Method run

lit_nlp/components/image_gradient_maps.py:219–312

Runs the component, given a model and input(s).

(
      self,
      inputs: list[JsonDict],
      model: lit_model.Model,
      dataset: lit_dataset.Dataset,
      model_outputs: Optional[list[JsonDict]] = None,
      config: Optional[JsonDict] = None,
  )

  """A base class for all interpreters that use the Saliency library."""

  def run(
      self,
      inputs: list[JsonDict],
      model: lit_model.Model,
      dataset: lit_dataset.Dataset,
      model_outputs: Optional[list[JsonDict]] = None,
      config: Optional[JsonDict] = None,
  ) -> Optional[list[JsonDict]]:
    """Runs the component, given a model and input(s)."""
    input_spec = model.input_spec()
    output_spec = model.output_spec()
    config = config or {}

    if not inputs:
      return []

    # Find all fields required for the interpretation.
    supported_fields = find_supported_fields(input_spec, output_spec)
    if supported_fields is None:
      return None

    grad_field_key = supported_fields.grad_field_key
    image_field_key = supported_fields.image_field_key
    grad_target_field_key = supported_fields.grad_target_field_key

    if target_config := config.get(TARGET_INFO_KEY):
      preds_field_key = target_config['field']
    else:
      preds_field_key = supported_fields.preds_field_key

    preds_field_spec = output_spec[preds_field_key]
    if not isinstance(preds_field_spec, _SUPPORTED_PRED_TYPES):
      logging.warning(
          "Image Salience is not compatible with field '%s'", preds_field_key
      )
      return None

    # Determine the shape of gradients by calling the model with a single input
    # and extracting the shape from the gradient output.
    first_example_preds = list(model.predict([inputs[0]]))[0]
    grad_shape = first_example_preds[grad_field_key].shape

    # If it is a multiclass model, find the labels with respect to which we
    # should compute the gradients.
    if isinstance(preds_field_spec, types.MulticlassPreds):
      # Get class labels.
      label_vocab = list(preds_field_spec.vocab)

      if (target_config := config.get(TARGET_INFO_KEY)) and (
          target_class := target_config.get('index')
      ):
        grad_target_labels = [label_vocab[target_class] for _ in inputs]
      else:
        # Run the model in order to find the gradient target labels.
        outputs = list(model.predict(inputs))
        grad_target_labels = []
        for model_input, model_output in zip(inputs, outputs):
          if model_input.get(grad_target_field_key) is not None: