          self.model.predict(self.input)
  
      def process_input(self, batch):
          """ Compile the detected faces for prediction """
          for index, face in enumerate(batch["detected_faces"]):
              face.load_aligned(face.image,
                                size=self.mask_in_size,
                                dtype='float32')
              self.input[index] = face.aligned["face"][..., :3]
          batch["feed"] = self.input - np.mean(self.input, axis=(1, 2))[:, None, None, :]
          return batch
  
      def predict(self, batch):
          """ Run model to get predictions """
          predictions = self.model.predict(batch["feed"])
 
</s> remove         for index, face in enumerate(batch["detected_faces"]):
            face.load_aligned(face.image,
                              size=self.mask_in_size,
                              dtype='float32')
            self.input[index] = face.aligned["face"][..., :3]
        batch["feed"] = self.input - np.mean(self.input, axis=(1, 2))[:, None, None, :]
 </s> add         input_ = [face.feed_face[..., :3] for face in batch["detected_faces"]]
        batch["feed"] = input_ - np.mean(input_, axis=(1, 2))[:, None, None, :]
        logger.trace("feed shape: %s", batch["feed"].shape) </s> remove         for index, face in enumerate(batch["detected_faces"]):
            face.load_aligned(face.image,
                              size=self.mask_in_size,
                              dtype='float32')
            self.input[index] = face.aligned["face"][..., :3]
        batch["feed"] = self.input / 255.
 </s> add         batch["feed"] = np.array([face.feed_face[..., :3]
                                  for face in batch["detected_faces"]], dtype="float32") / 255.0
        logger.trace("feed shape: %s", batch["feed"].shape) </s> remove         batch["feed"] = np.array([face.image for face in batch["detected_faces"]])
 </s> add         batch["feed"] = np.zeros((self.batchsize, self.input_size, self.input_size, 1),
                                 dtype="float32") </s> remove         batch["feed"] = np.array([face.image for face in batch["detected_faces"]])
 </s> add         batch["feed"] = np.zeros((self.batchsize, self.input_size, self.input_size, 1),
                                 dtype="float32") </s> remove         batch["feed"] = np.array([face.image for face in batch["detected_faces"]])
 </s> add         batch["feed"] = np.zeros((self.batchsize, self.input_size, self.input_size, 1),
                                 dtype="float32")
        input_ = np.array([face.feed_face[..., :3]
                           for face in batch["detected_faces"]], dtype="float32")
        batch["feed"] = input_ - np.mean(input_, axis=(1, 2))[:, None, None, :]
        logger.trace("feed shape: %s", batch["feed"].shape)
          self.model.predict(self.input)
  
      def process_input(self, batch):
          """ Compile the detected faces for prediction """
        input_ = np.array([face.feed_face[..., :3]
                           for face in batch["detected_faces"]], dtype="float32")
        batch["feed"] = input_ - np.mean(input_, axis=(1, 2))[:, None, None, :]
        logger.trace("feed shape: %s", batch["feed"].shape)
        input_ = np.array([face.feed_face[..., :3]
                           for face in batch["detected_faces"]], dtype="float32")
        batch["feed"] = input_ - np.mean(input_, axis=(1, 2))[:, None, None, :]
        logger.trace("feed shape: %s", batch["feed"].shape)
        input_ = np.array([face.feed_face[..., :3]
                           for face in batch["detected_faces"]], dtype="float32")
        batch["feed"] = input_ - np.mean(input_, axis=(1, 2))[:, None, None, :]
        logger.trace("feed shape: %s", batch["feed"].shape)
        input_ = np.array([face.feed_face[..., :3]
                           for face in batch["detected_faces"]], dtype="float32")
        batch["feed"] = input_ - np.mean(input_, axis=(1, 2))[:, None, None, :]
        logger.trace("feed shape: %s", batch["feed"].shape)
        input_ = np.array([face.feed_face[..., :3]
                           for face in batch["detected_faces"]], dtype="float32")
        batch["feed"] = input_ - np.mean(input_, axis=(1, 2))[:, None, None, :]
        logger.trace("feed shape: %s", batch["feed"].shape)
        input_ = np.array([face.feed_face[..., :3]
                           for face in batch["detected_faces"]], dtype="float32")
        batch["feed"] = input_ - np.mean(input_, axis=(1, 2))[:, None, None, :]
        logger.trace("feed shape: %s", batch["feed"].shape)
          return batch
  
      def predict(self, batch):
          """ Run model to get predictions """
          predictions = self.model.predict(batch["feed"])
keep keep keep keep replace replace replace replace replace replace keep keep keep keep keep
 Mask plugin cleanup

- PEP8 Fixes
- Remove config for non NN Masks
- Tidy up defaults helptext
- cli.py fix typos
- Remove unused imports and functions _base.py
- Standardize input_size param
- Enable and update documentation
- Change references from `aligner` to `masker`
- Change  input_size, output_size and coverage_ratio from kwargs to params
- Move load_aligned to batch input iterator
- Remove unnecessary self.input param
- Add softmax layer append function to KSession
- Remove references to KSession protected objects
- Standardize plugin output into finalize method
- Make masks full frame and add to lib.faces_detect
- Add masks to alignments.json (temporary zipped base64 solution) 
plugins/extract/mask/vgg_clear.py