              tensorboard.on_train_end(None)
  
  
  class Batcher():
      """ Batch images from a single side """
      def __init__(self, side, images, model, use_mask, batch_size, config):
          logger.debug("Initializing %s: side: '%s', num_images: %s, batch_size: %s, config: %s)",
                       self.__class__.__name__, side, len(images), batch_size, config)
          self.model = model
          self.use_mask = use_mask
 
</s> remove         logger.debug("Initializing %s: side: '%s', num_images: %s, batch_size: %s, config: %s)",
                     self.__class__.__name__, side, len(images), batch_size, config)
        self.model = model
        self.use_mask = use_mask
        self.side = side
        self.images = images
        self.config = config
        self.target = None
        self.samples = None
        self.mask = None

        generator = self.load_generator()
        self.feed = generator.minibatch_ab(images, batch_size, self.side)

        self.preview_feed = None
        self.timelapse_feed = None

    def load_generator(self):
        """ Pass arguments to TrainingDataGenerator and return object """
        logger.debug("Loading generator: %s", self.side)
        input_size = self.model.input_shape[0]
        output_shapes = self.model.output_shapes
 </s> add         logger.debug("Initializing %s: side: '%s', num_images: %s, use_mask: %s, batch_size: %s, "
                     "config: %s)",
                     self.__class__.__name__, side, len(images), use_mask, batch_size, config)
        self._model = model
        self._use_mask = use_mask
        self._side = side
        self._images = images
        self._config = config
        self._target = None
        self._samples = None
        self._masks = None

        generator = self._load_generator()
        self._feed = generator.minibatch_ab(images, batch_size, self._side)

        self._preview_feed = None
        self._timelapse_feed = None
        self._set_preview_feed()

    def _load_generator(self):
        """ Load the :class:`lib.training_data.TrainingDataGenerator` for this batcher """
        logger.debug("Loading generator: %s", self._side)
        input_size = self._model.input_shape[0]
        output_shapes = self._model.output_shapes </s> remove         self.model = model
        self.use_mask = use_mask
 </s> add         self._model = model
        self._use_mask = use_mask </s> remove                      "preview_images: %s, batchers: '%s')", self.__class__.__name__, model,
                     use_mask, coverage_ratio, preview_images, batchers)
        self.preview_images = preview_images
        self.samples = Samples(model, use_mask, coverage_ratio)
        self.model = model
        self.batchers = batchers
        self.output_file = None
 </s> add                      "image_count: %s, batchers: '%s')", self.__class__.__name__, model,
                     use_mask, coverage_ratio, image_count, batchers)
        self._num_images = image_count
        self._samples = Samples(model, use_mask, coverage_ratio)
        self._model = model
        self._batchers = batchers
        self._output_file = None </s> remove                      "training_opts: %s, landmarks: %s, config: %s)",
 </s> add                      "training_opts: %s, landmarks: %s, masks: %s, config: %s)", </s> remove         self.config = get_config(".".join(self.__module__.split(".")[-2:]), configfile=configfile)
        self.batch_size = batch_size
        self.model = model
        self.model.state.add_session_batchsize(batch_size)
        self.images = images
        self.sides = sorted(key for key in self.images.keys())

        self.process_training_opts()
        self.pingpong = PingPong(model, self.sides)

        self.batchers = {side: Batcher(side,
                                       images[side],
                                       self.model,
                                       self.use_mask,
                                       batch_size,
                                       self.config)
                         for side in self.sides}

        self.tensorboard = self.set_tensorboard()
        self.samples = Samples(self.model,
                               self.use_mask,
                               self.model.training_opts["coverage_ratio"],
                               self.model.training_opts["preview_scaling"])
        self.timelapse = Timelapse(self.model,
                                   self.use_mask,
                                   self.model.training_opts["coverage_ratio"],
                                   self.config.get("preview_images", 14),
                                   self.batchers)
 </s> add         self._config = _get_config(".".join(self.__module__.split(".")[-2:]),
                                   configfile=configfile)
        self._model = model
        self._model.state.add_session_batchsize(batch_size)
        self._images = images
        self._sides = sorted(key for key in self._images.keys())

        self._process_training_opts()
        self._pingpong = PingPong(model, self._sides)

        self._batchers = {side: Batcher(side,
                                        images[side],
                                        self._model,
                                        self._use_mask,
                                        batch_size,
                                        self._config)
                          for side in self._sides}

        self._tensorboard = self._set_tensorboard()
        self._samples = Samples(self._model,
                                self._use_mask,
                                self._model.training_opts["coverage_ratio"],
                                self._model.training_opts["preview_scaling"])
        self._timelapse = Timelapse(self._model,
                                    self._use_mask,
                                    self._model.training_opts["coverage_ratio"],
                                    self._config.get("preview_images", 14),
                                    self._batchers)
    """ Handles the processing of a Batch for a single side.

    Parameters
    ----------
    side: {"a" or "b"}
        The side that this :class:`Batcher` belongs to
    images: list
        The list of full paths to the training images for this :class:`Batcher`
    model: plugin from :mod:`plugins.train.model`
        The selected model that will be running this trainer
    use_mask: bool
        ``True`` if a mask is required for training otherwise ``False``
    batch_size: int
        The size of the batch to be processed at each iteration
    config: :class:`lib.config.FaceswapConfig`
        The configuration for this trainer
    """
              tensorboard.on_train_end(None)
  
  
  class Batcher():
    """ Handles the processing of a Batch for a single side.

    Parameters
    ----------
    side: {"a" or "b"}
        The side that this :class:`Batcher` belongs to
    images: list
        The list of full paths to the training images for this :class:`Batcher`
    model: plugin from :mod:`plugins.train.model`
        The selected model that will be running this trainer
    use_mask: bool
        ``True`` if a mask is required for training otherwise ``False``
    batch_size: int
        The size of the batch to be processed at each iteration
    config: :class:`lib.config.FaceswapConfig`
        The configuration for this trainer
    """
      def __init__(self, side, images, model, use_mask, batch_size, config):
          logger.debug("Initializing %s: side: '%s', num_images: %s, batch_size: %s, config: %s)",
                       self.__class__.__name__, side, len(images), batch_size, config)
          self.model = model
          self.use_mask = use_mask
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