207 lines
7.9 KiB
Python
207 lines
7.9 KiB
Python
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 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
|
|
|
|
|
|
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)
|
|
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
|