              var_y = self.blocks.upscale2x(var_y, mask_complexity // 2, fast=False)
              var_y = self.blocks.upscale2x(var_y, mask_complexity // 4, fast=False)
              var_y = self.blocks.upscale2x(var_y, mask_complexity // 8, fast=False)
  
              var_y = self.blocks.conv2d(var_y, 1, kernel_size=5, padding="same",
                                         activation="sigmoid", name="mask_out")
  
              outputs.append(var_y)
  
          return KerasModel([input_], outputs=outputs)
 
</s>
            var_y = Conv2DOutput(1, 5, name="mask_out")(var_y)
              var_y = self.blocks.upscale2x(var_y, mask_complexity // 2, fast=False)
              var_y = self.blocks.upscale2x(var_y, mask_complexity // 4, fast=False)
              var_y = self.blocks.upscale2x(var_y, mask_complexity // 8, fast=False)
  
            var_y = Conv2DOutput(1, 5, name="mask_out")(var_y)
            var_y = Conv2DOutput(1, 5, name="mask_out")(var_y)
  
              outputs.append(var_y)
  
          return KerasModel([input_], outputs=outputs)
keep keep keep keep replace replace 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
plugins/train/model/dlight.py