def _create_metadata()

in tensorflow_examples/lite/model_maker/core/task/metadata_writer_for_image_classifier.py [0:0]

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

  def _create_metadata(self):
    """Creates the metadata for an image classifier."""

    # Creates model info.
    model_meta = _metadata_fb.ModelMetadataT()
    model_meta.name = self.model_info.name
    model_meta.description = ("Identify the most prominent object in the "
                              "image from a set of %d categories." %
                              self.model_info.num_classes)
    model_meta.version = self.model_info.version
    model_meta.author = self.model_info.author
    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 = "image"
    input_meta.description = (
        "Input image to be classified. The expected image is {0} x {1}, with "
        "three channels (red, blue, and green) per pixel. Each value in the "
        "tensor is a single byte between {2} and {3}.".format(
            self.model_info.image_width, self.model_info.image_height,
            self.model_info.image_min, self.model_info.image_max))
    input_meta.content = _metadata_fb.ContentT()
    input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
    input_meta.content.contentProperties.colorSpace = (
        _metadata_fb.ColorSpaceType.RGB)
    input_meta.content.contentPropertiesType = (
        _metadata_fb.ContentProperties.ImageProperties)
    input_normalization = _metadata_fb.ProcessUnitT()
    input_normalization.optionsType = (
        _metadata_fb.ProcessUnitOptions.NormalizationOptions)
    input_normalization.options = _metadata_fb.NormalizationOptionsT()
    input_normalization.options.mean = self.model_info.mean
    input_normalization.options.std = self.model_info.std
    input_meta.processUnits = [input_normalization]
    input_stats = _metadata_fb.StatsT()
    input_stats.max = [self.model_info.image_max]
    input_stats.min = [self.model_info.image_min]
    input_meta.stats = input_stats

    # Creates output info.
    output_meta = _metadata_fb.TensorMetadataT()
    output_meta.name = "probability"
    output_meta.description = "Probabilities of the %d labels respectively." % self.model_info.num_classes
    output_meta.content = _metadata_fb.ContentT()
    output_meta.content.content_properties = _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.label_file_path)
    label_file.description = "Labels for objects that the model can recognize."
    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()