  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  
  class TrainingDataGenerator():
      """ A Training Data Generator for compiling data for feeding to a model.
  
      This class is called from :mod:`plugins.train.trainer._base` and launches a background
      iterator that compiles augmented data, target data and sample data.
  
 
</s> remove         self._masks = alignments.get("masks", None)
 </s> add         self._masks = dict(masks=alignments.get("masks", None),
                           eyes=alignments.get("masks_eye", None),
                           mouths=alignments.get("masks_mouth", None)) </s> remove     def _get_next(self, side):
        """ Return the next batch from the :class:`lib.training_data.TrainingDataGenerator` for
        this feeder ready for feeding into the model.

        Returns
        -------
        model_inputs: list
            A list of :class:`numpy.ndarray` for feeding into the model
        model_targets: list
            A list of :class:`numpy.ndarray` for comparing the output of the model
        """
        logger.trace("Generating targets")
        batch = next(self._feeds[side])
        targets_use_mask = (self._model.config["learn_mask"]
                            or self._model.config["penalized_mask_loss"])
        model_targets = batch["targets"] + batch["masks"] if targets_use_mask else batch["targets"]
        return batch["feed"], model_targets

    @classmethod
    def _compile_masks(cls, targets):
        """ Compile the masks into the targets for penalized loss.
 </s> add     def _compile_mask_targets(self, targets, masks, additional_masks):
        """ Compile the masks into the targets for penalized loss and for targeted learning. </s> remove     single output.

    Parameters
    ----------
    loss_functions: list
        A list of either a tuple of (:class:`keras.losses.Loss`, scalar weight) or just a
        :class:`keras.losses.Loss` function. If just the loss function is passed, then the weight
        is assumed to be 1.0 """
    def __init__(self, loss_functions):
        logger.debug("Initializing: %s: (loss_functions: %s)",
                     self.__class__.__name__, loss_functions)
 </s> add     single output and masking.
    """
    def __init__(self):
        logger.debug("Initializing: %s", self.__class__.__name__) </s> remove class PenalizedLoss():  # pylint:disable=too-few-public-methods
    """ Penalized Loss function.

    Applies the given loss function just to the masked area of the image.

    Parameters
    ----------
    loss_func: function
        The actual loss function to use
    mask_prop: float, optional
        The amount of mask propagation. Default: `1.0`
    """
    def __init__(self, loss_func, mask_prop=1.0):
        self._loss_func = loss_func
        self._mask_prop = mask_prop

    def __call__(self, y_true, y_pred):
        """ Apply the loss function to the masked area of the image.

        Parameters
        ----------
        y_true: tensor or variable
            The ground truth value. This should contain the mask in the 4th channel that will be
            split off for penalizing.
        y_pred: tensor or variable
            The predicted value

        Returns
        -------
        tensor
            The Loss value
        """
        mask = self._prepare_mask(K.expand_dims(y_true[..., -1], axis=-1))
        y_true = y_true[..., :-1]
        n_true = y_true * mask
        n_pred = y_pred * mask
        if isinstance(self._loss_func, DSSIMObjective):
            # Extract Image Patches in SSIM requires that y_pred be of a known shape, so
            # specifically reshape the tensor.
            n_pred = K.reshape(n_pred, K.int_shape(y_pred))
        return self._loss_func(n_true, n_pred)

    def _prepare_mask(self, mask):
        """ Prepare the masks for calculating loss

        Parameters
        ----------
        mask: :class:`numpy.ndarray`
            The masks for the current batch

        Returns
        -------
        tensor
            The prepared mask for applying to loss
        """
        mask_as_k_inv_prop = 1 - self._mask_prop
        mask = (mask * self._mask_prop) + mask_as_k_inv_prop
        return mask


 </s> add  </s> remove 
    Parameters
    ----------
    loss_functions: list
        A list of either a tuple of (:class:`keras.losses.Loss`, scalar weight) or just a
        :class:`keras.losses.Loss` function. If just the loss function is passed, then the weight
        is assumed to be 1.0 """
    def __init__(self, loss_functions):
        logger.debug("Initializing: %s: (loss_functions: %s)",
                     self.__class__.__name__, loss_functions)
 </s> add     """
    def __init__(self):
        logger.debug("Initializing: %s", self.__class__.__name__)
class TrainingDataGenerator():  # pylint:disable=too-few-public-methods
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  
class TrainingDataGenerator():  # pylint:disable=too-few-public-methods
      """ A Training Data Generator for compiling data for feeding to a model.
  
      This class is called from :mod:`plugins.train.trainer._base` and launches a background
      iterator that compiles augmented data, target data and sample data.
  
keep keep keep keep replace keep keep keep keep keep
 Add Mouth and Eye Priority to Loss options (#1054)

* Priority Training for Mouth and Eyes - Tensorflow

* Use chosen loss function for area multipliers

* loss multipliers for AMD

* Fix mask multipliers for plaid and roll PenalizedMaskLoss into LossWrapper

* losses_tf: roll PenalizedMaskLoss into LossWrapper 
lib/training_data.py