          ----------
          plugin_type: {'aligner', 'detector'}
              The plugin_type to be overriden
          batchsize: int
              The batchsize to use for this plugin type
          """
          logger.debug("Overriding batchsize for plugin_type: %s to: %s", plugin_type, batchsize)
          plugin = getattr(self, "_{}".format(plugin_type))
          plugin.batchsize = batchsize
  
 
</s> remove             The plugin_type to be overriden
 </s> add             The plugin_type to be overridden </s> remove         This should be set prior to :func:`launch` if the batchsize is to be manually overriden
 </s> add         This should be set prior to :func:`launch` if the batch size is to be manually overridden </s> remove         """ Set the batchsize for the given plugin based on given available vram """
 </s> add         """ Set the batch size for the given plugin based on given available vram """ </s> remove         """ Set the batchsize of a given :attr:`plugin_type` to the given :attr:`batchsize`.
 </s> add         """ Set the batch size of a given :attr:`plugin_type` to the given :attr:`batchsize`. </s> remove     def _launch_detector(self):
        """ Launch the face detector """
        logger.debug("Launching Detector")
        kwargs = dict(in_queue=self._queues["extract_detect_in"],
                      out_queue=self._queues["extract_align_in"])
        self._detector.initialize(**kwargs)
        self._detector.start()
        logger.debug("Launched Detector")

    def _launch_aligner(self):
        """ Launch the face aligner """
        logger.debug("Launching Aligner")
        kwargs = dict(in_queue=self._queues["extract_align_in"],
                      out_queue=self._queues["extract_mask_in"])
        self._aligner.initialize(**kwargs)
        self._aligner.start()
        logger.debug("Launched Aligner")

    def _launch_masker(self):
        """ Launch the face masker """
        logger.debug("Launching Masker")
        kwargs = dict(in_queue=self._queues["extract_mask_in"],
                      out_queue=self._queues["extract_mask_out"])
        self._masker.initialize(**kwargs)
        self._masker.start()
        logger.debug("Launched Masker")
 </s> add     def _launch_plugin(self, phase):
        """ Launch an extraction plugin """
        logger.debug("Launching %s plugin", phase)
        in_qname = "extract_{}_in".format(phase)
        if phase == self._final_phase:
            out_qname = "extract_{}_out".format(self._final_phase)
        else:
            next_phase = self._flow[self._flow.index(phase) + 1]
            out_qname = "extract_{}_in".format(next_phase)
        logger.debug("in_qname: %s, out_qname: %s", in_qname, out_qname)
        kwargs = dict(in_queue=self._queues[in_qname], out_queue=self._queues[out_qname])

        plugin = getattr(self, "_{}".format(phase))
        plugin.initialize(**kwargs)
        plugin.start()
        logger.debug("Launched %s plugin", phase)
            The batch size to use for this plugin type
          ----------
          plugin_type: {'aligner', 'detector'}
              The plugin_type to be overriden
          batchsize: int
            The batch size to use for this plugin type
          """
          logger.debug("Overriding batchsize for plugin_type: %s to: %s", plugin_type, batchsize)
          plugin = getattr(self, "_{}".format(plugin_type))
          plugin.batchsize = batchsize
  
keep keep keep keep replace keep keep keep keep keep
 Smart Mask - Extract code review

- Lint simple_tests.py
- Only reformat alignments file if it exists otherwise change filename
- Update legacy alignments to new format at all stages
- faces_detect.Mask.from_dict - logging format fix
- convert.py fix otf for new pipeline
- cli.py - Add note that masks not used. Revert convert masks
- faces_detect.py - Revert non-extract code
- Add .p and .pickle extensions for serializer
- plugins/extract revert some changes
- scripts/fsmedia - Revert code changes
- Pipeline - cleanup
- Consistant alpha channel stripping (fixes single-process)
- Store landmarks as numpy array
- Code attribution
- Normalize feed face and reference face to 0.0 - 1.0 in convert
- Lock in mask VRAM sized
- Add documentation to plugin_loader
- Update alignments tool to work with new format 
plugins/extract/pipeline.py