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simplify data loader

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Patrick Esser 2022-05-30 20:34:17 +00:00 committed by root
parent 74797d4099
commit ac1ca73d69
2 changed files with 67 additions and 86 deletions
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12
configs/stable-diffusion/dev_mn.yaml
View file

@ -81,7 +81,8 @@ data:
num_workers: 4
n_nodes: 4
train:
shards: '{000000..231349}.tar -'
shards: '{000000..231339}.tar -'
shuffle: 10000
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
@ -92,10 +93,9 @@ data:
params:
size: 256
shuffle: 0
n_examples: 100000
validation:
shards: '{000011..000012}.tar -' # TODO: wild guess, change
shards: '{231340..231349}.tar -'
shuffle: 0
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
@ -106,10 +106,6 @@ data:
params:
size: 256
shuffle: 0
n_examples: 60000 # TODO: find out
lightning:
callbacks:

139
ldm/data/laion.py
View file

@ -12,6 +12,7 @@ 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
@ -27,12 +28,10 @@ def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
: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)):
@ -40,21 +39,18 @@ def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
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,
test=None, num_workers=4, multinode=True,
**kwargs):
super().__init__(self)
print(f'Setting tar base to {tar_base}')
@ -64,9 +60,7 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
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 ??
self.multinode = multinode
def make_loader(self, dataset_config, train=True):
if 'image_transforms' in dataset_config:
@ -83,101 +77,47 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
else:
transforms_config = dict()
transform_dict = {dkey: load_partial_from_config(transforms_config[dkey]) if transforms_config[
dkey] != 'identity' else identity
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)
shardshuffle = 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')
from webdataset.handlers import warn_and_continue
tars = os.path.join(self.tar_base, dataset_config.shards)
dset = wds.WebDataset(
tars,
nodesplitter=nodesplitter,
shardshuffle=shardshuffle).shuffle(shuffle)
print(f'Loading webdataset with {len(dset.pipeline[0].urls)} shards.')
dset = (dset
.decode('pil', handler=warn_and_continue)
# .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
return loader
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
return self.make_loader(self.train)
def val_dataloader(self):
assert self.train is not None
loader, _ = self.make_loader(self.validation, train=False)
return loader
return self.make_loader(self.validation, train=False)
def test_dataloader(self):
assert self.train is not None
loader, _ = self.make_loader(self.test, train=False)
return loader
return self.make_loader(self.test, train=False)
if __name__ == "__main__":
def example00():
url = "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/000000.tar -"
dataset = wds.WebDataset(url)
example = next(iter(dataset))
@ -207,3 +147,48 @@ if __name__ == "__main__":
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.")
if __name__ == "__main__":
example01()