mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-08 03:26:47 -04:00
* Core Updates - Remove lib.utils.keras_backend_quiet and replace with get_backend() where relevant - Document lib.gpu_stats and lib.sys_info - Remove call to GPUStats.is_plaidml from convert and replace with get_backend() - lib.gui.menu - typofix * Update Dependencies Bump Tensorflow Version Check * Port extraction to tf2 * Add custom import finder for loading Keras or tf.keras depending on backend * Add `tensorflow` to KerasFinder search path * Basic TF2 training running * model.initializers - docstring fix * Fix and pass tests for tf2 * Replace Keras backend tests with faceswap backend tests * Initial optimizers update * Monkey patch tf.keras optimizer * Remove custom Adam Optimizers and Memory Saving Gradients * Remove multi-gpu option. Add Distribution to cli * plugins.train.model._base: Add Mirror, Central and Default distribution strategies * Update tensorboard kwargs for tf2 * Penalized Loss - Fix for TF2 and AMD * Fix syntax for tf2.1 * requirements typo fix * Explicit None for clipnorm if using a distribution strategy * Fix penalized loss for distribution strategies * Update Dlight * typo fix * Pin to TF2.2 * setup.py - Install tensorflow from pip if not available in Conda * Add reduction options and set default for mirrored distribution strategy * Explicitly use default strategy rather than nullcontext * lib.model.backup_restore documentation * Remove mirrored strategy reduction method and default based on OS * Initial restructure - training * Remove PingPong Start model.base refactor * Model saving and resuming enabled * More tidying up of model.base * Enable backup and snapshotting * Re-enable state file Remove loss names from state file Fix print loss function Set snapshot iterations correctly * Revert original model to Keras Model structure rather than custom layer Output full model and sub model summary Change NNBlocks to callables rather than custom keras layers * Apply custom Conv2D layer * Finalize NNBlock restructure Update Dfaker blocks * Fix reloading model under a different distribution strategy * Pass command line arguments through to trainer * Remove training_opts from model and reference params directly * Tidy up model __init__ * Re-enable tensorboard logging Suppress "Model Not Compiled" warning * Fix timelapse * lib.model.nnblocks - Bugfix residual block Port dfaker bugfix original * dfl-h128 ported * DFL SAE ported * IAE Ported * dlight ported * port lightweight * realface ported * unbalanced ported * villain ported * lib.cli.args - Update Batchsize + move allow_growth to config * Remove output shape definition Get image sizes per side rather than globally * Strip mask input from encoder * Fix learn mask and output learned mask to preview * Trigger Allow Growth prior to setting strategy * Fix GUI Graphing * GUI - Display batchsize correctly + fix training graphs * Fix penalized loss * Enable mixed precision training * Update analysis displayed batch to match input * Penalized Loss - Multi-GPU Fix * Fix all losses for TF2 * Fix Reflect Padding * Allow different input size for each side of the model * Fix conv-aware initialization on reload * Switch allow_growth order * Move mixed_precision to cli * Remove distrubution strategies * Compile penalized loss sub-function into LossContainer * Bump default save interval to 250 Generate preview on first iteration but don't save Fix iterations to start at 1 instead of 0 Remove training deprecation warnings Bump some scripts.train loglevels * Add ability to refresh preview on demand on pop-up window * Enable refresh of training preview from GUI * Fix Convert Debug logging in Initializers * Fix Preview Tool * Update Legacy TF1 weights to TF2 Catch stats error on loading stats with missing logs * lib.gui.popup_configure - Make more responsive + document * Multiple Outputs supported in trainer Original Model - Mask output bugfix * Make universal inference model for convert Remove scaling from penalized mask loss (now handled at input to y_true) * Fix inference model to work properly with all models * Fix multi-scale output for convert * Fix clipnorm issue with distribution strategies Edit error message on OOM * Update plaidml losses * Add missing file * Disable gmsd loss for plaidnl * PlaidML - Basic training working * clipnorm rewriting for mixed-precision * Inference model creation bugfixes * Remove debug code * Bugfix: Default clipnorm to 1.0 * Remove all mask inputs from training code * Remove mask inputs from convert * GUI - Analysis Tab - Docstrings * Fix rate in totals row * lib.gui - Only update display pages if they have focus * Save the model on first iteration * plaidml - Fix SSIM loss with penalized loss * tools.alignments - Remove manual and fix jobs * GUI - Remove case formatting on help text * gui MultiSelect custom widget - Set default values on init * vgg_face2 - Move to plugins.extract.recognition and use plugins._base base class cli - Add global GPU Exclude Option tools.sort - Use global GPU Exlude option for backend lib.model.session - Exclude all GPUs when running in CPU mode lib.cli.launcher - Set backend to CPU mode when all GPUs excluded * Cascade excluded devices to GPU Stats * Explicit GPU selection for Train and Convert * Reduce Tensorflow Min GPU Multiprocessor Count to 4 * remove compat.v1 code from extract * Force TF to skip mixed precision compatibility check if GPUs have been filtered * Add notes to config for non-working AMD losses * Rasie error if forcing extract to CPU mode * Fix loading of legace dfl-sae weights + dfl-sae typo fix * Remove unused requirements Update sphinx requirements Fix broken rst file locations * docs: lib.gui.display * clipnorm amd condition check * documentation - gui.display_analysis * Documentation - gui.popup_configure * Documentation - lib.logger * Documentation - lib.model.initializers * Documentation - lib.model.layers * Documentation - lib.model.losses * Documentation - lib.model.nn_blocks * Documetation - lib.model.normalization * Documentation - lib.model.session * Documentation - lib.plaidml_stats * Documentation: lib.training_data * Documentation: lib.utils * Documentation: plugins.train.model._base * GUI Stats: prevent stats from using GPU * Documentation - Original Model * Documentation: plugins.model.trainer._base * linting * unit tests: initializers + losses * unit tests: nn_blocks * bugfix - Exclude gpu devices in train, not include * Enable Exclude-Gpus in Extract * Enable exclude gpus in tools * Disallow multiple plugin types in a single model folder * Automatically add exclude_gpus argument in for cpu backends * Cpu backend fixes * Relax optimizer test threshold * Default Train settings - Set mask to Extended * Update Extractor cli help text Update to Python 3.8 * Fix FAN to run on CPU * lib.plaidml_tools - typofix * Linux installer - check for curl * linux installer - typo fix
322 lines
13 KiB
Python
322 lines
13 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 .custom_widgets import Tooltip
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from .stats import Calculations
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from .control_helper import set_slider_rounding
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from .utils import FileHandler, get_config, get_images, preview_trigger
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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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size = 256 if self.command == "convert" else 128
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get_images().load_latest_preview(thumbnail_size=int(size * get_config().scaling_factor),
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frame_dims=(self.winfo_width(), self.winfo_height()))
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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 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 preview immediately """
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logger.debug("Adding refresh option")
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btnrefresh = ttk.Button(self.optsframe,
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image=get_images().icons["reload"],
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command=preview_trigger().set)
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btnrefresh.pack(padx=2, side=tk.RIGHT)
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Tooltip(btnrefresh,
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text="Preview updates at every model save. Click to refresh now.",
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wraplength=200)
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logger.debug("Added refresh option")
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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 __init__(self, parent, tab_name, helptext, waittime, command=None):
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self.trace_var = None
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super().__init__(parent, tab_name, helptext, waittime, command)
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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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self.add_option_smoothing()
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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["reload"],
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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 at every model save. Click to refresh now.",
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wraplength=200)
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logger.debug("Added refresh option")
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def add_option_smoothing(self):
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""" Add refresh button to refresh graph immediately """
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logger.debug("Adding Smoothing Slider")
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tk_var = get_config().tk_vars["smoothgraph"]
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min_max = (0, 0.99)
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hlp = "Set the smoothing amount. 0 is no smoothing, 0.99 is maximum smoothing."
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ctl_frame = ttk.Frame(self.optsframe)
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ctl_frame.pack(padx=2, side=tk.RIGHT)
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lbl = ttk.Label(ctl_frame, text="Smoothing Amount:", anchor=tk.W)
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lbl.pack(pady=5, side=tk.LEFT, anchor=tk.N, expand=True)
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tbox = ttk.Entry(ctl_frame, width=6, textvariable=tk_var, justify=tk.RIGHT)
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tbox.pack(padx=(0, 5), side=tk.RIGHT)
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ctl = ttk.Scale(
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ctl_frame,
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variable=tk_var,
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command=lambda val, var=tk_var, dt=float, rn=2, mm=(0, 0.99):
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set_slider_rounding(val, var, dt, rn, mm))
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ctl["from_"] = min_max[0]
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ctl["to"] = min_max[1]
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ctl.pack(padx=5, pady=5, fill=tk.X, expand=True)
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for item in (tbox, ctl):
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Tooltip(item,
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text=hlp,
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wraplength=200)
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logger.debug("Added Smoothing Slider")
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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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smooth_amount_var = get_config().tk_vars["smoothgraph"]
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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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if self.trace_var is not None:
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smooth_amount_var.trace_vdelete("w", self.trace_var)
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self.trace_var = 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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if self.trace_var is None:
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self.trace_var = smooth_amount_var.trace("w", self.smooth_amount_callback)
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else:
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self.display_item = None
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if self.trace_var is not None:
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smooth_amount_var.trace_vdelete("w", self.trace_var)
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self.trace_var = 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.debug("Adding graph")
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existing = list(self.subnotebook_get_titles_ids().keys())
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loss_keys = [key for key in self.display_item.loss_keys if key != "total"]
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display_tabs = sorted(set(key[:-1].rstrip("_") for key in loss_keys))
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for loss_key in display_tabs:
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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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display_keys = [key for key in loss_keys if key.startswith(loss_key)]
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data = Calculations(session=get_config().session,
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display="loss",
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loss_keys=display_keys,
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selections=["raw", "smoothed"],
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smooth_amount=get_config().tk_vars["smoothgraph"].get())
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self.add_child(tabname, data)
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def smooth_amount_callback(self, *args):
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""" Update each graph's smooth amount on variable change """
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smooth_amount = get_config().tk_vars["smoothgraph"].get()
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logger.debug("Updating graph smooth_amount: (new_value: %s, args: %s)",
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smooth_amount, args)
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for graph in self.subnotebook.children.values():
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graph.calcs.args["smooth_amount"] = smooth_amount
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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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def close(self):
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""" Clear the plots from RAM """
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if self.trace_var is not None:
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get_config().tk_vars["smoothgraph"].trace_vdelete("w", self.trace_var)
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self.trace_var = None
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if self.subnotebook is None:
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logger.debug("No graphs to clear. Returning")
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return
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for name, graph in self.subnotebook.children.items():
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logger.debug("Clearing: %s", name)
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graph.clear()
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super().close()
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