  from lib.config import FaceswapConfig
  from lib.utils import full_path_split
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  ADDITIONAL_INFO = ("\nNB: Unless specifically stated, values changed here will only take effect "
 
</s> remove from lib.model.masks import get_available_masks
 </s> add  </s> remove from lib.model import masks
 </s> add  </s> remove from lib.queue_manager import queue_manager  # noqa pylint:disable=unused-import
 </s> add  </s> remove         set_system_verbosity(self.args.loglevel)
        thread = self.start_thread()
        # queue_manager.debug_monitor(1)
 </s> add         set_system_verbosity(self._args.loglevel)
        thread = self._start_thread()
        # from lib.queue_manager import queue_manager; queue_manager.debug_monitor(1)
from plugins.plugin_loader import PluginLoader
  from lib.config import FaceswapConfig
  from lib.utils import full_path_split
from plugins.plugin_loader import PluginLoader
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  ADDITIONAL_INFO = ("\nNB: Unless specifically stated, values changed here will only take effect "
keep add 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/_config.py