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import os
import pandas as pd
from pathlib import Path
import PIL
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_root,
tokenizer,
instance_prompt,
class_data_root=None,
class_prompt=None,
size=512,
repeats=100,
interpolation="bicubic",
identifier="*",
center_crop=False,
collate_fn=None):
super().__init__()
self.data_root = data_root
self.tokenizer = tokenizer
self.instance_prompt = instance_prompt
self.class_data_root = class_data_root
self.class_prompt = class_prompt
self.size = size
self.repeats = repeats
self.identifier = identifier
self.center_crop = center_crop
self.interpolation = interpolation
self.collate_fn = collate_fn
self.batch_size = batch_size
def prepare_data(self):
metadata = pd.read_csv(f'{self.data_root}/list.csv')
image_paths = [os.path.join(self.data_root, f_path) for f_path in metadata['image'].values]
captions = [caption for caption in metadata['caption'].values]
skips = [skip for skip in metadata['skip'].values]
self.data_full = [(img, cap) for img, cap, skip in zip(image_paths, captions, skips) if skip != "x"]
def setup(self, stage=None):
train_set_size = int(len(self.data_full) * 0.8)
valid_set_size = len(self.data_full) - train_set_size
self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size])
train_dataset = CSVDataset(self.data_train, self.tokenizer, instance_prompt=self.instance_prompt,
class_data_root=self.class_data_root,
class_prompt=self.class_prompt, size=self.size, repeats=self.repeats,
interpolation=self.interpolation, identifier=self.identifier,
center_crop=self.center_crop)
val_dataset = CSVDataset(self.data_val, self.tokenizer, instance_prompt=self.instance_prompt,
class_data_root=self.class_data_root,
class_prompt=self.class_prompt, size=self.size, interpolation=self.interpolation,
identifier=self.identifier, center_crop=self.center_crop)
self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size,
shuffle=True, collate_fn=self.collate_fn)
self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size, 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_prompt,
class_data_root=None,
class_prompt=None,
size=512,
repeats=1,
interpolation="bicubic",
identifier="*",
center_crop=False,
):
self.data = data
self.tokenizer = tokenizer
self.instance_prompt = instance_prompt
self.num_instance_images = len(self.data)
self._length = self.num_instance_images * repeats
self.identifier = identifier
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images = list(Path(class_data_root).iterdir())
self.num_class_images = len(self.class_images)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.interpolation = {"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
}[interpolation]
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
self.cache = {}
def __len__(self):
return self._length
def get_example(self, i):
image_path, text = self.data[i % self.num_instance_images]
if image_path in self.cache:
return self.cache[image_path]
example = {}
instance_image = Image.open(image_path)
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
text = text.format(self.identifier)
example["prompts"] = text
example["instance_images"] = instance_image
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images[i % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = class_image
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
self.cache[image_path] = example
return example
def __getitem__(self, i):
example = {}
unprocessed_example = self.get_example(i)
example["prompts"] = unprocessed_example["prompts"]
example["instance_images"] = self.image_transforms(unprocessed_example["instance_images"])
example["instance_prompt_ids"] = unprocessed_example["instance_prompt_ids"]
if self.class_data_root:
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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