  
          if "GAN" not in self.trainer:
              normalized_face = face / 255.0
              new_face = self.encoder(normalized_face)[0]
              new_face = numpy.clip( new_face * 255, 0, 255 ).astype( image.dtype )
          else:
              normalized_face = face / 255.0 * 2 - 1
              fake_output = self.encoder(normalized_face)
              if "128" in self.trainer: # TODO: Another hack to switch between 64 and 128
                  fake_output = fake_output[0]
 
</s> remove             if "128" in self.trainer: # TODO: Another hack to switch between 64 and 128
 </s> add             if "128" in self.trainer:  # TODO: Another hack to switch between 64 and 128
 </s> remove             mask = fake_output[:,:,:, :1]
            new_face = fake_output[:,:,:, 1:]
 </s> add             mask = fake_output[:, :, :, :1]
            new_face = fake_output[:, :, :, 1:]
 </s> remove             new_face = numpy.clip((new_face[0] + 1) * 255 / 2, 0, 255).astype( image.dtype )
 </s> add             new_face = numpy.clip((new_face[0] + 1) * 255 / 2, 0, 255).astype(image.dtype)
 </s> remove         face = cv2.warpAffine( image, mat, (size,size) )
        face = numpy.expand_dims( face, 0 )
        face_clipped = numpy.clip(face[0], 0, 255).astype( image.dtype )
 </s> add         face = cv2.warpAffine(image, mat, (size, size))
        face = numpy.expand_dims(face, 0)
        face_clipped = numpy.clip(face[0], 0, 255).astype(image.dtype)
 </s> remove         new_face = self.get_new_face(image,mat,size)
 </s> add         new_face = self.get_new_face(image, mat, size)

            new_face = numpy.clip(new_face * 255, 0, 255).astype(image.dtype)

  
          if "GAN" not in self.trainer:
              normalized_face = face / 255.0
              new_face = self.encoder(normalized_face)[0]
            new_face = numpy.clip(new_face * 255, 0, 255).astype(image.dtype)
          else:
              normalized_face = face / 255.0 * 2 - 1
              fake_output = self.encoder(normalized_face)
              if "128" in self.trainer: # TODO: Another hack to switch between 64 and 128
                  fake_output = fake_output[0]
keep keep keep keep replace keep keep keep keep keep
 Logging (#541)

* Convert prints to logger. Further logging improvements. Tidy  up

* Fix system verbosity. Allow SystemExit

* Fix reload extract bug

* Child Traceback handling

* Safer shutdown procedure

* Add shutdown event to queue manager

* landmarks_as_xy > property. GUI notes + linting. Aligner bugfix

* fix FaceFilter. Enable nFilter when no Filter is supplied

* Fix blurry face filter

* Continue on IO error. Better error handling

* Explicitly print stack trace tocrash log

* Windows Multiprocessing bugfix

* Add git info and conda version to crash log

* Windows/Anaconda mp bugfix

* Logging fixes for training 
plugins/convert/Convert_Masked.py