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