Source code for torchvision.datasets.voc
import collections
import os
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from xml.etree.ElementTree import Element as ET_Element
try:
from defusedxml.ElementTree import parse as ET_parse
except ImportError:
from xml.etree.ElementTree import parse as ET_parse
from PIL import Image
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
DATASET_YEAR_DICT = {
"2012": {
"url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar",
"filename": "VOCtrainval_11-May-2012.tar",
"md5": "6cd6e144f989b92b3379bac3b3de84fd",
"base_dir": os.path.join("VOCdevkit", "VOC2012"),
},
"2011": {
"url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2011/VOCtrainval_25-May-2011.tar",
"filename": "VOCtrainval_25-May-2011.tar",
"md5": "6c3384ef61512963050cb5d687e5bf1e",
"base_dir": os.path.join("TrainVal", "VOCdevkit", "VOC2011"),
},
"2010": {
"url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar",
"filename": "VOCtrainval_03-May-2010.tar",
"md5": "da459979d0c395079b5c75ee67908abb",
"base_dir": os.path.join("VOCdevkit", "VOC2010"),
},
"2009": {
"url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2009/VOCtrainval_11-May-2009.tar",
"filename": "VOCtrainval_11-May-2009.tar",
"md5": "a3e00b113cfcfebf17e343f59da3caa1",
"base_dir": os.path.join("VOCdevkit", "VOC2009"),
},
"2008": {
"url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2008/VOCtrainval_14-Jul-2008.tar",
"filename": "VOCtrainval_11-May-2012.tar",
"md5": "2629fa636546599198acfcfbfcf1904a",
"base_dir": os.path.join("VOCdevkit", "VOC2008"),
},
"2007": {
"url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar",
"filename": "VOCtrainval_06-Nov-2007.tar",
"md5": "c52e279531787c972589f7e41ab4ae64",
"base_dir": os.path.join("VOCdevkit", "VOC2007"),
},
"2007-test": {
"url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar",
"filename": "VOCtest_06-Nov-2007.tar",
"md5": "b6e924de25625d8de591ea690078ad9f",
"base_dir": os.path.join("VOCdevkit", "VOC2007"),
},
}
class _VOCBase(VisionDataset):
_SPLITS_DIR: str
_TARGET_DIR: str
_TARGET_FILE_EXT: str
def __init__(
self,
root: Union[str, Path],
year: str = "2012",
image_set: str = "train",
download: bool = False,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
transforms: Optional[Callable] = None,
):
super().__init__(root, transforms, transform, target_transform)
self.year = verify_str_arg(year, "year", valid_values=[str(yr) for yr in range(2007, 2013)])
valid_image_sets = ["train", "trainval", "val"]
if year == "2007":
valid_image_sets.append("test")
self.image_set = verify_str_arg(image_set, "image_set", valid_image_sets)
key = "2007-test" if year == "2007" and image_set == "test" else year
dataset_year_dict = DATASET_YEAR_DICT[key]
self.url = dataset_year_dict["url"]
self.filename = dataset_year_dict["filename"]
self.md5 = dataset_year_dict["md5"]
base_dir = dataset_year_dict["base_dir"]
voc_root = os.path.join(self.root, base_dir)
if download:
download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.md5)
if not os.path.isdir(voc_root):
raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
splits_dir = os.path.join(voc_root, "ImageSets", self._SPLITS_DIR)
split_f = os.path.join(splits_dir, image_set.rstrip("\n") + ".txt")
with open(os.path.join(split_f)) as f:
file_names = [x.strip() for x in f.readlines()]
image_dir = os.path.join(voc_root, "JPEGImages")
self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
target_dir = os.path.join(voc_root, self._TARGET_DIR)
self.targets = [os.path.join(target_dir, x + self._TARGET_FILE_EXT) for x in file_names]
assert len(self.images) == len(self.targets)
def __len__(self) -> int:
return len(self.images)
[docs]class VOCSegmentation(_VOCBase):
"""`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Segmentation Dataset.
Args:
root (str or ``pathlib.Path``): Root directory of the VOC Dataset.
year (string, optional): The dataset year, supports years ``"2007"`` to ``"2012"``.
image_set (string, optional): Select the image_set to use, ``"train"``, ``"trainval"`` or ``"val"``. If
``year=="2007"``, can also be ``"test"``.
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 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.
transforms (callable, optional): A function/transform that takes input sample and its target as entry
and returns a transformed version.
"""
_SPLITS_DIR = "Segmentation"
_TARGET_DIR = "SegmentationClass"
_TARGET_FILE_EXT = ".png"
@property
def masks(self) -> List[str]:
return self.targets
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is the image segmentation.
"""
img = Image.open(self.images[index]).convert("RGB")
target = Image.open(self.masks[index])
if self.transforms is not None:
img, target = self.transforms(img, target)
return img, target
[docs]class VOCDetection(_VOCBase):
"""`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Detection Dataset.
Args:
root (str or ``pathlib.Path``): Root directory of the VOC Dataset.
year (string, optional): The dataset year, supports years ``"2007"`` to ``"2012"``.
image_set (string, optional): Select the image_set to use, ``"train"``, ``"trainval"`` or ``"val"``. If
``year=="2007"``, can also be ``"test"``.
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.
(default: alphabetic indexing of VOC's 20 classes).
transform (callable, optional): A function/transform that takes in a PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, required): A function/transform that takes in the
target and transforms it.
transforms (callable, optional): A function/transform that takes input sample and its target as entry
and returns a transformed version.
"""
_SPLITS_DIR = "Main"
_TARGET_DIR = "Annotations"
_TARGET_FILE_EXT = ".xml"
@property
def annotations(self) -> List[str]:
return self.targets
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is a dictionary of the XML tree.
"""
img = Image.open(self.images[index]).convert("RGB")
target = self.parse_voc_xml(ET_parse(self.annotations[index]).getroot())
if self.transforms is not None:
img, target = self.transforms(img, target)
return img, target
@staticmethod
def parse_voc_xml(node: ET_Element) -> Dict[str, Any]:
voc_dict: Dict[str, Any] = {}
children = list(node)
if children:
def_dic: Dict[str, Any] = collections.defaultdict(list)
for dc in map(VOCDetection.parse_voc_xml, children):
for ind, v in dc.items():
def_dic[ind].append(v)
if node.tag == "annotation":
def_dic["object"] = [def_dic["object"]]
voc_dict = {node.tag: {ind: v[0] if len(v) == 1 else v for ind, v in def_dic.items()}}
if node.text:
text = node.text.strip()
if not children:
voc_dict[node.tag] = text
return voc_dict
