          config.gpu_options.visible_device_list = "0"
          set_session(tf.Session(config=config))
          logger.debug("Set Tensorflow 'allow_growth' option")
  
      def show(self, image, name=""):
          """ Generate the preview and write preview file output """
          logger.trace("Updating preview: (name: %s)", name)
          try:
              scriptpath = os.path.realpath(os.path.dirname(sys.argv[0]))
              if self.args.write_image:
                  logger.trace("Saving preview to disk")
 
</s> remove             if self.args.write_image:
 </s> add             if self._args.write_image: </s> remove     @staticmethod
    def set_tf_allow_growth():
        """ Allow TensorFlow to manage VRAM growth """
 </s> add         Enables the Tensorflow allow_growth option if requested in the command line arguments
        """ </s> remove                 with self.lock:
                    self.preview_buffer[name] = image
 </s> add                 with self._lock:
                    self._preview_buffer[name] = image </s> remove         """ Generate the preview if a preview iteration """
 </s> add         """ Generate the preview images.

        Parameters
        ----------
        do_preview: bool
            Whether the previews should be generated. ``True`` if they should ``False`` if they
            should not be generated, in which case currently stored previews should be deleted.
        """ </s> remove             self.samples = None
            self.target = None
 </s> add             self._samples = None
            self._target = None
            self._masks = None
    def _show(self, image, name=""):
        """ Generate the preview and write preview file output.

        Handles the output and display of preview images.

        Parameters
        ----------
        image: :class:`numpy.ndarray`
            The preview image to be displayed and/or written out
        name: str, optional
            The name of the image for saving or display purposes. If an empty string is passed
            then it will automatically be names. Default: ""
        """
          config.gpu_options.visible_device_list = "0"
          set_session(tf.Session(config=config))
          logger.debug("Set Tensorflow 'allow_growth' option")
  
    def _show(self, image, name=""):
        """ Generate the preview and write preview file output.

        Handles the output and display of preview images.

        Parameters
        ----------
        image: :class:`numpy.ndarray`
            The preview image to be displayed and/or written out
        name: str, optional
            The name of the image for saving or display purposes. If an empty string is passed
            then it will automatically be names. Default: ""
        """
    def _show(self, image, name=""):
        """ Generate the preview and write preview file output.

        Handles the output and display of preview images.

        Parameters
        ----------
        image: :class:`numpy.ndarray`
            The preview image to be displayed and/or written out
        name: str, optional
            The name of the image for saving or display purposes. If an empty string is passed
            then it will automatically be names. Default: ""
        """
          logger.trace("Updating preview: (name: %s)", name)
          try:
              scriptpath = os.path.realpath(os.path.dirname(sys.argv[0]))
              if self.args.write_image:
                  logger.trace("Saving preview to disk")
keep keep keep keep replace 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. 
scripts/train.py