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faceswap/lib/gui/display.py
torzdf d8557c1970
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
2020-08-12 10:36:41 +01:00

183 lines
6.8 KiB
Python

#!/usr/bin python3
""" Display Frame of the Faceswap GUI
This is the large right hand area of the GUI. At default, the Analysis tab is always displayed
here. Further optional tabs will also be displayed depending on the currently executing Faceswap
task. """
import logging
import tkinter as tk
from tkinter import ttk
from .display_analysis import Analysis
from .display_command import GraphDisplay, PreviewExtract, PreviewTrain
from .utils import get_config
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class DisplayNotebook(ttk.Notebook): # pylint: disable=too-many-ancestors
""" The tkinter Notebook that holds the display items.
Parameters
----------
parent: :class:`tk.PanedWindow`
The paned window that holds the Display Notebook
"""
def __init__(self, parent):
logger.debug("Initializing %s", self.__class__.__name__)
super().__init__(parent)
parent.add(self)
tk_vars = get_config().tk_vars
self._wrapper_var = tk_vars["display"]
self._runningtask = tk_vars["runningtask"]
self._set_wrapper_var_trace()
self._add_static_tabs()
# pylint:disable=unnecessary-comprehension
self._static_tabs = [child for child in self.tabs()]
self.bind("<<NotebookTabChanged>>", self._on_tab_change)
logger.debug("Initialized %s", self.__class__.__name__)
@property
def runningtask(self):
""" :class:`tkinter.BooleanVar`: The global tkinter variable that indicates whether a
Faceswap task is currently running or not. """
return self._runningtask
def _set_wrapper_var_trace(self):
""" Sets the trigger to update the displayed notebook's pages when the global tkinter
variable `display` is updated in the :class:`~lib.gui.wrapper.ProcessWrapper`. """
logger.debug("Setting wrapper var trace")
self._wrapper_var.trace("w", self._update_displaybook)
def _add_static_tabs(self):
""" Add the tabs to the Display Notebook that are permanently displayed.
Currently this is just the `Analysis` tab.
"""
logger.debug("Adding static tabs")
for tab in ("job queue", "analysis"):
if tab == "job queue":
continue # Not yet implemented
if tab == "analysis":
helptext = {"stats":
"Summary statistics for each training session"}
frame = Analysis(self, tab, helptext)
else:
frame = self._add_frame()
self.add(frame, text=tab.title())
def _add_frame(self):
""" Add a single frame for holding a static tab's contents.
Returns
-------
ttk.Frame
The frame, packed into position
"""
logger.debug("Adding frame")
frame = ttk.Frame(self)
frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=True, padx=5, pady=5)
return frame
def _command_display(self, command):
""" Build the relevant command specific tabs based on the incoming Faceswap command.
Parameters
----------
command: str
The Faceswap command that is being executed
"""
build_tabs = getattr(self, "_{}_tabs".format(command))
build_tabs()
def _extract_tabs(self, command="extract"):
""" Build the display tabs that are used for Faceswap extract and convert tasks.
Notes
-----
The same display tabs are used for both convert and extract tasks.
command: [`"extract"`, `"convert"`], optional
The command that the display tabs are being built for. Default: `"extract"`
"""
logger.debug("Build extract tabs")
helptext = ("Updates preview from output every 5 "
"seconds to limit disk contention")
PreviewExtract(self, "preview", helptext, 5000, command)
logger.debug("Built extract tabs")
def _train_tabs(self):
""" Build the display tabs that are used for the Faceswap train task."""
logger.debug("Build train tabs")
for tab in ("graph", "preview"):
if tab == "graph":
helptext = "Graph showing Loss vs Iterations"
GraphDisplay(self, "graph", helptext, 5000)
elif tab == "preview":
helptext = "Training preview. Updated on every save iteration"
PreviewTrain(self, "preview", helptext, 1000)
logger.debug("Built train tabs")
def _convert_tabs(self):
""" Build the display tabs that are used for the Faceswap convert task.
Notes
-----
The tabs displayed are the same as used for extract, so :func:`_extract_tabs` is called.
"""
logger.debug("Build convert tabs")
self._extract_tabs(command="convert")
logger.debug("Built convert tabs")
def _remove_tabs(self):
""" Remove all optional displayed command specific tabs from the notebook. """
for child in self.tabs():
if child in self._static_tabs:
continue
logger.debug("removing child: %s", child)
child_name = child.split(".")[-1]
child_object = self.children[child_name] # returns the OptionalDisplayPage object
child_object.close() # Call the OptionalDisplayPage close() method
self.forget(child)
def _update_displaybook(self, *args): # pylint: disable=unused-argument
""" Callback to be executed when the global tkinter variable `display`
(:attr:`wrapper_var`) is updated when a Faceswap task is executed.
Currently only updates when a core faceswap task (extract, train or convert) is executed.
Parameters
----------
args: tuple
Required for tkinter callback events, but unused.
"""
command = self._wrapper_var.get()
self._remove_tabs()
if not command or command not in ("extract", "train", "convert"):
return
self._command_display(command)
def _on_tab_change(self, event): # pylint:disable=unused-argument
""" Event trigger for tab change events.
Calls the selected tabs :func:`on_tab_select` method, if it exists, otherwise returns.
Parameters
----------
event: tkinter callback event
Required, but unused
"""
selected = self.select().split(".")[-1]
logger.debug("Selected tab: %s", selected)
selected_object = self.children[selected]
if hasattr(selected_object, "on_tab_select"):
logger.debug("Calling on_tab_select for '%s'", selected_object)
selected_object.on_tab_select()
else:
logger.debug("Object does not have on_tab_select method. Returning: '%s'",
selected_object)