#!/usr/bin python3 """ Main entry point to the training process of FaceSwap """ import logging import os import sys from threading import Lock from time import sleep import cv2 from lib.keypress import KBHit from lib.multithreading import MultiThread from lib.utils import (get_folder, get_image_paths, FaceswapError, _image_extensions) from plugins.plugin_loader import PluginLoader logger = logging.getLogger(__name__) # pylint: disable=invalid-name class Train(): # pylint:disable=too-few-public-methods """ The Faceswap Training Process. The training process is responsible for training a model on a set of source faces and a set of destination faces. The training process is self contained and should not be referenced by any other scripts, so it contains no public properties. Parameters ---------- arguments: argparse.Namespace The arguments to be passed to the training process as generated from Faceswap's command line arguments """ def __init__(self, arguments): logger.debug("Initializing %s: (args: %s", self.__class__.__name__, arguments) self._args = arguments if self._args.summary: # If just outputting summary we don't need to initialize everything return self._images = self._get_images() self._timelapse = self._set_timelapse() self._gui_preview_trigger = os.path.join(os.path.realpath(os.path.dirname(sys.argv[0])), "lib", "gui", ".cache", ".preview_trigger") self._stop = False self._save_now = False self._refresh_preview = False self._preview_buffer = dict() self._lock = Lock() logger.debug("Initialized %s", self.__class__.__name__) def _get_images(self): """ Check the image folders exist and contains images and obtain image paths. Returns ------- dict The image paths for each side. The key is the side, the value is the list of paths for that side. """ logger.debug("Getting image paths") images = dict() for side in ("a", "b"): image_dir = getattr(self._args, "input_{}".format(side)) if not os.path.isdir(image_dir): logger.error("Error: '%s' does not exist", image_dir) sys.exit(1) images[side] = get_image_paths(image_dir) if not images[side]: logger.error("Error: '%s' contains no images", image_dir) sys.exit(1) logger.info("Model A Directory: %s", self._args.input_a) logger.info("Model B Directory: %s", self._args.input_b) logger.debug("Got image paths: %s", [(key, str(len(val)) + " images") for key, val in images.items()]) self._validate_image_counts(images) return images @classmethod def _validate_image_counts(cls, images): """ Validate that there are sufficient images to commence training without raising an error. Confirms that there are at least 24 images in each folder. Whilst this is not enough images to train a Neural Network to any successful degree, it should allow the process to train without raising errors when generating previews. A warning is raised if there are fewer than 250 images on any side. Parameters ---------- images: dict The image paths for each side. The key is the side, the value is the list of paths for that side. """ counts = {side: len(paths) for side, paths in images.items()} msg = ("You need to provide a significant number of images to successfully train a Neural " "Network. Aim for between 500 - 5000 images per side.") if any(count < 25 for count in counts.values()): logger.error("At least one of your input folders contains fewer than 25 images.") logger.error(msg) sys.exit(1) if any(count < 250 for count in counts.values()): logger.warning("At least one of your input folders contains fewer than 250 images. " "Results are likely to be poor.") logger.warning(msg) def _set_timelapse(self): """ Set time-lapse paths if requested. Returns ------- dict The time-lapse keyword arguments for passing to the trainer """ if (not self._args.timelapse_input_a and not self._args.timelapse_input_b and not self._args.timelapse_output): return None if (not self._args.timelapse_input_a or not self._args.timelapse_input_b or not self._args.timelapse_output): raise FaceswapError("To enable the timelapse, you have to supply all the parameters " "(--timelapse-input-A, --timelapse-input-B and " "--timelapse-output).") timelapse_output = str(get_folder(self._args.timelapse_output)) for side in ("a", "b"): folder = getattr(self._args, "timelapse_input_{}".format(side)) if folder is not None and not os.path.isdir(folder): raise FaceswapError("The Timelapse path '{}' does not exist".format(folder)) training_folder = getattr(self._args, "input_{}".format(side)) if folder == training_folder: continue # Time-lapse folder is training folder filenames = [fname for fname in os.listdir(folder) if os.path.splitext(fname)[-1].lower() in _image_extensions] if not filenames: raise FaceswapError("The Timelapse path '{}' does not contain any valid " "images".format(folder)) # Time-lapse images must appear in the training set, as we need access to alignment and # mask info. Check filenames are there to save failing much later in the process. training_images = [os.path.basename(img) for img in self._images[side]] if not all(img in training_images for img in filenames): raise FaceswapError("All images in the Timelapse folder '{}' must exist in the " "training folder '{}'".format(folder, training_folder)) kwargs = {"input_a": self._args.timelapse_input_a, "input_b": self._args.timelapse_input_b, "output": timelapse_output} logger.debug("Timelapse enabled: %s", kwargs) return kwargs def process(self): """ The entry point for triggering the Training Process. Should only be called from :class:`lib.cli.launcher.ScriptExecutor` """ if self._args.summary: self._load_model() return logger.debug("Starting Training Process") logger.info("Training data directory: %s", self._args.model_dir) thread = self._start_thread() # from lib.queue_manager import queue_manager; queue_manager.debug_monitor(1) err = self._monitor(thread) self._end_thread(thread, err) logger.debug("Completed Training Process") def _start_thread(self): """ Put the :func:`_training` into a background thread so we can keep control. Returns ------- :class:`lib.multithreading.MultiThread` The background thread for running training """ logger.debug("Launching Trainer thread") thread = MultiThread(target=self._training) thread.start() logger.debug("Launched Trainer thread") return thread def _end_thread(self, thread, err): """ Output message and join thread back to main on termination. Parameters ---------- thread: :class:`lib.multithreading.MultiThread` The background training thread err: bool Whether an error has been detected in :func:`_monitor` """ logger.debug("Ending Training thread") if err: msg = "Error caught! Exiting..." log = logger.critical else: msg = ("Exit requested! The trainer will complete its current cycle, " "save the models and quit (This can take a couple of minutes " "depending on your training speed).") if not self._args.redirect_gui: msg += " If you want to kill it now, press Ctrl + c" log = logger.info log(msg) self._stop = True thread.join() sys.stdout.flush() logger.debug("Ended training thread") def _training(self): """ The training process to be run inside a thread. """ try: sleep(1) # Let preview instructions flush out to logger logger.debug("Commencing Training") logger.info("Loading data, this may take a while...") model = self._load_model() trainer = self._load_trainer(model) self._run_training_cycle(model, trainer) except KeyboardInterrupt: try: logger.debug("Keyboard Interrupt Caught. Saving Weights and exiting") model.save() trainer.clear_tensorboard() except KeyboardInterrupt: logger.info("Saving model weights has been cancelled!") sys.exit(0) except Exception as err: raise err def _load_model(self): """ Load the model requested for training. Returns ------- :file:`plugins.train.model` plugin The requested model plugin """ logger.debug("Loading Model") model_dir = str(get_folder(self._args.model_dir)) model = PluginLoader.get_model(self._args.trainer)( model_dir, self._args, predict=False) model.build() logger.debug("Loaded Model") return model def _load_trainer(self, model): """ Load the trainer requested for training. Parameters ---------- model: :file:`plugins.train.model` plugin The requested model plugin Returns ------- :file:`plugins.train.trainer` plugin The requested model trainer plugin """ logger.debug("Loading Trainer") trainer = PluginLoader.get_trainer(model.trainer) trainer = trainer(model, self._images, self._args.batch_size, self._args.configfile) logger.debug("Loaded Trainer") return trainer def _run_training_cycle(self, model, trainer): """ Perform the training cycle. Handles the background training, updating previews/time-lapse on each save interval, and saving the model. Parameters ---------- model: :file:`plugins.train.model` plugin The requested model plugin trainer: :file:`plugins.train.trainer` plugin The requested model trainer plugin """ logger.debug("Running Training Cycle") if self._args.write_image or self._args.redirect_gui or self._args.preview: display_func = self._show else: display_func = None for iteration in range(1, self._args.iterations + 1): logger.trace("Training iteration: %s", iteration) save_iteration = iteration % self._args.save_interval == 0 or iteration == 1 if save_iteration or self._save_now or self._refresh_preview: viewer = display_func else: viewer = None timelapse = self._timelapse if save_iteration else None trainer.train_one_step(viewer, timelapse) if self._stop: logger.debug("Stop received. Terminating") break if self._refresh_preview and viewer is not None: if self._args.redirect_gui: print("\n") logger.info("[Preview Updated]") if os.path.isfile(self._gui_preview_trigger): logger.debug("Removing gui trigger file: %s", self._gui_preview_trigger) os.remove(self._gui_preview_trigger) self._refresh_preview = False if save_iteration: logger.debug("Save Iteration: (iteration: %s", iteration) model.save() elif self._save_now: logger.debug("Save Requested: (iteration: %s", iteration) model.save() self._save_now = False logger.debug("Training cycle complete") model.save() trainer.clear_tensorboard() self._stop = True def _monitor(self, thread): """ Monitor the background :func:`_training` thread for key presses and errors. Returns ------- bool ``True`` if there has been an error in the background thread otherwise ``False`` """ is_preview = self._args.preview preview_trigger_set = False logger.debug("Launching Monitor") logger.info("===================================================") logger.info(" Starting") if is_preview: logger.info(" Using live preview") logger.info(" Press '%s' to save and quit", "Stop" if self._args.redirect_gui or self._args.colab else "ENTER") if not self._args.redirect_gui and not self._args.colab: logger.info(" Press 'S' to save model weights immediately") logger.info("===================================================") keypress = KBHit(is_gui=self._args.redirect_gui) err = False while True: try: if is_preview: with self._lock: for name, image in self._preview_buffer.items(): cv2.imshow(name, image) # pylint: disable=no-member cv_key = cv2.waitKey(1000) # pylint: disable=no-member else: cv_key = None if thread.has_error: logger.debug("Thread error detected") err = True break if self._stop: logger.debug("Stop received") break # Preview Monitor if is_preview and (cv_key == ord("\n") or cv_key == ord("\r")): logger.debug("Exit requested") break if is_preview and cv_key == ord("s"): print("\n") logger.info("Save requested") self._save_now = True if is_preview and cv_key == ord("r"): print("\n") logger.info("Refresh preview requested") self._refresh_preview = True # Console Monitor if keypress.kbhit(): console_key = keypress.getch() if console_key in ("\n", "\r"): logger.debug("Exit requested") break if console_key in ("s", "S"): logger.info("Save requested") self._save_now = True # GUI Preview trigger update monitor if self._args.redirect_gui: if not preview_trigger_set and os.path.isfile(self._gui_preview_trigger): print("\n") logger.info("Refresh preview requested") self._refresh_preview = True preview_trigger_set = True if preview_trigger_set and not self._refresh_preview: logger.debug("Resetting GUI preview trigger") preview_trigger_set = False sleep(1) except KeyboardInterrupt: logger.debug("Keyboard Interrupt received") break keypress.set_normal_term() logger.debug("Closed Monitor") return err def _show(self, image, name=""): """ Generate the preview and write preview file output. Handles the output and display of preview images. Parameters ---------- image: :class:`numpy.ndarray` The preview image to be displayed and/or written out name: str, optional The name of the image for saving or display purposes. If an empty string is passed then it will automatically be names. Default: "" """ logger.debug("Updating preview: (name: %s)", name) try: scriptpath = os.path.realpath(os.path.dirname(sys.argv[0])) if self._args.write_image: logger.debug("Saving preview to disk") img = "training_preview.jpg" imgfile = os.path.join(scriptpath, img) cv2.imwrite(imgfile, image) # pylint: disable=no-member logger.debug("Saved preview to: '%s'", img) if self._args.redirect_gui: logger.debug("Generating preview for GUI") img = ".gui_training_preview.jpg" imgfile = os.path.join(scriptpath, "lib", "gui", ".cache", "preview", img) cv2.imwrite(imgfile, image) # pylint: disable=no-member logger.debug("Generated preview for GUI: '%s'", img) if self._args.preview: logger.debug("Generating preview for display: '%s'", name) with self._lock: self._preview_buffer[name] = image logger.debug("Generated preview for display: '%s'", name) except Exception as err: logging.error("could not preview sample") raise err logger.debug("Updated preview: (name: %s)", name)