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