"""
"""
from PIL import Image
import os
import os.path
import numpy as np
import pickle
from typing import Any, Callable, Optional, Tuple
from .vision import VisionDataset
from .utils import check_integrity, download_and_extract_archive
[docs]class CIFAR10(VisionDataset):
r""" `CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
Args:
root (string): Root directory of dataset where directory
``cifar-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 an 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 = "cifar-10-batches-py"
url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
filename = "cifar-10-python.tar.gz"
tgz_md5 = "c58f30108f718f92721af3b95e74349a"
train_list = [
["data_batch_1", "c99cafc152244af753f735de768cd75f"],
["data_batch_2", "d4bba439e000b95fd0a9bffe97cbabec"],
["data_batch_3", "54ebc095f3ab1f0389bbae665268c751"],
["data_batch_4", "634d18415352ddfa80567beed471001a"],
["data_batch_5", "482c414d41f54cd18b22e5b47cb7c3cb"],
]
test_list = [
["test_batch", "40351d587109b95175f43aff81a1287e"],
]
meta = {
"filename": "batches.meta",
"key": "label_names",
"md5": "5ff9c542aee3614f3951f8cda6e48888",
}
def __init__(
self,
root: str,
train: bool = True,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
source_url: Optional[str] = None,
) -> None:
super(CIFAR10, self).__init__(
root, transform=transform, target_transform=target_transform
)
self.train = train # training set or test set
if source_url is not None:
self.url = source_url
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:
root = self.root
for fentry in self.train_list + self.test_list:
filename, md5 = fentry[0], fentry[1]
fpath = os.path.join(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:
return "Split: {}".format("Train" if self.train is True else "Test")
[docs]class CIFAR100(CIFAR10):
r""" `CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
This is a subclass of the `CIFAR10` Dataset.
"""
base_folder = "cifar-100-python"
url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
filename = "cifar-100-python.tar.gz"
tgz_md5 = "eb9058c3a382ffc7106e4002c42a8d85"
train_list = [
["train", "16019d7e3df5f24257cddd939b257f8d"],
]
test_list = [
["test", "f0ef6b0ae62326f3e7ffdfab6717acfc"],
]
meta = {
"filename": "meta",
"key": "fine_label_names",
"md5": "7973b15100ade9c7d40fb424638fde48",
}