""" Copyright [2022] Victor C Hall Licensed under the GNU Affero General Public License; You may not use this code except in compliance with the License. You may obtain a copy of the License at https://www.gnu.org/licenses/agpl-3.0.en.html Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import bisect import math import os import logging import copy import random from data.image_train_item import ImageTrainItem import data.aspects as aspects import data.resolver as resolver from colorama import Fore, Style import PIL PIL.Image.MAX_IMAGE_PIXELS = 715827880*4 # increase decompression bomb error limit to 4x default class DataLoaderMultiAspect(): """ Data loader for multi-aspect-ratio training and bucketing data_root: root folder of training data batch_size: number of images per batch flip_p: probability of flipping image horizontally (i.e. 0-0.5) """ def __init__(self, data_root, seed=555, debug_level=0, batch_size=1, flip_p=0.0, resolution=512, log_folder=None): self.data_root = data_root self.debug_level = debug_level self.flip_p = flip_p self.log_folder = log_folder self.seed = seed self.batch_size = batch_size self.has_scanned = False self.aspects = aspects.get_aspect_buckets(resolution=resolution, square_only=False) logging.info(f"* DLMA resolution {resolution}, buckets: {self.aspects}") self.__prepare_train_data() (self.rating_overall_sum, self.ratings_summed) = self.__sort_and_precalc_image_ratings() def __pick_multiplied_set(self, randomizer): """ Deals with multiply.txt whole and fractional numbers """ #print(f"Picking multiplied set from {len(self.prepared_train_data)}") data_copy = copy.deepcopy(self.prepared_train_data) # deep copy to avoid modifying original multiplier property epoch_size = len(self.prepared_train_data) picked_images = [] # add by whole number part first and decrement multiplier in copy for iti in data_copy: #print(f"check for whole number {iti.multiplier}: {iti.pathname}, remaining {iti.multiplier}") while iti.multiplier >= 1.0: picked_images.append(iti) #print(f"Adding {iti.multiplier}: {iti.pathname}, remaining {iti.multiplier}, , datalen: {len(picked_images)}") iti.multiplier -= 1.0 remaining = epoch_size - len(picked_images) assert remaining >= 0, "Something went wrong with the multiplier calculation" #print(f"Remaining to fill epoch after whole number adds: {remaining}") #print(f"Remaining in data copy: {len(data_copy)}") # add by renaming fractional numbers by random chance while remaining > 0: for iti in data_copy: if randomizer.uniform(0.0, 1.0) < iti.multiplier: #print(f"Adding {iti.multiplier}: {iti.pathname}, remaining {remaining}, datalen: {len(data_copy)}") picked_images.append(iti) remaining -= 1 data_copy.remove(iti) if remaining <= 0: break del data_copy return picked_images def get_shuffled_image_buckets(self, dropout_fraction: float = 1.0): """ returns the current list of images including their captions in a randomized order, sorted into buckets with same sized images if dropout_fraction < 1.0, only a subset of the images will be returned if dropout_fraction >= 1.0, repicks fractional multipliers based on folder/multiply.txt values swept at prescan :param dropout_fraction: must be between 0.0 and 1.0. :return: randomized list of (image, caption) pairs, sorted into same sized buckets """ self.seed += 1 randomizer = random.Random(self.seed) if dropout_fraction < 1.0: picked_images = self.__pick_random_subset(dropout_fraction, randomizer) else: picked_images = self.__pick_multiplied_set(randomizer) randomizer.shuffle(picked_images) buckets = {} batch_size = self.batch_size for image_caption_pair in picked_images: image_caption_pair.runt_size = 0 target_wh = image_caption_pair.target_wh if (target_wh[0],target_wh[1]) not in buckets: buckets[(target_wh[0],target_wh[1])] = [] buckets[(target_wh[0],target_wh[1])].append(image_caption_pair) if len(buckets) > 1: for bucket in buckets: truncate_count = len(buckets[bucket]) % batch_size if truncate_count > 0: runt_bucket = buckets[bucket][-truncate_count:] for item in runt_bucket: item.runt_size = truncate_count while len(runt_bucket) < batch_size: runt_bucket.append(random.choice(runt_bucket)) current_bucket_size = len(buckets[bucket]) buckets[bucket] = buckets[bucket][:current_bucket_size - truncate_count] buckets[bucket].extend(runt_bucket) # flatten the buckets image_caption_pairs = [] for bucket in buckets: image_caption_pairs.extend(buckets[bucket]) return image_caption_pairs def __sort_and_precalc_image_ratings(self) -> tuple[float, list[float]]: self.prepared_train_data = sorted(self.prepared_train_data, key=lambda img: img.caption.rating()) rating_overall_sum: float = 0.0 ratings_summed: list[float] = [] for image in self.prepared_train_data: rating_overall_sum += image.caption.rating() ratings_summed.append(rating_overall_sum) return rating_overall_sum, ratings_summed def __prepare_train_data(self, flip_p=0.0) -> list[ImageTrainItem]: """ Create ImageTrainItem objects with metadata for hydration later """ if not self.has_scanned: self.has_scanned = True logging.info(" Preloading images...") items = resolver.resolve(self.data_root, self.aspects, flip_p=flip_p, seed=self.seed) image_paths = set(map(lambda item: item.pathname, items)) print (f" * DLMA: {len(items)} images loaded from {len(image_paths)} files") self.prepared_train_data = items random.Random(self.seed).shuffle(self.prepared_train_data) self.__report_errors(items) def __report_errors(self, items: list[ImageTrainItem]): for item in items: if item.error is not None: logging.error(f"{Fore.LIGHTRED_EX} *** Error opening {Fore.LIGHTYELLOW_EX}{item.image_path}{Fore.LIGHTRED_EX} to get metadata. File may be corrupt and will be skipped.{Style.RESET_ALL}") logging.error(f" *** exception: {item.error}") undersized_items = [item for item in items if item.is_undersized] if len(undersized_items) > 0: underized_log_path = os.path.join(self.log_folder, "undersized_images.txt") logging.warning(f"{Fore.LIGHTRED_EX} ** Some images are smaller than the target size, consider using larger images{Style.RESET_ALL}") logging.warning(f"{Fore.LIGHTRED_EX} ** Check {underized_log_path} for more information.{Style.RESET_ALL}") with open(underized_log_path, "w") as undersized_images_file: undersized_images_file.write(f" The following images are smaller than the target size, consider removing or sourcing a larger copy:") for event in undersized_items: message = f" *** {event.image_path} with size: {event.image_size} is smaller than target size: {event.target_size}, consider using larger images" undersized_images_file.write(message) def __pick_random_subset(self, dropout_fraction: float, picker: random.Random) -> list[ImageTrainItem]: """ Picks a random subset of all images - The size of the subset is limited by dropout_faction - The chance of an image to be picked is influenced by its rating. Double that rating -> double the chance :param dropout_fraction: must be between 0.0 and 1.0 :param picker: seeded random picker :return: list of picked ImageTrainItem """ prepared_train_data = self.prepared_train_data.copy() ratings_summed = self.ratings_summed.copy() rating_overall_sum = self.rating_overall_sum num_images = len(prepared_train_data) num_images_to_pick = math.ceil(num_images * dropout_fraction) num_images_to_pick = max(min(num_images_to_pick, num_images), 0) # logging.info(f"Picking {num_images_to_pick} images out of the {num_images} in the dataset for drop_fraction {dropout_fraction}") picked_images: list[ImageTrainItem] = [] while num_images_to_pick > len(picked_images): # find random sample in dataset point = picker.uniform(0.0, rating_overall_sum) pos = min(bisect.bisect_left(ratings_summed, point), len(prepared_train_data) -1 ) # pick random sample picked_image = prepared_train_data[pos] picked_images.append(picked_image) # kick picked item out of data set to not pick it again rating_overall_sum = max(rating_overall_sum - picked_image.caption.rating(), 0.0) ratings_summed.pop(pos) prepared_train_data.pop(pos) return picked_images