mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-07 10:37:19 -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
234 lines
9.1 KiB
Python
234 lines
9.1 KiB
Python
#!/usr/bin python3
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""" Command specific tabs of Display Frame of the Faceswap GUI """
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import datetime
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import logging
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import os
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import tkinter as tk
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from tkinter import ttk
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from .display_graph import TrainingGraph
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from .display_page import DisplayOptionalPage
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from .tooltip import Tooltip
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from .stats import Calculations
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from .utils import FileHandler, get_config, get_images
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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class PreviewExtract(DisplayOptionalPage): # pylint: disable=too-many-ancestors
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""" Tab to display output preview images for extract and convert """
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def display_item_set(self):
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""" Load the latest preview if available """
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logger.trace("Loading latest preview")
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get_images().load_latest_preview()
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self.display_item = get_images().previewoutput
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def display_item_process(self):
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""" Display the preview """
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logger.trace("Displaying preview")
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if not self.subnotebook.children:
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self.add_child()
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else:
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self.update_child()
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def add_child(self):
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""" Add the preview label child """
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logger.debug("Adding child")
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preview = self.subnotebook_add_page(self.tabname, widget=None)
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lblpreview = ttk.Label(preview, image=get_images().previewoutput[1])
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lblpreview.pack(side=tk.TOP, anchor=tk.NW)
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Tooltip(lblpreview, text=self.helptext, wraplength=200)
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def update_child(self):
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""" Update the preview image on the label """
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logger.trace("Updating preview")
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for widget in self.subnotebook_get_widgets():
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widget.configure(image=get_images().previewoutput[1])
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def save_items(self):
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""" Open save dialogue and save preview """
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location = FileHandler("dir", None).retfile
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if not location:
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return
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filename = "extract_convert_preview"
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now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = os.path.join(location,
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"{}_{}.{}".format(filename,
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now,
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"png"))
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get_images().previewoutput[0].save(filename)
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logger.debug("Saved preview to %s", filename)
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print("Saved preview to {}".format(filename))
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class PreviewTrain(DisplayOptionalPage): # pylint: disable=too-many-ancestors
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""" Training preview image(s) """
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def __init__(self, *args, **kwargs):
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self.update_preview = get_config().tk_vars["updatepreview"]
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super().__init__(*args, **kwargs)
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def display_item_set(self):
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""" Load the latest preview if available """
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logger.trace("Loading latest preview")
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if not self.update_preview.get():
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logger.trace("Preview not updated")
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return
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get_images().load_training_preview()
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self.display_item = get_images().previewtrain
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def display_item_process(self):
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""" Display the preview(s) resized as appropriate """
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logger.trace("Displaying preview")
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sortednames = sorted(list(get_images().previewtrain.keys()))
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existing = self.subnotebook_get_titles_ids()
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should_update = self.update_preview.get()
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for name in sortednames:
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if name not in existing.keys():
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self.add_child(name)
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elif should_update:
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tab_id = existing[name]
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self.update_child(tab_id, name)
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if should_update:
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self.update_preview.set(False)
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def add_child(self, name):
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""" Add the preview canvas child """
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logger.debug("Adding child")
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preview = PreviewTrainCanvas(self.subnotebook, name)
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preview = self.subnotebook_add_page(name, widget=preview)
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Tooltip(preview, text=self.helptext, wraplength=200)
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self.vars["modified"].set(get_images().previewtrain[name][2])
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def update_child(self, tab_id, name):
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""" Update the preview canvas """
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logger.debug("Updating preview")
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if self.vars["modified"].get() != get_images().previewtrain[name][2]:
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self.vars["modified"].set(get_images().previewtrain[name][2])
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widget = self.subnotebook_page_from_id(tab_id)
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widget.reload()
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def save_items(self):
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""" Open save dialogue and save preview """
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location = FileHandler("dir", None).retfile
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if not location:
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return
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for preview in self.subnotebook.children.values():
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preview.save_preview(location)
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class PreviewTrainCanvas(ttk.Frame): # pylint: disable=too-many-ancestors
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""" Canvas to hold a training preview image """
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def __init__(self, parent, previewname):
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logger.debug("Initializing %s: (previewname: '%s')", self.__class__.__name__, previewname)
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ttk.Frame.__init__(self, parent)
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self.name = previewname
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get_images().resize_image(self.name, None)
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self.previewimage = get_images().previewtrain[self.name][1]
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self.canvas = tk.Canvas(self, bd=0, highlightthickness=0)
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self.canvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)
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self.imgcanvas = self.canvas.create_image(0,
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0,
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image=self.previewimage,
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anchor=tk.NW)
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self.bind("<Configure>", self.resize)
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logger.debug("Initialized %s:", self.__class__.__name__)
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def resize(self, event):
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""" Resize the image to fit the frame, maintaining aspect ratio """
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logger.trace("Resizing preview image")
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framesize = (event.width, event.height)
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# Sometimes image is resized before frame is drawn
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framesize = None if framesize == (1, 1) else framesize
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get_images().resize_image(self.name, framesize)
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self.reload()
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def reload(self):
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""" Reload the preview image """
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logger.trace("Reloading preview image")
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self.previewimage = get_images().previewtrain[self.name][1]
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self.canvas.itemconfig(self.imgcanvas, image=self.previewimage)
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def save_preview(self, location):
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""" Save the figure to file """
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filename = self.name
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now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = os.path.join(location,
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"{}_{}.{}".format(filename,
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now,
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"png"))
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get_images().previewtrain[self.name][0].save(filename)
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logger.debug("Saved preview to %s", filename)
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print("Saved preview to {}".format(filename))
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class GraphDisplay(DisplayOptionalPage): # pylint: disable=too-many-ancestors
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""" The Graph Tab of the Display section """
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def add_options(self):
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""" Add the additional options """
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self.add_option_refresh()
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super().add_options()
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def add_option_refresh(self):
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""" Add refresh button to refresh graph immediately """
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logger.debug("Adding refresh option")
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tk_var = get_config().tk_vars["refreshgraph"]
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btnrefresh = ttk.Button(self.optsframe,
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image=get_images().icons["reset"],
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command=lambda: tk_var.set(True))
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btnrefresh.pack(padx=2, side=tk.RIGHT)
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Tooltip(btnrefresh,
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text="Graph updates every 100 iterations. Click to refresh now.",
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wraplength=200)
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def display_item_set(self):
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""" Load the graph(s) if available """
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session = get_config().session
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if session.initialized and session.logging_disabled:
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logger.trace("Logs disabled. Hiding graph")
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self.set_info("Graph is disabled as 'no-logs' has been selected")
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self.display_item = None
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elif session.initialized:
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logger.trace("Loading graph")
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self.display_item = session
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else:
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self.display_item = None
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def display_item_process(self):
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""" Add a single graph to the graph window """
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logger.trace("Adding graph")
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existing = list(self.subnotebook_get_titles_ids().keys())
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for loss_key in self.display_item.loss_keys:
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tabname = loss_key.replace("_", " ").title()
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if tabname in existing:
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continue
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data = Calculations(session=get_config().session,
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display="loss",
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loss_keys=[loss_key],
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selections=["raw", "trend"])
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self.add_child(tabname, data)
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def add_child(self, name, data):
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""" Add the graph for the selected keys """
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logger.debug("Adding child: %s", name)
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graph = TrainingGraph(self.subnotebook, data, "Loss")
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graph.build()
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graph = self.subnotebook_add_page(name, widget=graph)
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Tooltip(graph, text=self.helptext, wraplength=200)
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def save_items(self):
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""" Open save dialogue and save graphs """
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graphlocation = FileHandler("dir", None).retfile
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if not graphlocation:
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return
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for graph in self.subnotebook.children.values():
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graph.save_fig(graphlocation)
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