  
          logger.debug("Enabling TensorBoard Logging")
          tensorboard = dict()
  
          for side in self.sides:
              logger.debug("Setting up TensorBoard Logging. Side: %s", side)
              log_dir = os.path.join(str(self.model.model_dir),
                                     "{}_logs".format(self.model.name),
                                     side,
                                     "session_{}".format(self.model.state.session_id))
 
</s> remove             log_dir = os.path.join(str(self.model.model_dir),
                                   "{}_logs".format(self.model.name),
 </s> add             log_dir = os.path.join(str(self._model.model_dir),
                                   "{}_logs".format(self._model.name), </s> remove                                    "session_{}".format(self.model.state.session_id))
            tbs = tf.keras.callbacks.TensorBoard(log_dir=log_dir, **self.tensorboard_kwargs)
            tbs.set_model(self.model.predictors[side])
 </s> add                                    "session_{}".format(self._model.state.session_id))
            tbs = tf.keras.callbacks.TensorBoard(log_dir=log_dir, **self._tensorboard_kwargs)
            tbs.set_model(self._model.predictors[side]) </s> remove         """ Indicate training end to Tensorboard """
        if not self.tensorboard:
 </s> add         """ Stop Tensorboard logging.

        Tensorboard logging needs to be explicitly shutdown on training termination. Called from
        :class:`scripts.train.Train` when training is stopped.
         """
        if not self._tensorboard: </s> remove     def log_tensorboard(self, side, loss):
        """ Log loss to TensorBoard log """
        if not self.tensorboard:
 </s> add     def _log_tensorboard(self, side, loss):
        """ Log current loss to Tensorboard log files

        Parameters
        ----------
        side: {"a", "b"}
            The side to store the loss for
        loss: list
            The list of loss ``floats`` for this side
        """
        if not self._tensorboard: </s> remove                 for log in zip(self.model.state.loss_names[side], loss)}
        self.tensorboard[side].on_batch_end(self.model.state.iterations, logs)
 </s> add                 for log in zip(self._model.state.loss_names[side], loss)}
        self._tensorboard[side].on_batch_end(self._model.state.iterations, logs)
        for side in self._sides:
  
          logger.debug("Enabling TensorBoard Logging")
          tensorboard = dict()
  
        for side in self._sides:
              logger.debug("Setting up TensorBoard Logging. Side: %s", side)
              log_dir = os.path.join(str(self.model.model_dir),
                                     "{}_logs".format(self.model.name),
                                     side,
                                     "session_{}".format(self.model.state.session_id))
keep keep keep keep replace keep keep keep keep keep
 Smart Masks - Training Implementation (#914)

* Smart Masks - Training

- Reinstate smart mask training code
- Reinstate mask_type back to model.config
- change 'replicate_input_mask to 'learn_mask'
- Add learn mask option
- Add mask loading from alignments to plugins.train.trainer
- Add mask_blur and mask threshold options
- _base.py - Pass mask options through training_opts dict
- plugins.train.model - check for mask_type not None for learn_mask and penalized_mask_loss
- Limit alignments loading to just those faces that appear in the training folder
- Raise error if not all training images have an alignment, and alignment file is required
- lib.training_data - Mask generation code
- lib.faces_detect - cv2 dimension stripping bugfix
- Remove cv2 linting code

* Update mask helptext in cli.py

* Fix Warp to Landmarks
Remove SHA1 hashing from training data

* Update mask training config

* Capture missing masks at training init

* lib.image.read_image_batch - Return filenames with batch for ordering

* scripts.train - Documentation

* plugins.train.trainer - documentation

* Ensure backward compatibility.
Fix convert for new predicted masks

* Update removed masks to components for legacy models. 
plugins/train/trainer/_base.py