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from __future__ import annotations
import ast
import copy
from curses import meta
from email.mime import image
import json
import logging
import math
import os
import random
import sys
import time
import io
import itertools
import braceexpand
from dataclasses import dataclass
from multiprocessing import Value
import pyarrow as pa
import numpy as np
import pandas as pd
import functools
import torch
import torchvision.datasets as datasets
import torchvision.transforms.functional as TF
import torch.distributed as dist
import webdataset as wds
from PIL import Image
from torchvision.transforms import InterpolationMode
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler, IterableDataset, get_worker_info
from torch.utils.data.distributed import DistributedSampler, Sampler
from webdataset.filters import _shuffle
from webdataset.tariterators import base_plus_ext, url_opener, tar_file_expander, valid_sample
from open_clip import transform
try:
import horovod.torch as hvd
except ImportError:
hvd = None
try:
from petrel_client.client import Client
except ImportError as E:
"petrel_client.client cannot be imported"
pass
def pil_loader(img_str):
buff = io.BytesIO(img_str)
return Image.open(buff).convert("RGB")
@functools.lru_cache()
def _get_global_gloo_group():
"""
Return a process group based on gloo backend, containing all the ranks
The result is cached.
"""
if dist.get_backend() == "nccl":
return dist.new_group(backend="gloo")
else:
return dist.group.WORLD
def all_gather(data, group=None):
"""
Run all_gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Returns:
list[data]: list of data gathered from each rank
"""
if dist.get_world_size() == 1:
return [data]
if group is None:
group = _get_global_gloo_group() # use CPU group by default, to reduce GPU RAM usage.
world_size = dist.get_world_size(group)
if world_size == 1:
return [data]
output = [None for _ in range(world_size)]
dist.all_gather_object(output, data, group=group)
return output
def shared_random_seed():
"""
Returns:
int: a random number that is the same across all workers.
If workers need a shared RNG, they can use this shared seed to
create one.
All workers must call this function, otherwise it will deadlock.
"""
ints = np.random.randint(2**31)
all_ints = all_gather(ints)
return all_ints[0]
class TrainingSampler(Sampler):
"""
In training, we only care about the "infinite stream" of training data.
So this sampler produces an infinite stream of indices and
all workers cooperate to correctly shuffle the indices and sample different indices.
The samplers in each worker effectively produces `indices[worker_id::num_workers]`
where `indices` is an infinite stream of indices consisting of
`shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True)
or `range(size) + range(size) + ...` (if shuffle is False)
"""
def __init__(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True, seed = None):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) -1
self.total_size = len(dataset)
self.shuffle = shuffle
# self.dataset_repeat = dataset_repeat
if seed is None:
seed = shared_random_seed()
self.seed = int(seed)
def __len__(self):
return self.num_samples
def __iter__(self):
start = self.rank
yield from itertools.islice(self._infinite_indices(), start, None, self.num_replicas)
def _infinite_indices(self):
g = torch.Generator()
g.manual_seed(self.seed)
while True:
if self.shuffle:
yield from torch.randperm(self.total_size, generator=g).tolist()
else:
yield from torch.arange(self.total_size).tolist()
class TCSLoader(object):
def __init__(self, time_limit=3):
conf_path = os.environ.get('CEPH_CONFIG', './petreloss.config')
self.client = Client(conf_path)
self.time_limit = time_limit
def __call__(self, fn):
try:
img_value_str = self.client.get(fn)
img = pil_loader(img_value_str)
return img
except Exception as e:
print('Read image failed ({})'.format(fn))
raise e
class CsvDataset(Dataset):
def __init__(self, input_filename, transforms, img_key, caption_key, sep="\t", tokenizer=None):
logging.debug(f'Loading csv data from {input_filename}.')
df = pd.read_csv(input_filename, sep=sep)
self.images = df[img_key].tolist()
self.captions = df[caption_key].tolist()
self.transforms = transforms
logging.debug('Done loading data.')
self.tokenize = tokenizer
def __len__(self):
return len(self.captions)
def __getitem__(self, idx):
images = self.transforms(Image.open(str(self.images[idx])))
texts = self.tokenize([str(self.captions[idx])])[0]
return images, texts
class SharedEpoch:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
def set_value(self, epoch):
self.shared_epoch.value = epoch
def get_value(self):
return self.shared_epoch.value
@dataclass
class DataInfo:
dataloader: DataLoader
data_type: str
sampler: DistributedSampler = None
shared_epoch: SharedEpoch = None
def set_epoch(self, epoch):
if self.shared_epoch is not None:
self.shared_epoch.set_value(epoch)
if self.sampler is not None and isinstance(self.sampler, DistributedSampler):
self.sampler.set_epoch(epoch)
def expand_urls(urls, weights=None):
if weights is None:
expanded_urls = wds.shardlists.expand_urls(urls)
return expanded_urls, None
if isinstance(urls, str):
urllist = urls.split("::")
weights = weights.split('::')
assert len(weights) == len(urllist), f"Expected the number of data components ({len(urllist)}) and weights({len(weights)}) to match."
weights = [float(weight) for weight in weights]
all_urls, all_weights = [], []
for url, weight in zip(urllist, weights):
expanded_url = list(braceexpand.braceexpand(url))
expanded_weights = [weight for _ in expanded_url]
all_urls.extend(expanded_url)
all_weights.extend(expanded_weights)
return all_urls, all_weights
else:
all_urls = list(urls)
return all_urls, weights
def get_dataset_size(shards):
shards_list, _ = expand_urls(shards)
dir_path = os.path.dirname(shards_list[0])
sizes_filename = os.path.join(dir_path, 'sizes.json')
len_filename = os.path.join(dir_path, '__len__')
if os.path.exists(sizes_filename):
sizes = json.load(open(sizes_filename, 'r'))
total_size = sum([int(sizes[os.path.basename(shard)]) for shard in shards_list])
elif os.path.exists(len_filename):
# FIXME this used to be eval(open(...)) but that seemed rather unsafe
total_size = ast.literal_eval(open(len_filename, 'r').read())
else:
total_size = None # num samples undefined
# some common dataset sizes (at time of authors last download)
# CC3M (train): 2905954
# CC12M: 10968539
# LAION-400M: 407332084
# LAION-2B (english): 2170337258
num_shards = len(shards_list)
return total_size, num_shards
def get_imagenet(args, preprocess_fns, split):
assert split in ["train", "val", "v2"]
is_train = split == "train"
preprocess_train, preprocess_val = preprocess_fns
if split == "v2":
from imagenetv2_pytorch import ImageNetV2Dataset
dataset = ImageNetV2Dataset(location=args.imagenet_v2, transform=preprocess_val)
else:
if is_train:
data_path = args.imagenet_train
preprocess_fn = preprocess_train
else:
data_path = args.imagenet_val
preprocess_fn = preprocess_val
assert data_path
dataset = datasets.ImageFolder(data_path, transform=preprocess_fn)
if is_train:
idxs = np.zeros(len(dataset.targets))
target_array = np.array(dataset.targets)
k = 50
for c in range(1000):
m = target_array == c
n = len(idxs[m])
arr = np.zeros(n)
arr[:k] = 1
np.random.shuffle(arr)
idxs[m] = arr
idxs = idxs.astype('int')
sampler = SubsetRandomSampler(np.where(idxs)[0])
else:
sampler = None
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
num_workers=args.workers,
sampler=sampler,
)
return DataInfo(dataloader=dataloader, sampler=sampler, data_type='classification')
def count_samples(dataloader):
os.environ["WDS_EPOCH"] = "0"
n_elements, n_batches = 0, 0
for images, texts in dataloader:
n_batches += 1
n_elements += len(images)
assert len(images) == len(texts)
return n_elements, n_batches
def filter_no_caption_or_no_image(sample):
has_caption = ('txt' in sample)
has_image = ('png' in sample or 'jpg' in sample or 'jpeg' in sample or 'webp' in sample)
return has_caption and has_image
def log_and_continue(exn):
"""Call in an exception handler to ignore any exception, issue a warning, and continue."""
logging.warning(f'Handling webdataset error ({repr(exn)}). Ignoring.')
return True
def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
"""Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext)
:param lcase: convert suffixes to lower case (Default value = True)
"""
current_sample = None
for filesample in data:
assert isinstance(filesample, dict)
fname, value = filesample["fname"], filesample["data"]
prefix, suffix = keys(fname)
if prefix is None:
continue
if lcase:
suffix = suffix.lower()
# FIXME webdataset version throws if suffix in current_sample, but we have a potential for
# this happening in the current LAION400m dataset if a tar ends with same prefix as the next
# begins, rare, but can happen since prefix aren't unique across tar files in that dataset
if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample:
if valid_sample(current_sample):
yield current_sample
current_sample = dict(__key__=prefix, __url__=filesample["__url__"])
if suffixes is None or suffix in suffixes:
current_sample[suffix] = value
if valid_sample(current_sample):
yield current_sample
def tarfile_to_samples_nothrow(src, handler=log_and_continue):
# NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw
streams = url_opener(src, handler=handler)
files = tar_file_expander(streams, handler=handler)
samples = group_by_keys_nothrow(files, handler=handler)
return samples
def pytorch_worker_seed(increment=0):
"""get dataloader worker seed from pytorch"""
worker_info = get_worker_info()
if worker_info is not None:
# favour using the seed already created for pytorch dataloader workers if it exists
seed = worker_info.seed
if increment:
# space out seed increments so they can't overlap across workers in different iterations
seed += increment * max(1, worker_info.num_workers)
return seed
# fallback to wds rank based seed
return wds.utils.pytorch_worker_seed()
_SHARD_SHUFFLE_SIZE = 2000
_SHARD_SHUFFLE_INITIAL = 500
_SAMPLE_SHUFFLE_SIZE = 5000
_SAMPLE_SHUFFLE_INITIAL = 1000
class detshuffle2(wds.PipelineStage):
def __init__(
self,
bufsize=1000,
initial=100,
seed=0,
epoch=-1,
):
self.bufsize = bufsize
self.initial = initial
self.seed = seed
self.epoch = epoch
def run(self, src):
if isinstance(self.epoch, SharedEpoch):
epoch = self.epoch.get_value()
else:
# NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train)
# situation as different workers may wrap at different times (or not at all).
self.epoch += 1
epoch = self.epoch
rng = random.Random()
if self.seed < 0:
# If seed is negative, we use the worker's seed, this will be different across all nodes/workers
seed = pytorch_worker_seed(epoch)
else:
# This seed to be deterministic AND the same across all nodes/workers in each epoch
seed = self.seed + epoch
rng.seed(seed)
return _shuffle(src, self.bufsize, self.initial, rng)
class ResampledShards2(IterableDataset):
"""An iterable dataset yielding a list of urls."""
def __init__(
self,
urls,
weights=None,
nshards=sys.maxsize,
worker_seed=None,
deterministic=False,
epoch=-1,
):
"""Sample shards from the shard list with replacement.
:param urls: a list of URLs as a Python list or brace notation string
"""
super().__init__()
urls, weights = expand_urls(urls, weights)
self.urls = urls
self.weights = weights
if self.weights is not None:
assert len(self.urls) == len(self.weights), f"Number of urls {len(self.urls)} and weights {len(self.weights)} should match."
assert isinstance(self.urls[0], str)
self.nshards = nshards
self.rng = random.Random()
self.worker_seed = worker_seed
self.deterministic = deterministic
self.epoch = epoch
def __iter__(self):
"""Return an iterator over the shards."""
if isinstance(self.epoch, SharedEpoch):
epoch = self.epoch.get_value()
else:
# NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train)
# situation as different workers may wrap at different times (or not at all).
self.epoch += 1
epoch = self.epoch
if self.deterministic:
# reset seed w/ epoch if deterministic
if self.worker_seed is None:
# pytorch worker seed should be deterministic due to being init by arg.seed + rank + worker id
seed = pytorch_worker_seed(epoch)
else:
seed = self.worker_seed() + epoch
self.rng.seed(seed)
for _ in range(self.nshards):
if self.weights is None:
yield dict(url=self.rng.choice(self.urls))
else:
yield dict(url=self.rng.choices(self.urls, weights=self.weights, k=1)[0])
def get_wds_dataset(args, preprocess_img, is_train, epoch=0, floor=False, tokenizer=None):
input_shards = args.train_data if is_train else args.val_data
assert input_shards is not None
resampled = getattr(args, 'dataset_resampled', False) and is_train
num_samples, num_shards = get_dataset_size(input_shards)
if not num_samples:
if is_train:
num_samples = args.train_num_samples
if not num_samples:
raise RuntimeError(
'Currently, number of dataset samples must be specified for training dataset. '
'Please specify via `--train-num-samples` if no dataset length info present.')
else:
num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified
shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc
if resampled:
pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)]
else:
assert args.train_data_upsampling_factors is None, "--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled)."
pipeline = [wds.SimpleShardList(input_shards)]
# at this point we have an iterator over all the shards
if is_train:
if not resampled:
pipeline.extend([
detshuffle2(
bufsize=_SHARD_SHUFFLE_SIZE,
initial=_SHARD_SHUFFLE_INITIAL,
seed=args.seed,
epoch=shared_epoch,
),
wds.split_by_node,
wds.split_by_worker,
])
pipeline.extend([
# at this point, we have an iterator over the shards assigned to each worker at each node
tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue),
wds.shuffle(
bufsize=_SAMPLE_SHUFFLE_SIZE,
initial=_SAMPLE_SHUFFLE_INITIAL,
),
])
else:
pipeline.extend([
wds.split_by_worker,
# at this point, we have an iterator over the shards assigned to each worker
wds.tarfile_to_samples(handler=log_and_continue),
])
pipeline.extend([
wds.select(filter_no_caption_or_no_image),
wds.decode("pilrgb", handler=log_and_continue),
wds.rename(image="jpg;png;jpeg;webp", text="txt"),
wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]),
wds.to_tuple("image", "text"),
wds.batched(args.batch_size, partial=not is_train)
])
dataset = wds.DataPipeline(*pipeline)
if is_train:
if not resampled:
assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers'
# roll over and repeat a few samples to get same number of full batches on each node
round_fn = math.floor if floor else math.ceil
global_batch_size = args.batch_size * args.world_size
num_batches = round_fn(num_samples / global_batch_size)
num_workers = max(1, args.workers)
num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker
num_batches = num_worker_batches * num_workers
num_samples = num_batches * global_batch_size
dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this
else:
# last batches are partial, eval is done on single (master) node
num_batches = math.ceil(num_samples / args.batch_size)
dataloader = wds.WebLoader(
dataset,
batch_size=None,
shuffle=False,
num_workers=args.workers,
persistent_workers=True,
)
# FIXME not clear which approach is better, with_epoch before vs after dataloader?
# hoping to resolve via https://github.com/webdataset/webdataset/issues/169
# if is_train:
# # roll over and repeat a few samples to get same number of full batches on each node
# global_batch_size = args.batch_size * args.world_size
# num_batches = math.ceil(num_samples / global_batch_size)
# num_workers = max(1, args.workers)
# num_batches = math.ceil(num_batches / num_workers) * num_workers
# num_samples = num_batches * global_batch_size
# dataloader = dataloader.with_epoch(num_batches)
# else:
# # last batches are partial, eval is done on single (master) node
# num_batches = math.ceil(num_samples / args.batch_size)
# add meta-data to dataloader instance for convenience
dataloader.num_batches = num_batches
dataloader.num_samples = num_samples
return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch, data_type='image-text')
def get_csv_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None):
input_filename = args.train_data if is_train else args.val_data
assert input_filename
dataset = CsvDataset(
input_filename,
preprocess_fn,
img_key=args.csv_img_key,
caption_key=args.csv_caption_key,
sep=args.csv_separator,
tokenizer=tokenizer
)
num_samples = len(dataset)
sampler = DistributedSampler(dataset) if args.distributed and is_train else None
shuffle = is_train and not args.distributed and sampler is None
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=is_train,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader=dataloader, sampler=sampler, data_type='image-text')
class SyntheticDataset(Dataset):
def __init__(self, transform=None, image_size=(224, 224), caption="Dummy caption", dataset_size=100, tokenizer=None):
self.transform = transform
self.image_size = image_size
self.caption = caption
self.image = Image.new('RGB', image_size)
self.dataset_size = dataset_size
self.preprocess_txt = lambda text: tokenizer(text)[0]
def __len__(self):
return self.dataset_size
def __getitem__(self, idx):
if self.transform is not None:
image = self.transform(self.image)
return image, self.preprocess_txt(self.caption)
def get_synthetic_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None):
image_size = preprocess_fn.transforms[0].size
dataset = SyntheticDataset(
transform=preprocess_fn, image_size=image_size, dataset_size=args.train_num_samples, tokenizer=tokenizer)
num_samples = len(dataset)
sampler = DistributedSampler(dataset) if args.distributed and is_train else None
shuffle = is_train and not args.distributed and sampler is None
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=is_train,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader=dataloader, sampler=sampler, data_type='image-text')
class PreferenceDataset(Dataset):
def __init__(self, meta_file, image_folder, transforms, tokenizer, extra_data=(None, None)):
extra_meta, extra_folder = extra_data
self.transforms = transforms
self.tokenizer = tokenizer
self.open_image = Image.open
if image_folder.startswith('s3://'):
loader = TCSLoader()
self.open_image = loader
if meta_file is not None:
with open(meta_file, 'r') as f:
self.table = pa.Table.from_pylist(json.load(f))
self.image_folder = image_folder
else:
# self.captions = pa.array()
self.table = []
if extra_meta:
with open(extra_meta, 'r') as f:
meta = json.load(f)
self.files = [t['files'] for t in meta]
self.extra_captions = [t['caption'] for t in meta]
self.extra_label = [t['human_preference'] for t in meta]
self.extra_image_folder = extra_folder
else:
self.extra_captions = []
def __len__(self):
return len(self.table) + len(self.extra_captions)
def __getitem__(self, idx):
try:
if idx < len(self.table):
images = [self.transforms(self.open_image(os.path.join(self.image_folder, file_names))) for file_names in self.table.column('file_path')[idx].as_py()]
if not len(set([i.size() for i in images])) == 1:
return self.__getitem__((idx + 1) % len(self))
label = self.table.column('pap_pref')[idx].as_py()
caption = self.tokenizer(self.table.column('prompt')[idx].as_py())
else:
idx = idx - len(self.captions)
images = [self.transforms(self.open_image(os.path.join(self.extra_image_folder, f))) for f in self.files[idx]]
label = self.extra_label[idx]
caption = self.tokenizer(self.extra_captions[idx])
if not len(set([i.size() for i in images])) == 1:
return self.__getitem__((idx + 1) % len(self))
else:
return images, label, caption
except:
return self.__getitem__((idx + 1) % len(self))
class HPDDataset(Dataset):
def __init__(self, meta_file, image_folder, transforms, tokenizer, is_train=True):
self.transforms = transforms
self.tokenizer = tokenizer
self.open_image = Image.open
self.is_train = is_train
if image_folder.startswith('s3://'):
loader = TCSLoader()
self.open_image = loader
if meta_file is not None:
with open(meta_file, 'r') as f:
self.table = pa.Table.from_pylist(json.load(f))
self.image_folder = image_folder
else:
self.table = []
def __len__(self):
return len(self.table)
def __getitem__(self, idx):
try:
if self.is_train:
images = [self.transforms(self.open_image(os.path.join(self.image_folder, file_names))) for file_names in self.table.column('file_path')[idx].as_py()]
if not len(set([i.size() for i in images])) == 1:
return self.__getitem__((idx + 1) % len(self))
label = self.table.column('human_preference')[idx].as_py()
caption = self.tokenizer(self.table.column('prompt')[idx].as_py())
# num_per_prompt = self.table.column('num_per_prompt')[idx].as_py()
return images, label, caption
else:
images = [self.transforms(self.open_image(os.path.join(self.image_folder, file_names))) for file_names in self.table.column('file_path')[idx].as_py()]
if not len(set([i.size() for i in images])) == 1:
return self.__getitem__((idx + 1) % len(self))
label = self.table.column('human_preference')[idx].as_py()
caption = self.tokenizer(self.table.column('prompt')[idx].as_py())
return images, label, caption
except:
return self.__getitem__((idx + 1) % len(self))
class RatingDataset(Dataset):
def __init__(self, meta_file, image_folder, transforms):
self.transforms = transforms
self.image_folder = image_folder
self.open_image = Image.open
self.max_size = 224
if image_folder.startswith('s3://'):
loader = TCSLoader()
self.open_image = loader
with open(meta_file, 'r') as f:
self.table = pa.Table.from_pylist(json.load(f))
def __len__(self):
return len(self.table)
def __getitem__(self, idx):
try:
images = self.transforms(self.open_image(os.path.join(self.image_folder, self.table.column('path')[idx].as_py())))
img_weight, img_height = images.shape[1:]
if img_weight != self.max_size or img_height != self.max_size:
return self.__getitem__((idx + 10) % len(self))
label = self.table.column('rating')[idx].as_py()
return images, label
except:
return self.__getitem__((idx + 1) % len(self))
class RankingDataset(Dataset):
def __init__(self, meta_file, image_folder, transforms, tokenizer):
self.transforms = transforms
self.image_folder = image_folder
self.open_image = Image.open
if image_folder.startswith('s3://'):
loader = TCSLoader()
self.open_image = loader
self.tokenizer = tokenizer
with open(meta_file, 'r') as f:
self.table = pa.Table.from_pylist(json.load(f))
def __len__(self):
return len(self.table)
def __getitem__(self, idx):
try:
images = [self.transforms(self.open_image(os.path.join(self.image_folder, file_names))) for file_names in self.table.column('image_path')[idx].as_py()]
label = self.table.column('rank')[idx].as_py()
caption = self.tokenizer(self.table.column('prompt')[idx].as_py())
return images, label, caption
except:
return self.__getitem__((idx + 1) % len(self))
class RegionDataset(Dataset):
def __init__(self, meta_file, image_folder, transforms):
self.transforms = transforms
self.image_folder = image_folder
self.open_image = Image.open
with open(meta_file,'r') as f:
self.table = pa.Table.from_pylist(json.load(f))
def __len__(self):
return len(self.table)
def __getitem__(self, idx):
try:
img = self.open_image(os.path.join(self.image_folder, self.table.column('image_path')[idx].as_py()))
mask = self.open_image(os.path.join(self.image_folder, self.table.column('mask_path')[idx].as_py()))
img.putalpha(mask)
masked_image = self.transforms(img)
image = masked_image[:3]
mask = masked_image[3]
return image, mask
except:
return self.__getitem__((idx + 1) % len(self))
class ImageRewardDataset(Dataset):
def __init__(self, meta_file, image_folder,transforms, tokenizer):
self.transforms = transforms
self.image_folder = image_folder
self.open_image = Image.open
self.tokenizer = tokenizer
with open(meta_file, 'r') as f:
self.table = pa.Table.from_pylist(json.load(f))
def __len__(self):
return len(self.table)
def __getitem__(self, idx):
images = [self.transforms(self.open_image(os.path.join(self.image_folder, file_names))) for file_names in self.table.column('generations')[idx].as_py()]
label = self.table.column('ranking')[idx].as_py()
caption = self.tokenizer(self.table.column('prompt')[idx].as_py())
return images, label, caption
def set_env_vars(something):
os.environ['http_proxy'] = ''
os.environ['https_proxy'] = ''
def collate_rating(batch):
images = [sample[0] for sample in batch]
labels = torch.tensor([sample[1] for sample in batch])
images = torch.stack(images)
return images, labels
def get_rating_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None):
# only training data
assert is_train
dataset = RatingDataset(meta_file=args.train_data,
image_folder=args.train_folder,
transforms=preprocess_fn)
num_samples = len(dataset)
sampler = TrainingSampler(dataset) if args.distributed else None
shuffle = is_train and not args.distributed
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=is_train,
collate_fn=collate_rating,
worker_init_fn=set_env_vars,
persistent_workers=True,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader=dataloader, sampler=sampler, data_type='rating')
def collate_pref(batch):
images = [torch.stack(sample[0]) for sample in batch]
num_images = torch.tensor([g.size(0) for g in images])
labels = torch.tensor([sample[1] for sample in batch])
captions = torch.cat([sample[2] for sample in batch])
images = torch.cat(images)
return images, num_images, labels, captions
def get_preference_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None, extra_val=False):
if is_train:
extra_data = (args.extra_train_data, args.extra_train_folder)
dataset = PreferenceDataset(meta_file=args.train_data if is_train else args.val_data,
image_folder=args.train_folder if is_train else args.val_folder,
transforms=preprocess_fn, tokenizer=tokenizer, extra_data=extra_data)
else:
if extra_val:
dataset = PreferenceDataset(meta_file=None,
image_folder=None,
transforms=preprocess_fn, tokenizer=tokenizer, extra_data=(args.extra_val_data, args.extra_val_folder))
else:
dataset = PreferenceDataset(meta_file=args.val_data,
image_folder=args.val_folder,
transforms=preprocess_fn, tokenizer=tokenizer)
num_samples = len(dataset)
sampler = TrainingSampler(dataset) if args.distributed and is_train else None
shuffle = is_train and not args.distributed and sampler is None
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=is_train,
collate_fn=collate_pref,
worker_init_fn=set_env_vars,
persistent_workers=True,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader=dataloader, sampler=sampler, data_type='preference')
def collate_HPD(batch):
image_1 = torch.stack([sample[0][0] for sample in batch])
image_2 = torch.stack([sample[0][1] for sample in batch])
label_1 = torch.tensor([sample[1][0] for sample in batch])
label_2 = torch.tensor([sample[1][1] for sample in batch])
labels = torch.cat([label_1, label_2], dim=0)
captions = torch.cat([sample[2] for sample in batch])
images = torch.cat([image_1, image_2])
return images, labels, captions
def get_HPD_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None):
dataset = HPDDataset(meta_file=args.train_data if is_train else args.val_data,
image_folder=args.train_folder if is_train else args.val_folder,
transforms=preprocess_fn, tokenizer=tokenizer, is_train=is_train)
num_samples = len(dataset)
sampler = TrainingSampler(dataset) if args.distributed and is_train else None
shuffle = is_train and not args.distributed and sampler is None
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=is_train,
collate_fn=collate_HPD if is_train else collate_pref,
worker_init_fn=set_env_vars,
persistent_workers=True,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader=dataloader, sampler=sampler, data_type='HPD' if is_train else 'preference')
def get_ranking_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None):
if is_train:
dataset = RankingDataset(meta_file=args.train_data,
image_folder=args.train_folder, transforms=preprocess_fn, tokenizer=tokenizer)
else:
dataset = RankingDataset(meta_file=args.val_data,
image_folder=args.val_folder, transforms=preprocess_fn, tokenizer=tokenizer)
num_samples = len(dataset)
sampler = TrainingSampler(dataset) if args.distributed and is_train else None
shuffle = is_train and not args.distributed and sampler is None
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=is_train,
collate_fn=collate_rank,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader=dataloader, sampler=sampler, data_type='ranking')
def get_regional_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None):
if is_train:
dataset = RegionDataset(
meta_file=args.train_data,
image_folder=args.train_folder,
transforms=preprocess_fn
)
else:
dataset = RegionDataset(
meta_file=args.val_data,
image_folder=args.val_folder,
transforms=preprocess_fn
)
num_samples = len(dataset)
sampler = TrainingSampler(dataset) if args.distributed else None
shuffle = is_train and not args.distributed
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=is_train,
worker_init_fn=set_env_vars,
persistent_workers=True,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader=dataloader, sampler=sampler, data_type='regional')
def collate_rank(batch):
images = [torch.stack(sample[0]) for sample in batch]
num_images = torch.tensor([g.size(0) for g in images])
labels = [torch.tensor(sample[1]) for sample in batch]
captions = torch.cat([sample[2] for sample in batch])
images = torch.cat(images)
labels = torch.cat(labels)
return images, num_images, labels, captions
def get_imagereward_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None):
#only support evaluation
if not is_train:
dataset = ImageRewardDataset(
meta_file=args.val_data,
image_folder = args.val_folder,
transforms=preprocess_fn,
tokenizer=tokenizer
)
num_samples = len(dataset)
sampler = TrainingSampler(dataset) if args.distributed and is_train else None
shuffle = is_train and not args.distributed
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=is_train,
worker_init_fn=set_env_vars,
collate_fn=collate_rank,
persistent_workers=True,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader=dataloader, sampler=sampler, data_type='ImageReward')
def get_dataset_fn(data_path, dataset_type):
if dataset_type == "webdataset":
return get_wds_dataset
elif dataset_type == "csv":
return get_csv_dataset
elif dataset_type == "synthetic":
return get_synthetic_dataset
elif dataset_type == "auto":
ext = data_path.split('.')[-1]
if ext in ['csv', 'tsv']:
return get_csv_dataset
elif ext in ['tar']:
return get_wds_dataset
else:
raise ValueError(
f"Tried to figure out dataset type, but failed for extension {ext}.")
elif dataset_type == "preference":
return get_preference_dataset
elif dataset_type == "rating":
return get_rating_dataset
elif dataset_type == 'ranking':
return get_ranking_dataset
elif dataset_type == 'regional':
return get_regional_dataset
elif dataset_type == 'ImageReward':
return get_imagereward_dataset
elif dataset_type == "HPD":
return get_HPD_dataset
else:
raise ValueError(f"Unsupported dataset type: {dataset_type}")
def get_data(args, preprocess_fns, epoch=0, tokenizer=None):
preprocess_train, preprocess_val = preprocess_fns
data = {}
if args.train_data or args.dataset_type == "synthetic":
assert len(args.train_data) == len(args.dataset_type) == len(args.batch_size) == len(args.workers) == len(args.train_folder) == len(args.train_data_sample_ratio) == len(args.ignore_in_train)
for train_data, dataset_type, batch_size, workers, train_folder, train_data_sample_ratio, ignore in zip(args.train_data, args.dataset_type, args.batch_size, args.workers, args.train_folder, args.train_data_sample_ratio, args.ignore_in_train):
if ignore:
continue
if 'train' not in data:
data['train'] = []
new_args = copy.deepcopy(args)
new_args.train_data = train_data
new_args.dataset_type = dataset_type
new_args.batch_size = batch_size
new_args.workers = workers
new_args.train_folder = train_folder
new_args.train_data_sample_ratio = train_data_sample_ratio
dataset = get_dataset_fn(new_args.train_data, new_args.dataset_type)(
new_args, preprocess_train, is_train=True, epoch=epoch, tokenizer=tokenizer)
data['train'].append(dataset)
if args.val_data[0]:
assert len(args.val_data) == len(args.dataset_type) == len(args.batch_size) == len(args.workers) == len(args.val_folder) == len(args.ignore_in_val)
# data['val'] = []
for val_data, dataset_type, batch_size, workers, val_folder ,ignore in zip(args.val_data, args.dataset_type, args.batch_size, args.workers, args.val_folder, args.ignore_in_val):
if ignore:
continue
if 'val' not in data:
data['val'] = []
new_args = copy.deepcopy(args)
new_args.val_data = val_data
new_args.dataset_type = dataset_type
new_args.batch_size = batch_size
new_args.workers = workers
new_args.val_folder = val_folder
dataset = get_dataset_fn(new_args.val_data, new_args.dataset_type)(
new_args, preprocess_val, is_train=False, tokenizer=tokenizer)
data['val'].append(dataset)
if args.extra_val_data:
assert False
data["extra_val"] = get_dataset_fn(args.val_data, args.dataset_type)(
args, preprocess_val, is_train=False, tokenizer=tokenizer, extra_val=True)
if args.imagenet_val is not None:
data["imagenet-val"] = get_imagenet(args, preprocess_fns, "val")
if args.imagenet_v2 is not None:
data["imagenet-v2"] = get_imagenet(args, preprocess_fns, "v2")
return data
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