Source code for libauc.datasets.cifar

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

import numpy as np
from PIL import Image

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

[docs] class CIFAR10(VisionDataset): """`CIFAR10 <>`_ 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 = "" 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 = True, return_index: bool = False, ) -> None: super().__init__(root, transform=transform, target_transform=target_transform) self.train = train # training set or test set if 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 Any = [] self.targets = [] self.return_index = return_index # 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")["data"]) if "labels" in entry: self.targets.extend(entry["labels"]) else: self.targets.extend(entry["fine_labels"]) = np.vstack(, 3, 32, 32) =, 2, 3, 1)) # convert to HWC self.targets = np.array(self.targets).astype(np.int32) 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)}
[docs] def as_array(self): return, self.targets
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 =[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) if self.return_index: return img, target, index return img, target def __len__(self) -> int: return len( 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
[docs] 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)
[docs] def extra_repr(self) -> str: split = "Train" if self.train is True else "Test" return f"Split: {split}"
[docs] class CIFAR100(CIFAR10): """`CIFAR100 <>`_ Dataset. This is a subclass of the `CIFAR10` Dataset. """ base_folder = "cifar-100-python" url = "" 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", }