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open-mmlab · Dec 27, 2023 · 49a564a · 49a564a
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diff --git a/mmdet3d/datasets/convert_utils.py b/mmdet3d/datasets/convert_utils.py
@@ -10,7 +10,7 @@
 from shapely.geometry import MultiPoint, box
 from shapely.geometry.polygon import Polygon
 
-from mmdet3d.structures import Box3DMode, LiDARInstance3DBoxes, points_cam2img
+from mmdet3d.structures import Box3DMode, CameraInstance3DBoxes, points_cam2img
 from mmdet3d.structures.ops import box_np_ops
 
 kitti_categories = ('Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
@@ -318,23 +318,21 @@ def get_kitti_style_2d_boxes(info: dict,
 def convert_annos(info: dict, cam_idx: int) -> dict:
     """Convert front-cam anns to i-th camera (KITTI-style info)."""
     rect = info['calib']['R0_rect'].astype(np.float32)
-    if cam_idx == 0:
-        lidar2cami = info['calib']['Tr_velo_to_cam'].astype(np.float32)
-    else:
-        lidar2cami = info['calib'][f'Tr_velo_to_cam{cam_idx}'].astype(
-            np.float32)
+    lidar2cam0 = info['calib']['Tr_velo_to_cam'].astype(np.float32)
+    lidar2cami = info['calib'][f'Tr_velo_to_cam{cam_idx}'].astype(np.float32)
     annos = info['annos']
     converted_annos = copy.deepcopy(annos)
     loc = annos['location']
     dims = annos['dimensions']
     rots = annos['rotation_y']
     gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
                                   axis=1).astype(np.float32)
-    # BC-breaking: gt_bboxes_3d is already in lidar coordinates
+    # convert gt_bboxes_3d to velodyne coordinates
+    gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
+        Box3DMode.LIDAR, np.linalg.inv(rect @ lidar2cam0), correct_yaw=True)
     # convert gt_bboxes_3d to cam coordinates
-    gt_bboxes_3d = LiDARInstance3DBoxes(gt_bboxes_3d).convert_to(
+    gt_bboxes_3d = gt_bboxes_3d.convert_to(
         Box3DMode.CAM, rect @ lidar2cami, correct_yaw=True).numpy()
-
     converted_annos['location'] = gt_bboxes_3d[:, :3]
     converted_annos['dimensions'] = gt_bboxes_3d[:, 3:6]
     converted_annos['rotation_y'] = gt_bboxes_3d[:, 6]

diff --git a/projects/DSVT/README.md b/projects/DSVT/README.md
@@ -57,18 +57,24 @@ python tools/test.py projects/DSVT/configs/dsvt_voxel032_res-second_secfpn_8xb1-
 
 ### Training commands
 
-The support of training DSVT is on the way.
+In MMDetection3D's root directory, run the following command to test the model:
+
+```bash
+tools/dist_train.sh projects/DSVT/configs/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class.py 8 --sync_bn torch
+```
 
 ## Results and models
 
 ### Waymo
 
-|                                     Middle Encoder                                     |                                          Backbone                                           | Load Interval | Voxel type (voxel size) | Multi-Class NMS | Multi-frames | Mem (GB) | Inf time (fps) | mAP@L1 | mAPH@L1 | mAP@L2 | **mAPH@L2** | Download |
-| :------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: | :-----------: | :---------------------: | :-------------: | :----------: | :------: | :------------: | :----: | :-----: | :----: | :---------: | :------: |
-| [DSVT](./configs/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class.py) | [ResSECOND](./configs/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class.py) |       5       |      voxel (0.32)       |        ✓        |      ×       |          |                |  75.2  |  72.2   |  68.9  |    66.1     |          |
+|                                     Middle Encoder                                     |                                          Backbone                                           | Load Interval | Voxel type (voxel size) | Multi-Class NMS | Multi-frames | mAP@L1 | mAPH@L1 | mAP@L2 | **mAPH@L2** | Download  |
+| :------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: | :-----------: | :---------------------: | :-------------: | :----------: | :----: | :-----: | :----: | :---------: | :-------: |
+| [DSVT](./configs/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class.py) | [ResSECOND](./configs/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class.py) |       5       |      voxel (0.32)       |        ✓        |      ×       |  75.5  |  72.4   |  69.2  |    66.3     | [log](<>) |
 
 **Note** that `ResSECOND` denotes the base block in SECOND has residual layers.
 
+**Note** Regrettably, we are unable to provide the pre-trained model weights due to [Waymo Dataset License Agreement](https://waymo.com/open/terms/). However, we can provide the training logs.
+
 ## Citation
 
 ```latex

diff --git a/projects/DSVT/configs/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class.py b/projects/DSVT/configs/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class.py
@@ -6,7 +6,6 @@
 voxel_size = [0.32, 0.32, 6]
 grid_size = [468, 468, 1]
 point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4.0]
-# data_root = 'data/waymo_mini/kitti_format/'
 data_root = 'data/waymo/kitti_format/'
 class_names = ['Car', 'Pedestrian', 'Cyclist']
 metainfo = dict(classes=class_names)
@@ -194,7 +193,7 @@
     dataset=dict(
         type=dataset_type,
         data_root=data_root,
-        ann_file='waymo_wo_cam_ins_infos_train.pkl',
+        ann_file='waymo_infos_train.pkl',
         data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
         pipeline=train_pipeline,
         modality=input_modality,
@@ -216,7 +215,7 @@
         type=dataset_type,
         data_root=data_root,
         data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
-        ann_file='waymo_wo_cam_ins_infos_val.pkl',
+        ann_file='waymo_infos_val.pkl',
         pipeline=test_pipeline,
         modality=input_modality,
         test_mode=True,
@@ -225,32 +224,23 @@
         backend_args=backend_args))
 test_dataloader = val_dataloader
 
-# val_evaluator = dict(
-#     type='WaymoMetric',
-#     ann_file='./data/waymo_mini/kitti_format/waymo_infos_val.pkl',
-#     waymo_bin_file='./data/waymo_mini/waymo_format/gt_mini.bin',
-#     backend_args=backend_args,
-#     convert_kitti_format=False)
 val_evaluator = dict(
     type='WaymoMetric',
-    ann_file='./data/waymo/kitti_format/waymo_infos_val.pkl',
     waymo_bin_file='./data/waymo/waymo_format/gt.bin',
-    backend_args=backend_args,
-    convert_kitti_format=False)
+    result_prefix='./dsvt_pred')
 test_evaluator = val_evaluator
 
-vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]
-# vis_backends = [dict(type='LocalVisBackend')]
+# vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]
+vis_backends = [dict(type='LocalVisBackend')]
 visualizer = dict(
     type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+# schedules
 lr = 1e-5
-# This schedule is mainly used by models on nuScenes dataset
-# max_norm=10 is better for SECOND
 optim_wrapper = dict(
     type='OptimWrapper',
     optimizer=dict(type='AdamW', lr=lr, weight_decay=0.05, betas=(0.9, 0.99)),
     clip_grad=dict(max_norm=10, norm_type=2))
-# learning rate
 param_scheduler = [
     dict(
         type='CosineAnnealingLR',

diff --git a/tools/dataset_converters/kitti_converter.py b/tools/dataset_converters/kitti_converter.py
@@ -5,7 +5,6 @@
 import mmcv
 import mmengine
 import numpy as np
-from mmengine import logging, print_log
 from nuscenes.utils.geometry_utils import view_points
 
 from mmdet3d.structures import points_cam2img
@@ -250,12 +249,6 @@ def create_waymo_info_file(data_path,
         max_sweeps (int, optional): Max sweeps before the detection frame
             to be used. Default: 5.
     """
-    print_log(
-        'Deprecation Warning: related functions has been migrated to '
-        '`Waymo2KITTI.create_waymo_info_file`. It will be removed in '
-        'the future!',
-        logger='current',
-        level=logging.WARNING)
     imageset_folder = Path(data_path) / 'ImageSets'
     train_img_ids = _read_imageset_file(str(imageset_folder / 'train.txt'))
     val_img_ids = _read_imageset_file(str(imageset_folder / 'val.txt'))

diff --git a/tools/dataset_converters/kitti_data_utils.py b/tools/dataset_converters/kitti_data_utils.py
@@ -148,14 +148,10 @@ def get_label_anno(label_path):
                                         for x in content]).reshape(-1, 3)
     annotations['rotation_y'] = np.array([float(x[14])
                                           for x in content]).reshape(-1)
-    if len(content) != 0 and len(content[0]) >= 16:  # have score
+    if len(content) != 0 and len(content[0]) == 16:  # have score
         annotations['score'] = np.array([float(x[15]) for x in content])
     else:
         annotations['score'] = np.zeros((annotations['bbox'].shape[0], ))
-    # have num_lidar_points_in_box, given in waymo
-    if len(content) != 0 and len(content[0]) == 17:
-        annotations['num_points_in_gt'] = np.array(
-            [int(x[16]) for x in content])
     index = list(range(num_objects)) + [-1] * (num_gt - num_objects)
     annotations['index'] = np.array(index, dtype=np.int32)
     annotations['group_ids'] = np.arange(num_gt, dtype=np.int32)
@@ -556,7 +552,6 @@ def gather(self, image_ids):
         image_infos = mmengine.track_parallel_progress(self.gather_single,
                                                        image_ids,
                                                        self.num_worker)
-        # image_infos = mmengine.track_progress(self.gather_single, image_ids)
         return list(image_infos)