stable-diffusion-finetune/ldm/data/laion.py

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import webdataset as wds
from PIL import Image
import io
import os
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import torchvision
from PIL import Image
import glob
import random
import numpy as np
import pytorch_lightning as pl
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from tqdm import tqdm
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from omegaconf import OmegaConf
from einops import rearrange
import torch
from ldm.util import instantiate_from_config
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
"""
batched = {key: [] for key in samples[0]}
# assert isinstance(samples[0][first_key], (list, tuple)), type(samples[first_key])
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:
# import torch
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])
# result.append(b)
return result
class WebDataModuleFromConfig(pl.LightningDataModule):
def __init__(self, tar_base, batch_size, train=None, validation=None,
test=None, num_workers=4, load_ddp=True, n_nodes=1,
**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.load_ddp = load_ddp
self.multinode = n_nodes > 1
self.n_nodes = n_nodes # n gpu ??
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})
shuffle = dataset_config.get('shuffle', 0)
# TODO fid strategy when n exmples not known beforehand
n_examples = dataset_config.get('n_examples', 1e6) // self.n_nodes
shards_to_load = dataset_config.shards
dset_name = 'unknown'
if isinstance(shards_to_load, str):
print(f'Loading tars based on the string {shards_to_load}')
tars = os.path.join(self.tar_base, shards_to_load)
start_shard_id, end_shard_id = dataset_config.shards.split('{')[-1].split('}')[0].split('..')
n_shards = int(end_shard_id) - int(start_shard_id) + 1
dset_name = dataset_config.shards.split('-')[0]
elif isinstance(shards_to_load, int):
print(f'Creating tar list, max shard is {shards_to_load}')
try:
tars = [tf for tf in natsorted(glob(os.path.join(self.tar_base, '*.tar'))) if
int(tf.split('/')[-1].split('.')[0]) < shards_to_load]
n_shards = len(tars)
random.shuffle(tars)
except ValueError as e:
print('tarfile names should follow the pattern <zero_padded_number>.tar . Check names of the files')
raise e
else:
raise ValueError(
'shards should be either a string containing consecutive shards or an int defining the max shard number')
print(f'Got {n_shards} shard files in datafolder for {"training" if train else "validation"}')
# if self.num_workers > 0:
# assert n_shards % self.num_workers == 0 , f'Number of workers which is {self.num_workers} does not evenly divide number of shards which is {n_shards}'
print(f'Loading webdataset based dataloader based on {n_shards} of {dset_name} dataset.')
# start creating the dataset
nodesplitter = wds.shardlists.split_by_node if self.multinode else wds.shardlists.single_node_only
epoch_length = n_examples // (self.batch_size)
dset = wds.WebDataset(tars, nodesplitter=nodesplitter).shuffle(shuffle)
with_epoch_args = {'nsamples': n_examples, 'nbatches': epoch_length}
if 'filters' in dataset_config:
for stage in tqdm(dataset_config.filters,
desc=f'Applying the following filters: {[f for f in dataset_config.filters]}'):
f = getattr(dset, stage)
dset = f(dset, *dataset_config.filters[stage].args,
**dataset_config.filters[stage].get('kwargs', dict()))
print(f'Dataset holding {len(dset.pipeline[0].urls)} shards')
dset = (dset
.decode('pil')
# .to_tuple("jpg;png;jpeg pickle cls hls")
# .map_tuple(image_transforms,load_partial_from_config(nns_transform) if 'target' in nns_transform else identity,identity,identity)
.map_dict(**transform_dict)
.repeat()
.batched(self.batch_size, partial=False,
collation_fn=dict_collation_fn)
.with_length(n_examples)
.with_epoch(**with_epoch_args)
)
loader = wds.WebLoader(dset, batch_size=None, shuffle=False,
num_workers=self.num_workers)
return loader, n_examples
def train_dataloader(self):
assert self.train is not None
loader, dset_size = self.make_loader(self.train)
# if self.load_ddp:
# loader = loader.ddp_equalize(dset_size // self.batch_size)
return loader
def val_dataloader(self):
assert self.train is not None
loader, _ = self.make_loader(self.validation, train=False)
return loader
def test_dataloader(self):
assert self.train is not None
loader, _ = self.make_loader(self.test, train=False)
return loader
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if __name__ == "__main__":
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)
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image.save(os.path.join(outdir, example["__key__"] + ".png"))
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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