          err = False
          while True:
              try:
                  if is_preview:
                      with self.lock:
                          for name, image in self.preview_buffer.items():
                              cv2.imshow(name, image)  # pylint: disable=no-member
                      cv_key = cv2.waitKey(1000)  # pylint: disable=no-member
                  else:
                      cv_key = None
  
 
</s> remove         keypress = KBHit(is_gui=self.args.redirect_gui)
 </s> add         keypress = KBHit(is_gui=self._args.redirect_gui) </s> remove                 with self.lock:
                    self.preview_buffer[name] = image
 </s> add                 with self._lock:
                    self._preview_buffer[name] = image </s> remove             if self.args.preview:
 </s> add             if self._args.preview: </s> remove             if self.args.redirect_gui:
 </s> add             if self._args.redirect_gui: </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
        """
                    with self._lock:
                        for name, image in self._preview_buffer.items():
          err = False
          while True:
              try:
                  if is_preview:
                    with self._lock:
                        for name, image in self._preview_buffer.items():
                    with self._lock:
                        for name, image in self._preview_buffer.items():
                              cv2.imshow(name, image)  # pylint: disable=no-member
                      cv_key = cv2.waitKey(1000)  # pylint: disable=no-member
                  else:
                      cv_key = None
  
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