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import math
import pandas as pd
from pathlib import Path
import pytorch_lightning as pl
from PIL import Image
from torch.utils.data import Dataset, DataLoader, random_split
from torchvision import transforms
from typing import NamedTuple, List
class CSVDataItem(NamedTuple):
instance_image_path: Path
class_image_path: Path
prompt: str
nprompt: str
class CSVDataModule(pl.LightningDataModule):
def __init__(
self,
batch_size,
data_file,
tokenizer,
instance_identifier,
class_identifier=None,
class_subdir="cls",
num_class_images=100,
size=512,
repeats=100,
interpolation="bicubic",
center_crop=False,
valid_set_size=None,
generator=None,
collate_fn=None
):
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.tokenizer = tokenizer
self.instance_identifier = instance_identifier
self.class_identifier = class_identifier
self.size = size
self.repeats = repeats
self.center_crop = center_crop
self.interpolation = interpolation
self.valid_set_size = valid_set_size
self.generator = generator
self.collate_fn = collate_fn
self.batch_size = batch_size
def prepare_subdata(self, data, num_class_images=1):
image_multiplier = max(math.ceil(num_class_images / len(data)), 1)
return [
CSVDataItem(
self.data_root.joinpath(item.image),
self.class_root.joinpath(f"{Path(item.image).stem}_{i}{Path(item.image).suffix}"),
item.prompt,
item.nprompt if "nprompt" in item else ""
)
for item in data
for i in range(image_multiplier)
]
def prepare_data(self):
metadata = pd.read_csv(self.data_file)
metadata = [item for item in metadata.itertuples() if "skip" not in item or item.skip != "x"]
num_images = len(metadata)
valid_set_size = int(num_images * 0.2)
if self.valid_set_size:
valid_set_size = min(valid_set_size, self.valid_set_size)
valid_set_size = max(valid_set_size, 1)
train_set_size = num_images - valid_set_size
data_train, data_val = random_split(metadata, [train_set_size, valid_set_size], self.generator)
self.data_train = self.prepare_subdata(data_train, self.num_class_images)
self.data_val = self.prepare_subdata(data_val)
def setup(self, stage=None):
train_dataset = CSVDataset(self.data_train, self.tokenizer, batch_size=self.batch_size,
instance_identifier=self.instance_identifier, class_identifier=self.class_identifier,
num_class_images=self.num_class_images,
size=self.size, interpolation=self.interpolation,
center_crop=self.center_crop, repeats=self.repeats)
val_dataset = CSVDataset(self.data_val, self.tokenizer, batch_size=self.batch_size,
instance_identifier=self.instance_identifier,
size=self.size, interpolation=self.interpolation,
center_crop=self.center_crop, repeats=self.repeats)
self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size,
shuffle=True, pin_memory=True, collate_fn=self.collate_fn)
self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size,
pin_memory=True, collate_fn=self.collate_fn)
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],
tokenizer,
instance_identifier,
batch_size=1,
class_identifier=None,
num_class_images=0,
size=512,
repeats=1,
interpolation="bicubic",
center_crop=False,
):
self.data = data
self.tokenizer = tokenizer
self.batch_size = batch_size
self.instance_identifier = instance_identifier
self.class_identifier = class_identifier
self.num_class_images = num_class_images
self.cache = {}
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_example(self, i):
item = self.data[i % self.num_instance_images]
cache_key = f"{item.instance_image_path}_{item.class_image_path}"
if cache_key in self.cache:
return self.cache[cache_key]
example = {}
example["prompts"] = item.prompt
example["nprompts"] = item.nprompt
if item.instance_image_path in self.image_cache:
instance_image = self.image_cache[item.instance_image_path]
else:
instance_image = Image.open(item.instance_image_path)
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
self.image_cache[item.instance_image_path] = instance_image
example["instance_images"] = instance_image
example["instance_prompt_ids"] = self.tokenizer(
item.prompt.format(self.instance_identifier),
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
if self.num_class_images != 0:
class_image = Image.open(item.class_image_path)
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = class_image
example["class_prompt_ids"] = self.tokenizer(
item.prompt.format(self.class_identifier),
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
self.cache[item.instance_image_path] = example
return example
def __getitem__(self, i):
example = {}
unprocessed_example = self.get_example(i)
example["prompts"] = unprocessed_example["prompts"]
example["nprompts"] = unprocessed_example["nprompts"]
example["instance_images"] = self.image_transforms(unprocessed_example["instance_images"])
example["instance_prompt_ids"] = unprocessed_example["instance_prompt_ids"]
if self.class_identifier is not None:
example["class_images"] = self.image_transforms(unprocessed_example["class_images"])
example["class_prompt_ids"] = unprocessed_example["class_prompt_ids"]
return example
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