mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-07 10:43:27 -04:00
* 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
121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
#!/usr/bin python3
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""" Display Frame of the Faceswap GUI
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What is displayed in the Display Frame varies
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depending on what tasked is being run """
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import logging
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import tkinter as tk
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from tkinter import ttk
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from .display_analysis import Analysis
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from .display_command import GraphDisplay, PreviewExtract, PreviewTrain
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from .utils import get_config
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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class DisplayNotebook(ttk.Notebook): # pylint: disable=too-many-ancestors
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""" The display tabs """
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def __init__(self, parent):
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logger.debug("Initializing %s", self.__class__.__name__)
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ttk.Notebook.__init__(self, parent, width=780)
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parent.add(self)
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tk_vars = get_config().tk_vars
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self.wrapper_var = tk_vars["display"]
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self.runningtask = tk_vars["runningtask"]
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self.set_wrapper_var_trace()
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self.add_static_tabs()
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self.static_tabs = [child for child in self.tabs()]
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logger.debug("Initialized %s", self.__class__.__name__)
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def set_wrapper_var_trace(self):
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""" Set the trigger actions for the display vars
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when they have been triggered in the Process Wrapper """
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logger.debug("Setting wrapper var trace")
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self.wrapper_var.trace("w", self.update_displaybook)
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def add_static_tabs(self):
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""" Add tabs that are permanently available """
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logger.debug("Adding static tabs")
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for tab in ("job queue", "analysis"):
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if tab == "job queue":
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continue # Not yet implemented
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if tab == "analysis":
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helptext = {"stats":
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"Summary statistics for each training session"}
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frame = Analysis(self, tab, helptext)
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else:
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frame = self.add_frame()
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self.add(frame, text=tab.title())
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def add_frame(self):
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""" Add a single frame for holding tab's contents """
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logger.debug("Adding frame")
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frame = ttk.Frame(self)
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frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=True, padx=5, pady=5)
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return frame
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def command_display(self, command):
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""" Select what to display based on incoming
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command """
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build_tabs = getattr(self, "{}_tabs".format(command))
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build_tabs()
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def extract_tabs(self):
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""" Build the extract tabs """
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logger.debug("Build extract tabs")
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helptext = ("Updates preview from output every 5 "
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"seconds to limit disk contention")
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PreviewExtract(self, "preview", helptext, 5000)
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logger.debug("Built extract tabs")
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def train_tabs(self):
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""" Build the train tabs """
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logger.debug("Build train tabs")
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for tab in ("graph", "preview"):
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if tab == "graph":
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helptext = "Graph showing Loss vs Iterations"
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GraphDisplay(self, "graph", helptext, 5000)
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elif tab == "preview":
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helptext = "Training preview. Updated on every save iteration"
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PreviewTrain(self, "preview", helptext, 1000)
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logger.debug("Built train tabs")
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def convert_tabs(self):
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""" Build the convert tabs
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Currently identical to Extract, so just call that """
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logger.debug("Build convert tabs")
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self.extract_tabs()
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logger.debug("Built convert tabs")
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def remove_tabs(self):
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""" Remove all command specific tabs """
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for child in self.tabs():
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if child in self.static_tabs:
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continue
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logger.debug("removing child: %s", child)
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child_name = child.split(".")[-1]
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child_object = self.children[child_name]
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self.destroy_tabs_children(child_object)
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self.forget(child)
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@staticmethod
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def destroy_tabs_children(tab):
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""" Destroy all tabs children
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Children must be destroyed as forget only hides display
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"""
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logger.debug("Destroying children for tab: %s", tab)
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for child in tab.winfo_children():
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logger.debug("Destroying child: %s", child)
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child.destroy()
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def update_displaybook(self, *args): # pylint: disable=unused-argument
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""" Set the display tabs based on executing task """
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command = self.wrapper_var.get()
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self.remove_tabs()
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if not command or command not in ("extract", "train", "convert"):
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return
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self.command_display(command)
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