add VOCDataset

mmdet/datasets/__init__.py

 from .custom import CustomDataset
 from .coco import CocoDataset
+from .voc import VOCDataset
 from .loader import GroupSampler, DistributedGroupSampler, build_dataloader
 from .utils import to_tensor, random_scale, show_ann, get_dataset
 from .concat_dataset import ConcatDataset
 from .repeat_dataset import RepeatDataset
 
 __all__ = [
-    'CustomDataset', 'CocoDataset', 'GroupSampler', 'DistributedGroupSampler',
-    'build_dataloader', 'to_tensor', 'random_scale', 'show_ann',
-    'get_dataset', 'ConcatDataset', 'RepeatDataset',
+    'CustomDataset', 'CocoDataset', 'VOCDataset', 'GroupSampler',
+    'DistributedGroupSampler', 'build_dataloader', 'to_tensor', 'random_scale',
+    'show_ann', 'get_dataset', 'ConcatDataset', 'RepeatDataset'
 ]

mmdet/datasets/voc.py

+import os.path as osp
+import xml.etree.ElementTree as ET
+
+import mmcv
+import numpy as np
+
+from .custom import CustomDataset
+
+
+class VOCDataset(CustomDataset):
+
+    CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+               'tvmonitor')
+
+    def __init__(self, **kwargs):
+        assert not kwargs.get('with_mask', False)
+        super(VOCDataset, self).__init__(**kwargs)
+        self.cat2label = {cat: i + 1 for i, cat in enumerate(self.CLASSES)}
+
+    def load_annotations(self, ann_file):
+        self.img_infos = []
+        img_ids = mmcv.list_from_file(ann_file)
+        for img_id in img_ids:
+            filename = 'JPEGImages/{}.jpg'.format(img_id)
+            xml_path = osp.join(self.img_prefix, 'Annotations',
+                                '{}.xml'.format(img_id))
+            tree = ET.parse(xml_path)
+            root = tree.getroot()
+            size = root.find('size')
+            width = int(size.find('width').text)
+            height = int(size.find('height').text)
+            self.img_infos.append(
+                dict(id=img_id, filename=filename, width=width, height=height))
+        return self.img_infos
+
+    def get_ann_info(self, idx):
+        img_id = self.img_infos[idx]['id']
+        xml_path = osp.join(self.img_prefix, 'Annotations',
+                            '{}.xml'.format(img_id))
+        tree = ET.parse(xml_path)
+        root = tree.getroot()
+        bboxes = []
+        labels = []
+        bboxes_ignore = []
+        labels_ignore = []
+        for obj in root.findall('object'):
+            name = obj.find('name').text
+            label = self.cat2label[name]
+            difficult = int(obj.find('difficult').text)
+            bnd_box = obj.find('bndbox')
+            bbox = [
+                int(bnd_box.find('xmin').text),
+                int(bnd_box.find('ymin').text),
+                int(bnd_box.find('xmax').text),
+                int(bnd_box.find('ymax').text)
+            ]
+            if difficult:
+                bboxes_ignore.append(bbox)
+                labels_ignore.append(label)
+            else:
+                bboxes.append(bbox)
+                labels.append(label)
+        if not bboxes:
+            bboxes = np.zeros((0, 4))
+            labels = np.zeros((0, ))
+        else:
+            bboxes = np.array(bboxes, ndmin=2) - 1
+            labels = np.array(labels)
+        if not bboxes_ignore:
+            bboxes_ignore = np.zeros((0, 4))
+            labels_ignore = np.zeros((0, ))
+        else:
+            bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1
+            labels_ignore = np.array(labels_ignore)
+        ann = dict(
+            bboxes=bboxes.astype(np.float32),
+            labels=labels.astype(np.int64),
+            bboxes_ignore=bboxes_ignore.astype(np.float32),
+            labels_ignore=labels_ignore.astype(np.int64))
+        return ann