  
      # Extraction pipeline
      def align_faces(self, detector_name, aligner_name, multiprocess):
          """ Use the requested detectors to retrieve landmarks for filter images """
          extractor = Extractor(detector_name, aligner_name, multiprocess=multiprocess)
          self.run_extractor(extractor)
          del extractor
          self.load_aligned_face()
  
      def run_extractor(self, extractor):
 
</s> remove     def align_faces(self, detector_name, aligner_name, multiprocess):
 </s> add     def align_faces(self, detector_name, aligner_name, masker_name, multiprocess):
 </s> remove #!/usr/bin/env python3
""" Plugin loader for extract, training and model tasks """

import logging
import os
from importlib import import_module

logger = logging.getLogger(__name__)  # pylint: disable=invalid-name


class PluginLoader():
    """ Plugin loader for extract, training and model tasks """
    @staticmethod
    def get_detector(name, disable_logging=False):
        """ Return requested detector plugin """
        return PluginLoader._import("extract.detect", name, disable_logging)

    @staticmethod
    def get_aligner(name, disable_logging=False):
        """ Return requested detector plugin """
        return PluginLoader._import("extract.align", name, disable_logging)

    @staticmethod
    def get_model(name, disable_logging=False):
        """ Return requested model plugin """
        return PluginLoader._import("train.model", name, disable_logging)

    @staticmethod
    def get_trainer(name, disable_logging=False):
        """ Return requested trainer plugin """
        return PluginLoader._import("train.trainer", name, disable_logging)

    @staticmethod
    def get_converter(category, name, disable_logging=False):
        """ Return the converter sub plugin """
        return PluginLoader._import("convert.{}".format(category), name, disable_logging)

    @staticmethod
    def _import(attr, name, disable_logging):
        """ Import the plugin's module """
        name = name.replace("-", "_")
        ttl = attr.split(".")[-1].title()
        if not disable_logging:
            logger.info("Loading %s from %s plugin...", ttl, name.title())
        attr = "model" if attr == "Trainer" else attr.lower()
        mod = ".".join(("plugins", attr, name))
        module = import_module(mod)
        return getattr(module, ttl)

    @staticmethod
    def get_available_extractors(extractor_type):
        """ Return a list of available aligners/detectors """
        extractpath = os.path.join(os.path.dirname(__file__),
                                   "extract",
                                   extractor_type)
        extractors = sorted(item.name.replace(".py", "").replace("_", "-")
                            for item in os.scandir(extractpath)
                            if not item.name.startswith("_")
                            and not item.name.endswith("defaults.py")
                            and item.name.endswith(".py")
                            and item.name != "manual.py")
        return extractors

    @staticmethod
    def get_available_models():
        """ Return a list of available models """
        modelpath = os.path.join(os.path.dirname(__file__), "train", "model")
        models = sorted(item.name.replace(".py", "").replace("_", "-")
                        for item in os.scandir(modelpath)
                        if not item.name.startswith("_")
                        and not item.name.endswith("defaults.py")
                        and item.name.endswith(".py"))
        return models

    @staticmethod
    def get_default_model():
        """ Return the default model """
        models = PluginLoader.get_available_models()
        return 'original' if 'original' in models else models[0]

    @staticmethod
    def get_available_convert_plugins(convert_category, add_none=True):
        """ Return a list of available models """
        convertpath = os.path.join(os.path.dirname(__file__),
                                   "convert",
                                   convert_category)
        converters = sorted(item.name.replace(".py", "").replace("_", "-")
                            for item in os.scandir(convertpath)
                            if not item.name.startswith("_")
                            and not item.name.endswith("defaults.py")
                            and item.name.endswith(".py"))
        if add_none:
            converters.insert(0, "none")
        return converters
 </s> add #!/usr/bin/env python3
""" Plugin loader for extract, training and model tasks """

import logging
import os
from importlib import import_module

logger = logging.getLogger(__name__)  # pylint: disable=invalid-name


class PluginLoader():
    """ Plugin loader for extract, training and model tasks """
    @staticmethod
    def get_detector(name, disable_logging=False):
        """ Return requested detector plugin """
        return PluginLoader._import("extract.detect", name, disable_logging)

    @staticmethod
    def get_aligner(name, disable_logging=False):
        """ Return requested detector plugin """
        return PluginLoader._import("extract.align", name, disable_logging)

    @staticmethod
    def get_masker(name, disable_logging=False):
        """ Return requested detector plugin """
        return PluginLoader._import("extract.mask", name, disable_logging)

    @staticmethod
    def get_model(name, disable_logging=False):
        """ Return requested model plugin """
        return PluginLoader._import("train.model", name, disable_logging)

    @staticmethod
    def get_trainer(name, disable_logging=False):
        """ Return requested trainer plugin """
        return PluginLoader._import("train.trainer", name, disable_logging)

    @staticmethod
    def get_converter(category, name, disable_logging=False):
        """ Return the converter sub plugin """
        return PluginLoader._import("convert.{}".format(category), name, disable_logging)

    @staticmethod
    def _import(attr, name, disable_logging):
        """ Import the plugin's module """
        name = name.replace("-", "_")
        ttl = attr.split(".")[-1].title()
        if not disable_logging:
            logger.info("Loading %s from %s plugin...", ttl, name.title())
        attr = "model" if attr == "Trainer" else attr.lower()
        mod = ".".join(("plugins", attr, name))
        module = import_module(mod)
        return getattr(module, ttl)

    @staticmethod
    def get_available_extractors(extractor_type):
        """ Return a list of available aligners/detectors """
        extractpath = os.path.join(os.path.dirname(__file__),
                                   "extract",
                                   extractor_type)
        extractors = sorted(item.name.replace(".py", "").replace("_", "-")
                            for item in os.scandir(extractpath)
                            if not item.name.startswith("_")
                            and not item.name.endswith("defaults.py")
                            and item.name.endswith(".py")
                            and item.name != "manual.py")
        return extractors

    @staticmethod
    def get_available_models():
        """ Return a list of available models """
        modelpath = os.path.join(os.path.dirname(__file__), "train", "model")
        models = sorted(item.name.replace(".py", "").replace("_", "-")
                        for item in os.scandir(modelpath)
                        if not item.name.startswith("_")
                        and not item.name.endswith("defaults.py")
                        and item.name.endswith(".py"))
        return models

    @staticmethod
    def get_default_model():
        """ Return the default model """
        models = PluginLoader.get_available_models()
        return 'original' if 'original' in models else models[0]

    @staticmethod
    def get_available_convert_plugins(convert_category, add_none=True):
        """ Return a list of available models """
        convertpath = os.path.join(os.path.dirname(__file__),
                                   "convert",
                                   convert_category)
        converters = sorted(item.name.replace(".py", "").replace("_", "-")
                            for item in os.scandir(convertpath)
                            if not item.name.startswith("_")
                            and not item.name.endswith("defaults.py")
                            and item.name.endswith(".py"))
        if add_none:
            converters.insert(0, "none")
        return converters </s> remove         images = [batch["targets"][self.model.largest_face_index]]
        if self.use_mask:
            images = images + [batch["masks"]]
        sample = self.compile_sample(batchsize, samples=batch["samples"], images=images)
 </s> add         images = batch["targets"][self.model.largest_face_index]
        masks = batch["masks"][0]
        sample = self.compile_sample(batchsize, 
                                     samples=batch["samples"],
                                     images=images,
                                     masks=masks) </s> remove         retval = bool(self.model.training_opts.get("mask_type", None))
 </s> add         retval = (self.model.training_opts.get("replicate_input_mask", False) or
                  self.model.training_opts.get("penalized_mask_loss", True)) </s> remove     def _set__mask_class(self):
        """ Returns the correct mask class from :mod:`lib`.model.masks` as defined in the
        :attr:`mask_type` parameter. """
        mask_type = self._training_opts.get("mask_type", None)
        if mask_type:
            logger.debug("Mask type: '%s'", mask_type)
            _mask_class = getattr(masks, mask_type)
        else:
            _mask_class = None
        logger.debug("Mask class: %s", _mask_class)
        return _mask_class

 </s> add 
        extractor = Extractor(detector_name,
                              aligner_name,
                              masker_name,
                              multiprocess=multiprocess)

  
      # Extraction pipeline
      def align_faces(self, detector_name, aligner_name, multiprocess):
          """ Use the requested detectors to retrieve landmarks for filter images """
        extractor = Extractor(detector_name,
                              aligner_name,
                              masker_name,
                              multiprocess=multiprocess)
          self.run_extractor(extractor)
          del extractor
          self.load_aligned_face()
  
      def run_extractor(self, extractor):
keep keep keep keep replace keep keep keep keep keep
 Smart Mask Exposure for Extraction & Training (#831)

Smart Masks - Initial Commit 
lib/face_filter.py