                  feeds[side] = self.resize_sample(side, faces, self.model.input_shape[0])
                  feeds[side] = feeds[side].reshape((-1, ) + self.model.input_shape)
              else:
                  feeds[side] = faces
              if self.use_mask:
                  mask = samples[-1]
                  feeds[side] = [feeds[side], mask]
  
          preds = self.get_predictions(feeds["a"], feeds["b"])
  
 
</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         preds = self.get_predictions(feeds["a"], feeds["b"])
 </s> add         preds = self._get_predictions(feeds["a"], feeds["b"]) </s> remove         target_size = int(full_size * self.coverage_ratio)
 </s> add         target_size = int(full_size * self._coverage_ratio) </s> remove             frame = self.frame_overlay(full, target_size, (0, 0, 255))
 </s> add             frame = self._frame_overlay(full, target_size, (0, 0, 255)) </s> remove     def transform_landmarks(self, alignments):
        """ For each face transform landmarks and return """
        landmarks = dict()
        for _, faces, _, _ in alignments.yield_faces():
            for face in faces:
 </s> add     def _to_detected_faces(self, alignments, side):
        """ Convert alignments to DetectedFace objects.

        Filter the detected faces to only those that exist in the training folders.

        Parameters
        ----------
        alignments: :class:`lib.alignments.Alignments`
            The alignments for the current faces
        side: {"a" or "b"}
            The side being processed

        Returns
        -------
        list
            List of :class:`lib.faces_detect.DetectedFace` objects
        """
        skip_count = 0
        side_hashes = set(self._hashes[side]["hashes"])
        detected_faces = []
        for _, faces, _, filename in alignments.yield_faces():
            for idx, face in enumerate(faces):
                if not self._validate_face(face, filename, idx, side, side_hashes):
                    skip_count += 1
                    continue
            if self._use_mask:
                  feeds[side] = self.resize_sample(side, faces, self.model.input_shape[0])
                  feeds[side] = feeds[side].reshape((-1, ) + self.model.input_shape)
              else:
                  feeds[side] = faces
            if self._use_mask:
                  mask = samples[-1]
                  feeds[side] = [feeds[side], mask]
  
          preds = self.get_predictions(feeds["a"], feeds["b"])
  
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