Source code for torchvision.datasets.caltech
from PIL import Image
import os
import os.path
from typing import Any, Callable, List, Optional, Union, Tuple
from .vision import VisionDataset
from .utils import download_and_extract_archive, verify_str_arg
[docs]class Caltech101(VisionDataset):
"""`Caltech 101 <http://www.vision.caltech.edu/Image_Datasets/Caltech101/>`_ Dataset.
.. warning::
This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.
Args:
root (string): Root directory of dataset where directory
``caltech101`` exists or will be saved to if download is set to True.
target_type (string or list, optional): Type of target to use, ``category`` or
``annotation``. Can also be a list to output a tuple with all specified target types.
``category`` represents the target class, and ``annotation`` is a list of points
from a hand-generated outline. Defaults to ``category``.
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.
"""
def __init__(
self,
root: str,
target_type: Union[List[str], str] = "category",
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
) -> None:
super(Caltech101, self).__init__(os.path.join(root, 'caltech101'),
transform=transform,
target_transform=target_transform)
os.makedirs(self.root, exist_ok=True)
if not isinstance(target_type, list):
target_type = [target_type]
self.target_type = [verify_str_arg(t, "target_type", ("category", "annotation"))
for t in target_type]
if download:
self.download()
if not self._check_integrity():
raise RuntimeError('Dataset not found or corrupted.' +
' You can use download=True to download it')
self.categories = sorted(os.listdir(os.path.join(self.root, "101_ObjectCategories")))
self.categories.remove("BACKGROUND_Google") # this is not a real class
# For some reason, the category names in "101_ObjectCategories" and
# "Annotations" do not always match. This is a manual map between the
# two. Defaults to using same name, since most names are fine.
name_map = {"Faces": "Faces_2",
"Faces_easy": "Faces_3",
"Motorbikes": "Motorbikes_16",
"airplanes": "Airplanes_Side_2"}
self.annotation_categories = list(map(lambda x: name_map[x] if x in name_map else x, self.categories))
self.index: List[int] = []
self.y = []
for (i, c) in enumerate(self.categories):
n = len(os.listdir(os.path.join(self.root, "101_ObjectCategories", c)))
self.index.extend(range(1, n + 1))
self.y.extend(n * [i])
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where the type of target specified by target_type.
"""
import scipy.io
img = Image.open(os.path.join(self.root,
"101_ObjectCategories",
self.categories[self.y[index]],
"image_{:04d}.jpg".format(self.index[index])))
target: Any = []
for t in self.target_type:
if t == "category":
target.append(self.y[index])
elif t == "annotation":
data = scipy.io.loadmat(os.path.join(self.root,
"Annotations",
self.annotation_categories[self.y[index]],
"annotation_{:04d}.mat".format(self.index[index])))
target.append(data["obj_contour"])
target = tuple(target) if len(target) > 1 else target[0]
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 _check_integrity(self) -> bool:
# can be more robust and check hash of files
return os.path.exists(os.path.join(self.root, "101_ObjectCategories"))
def __len__(self) -> int:
return len(self.index)
def download(self) -> None:
if self._check_integrity():
print('Files already downloaded and verified')
return
download_and_extract_archive(
"http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz",
self.root,
filename="101_ObjectCategories.tar.gz",
md5="b224c7392d521a49829488ab0f1120d9")
download_and_extract_archive(
"http://www.vision.caltech.edu/Image_Datasets/Caltech101/Annotations.tar",
self.root,
filename="101_Annotations.tar",
md5="6f83eeb1f24d99cab4eb377263132c91")
def extra_repr(self) -> str:
return "Target type: {target_type}".format(**self.__dict__)
[docs]class Caltech256(VisionDataset):
"""`Caltech 256 <http://www.vision.caltech.edu/Image_Datasets/Caltech256/>`_ Dataset.
Args:
root (string): Root directory of dataset where directory
``caltech256`` exists or will be saved to if download is set to True.
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.
"""
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
) -> None:
super(Caltech256, self).__init__(os.path.join(root, 'caltech256'),
transform=transform,
target_transform=target_transform)
os.makedirs(self.root, exist_ok=True)
if download:
self.download()
if not self._check_integrity():
raise RuntimeError('Dataset not found or corrupted.' +
' You can use download=True to download it')
self.categories = sorted(os.listdir(os.path.join(self.root, "256_ObjectCategories")))
self.index: List[int] = []
self.y = []
for (i, c) in enumerate(self.categories):
n = len(os.listdir(os.path.join(self.root, "256_ObjectCategories", c)))
self.index.extend(range(1, n + 1))
self.y.extend(n * [i])
[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 = Image.open(os.path.join(self.root,
"256_ObjectCategories",
self.categories[self.y[index]],
"{:03d}_{:04d}.jpg".format(self.y[index] + 1, self.index[index])))
target = self.y[index]
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 _check_integrity(self) -> bool:
# can be more robust and check hash of files
return os.path.exists(os.path.join(self.root, "256_ObjectCategories"))
def __len__(self) -> int:
return len(self.index)
def download(self) -> None:
if self._check_integrity():
print('Files already downloaded and verified')
return
download_and_extract_archive(
"http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tar",
self.root,
filename="256_ObjectCategories.tar",
md5="67b4f42ca05d46448c6bb8ecd2220f6d")
