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""" Dataset Factory
Hacked together by / Copyright 2021, Ross Wightman
"""
import os
from typing import Optional
from torchvision.datasets import CIFAR100, CIFAR10, MNIST, KMNIST, FashionMNIST, ImageFolder
try:
from torchvision.datasets import Places365
has_places365 = True
except ImportError:
has_places365 = False
try:
from torchvision.datasets import INaturalist
has_inaturalist = True
except ImportError:
has_inaturalist = False
try:
from torchvision.datasets import QMNIST
has_qmnist = True
except ImportError:
has_qmnist = False
try:
from torchvision.datasets import ImageNet
has_imagenet = True
except ImportError:
has_imagenet = False
from .dataset import IterableImageDataset, ImageDataset
_TORCH_BASIC_DS = dict(
cifar10=CIFAR10,
cifar100=CIFAR100,
mnist=MNIST,
kmnist=KMNIST,
fashion_mnist=FashionMNIST,
)
_TRAIN_SYNONYM = dict(train=None, training=None)
_EVAL_SYNONYM = dict(val=None, valid=None, validation=None, eval=None, evaluation=None)
def _search_split(root, split):
# look for sub-folder with name of split in root and use that if it exists
split_name = split.split('[')[0]
try_root = os.path.join(root, split_name)
if os.path.exists(try_root):
return try_root
def _try(syn):
for s in syn:
try_root = os.path.join(root, s)
if os.path.exists(try_root):
return try_root
return root
if split_name in _TRAIN_SYNONYM:
root = _try(_TRAIN_SYNONYM)
elif split_name in _EVAL_SYNONYM:
root = _try(_EVAL_SYNONYM)
return root
def create_dataset(
name: str,
root: Optional[str] = None,
split: str = 'validation',
search_split: bool = True,
class_map: dict = None,
load_bytes: bool = False,
is_training: bool = False,
download: bool = False,
batch_size: int = 1,
num_samples: Optional[int] = None,
seed: int = 42,
repeats: int = 0,
input_img_mode: str = 'RGB',
trust_remote_code: bool = False,
**kwargs,
):
""" Dataset factory method
In parentheses after each arg are the type of dataset supported for each arg, one of:
* Folder - default, timm folder (or tar) based ImageDataset
* Torch - torchvision based datasets
* HFDS - Hugging Face Datasets
* HFIDS - Hugging Face Datasets Iterable (streaming mode, with IterableDataset)
* TFDS - Tensorflow-datasets wrapper in IterabeDataset interface via IterableImageDataset
* WDS - Webdataset
* All - any of the above
Args:
name: Dataset name, empty is okay for folder based datasets
root: Root folder of dataset (All)
split: Dataset split (All)
search_split: Search for split specific child fold from root so one can specify
`imagenet/` instead of `/imagenet/val`, etc on cmd line / config. (Folder, Torch)
class_map: Specify class -> index mapping via text file or dict (Folder)
load_bytes: Load data, return images as undecoded bytes (Folder)
download: Download dataset if not present and supported (HFIDS, TFDS, Torch)
is_training: Create dataset in train mode, this is different from the split.
For Iterable / TDFS it enables shuffle, ignored for other datasets. (TFDS, WDS, HFIDS)
batch_size: Batch size hint for iterable datasets (TFDS, WDS, HFIDS)
seed: Seed for iterable datasets (TFDS, WDS, HFIDS)
repeats: Dataset repeats per iteration i.e. epoch (TFDS, WDS, HFIDS)
input_img_mode: Input image color conversion mode e.g. 'RGB', 'L' (folder, TFDS, WDS, HFDS, HFIDS)
trust_remote_code: Trust remote code in Hugging Face Datasets if True (HFDS, HFIDS)
**kwargs: Other args to pass through to underlying Dataset and/or Reader classes
Returns:
Dataset object
"""
kwargs = {k: v for k, v in kwargs.items() if v is not None}
name = name.lower()
if name.startswith('torch/'):
name = name.split('/', 2)[-1]
torch_kwargs = dict(root=root, download=download, **kwargs)
if name in _TORCH_BASIC_DS:
ds_class = _TORCH_BASIC_DS[name]
use_train = split in _TRAIN_SYNONYM
ds = ds_class(train=use_train, **torch_kwargs)
elif name == 'inaturalist' or name == 'inat':
assert has_inaturalist, 'Please update to PyTorch 1.10, torchvision 0.11+ for Inaturalist'
target_type = 'full'
split_split = split.split('/')
if len(split_split) > 1:
target_type = split_split[0].split('_')
if len(target_type) == 1:
target_type = target_type[0]
split = split_split[-1]
if split in _TRAIN_SYNONYM:
split = '2021_train'
elif split in _EVAL_SYNONYM:
split = '2021_valid'
ds = INaturalist(version=split, target_type=target_type, **torch_kwargs)
elif name == 'places365':
assert has_places365, 'Please update to a newer PyTorch and torchvision for Places365 dataset.'
if split in _TRAIN_SYNONYM:
split = 'train-standard'
elif split in _EVAL_SYNONYM:
split = 'val'
ds = Places365(split=split, **torch_kwargs)
elif name == 'qmnist':
assert has_qmnist, 'Please update to a newer PyTorch and torchvision for QMNIST dataset.'
use_train = split in _TRAIN_SYNONYM
ds = QMNIST(train=use_train, **torch_kwargs)
elif name == 'imagenet':
assert has_imagenet, 'Please update to a newer PyTorch and torchvision for ImageNet dataset.'
if split in _EVAL_SYNONYM:
split = 'val'
ds = ImageNet(split=split, **torch_kwargs)
elif name == 'image_folder' or name == 'folder':
# in case torchvision ImageFolder is preferred over timm ImageDataset for some reason
if search_split and os.path.isdir(root):
# look for split specific sub-folder in root
root = _search_split(root, split)
ds = ImageFolder(root, **kwargs)
else:
assert False, f"Unknown torchvision dataset {name}"
elif name.startswith('hfds/'):
# NOTE right now, HF datasets default arrow format is a random-access Dataset,
# There will be a IterableDataset variant too, TBD
ds = ImageDataset(
root,
reader=name,
split=split,
class_map=class_map,
input_img_mode=input_img_mode,
trust_remote_code=trust_remote_code,
**kwargs,
)
elif name.startswith('hfids/'):
ds = IterableImageDataset(
root,
reader=name,
split=split,
class_map=class_map,
is_training=is_training,
download=download,
batch_size=batch_size,
num_samples=num_samples,
repeats=repeats,
seed=seed,
input_img_mode=input_img_mode,
trust_remote_code=trust_remote_code,
**kwargs,
)
elif name.startswith('tfds/'):
ds = IterableImageDataset(
root,
reader=name,
split=split,
class_map=class_map,
is_training=is_training,
download=download,
batch_size=batch_size,
num_samples=num_samples,
repeats=repeats,
seed=seed,
input_img_mode=input_img_mode,
**kwargs
)
elif name.startswith('wds/'):
ds = IterableImageDataset(
root,
reader=name,
split=split,
class_map=class_map,
is_training=is_training,
batch_size=batch_size,
num_samples=num_samples,
repeats=repeats,
seed=seed,
input_img_mode=input_img_mode,
**kwargs
)
else:
# FIXME support more advance split cfg for ImageFolder/Tar datasets in the future
if search_split and os.path.isdir(root):
# look for split specific sub-folder in root
root = _search_split(root, split)
ds = ImageDataset(
root,
reader=name,
class_map=class_map,
load_bytes=load_bytes,
input_img_mode=input_img_mode,
**kwargs,
)
return ds
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