          self.clear_session()
          filename = FileHandler("open", "session").retfile
          if not filename:
              return
          filename = filename.name
          loaded_data = SavedSessions(filename).sessions
          msg = filename
          if len(filename) > 70:
              msg = "...{}".format(filename[-70:])
          self.set_session_summary(loaded_data, msg)
          self.vars["filename"].set(filename)
  
      def reset_session(self):
          """ Load previously saved sessions """
          self.clear_session()
          if self.session.stats["iterations"] == 0:
 
</s> remove         filename = FileHandler("open", "session").retfile
        if not filename:
 </s> add         fullpath = FileHandler("filename", "state").retfile
        if not fullpath:
            return
        logger.debug("state_file: '%s'", fullpath)
        model_dir, state_file = os.path.split(fullpath)
        logger.debug("model_dir: '%s'", model_dir)
        model_name = self.get_model_name(model_dir, state_file)
        if not model_name: </s> remove         if self.session.stats["iterations"] == 0:
 </s> add         session = get_config().session
        if not session.initialized:
            logger.debug("Training not running") </s> remove         """ Load previously saved sessions """
 </s> add         """ Reset currently training sessions """
        logger.debug("Reset current training session") </s> remove         loaded_data = self.session.historical.sessions
 </s> add 
        self.session = Session(model_dir=model_dir, model_name=model_name)
        self.session.initialize_session(is_training=False)
        msg = os.path.split(state_file)[0]
        if len(msg) > 70:
            msg = "...{}".format(msg[-70:])
        self.set_session_summary(msg)

    @staticmethod
    def get_model_name(model_dir, state_file):
        """ Get the state file from the model directory """
        logger.debug("Getting model name")
        model_name = state_file.replace("_state.json", "")
        logger.debug("model_name: %s", model_name)
        logs_dir = os.path.join(model_dir, "{}_logs".format(model_name))
        if not os.path.isdir(logs_dir):
            logger.warning("No logs folder found in folder: '%s'", logs_dir)
            return None
        return model_name
          self.clear_session()
          filename = FileHandler("open", "session").retfile
          if not filename:
              return
        self.session = Session(model_dir=model_dir, model_name=model_name)
        self.session.initialize_session(is_training=False)
        msg = os.path.split(state_file)[0]
        if len(msg) > 70:
            msg = "...{}".format(msg[-70:])
        self.set_session_summary(msg)

    @staticmethod
    def get_model_name(model_dir, state_file):
        """ Get the state file from the model directory """
        logger.debug("Getting model name")
        model_name = state_file.replace("_state.json", "")
        logger.debug("model_name: %s", model_name)
        logs_dir = os.path.join(model_dir, "{}_logs".format(model_name))
        if not os.path.isdir(logs_dir):
            logger.warning("No logs folder found in folder: '%s'", logs_dir)
            return None
        return model_name
        self.session = Session(model_dir=model_dir, model_name=model_name)
        self.session.initialize_session(is_training=False)
        msg = os.path.split(state_file)[0]
        if len(msg) > 70:
            msg = "...{}".format(msg[-70:])
        self.set_session_summary(msg)

    @staticmethod
    def get_model_name(model_dir, state_file):
        """ Get the state file from the model directory """
        logger.debug("Getting model name")
        model_name = state_file.replace("_state.json", "")
        logger.debug("model_name: %s", model_name)
        logs_dir = os.path.join(model_dir, "{}_logs".format(model_name))
        if not os.path.isdir(logs_dir):
            logger.warning("No logs folder found in folder: '%s'", logs_dir)
            return None
        return model_name
        self.session = Session(model_dir=model_dir, model_name=model_name)
        self.session.initialize_session(is_training=False)
        msg = os.path.split(state_file)[0]
        if len(msg) > 70:
            msg = "...{}".format(msg[-70:])
        self.set_session_summary(msg)

    @staticmethod
    def get_model_name(model_dir, state_file):
        """ Get the state file from the model directory """
        logger.debug("Getting model name")
        model_name = state_file.replace("_state.json", "")
        logger.debug("model_name: %s", model_name)
        logs_dir = os.path.join(model_dir, "{}_logs".format(model_name))
        if not os.path.isdir(logs_dir):
            logger.warning("No logs folder found in folder: '%s'", logs_dir)
            return None
        return model_name
        self.session = Session(model_dir=model_dir, model_name=model_name)
        self.session.initialize_session(is_training=False)
        msg = os.path.split(state_file)[0]
        if len(msg) > 70:
            msg = "...{}".format(msg[-70:])
        self.set_session_summary(msg)

    @staticmethod
    def get_model_name(model_dir, state_file):
        """ Get the state file from the model directory """
        logger.debug("Getting model name")
        model_name = state_file.replace("_state.json", "")
        logger.debug("model_name: %s", model_name)
        logs_dir = os.path.join(model_dir, "{}_logs".format(model_name))
        if not os.path.isdir(logs_dir):
            logger.warning("No logs folder found in folder: '%s'", logs_dir)
            return None
        return model_name
        self.session = Session(model_dir=model_dir, model_name=model_name)
        self.session.initialize_session(is_training=False)
        msg = os.path.split(state_file)[0]
        if len(msg) > 70:
            msg = "...{}".format(msg[-70:])
        self.set_session_summary(msg)

    @staticmethod
    def get_model_name(model_dir, state_file):
        """ Get the state file from the model directory """
        logger.debug("Getting model name")
        model_name = state_file.replace("_state.json", "")
        logger.debug("model_name: %s", model_name)
        logs_dir = os.path.join(model_dir, "{}_logs".format(model_name))
        if not os.path.isdir(logs_dir):
            logger.warning("No logs folder found in folder: '%s'", logs_dir)
            return None
        return model_name
        self.session = Session(model_dir=model_dir, model_name=model_name)
        self.session.initialize_session(is_training=False)
        msg = os.path.split(state_file)[0]
        if len(msg) > 70:
            msg = "...{}".format(msg[-70:])
        self.set_session_summary(msg)

    @staticmethod
    def get_model_name(model_dir, state_file):
        """ Get the state file from the model directory """
        logger.debug("Getting model name")
        model_name = state_file.replace("_state.json", "")
        logger.debug("model_name: %s", model_name)
        logs_dir = os.path.join(model_dir, "{}_logs".format(model_name))
        if not os.path.isdir(logs_dir):
            logger.warning("No logs folder found in folder: '%s'", logs_dir)
            return None
        return model_name
        self.session = Session(model_dir=model_dir, model_name=model_name)
        self.session.initialize_session(is_training=False)
        msg = os.path.split(state_file)[0]
        if len(msg) > 70:
            msg = "...{}".format(msg[-70:])
        self.set_session_summary(msg)

    @staticmethod
    def get_model_name(model_dir, state_file):
        """ Get the state file from the model directory """
        logger.debug("Getting model name")
        model_name = state_file.replace("_state.json", "")
        logger.debug("model_name: %s", model_name)
        logs_dir = os.path.join(model_dir, "{}_logs".format(model_name))
        if not os.path.isdir(logs_dir):
            logger.warning("No logs folder found in folder: '%s'", logs_dir)
            return None
        return model_name
  
      def reset_session(self):
          """ Load previously saved sessions """
          self.clear_session()
          if self.session.stats["iterations"] == 0:
keep keep keep keep replace replace replace replace replace replace replace keep keep keep keep keep
 model_refactor (#571) (#572)

* model_refactor (#571)

* original model to new structure

* IAE model to new structure

* OriginalHiRes to new structure

* Fix trainer for different resolutions

* Initial config implementation

* Configparse library added

* improved training data loader

* dfaker model working

* Add logging to training functions

* Non blocking input for cli training

* Add error handling to threads. Add non-mp queues to queue_handler

* Improved Model Building and NNMeta

* refactor lib/models

* training refactor. DFL H128 model Implementation

* Dfaker - use hashes

* Move timelapse. Remove perceptual loss arg

* Update INSTALL.md. Add logger formatting. Update Dfaker training

* DFL h128 partially ported

* Add mask to dfaker (#573)

* Remove old models. Add mask to dfaker

* dfl mask. Make masks selectable in config (#575)

* DFL H128 Mask. Mask type selectable in config.

* remove gan_v2_2

* Creating Input Size config for models

Creating Input Size config for models

Will be used downstream in converters.

Also name change of image_shape to input_shape to clarify ( for future models with potentially different output_shapes)

* Add mask loss options to config

* MTCNN options to config.ini. Remove GAN config. Update USAGE.md

* Add sliders for numerical values in GUI

* Add config plugins menu to gui. Validate config

* Only backup model if loss has dropped. Get training working again

* bugfixes

* Standardise loss printing

* GUI idle cpu fixes. Graph loss fix.

* mutli-gpu logging bugfix

* Merge branch 'staging' into train_refactor

* backup state file

* Crash protection: Only backup if both total losses have dropped

* Port OriginalHiRes_RC4 to train_refactor (OriginalHiRes)

* Load and save model structure with weights

* Slight code update

* Improve config loader. Add subpixel opt to all models. Config to state

* Show samples... wrong input

* Remove AE topology. Add input/output shapes to State

* Port original_villain (birb/VillainGuy) model to faceswap

* Add plugin info to GUI config pages

* Load input shape from state. IAE Config options.

* Fix transform_kwargs.
Coverage to ratio.
Bugfix mask detection

* Suppress keras userwarnings.
Automate zoom.
Coverage_ratio to model def.

* Consolidation of converters & refactor (#574)

* Consolidation of converters & refactor

Initial Upload of alpha

Items
- consolidate convert_mased & convert_adjust into one converter
-add average color adjust to convert_masked
-allow mask transition blur size to be a fixed integer of pixels and a fraction of the facial mask size
-allow erosion/dilation size to be a fixed integer of pixels and a fraction of the facial mask size
-eliminate redundant type conversions to avoid multiple round-off errors
-refactor loops for vectorization/speed
-reorganize for clarity & style changes

TODO
- bug/issues with warping the new face onto a transparent old image...use a cleanup mask for now
- issues with mask border giving black ring at zero erosion .. investigate
- remove GAN ??
- test enlargment factors of umeyama standard face .. match to coverage factor
- make enlargment factor a model parameter
- remove convert_adjusted and referencing code when finished

* Update Convert_Masked.py

default blur size of 2 to match original...
description of enlargement tests
breakout matrxi scaling into def

* Enlargment scale as a cli parameter

* Update cli.py

* dynamic interpolation algorithm

Compute x & y scale factors from the affine matrix on the fly by QR decomp.
Choose interpolation alogrithm for the affine warp based on an upsample or downsample for each image

* input size
input size from config

* fix issues with <1.0 erosion

* Update convert.py

* Update Convert_Adjust.py

more work on the way to merginf

* Clean up help note on sharpen

* cleanup seamless

* Delete Convert_Adjust.py

* Update umeyama.py

* Update training_data.py

* swapping

* segmentation stub

* changes to convert.str

* Update masked.py

* Backwards compatibility fix for models
Get converter running

* Convert:
Move masks to class.
bugfix blur_size
some linting

* mask fix

* convert fixes

- missing facehull_rect re-added
- coverage to %
- corrected coverage logic
- cleanup of gui option ordering

* Update cli.py

* default for blur

* Update masked.py

* added preliminary low_mem version of OriginalHighRes model plugin

* Code cleanup, minor fixes

* Update masked.py

* Update masked.py

* Add dfl mask to convert

* histogram fix & seamless location

* update

* revert

* bugfix: Load actual configuration in gui

* Standardize nn_blocks

* Update cli.py

* Minor code amends

* Fix Original HiRes model

* Add masks to preview output for mask trainers
refactor trainer.__base.py

* Masked trainers converter support

* convert bugfix

* Bugfix: Converter for masked (dfl/dfaker) trainers

* Additional Losses (#592)

* initial upload

* Delete blur.py

* default initializer = He instead of Glorot (#588)

* Allow kernel_initializer to be overridable

* Add ICNR Initializer option for upscale on all models.

* Hopefully fixes RSoDs with original-highres model plugin

* remove debug line

* Original-HighRes model plugin Red Screen of Death fix, take #2

* Move global options to _base. Rename Villain model

* clipnorm and res block biases

* scale the end of res block

* res block

* dfaker pre-activation res

* OHRES pre-activation

* villain pre-activation

* tabs/space in nn_blocks

* fix for histogram with mask all set to zero

* fix to prevent two networks with same name

* GUI: Wider tooltips. Improve TQDM capture

* Fix regex bug

* Convert padding=48 to ratio of image size

* Add size option to alignments tool extract

* Pass through training image size to convert from model

* Convert: Pull training coverage from model

* convert: coverage, blur and erode to percent

* simplify matrix scaling

* ordering of sliders in train

* Add matrix scaling to utils. Use interpolation in lib.aligner transform

* masked.py Import get_matrix_scaling from utils

* fix circular import

* Update masked.py

* quick fix for matrix scaling

* testing thus for now

* tqdm regex capture bugfix

* Minor ammends

* blur size cleanup

* Remove coverage option from convert (Now cascades from model)

* Implement convert for all model types

* Add mask option and coverage option to all existing models

* bugfix for model loading on convert

* debug print removal

* Bugfix for masks in dfl_h128 and iae

* Update preview display. Add preview scaling to cli

* mask notes

* Delete training_data_v2.py

errant file

* training data variables

* Fix timelapse function

* Add new config items to state file for legacy purposes

* Slight GUI tweak

* Raise exception if problem with loaded model

* Add Tensorboard support (Logs stored in model directory)

* ICNR fix

* loss bugfix

* convert bugfix

* Move ini files to config folder. Make TensorBoard optional

* Fix training data for unbalanced inputs/outputs

* Fix config "none" test

* Keep helptext in .ini files when saving config from GUI

* Remove frame_dims from alignments

* Add no-flip and warp-to-landmarks cli options

* Revert OHR to RC4_fix version

* Fix lowmem mode on OHR model

* padding to variable

* Save models in parallel threads

* Speed-up of res_block stability

* Automated Reflection Padding

* Reflect Padding as a training option

Includes auto-calculation of proper padding shapes, input_shapes, output_shapes

Flag included in config now

* rest of reflect padding

* Move TB logging to cli. Session info to state file

* Add session iterations to state file

* Add recent files to menu. GUI code tidy up

* [GUI] Fix recent file list update issue

* Add correct loss names to TensorBoard logs

* Update live graph to use TensorBoard and remove animation

* Fix analysis tab. GUI optimizations

* Analysis Graph popup to Tensorboard Logs

* [GUI] Bug fix for graphing for models with hypens in name

* [GUI] Correctly split loss to tabs during training

* [GUI] Add loss type selection to analysis graph

* Fix store command name in recent files. Switch to correct tab on open

* [GUI] Disable training graph when 'no-logs' is selected

* Fix graphing race condition

* rename original_hires model to unbalanced 
lib/gui/display_analysis.py