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import os
import numpy as np
import pandas as pd
from pathlib import Path
import math
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,
size=512,
repeats=100,
interpolation="bicubic",
placeholder_token="*",
center_crop=False):
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.tokenizer = tokenizer
self.size = size
self.repeats = repeats
self.placeholder_token = placeholder_token
self.center_crop = center_crop
self.interpolation = interpolation
self.batch_size = batch_size
def prepare_data(self):
metadata = pd.read_csv(self.data_file)
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, size=self.size, repeats=self.repeats, interpolation=self.interpolation,
placeholder_token=self.placeholder_token, center_crop=self.center_crop)
val_dataset = CSVDataset(self.data_val, self.tokenizer, size=self.size, interpolation=self.interpolation,
placeholder_token=self.placeholder_token, center_crop=self.center_crop)
self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True)
self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size)
def train_dataloader(self):
return self.train_dataloader_
def val_dataloader(self):
return self.val_dataloader_
class CSVDataset(Dataset):
def __init__(self,
data,
tokenizer,
size=512,
repeats=1,
interpolation="bicubic",
placeholder_token="*",
center_crop=False,
batch_size=1,
):
self.data = data
self.tokenizer = tokenizer
self.placeholder_token = placeholder_token
self.batch_size = batch_size
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 math.ceil(self._length / self.batch_size) * self.batch_size
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.placeholder_token)
example["prompts"] = text
example["pixel_values"] = instance_image
example["input_ids"] = self.tokenizer(
text,
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids[0]
self.cache[image_path] = example
return example
def __getitem__(self, i):
example = {}
unprocessed_example = self.get_example(i)
example["prompts"] = unprocessed_example["prompts"]
example["input_ids"] = unprocessed_example["input_ids"]
example["pixel_values"] = self.image_transforms(unprocessed_example["pixel_values"])
return example
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