add ImageNet localization dataset

chainercv/datasets/__init__.py

@@ -18,8 +18,11 @@
 from chainercv.datasets.directory_parsing_label_dataset import directory_parsing_label_names  # NOQA
 from chainercv.datasets.directory_parsing_label_dataset import DirectoryParsingLabelDataset  # NOQA
 from chainercv.datasets.imagenet.imagenet_det_bbox_dataset import ImagenetDetBboxDataset  # NOQA
+from chainercv.datasets.imagenet.imagenet_loc_bbox_dataset import ImagenetLocBboxDataset  # NOQA
 from chainercv.datasets.imagenet.imagenet_utils import imagenet_det_bbox_label_names  # NOQA
 from chainercv.datasets.imagenet.imagenet_utils import imagenet_det_synset_ids  # NOQA
+from chainercv.datasets.imagenet.imagenet_utils import imagenet_loc_bbox_label_names  # NOQA
+from chainercv.datasets.imagenet.imagenet_utils import imagenet_loc_synset_ids  # NOQA
 from chainercv.datasets.mixup_soft_label_dataset import MixUpSoftLabelDataset  # NOQA
 from chainercv.datasets.online_products.online_products_dataset import online_products_super_label_names  # NOQA
 from chainercv.datasets.online_products.online_products_dataset import OnlineProductsDataset  # NOQA

chainercv/datasets/imagenet/imagenet_loc_bbox_dataset.py

@@ -0,0 +1,87 @@
+import os
+
+from chainer.dataset import download
+
+from chainercv.chainer_experimental.datasets.sliceable import GetterDataset
+from chainercv.datasets.imagenet.imagenet_utils import imagenet_loc_synset_ids
+from chainercv.datasets.voc.voc_utils import parse_voc_bbox_annotation
+from chainercv.utils import read_image
+
+
+class ImagenetLocBboxDataset(GetterDataset):
+
+    """ILSVRC2012 ImageNet localization dataset.
+
+    The data is distributed on `the official Kaggle page`_.
+
+    .. _`the official Kaggle page`: https://www.kaggle.com/c/
+        imagenet-object-localization-challenge
+
+    Please refer to the readme of ILSVRC2012 dev kit for a comprehensive
+    documentation. Note that the detection part of ILSVRC has not changed since
+    2012.
+
+    Every image in the training and validation sets has a single
+    image-level label specifying the presence of one object category.
+
+    Args:
+        data_dir (string): Path to the root of the training data. If this is
+            :obj:`auto`,
+            :obj:`$CHAINER_DATASET_ROOT/pfnet/chainercv/imagenet` is used.
+        split ({'train', 'val'}): Selects a split of the dataset.
+
+    This dataset returns the following data.
+
+    .. csv-table::
+        :header: name, shape, dtype, format
+
+        :obj:`img`, ":math:`(3, H, W)`", :obj:`float32`, \
+        "RGB, :math:`[0, 255]`"
+        :obj:`bbox`, ":math:`(R, 4)`", :obj:`float32`, \
+        ":math:`(y_{min}, x_{min}, y_{max}, x_{max})`"
+        :obj:`label`, ":math:`(R,)`", :obj:`int32`, \
+        ":math:`[0, \#fg\_class - 1]`"
+
+    """
+
+    def __init__(self, data_dir='auto', split='train'):
+        super(ImagenetLocBboxDataset, self).__init__()
+        if data_dir == 'auto':
+            data_dir = download.get_dataset_directory(
+                'pfnet/chainercv/imagenet')
+        self.base_dir = os.path.join(data_dir, 'ILSVRC')
+        imageset_dir = os.path.join(self.base_dir, 'ImageSets/CLS-LOC')
+
+        ids = []
+        if split == 'train':
+            imageset_path = os.path.join(imageset_dir, 'train_loc.txt')
+        elif split == 'val':
+            imageset_path = os.path.join(imageset_dir, 'val.txt')
+        with open(imageset_path) as f:
+            for l in f:
+                id_ = l.split()[0]
+                ids.append(id_)
+        self.ids = ids
+        self.split = split
+
+        self.add_getter('img', self._get_image)
+        self.add_getter(('bbox', 'label'), self._get_inst_anno)
+
+    def __len__(self):
+        return len(self.ids)
+
+    def _get_image(self, i):
+        img_path = os.path.join(
+            self.base_dir, 'Data/CLS-LOC', self.split,
+            self.ids[i] + '.JPEG')
+        img = read_image(img_path, color=True)
+        return img
+
+    def _get_inst_anno(self, i):
+        anno_path = os.path.join(
+            self.base_dir, 'Annotations/CLS-LOC', self.split,
+            self.ids[i] + '.xml')
+        bbox, label, _ = parse_voc_bbox_annotation(
+            anno_path, imagenet_loc_synset_ids,
+            skip_names_not_in_label_names=False)
+        return bbox, label