VOCSegmentation, VOCDetection, linting passing, examples. (#663)

* VOC Dataset, linted, flak8 passing, samples on gist.

* Double backtick on values.

* Apply suggestions from code review

Add suggestions from @ellisbrown, using dict of dicts instead of array index.
Co-Authored-By: bpinaya <bpg_92@hotmail.com>

* Fixed errors with the new comments.

* Added documentation on RST

* Removed getBB, added parse_voc_xml, variable naming change.

* Removed unused variable, removed VOC_CLASSES, two new gists for test.

docs/source/datasets.rst

@@ -149,3 +149,15 @@ Flickr
 .. autoclass:: Flickr30k
   :members: __getitem__
   :special-members:
+
+VOC
+~~~~~~
+
+
+.. autoclass:: VOCSegmentation
+  :members: __getitem__
+  :special-members:
+
+.. autoclass:: VOCDetection
+  :members: __getitem__
+  :special-members:

torchvision/datasets/__init__.py

@@ -11,10 +11,12 @@ from .semeion import SEMEION
 from .omniglot import Omniglot
 from .sbu import SBU
 from .flickr import Flickr8k, Flickr30k
+from .voc import VOCSegmentation, VOCDetection
 
 __all__ = ('LSUN', 'LSUNClass',
            'ImageFolder', 'DatasetFolder', 'FakeData',
            'CocoCaptions', 'CocoDetection',
            'CIFAR10', 'CIFAR100', 'EMNIST', 'FashionMNIST',
            'MNIST', 'STL10', 'SVHN', 'PhotoTour', 'SEMEION',
-           'Omniglot', 'SBU', 'Flickr8k', 'Flickr30k')
+           'Omniglot', 'SBU', 'Flickr8k', 'Flickr30k',
+           'VOCSegmentation', 'VOCDetection')

torchvision/datasets/voc.py

+import os
+import sys
+import tarfile
+import collections
+import torch.utils.data as data
+if sys.version_info[0] == 2:
+    import xml.etree.cElementTree as ET
+else:
+    import xml.etree.ElementTree as ET
+
+from PIL import Image
+from .utils import download_url, check_integrity
+
+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': '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': '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': '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': '59065e4b188729180974ef6572f6a212',
+        'base_dir': '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': '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': 'VOCdevkit/VOC2007'
+    }
+}
+
+
+class VOCSegmentation(data.Dataset):
+    """`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Segmentation Dataset.
+
+    Args:
+        root (string): 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``
+        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.
+    """
+
+    def __init__(self,
+                 root,
+                 year='2012',
+                 image_set='train',
+                 download=False,
+                 transform=None,
+                 target_transform=None):
+        self.root = root
+        self.year = year
+        self.url = DATASET_YEAR_DICT[year]['url']
+        self.filename = DATASET_YEAR_DICT[year]['filename']
+        self.md5 = DATASET_YEAR_DICT[year]['md5']
+        self.transform = transform
+        self.target_transform = target_transform
+        self.image_set = image_set
+        base_dir = DATASET_YEAR_DICT[year]['base_dir']
+        voc_root = os.path.join(self.root, base_dir)
+        image_dir = os.path.join(voc_root, 'JPEGImages')
+        mask_dir = os.path.join(voc_root, 'SegmentationClass')
+
+        if download:
+            download_extract(self.url, self.root, self.filename, 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/Segmentation')
+
+        split_f = os.path.join(splits_dir, image_set.rstrip('\n') + '.txt')
+
+        if not os.path.exists(split_f):
+            raise ValueError(
+                'Wrong image_set entered! Please use image_set="train" '
+                'or image_set="trainval" or image_set="val"')
+
+        with open(os.path.join(split_f), "r") as f:
+            file_names = [x.strip() for x in f.readlines()]
+
+        self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
+        self.masks = [os.path.join(mask_dir, x + ".png") for x in file_names]
+        assert (len(self.images) == len(self.masks))
+
+    def __getitem__(self, index):
+        """
+        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.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):
+        return len(self.images)
+
+
+class VOCDetection(data.Dataset):
+    """`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Detection Dataset.
+
+    Args:
+        root (string): 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``
+        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 an 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.
+    """
+
+    def __init__(self,
+                 root,
+                 year='2012',
+                 image_set='train',
+                 download=False,
+                 transform=None,
+                 target_transform=None):
+        self.root = root
+        self.year = year
+        self.url = DATASET_YEAR_DICT[year]['url']
+        self.filename = DATASET_YEAR_DICT[year]['filename']
+        self.md5 = DATASET_YEAR_DICT[year]['md5']
+        self.transform = transform
+        self.target_transform = target_transform
+        self.image_set = image_set
+
+        base_dir = DATASET_YEAR_DICT[year]['base_dir']
+        voc_root = os.path.join(self.root, base_dir)
+        image_dir = os.path.join(voc_root, 'JPEGImages')
+        annotation_dir = os.path.join(voc_root, 'Annotations')
+
+        if download:
+            download_extract(self.url, self.root, self.filename, 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/Main')
+
+        split_f = os.path.join(splits_dir, image_set.rstrip('\n') + '.txt')
+
+        if not os.path.exists(split_f):
+            raise ValueError(
+                'Wrong image_set entered! Please use image_set="train" '
+                'or image_set="trainval" or image_set="val" or a valid'
+                'image_set from the VOC ImageSets/Main folder.')
+
+        with open(os.path.join(split_f), "r") as f:
+            file_names = [x.strip() for x in f.readlines()]
+
+        self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
+        self.annotations = [os.path.join(annotation_dir, x + ".xml") for x in file_names]
+        assert (len(self.images) == len(self.annotations))
+
+    def __getitem__(self, index):
+        """
+        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.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):
+        return len(self.images)
+
+    def parse_voc_xml(self, node):
+        voc_dict = {}
+        children = list(node)
+        if children:
+            def_dic = collections.defaultdict(list)
+            for dc in map(self.parse_voc_xml, children):
+                for ind, v in dc.items():
+                    def_dic[ind].append(v)
+            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
+
+
+def download_extract(url, root, filename, md5):
+    download_url(url, root, filename, md5)
+    with tarfile.open(os.path.join(root, filename), "r") as tar:
+        tar.extractall(path=root)