509 lines
18 KiB
Python
509 lines
18 KiB
Python
import webdataset as wds
|
|
import kornia
|
|
from PIL import Image
|
|
import io
|
|
import os
|
|
import torchvision
|
|
from PIL import Image
|
|
import glob
|
|
import random
|
|
import numpy as np
|
|
import pytorch_lightning as pl
|
|
from tqdm import tqdm
|
|
from omegaconf import OmegaConf
|
|
from einops import rearrange
|
|
import torch
|
|
from webdataset.handlers import warn_and_continue
|
|
|
|
|
|
from ldm.util import instantiate_from_config
|
|
from ldm.data.inpainting.synthetic_mask import gen_large_mask, MASK_MODES
|
|
from ldm.data.base import PRNGMixin
|
|
|
|
|
|
class DataWithWings(torch.utils.data.IterableDataset):
|
|
def __init__(self, min_size, transform=None, target_transform=None):
|
|
self.min_size = min_size
|
|
self.transform = transform if transform is not None else nn.Identity()
|
|
self.target_transform = target_transform if target_transform is not None else nn.Identity()
|
|
self.kv = OnDiskKV(file='/home/ubuntu/laion5B-watermark-safety-ordered', key_format='q', value_format='ee')
|
|
self.kv_aesthetic = OnDiskKV(file='/home/ubuntu/laion5B-aesthetic-tags-kv', key_format='q', value_format='e')
|
|
self.pwatermark_threshold = 0.8
|
|
self.punsafe_threshold = 0.5
|
|
self.aesthetic_threshold = 5.
|
|
self.total_samples = 0
|
|
self.samples = 0
|
|
location = 'pipe:aws s3 cp --quiet s3://s-datasets/laion5b/laion2B-data/{000000..231349}.tar -'
|
|
|
|
self.inner_dataset = wds.DataPipeline(
|
|
wds.ResampledShards(location),
|
|
wds.tarfile_to_samples(handler=wds.warn_and_continue),
|
|
wds.shuffle(1000, handler=wds.warn_and_continue),
|
|
wds.decode('pilrgb', handler=wds.warn_and_continue),
|
|
wds.map(self._add_tags, handler=wds.ignore_and_continue),
|
|
wds.select(self._filter_predicate),
|
|
wds.map_dict(jpg=self.transform, txt=self.target_transform, punsafe=self._punsafe_to_class, handler=wds.warn_and_continue),
|
|
wds.to_tuple('jpg', 'txt', 'punsafe', handler=wds.warn_and_continue),
|
|
)
|
|
|
|
@staticmethod
|
|
def _compute_hash(url, text):
|
|
if url is None:
|
|
url = ''
|
|
if text is None:
|
|
text = ''
|
|
total = (url + text).encode('utf-8')
|
|
return mmh3.hash64(total)[0]
|
|
|
|
def _add_tags(self, x):
|
|
hsh = self._compute_hash(x['json']['url'], x['txt'])
|
|
pwatermark, punsafe = self.kv[hsh]
|
|
aesthetic = self.kv_aesthetic[hsh][0]
|
|
return {**x, 'pwatermark': pwatermark, 'punsafe': punsafe, 'aesthetic': aesthetic}
|
|
|
|
def _punsafe_to_class(self, punsafe):
|
|
return torch.tensor(punsafe >= self.punsafe_threshold).long()
|
|
|
|
def _filter_predicate(self, x):
|
|
try:
|
|
return x['pwatermark'] < self.pwatermark_threshold and x['aesthetic'] >= self.aesthetic_threshold and x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size
|
|
except:
|
|
return False
|
|
|
|
def __iter__(self):
|
|
return iter(self.inner_dataset)
|
|
|
|
|
|
def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
|
|
"""Take a list of samples (as dictionary) and create a batch, preserving the keys.
|
|
If `tensors` is True, `ndarray` objects are combined into
|
|
tensor batches.
|
|
:param dict samples: list of samples
|
|
:param bool tensors: whether to turn lists of ndarrays into a single ndarray
|
|
:returns: single sample consisting of a batch
|
|
:rtype: dict
|
|
"""
|
|
keys = set.intersection(*[set(sample.keys()) for sample in samples])
|
|
batched = {key: [] for key in keys}
|
|
|
|
for s in samples:
|
|
[batched[key].append(s[key]) for key in batched]
|
|
|
|
result = {}
|
|
for key in batched:
|
|
if isinstance(batched[key][0], (int, float)):
|
|
if combine_scalars:
|
|
result[key] = np.array(list(batched[key]))
|
|
elif isinstance(batched[key][0], torch.Tensor):
|
|
if combine_tensors:
|
|
result[key] = torch.stack(list(batched[key]))
|
|
elif isinstance(batched[key][0], np.ndarray):
|
|
if combine_tensors:
|
|
result[key] = np.array(list(batched[key]))
|
|
else:
|
|
result[key] = list(batched[key])
|
|
return result
|
|
|
|
|
|
class WebDataModuleFromConfig(pl.LightningDataModule):
|
|
def __init__(self, tar_base, batch_size, train=None, validation=None,
|
|
test=None, num_workers=4, multinode=True, min_size=None,
|
|
max_pwatermark=1.0,
|
|
**kwargs):
|
|
super().__init__(self)
|
|
print(f'Setting tar base to {tar_base}')
|
|
self.tar_base = tar_base
|
|
self.batch_size = batch_size
|
|
self.num_workers = num_workers
|
|
self.train = train
|
|
self.validation = validation
|
|
self.test = test
|
|
self.multinode = multinode
|
|
self.min_size = min_size # filter out very small images
|
|
self.max_pwatermark = max_pwatermark # filter out watermarked images
|
|
|
|
def make_loader(self, dataset_config, train=True):
|
|
if 'image_transforms' in dataset_config:
|
|
image_transforms = [instantiate_from_config(tt) for tt in dataset_config.image_transforms]
|
|
else:
|
|
image_transforms = []
|
|
|
|
image_transforms.extend([torchvision.transforms.ToTensor(),
|
|
torchvision.transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
|
|
image_transforms = torchvision.transforms.Compose(image_transforms)
|
|
|
|
if 'transforms' in dataset_config:
|
|
transforms_config = OmegaConf.to_container(dataset_config.transforms)
|
|
else:
|
|
transforms_config = dict()
|
|
|
|
transform_dict = {dkey: load_partial_from_config(transforms_config[dkey])
|
|
if transforms_config[dkey] != 'identity' else identity
|
|
for dkey in transforms_config}
|
|
img_key = dataset_config.get('image_key', 'jpeg')
|
|
transform_dict.update({img_key: image_transforms})
|
|
|
|
if 'postprocess' in dataset_config:
|
|
postprocess = instantiate_from_config(dataset_config['postprocess'])
|
|
else:
|
|
postprocess = None
|
|
|
|
shuffle = dataset_config.get('shuffle', 0)
|
|
shardshuffle = shuffle > 0
|
|
|
|
nodesplitter = wds.shardlists.split_by_node if self.multinode else wds.shardlists.single_node_only
|
|
|
|
if self.tar_base == "__improvedaesthetic__":
|
|
print("## Warning, loading the same improved aesthetic dataset "
|
|
"for all splits and ignoring shards parameter.")
|
|
tars = "pipe:aws s3 cp s3://s-laion/improved-aesthetics-laion-2B-en-subsets/aesthetics_tars/{000000..060207}.tar -"
|
|
else:
|
|
tars = os.path.join(self.tar_base, dataset_config.shards)
|
|
|
|
dset = wds.WebDataset(
|
|
tars,
|
|
nodesplitter=nodesplitter,
|
|
shardshuffle=shardshuffle,
|
|
handler=wds.warn_and_continue).repeat().shuffle(shuffle)
|
|
print(f'Loading webdataset with {len(dset.pipeline[0].urls)} shards.')
|
|
|
|
dset = (dset
|
|
.select(self.filter_keys)
|
|
.decode('pil', handler=wds.warn_and_continue)
|
|
.select(self.filter_size)
|
|
.map_dict(**transform_dict, handler=wds.warn_and_continue)
|
|
)
|
|
if postprocess is not None:
|
|
dset = dset.map(postprocess)
|
|
dset = (dset
|
|
.batched(self.batch_size, partial=False,
|
|
collation_fn=dict_collation_fn)
|
|
)
|
|
|
|
loader = wds.WebLoader(dset, batch_size=None, shuffle=False,
|
|
num_workers=self.num_workers)
|
|
|
|
return loader
|
|
|
|
def filter_size(self, x):
|
|
try:
|
|
valid = True
|
|
if self.min_size is not None and self.min_size > 1:
|
|
try:
|
|
valid = valid and x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size
|
|
except Exception:
|
|
valid = False
|
|
if self.max_pwatermark is not None and self.max_pwatermark < 1.0:
|
|
try:
|
|
valid = valid and x['json']['pwatermark'] <= self.max_pwatermark
|
|
except Exception:
|
|
valid = False
|
|
return valid
|
|
except Exception:
|
|
return False
|
|
|
|
def filter_keys(self, x):
|
|
try:
|
|
return ("jpg" in x) and ("txt" in x)
|
|
except Exception:
|
|
return False
|
|
|
|
def train_dataloader(self):
|
|
return self.make_loader(self.train)
|
|
|
|
def val_dataloader(self):
|
|
return self.make_loader(self.validation, train=False)
|
|
|
|
def test_dataloader(self):
|
|
return self.make_loader(self.test, train=False)
|
|
|
|
|
|
from ldm.modules.image_degradation import degradation_fn_bsr_light
|
|
|
|
class AddLR(object):
|
|
def __init__(self, factor):
|
|
self.factor = factor
|
|
|
|
def pt2np(self, x):
|
|
x = ((x+1.0)*127.5).clamp(0, 255).to(dtype=torch.uint8).detach().cpu().numpy()
|
|
return x
|
|
|
|
def np2pt(self, x):
|
|
x = torch.from_numpy(x)/127.5-1.0
|
|
return x
|
|
|
|
def __call__(self, sample):
|
|
# sample['jpg'] is tensor hwc in [-1, 1] at this point
|
|
x = self.pt2np(sample['jpg'])
|
|
x = degradation_fn_bsr_light(x, sf=self.factor)['image']
|
|
x = self.np2pt(x)
|
|
sample['lr'] = x
|
|
return sample
|
|
|
|
|
|
class AddMask(PRNGMixin):
|
|
def __init__(self, mode="512train", p_drop=0.):
|
|
super().__init__()
|
|
assert mode in list(MASK_MODES.keys()), f'unknown mask generation mode "{mode}"'
|
|
self.make_mask = MASK_MODES[mode]
|
|
self.p_drop = p_drop
|
|
|
|
def __call__(self, sample):
|
|
# sample['jpg'] is tensor hwc in [-1, 1] at this point
|
|
x = sample['jpg']
|
|
mask = self.make_mask(self.prng, x.shape[0], x.shape[1])
|
|
if self.prng.choice(2, p=[1 - self.p_drop, self.p_drop]):
|
|
mask = np.ones_like(mask)
|
|
mask[mask < 0.5] = 0
|
|
mask[mask > 0.5] = 1
|
|
mask = torch.from_numpy(mask[..., None])
|
|
sample['mask'] = mask
|
|
sample['masked_image'] = x * (mask < 0.5)
|
|
return sample
|
|
|
|
|
|
class AddEdge(PRNGMixin):
|
|
def __init__(self, mode="512train", mask_edges=True):
|
|
super().__init__()
|
|
assert mode in list(MASK_MODES.keys()), f'unknown mask generation mode "{mode}"'
|
|
self.make_mask = MASK_MODES[mode]
|
|
self.n_down_choices = [0]
|
|
self.sigma_choices = [1, 2]
|
|
self.mask_edges = mask_edges
|
|
|
|
@torch.no_grad()
|
|
def __call__(self, sample):
|
|
# sample['jpg'] is tensor hwc in [-1, 1] at this point
|
|
x = sample['jpg']
|
|
|
|
mask = self.make_mask(self.prng, x.shape[0], x.shape[1])
|
|
mask[mask < 0.5] = 0
|
|
mask[mask > 0.5] = 1
|
|
mask = torch.from_numpy(mask[..., None])
|
|
sample['mask'] = mask
|
|
|
|
n_down_idx = self.prng.choice(len(self.n_down_choices))
|
|
sigma_idx = self.prng.choice(len(self.sigma_choices))
|
|
|
|
n_choices = len(self.n_down_choices)*len(self.sigma_choices)
|
|
raveled_idx = np.ravel_multi_index((n_down_idx, sigma_idx),
|
|
(len(self.n_down_choices), len(self.sigma_choices)))
|
|
normalized_idx = raveled_idx/max(1, n_choices-1)
|
|
|
|
n_down = self.n_down_choices[n_down_idx]
|
|
sigma = self.sigma_choices[sigma_idx]
|
|
|
|
kernel_size = 4*sigma+1
|
|
kernel_size = (kernel_size, kernel_size)
|
|
sigma = (sigma, sigma)
|
|
canny = kornia.filters.Canny(
|
|
low_threshold=0.1,
|
|
high_threshold=0.2,
|
|
kernel_size=kernel_size,
|
|
sigma=sigma,
|
|
hysteresis=True,
|
|
)
|
|
y = (x+1.0)/2.0 # in 01
|
|
y = y.unsqueeze(0).permute(0, 3, 1, 2).contiguous()
|
|
|
|
# down
|
|
for i_down in range(n_down):
|
|
size = min(y.shape[-2], y.shape[-1])//2
|
|
y = kornia.geometry.transform.resize(y, size, antialias=True)
|
|
|
|
# edge
|
|
_, y = canny(y)
|
|
|
|
if n_down > 0:
|
|
size = x.shape[0], x.shape[1]
|
|
y = kornia.geometry.transform.resize(y, size, interpolation="nearest")
|
|
|
|
y = y.permute(0, 2, 3, 1)[0].expand(-1, -1, 3).contiguous()
|
|
y = y*2.0-1.0
|
|
|
|
if self.mask_edges:
|
|
sample['masked_image'] = y * (mask < 0.5)
|
|
else:
|
|
sample['masked_image'] = y
|
|
sample['mask'] = torch.zeros_like(sample['mask'])
|
|
|
|
# concat normalized idx
|
|
sample['smoothing_strength'] = torch.ones_like(sample['mask'])*normalized_idx
|
|
|
|
return sample
|
|
|
|
|
|
def example00():
|
|
url = "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/000000.tar -"
|
|
dataset = wds.WebDataset(url)
|
|
example = next(iter(dataset))
|
|
for k in example:
|
|
print(k, type(example[k]))
|
|
|
|
print(example["__key__"])
|
|
for k in ["json", "txt"]:
|
|
print(example[k].decode())
|
|
|
|
image = Image.open(io.BytesIO(example["jpg"]))
|
|
outdir = "tmp"
|
|
os.makedirs(outdir, exist_ok=True)
|
|
image.save(os.path.join(outdir, example["__key__"] + ".png"))
|
|
|
|
|
|
def load_example(example):
|
|
return {
|
|
"key": example["__key__"],
|
|
"image": Image.open(io.BytesIO(example["jpg"])),
|
|
"text": example["txt"].decode(),
|
|
}
|
|
|
|
|
|
for i, example in tqdm(enumerate(dataset)):
|
|
ex = load_example(example)
|
|
print(ex["image"].size, ex["text"])
|
|
if i >= 100:
|
|
break
|
|
|
|
|
|
def example01():
|
|
# the first laion shards contain ~10k examples each
|
|
url = "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/{000000..000002}.tar -"
|
|
|
|
batch_size = 3
|
|
shuffle_buffer = 10000
|
|
dset = wds.WebDataset(
|
|
url,
|
|
nodesplitter=wds.shardlists.split_by_node,
|
|
shardshuffle=True,
|
|
)
|
|
dset = (dset
|
|
.shuffle(shuffle_buffer, initial=shuffle_buffer)
|
|
.decode('pil', handler=warn_and_continue)
|
|
.batched(batch_size, partial=False,
|
|
collation_fn=dict_collation_fn)
|
|
)
|
|
|
|
num_workers = 2
|
|
loader = wds.WebLoader(dset, batch_size=None, shuffle=False, num_workers=num_workers)
|
|
|
|
batch_sizes = list()
|
|
keys_per_epoch = list()
|
|
for epoch in range(5):
|
|
keys = list()
|
|
for batch in tqdm(loader):
|
|
batch_sizes.append(len(batch["__key__"]))
|
|
keys.append(batch["__key__"])
|
|
|
|
for bs in batch_sizes:
|
|
assert bs==batch_size
|
|
print(f"{len(batch_sizes)} batches of size {batch_size}.")
|
|
batch_sizes = list()
|
|
|
|
keys_per_epoch.append(keys)
|
|
for i_batch in [0, 1, -1]:
|
|
print(f"Batch {i_batch} of epoch {epoch}:")
|
|
print(keys[i_batch])
|
|
print("next epoch.")
|
|
|
|
|
|
def example02():
|
|
from omegaconf import OmegaConf
|
|
from torch.utils.data.distributed import DistributedSampler
|
|
from torch.utils.data import IterableDataset
|
|
from torch.utils.data import DataLoader, RandomSampler, Sampler, SequentialSampler
|
|
from pytorch_lightning.trainer.supporters import CombinedLoader, CycleIterator
|
|
|
|
#config = OmegaConf.load("configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml")
|
|
#config = OmegaConf.load("configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml")
|
|
config = OmegaConf.load("configs/stable-diffusion/txt2img-v2-clip-encoder-improved_aesthetics-256.yaml")
|
|
datamod = WebDataModuleFromConfig(**config["data"]["params"])
|
|
dataloader = datamod.train_dataloader()
|
|
|
|
for batch in dataloader:
|
|
print(batch.keys())
|
|
print(batch["jpg"].shape)
|
|
break
|
|
|
|
|
|
def example03():
|
|
# improved aesthetics
|
|
tars = "pipe:aws s3 cp s3://s-laion/improved-aesthetics-laion-2B-en-subsets/aesthetics_tars/{000000..060207}.tar -"
|
|
dataset = wds.WebDataset(tars)
|
|
|
|
def filter_keys(x):
|
|
try:
|
|
return ("jpg" in x) and ("txt" in x)
|
|
except Exception:
|
|
return False
|
|
|
|
def filter_size(x):
|
|
try:
|
|
return x['json']['original_width'] >= 512 and x['json']['original_height'] >= 512
|
|
except Exception:
|
|
return False
|
|
|
|
def filter_watermark(x):
|
|
try:
|
|
return x['json']['pwatermark'] < 0.5
|
|
except Exception:
|
|
return False
|
|
|
|
dataset = (dataset
|
|
.select(filter_keys)
|
|
.decode('pil', handler=wds.warn_and_continue))
|
|
n_save = 20
|
|
n_total = 0
|
|
n_large = 0
|
|
n_large_nowm = 0
|
|
for i, example in enumerate(dataset):
|
|
n_total += 1
|
|
if filter_size(example):
|
|
n_large += 1
|
|
if filter_watermark(example):
|
|
n_large_nowm += 1
|
|
if n_large_nowm < n_save+1:
|
|
image = example["jpg"]
|
|
image.save(os.path.join("tmp", f"{n_large_nowm-1:06}.png"))
|
|
|
|
if i%500 == 0:
|
|
print(i)
|
|
print(f"Large: {n_large}/{n_total} | {n_large/n_total*100:.2f}%")
|
|
if n_large > 0:
|
|
print(f"No Watermark: {n_large_nowm}/{n_large} | {n_large_nowm/n_large*100:.2f}%")
|
|
|
|
|
|
|
|
def example04():
|
|
# improved aesthetics
|
|
for i_shard in range(60208)[::-1]:
|
|
print(i_shard)
|
|
tars = "pipe:aws s3 cp s3://s-laion/improved-aesthetics-laion-2B-en-subsets/aesthetics_tars/{:06}.tar -".format(i_shard)
|
|
dataset = wds.WebDataset(tars)
|
|
|
|
def filter_keys(x):
|
|
try:
|
|
return ("jpg" in x) and ("txt" in x)
|
|
except Exception:
|
|
return False
|
|
|
|
def filter_size(x):
|
|
try:
|
|
return x['json']['original_width'] >= 512 and x['json']['original_height'] >= 512
|
|
except Exception:
|
|
return False
|
|
|
|
dataset = (dataset
|
|
.select(filter_keys)
|
|
.decode('pil', handler=wds.warn_and_continue))
|
|
try:
|
|
example = next(iter(dataset))
|
|
except Exception:
|
|
print(f"Error @ {i_shard}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
#example01()
|
|
#example02()
|
|
example03()
|
|
#example04()
|