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import math
import torch
import json
import numpy as np
from pathlib import Path
from PIL import Image
from torch.utils.data import Dataset, DataLoader, random_split
from torchvision import transforms
from typing import Dict, NamedTuple, List, Optional, Union, Callable
from models.clip.prompt import PromptProcessor
def prepare_prompt(prompt: Union[str, Dict[str, str]]):
return {"content": prompt} if isinstance(prompt, str) else prompt
def keywords_to_prompt(prompt: list[str], dropout: float = 0, shuffle: bool = False) -> str:
if dropout != 0:
prompt = [keyword for keyword in prompt if np.random.random() > dropout]
if shuffle:
np.random.shuffle(prompt)
return ", ".join(prompt)
def prompt_to_keywords(prompt: str, expansions: dict[str, str]) -> list[str]:
def expand_keyword(keyword: str) -> list[str]:
return [keyword] + expansions[keyword].split(", ") if keyword in expansions else [keyword]
return [
kw
for keyword in prompt.split(", ")
for kw in expand_keyword(keyword)
if keyword != ""
]
class CSVDataItem(NamedTuple):
instance_image_path: Path
class_image_path: Path
prompt: list[str]
cprompt: str
nprompt: str
collection: list[str]
class CSVDataModule():
def __init__(
self,
batch_size: int,
data_file: str,
prompt_processor: PromptProcessor,
class_subdir: str = "cls",
num_class_images: int = 1,
size: int = 768,
repeats: int = 1,
dropout: float = 0,
interpolation: str = "bicubic",
center_crop: bool = False,
template_key: str = "template",
valid_set_size: Optional[int] = None,
generator: Optional[torch.Generator] = None,
filter: Optional[Callable[[CSVDataItem], bool]] = None,
collate_fn=None,
num_workers: int = 0
):
super().__init__()
self.data_file = Path(data_file)
if not self.data_file.is_file():
raise ValueError("data_file must be a file")
self.data_root = self.data_file.parent
self.class_root = self.data_root.joinpath(class_subdir)
self.class_root.mkdir(parents=True, exist_ok=True)
self.num_class_images = num_class_images
self.prompt_processor = prompt_processor
self.size = size
self.repeats = repeats
self.dropout = dropout
self.center_crop = center_crop
self.template_key = template_key
self.interpolation = interpolation
self.valid_set_size = valid_set_size
self.generator = generator
self.filter = filter
self.collate_fn = collate_fn
self.num_workers = num_workers
self.batch_size = batch_size
def prepare_items(self, template, expansions, data) -> list[CSVDataItem]:
image = template["image"] if "image" in template else "{}"
prompt = template["prompt"] if "prompt" in template else "{content}"
cprompt = template["cprompt"] if "cprompt" in template else "{content}"
nprompt = template["nprompt"] if "nprompt" in template else "{content}"
return [
CSVDataItem(
self.data_root.joinpath(image.format(item["image"])),
None,
prompt_to_keywords(
prompt.format(**prepare_prompt(item["prompt"] if "prompt" in item else "")),
expansions
),
keywords_to_prompt(prompt_to_keywords(
cprompt.format(**prepare_prompt(item["prompt"] if "prompt" in item else "")),
expansions
)),
keywords_to_prompt(prompt_to_keywords(
nprompt.format(**prepare_prompt(item["nprompt"] if "nprompt" in item else "")),
expansions
)),
item["collection"].split(", ") if "collection" in item else []
)
for item in data
]
def filter_items(self, items: list[CSVDataItem]) -> list[CSVDataItem]:
if self.filter is None:
return items
return [item for item in items if self.filter(item)]
def pad_items(self, items: list[CSVDataItem], num_class_images: int = 1) -> list[CSVDataItem]:
image_multiplier = max(num_class_images, 1)
return [
CSVDataItem(
item.instance_image_path,
self.class_root.joinpath(f"{item.instance_image_path.stem}_{i}{item.instance_image_path.suffix}"),
item.prompt,
item.cprompt,
item.nprompt,
item.collection,
)
for item in items
for i in range(image_multiplier)
]
def prepare_data(self):
with open(self.data_file, 'rt') as f:
metadata = json.load(f)
template = metadata[self.template_key] if self.template_key in metadata else {}
expansions = metadata["expansions"] if "expansions" in metadata else {}
items = metadata["items"] if "items" in metadata else []
items = self.prepare_items(template, expansions, items)
items = self.filter_items(items)
num_images = len(items)
valid_set_size = self.valid_set_size if self.valid_set_size is not None else int(num_images * 0.2)
valid_set_size = max(valid_set_size, 1)
train_set_size = num_images - valid_set_size
data_train, data_val = random_split(items, [train_set_size, valid_set_size], self.generator)
self.data_train = self.pad_items(data_train, self.num_class_images)
self.data_val = self.pad_items(data_val)
def setup(self, stage=None):
train_dataset = CSVDataset(self.data_train, self.prompt_processor, batch_size=self.batch_size,
num_class_images=self.num_class_images,
size=self.size, interpolation=self.interpolation,
center_crop=self.center_crop, repeats=self.repeats, dropout=self.dropout)
val_dataset = CSVDataset(self.data_val, self.prompt_processor, batch_size=self.batch_size,
size=self.size, interpolation=self.interpolation,
center_crop=self.center_crop)
self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size,
shuffle=True, pin_memory=True, collate_fn=self.collate_fn,
num_workers=self.num_workers)
self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size,
pin_memory=True, collate_fn=self.collate_fn,
num_workers=self.num_workers)
def train_dataloader(self):
return self.train_dataloader_
def val_dataloader(self):
return self.val_dataloader_
class CSVDataset(Dataset):
def __init__(
self,
data: List[CSVDataItem],
prompt_processor: PromptProcessor,
batch_size: int = 1,
num_class_images: int = 0,
size: int = 768,
repeats: int = 1,
dropout: float = 0,
interpolation: str = "bicubic",
center_crop: bool = False,
):
self.data = data
self.prompt_processor = prompt_processor
self.batch_size = batch_size
self.num_class_images = num_class_images
self.dropout = dropout
self.image_cache = {}
self.num_instance_images = len(self.data)
self._length = self.num_instance_images * repeats
self.interpolation = {"linear": transforms.InterpolationMode.NEAREST,
"bilinear": transforms.InterpolationMode.BILINEAR,
"bicubic": transforms.InterpolationMode.BICUBIC,
"lanczos": transforms.InterpolationMode.LANCZOS,
}[interpolation]
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=self.interpolation),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return math.ceil(self._length / self.batch_size) * self.batch_size
def get_image(self, path):
if path in self.image_cache:
return self.image_cache[path]
image = Image.open(path)
if not image.mode == "RGB":
image = image.convert("RGB")
self.image_cache[path] = image
return image
def get_example(self, i):
item = self.data[i % self.num_instance_images]
example = {}
example["prompts"] = item.prompt
example["cprompts"] = item.cprompt
example["nprompts"] = item.nprompt
example["instance_images"] = self.get_image(item.instance_image_path)
if self.num_class_images != 0:
example["class_images"] = self.get_image(item.class_image_path)
return example
def __getitem__(self, i):
unprocessed_example = self.get_example(i)
example = {}
example["prompts"] = keywords_to_prompt(unprocessed_example["prompts"], self.dropout, True)
example["cprompts"] = unprocessed_example["cprompts"]
example["nprompts"] = unprocessed_example["nprompts"]
example["instance_images"] = self.image_transforms(unprocessed_example["instance_images"])
example["instance_prompt_ids"] = self.prompt_processor.get_input_ids(example["prompts"])
if self.num_class_images != 0:
example["class_images"] = self.image_transforms(unprocessed_example["class_images"])
example["class_prompt_ids"] = self.prompt_processor.get_input_ids(example["cprompts"])
return example
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