EveryDream2trainer/data/data_loader.py

"""
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 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())
        expected_epoch_size = math.floor(sum([i.multiplier for i in self.prepared_train_data]))
        if 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 {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 = []
        fractional_images_per_directory = defaultdict(list[ImageTrainItem])
        for iti in self.prepared_train_data:
            multiplier = iti.multiplier
            while multiplier >= 1:
                picked_images.append(iti)
                multiplier -= 1
            # fractional remainders must be dealt with separately
            if multiplier > 0:
                directory = os.path.dirname(iti.pathname)
                fractional_images_per_directory[directory].append(iti)

        # resolve fractional parts per-directory
        for _, fractional_items in fractional_images_per_directory.items():
            randomizer.shuffle(fractional_items)
            multiplier = fractional_items[0].multiplier % 1.0
            count_to_take = math.ceil(multiplier * len(fractional_items))
            picked_images.extend(fractional_items[:count_to_take])

        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)