Datasets:

alexey-zhavoronkin
/

CINIC10

	import os.path
	import pickle
	from pathlib import Path
	from typing import Any, Callable, Optional, Tuple, Union

	import numpy as np
	from PIL import Image

	from torchvision.datasets.utils import check_integrity, download_and_extract_archive
	from torchvision.datasets.vision import VisionDataset


	class CINIC10(VisionDataset):
	"""`CINIC10 <https://github.com/BayesWatch/cinic-10>`_ Dataset.

	Args:
	root (str or ``pathlib.Path``): Root directory of dataset where directory
	``cinic-10-batches-py`` exists or will be saved to if download is set to True.
	train (bool, optional): If True, creates dataset from training set, otherwise
	creates from test set.
	transform (callable, optional): A function/transform that takes in a PIL image
	and returns a transformed version. E.g, ``transforms.RandomCrop``
	target_transform (callable, optional): A function/transform that takes in the
	target and transforms it.
	download (bool, optional): If true, downloads the dataset from the internet and
	puts it in root directory. If dataset is already downloaded, it is not
	downloaded again.

	"""

	base_folder = "cinic-10-batches-py"
	url = "https://huggingface.co/datasets/alexey-zhavoronkin/CINIC10/resolve/main/cinic-10-python.tar.gz?download=true"
	filename = "cinic-10-python.tar.gz"
	tgz_md5 = None
	train_list = [
	["data_batch_1", None],
	["data_batch_2", None],
	["data_batch_3", None],
	["data_batch_4", None],
	["data_batch_5", None],
	["data_batch_6", None],
	["data_batch_7", None],
	["data_batch_8", None],
	["data_batch_9", None],
	["data_batch_10", None],
	["data_batch_11", None],
	["data_batch_12", None],
	["data_batch_13", None],
	["data_batch_14", None],


	]

	test_list = [
	["test_batch_1", None],
	["test_batch_2", None],
	["test_batch_3", None],
	["test_batch_4", None],
	["test_batch_5", None],
	["test_batch_6", None],
	["test_batch_7", None],


	]
	meta = {
	"filename": "batches.meta",
	"key": "label_names",
	"md5": None,
	}

	def __init__(
	self,
	root: Union[str, Path],
	train: bool = True,
	transform: Optional[Callable] = None,
	target_transform: Optional[Callable] = None,
	download: bool = False,
	) -> None:

	super().__init__(root, transform=transform, target_transform=target_transform)

	self.train = train # training set or test set

	if download:
	self.download()

	if not self._check_integrity():
	raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")

	if self.train:
	downloaded_list = self.train_list
	else:
	downloaded_list = self.test_list

	self.data: Any = []
	self.targets = []

	# now load the picked numpy arrays
	for file_name, checksum in downloaded_list:
	file_path = os.path.join(self.root, self.base_folder, file_name)
	with open(file_path, "rb") as f:
	entry = pickle.load(f, encoding="latin1")
	self.data.append(entry["data"])
	if "labels" in entry:
	self.targets.extend(entry["labels"])
	else:
	self.targets.extend(entry["fine_labels"])

	self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
	self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC

	self._load_meta()

	def _load_meta(self) -> None:
	path = os.path.join(self.root, self.base_folder, self.meta["filename"])
	if not check_integrity(path, self.meta["md5"]):
	raise RuntimeError("Dataset metadata file not found or corrupted. You can use download=True to download it")
	with open(path, "rb") as infile:
	data = pickle.load(infile, encoding="latin1")
	self.classes = data[self.meta["key"]]
	self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)}

	def __getitem__(self, index: int) -> Tuple[Any, Any]:
	"""
	Args:
	index (int): Index

	Returns:
	tuple: (image, target) where target is index of the target class.
	"""
	img, target = self.data[index], self.targets[index]

	# doing this so that it is consistent with all other datasets
	# to return a PIL Image
	img = Image.fromarray(img)

	if self.transform is not None:
	img = self.transform(img)

	if self.target_transform is not None:
	target = self.target_transform(target)

	return img, target

	def __len__(self) -> int:
	return len(self.data)

	def _check_integrity(self) -> bool:
	for filename, md5 in self.train_list + self.test_list:
	fpath = os.path.join(self.root, self.base_folder, filename)
	if not check_integrity(fpath, md5):
	return False
	return True

	def download(self) -> None:
	if self._check_integrity():
	print("Files already downloaded and verified")
	return
	download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)

	def extra_repr(self) -> str:
	split = "Train" if self.train is True else "Test"
	return f"Split: {split}"