import webdataset as wds 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, make_lama_mask, make_narrow_lama_mask, make_512_lama_mask 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): if self.min_size is None: return True try: return x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size and x['json']['pwatermark'] <= self.max_pwatermark 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, size=512): super().__init__() self.make_mask = make_512_lama_mask if size == 512 else make_lama_mask 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 sample['masked_image'] = x * (mask < 0.5) 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_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 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()