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import os
import numpy as np
import PIL
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
import random
import tqdm
from modules import devices
import re
re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
class PersonalizedBase(Dataset):
def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
self.placeholder_token = placeholder_token
self.size = size
self.width = width
self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
self.dataset = []
with open(template_file, "r") as file:
lines = [x.strip() for x in file.readlines()]
self.lines = lines
assert data_root, 'dataset directory not specified'
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
print("Preparing dataset...")
for path in tqdm.tqdm(self.image_paths):
image = Image.open(path)
image = image.convert('RGB')
image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
filename = os.path.basename(path)
filename_tokens = os.path.splitext(filename)[0]
filename_tokens = re_tag.findall(filename_tokens)
npimage = np.array(image).astype(np.uint8)
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
torchdata = torch.moveaxis(torchdata, 2, 0)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
init_latent = init_latent.to(devices.cpu)
self.dataset.append((init_latent, filename_tokens))
self.length = len(self.dataset) * repeats
self.initial_indexes = np.arange(self.length) % len(self.dataset)
self.indexes = None
self.shuffle()
def shuffle(self):
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
def __len__(self):
return self.length
def __getitem__(self, i):
if i % len(self.dataset) == 0:
self.shuffle()
index = self.indexes[i % len(self.indexes)]
x, filename_tokens = self.dataset[index]
text = random.choice(self.lines)
text = text.replace("[name]", self.placeholder_token)
text = text.replace("[filewords]", ' '.join(filename_tokens))
return x, text
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