  
      @property
      def input_mask(self):
          """ Return the input mask """
          mask = np.zeros(self.model.state.mask_shapes[0], dtype="float32")
          retval = np.expand_dims(mask, 0)
          return retval
  
      @property
      def has_predicted_mask(self):
          """ Return whether this model has a predicted mask """
          return bool(self.model.state.mask_shapes)
 
</s> remove         dummy = np.zeros((20, 20, 3)).astype("uint8")
 </s> add         dummy = np.zeros((20, 20, 3), dtype="uint8") </s> remove         if swap:
            model = self.predictors["a"]
        else:
            model = self.predictors["b"]
 </s> add         side = "a" if swap else "b"
        model = self.predictors[side] </s> remove         factor = dict(gaussian=0.8,
                      normalized=0.5)
 </s> add         factor = dict(gaussian=0.8, normalized=0.5) </s> remove                       choices=get_available_masks(), gui_radio=True,
 </s> add                       choices=get_available_masks(), </s> remove                 face_func = PenalizedLoss(mask[0], loss_func)
                logger.debug("final face loss: %s", face_func)
                loss_funcs.append(face_func)
                if not predict and side.lower() == "a":
                    logger.verbose("Penalizing mask for Loss")
 </s> add                 loss_funcs.append(PenalizedLoss(mask[0], loss_dict[img_loss_config]))
                logger.debug("final face loss: %s", img_loss_config)
        mask = np.zeros((1, ) + self.model.state.mask_shapes[0], dtype="float32")
        return mask
  
      @property
      def input_mask(self):
          """ Return the input mask """
        mask = np.zeros((1, ) + self.model.state.mask_shapes[0], dtype="float32")
        return mask
        mask = np.zeros((1, ) + self.model.state.mask_shapes[0], dtype="float32")
        return mask
        mask = np.zeros((1, ) + self.model.state.mask_shapes[0], dtype="float32")
        return mask
  
      @property
      def has_predicted_mask(self):
          """ Return whether this model has a predicted mask """
          return bool(self.model.state.mask_shapes)
keep keep keep keep replace replace replace keep keep keep keep keep
 documentation, pep8, style, clarity updates  - Prep for Segmentation (#812)

* documentation, pep8, style, clarity updates

* Update cli.py

* Update _config.py

remove extra mask and coverage

mask type as dropdown

* Update training_data.py

move coverage / LR to global
cut down on loss description

style change
losses working in PR

* simpler logging

* legacy update 
scripts/convert.py