import json import logging import os import random import typing import zipfile import PIL.Image as Image import tqdm from colorama import Fore, Style from data.image_train_item import ImageCaption, ImageTrainItem class DataResolver: def __init__(self, aspects: list[typing.Tuple[int, int]], flip_p=0.0, seed=555): self.seed = seed self.aspects = aspects self.flip_p = flip_p def image_train_items(self, data_root: str) -> list[ImageTrainItem]: """ Get the list of `ImageTrainItem` for the given data root. :param data_root: The data root, a directory, a file, etc.. :return: The list of `ImageTrainItem`. """ raise NotImplementedError() def image_train_item(self, image_path: str, caption: ImageCaption, multiplier: float=1) -> ImageTrainItem: return ImageTrainItem( image=None, caption=caption, aspects=self.aspects, pathname=image_path, flip_p=self.flip_p, multiplier=multiplier ) class JSONResolver(DataResolver): def image_train_items(self, json_path: str) -> list[ImageTrainItem]: """ Create `ImageTrainItem` objects with metadata for hydration later. Extracts images and captions from a JSON file. :param json_path: The path to the JSON file. """ items = [] with open(json_path, encoding='utf-8', mode='r') as f: json_data = json.load(f) for data in tqdm.tqdm(json_data): caption = JSONResolver.image_caption(data) if caption: image_value = JSONResolver.get_image_value(data) item = self.image_train_item(image_value, caption) if item: items.append(item) return items @staticmethod def get_image_value(json_data: dict) -> typing.Optional[str]: """ Get the image from the json data if possible. :param json_data: The json data, a dict. :return: The image, or None if not found. """ image_value = json_data.get("image", None) if isinstance(image_value, str): image_value = image_value.strip() if os.path.exists(image_value): return image_value @staticmethod def get_caption_value(json_data: dict) -> typing.Optional[str]: """ Get the caption from the json data if possible. :param json_data: The json data, a dict. :return: The caption, or None if not found. """ caption_value = json_data.get("caption", None) if isinstance(caption_value, str): return caption_value.strip() @staticmethod def image_caption(json_data: dict) -> typing.Optional[ImageCaption]: """ Get the caption from the json data if possible. :param json_data: The json data, a dict. :return: The `ImageCaption`, or None if not found. """ image_value = JSONResolver.get_image_value(json_data) caption_value = JSONResolver.get_caption_value(json_data) if image_value: if caption_value: return ImageCaption.resolve(caption_value) return ImageCaption.from_file(image_value) class DirectoryResolver(DataResolver): def image_train_items(self, data_root: str) -> list[ImageTrainItem]: """ Create `ImageTrainItem` objects with metadata for hydration later. Unzips all zip files in `data_root` and then recursively searches the `data_root` for images and captions. :param data_root: The root directory to recurse through """ DirectoryResolver.unzip_all(data_root) image_paths = list(DirectoryResolver.recurse_data_root(data_root)) items = [] multipliers = {} randomizer = random.Random(self.seed) for pathname in tqdm.tqdm(image_paths): current_dir = os.path.dirname(pathname) if current_dir not in multipliers: multiply_txt_path = os.path.join(current_dir, "multiply.txt") if os.path.exists(multiply_txt_path): try: with open(multiply_txt_path, 'r') as f: val = float(f.read().strip()) multipliers[current_dir] = val logging.info(f" * DLMA multiply.txt in {current_dir} set to {val}") except Exception as e: logging.warning(f" * {Fore.LIGHTYELLOW_EX}Error trying to read multiply.txt for {current_dir}: {Style.RESET_ALL}{e}") multipliers[current_dir] = 1.0 else: multipliers[current_dir] = 1.0 caption = ImageCaption.resolve(pathname) item = self.image_train_item(pathname, caption, multiplier=multipliers[current_dir]) cur_file_multiplier = multipliers[current_dir] while cur_file_multiplier >= 1.0: items.append(item) cur_file_multiplier -= 1 if cur_file_multiplier > 0: if randomizer.random() < cur_file_multiplier: items.append(item) return items @staticmethod def unzip_all(path): try: for root, dirs, files in os.walk(path): for file in files: if file.endswith('.zip'): logging.info(f"Unzipping {file}") with zipfile.ZipFile(path, 'r') as zip_ref: zip_ref.extractall(path) except Exception as e: logging.error(f"Error unzipping files {e}") @staticmethod def recurse_data_root(recurse_root): for f in os.listdir(recurse_root): current = os.path.join(recurse_root, f) if os.path.isfile(current): ext = os.path.splitext(f)[1].lower() if ext in ['.jpg', '.jpeg', '.png', '.bmp', '.webp', '.jfif']: yield current for d in os.listdir(recurse_root): current = os.path.join(recurse_root, d) if os.path.isdir(current): yield from DirectoryResolver.recurse_data_root(current) def strategy(data_root: str): if os.path.isfile(data_root) and data_root.endswith('.json'): return JSONResolver if os.path.isdir(data_root): return DirectoryResolver raise ValueError(f"data_root '{data_root}' is not a valid directory or JSON file.") def resolve_root(path: str, aspects: list[float], flip_p: float = 0.0, seed=555) -> list[ImageTrainItem]: """ :param data_root: Directory or JSON file. :param aspects: The list of aspect ratios to use :param flip_p: The probability of flipping the image """ if os.path.isfile(path) and path.endswith('.json'): return JSONResolver(aspects, flip_p, seed).image_train_items(path) if os.path.isdir(path): return DirectoryResolver(aspects, flip_p, seed).image_train_items(path) raise ValueError(f"data_root '{path}' is not a valid directory or JSON file.") def resolve(value: typing.Union[dict, str], aspects: list[float], flip_p: float=0.0, seed=555) -> list[ImageTrainItem]: """ Resolve the training data from the value. :param value: The value to resolve, either a dict or a string. :param aspects: The list of aspect ratios to use :param flip_p: The probability of flipping the image """ if isinstance(value, str): return resolve_root(value, aspects, flip_p) if isinstance(value, dict): resolver = value.get('resolver', None) match resolver: case 'directory' | 'json': path = value.get('path', None) return resolve_root(path, aspects, flip_p, seed) case 'multi': items = [] for resolver in value.get('resolvers', []): items += resolve(resolver, aspects, flip_p, seed) return items case _: raise ValueError(f"Cannot resolve training data for resolver value '{resolver}'")