              return
          logger.debug("Remove current training Analysis callback received")
          self.clear_session()
  
      def set_vars(self):
          """ Analysis specific vars """
          selected_id = tk.StringVar()
          return {"selected_id": selected_id}
  
      def add_main_frame(self):
          """ Add the main frame to the sub-notebook
              to hold stats and session data """
          logger.debug("Adding main frame")
          mainframe = self.subnotebook_add_page("stats")
          self.stats = StatsData(mainframe,
                                 self.vars["selected_id"],
                                 self.helptext["stats"])
          logger.debug("Added main frame")
  
      def add_options(self):
          """ Add the options bar """
          logger.debug("Adding options")
          self.reset_session_info()
          options = Options(self)
          options.add_options()
          logger.debug("Added options")
  
      def reset_session_info(self):
          """ Reset the session info status to default """
          logger.debug("Resetting session info")
          self.set_info("No session data loaded")
  
      def populate_from_folder(self, *args):  # pylint:disable=unused-argument
 
</s> remove         self.clear_session()
 </s> add         self._clear_session() </s> remove     def populate_from_folder(self, *args):  # pylint:disable=unused-argument
        """ Populate the Analysis tab from just a model folder. Triggered
        when tkinter variable ``analysis_folder`` is set.
 </s> add     def _populate_from_folder(self, *args):  # pylint:disable=unused-argument
        """ Populate the Analysis tab from a model folder.

        Triggered when :attr:`vars` ``analysis_folder`` variable is is set. </s> remove     def remove_current_session(self, *args):  # pylint:disable=unused-argument
        """ Remove the current session data on a istraining=False callback """
        if get_config().tk_vars["istraining"].get():
 </s> add     def _remove_current_session(self, *args):  # pylint:disable=unused-argument
        """ Remove the current session data on a is_training=False callback """
        if self.vars["is_training"].get(): </s> remove     def set_callbacks(self):
        """ Add a callback to update analysis when the training graph is updated """
        tkv = get_config().tk_vars
        tkv["refreshgraph"].trace("w", self.update_current_session)
        tkv["istraining"].trace("w", self.remove_current_session)
        tkv["analysis_folder"].trace("w", self.populate_from_folder)
 </s> add     def set_vars(self):
        """ Set the analysis specific tkinter variables to :attr:`vars`.

        The tracked variables are the global variables that:
            * Trigger when a graph refresh has been requested.
            * Trigger training is commenced or halted
            * The variable holding the location of the current Tensorboard log folder.

        Returns
        -------
        dict
            The dictionary of variable names to tkinter variables
        """
        return dict(selected_id=tk.StringVar(),
                    refresh_graph=get_config().tk_vars["refreshgraph"],
                    is_training=get_config().tk_vars["istraining"],
                    analysis_folder=get_config().tk_vars["analysis_folder"])

    def on_tab_select(self):
        """ Callback for when the analysis tab is selected.

        If Faceswap is currently training a model, then update the statistics with the latest
        values.
        """
        if not self.vars["is_training"].get():
            return
        logger.debug("Analysis update callback received")
        self._reset_session()

    def _get_main_frame(self):
        """ Get the main frame to the sub-notebook to hold stats and session data.

        Returns
        -------
        :class:`StatsData`
            The frame that holds the analysis statistics for the Analysis notebook page
        """
        logger.debug("Getting main stats frame")
        mainframe = self.subnotebook_add_page("stats")
        retval = StatsData(mainframe, self.vars["selected_id"], self.helptext["stats"])
        logger.debug("got main frame: %s", retval)
        return retval

    def _set_callbacks(self):
        """ Adds callbacks to update the analysis summary statistics and add them to :attr:`vars`

        Training graph refresh - Updates the stats for the current training session when the graph
        has been updated.

        When training is commenced - Removes the currently displayed session.

        When the analysis folder has been populated - Updates the stats from that folder.
        """
        self.vars["refresh_graph"].trace("w", self._update_current_session)
        self.vars["is_training"].trace("w", self._remove_current_session)
        self.vars["analysis_folder"].trace("w", self._populate_from_folder) </s> remove         self.reset_session()
 </s> add         self._reset_session()
    def _reset_session_info(self):
              return
          logger.debug("Remove current training Analysis callback received")
          self.clear_session()
  
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
    def _reset_session_info(self):
          """ Reset the session info status to default """
          logger.debug("Resetting session info")
          self.set_info("No session data loaded")
  
      def populate_from_folder(self, *args):  # pylint:disable=unused-argument
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Faceswap 2.0 (#1045)

* Core Updates
    - Remove lib.utils.keras_backend_quiet and replace with get_backend() where relevant
    - Document lib.gpu_stats and lib.sys_info
    - Remove call to GPUStats.is_plaidml from convert and replace with get_backend()
    - lib.gui.menu - typofix

* Update Dependencies
Bump Tensorflow Version Check

* Port extraction to tf2

* Add custom import finder for loading Keras or tf.keras depending on backend

* Add `tensorflow` to KerasFinder search path

* Basic TF2 training running

* model.initializers - docstring fix

* Fix and pass tests for tf2

* Replace Keras backend tests with faceswap backend tests

* Initial optimizers update

* Monkey patch tf.keras optimizer

* Remove custom Adam Optimizers and Memory Saving Gradients

* Remove multi-gpu option. Add Distribution to cli

* plugins.train.model._base: Add Mirror, Central and Default distribution strategies

* Update tensorboard kwargs for tf2

* Penalized Loss - Fix for TF2 and AMD

* Fix syntax for tf2.1

* requirements typo fix

* Explicit None for clipnorm if using a distribution strategy

* Fix penalized loss for distribution strategies

* Update Dlight

* typo fix

* Pin to TF2.2

* setup.py - Install tensorflow from pip if not available in Conda

* Add reduction options and set default for mirrored distribution strategy

* Explicitly use default strategy rather than nullcontext

* lib.model.backup_restore documentation

* Remove mirrored strategy reduction method and default based on OS

* Initial restructure - training

* Remove PingPong
Start model.base refactor

* Model saving and resuming enabled

* More tidying up of model.base

* Enable backup and snapshotting

* Re-enable state file
Remove loss names from state file
Fix print loss function
Set snapshot iterations correctly

* Revert original model to Keras Model structure rather than custom layer
Output full model and sub model summary
Change NNBlocks to callables rather than custom keras layers

* Apply custom Conv2D layer

* Finalize NNBlock restructure
Update Dfaker blocks

* Fix reloading model under a different distribution strategy

* Pass command line arguments through to trainer

* Remove training_opts from model and reference params directly

* Tidy up model __init__

* Re-enable tensorboard logging
Suppress "Model Not Compiled" warning

* Fix timelapse

* lib.model.nnblocks - Bugfix residual block
Port dfaker
bugfix original

* dfl-h128 ported

* DFL SAE ported

* IAE Ported

* dlight ported

* port lightweight

* realface ported

* unbalanced ported

* villain ported

* lib.cli.args - Update Batchsize + move allow_growth to config

* Remove output shape definition
Get image sizes per side rather than globally

* Strip mask input from encoder

* Fix learn mask and output learned mask to preview

* Trigger Allow Growth prior to setting strategy

* Fix GUI Graphing

* GUI - Display batchsize correctly + fix training graphs

* Fix penalized loss

* Enable mixed precision training

* Update analysis displayed batch to match input

* Penalized Loss - Multi-GPU Fix

* Fix all losses for TF2

* Fix Reflect Padding

* Allow different input size for each side of the model

* Fix conv-aware initialization on reload

* Switch allow_growth order

* Move mixed_precision to cli

* Remove distrubution strategies

* Compile penalized loss sub-function into LossContainer

* Bump default save interval to 250
Generate preview on first iteration but don't save
Fix iterations to start at 1 instead of 0
Remove training deprecation warnings
Bump some scripts.train loglevels

* Add ability to refresh preview on demand on pop-up window

* Enable refresh of training preview from GUI

* Fix Convert
Debug logging in Initializers

* Fix Preview Tool

* Update Legacy TF1 weights to TF2
Catch stats error on loading stats with missing logs

* lib.gui.popup_configure - Make more responsive + document

* Multiple Outputs supported in trainer
Original Model - Mask output bugfix

* Make universal inference model for convert
Remove scaling from penalized mask loss (now handled at input to y_true)

* Fix inference model to work properly with all models

* Fix multi-scale output for convert

* Fix clipnorm issue with distribution strategies
Edit error message on OOM

* Update plaidml losses

* Add missing file

* Disable gmsd loss for plaidnl

* PlaidML - Basic training working

* clipnorm rewriting for mixed-precision

* Inference model creation bugfixes

* Remove debug code

* Bugfix: Default clipnorm to 1.0

* Remove all mask inputs from training code

* Remove mask inputs from convert

* GUI - Analysis Tab - Docstrings

* Fix rate in totals row

* lib.gui - Only update display pages if they have focus

* Save the model on first iteration

* plaidml - Fix SSIM loss with penalized loss

* tools.alignments - Remove manual and fix jobs

* GUI - Remove case formatting on help text

* gui MultiSelect custom widget - Set default values on init

* vgg_face2 - Move to plugins.extract.recognition and use plugins._base base class
cli - Add global GPU Exclude Option
tools.sort - Use global GPU Exlude option for backend
lib.model.session - Exclude all GPUs when running in CPU mode
lib.cli.launcher - Set backend to CPU mode when all GPUs excluded

* Cascade excluded devices to GPU Stats

* Explicit GPU selection for Train and Convert

* Reduce Tensorflow Min GPU Multiprocessor Count to 4

* remove compat.v1 code from extract

* Force TF to skip mixed precision compatibility check if GPUs have been filtered

* Add notes to config for non-working AMD losses

* Rasie error if forcing extract to CPU mode

* Fix loading of legace dfl-sae weights + dfl-sae typo fix

* Remove unused requirements
Update sphinx requirements
Fix broken rst file locations

* docs: lib.gui.display

* clipnorm amd condition check

* documentation - gui.display_analysis

* Documentation - gui.popup_configure

* Documentation - lib.logger

* Documentation - lib.model.initializers

* Documentation - lib.model.layers

* Documentation - lib.model.losses

* Documentation - lib.model.nn_blocks

* Documetation - lib.model.normalization

* Documentation - lib.model.session

* Documentation - lib.plaidml_stats

* Documentation: lib.training_data

* Documentation: lib.utils

* Documentation: plugins.train.model._base

* GUI Stats: prevent stats from using GPU

* Documentation - Original Model

* Documentation: plugins.model.trainer._base

* linting

* unit tests: initializers + losses

* unit tests: nn_blocks

* bugfix - Exclude gpu devices in train, not include

* Enable Exclude-Gpus in Extract

* Enable exclude gpus in tools

* Disallow multiple plugin types in a single model folder

* Automatically add exclude_gpus argument in for cpu backends

* Cpu backend fixes

* Relax optimizer test threshold

* Default Train settings - Set mask to Extended

* Update Extractor cli help text
Update to Python 3.8

* Fix FAN to run on CPU

* lib.plaidml_tools - typofix

* Linux installer - check for curl

* linux installer - typo fix 
lib/gui/display_analysis.py