              if self.use_mask:
                  mask = samples[-1]
                  feeds[side] = [feeds[side], mask]
  
          preds = self.get_predictions(feeds["a"], feeds["b"])
  
          for side, samples in self.images.items():
              other_side = "a" if side == "b" else "b"
              predictions = [preds["{0}_{0}".format(side)],
                             preds["{}_{}".format(other_side, side)]]
 
</s> remove             display = self.to_full_frame(side, samples, predictions)
            headers[side] = self.get_headers(side, display[0].shape[1])
 </s> add             display = self._to_full_frame(side, samples, predictions)
            headers[side] = self._get_headers(side, display[0].shape[1]) </s> remove             if self.use_mask:
 </s> add             if self._use_mask: </s> remove             if self.model.input_shape[0] / faces.shape[1] != 1.0:
                feeds[side] = self.resize_sample(side, faces, self.model.input_shape[0])
                feeds[side] = feeds[side].reshape((-1, ) + self.model.input_shape)
 </s> add             if self._model.input_shape[0] / faces.shape[1] != 1.0:
                feeds[side] = self._resize_sample(side, faces, self._model.input_shape[0])
                feeds[side] = feeds[side].reshape((-1, ) + self._model.input_shape) </s> remove     def get_predictions(self, feed_a, feed_b):
        """ Return the sample predictions from the model """
 </s> add     def _get_predictions(self, feed_a, feed_b):
        """ Feed the samples to the model and return predictions

        Parameters
        ----------
        feed_a: list
            List of :class:`numpy.ndarray` of feed images for the "a" side
        feed_a: list
            List of :class:`numpy.ndarray` of feed images for the "b" side

        Returns
        list:
            List of :class:`numpy.ndarray` of predictions received from the model
        """ </s> remove         height = int(64 * self.scaling)
 </s> add         height = int(64 * self._scaling)
        preds = self._get_predictions(feeds["a"], feeds["b"])
              if self.use_mask:
                  mask = samples[-1]
                  feeds[side] = [feeds[side], mask]
  
        preds = self._get_predictions(feeds["a"], feeds["b"])
  
          for side, samples in self.images.items():
              other_side = "a" if side == "b" else "b"
              predictions = [preds["{0}_{0}".format(side)],
                             preds["{}_{}".format(other_side, side)]]
keep keep keep keep replace keep keep keep keep keep
 Smart Masks - Training Implementation (#914)

* Smart Masks - Training

- Reinstate smart mask training code
- Reinstate mask_type back to model.config
- change 'replicate_input_mask to 'learn_mask'
- Add learn mask option
- Add mask loading from alignments to plugins.train.trainer
- Add mask_blur and mask threshold options
- _base.py - Pass mask options through training_opts dict
- plugins.train.model - check for mask_type not None for learn_mask and penalized_mask_loss
- Limit alignments loading to just those faces that appear in the training folder
- Raise error if not all training images have an alignment, and alignment file is required
- lib.training_data - Mask generation code
- lib.faces_detect - cv2 dimension stripping bugfix
- Remove cv2 linting code

* Update mask helptext in cli.py

* Fix Warp to Landmarks
Remove SHA1 hashing from training data

* Update mask training config

* Capture missing masks at training init

* lib.image.read_image_batch - Return filenames with batch for ordering

* scripts.train - Documentation

* plugins.train.trainer - documentation

* Ensure backward compatibility.
Fix convert for new predicted masks

* Update removed masks to components for legacy models. 
plugins/train/trainer/_base.py