""" 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 logging import os.path from collections import defaultdict import math import copy import random from data.image_train_item import ImageTrainItem import PIL.Image 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 image_train_items: list of `ImageTrainItem` objects seed: random seed batch_size: number of images per batch """ def __init__(self, image_train_items: list[ImageTrainItem], seed=555, batch_size=1): self.seed = seed self.batch_size = batch_size self.prepared_train_data = image_train_items random.Random(self.seed).shuffle(self.prepared_train_data) self.prepared_train_data = sorted(self.prepared_train_data, key=lambda img: img.caption.rating()) self.expected_epoch_size = math.floor(sum([i.multiplier for i in self.prepared_train_data])) if self.expected_epoch_size != len(self.prepared_train_data): logging.info(f" * DLMA initialized with {len(image_train_items)} source images. After applying multipliers, each epoch will train on at least {self.expected_epoch_size} images.") else: logging.info(f" * DLMA initialized with {len(image_train_items)} images.") self.rating_overall_sum: float = 0.0 self.ratings_summed: list[float] = [] self.__update_rating_sums() def __pick_multiplied_set(self, randomizer: random.Random): """ Deals with multiply.txt whole and fractional numbers """ picked_images = [] data_copy = copy.deepcopy(self.prepared_train_data) # deep copy to avoid modifying original multiplier property for iti in data_copy: while iti.multiplier >= 1: picked_images.append(iti) iti.multiplier -= 1 remaining = self.expected_epoch_size - len(picked_images) assert remaining >= 0, "Something went wrong with the multiplier calculation" # resolve fractional parts, ensure each is only added max once while remaining > 0: for iti in data_copy: if randomizer.random() < iti.multiplier: picked_images.append(iti) iti.multiplier = 0 remaining -= 1 if remaining <= 0: break return picked_images def get_shuffled_image_buckets(self, dropout_fraction: float = 1.0) -> list[ImageTrainItem]: """ Returns the current list of `ImageTrainItem` in 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 `ImageTrainItem` objects """ 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) 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 items: list[ImageTrainItem] = [] for bucket in buckets: items.extend(buckets[bucket]) return items 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 def __update_rating_sums(self): self.rating_overall_sum: float = 0.0 self.ratings_summed: list[float] = [] for item in self.prepared_train_data: self.rating_overall_sum += item.caption.rating() self.ratings_summed.append(self.rating_overall_sum)