                                "warp_to_landmarks": warp_to_landmarks,
                                "augment_color": augment_color,
                                "no_flip": no_flip,
                                "pingpong": self.vram_savings.pingpong,
                                "snapshot_interval": snapshot_interval}
  
          if self.multiple_models_in_folder:
              deprecation_warning("Support for multiple model types within the same folder",
                                  additional_info="Please split each model into separate folders to "
                                                  "avoid issues in future.")
 
</s> remove         self.set_training_data()
 </s> add  </s> remove     def get_images(self):
        """ Check the image folders exist, contain images and return the image
        objects """
 </s> add     def _get_images(self):
        """ Check the image folders exist and contains images and obtain image paths.

        Returns
        -------
        dict
            The image paths for each side. The key is the side, the value is the list of paths
            for that side.
        """ </s> remove                 preds[key] = val[self.model.largest_face_index]
 </s> add                 preds[key] = val[self._model.largest_face_index] </s> remove         if hasattr(self.args, "no_augment_color") and self.args.no_flip:
 </s> add         if hasattr(self._args, "no_augment_color") and self._args.no_flip:
                              "snapshot_interval": snapshot_interval,
                              "training_size": self.state.training_size,
                              "no_logs": self.state.current_session["no_logs"],
                              "coverage_ratio": self.calculate_coverage_ratio(),
                              "mask_type": self.config["mask_type"],
                              "mask_blur_kernel": self.config["mask_blur_kernel"],
                              "mask_threshold": self.config["mask_threshold"],
                              "learn_mask": (self.config["learn_mask"] and
                                             self.config["mask_type"] is not None),
                              "penalized_mask_loss": (self.config["penalized_mask_loss"] and
                                                      self.config["mask_type"] is not None)}
        logger.debug("training_opts: %s", self.training_opts)
                                "warp_to_landmarks": warp_to_landmarks,
                                "augment_color": augment_color,
                                "no_flip": no_flip,
                                "pingpong": self.vram_savings.pingpong,
                              "snapshot_interval": snapshot_interval,
                              "training_size": self.state.training_size,
                              "no_logs": self.state.current_session["no_logs"],
                              "coverage_ratio": self.calculate_coverage_ratio(),
                              "mask_type": self.config["mask_type"],
                              "mask_blur_kernel": self.config["mask_blur_kernel"],
                              "mask_threshold": self.config["mask_threshold"],
                              "learn_mask": (self.config["learn_mask"] and
                                             self.config["mask_type"] is not None),
                              "penalized_mask_loss": (self.config["penalized_mask_loss"] and
                                                      self.config["mask_type"] is not None)}
        logger.debug("training_opts: %s", self.training_opts)
  
          if self.multiple_models_in_folder:
              deprecation_warning("Support for multiple model types within the same folder",
                                  additional_info="Please split each model into separate folders to "
                                                  "avoid issues in future.")
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/model/_base.py