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import os
import numpy as np
import PIL
import torch
from PIL import Image
from torch.utils.data import Dataset, DataLoader, Sampler
from torchvision import transforms
from collections import defaultdict
from random import shuffle, choices
import random
import tqdm
from modules import devices, shared
import re
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
re_numbers_at_start = re.compile(r"^[-\d]+\s*")
class DatasetEntry:
def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
self.filename = filename
self.filename_text = filename_text
self.weight = weight
self.latent_dist = latent_dist
self.latent_sample = latent_sample
self.cond = cond
self.cond_text = cond_text
self.pixel_values = pixel_values
class PersonalizedBase(Dataset):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
self.placeholder_token = placeholder_token
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'
assert os.path.isdir(data_root), "Dataset directory doesn't exist"
assert os.listdir(data_root), "Dataset directory is empty"
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
self.shuffle_tags = shuffle_tags
self.tag_drop_out = tag_drop_out
groups = defaultdict(list)
print("Preparing dataset...")
for path in tqdm.tqdm(self.image_paths):
alpha_channel = None
if shared.state.interrupted:
raise Exception("interrupted")
try:
image = Image.open(path)
#Currently does not work for single color transparency
#We would need to read image.info['transparency'] for that
if use_weight and 'A' in image.getbands():
alpha_channel = image.getchannel('A')
image = image.convert('RGB')
if not varsize:
image = image.resize((width, height), PIL.Image.BICUBIC)
except Exception:
continue
text_filename = os.path.splitext(path)[0] + ".txt"
filename = os.path.basename(path)
if os.path.exists(text_filename):
with open(text_filename, "r", encoding="utf8") as file:
filename_text = file.read()
else:
filename_text = os.path.splitext(filename)[0]
filename_text = re.sub(re_numbers_at_start, '', filename_text)
if re_word:
tokens = re_word.findall(filename_text)
filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
npimage = np.array(image).astype(np.uint8)
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
latent_sample = None
with devices.autocast():
latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
#Perform latent sampling, even for random sampling.
#We need the sample dimensions for the weights
if latent_sampling_method == "deterministic":
if isinstance(latent_dist, DiagonalGaussianDistribution):
# Works only for DiagonalGaussianDistribution
latent_dist.std = 0
else:
latent_sampling_method = "once"
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
if use_weight and alpha_channel is not None:
channels, *latent_size = latent_sample.shape
weight_img = alpha_channel.resize(latent_size)
npweight = np.array(weight_img).astype(np.float32)
#Repeat for every channel in the latent sample
weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
#Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
weight -= weight.min()
weight /= weight.mean()
elif use_weight:
#If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
weight = torch.ones(latent_sample.shape)
else:
weight = None
if latent_sampling_method == "random":
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
else:
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
if not (self.tag_drop_out != 0 or self.shuffle_tags):
entry.cond_text = self.create_text(filename_text)
if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
with devices.autocast():
entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
groups[image.size].append(len(self.dataset))
self.dataset.append(entry)
del torchdata
del latent_dist
del latent_sample
del weight
self.length = len(self.dataset)
self.groups = list(groups.values())
assert self.length > 0, "No images have been found in the dataset."
self.batch_size = min(batch_size, self.length)
self.gradient_step = min(gradient_step, self.length // self.batch_size)
self.latent_sampling_method = latent_sampling_method
if len(groups) > 1:
print("Buckets:")
for (w, h), ids in sorted(groups.items(), key=lambda x: x[0]):
print(f" {w}x{h}: {len(ids)}")
print()
def create_text(self, filename_text):
text = random.choice(self.lines)
tags = filename_text.split(',')
if self.tag_drop_out != 0:
tags = [t for t in tags if random.random() > self.tag_drop_out]
if self.shuffle_tags:
random.shuffle(tags)
text = text.replace("[filewords]", ','.join(tags))
text = text.replace("[name]", self.placeholder_token)
return text
def __len__(self):
return self.length
def __getitem__(self, i):
entry = self.dataset[i]
if self.tag_drop_out != 0 or self.shuffle_tags:
entry.cond_text = self.create_text(entry.filename_text)
if self.latent_sampling_method == "random":
entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
return entry
class GroupedBatchSampler(Sampler):
def __init__(self, data_source: PersonalizedBase, batch_size: int):
super().__init__(data_source)
n = len(data_source)
self.groups = data_source.groups
self.len = n_batch = n // batch_size
expected = [len(g) / n * n_batch * batch_size for g in data_source.groups]
self.base = [int(e) // batch_size for e in expected]
self.n_rand_batches = nrb = n_batch - sum(self.base)
self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected]
self.batch_size = batch_size
def __len__(self):
return self.len
def __iter__(self):
b = self.batch_size
for g in self.groups:
shuffle(g)
batches = []
for g in self.groups:
batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b))
for _ in range(self.n_rand_batches):
rand_group = choices(self.groups, self.probs)[0]
batches.append(choices(rand_group, k=b))
shuffle(batches)
yield from batches
class PersonalizedDataLoader(DataLoader):
def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory)
if latent_sampling_method == "random":
self.collate_fn = collate_wrapper_random
else:
self.collate_fn = collate_wrapper
class BatchLoader:
def __init__(self, data):
self.cond_text = [entry.cond_text for entry in data]
self.cond = [entry.cond for entry in data]
self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
if all(entry.weight is not None for entry in data):
self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
else:
self.weight = None
#self.emb_index = [entry.emb_index for entry in data]
#print(self.latent_sample.device)
def pin_memory(self):
self.latent_sample = self.latent_sample.pin_memory()
return self
def collate_wrapper(batch):
return BatchLoader(batch)
class BatchLoaderRandom(BatchLoader):
def __init__(self, data):
super().__init__(data)
def pin_memory(self):
return self
def collate_wrapper_random(batch):
return BatchLoaderRandom(batch) |