Merge branch 'fix_gt_database' into 'master'

Fix gt database

See merge request open-mmlab/mmdet.3d!90

mmdet3d/datasets/kitti_dataset.py

@@ -116,7 +116,8 @@ class KittiDataset(Custom3DDataset):
             gt_bboxes_3d=gt_bboxes_3d,
             gt_labels_3d=gt_labels_3d,
             bboxes=gt_bboxes,
-            labels=gt_labels)
+            labels=gt_labels,
+            gt_names=gt_names)
         return anns_results
 
     def drop_arrays_by_name(self, gt_names, used_classes):

mmdet3d/datasets/nuscenes_dataset.py

@@ -182,7 +182,7 @@ class NuScenesDataset(Custom3DDataset):
         anns_results = dict(
             gt_bboxes_3d=gt_bboxes_3d,
             gt_labels_3d=gt_labels_3d,
-        )
+            gt_names=gt_names_3d)
         return anns_results
 
     def _format_bbox(self, results, jsonfile_prefix=None):

tools/data_converter/create_gt_database.py

@@ -144,27 +144,52 @@ def create_groundtruth_database(dataset_class_name,
     print(f'Create GT Database of {dataset_class_name}')
     dataset_cfg = dict(
         type=dataset_class_name,
-        root_path=data_path,
+        data_root=data_path,
         ann_file=info_path,
     )
     if dataset_class_name == 'KittiDataset':
+        file_client_args = dict(backend='disk')
         dataset_cfg.update(
-            training=True,
+            test_mode=False,
             split='training',
             modality=dict(
                 use_lidar=True,
                 use_depth=False,
                 use_lidar_intensity=True,
                 use_camera=with_mask,
-            ))
+            ),
+            pipeline=[
+                dict(
+                    type='LoadPointsFromFile',
+                    load_dim=4,
+                    use_dim=4,
+                    file_client_args=file_client_args),
+                dict(
+                    type='LoadAnnotations3D',
+                    with_bbox_3d=True,
+                    with_label_3d=True,
+                    file_client_args=file_client_args)
+            ])
+
+    elif dataset_class_name == 'NuScenesDataset':
+        dataset_cfg.update(pipeline=[
+            dict(type='LoadPointsFromFile', load_dim=5, use_dim=5),
+            dict(
+                type='LoadPointsFromMultiSweeps',
+                sweeps_num=10,
+            ),
+            dict(
+                type='LoadAnnotations3D',
+                with_bbox_3d=True,
+                with_label_3d=True)
+        ])
     dataset = build_dataset(dataset_cfg)
 
     if database_save_path is None:
-        database_save_path = osp.join(data_path,
-                                      '{}_gt_database'.format(info_prefix))
+        database_save_path = osp.join(data_path, f'{info_prefix}_gt_database')
     if db_info_save_path is None:
-        db_info_save_path = osp.join(
-            data_path, '{}_dbinfos_train.pkl'.format(info_prefix))
+        db_info_save_path = osp.join(data_path,
+                                     f'{info_prefix}_dbinfos_train.pkl')
     mmcv.mkdir_or_exist(database_save_path)
     all_db_infos = dict()
 
@@ -178,14 +203,15 @@ def create_groundtruth_database(dataset_class_name,
 
     group_counter = 0
     for j in track_iter_progress(list(range(len(dataset)))):
-        image_idx = j
-        annos = dataset.get_sensor_data(j)
-        image_idx = annos['sample_idx']
-        points = annos['points']
-        gt_boxes_3d = annos['gt_bboxes_3d']
+        input_dict = dataset.get_data_info(j)
+        dataset.pre_pipeline(input_dict)
+        example = dataset.pipeline(input_dict)
+        annos = example['ann_info']
+        image_idx = example['sample_idx']
+        points = example['points']
+        gt_boxes_3d = annos['gt_bboxes_3d'].tensor.numpy()
         names = annos['gt_names']
         group_dict = dict()
-        group_ids = np.full([gt_boxes_3d.shape[0]], -1, dtype=np.int64)
         if 'group_ids' in annos:
             group_ids = annos['group_ids']
         else:
@@ -200,9 +226,9 @@ def create_groundtruth_database(dataset_class_name,
         if with_mask:
             # prepare masks
             gt_boxes = annos['gt_bboxes']
-            img_path = annos['filename'].split('/')[-1]
+            img_path = osp.split(example['img_info']['filename'])[-1]
             if img_path not in file2id.keys():
-                print('skip image {} for empty mask'.format(img_path))
+                print(f'skip image {img_path} for empty mask')
                 continue
             img_id = file2id[img_path]
             kins_annIds = coco.getAnnIds(imgIds=img_id)
@@ -230,7 +256,8 @@ def create_groundtruth_database(dataset_class_name,
 
         for i in range(num_obj):
             filename = f'{image_idx}_{names[i]}_{i}.bin'
-            filepath = osp.join(database_save_path, filename)
+            abs_filepath = osp.join(database_save_path, filename)
+            rel_filepath = osp.join(f'{info_prefix}_gt_database', filename)
 
             # save point clouds and image patches for each object
             gt_points = points[point_indices[:, i]]
@@ -240,22 +267,18 @@ def create_groundtruth_database(dataset_class_name,
                 if object_masks[i].sum() == 0 or not valid_inds[i]:
                     # Skip object for empty or invalid mask
                     continue
-                img_patch_path = filepath + '.png'
-                mask_patch_path = filepath + '.mask.png'
+                img_patch_path = abs_filepath + '.png'
+                mask_patch_path = abs_filepath + '.mask.png'
                 mmcv.imwrite(object_img_patches[i], img_patch_path)
                 mmcv.imwrite(object_masks[i], mask_patch_path)
 
-            with open(filepath, 'w') as f:
+            with open(abs_filepath, 'w') as f:
                 gt_points.tofile(f)
 
             if (used_classes is None) or names[i] in used_classes:
-                if relative_path:
-                    db_path = osp.join(data_path, filename)
-                else:
-                    db_path = filepath
                 db_info = {
                     'name': names[i],
-                    'path': db_path,
+                    'path': rel_filepath,
                     'image_idx': image_idx,
                     'gt_idx': i,
                     'box3d_lidar': gt_boxes_3d[i],

tools/data_converter/kitti_converter.py

@@ -92,9 +92,8 @@ def create_kitti_info_file(data_path,
         relative_path (bool): Whether to use relative path.
     """
     imageset_folder = Path(data_path) / 'ImageSets'
-    train_img_ids = _read_imageset_file(
-        str(imageset_folder / 'train_6014.txt'))
-    val_img_ids = _read_imageset_file(str(imageset_folder / 'val_1467.txt'))
+    train_img_ids = _read_imageset_file(str(imageset_folder / 'train.txt'))
+    val_img_ids = _read_imageset_file(str(imageset_folder / 'val.txt'))
     test_img_ids = _read_imageset_file(str(imageset_folder / 'test.txt'))
 
     print('Generate info. this may take several minutes.')