merge dev-1.x

projects/DSVT/README.md

@@ -57,17 +57,25 @@ 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     | \[log\](\<https://download.openmmlab.com/mmdetection3d/v1.1.0_models/dsvt/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130.log) |
+
+**Note**:
+
+- `ResSECOND` denotes the base block in SECOND has residual layers.
 
-**Note** that `ResSECOND` denotes the base block in SECOND has residual layers.
+- Regrettably, we are unable to provide the pre-trained model weights due to [Waymo Dataset License Agreement](https://waymo.com/open/terms/), so we only provide the training logs as shown above.
 
 ## Citation
 

projects/DSVT/configs/dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class.py

@@ -158,9 +158,8 @@
         rot_range=[-0.78539816, 0.78539816],
         scale_ratio_range=[0.95, 1.05],
         translation_std=[0.5, 0.5, 0.5]),
-    dict(type='DSVTPointsRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='DSVTObjectRangeFilter', point_cloud_range=point_cloud_range),
-    # dict(type='ObjectNameFilter', classes=class_names),
+    dict(type='PointsRangeFilter3D', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter3D', point_cloud_range=point_cloud_range),
     dict(type='PointShuffle'),
     dict(
         type='Pack3DDetInputs',
@@ -176,7 +175,7 @@
         norm_intensity=True,
         norm_elongation=True,
         backend_args=backend_args),
-    dict(type='DSVTPointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointsRangeFilter3D', point_cloud_range=point_cloud_range),
     dict(
         type='Pack3DDetInputs',
         keys=['points'],
@@ -188,12 +187,11 @@
     batch_size=1,
     num_workers=4,
     persistent_workers=True,
-    # sampler=dict(type='DefaultSampler', shuffle=False),
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
         type=dataset_type,
         data_root=data_root,
-        ann_file='waymo_train.pkl',
+        ann_file='waymo_infos_train.pkl',
         data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
         pipeline=train_pipeline,
         modality=input_modality,
@@ -227,7 +225,7 @@
 val_evaluator = dict(
     type='WaymoMetric',
     waymo_bin_file='./data/waymo/waymo_format/gt.bin',
-    result_prefix='./dsvt_pred.bin')
+    result_prefix='./dsvt_pred')
 test_evaluator = val_evaluator
 
 # vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]
@@ -281,6 +279,51 @@
 # runtime settings
 train_cfg = dict(by_epoch=True, max_epochs=12, val_interval=1)
 
+# schedules
+lr = 1e-5
+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))
+param_scheduler = [
+    dict(
+        type='CosineAnnealingLR',
+        T_max=1.2,
+        eta_min=lr * 100,
+        begin=0,
+        end=1.2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    dict(
+        type='CosineAnnealingLR',
+        T_max=10.8,
+        eta_min=lr * 1e-4,
+        begin=1.2,
+        end=12,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    # momentum scheduler
+    dict(
+        type='CosineAnnealingMomentum',
+        T_max=1.2,
+        eta_min=0.85,
+        begin=0,
+        end=1.2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    dict(
+        type='CosineAnnealingMomentum',
+        T_max=10.8,
+        eta_min=0.95,
+        begin=1.2,
+        end=12,
+        by_epoch=True,
+        convert_to_iter_based=True)
+]
+
+# runtime settings
+train_cfg = dict(by_epoch=True, max_epochs=12, val_interval=1)
+
 # runtime settings
 val_cfg = dict()
 test_cfg = dict()

projects/DSVT/dsvt/__init__.py

@@ -5,11 +5,11 @@
 from .dynamic_pillar_vfe import DynamicPillarVFE3D
 from .map2bev import PointPillarsScatter3D
 from .res_second import ResSECOND
-from .transforms_3d import DSVTObjectRangeFilter, DSVTPointsRangeFilter
+from .transforms_3d import ObjectRangeFilter3D, PointsRangeFilter3D
 from .utils import DSVTBBoxCoder
 
 __all__ = [
     'DSVTCenterHead', 'DSVT', 'DSVTMiddleEncoder', 'DynamicPillarVFE3D',
     'PointPillarsScatter3D', 'ResSECOND', 'DSVTBBoxCoder',
-    'DSVTObjectRangeFilter', 'DSVTPointsRangeFilter', 'DisableAugHook'
+    'ObjectRangeFilter3D', 'PointsRangeFilter3D', 'DisableAugHook'
 ]

projects/DSVT/dsvt/dsvt_head.py

@@ -26,10 +26,10 @@ class DSVTCenterHead(CenterHead):
     """
 
     def __init__(self,
-                 *args,
                  loss_iou=dict(
                      type='mmdet.L1Loss', reduction='mean', loss_weight=1),
                  loss_reg_iou=None,
+                 *args,
                  **kwargs):
         super(DSVTCenterHead, self).__init__(*args, **kwargs)
         self.loss_iou = MODELS.build(loss_iou)
@@ -165,6 +165,7 @@ def calc_iou_loss(self, iou_preds, batch_box_preds, mask, ind, gt_boxes):
             gt_boxes: List of batch groundtruth boxes.
 
         Returns:
+            Tensor: IoU Loss.
         """
         if mask.sum() == 0:
             return iou_preds.new_zeros((1))
@@ -256,7 +257,7 @@ def get_targets_single(self,
         """Generate training targets for a single sample.
 
         Args:
-            gt_instances_3d (:obj:`InstanceData`): Gt_instances of
+            gt_instances_3d (:obj:`InstanceData`): Gt_instances_3d of
                 single data sample. It usually includes
                 ``bboxes_3d`` and ``labels_3d`` attributes.
 
@@ -401,7 +402,7 @@ def loss_by_feat(self, preds_dicts: Tuple[List[dict]],
                 tasks head, and the internal list indicate different
                 FPN level.
             batch_gt_instances_3d (list[:obj:`InstanceData`]): Batch of
-                gt_instances. It usually includes ``bboxes_3d`` and
+                gt_instances_3d. It usually includes ``bboxes_3d`` and
                 ``labels_3d`` attributes.
 
         Returns:

projects/DSVT/dsvt/dynamic_pillar_vfe.py

@@ -77,7 +77,6 @@ def __init__(self, with_distance, use_absolute_xyz, use_norm, num_filters,
         self.voxel_x = voxel_size[0]
         self.voxel_y = voxel_size[1]
         self.voxel_z = voxel_size[2]
-        # TODO: remove it after 对齐精度
         point_cloud_range = np.array(point_cloud_range).astype(np.float32)
         self.x_offset = self.voxel_x / 2 + point_cloud_range[0]
         self.y_offset = self.voxel_y / 2 + point_cloud_range[1]

projects/DSVT/dsvt/res_second.py

@@ -76,6 +76,8 @@ class ResSECOND(BaseModule):
         out_channels (list[int]): Output channels for multi-scale feature maps.
         blocks_nums (list[int]): Number of blocks in each stage.
         layer_strides (list[int]): Strides of each stage.
+        init_cfg (dict, optional): Config for weight initialization.
+            Defaults to None.
     """
 
     def __init__(self,

projects/DSVT/dsvt/transforms_3d.py

@@ -7,7 +7,7 @@
 
 
 @TRANSFORMS.register_module()
-class DSVTObjectRangeFilter(BaseTransform):
+class ObjectRangeFilter3D(BaseTransform):
     """Filter objects by the range. It differs from `ObjectRangeFilter` by
     using `in_range_3d` instead of `in_range_bev`.
 
@@ -61,7 +61,7 @@ def __repr__(self) -> str:
 
 
 @TRANSFORMS.register_module()
-class DSVTPointsRangeFilter(BaseTransform):
+class PointsRangeFilter3D(BaseTransform):
     """Filter points by the range. It differs from `PointRangeFilter` by using
     `in_range_bev` instead of `in_range_3d`.
 

projects/DSVT/dsvt/utils.py

@@ -313,13 +313,13 @@ def center_to_corner2d(center, dim):
 @weighted_loss
 def diou3d_loss(pred_boxes, gt_boxes, eps: float = 1e-7):
     """
-    https://github.com/agent-sgs/PillarNet/blob/master/det3d/core/utils/center_utils.py # noqa
+    modified from https://github.com/agent-sgs/PillarNet/blob/master/det3d/core/utils/center_utils.py # noqa
     Args:
         pred_boxes (N, 7):
         gt_boxes (N, 7):
 
     Returns:
-        _type_: _description_
+        Tensor: Distance-IoU Loss.
     """
     assert pred_boxes.shape[0] == gt_boxes.shape[0]
 
@@ -371,14 +371,15 @@ def diou3d_loss(pred_boxes, gt_boxes, eps: float = 1e-7):
 @MODELS.register_module()
 class DIoU3DLoss(nn.Module):
     r"""3D bboxes Implementation of `Distance-IoU Loss: Faster and Better
-    Learning for Bounding Box Regression https://arxiv.org/abs/1911.08287`_.
+    Learning for Bounding Box Regression <https://arxiv.org/abs/1911.08287>`_.
 
     Code is modified from https://github.com/Zzh-tju/DIoU.
 
     Args:
-        eps (float): Epsilon to avoid log(0).
+        eps (float): Epsilon to avoid log(0). Defaults to 1e-6.
         reduction (str): Options are "none", "mean" and "sum".
-        loss_weight (float): Weight of loss.
+            Defaults to "mean".
+        loss_weight (float): Weight of loss. Defaults to 1.0.
     """
 
     def __init__(self,