Files
faceswap/plugins/plugin_loader.py
T
torzdf 837bc2d51d Faceswap 3 (#1516)
* FaceSwap 3 (#1515)
* Update extract pipeline
* Update requirements + setup for nvidia
* Remove allow-growth option
* tf.keras to keras updates
* lib.model.losses - Port + fix all loss functions for Keras3
* lib.model - port initializers, layers. normalization to Keras3
* lib.model.autoclip to Keras 3
* Update mixed precision layer storage
* model file to .keras format
* Restructure nn_blocks to initialize layers in __init__
* Tensorboard
  - Trainer: Add Torch compatible Tensorboard callbacks
  - GUI event reader remove TF dependency
* Loss logging
  - Flush TB logs on save
  - Replace TB live iterator for GUI
* Backup models on total loss drop rather than per side
* Update all models to Keras3 Compat
* Remove lib.model.session
* Update clip ViT to Keras 3
* plugins.extract.mask.unet-dfl - Fix for Keras3/Torch backend
* Port AdaBelief to Keras 3
* setup.py:
  - Add --dev flag for dev tool install
* Fix Keras 3 syntax
* Fix LR Finder for Keras 3
* Fix mixed precision switching for Keras  3
* Add more optimizers + open up config setting
* train: Remove updating FS1 weights to FS2 models
* Alignments: Remove support for legacy .json files
* tools.model:
  - Remove TF Saved Format saving
  - Fix Backup/Restore + Nan-Scan
* Fix inference model creation for Keras 3
* Preview tool: Fix for Keras3
* setup.py: Configure keras backend
* train: Migration of FS2 models to FS3
* Training: Default coverage to 100%
* Remove DirectML backend
* Update setup for MacOS
* GUI: Force line reading to UTF-8
* Remove redundant Tensorflow references
* Remove redundant code
* Legacy model loading: Fix TFLamdaOp scalar ops and DepthwiseConv2D
* Add vertical offset option for training
* Github actions: Add more python versions
* Add python version to workflow names
* Github workflow: Exclude Python 3.12 for macOS
* Implement custom training loop
* Fs3 - Add RTX5xxx and ROCm 6.1-6.4 support (#1511)
* setup.py: Add Cuda/ROCm version select options
* bump minimum python version to 3.11
* Switch from setup.cgf to pyproject.toml
* Documentation: Update all docs to use automodapi
* Allow sysinfo to run with missing packages + correctly install tk under Linux
* Bugfix: dot naming convention in clip models
* lib.config: Centralise globally rather than passing as object
- Add torch DataParallel for multi-gpu training
  - GUI: Group switches together when generating cli args
  - CLI: Remove deprecated multi-character argparse args
  - Refactor:
    - Centralise tensorboard reading/writing + unit tests
    - Create trainer plugin interfaces + add original + distributed
* Update installers
2025-12-21 02:45:11 +00:00

304 lines
11 KiB
Python

#!/usr/bin/env python3
""" Plugin loader for Faceswap extract, training and convert tasks """
from __future__ import annotations
import logging
import os
import typing as T
from importlib import import_module
from lib.utils import get_module_objects
if T.TYPE_CHECKING:
from collections.abc import Callable
from plugins.extract.detect._base import Detector
from plugins.extract.align._base import Aligner
from plugins.extract.mask._base import Masker
from plugins.extract.recognition._base import Identity
from plugins.train.model._base import ModelBase
from plugins.train.trainer._base import TrainerBase
logger = logging.getLogger(__name__)
class PluginLoader():
""" Retrieve, or get information on, Faceswap plugins
Return a specific plugin, list available plugins, or get the default plugin for a
task.
Example
-------
>>> from plugins.plugin_loader import PluginLoader
>>> align_plugins = PluginLoader.get_available_extractors('align')
>>> aligner = PluginLoader.get_aligner('cv2-dnn')
"""
@staticmethod
def get_detector(name: str, disable_logging: bool = False) -> type[Detector]:
""" Return requested detector plugin
Parameters
----------
name: str
The name of the requested detector plugin
disable_logging: bool, optional
Whether to disable the INFO log message that the plugin is being imported.
Default: `False`
Returns
-------
:class:`plugins.extract.detect` object:
An extraction detector plugin
"""
return PluginLoader._import("extract.detect", name, disable_logging)
@staticmethod
def get_aligner(name: str, disable_logging: bool = False) -> type[Aligner]:
""" Return requested aligner plugin
Parameters
----------
name: str
The name of the requested aligner plugin
disable_logging: bool, optional
Whether to disable the INFO log message that the plugin is being imported.
Default: `False`
Returns
-------
:class:`plugins.extract.align` object:
An extraction aligner plugin
"""
return PluginLoader._import("extract.align", name, disable_logging)
@staticmethod
def get_masker(name: str, disable_logging: bool = False) -> type[Masker]:
""" Return requested masker plugin
Parameters
----------
name: str
The name of the requested masker plugin
disable_logging: bool, optional
Whether to disable the INFO log message that the plugin is being imported.
Default: `False`
Returns
-------
:class:`plugins.extract.mask` object:
An extraction masker plugin
"""
return PluginLoader._import("extract.mask", name, disable_logging)
@staticmethod
def get_recognition(name: str, disable_logging: bool = False) -> type[Identity]:
""" Return requested recognition plugin
Parameters
----------
name: str
The name of the requested reccognition plugin
disable_logging: bool, optional
Whether to disable the INFO log message that the plugin is being imported.
Default: `False`
Returns
-------
:class:`plugins.extract.recognition` object:
An extraction recognition plugin
"""
return PluginLoader._import("extract.recognition", name, disable_logging)
@staticmethod
def get_model(name: str, disable_logging: bool = False) -> type[ModelBase]:
""" Return requested training model plugin
Parameters
----------
name: str
The name of the requested training model plugin
disable_logging: bool, optional
Whether to disable the INFO log message that the plugin is being imported.
Default: `False`
Returns
-------
:class:`plugins.train.model` object:
A training model plugin
"""
return PluginLoader._import("train.model", name, disable_logging)
@staticmethod
def get_trainer(name: str, disable_logging: bool = False) -> type[TrainerBase]:
""" Return requested training trainer plugin
Parameters
----------
name: str
The name of the requested training trainer plugin
disable_logging: bool, optional
Whether to disable the INFO log message that the plugin is being imported.
Default: `False`
Returns
-------
:class:`plugins.train.trainer` object:
A training trainer plugin
"""
return PluginLoader._import("train.trainer", name, disable_logging)
@staticmethod
def get_converter(category: str, name: str, disable_logging: bool = False) -> Callable:
""" Return requested converter plugin
Converters work slightly differently to other faceswap plugins. They are created to do a
specific task (e.g. color adjustment, mask blending etc.), so multiple plugins will be
loaded in the convert phase, rather than just one plugin for the other phases.
Parameters
----------
name: str
The name of the requested converter plugin
disable_logging: bool, optional
Whether to disable the INFO log message that the plugin is being imported.
Default: `False`
Returns
-------
:class:`plugins.convert` object:
A converter sub plugin
"""
return PluginLoader._import(f"convert.{category}", name, disable_logging)
@staticmethod
def _import(attr: str, name: str, disable_logging: bool):
""" Import the plugin's module
Parameters
----------
name: str
The name of the requested converter plugin
disable_logging: bool
Whether to disable the INFO log message that the plugin is being imported.
Returns
-------
:class:`plugin` object:
A plugin
"""
name = name.replace("-", "_")
ttl = attr.split(".")[-1].title()
if not disable_logging:
logger.info("Loading %s from %s plugin...", ttl, name.title())
attr = "model" if attr == "Trainer" else attr.lower()
mod = ".".join(("plugins", attr, name))
module = import_module(mod)
return getattr(module, ttl)
@staticmethod
def get_available_extractors(extractor_type: T.Literal["align", "detect", "mask"],
add_none: bool = False,
extend_plugin: bool = False) -> list[str]:
""" Return a list of available extractors of the given type
Parameters
----------
extractor_type: {'align', 'detect', 'mask'}
The type of extractor to return the plugins for
add_none: bool, optional
Append "none" to the list of returned plugins. Default: False
extend_plugin: bool, optional
Some plugins have configuration options that mean that multiple 'pseudo-plugins'
can be generated based on their settings. An example of this is the bisenet-fp mask
which, whilst selected as 'bisenet-fp' can be stored as 'bisenet-fp-face' and
'bisenet-fp-head' depending on whether hair has been included in the mask or not.
``True`` will generate each pseudo-plugin, ``False`` will generate the original
plugin name. Default: ``False``
Returns
-------
list:
A list of the available extractor plugin names for the given type
"""
extractpath = os.path.join(os.path.dirname(__file__),
"extract",
extractor_type)
extractors = [item.name.replace(".py", "").replace("_", "-")
for item in os.scandir(extractpath)
if not item.name.startswith("_")
and not item.name.endswith("defaults.py")
and item.name.endswith(".py")]
extendable = ["bisenet-fp", "custom"]
if extend_plugin and extractor_type == "mask" and any(ext in extendable
for ext in extractors):
for msk in extendable:
extractors.remove(msk)
extractors.extend([f"{msk}_face", f"{msk}_head"])
extractors = sorted(extractors)
if add_none:
extractors.insert(0, "none")
return extractors
@staticmethod
def get_available_models() -> list[str]:
""" Return a list of available training models
Returns
-------
list:
A list of the available training model plugin names
"""
modelpath = os.path.join(os.path.dirname(__file__), "train", "model")
models = sorted(item.name.replace(".py", "").replace("_", "-")
for item in os.scandir(modelpath)
if not item.name.startswith("_")
and not item.name.endswith("defaults.py")
and item.name.endswith(".py"))
return models
@staticmethod
def get_default_model() -> str:
""" Return the default training model plugin name
Returns
-------
str:
The default faceswap training model
"""
models = PluginLoader.get_available_models()
return 'original' if 'original' in models else models[0]
@staticmethod
def get_available_convert_plugins(convert_category: str, add_none: bool = True) -> list[str]:
""" Return a list of available converter plugins in the given category
Parameters
----------
convert_category: {'color', 'mask', 'scaling', 'writer'}
The category of converter plugin to return the plugins for
add_none: bool, optional
Append "none" to the list of returned plugins. Default: True
Returns
-------
list
A list of the available converter plugin names in the given category
"""
convertpath = os.path.join(os.path.dirname(__file__),
"convert",
convert_category)
converters = sorted(item.name.replace(".py", "").replace("_", "-")
for item in os.scandir(convertpath)
if not item.name.startswith("_")
and not item.name.endswith("defaults.py")
and item.name.endswith(".py"))
if add_none:
converters.insert(0, "none")
return converters
__all__ = get_module_objects(__name__)