Source code for torchvision.datasets.mnist
import codecs
import os
import os.path
import shutil
import string
import sys
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple
from urllib.error import URLError
import numpy as np
import torch
from PIL import Image
from .utils import _flip_byte_order, check_integrity, download_and_extract_archive, extract_archive, verify_str_arg
from .vision import VisionDataset
[docs]class MNIST(VisionDataset):
"""`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset.
Args:
root (string): Root directory of dataset where ``MNIST/raw/train-images-idx3-ubyte``
and ``MNIST/raw/t10k-images-idx3-ubyte`` exist.
train (bool, optional): If True, creates dataset from ``train-images-idx3-ubyte``,
otherwise from ``t10k-images-idx3-ubyte``.
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.
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.
"""
mirrors = [
"http://yann.lecun.com/exdb/mnist/",
"https://ossci-datasets.s3.amazonaws.com/mnist/",
]
resources = [
("train-images-idx3-ubyte.gz", "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
("train-labels-idx1-ubyte.gz", "d53e105ee54ea40749a09fcbcd1e9432"),
("t10k-images-idx3-ubyte.gz", "9fb629c4189551a2d022fa330f9573f3"),
("t10k-labels-idx1-ubyte.gz", "ec29112dd5afa0611ce80d1b7f02629c"),
]
training_file = "training.pt"
test_file = "test.pt"
classes = [
"0 - zero",
"1 - one",
"2 - two",
"3 - three",
"4 - four",
"5 - five",
"6 - six",
"7 - seven",
"8 - eight",
"9 - nine",
]
@property
def train_labels(self):
warnings.warn("train_labels has been renamed targets")
return self.targets
@property
def test_labels(self):
warnings.warn("test_labels has been renamed targets")
return self.targets
@property
def train_data(self):
warnings.warn("train_data has been renamed data")
return self.data
@property
def test_data(self):
warnings.warn("test_data has been renamed data")
return self.data
def __init__(
self,
root: str,
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 self._check_legacy_exist():
self.data, self.targets = self._load_legacy_data()
return
if download:
self.download()
if not self._check_exists():
raise RuntimeError("Dataset not found. You can use download=True to download it")
self.data, self.targets = self._load_data()
def _check_legacy_exist(self):
processed_folder_exists = os.path.exists(self.processed_folder)
if not processed_folder_exists:
return False
return all(
check_integrity(os.path.join(self.processed_folder, file)) for file in (self.training_file, self.test_file)
)
def _load_legacy_data(self):
# This is for BC only. We no longer cache the data in a custom binary, but simply read from the raw data
# directly.
data_file = self.training_file if self.train else self.test_file
return torch.load(os.path.join(self.processed_folder, data_file))
def _load_data(self):
image_file = f"{'train' if self.train else 't10k'}-images-idx3-ubyte"
data = read_image_file(os.path.join(self.raw_folder, image_file))
label_file = f"{'train' if self.train else 't10k'}-labels-idx1-ubyte"
targets = read_label_file(os.path.join(self.raw_folder, label_file))
return data, targets
[docs] 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], int(self.targets[index])
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img.numpy(), mode="L")
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)
@property
def raw_folder(self) -> str:
return os.path.join(self.root, self.__class__.__name__, "raw")
@property
def processed_folder(self) -> str:
return os.path.join(self.root, self.__class__.__name__, "processed")
@property
def class_to_idx(self) -> Dict[str, int]:
return {_class: i for i, _class in enumerate(self.classes)}
def _check_exists(self) -> bool:
return all(
check_integrity(os.path.join(self.raw_folder, os.path.splitext(os.path.basename(url))[0]))
for url, _ in self.resources
)
def download(self) -> None:
"""Download the MNIST data if it doesn't exist already."""
if self._check_exists():
return
os.makedirs(self.raw_folder, exist_ok=True)
# download files
for filename, md5 in self.resources:
for mirror in self.mirrors:
url = f"{mirror}{filename}"
try:
print(f"Downloading {url}")
download_and_extract_archive(url, download_root=self.raw_folder, filename=filename, md5=md5)
except URLError as error:
print(f"Failed to download (trying next):\n{error}")
continue
finally:
print()
break
else:
raise RuntimeError(f"Error downloading {filename}")
def extra_repr(self) -> str:
split = "Train" if self.train is True else "Test"
return f"Split: {split}"
[docs]class FashionMNIST(MNIST):
"""`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ Dataset.
Args:
root (string): Root directory of dataset where ``FashionMNIST/raw/train-images-idx3-ubyte``
and ``FashionMNIST/raw/t10k-images-idx3-ubyte`` exist.
train (bool, optional): If True, creates dataset from ``train-images-idx3-ubyte``,
otherwise from ``t10k-images-idx3-ubyte``.
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.
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.
"""
mirrors = ["http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/"]
resources = [
("train-images-idx3-ubyte.gz", "8d4fb7e6c68d591d4c3dfef9ec88bf0d"),
("train-labels-idx1-ubyte.gz", "25c81989df183df01b3e8a0aad5dffbe"),
("t10k-images-idx3-ubyte.gz", "bef4ecab320f06d8554ea6380940ec79"),
("t10k-labels-idx1-ubyte.gz", "bb300cfdad3c16e7a12a480ee83cd310"),
]
classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
[docs]class KMNIST(MNIST):
"""`Kuzushiji-MNIST <https://github.com/rois-codh/kmnist>`_ Dataset.
Args:
root (string): Root directory of dataset where ``KMNIST/raw/train-images-idx3-ubyte``
and ``KMNIST/raw/t10k-images-idx3-ubyte`` exist.
train (bool, optional): If True, creates dataset from ``train-images-idx3-ubyte``,
otherwise from ``t10k-images-idx3-ubyte``.
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.
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.
"""
mirrors = ["http://codh.rois.ac.jp/kmnist/dataset/kmnist/"]
resources = [
("train-images-idx3-ubyte.gz", "bdb82020997e1d708af4cf47b453dcf7"),
("train-labels-idx1-ubyte.gz", "e144d726b3acfaa3e44228e80efcd344"),
("t10k-images-idx3-ubyte.gz", "5c965bf0a639b31b8f53240b1b52f4d7"),
("t10k-labels-idx1-ubyte.gz", "7320c461ea6c1c855c0b718fb2a4b134"),
]
classes = ["o", "ki", "su", "tsu", "na", "ha", "ma", "ya", "re", "wo"]
[docs]class EMNIST(MNIST):
"""`EMNIST <https://www.westernsydney.edu.au/bens/home/reproducible_research/emnist>`_ Dataset.
Args:
root (string): Root directory of dataset where ``EMNIST/raw/train-images-idx3-ubyte``
and ``EMNIST/raw/t10k-images-idx3-ubyte`` exist.
split (string): The dataset has 6 different splits: ``byclass``, ``bymerge``,
``balanced``, ``letters``, ``digits`` and ``mnist``. This argument specifies
which one to use.
train (bool, optional): If True, creates dataset from ``training.pt``,
otherwise from ``test.pt``.
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.
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.
"""
url = "https://www.itl.nist.gov/iaui/vip/cs_links/EMNIST/gzip.zip"
md5 = "58c8d27c78d21e728a6bc7b3cc06412e"
splits = ("byclass", "bymerge", "balanced", "letters", "digits", "mnist")
# Merged Classes assumes Same structure for both uppercase and lowercase version
_merged_classes = {"c", "i", "j", "k", "l", "m", "o", "p", "s", "u", "v", "w", "x", "y", "z"}
_all_classes = set(string.digits + string.ascii_letters)
classes_split_dict = {
"byclass": sorted(list(_all_classes)),
"bymerge": sorted(list(_all_classes - _merged_classes)),
"balanced": sorted(list(_all_classes - _merged_classes)),
"letters": ["N/A"] + list(string.ascii_lowercase),
"digits": list(string.digits),
"mnist": list(string.digits),
}
def __init__(self, root: str, split: str, **kwargs: Any) -> None:
self.split = verify_str_arg(split, "split", self.splits)
self.training_file = self._training_file(split)
self.test_file = self._test_file(split)
super().__init__(root, **kwargs)
self.classes = self.classes_split_dict[self.split]
@staticmethod
def _training_file(split) -> str:
return f"training_{split}.pt"
@staticmethod
def _test_file(split) -> str:
return f"test_{split}.pt"
@property
def _file_prefix(self) -> str:
return f"emnist-{self.split}-{'train' if self.train else 'test'}"
@property
def images_file(self) -> str:
return os.path.join(self.raw_folder, f"{self._file_prefix}-images-idx3-ubyte")
@property
def labels_file(self) -> str:
return os.path.join(self.raw_folder, f"{self._file_prefix}-labels-idx1-ubyte")
def _load_data(self):
return read_image_file(self.images_file), read_label_file(self.labels_file)
def _check_exists(self) -> bool:
return all(check_integrity(file) for file in (self.images_file, self.labels_file))
def download(self) -> None:
"""Download the EMNIST data if it doesn't exist already."""
if self._check_exists():
return
os.makedirs(self.raw_folder, exist_ok=True)
download_and_extract_archive(self.url, download_root=self.raw_folder, md5=self.md5)
gzip_folder = os.path.join(self.raw_folder, "gzip")
for gzip_file in os.listdir(gzip_folder):
if gzip_file.endswith(".gz"):
extract_archive(os.path.join(gzip_folder, gzip_file), self.raw_folder)
shutil.rmtree(gzip_folder)
[docs]class QMNIST(MNIST):
"""`QMNIST <https://github.com/facebookresearch/qmnist>`_ Dataset.
Args:
root (string): Root directory of dataset whose ``raw``
subdir contains binary files of the datasets.
what (string,optional): Can be 'train', 'test', 'test10k',
'test50k', or 'nist' for respectively the mnist compatible
training set, the 60k qmnist testing set, the 10k qmnist
examples that match the mnist testing set, the 50k
remaining qmnist testing examples, or all the nist
digits. The default is to select 'train' or 'test'
according to the compatibility argument 'train'.
compat (bool,optional): A boolean that says whether the target
for each example is class number (for compatibility with
the MNIST dataloader) or a torch vector containing the
full qmnist information. Default=True.
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.
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.
train (bool,optional,compatibility): When argument 'what' is
not specified, this boolean decides whether to load the
training set or the testing set. Default: True.
"""
subsets = {"train": "train", "test": "test", "test10k": "test", "test50k": "test", "nist": "nist"}
resources: Dict[str, List[Tuple[str, str]]] = { # type: ignore[assignment]
"train": [
(
"https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-train-images-idx3-ubyte.gz",
"ed72d4157d28c017586c42bc6afe6370",
),
(
"https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-train-labels-idx2-int.gz",
"0058f8dd561b90ffdd0f734c6a30e5e4",
),
],
"test": [
(
"https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-test-images-idx3-ubyte.gz",
"1394631089c404de565df7b7aeaf9412",
),
(
"https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-test-labels-idx2-int.gz",
"5b5b05890a5e13444e108efe57b788aa",
),
],
"nist": [
(
"https://raw.githubusercontent.com/facebookresearch/qmnist/master/xnist-images-idx3-ubyte.xz",
"7f124b3b8ab81486c9d8c2749c17f834",
),
(
"https://raw.githubusercontent.com/facebookresearch/qmnist/master/xnist-labels-idx2-int.xz",
"5ed0e788978e45d4a8bd4b7caec3d79d",
),
],
}
classes = [
"0 - zero",
"1 - one",
"2 - two",
"3 - three",
"4 - four",
"5 - five",
"6 - six",
"7 - seven",
"8 - eight",
"9 - nine",
]
def __init__(
self, root: str, what: Optional[str] = None, compat: bool = True, train: bool = True, **kwargs: Any
) -> None:
if what is None:
what = "train" if train else "test"
self.what = verify_str_arg(what, "what", tuple(self.subsets.keys()))
self.compat = compat
self.data_file = what + ".pt"
self.training_file = self.data_file
self.test_file = self.data_file
super().__init__(root, train, **kwargs)
@property
def images_file(self) -> str:
(url, _), _ = self.resources[self.subsets[self.what]]
return os.path.join(self.raw_folder, os.path.splitext(os.path.basename(url))[0])
@property
def labels_file(self) -> str:
_, (url, _) = self.resources[self.subsets[self.what]]
return os.path.join(self.raw_folder, os.path.splitext(os.path.basename(url))[0])
def _check_exists(self) -> bool:
return all(check_integrity(file) for file in (self.images_file, self.labels_file))
def _load_data(self):
data = read_sn3_pascalvincent_tensor(self.images_file)
if data.dtype != torch.uint8:
raise TypeError(f"data should be of dtype torch.uint8 instead of {data.dtype}")
if data.ndimension() != 3:
raise ValueError("data should have 3 dimensions instead of {data.ndimension()}")
targets = read_sn3_pascalvincent_tensor(self.labels_file).long()
if targets.ndimension() != 2:
raise ValueError(f"targets should have 2 dimensions instead of {targets.ndimension()}")
if self.what == "test10k":
data = data[0:10000, :, :].clone()
targets = targets[0:10000, :].clone()
elif self.what == "test50k":
data = data[10000:, :, :].clone()
targets = targets[10000:, :].clone()
return data, targets
def download(self) -> None:
"""Download the QMNIST data if it doesn't exist already.
Note that we only download what has been asked for (argument 'what').
"""
if self._check_exists():
return
os.makedirs(self.raw_folder, exist_ok=True)
split = self.resources[self.subsets[self.what]]
for url, md5 in split:
download_and_extract_archive(url, self.raw_folder, md5=md5)
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]:
# redefined to handle the compat flag
img, target = self.data[index], self.targets[index]
img = Image.fromarray(img.numpy(), mode="L")
if self.transform is not None:
img = self.transform(img)
if self.compat:
target = int(target[0])
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def extra_repr(self) -> str:
return f"Split: {self.what}"
def get_int(b: bytes) -> int:
return int(codecs.encode(b, "hex"), 16)
SN3_PASCALVINCENT_TYPEMAP = {
8: torch.uint8,
9: torch.int8,
11: torch.int16,
12: torch.int32,
13: torch.float32,
14: torch.float64,
}
def read_sn3_pascalvincent_tensor(path: str, strict: bool = True) -> torch.Tensor:
"""Read a SN3 file in "Pascal Vincent" format (Lush file 'libidx/idx-io.lsh').
Argument may be a filename, compressed filename, or file object.
"""
# read
with open(path, "rb") as f:
data = f.read()
# parse
if sys.byteorder == "little":
magic = get_int(data[0:4])
nd = magic % 256
ty = magic // 256
else:
nd = get_int(data[0:1])
ty = get_int(data[1:2]) + get_int(data[2:3]) * 256 + get_int(data[3:4]) * 256 * 256
assert 1 <= nd <= 3
assert 8 <= ty <= 14
torch_type = SN3_PASCALVINCENT_TYPEMAP[ty]
s = [get_int(data[4 * (i + 1) : 4 * (i + 2)]) for i in range(nd)]
if sys.byteorder == "big":
for i in range(len(s)):
s[i] = int.from_bytes(s[i].to_bytes(4, byteorder="little"), byteorder="big", signed=False)
parsed = torch.frombuffer(bytearray(data), dtype=torch_type, offset=(4 * (nd + 1)))
# The MNIST format uses the big endian byte order, while `torch.frombuffer` uses whatever the system uses. In case
# that is little endian and the dtype has more than one byte, we need to flip them.
if sys.byteorder == "little" and parsed.element_size() > 1:
parsed = _flip_byte_order(parsed)
assert parsed.shape[0] == np.prod(s) or not strict
return parsed.view(*s)
def read_label_file(path: str) -> torch.Tensor:
x = read_sn3_pascalvincent_tensor(path, strict=False)
if x.dtype != torch.uint8:
raise TypeError(f"x should be of dtype torch.uint8 instead of {x.dtype}")
if x.ndimension() != 1:
raise ValueError(f"x should have 1 dimension instead of {x.ndimension()}")
return x.long()
def read_image_file(path: str) -> torch.Tensor:
x = read_sn3_pascalvincent_tensor(path, strict=False)
if x.dtype != torch.uint8:
raise TypeError(f"x should be of dtype torch.uint8 instead of {x.dtype}")
if x.ndimension() != 3:
raise ValueError(f"x should have 3 dimension instead of {x.ndimension()}")
return x
