  
      def init_model(self):
          self.model = KSession(self.name, self.model_path, model_kwargs=dict())
          self.model.load_model()
          o = keras.layers.core.Activation('softmax',
                                           name='softmax')(self.model._model.layers[-1].output)
          self.model._model = keras.models.Model(inputs=self.model._model.input, outputs=[o])
          self.input = np.zeros((self.batchsize, self.mask_in_size, self.mask_in_size, 3),
                                dtype="float32")
          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,
 
</s>
        self.model.append_softmax_activation(layer_index=-1)
        placeholder = np.zeros((self.batchsize, self.input_size, self.input_size, 3),
                               dtype="float32")
        self.model.predict(placeholder)
  
      def init_model(self):
          self.model = KSession(self.name, self.model_path, model_kwargs=dict())
          self.model.load_model()
        self.model.append_softmax_activation(layer_index=-1)
        placeholder = np.zeros((self.batchsize, self.input_size, self.input_size, 3),
                               dtype="float32")
        self.model.predict(placeholder)
        self.model.append_softmax_activation(layer_index=-1)
        placeholder = np.zeros((self.batchsize, self.input_size, self.input_size, 3),
                               dtype="float32")
        self.model.predict(placeholder)
        self.model.append_softmax_activation(layer_index=-1)
        placeholder = np.zeros((self.batchsize, self.input_size, self.input_size, 3),
                               dtype="float32")
        self.model.predict(placeholder)
        self.model.append_softmax_activation(layer_index=-1)
        placeholder = np.zeros((self.batchsize, self.input_size, self.input_size, 3),
                               dtype="float32")
        self.model.predict(placeholder)
        self.model.append_softmax_activation(layer_index=-1)
        placeholder = np.zeros((self.batchsize, self.input_size, self.input_size, 3),
                               dtype="float32")
        self.model.predict(placeholder)
        self.model.append_softmax_activation(layer_index=-1)
        placeholder = np.zeros((self.batchsize, self.input_size, self.input_size, 3),
                               dtype="float32")
        self.model.predict(placeholder)
  
      def process_input(self, batch):
          """ Compile the detected faces for prediction """
          for index, face in enumerate(batch["detected_faces"]):
              face.load_aligned(face.image,
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_obstructed.py