetadata = [output_meta]
    model_meta.subgraphMetadata = [subgraph]

    b = flatbuffers.Builder(0)
    b.Finish(
        model_meta.Pack(b),
        _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
    self.metadata_buf = b.Output()