Datasets:
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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}" |