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import math
import os
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
class CSVDataModule(pl.LightningDataModule):
def __init__(self,
batch_size,
data_file,
tokenizer,
instance_identifier,
class_identifier=None,
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("db_cls")
self.class_root.mkdir(parents=True, exist_ok=True)
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_data(self):
metadata = pd.read_csv(self.data_file)
instance_image_paths = [self.data_root.joinpath(f) for f in metadata['image'].values]
class_image_paths = [self.class_root.joinpath(Path(f).name) for f in metadata['image'].values]
prompts = metadata['prompt'].values
nprompts = metadata['nprompt'].values if 'nprompt' in metadata else [""] * len(instance_image_paths)
skips = metadata['skip'].values if 'skip' in metadata else [""] * len(instance_image_paths)
self.data = [(i, c, p, n)
for i, c, p, n, s
in zip(instance_image_paths, class_image_paths, prompts, nprompts, skips)
if s != "x"]
def setup(self, stage=None):
valid_set_size = int(len(self.data) * 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 = len(self.data) - valid_set_size
self.data_train, self.data_val = random_split(self.data, [train_set_size, valid_set_size], self.generator)
train_dataset = CSVDataset(self.data_train, self.tokenizer,
instance_identifier=self.instance_identifier, class_identifier=self.class_identifier,
size=self.size, interpolation=self.interpolation,
center_crop=self.center_crop, repeats=self.repeats)
val_dataset = CSVDataset(self.data_val, self.tokenizer,
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, drop_last=True,
shuffle=True, pin_memory=True, collate_fn=self.collate_fn)
self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size, drop_last=True,
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,
tokenizer,
instance_identifier,
class_identifier=None,
size=512,
repeats=1,
interpolation="bicubic",
center_crop=False,
):
self.data = data
self.tokenizer = tokenizer
self.instance_identifier = instance_identifier
self.class_identifier = class_identifier
self.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 self._length
def get_example(self, i):
instance_image_path, class_image_path, prompt, nprompt = self.data[i % self.num_instance_images]
if instance_image_path in self.cache:
return self.cache[instance_image_path]
example = {}
example["prompts"] = prompt
example["nprompts"] = nprompt
instance_image = Image.open(instance_image_path)
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
instance_prompt = prompt.format(self.instance_identifier)
example["instance_images"] = instance_image
example["instance_prompt_ids"] = self.tokenizer(
instance_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
if self.class_identifier is not None:
class_image = Image.open(class_image_path)
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
class_prompt = prompt.format(self.class_identifier)
example["class_images"] = class_image
example["class_prompt_ids"] = self.tokenizer(
class_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
self.cache[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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