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* Remove tensorflow_probability requirement * setup.py - fix progress bars * requirements.txt: Remove pre python 3.9 packages * update apple requirements.txt * update INSTALL.md * Remove python<3.9 code * setup.py - fix Windows Installer * typing: python3.9 compliant * Update pytest and readthedocs python versions * typing fixes * Python Version updates - Reduce max version to 3.10 - Default to 3.10 in installers - Remove incompatible 3.11 tests * Update dependencies * Downgrade imageio dep for Windows * typing: merge optional unions and fixes * Updates - min python version 3.10 - typing to python 3.10 spec - remove pre-tf2.10 code - Add conda tests * train: re-enable optimizer saving * Update dockerfiles * Update setup.py - Apple Conda deps to setup.py - Better Cuda + dependency handling * bugfix: Patch logging to prevent Autograph errors * Update dockerfiles * Setup.py - Setup.py - stdout to utf-8 * Add more OSes to github Actions * suppress mac-os end to end test
622 lines
33 KiB
Python
622 lines
33 KiB
Python
#!/usr/bin/env python3
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""" Default configurations for models """
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import gettext
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import logging
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import os
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from lib.config import FaceswapConfig
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from plugins.plugin_loader import PluginLoader
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# LOCALES
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_LANG = gettext.translation("plugins.train._config", localedir="locales", fallback=True)
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_ = _LANG.gettext
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logger = logging.getLogger(__name__)
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ADDITIONAL_INFO = _("\nNB: Unless specifically stated, values changed here will only take effect "
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"when creating a new model.")
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_LOSS_HELP = {
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"ffl": _(
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"Focal Frequency Loss. Analyzes the frequency spectrum of the images rather than the "
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"images themselves. This loss function can be used on its own, but the original paper "
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"found increased benefits when using it as a complementary loss to another spacial loss "
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"function (e.g. MSE). Ref: Focal Frequency Loss for Image Reconstruction and Synthesis "
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"https://arxiv.org/pdf/2012.12821.pdf NB: This loss does not currently work on AMD "
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"cards."),
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"flip": _(
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"Nvidia FLIP. A perceptual loss measure that approximates the difference perceived by "
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"humans as they alternate quickly (or flip) between two images. Used on its own and this "
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"loss function creates a distinct grid on the output. However it can be helpful when "
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"used as a complimentary loss function. Ref: FLIP: A Difference Evaluator for "
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"Alternating Images: "
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"https://research.nvidia.com/sites/default/files/node/3260/FLIP_Paper.pdf"),
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"gmsd": _(
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"Gradient Magnitude Similarity Deviation seeks to match the global standard deviation of "
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"the pixel to pixel differences between two images. Similar in approach to SSIM. Ref: "
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"Gradient Magnitude Similarity Deviation: An Highly Efficient Perceptual Image Quality "
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"Index https://arxiv.org/ftp/arxiv/papers/1308/1308.3052.pdf"),
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"l_inf_norm": _(
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"The L_inf norm will reduce the largest individual pixel error in an image. As "
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"each largest error is minimized sequentially, the overall error is improved. This loss "
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"will be extremely focused on outliers."),
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"laploss": _(
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"Laplacian Pyramid Loss. Attempts to improve results by focussing on edges using "
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"Laplacian Pyramids. As this loss function gives priority to edges over other low-"
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"frequency information, like color, it should not be used on its own. The original "
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"implementation uses this loss as a complimentary function to MSE. "
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"Ref: Optimizing the Latent Space of Generative Networks "
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"https://arxiv.org/abs/1707.05776"),
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"lpips_alex": _(
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"LPIPS is a perceptual loss that uses the feature outputs of other pretrained models as a "
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"loss metric. Be aware that this loss function will use more VRAM. Used on its own and "
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"this loss will create a distinct moire pattern on the output, however it can be helpful "
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"as a complimentary loss function. The output of this function is strong, so depending "
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"on your chosen primary loss function, you are unlikely going to want to set the weight "
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"above about 25%. Ref: The Unreasonable Effectiveness of Deep Features as a Perceptual "
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"Metric http://arxiv.org/abs/1801.03924\nThis variant uses the AlexNet backbone. A fairly "
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"light and old model which performed best in the paper's original implementation.\nNB: "
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"For AMD Users the final linear layer is not implemented."),
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"lpips_squeeze": _(
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"Same as lpips_alex, but using the SqueezeNet backbone. A more lightweight "
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"version of AlexNet.\nNB: For AMD Users the final linear layer is not implemented."),
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"lpips_vgg16": _(
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"Same as lpips_alex, but using the VGG16 backbone. A more heavyweight model.\n"
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"NB: For AMD Users the final linear layer is not implemented."),
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"logcosh": _(
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"log(cosh(x)) acts similar to MSE for small errors and to MAE for large errors. Like "
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"MSE, it is very stable and prevents overshoots when errors are near zero. Like MAE, it "
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"is robust to outliers."),
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"mae": _(
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"Mean absolute error will guide reconstructions of each pixel towards its median value in "
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"the training dataset. Robust to outliers but as a median, it can potentially ignore some "
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"infrequent image types in the dataset."),
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"mse": _(
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"Mean squared error will guide reconstructions of each pixel towards its average value in "
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"the training dataset. As an avg, it will be susceptible to outliers and typically "
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"produces slightly blurrier results. Ref: Multi-Scale Structural Similarity for Image "
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"Quality Assessment https://www.cns.nyu.edu/pub/eero/wang03b.pdf"),
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"ms_ssim": _(
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"Multiscale Structural Similarity Index Metric is similar to SSIM except that it "
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"performs the calculations along multiple scales of the input image."),
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"smooth_loss": _(
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"Smooth_L1 is a modification of the MAE loss to correct two of its disadvantages. "
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"This loss has improved stability and guidance for small errors. Ref: A General and "
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"Adaptive Robust Loss Function https://arxiv.org/pdf/1701.03077.pdf"),
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"ssim": _(
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"Structural Similarity Index Metric is a perception-based loss that considers changes in "
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"texture, luminance, contrast, and local spatial statistics of an image. Potentially "
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"delivers more realistic looking images. Ref: Image Quality Assessment: From Error "
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"Visibility to Structural Similarity http://www.cns.nyu.edu/pub/eero/wang03-reprint.pdf"),
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"pixel_gradient_diff": _(
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"Instead of minimizing the difference between the absolute value of each "
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"pixel in two reference images, compute the pixel to pixel spatial difference in each "
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"image and then minimize that difference between two images. Allows for large color "
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"shifts, but maintains the structure of the image."),
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"none": _("Do not use an additional loss function.")}
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_NON_PRIMARY_LOSS = ["flip", "lpips_alex", "lpips_squeeze", "lpips_vgg16", "none"]
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class Config(FaceswapConfig):
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""" Config File for Models """
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# pylint: disable=too-many-statements
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def set_defaults(self) -> None:
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""" Set the default values for config """
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logger.debug("Setting defaults")
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self._set_globals()
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self._set_loss()
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self._defaults_from_plugin(os.path.dirname(__file__))
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def _set_globals(self) -> None:
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""" Set the global options for training """
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logger.debug("Setting global config")
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section = "global"
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self.add_section(section,
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_("Options that apply to all models") + ADDITIONAL_INFO)
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self.add_item(
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section=section,
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title="centering",
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datatype=str,
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gui_radio=True,
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default="face",
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choices=["face", "head", "legacy"],
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fixed=True,
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group=_("face"),
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info=_(
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"How to center the training image. The extracted images are centered on the "
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"middle of the skull based on the face's estimated pose. A subsection of these "
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"images are used for training. The centering used dictates how this subsection "
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"will be cropped from the aligned images."
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"\n\tface: Centers the training image on the center of the face, adjusting for "
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"pitch and yaw."
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"\n\thead: Centers the training image on the center of the head, adjusting for "
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"pitch and yaw. NB: You should only select head centering if you intend to "
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"include the full head (including hair) in the final swap. This may give mixed "
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"results. Additionally, it is only worth choosing head centering if you are "
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"training with a mask that includes the hair (e.g. BiSeNet-FP-Head)."
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"\n\tlegacy: The 'original' extraction technique. Centers the training image "
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"near the tip of the nose with no adjustment. Can result in the edges of the "
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"face appearing outside of the training area."))
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self.add_item(
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section=section,
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title="coverage",
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datatype=float,
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default=87.5,
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min_max=(62.5, 100.0),
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rounding=2,
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fixed=True,
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group=_("face"),
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info=_(
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"How much of the extracted image to train on. A lower coverage will limit the "
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"model's scope to a zoomed-in central area while higher amounts can include the "
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"entire face. A trade-off exists between lower amounts given more detail "
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"versus higher amounts avoiding noticeable swap transitions. For 'Face' "
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"centering you will want to leave this above 75%. For Head centering you will "
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"most likely want to set this to 100%. Sensible values for 'Legacy' "
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"centering are:"
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"\n\t62.5% spans from eyebrow to eyebrow."
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"\n\t75.0% spans from temple to temple."
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"\n\t87.5% spans from ear to ear."
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"\n\t100.0% is a mugshot."))
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self.add_item(
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section=section,
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title="icnr_init",
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datatype=bool,
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default=False,
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group=_("initialization"),
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info=_(
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"Use ICNR to tile the default initializer in a repeating pattern. "
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"This strategy is designed for pairing with sub-pixel / pixel shuffler "
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"to reduce the 'checkerboard effect' in image reconstruction. "
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"\n\t https://arxiv.org/ftp/arxiv/papers/1707/1707.02937.pdf"))
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self.add_item(
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section=section,
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title="conv_aware_init",
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datatype=bool,
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default=False,
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group=_("initialization"),
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info=_(
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"Use Convolution Aware Initialization for convolutional layers. "
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"This can help eradicate the vanishing and exploding gradient problem "
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"as well as lead to higher accuracy, lower loss and faster convergence.\nNB:"
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"\n\t This can use more VRAM when creating a new model so you may want to "
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"lower the batch size for the first run. The batch size can be raised "
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"again when reloading the model. "
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"\n\t Multi-GPU is not supported for this option, so you should start the model "
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"on a single GPU. Once training has started, you can stop training, enable "
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"multi-GPU and resume."
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"\n\t Building the model will likely take several minutes as the calculations "
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"for this initialization technique are expensive. This will only impact starting "
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"a new model."))
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self.add_item(
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section=section,
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title="optimizer",
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datatype=str,
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gui_radio=True,
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group=_("optimizer"),
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default="adam",
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choices=["adabelief", "adam", "nadam", "rms-prop"],
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info=_(
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"The optimizer to use."
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"\n\t adabelief - Adapting Stepsizes by the Belief in Observed Gradients. An "
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"optimizer with the aim to converge faster, generalize better and remain more "
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"stable. (https://arxiv.org/abs/2010.07468). NB: Epsilon for AdaBelief needs to "
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"be set to a smaller value than other Optimizers. Generally setting the 'Epsilon "
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"Exponent' to around '-16' should work."
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"\n\t adam - Adaptive Moment Optimization. A stochastic gradient descent method "
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"that is based on adaptive estimation of first-order and second-order moments."
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"\n\t nadam - Adaptive Moment Optimization with Nesterov Momentum. Much like "
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"Adam but uses a different formula for calculating momentum."
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"\n\t rms-prop - Root Mean Square Propagation. Maintains a moving (discounted) "
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"average of the square of the gradients. Divides the gradient by the root of "
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"this average."))
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self.add_item(
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section=section,
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title="learning_rate",
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datatype=float,
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default=5e-5,
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min_max=(1e-6, 1e-4),
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rounding=6,
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fixed=False,
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group=_("optimizer"),
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info=_(
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"Learning rate - how fast your network will learn (how large are the "
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"modifications to the model weights after one batch of training). Values that "
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"are too large might result in model crashes and the inability of the model to "
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"find the best solution. Values that are too small might be unable to escape "
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"from dead-ends and find the best global minimum."))
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self.add_item(
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section=section,
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title="epsilon_exponent",
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datatype=int,
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default=-7,
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min_max=(-20, 0),
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rounding=1,
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fixed=False,
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group=_("optimizer"),
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info=_(
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"The epsilon adds a small constant to weight updates to attempt to avoid 'divide "
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"by zero' errors. Unless you are using the AdaBelief Optimizer, then Generally "
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"this option should be left at default value, For AdaBelief, setting this to "
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"around '-16' should work.\n"
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"In all instances if you are getting 'NaN' loss values, and have been unable to "
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"resolve the issue any other way (for example, increasing batch size, or "
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"lowering learning rate), then raising the epsilon can lead to a more stable "
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"model. It may, however, come at the cost of slower training and a less accurate "
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"final result.\n"
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"NB: The value given here is the 'exponent' to the epsilon. For example, "
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"choosing '-7' will set the epsilon to 1e-7. Choosing '-3' will set the epsilon "
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"to 0.001 (1e-3)."))
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self.add_item(
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section=section,
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title="save_optimizer",
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datatype=str,
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group=_("optimizer"),
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default="exit",
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fixed=False,
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gui_radio=True,
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choices=["never", "always", "exit"],
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info=_(
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"When to save the Optimizer Weights. Saving the optimizer weights is not "
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"necessary and will increase the model file size 3x (and by extension the amount "
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"of time it takes to save the model). However, it can be useful to save these "
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"weights if you want to guarantee that a resumed model carries off exactly from "
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"where it left off, rather than spending a few hundred iterations catching up."
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"\n\t never - Don't save optimizer weights."
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"\n\t always - Save the optimizer weights at every save iteration. Model saving "
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"will take longer, due to the increased file size, but you will always have the "
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"last saved optimizer state in your model file."
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"\n\t exit - Only save the optimizer weights when explicitly terminating a "
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"model. This can be when the model is actively stopped or when the target "
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"iterations are met. Note: If the training session ends because of another "
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"reason (e.g. power outage, Out of Memory Error, NaN detected) then the "
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"optimizer weights will NOT be saved."))
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self.add_item(
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section=section,
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title="autoclip",
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datatype=bool,
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default=False,
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info=_(
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"Apply AutoClipping to the gradients. AutoClip analyzes the "
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"gradient weights and adjusts the normalization value dynamically to fit the "
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"data. Can help prevent NaNs and improve model optimization at the expense of "
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"VRAM. Ref: AutoClip: Adaptive Gradient Clipping for Source Separation Networks "
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"https://arxiv.org/abs/2007.14469"),
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fixed=False,
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gui_radio=True,
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group=_("optimizer"))
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self.add_item(
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section=section,
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title="reflect_padding",
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datatype=bool,
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default=False,
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group=_("network"),
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info=_(
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"Use reflection padding rather than zero padding with convolutions. "
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"Each convolution must pad the image boundaries to maintain the proper "
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"sizing. More complex padding schemes can reduce artifacts at the "
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"border of the image."
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"\n\t http://www-cs.engr.ccny.cuny.edu/~wolberg/cs470/hw/hw2_pad.txt"))
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self.add_item(
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section=section,
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title="allow_growth",
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datatype=bool,
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default=False,
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group=_("network"),
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fixed=False,
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info=_(
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"Enable the Tensorflow GPU 'allow_growth' configuration option. "
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"This option prevents Tensorflow from allocating all of the GPU VRAM at launch "
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"but can lead to higher VRAM fragmentation and slower performance. Should only "
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"be enabled if you are receiving errors regarding 'cuDNN fails to initialize' "
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"when commencing training."))
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self.add_item(
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section=section,
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title="mixed_precision",
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datatype=bool,
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default=False,
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fixed=False,
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group=_("network"),
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info=_(
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"NVIDIA GPUs can run operations in float16 faster than in "
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"float32. Mixed precision allows you to use a mix of float16 with float32, to "
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"get the performance benefits from float16 and the numeric stability benefits "
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"from float32.\n\nThis is untested on DirectML backend, but will run on most "
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"Nvidia models. it will only speed up training on more recent GPUs. Those with "
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"compute capability 7.0 or higher will see the greatest performance benefit from "
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"mixed precision because they have Tensor Cores. Older GPUs offer no math "
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"performance benefit for using mixed precision, however memory and bandwidth "
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"savings can enable some speedups. Generally RTX GPUs and later will offer the "
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"most benefit."))
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self.add_item(
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section=section,
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title="nan_protection",
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datatype=bool,
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default=True,
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group=_("network"),
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info=_(
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"If a 'NaN' is generated in the model, this means that the model has corrupted "
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"and the model is likely to start deteriorating from this point on. Enabling NaN "
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"protection will stop training immediately in the event of a NaN. The last save "
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"will not contain the NaN, so you may still be able to rescue your model."),
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fixed=False)
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self.add_item(
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section=section,
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title="convert_batchsize",
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datatype=int,
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default=16,
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min_max=(1, 32),
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rounding=1,
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fixed=False,
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group=_("convert"),
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info=_(
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"[GPU Only]. The number of faces to feed through the model at once when running "
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"the Convert process.\n\nNB: Increasing this figure is unlikely to improve "
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"convert speed, however, if you are getting Out of Memory errors, then you may "
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"want to reduce the batch size."))
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def _set_loss(self) -> None:
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# pylint:disable=line-too-long
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""" Set the default loss options.
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Loss Documentation
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MAE https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0
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MSE https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0
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LogCosh https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0
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L_inf_norm https://medium.com/@montjoile/l0-norm-l1-norm-l2-norm-l-infinity-norm-7a7d18a4f40c
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""" # noqa
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# pylint:enable=line-too-long
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logger.debug("Setting Loss config")
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section = "global.loss"
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self.add_section(section,
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_("Loss configuration options\n"
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"Loss is the mechanism by which a Neural Network judges how well it "
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"thinks that it is recreating a face.") + ADDITIONAL_INFO)
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self.add_item(
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section=section,
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title="loss_function",
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datatype=str,
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group=_("loss"),
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default="ssim",
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fixed=False,
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choices=[x for x in sorted(_LOSS_HELP) if x not in _NON_PRIMARY_LOSS],
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info=(_("The loss function to use.") +
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"\n\n\t" + "\n\n\t".join(f"{k}: {v}"
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for k, v in sorted(_LOSS_HELP.items())
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if k not in _NON_PRIMARY_LOSS)))
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self.add_item(
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section=section,
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title="loss_function_2",
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datatype=str,
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group=_("loss"),
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default="mse",
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fixed=False,
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choices=list(sorted(_LOSS_HELP)),
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info=(_("The second loss function to use. If using a structural based loss (such as "
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"SSIM, MS-SSIM or GMSD) it is common to add an L1 regularization(MAE) or L2 "
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"regularization (MSE) function. You can adjust the weighting of this loss "
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"function with the loss_weight_2 option.") +
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"\n\n\t" + "\n\n\t".join(f"{k}: {v}" for k, v in sorted(_LOSS_HELP.items()))))
|
|
self.add_item(
|
|
section=section,
|
|
title="loss_weight_2",
|
|
datatype=int,
|
|
group=_("loss"),
|
|
min_max=(0, 400),
|
|
rounding=1,
|
|
default=100,
|
|
fixed=False,
|
|
info=_(
|
|
"The amount of weight to apply to the second loss function.\n\n"
|
|
"\n\nThe value given here is as a percentage denoting how much the selected "
|
|
"function should contribute to the overall loss cost of the model. For example:"
|
|
"\n\t 100 - The loss calculated for the second loss function will be applied at "
|
|
"its full amount towards the overall loss score. "
|
|
"\n\t 25 - The loss calculated for the second loss function will be reduced by a "
|
|
"quarter prior to adding to the overall loss score. "
|
|
"\n\t 400 - The loss calculated for the second loss function will be mulitplied "
|
|
"4 times prior to adding to the overall loss score. "
|
|
"\n\t 0 - Disables the second loss function altogether."))
|
|
self.add_item(
|
|
section=section,
|
|
title="loss_function_3",
|
|
datatype=str,
|
|
group=_("loss"),
|
|
default="none",
|
|
fixed=False,
|
|
choices=list(sorted(_LOSS_HELP)),
|
|
info=(_("The third loss function to use. You can adjust the weighting of this loss "
|
|
"function with the loss_weight_3 option.") +
|
|
"\n\n\t" +
|
|
"\n\n\t".join(f"{k}: {v}" for k, v in sorted(_LOSS_HELP.items()))))
|
|
self.add_item(
|
|
section=section,
|
|
title="loss_weight_3",
|
|
datatype=int,
|
|
group=_("loss"),
|
|
min_max=(0, 400),
|
|
rounding=1,
|
|
default=0,
|
|
fixed=False,
|
|
info=_(
|
|
"The amount of weight to apply to the third loss function.\n\n"
|
|
"\n\nThe value given here is as a percentage denoting how much the selected "
|
|
"function should contribute to the overall loss cost of the model. For example:"
|
|
"\n\t 100 - The loss calculated for the third loss function will be applied at "
|
|
"its full amount towards the overall loss score. "
|
|
"\n\t 25 - The loss calculated for the third loss function will be reduced by a "
|
|
"quarter prior to adding to the overall loss score. "
|
|
"\n\t 400 - The loss calculated for the third loss function will be mulitplied 4 "
|
|
"times prior to adding to the overall loss score. "
|
|
"\n\t 0 - Disables the third loss function altogether."))
|
|
self.add_item(
|
|
section=section,
|
|
title="loss_function_4",
|
|
datatype=str,
|
|
group=_("loss"),
|
|
default="none",
|
|
fixed=False,
|
|
choices=list(sorted(_LOSS_HELP)),
|
|
info=(_("The fourth loss function to use. You can adjust the weighting of this loss "
|
|
"function with the loss_weight_3 option.") +
|
|
"\n\n\t" +
|
|
"\n\n\t".join(f"{k}: {v}" for k, v in sorted(_LOSS_HELP.items()))))
|
|
self.add_item(
|
|
section=section,
|
|
title="loss_weight_4",
|
|
datatype=int,
|
|
group=_("loss"),
|
|
min_max=(0, 400),
|
|
rounding=1,
|
|
default=0,
|
|
fixed=False,
|
|
info=_(
|
|
"The amount of weight to apply to the fourth loss function.\n\n"
|
|
"\n\nThe value given here is as a percentage denoting how much the selected "
|
|
"function should contribute to the overall loss cost of the model. For example:"
|
|
"\n\t 100 - The loss calculated for the fourth loss function will be applied at "
|
|
"its full amount towards the overall loss score. "
|
|
"\n\t 25 - The loss calculated for the fourth loss function will be reduced by a "
|
|
"quarter prior to adding to the overall loss score. "
|
|
"\n\t 400 - The loss calculated for the fourth loss function will be mulitplied "
|
|
"4 times prior to adding to the overall loss score. "
|
|
"\n\t 0 - Disables the fourth loss function altogether."))
|
|
self.add_item(
|
|
section=section,
|
|
title="mask_loss_function",
|
|
datatype=str,
|
|
group=_("loss"),
|
|
default="mse",
|
|
fixed=False,
|
|
choices=["mae", "mse"],
|
|
info=_(
|
|
"The loss function to use when learning a mask."
|
|
"\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 average, it will be "
|
|
"susceptible to outliers and typically produces slightly blurrier results."))
|
|
self.add_item(
|
|
section=section,
|
|
title="eye_multiplier",
|
|
datatype=int,
|
|
group=_("loss"),
|
|
min_max=(1, 40),
|
|
rounding=1,
|
|
default=3,
|
|
fixed=False,
|
|
info=_(
|
|
"The amount of priority to give to the eyes.\n\nThe value given here is as a "
|
|
"multiplier of the main loss score. For example:"
|
|
"\n\t 1 - The eyes will receive the same priority as the rest of the face. "
|
|
"\n\t 10 - The eyes will be given a score 10 times higher than the rest of the "
|
|
"face."
|
|
"\n\nNB: Penalized Mask Loss must be enable to use this option."))
|
|
self.add_item(
|
|
section=section,
|
|
title="mouth_multiplier",
|
|
datatype=int,
|
|
group=_("loss"),
|
|
min_max=(1, 40),
|
|
rounding=1,
|
|
default=2,
|
|
fixed=False,
|
|
info=_(
|
|
"The amount of priority to give to the mouth.\n\nThe value given here is as a "
|
|
"multiplier of the main loss score. For Example:"
|
|
"\n\t 1 - The mouth will receive the same priority as the rest of the face. "
|
|
"\n\t 10 - The mouth will be given a score 10 times higher than the rest of the "
|
|
"face."
|
|
"\n\nNB: Penalized Mask Loss must be enable to use this option."))
|
|
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, reconstruction 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="mask_type",
|
|
datatype=str,
|
|
default="extended",
|
|
choices=PluginLoader.get_available_extractors("mask",
|
|
add_none=True, extend_plugin=True),
|
|
group=_("mask"),
|
|
gui_radio=True,
|
|
info=_(
|
|
"The mask to be used for training. If you have selected 'Learn Mask' or "
|
|
"'Penalized Mask Loss' you must select a value other than 'none'. The required "
|
|
"mask should have been selected as part of the Extract process. If it does not "
|
|
"exist in the alignments file then it will be generated prior to training "
|
|
"commencing."
|
|
"\n\tnone: Don't use a mask."
|
|
"\n\tbisenet-fp_face: Relatively lightweight NN based mask that provides more "
|
|
"refined control over the area to be masked (configurable in mask settings). "
|
|
"Use this version of bisenet-fp if your model is trained with 'face' or "
|
|
"'legacy' centering."
|
|
"\n\tbisenet-fp_head: Relatively lightweight NN based mask that provides more "
|
|
"refined control over the area to be masked (configurable in mask settings). "
|
|
"Use this version of bisenet-fp if your model is trained with 'head' centering."
|
|
"\n\tcomponents: Mask designed to provide facial segmentation based on the "
|
|
"positioning of landmark locations. A convex hull is constructed around the "
|
|
"exterior of the landmarks to create a mask."
|
|
"\n\tcustom_face: Custom user created, face centered mask."
|
|
"\n\tcustom_head: Custom user created, head centered mask."
|
|
"\n\textended: Mask designed to provide facial segmentation based on the "
|
|
"positioning of landmark locations. A convex hull is constructed around the "
|
|
"exterior of the landmarks and the mask is extended upwards onto the forehead."
|
|
"\n\tvgg-clear: Mask designed to provide smart segmentation of mostly frontal "
|
|
"faces clear of obstructions. Profile faces and obstructions may result in "
|
|
"sub-par performance."
|
|
"\n\tvgg-obstructed: Mask designed to provide smart segmentation of mostly "
|
|
"frontal faces. The mask model has been specifically trained to recognize "
|
|
"some facial obstructions (hands and eyeglasses). Profile faces may result in "
|
|
"sub-par performance."
|
|
"\n\tunet-dfl: Mask designed to provide smart segmentation of mostly frontal "
|
|
"faces. The mask model has been trained by community members and will need "
|
|
"testing for further description. Profile faces may result in sub-par "
|
|
"performance."))
|
|
self.add_item(
|
|
section=section,
|
|
title="mask_blur_kernel",
|
|
datatype=int,
|
|
min_max=(0, 9),
|
|
rounding=1,
|
|
default=3,
|
|
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. The size is in pixels (calculated from "
|
|
"a 128px mask). Set to 0 to not apply gaussian blur. This value should be odd, "
|
|
"if an even number is passed in then it will be rounded to the next odd number."))
|
|
self.add_item(
|
|
section=section,
|
|
title="mask_threshold",
|
|
datatype=int,
|
|
default=4,
|
|
min_max=(0, 50),
|
|
rounding=1,
|
|
group=_("mask"),
|
|
info=_(
|
|
"Sets pixels that are near white to white and near black to black. Set to 0 for "
|
|
"off."))
|
|
self.add_item(
|
|
section=section,
|
|
title="learn_mask",
|
|
datatype=bool,
|
|
default=False,
|
|
group=_("mask"),
|
|
info=_(
|
|
"Dedicate a portion of the model to learning how to duplicate the input "
|
|
"mask. Increases VRAM usage in exchange for learning a quick ability to try "
|
|
"to replicate more complex mask models."))
|