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