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faceswap/lib/config.py
torzdf cd00859c40
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
2019-02-09 18:35:12 +00:00

301 lines
13 KiB
Python

#!/usr/bin/env python3
""" Default configurations for faceswap
Extends out configparser funcionality
by checking for default config updates
and returning data in it's correct format """
import logging
import os
import sys
from collections import OrderedDict
from configparser import ConfigParser
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class FaceswapConfig():
""" Config Items """
def __init__(self, section):
""" Init Configuration """
logger.debug("Initializing: %s", self.__class__.__name__)
self.configfile = self.get_config_file()
self.config = ConfigParser(allow_no_value=True)
self.defaults = OrderedDict()
self.config.optionxform = str
self.section = section
self.set_defaults()
self.handle_config()
logger.debug("Initialized: %s", self.__class__.__name__)
def set_defaults(self):
""" Override for plugin specific config defaults
Should be a series of self.add_section() and self.add_item() calls
e.g:
section = "sect_1"
self.add_section(title=section,
info="Section 1 Information")
self.add_item(section=section,
title="option_1",
datatype=bool,
default=False,
info="sect_1 option_1 information")
"""
raise NotImplementedError
@property
def config_dict(self):
""" Collate global options and requested section into a dictionary
with the correct datatypes """
conf = dict()
for sect in ("global", self.section):
if sect not in self.config.sections():
continue
for key in self.config[sect]:
if key.startswith(("#", "\n")): # Skip comments
continue
conf[key] = self.get(sect, key)
return conf
def get(self, section, option):
""" Return a config item in it's correct format """
logger.debug("Getting config item: (section: '%s', option: '%s')", section, option)
datatype = self.defaults[section][option]["type"]
if datatype == bool:
func = self.config.getboolean
elif datatype == int:
func = self.config.getint
elif datatype == float:
func = self.config.getfloat
else:
func = self.config.get
retval = func(section, option)
if isinstance(retval, str) and retval.lower() == "none":
retval = None
logger.debug("Returning item: (type: %s, value: %s)", datatype, retval)
return retval
def get_config_file(self):
""" Return the config file from the calling folder """
dirname = os.path.dirname(sys.modules[self.__module__].__file__)
folder, fname = os.path.split(dirname)
retval = os.path.join(os.path.dirname(folder), "config", "{}.ini".format(fname))
logger.debug("Config File location: '%s'", retval)
return retval
def add_section(self, title=None, info=None):
""" Add a default section to config file """
logger.debug("Add section: (title: '%s', info: '%s')", title, info)
if None in (title, info):
raise ValueError("Default config sections must have a title and "
"information text")
self.defaults[title] = OrderedDict()
self.defaults[title]["helptext"] = info
def add_item(self, section=None, title=None, datatype=str,
default=None, info=None, rounding=None, min_max=None, choices=None):
""" Add a default item to a config section
For int or float values, rounding and min_max must be set
This is for the slider in the GUI. The min/max values are not enforced:
rounding: sets the decimal places for floats or the step interval for ints.
min_max: tuple of min and max accepted values
For str values choices can be set to validate input and create a combo box
in the GUI
"""
logger.debug("Add item: (section: '%s', title: '%s', datatype: '%s', default: '%s', "
"info: '%s', rounding: '%s', min_max: %s, choices: %s)",
section, title, datatype, default, info, rounding, min_max, choices)
choices = list() if not choices else choices
if None in (section, title, default, info):
raise ValueError("Default config items must have a section, "
"title, defult and "
"information text")
if not self.defaults.get(section, None):
raise ValueError("Section does not exist: {}".format(section))
if datatype not in (str, bool, float, int):
raise ValueError("'datatype' must be one of str, bool, float or "
"int: {} - {}".format(section, title))
if datatype in (float, int) and (rounding is None or min_max is None):
raise ValueError("'rounding' and 'min_max' must be set for numerical options")
if not isinstance(choices, (list, tuple)):
raise ValueError("'choices' must be a list or tuple")
self.defaults[section][title] = {"default": default,
"helptext": info,
"type": datatype,
"rounding": rounding,
"min_max": min_max,
"choices": choices}
def check_exists(self):
""" Check that a config file exists """
if not os.path.isfile(self.configfile):
logger.debug("Config file does not exist: '%s'", self.configfile)
return False
logger.debug("Config file exists: '%s'", self.configfile)
return True
def create_default(self):
""" Generate a default config if it does not exist """
logger.debug("Creating default Config")
for section, items in self.defaults.items():
logger.debug("Adding section: '%s')", section)
self.insert_config_section(section, items["helptext"])
for item, opt in items.items():
logger.debug("Adding option: (item: '%s', opt: '%s'", item, opt)
if item == "helptext":
continue
self.insert_config_item(section,
item,
opt["default"],
opt)
self.save_config()
def insert_config_section(self, section, helptext, config=None):
""" Insert a section into the config """
logger.debug("Inserting section: (section: '%s', helptext: '%s', config: '%s')",
section, helptext, config)
config = self.config if config is None else config
helptext = self.format_help(helptext, is_section=True)
config.add_section(section)
config.set(section, helptext)
logger.debug("Inserted section: '%s'", section)
def insert_config_item(self, section, item, default, option,
config=None):
""" Insert an item into a config section """
logger.debug("Inserting item: (section: '%s', item: '%s', default: '%s', helptext: '%s', "
"config: '%s')", section, item, default, option["helptext"], config)
config = self.config if config is None else config
helptext = option["helptext"]
helptext += self.set_helptext_choices(option)
helptext += "\n[Default: {}]".format(default)
helptext = self.format_help(helptext, is_section=False)
config.set(section, helptext)
config.set(section, item, str(default))
logger.debug("Inserted item: '%s'", item)
@staticmethod
def set_helptext_choices(option):
""" Set the helptext choices """
choices = ""
if option["choices"]:
choices = "\nChoose from: {}".format(option["choices"])
elif option["type"] == bool:
choices = "\nChoose from: True, False"
elif option["type"] == int:
cmin, cmax = option["min_max"]
choices = "\nSelect an integer between {} and {}".format(cmin, cmax)
elif option["type"] == float:
cmin, cmax = option["min_max"]
choices = "\nSelect a decimal number between {} and {}".format(cmin, cmax)
return choices
@staticmethod
def format_help(helptext, is_section=False):
""" Format comments for default ini file """
logger.debug("Formatting help: (helptext: '%s', is_section: '%s')", helptext, is_section)
helptext = '# {}'.format(helptext.replace("\n", "\n# "))
if is_section:
helptext = helptext.upper()
else:
helptext = "\n{}".format(helptext)
logger.debug("formatted help: '%s'", helptext)
return helptext
def load_config(self):
""" Load values from config """
logger.info("Loading config: '%s'", self.configfile)
self.config.read(self.configfile)
def save_config(self):
""" Save a config file """
logger.info("Updating config at: '%s'", self.configfile)
f_cfgfile = open(self.configfile, "w")
self.config.write(f_cfgfile)
f_cfgfile.close()
def validate_config(self):
""" Check for options in default config against saved config
and add/remove as appropriate """
logger.debug("Validating config")
if self.check_config_change():
self.add_new_config_items()
self.check_config_choices()
logger.debug("Validated config")
def add_new_config_items(self):
""" Add new items to the config file """
logger.debug("Updating config")
new_config = ConfigParser(allow_no_value=True)
for section, items in self.defaults.items():
self.insert_config_section(section, items["helptext"], new_config)
for item, opt in items.items():
if item == "helptext":
continue
if section not in self.config.sections():
logger.debug("Adding new config section: '%s'", section)
opt_value = opt["default"]
else:
opt_value = self.config[section].get(item, opt["default"])
self.insert_config_item(section,
item,
opt_value,
opt,
new_config)
self.config = new_config
self.config.optionxform = str
self.save_config()
logger.debug("Updated config")
def check_config_choices(self):
""" Check that config items are valid choices """
logger.debug("Checking config choices")
for section, items in self.defaults.items():
for item, opt in items.items():
if item == "helptext" or not opt["choices"]:
continue
opt_value = self.config.get(section, item)
if opt_value.lower() == "none" and any(choice.lower() == "none"
for choice in opt["choices"]):
continue
if opt_value not in opt["choices"]:
default = str(opt["default"])
logger.warning("'%s' is not a valid config choice for '%s': '%s'. Defaulting "
"to: '%s'", opt_value, section, item, default)
self.config.set(section, item, default)
logger.debug("Checked config choices")
def check_config_change(self):
""" Check whether new default items have been added or removed
from the config file compared to saved version """
if set(self.config.sections()) != set(self.defaults.keys()):
logger.debug("Default config has new section(s)")
return True
for section, items in self.defaults.items():
opts = [opt for opt in items.keys() if opt != "helptext"]
exists = [opt for opt in self.config[section].keys()
if not opt.startswith(("# ", "\n# "))]
if set(exists) != set(opts):
logger.debug("Default config has new item(s)")
return True
logger.debug("Default config has not changed")
return False
def handle_config(self):
""" Handle the config """
logger.debug("Handling config")
if not self.check_exists():
self.create_default()
self.load_config()
self.validate_config()
logger.debug("Handled config")