[Fix]: fix label type bug when using dbsampler (#111)

* [Fix]: fix label type bug when using dbsampler

* Unify cat_id for more general usage

* fix CI bugs

* keep astype np.long

.github/workflows/build.yml

@@ -93,7 +93,7 @@ jobs:
           coverage report -m
       # Only upload coverage report for python3.7 && pytorch1.5
       - name: Upload coverage to Codecov
-        if: ${{matrix.torch == '1.5.0' && matrix.python-version == '3.7'}}
+        if: ${{matrix.torch == '1.5.0+cu101' && matrix.python-version == '3.7'}}
         uses: codecov/codecov-action@v1.0.10
         with:
           file: ./coverage.xml

mmdet3d/datasets/custom_3d.py

@@ -57,6 +57,7 @@ def __init__(self,
         self.box_type_3d, self.box_mode_3d = get_box_type(box_type_3d)
 
         self.CLASSES = self.get_classes(classes)
+        self.cat2id = {name: i for i, name in enumerate(self.CLASSES)}
         self.data_infos = self.load_annotations(self.ann_file)
 
         if pipeline is not None:
@@ -300,7 +301,7 @@ def _set_group_flag(self):
         """Set flag according to image aspect ratio.
 
         Images with aspect ratio greater than 1 will be set as group 1,
-        otherwise group 0. In 3D datasets, they are all the same, thus
-        are all zeros.
+        otherwise group 0. In 3D datasets, they are all the same, thus are all
+        zeros.
         """
         self.flag = np.zeros(len(self), dtype=np.uint8)

mmdet3d/datasets/dataset_wrappers.py

@@ -18,6 +18,7 @@ class CBGSDataset(object):
     def __init__(self, dataset):
         self.dataset = dataset
         self.CLASSES = dataset.CLASSES
+        self.cat2id = {name: i for i, name in enumerate(self.CLASSES)}
         self.sample_indices = self._get_sample_indices()
         # self.dataset.data_infos = self.data_infos
         if hasattr(self.dataset, 'flag'):
@@ -34,22 +35,23 @@ def _get_sample_indices(self):
         Returns:
             list[dict]: List of annotations after class sampling.
         """
-        class_sample_idxs = {name: [] for name in self.CLASSES}
+        class_sample_idxs = {cat_id: [] for cat_id in self.cat2id.values()}
         for idx in range(len(self.dataset)):
-            class_sample_idx = self.dataset.get_cat_ids(idx)
-            for key in class_sample_idxs.keys():
-                class_sample_idxs[key] += class_sample_idx[key]
-        duplicated_samples = sum([len(v) for _, v in class_sample_idx.items()])
+            sample_cat_ids = self.dataset.get_cat_ids(idx)
+            for cat_id in sample_cat_ids:
+                class_sample_idxs[cat_id].append(idx)
+        duplicated_samples = sum(
+            [len(v) for _, v in class_sample_idxs.items()])
         class_distribution = {
             k: len(v) / duplicated_samples
-            for k, v in class_sample_idx.items()
+            for k, v in class_sample_idxs.items()
         }
 
         sample_indices = []
 
         frac = 1.0 / len(self.CLASSES)
         ratios = [frac / v for v in class_distribution.values()]
-        for cls_inds, ratio in zip(list(class_sample_idx.values()), ratios):
+        for cls_inds, ratio in zip(list(class_sample_idxs.values()), ratios):
             sample_indices += np.random.choice(cls_inds,
                                                int(len(cls_inds) *
                                                    ratio)).tolist()

mmdet3d/datasets/nuscenes_dataset.py

@@ -150,17 +150,18 @@ def get_cat_ids(self, idx):
                 contains such boxes, store a list containing idx,
                 otherwise, store empty list.
         """
-        class_sample_idx = {name: [] for name in self.CLASSES}
         info = self.data_infos[idx]
         if self.use_valid_flag:
             mask = info['valid_flag']
             gt_names = set(info['gt_names'][mask])
         else:
             gt_names = set(info['gt_names'])
+
+        cat_ids = []
         for name in gt_names:
             if name in self.CLASSES:
-                class_sample_idx[name].append(idx)
-        return class_sample_idx
+                cat_ids.append(self.cat2id[name])
+        return cat_ids
 
     def load_annotations(self, ann_file):
         """Load annotations from ann_file.

mmdet3d/datasets/pipelines/dbsampler.py

@@ -259,9 +259,9 @@ def sample_all(self, gt_bboxes, gt_labels, img=None):
                 count += 1
 
                 s_points_list.append(s_points)
-            # gt_names = np.array([s['name'] for s in sampled]),
-            # gt_labels = np.array([self.cat2label(s) for s in gt_names])
-            gt_labels = np.array([self.cat2label[s['name']] for s in sampled])
+
+            gt_labels = np.array([self.cat2label[s['name']] for s in sampled],
+                                 dtype=np.long)
             ret = {
                 'gt_labels_3d':
                 gt_labels,

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -198,7 +198,7 @@ def __call__(self, input_dict):
                 input_dict['img'] = sampled_dict['img']
 
         input_dict['gt_bboxes_3d'] = gt_bboxes_3d
-        input_dict['gt_labels_3d'] = gt_labels_3d
+        input_dict['gt_labels_3d'] = gt_labels_3d.astype(np.long)
         input_dict['points'] = points
 
         return input_dict

tests/test_dataset/test_dataset_wrappers.py

@@ -62,13 +62,13 @@ def test_getitem():
             # and box_type_3d='Depth' in sunrgbd and scannet dataset.
             box_type_3d='LiDAR'))
     nus_dataset = build_dataset(dataset_cfg)
-    assert len(nus_dataset) == 10
+    assert len(nus_dataset) == 20
     data = nus_dataset[0]
-    assert data['img_metas'].data['flip'] is True
-    assert data['img_metas'].data['pcd_horizontal_flip'] is True
-    assert data['points']._data.shape == (537, 5)
-
-    data = nus_dataset[1]
     assert data['img_metas'].data['flip'] is False
     assert data['img_metas'].data['pcd_horizontal_flip'] is False
+    assert data['points']._data.shape == (901, 5)
+
+    data = nus_dataset[1]
+    assert data['img_metas'].data['flip'] is True
+    assert data['img_metas'].data['pcd_horizontal_flip'] is True
     assert data['points']._data.shape == (537, 5)

tests/test_pipeline/test_transforms_3d.py

@@ -78,6 +78,7 @@ def test_object_sample():
             gt_labels.append(CLASSES.index(cat))
         else:
             gt_labels.append(-1)
+    gt_labels = np.array(gt_labels, dtype=np.long)
     input_dict = dict(
         points=points, gt_bboxes_3d=gt_bboxes_3d, gt_labels_3d=gt_labels)
     input_dict = object_sample(input_dict)