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