def add_comboboxes(self, parent, defaults):
""" Add the comboboxes to the Action Frame """
for opt in self.options:
if opt == "mask_type":
choices = get_available_masks() + ["predicted"]
else:
choices = PluginLoader.get_available_convert_plugins(opt, True)
choices = [self.format_to_display(choice) for choice in choices]
ctl = ControlBuilder(parent,
opt,
str,
defaults[opt],
choices=choices,
is_radio=False,
label_width=10,
control_width=12)
self.tk_vars[opt] = ctl.tk_var
def set_globals(self):
""" Set the global options for training """
logger.debug("Setting global config")
section = "global"
self.add_section(title=section,
info="Options that apply to all models" + ADDITIONAL_INFO)
self.add_item(section=section, title="mask_type", datatype=str, default="none",
choices=get_available_masks(), gui_radio=True,
info="The mask to be used for training:"
"\n\t none: Doesn't use any mask."
"\n\t components: An improved face hull mask using a facehull of 8 "
"facial parts"
"\n\t dfl_full: An improved face hull mask using a facehull of 3 "
"facial parts"
"\n\t extended: Based on components mask. Extends the eyebrow points "
"to further up the forehead. May perform badly on difficult angles."
"\n\t facehull: Face cutout based on landmarks")
self.add_item(
section=section, title="icnr_init", datatype=bool, default=False,
info="Use ICNR Kernel Initializer for upscaling.\nThis can help reduce the "
"'checkerboard effect' when upscaling the image.")
self.add_item(
section=section, title="subpixel_upscaling", datatype=bool, default=False,
info="Use subpixel upscaling rather than pixel shuffler.\n"
"Might increase speed at cost of VRAM")
self.add_item(
section=section, title="reflect_padding", datatype=bool, default=False,
info="Use reflect padding rather than zero padding. Only enable this option if the "
"model you are training has a distinct line appearing around the edge of the "
"swap area.")
self.add_item(
section=section, title="dssim_loss", datatype=bool, default=True,
info="Use DSSIM for Loss rather than Mean Absolute Error\n"
"May increase overall quality.")
self.add_item(
section=section, title="penalized_mask_loss", datatype=bool, default=True,
info="If using a mask, This penalizes the loss for the masked area, to give higher "
"priority to the face area. \nShould increase overall quality and speed up "
"training. This should probably be left at True")
self.add_item(
section=section, title="preview_images", datatype=int, default=14, min_max=(2, 16),
rounding=2, fixed=False,
info="Number of sample faces to display for each side in the preview when training.")
logger.debug("Set global config")
def set_globals(self):
"""
Set the global options for training
Loss Documentation
MAE https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine
-learners-should-know-4fb140e9d4b0
MSE https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine
-learners-should-know-4fb140e9d4b0
LogCosh https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine
-learners-should-know-4fb140e9d4b0
Smooth L1 https://arxiv.org/pdf/1701.03077.pdf
L_inf_norm https://medium.com/@montjoile/l0-norm-l1-norm-l2-norm-l-infinity
-norm-7a7d18a4f40c
SSIM http://www.cns.nyu.edu/pub/eero/wang03-reprint.pdf
GMSD https://arxiv.org/ftp/arxiv/papers/1308/1308.3052.pdf
"""
logger.debug("Setting global config")
section = "global"
self.add_section(title=section,
info="Options that apply to all models" +
ADDITIONAL_INFO)
self.add_item(
section=section,
title="coverage",
datatype=float,
default=68.75,
min_max=(62.5, 100.0),
rounding=2,
fixed=True,
group="face",
info=
"How much of the extracted image to train on. A lower coverage will limit the "
"model's scope to a zoomed-in central area while higher amounts can include the "
"entire face. A trade-off exists between lower amounts given more detail "
"versus higher amounts avoiding noticeable swap transitions. Sensible values to "
"use are:"
"\n\t62.5%% spans from eyebrow to eyebrow."
"\n\t75.0%% spans from temple to temple."
"\n\t87.5%% spans from ear to ear."
"\n\t100.0%% is a mugshot.")
self.add_item(
section=section,
title="mask_type",
datatype=str,
default="none",
choices=get_available_masks(),
group="mask",
info="The mask to be used for training:"
"\n\t none: Doesn't use any mask."
"\n\t components: An improved face hull mask using a facehull of 8 facial parts"
"\n\t dfl_full: An improved face hull mask using a facehull of 3 facial parts"
"\n\t extended: Based on components mask. Extends the eyebrow points to further "
"up the forehead. May perform badly on difficult angles."
"\n\t facehull: Face cutout based on landmarks")
self.add_item(
section=section,
title="mask_blur",
datatype=bool,
default=False,
group="mask",
info=
"Apply gaussian blur to the mask input. This has the effect of smoothing the "
"edges of the mask, which can help with poorly calculated masks, and give less "
"of a hard edge to the predicted mask.")
self.add_item(
section=section,
title="icnr_init",
datatype=bool,
default=False,
group="initialization",
info=
"Use ICNR to tile the default initializer in a repeating pattern. "
"This strategy is designed for pairing with sub-pixel / pixel shuffler "
"to reduce the 'checkerboard effect' in image reconstruction. "
"\n\t https://arxiv.org/ftp/arxiv/papers/1707/1707.02937.pdf")
self.add_item(
section=section,
title="conv_aware_init",
datatype=bool,
default=False,
group="initialization",
info=
"Use Convolution Aware Initialization for convolutional layers. "
"This can help eradicate the vanishing and exploding gradient problem "
"as well as lead to higher accuracy, lower loss and faster convergence.\nNB:"
"\n\t This can use more VRAM when creating a new model so you may want to "
"lower the batch size for the first run. The batch size can be raised "
"again when reloading the model. "
"\n\t Multi-GPU is not supported for this option, so you should start the model "
"on a single GPU. Once training has started, you can stop training, enable "
"multi-GPU and resume."
"\n\t Building the model will likely take several minutes as the calculations "
"for this initialization technique are expensive. This will only impact starting "
"a new model.")
self.add_item(
section=section,
title="subpixel_upscaling",
datatype=bool,
default=False,
group="network",
info=
"Use subpixel upscaling rather than pixel shuffler. These techniques "
"are both designed to produce better resolving upscaling than other "
"methods. Each perform the same operations, but using different TF opts."
"\n\t https://arxiv.org/pdf/1609.05158.pdf")
self.add_item(
section=section,
title="reflect_padding",
datatype=bool,
default=False,
group="network",
info=
"Use reflection padding rather than zero padding with convolutions. "
"Each convolution must pad the image boundaries to maintain the proper "
"sizing. More complex padding schemes can reduce artifacts at the "
"border of the image."
"\n\t http://www-cs.engr.ccny.cuny.edu/~wolberg/cs470/hw/hw2_pad.txt"
)
self.add_item(
section=section,
title="penalized_mask_loss",
datatype=bool,
default=True,
group="loss",
info=
"Image loss function is weighted by mask presence. For areas of "
"the image without the facial mask, reconstuction errors will be "
"ignored while the masked face area is prioritized. May increase "
"overall quality by focusing attention on the core face area.")
self.add_item(
section=section,
title="loss_function",
datatype=str,
group="loss",
default="mae",
choices=[
"mae", "mse", "logcosh", "smooth_loss", "l_inf_norm", "ssim",
"gmsd", "pixel_gradient_diff"
],
info=
"\n\t MAE - Mean absolute error will guide reconstructions of each pixel "
"towards its median value in the training dataset. Robust to outliers but as "
"a median, it can potentially ignore some infrequent image types in the dataset."
"\n\t MSE - Mean squared error will guide reconstructions of each pixel "
"towards its average value in the training dataset. As an avg, it will be "
"suspectible to outliers and typically produces slightly blurrier results."
"\n\t LogCosh - log(cosh(x)) acts similiar to MSE for small errors and to "
"MAE for large errors. Like MSE, it is very stable and prevents overshoots "
"when errors are near zero. Like MAE, it is robust to outliers."
"\n\t Smooth_L1 --- Modification of the MAE loss to correct two of its "
"disadvantages. This loss has improved stability and guidance for small errors."
"\n\t L_inf_norm --- The L_inf norm will reduce the largest individual pixel "
"error in an image. As each largest error is minimized sequentially, the "
"overall error is improved. This loss will be extremely focused on outliers."
"\n\t SSIM - Structural Similarity Index Metric is a perception-based "
"loss that considers changes in texture, luminance, contrast, and local spatial "
"statistics of an image. Potentially delivers more realistic looking images."
"\n\t GMSD - Gradient Magnitude Similarity Deviation seeks to match "
"the global standard deviation of the pixel to pixel differences between two "
"images. Similiar in approach to SSIM."
"\n\t Pixel_Gradient_Difference - Instead of minimizing the difference between "
"the absolute value of each pixel in two reference images, compute the pixel to "
"pixel spatial difference in each image and then minimize that difference "
"between two images. Allows for large color shifts,but maintains the structure "
"of the image.\n")
self.add_item(
section=section,
title="learning_rate",
datatype=float,
default=5e-5,
min_max=(1e-6, 1e-4),
rounding=6,
fixed=False,
group="optimizer",
info=
"Learning rate - how fast your network will learn (how large are "
"the modifications to the model weights after one batch of training). "
"Values that are too large might result in model crashes and the "
"inability of the model to find the best solution. "
"Values that are too small might be unable to escape from dead-ends "
"and find the best global minimum.")
#!/usr/bin/env python3
""" Default configurations for models """
import logging
from lib.config import FaceswapConfig
from lib.model.masks import get_available_masks
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
MASK_TYPES = get_available_masks()
MASK_INFO = (
"The mask to be used for training:"
"\n\t none: Doesn't use any mask."
"\n\t components: An improved face hull mask using a facehull of 8 facial parts"
"\n\t dfl_full: An improved face hull mask using a facehull of 3 facial parts"
"\n\t facehull: Face cutout based on landmarks")
COVERAGE_INFO = (
"How much of the extracted image to train on. Generally the model is optimized\n"
"to the default value. Sensible values to use are:"
"\n\t62.5%% spans from eyebrow to eyebrow."
"\n\t75.0%% spans from temple to temple."
"\n\t87.5%% spans from ear to ear."
"\n\t100.0%% is a mugshot.")
ADDITIONAL_INFO = (
"\nNB: Unless specifically stated, values changed here will only take effect "
"when creating a new model.")
class Config(FaceswapConfig):
""" Config File for Models """
def set_globals(self):
""" Set the global options for training """
logger.debug("Setting global config")
section = "global"
self.add_section(title=section,
info="Options that apply to all models" +
ADDITIONAL_INFO)
self.add_item(
section=section,
title="mask_type",
datatype=str,
default="none",
choices=get_available_masks(),
gui_radio=True,
info="The mask to be used for training:"
"\n\t none: Doesn't use any mask."
"\n\t components: An improved face hull mask using a facehull of 8 "
"facial parts"
"\n\t dfl_full: An improved face hull mask using a facehull of 3 "
"facial parts"
"\n\t extended: Based on components mask. Extends the eyebrow points "
"to further up the forehead. May perform badly on difficult angles."
"\n\t facehull: Face cutout based on landmarks")
self.add_item(
section=section,
title="icnr_init",
datatype=bool,
default=False,
info=
"Use ICNR Kernel Initializer for upscaling.\nThis can help reduce the "
"'checkerboard effect' when upscaling the image.")
self.add_item(
section=section,
title="conv_aware_init",
datatype=bool,
default=False,
info=
"Use Convolution Aware Initialization for convolutional layers\nThis can help "
"eradicate the vanishing and exploding gradient problem as well as lead to "
"higher accuracy, lower loss and faster convergence."
"\nNB This can use more VRAM when creating a new model so you may want to lower "
"the batch size for the first run. The batch size can be raised again when "
"reloading the model."
"\nNB: Building the model will likely take several minutes as the caluclations "
"for this initialization technique are expensive.")
self.add_item(
section=section,
title="subpixel_upscaling",
datatype=bool,
default=False,
info="Use subpixel upscaling rather than pixel shuffler.\n"
"Might increase speed at cost of VRAM")
self.add_item(
section=section,
title="reflect_padding",
datatype=bool,
default=False,
info=
"Use reflect padding rather than zero padding. Only enable this option if the "
"model you are training has a distinct line appearing around the edge of the "
"swap area.")
self.add_item(
section=section,
title="dssim_loss",
datatype=bool,
default=True,
info="Use DSSIM for Loss rather than Mean Absolute Error\n"
"May increase overall quality.")
self.add_item(
section=section,
title="penalized_mask_loss",
datatype=bool,
default=True,
info=
"If using a mask, This penalizes the loss for the masked area, to give higher "
"priority to the face area. \nShould increase overall quality and speed up "
"training. This should probably be left at True")
logger.debug("Set global config")
#!/usr/bin/env python3
""" Default configurations for models """
import logging
from lib.config import FaceswapConfig
from lib.model.masks import get_available_masks
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
MASK_TYPES = get_available_masks()
MASK_INFO = ("The mask to be used for training:"
"\n\t none: Doesn't use any mask."
"\n\t components: An improved face hull mask using a facehull of 8 facial parts"
"\n\t dfl_full: An improved face hull mask using a facehull of 3 facial parts"
"\n\t facehull: Face cutout based on landmarks")
COVERAGE_INFO = ("How much of the extracted image to train on. Generally the model is optimized\n"
"to the default value. Sensible values to use are:"
"\n\t62.5%% spans from eyebrow to eyebrow."
"\n\t75.0%% spans from temple to temple."
"\n\t87.5%% spans from ear to ear."
"\n\t100.0%% is a mugshot.")
ADDITIONAL_INFO = ("\nNB: Unless specifically stated, values changed here will only take effect "
"when creating a new model.")
class Config(FaceswapConfig):
""" Config File for Models """
# pylint: disable=too-many-statements
def set_defaults(self):
""" Set the default values for config """