[Feature] Support PointRCNN backbone (#974)

* [Refactor] Main code modification for coordinate system refactor (#677)

* [Enhance] Add script for data update (#774)

* Fixed wrong config paths and fixed a bug in test

* Fixed metafile

* Coord sys refactor (main code)

* Update test_waymo_dataset.py

* Manually resolve conflict

* Removed unused lines and fixed imports

* remove coord2box and box2coord

* update dir_limit_offset

* Some minor improvements

* Removed some \s in comments

* Revert a change

* Change Box3DMode to Coord3DMode where points are converted

* Fix points_in_bbox function

* Fix Imvoxelnet config

* Revert adding a line

* Fix rotation bug when batch size is 0

* Keep sign of dir_scores as before

* Fix several comments

* Add a comment

* Fix docstring

* Add data update scripts

* Fix comments

* fix import (#839)

* [Enhance]  refactor  iou_neg_piecewise_sampler.py (#842)

* [Refactor] Main code modification for coordinate system refactor (#677)

* [Enhance] Add script for data update (#774)

* Fixed wrong config paths and fixed a bug in test

* Fixed metafile

* Coord sys refactor (main code)

* Update test_waymo_dataset.py

* Manually resolve conflict

* Removed unused lines and fixed imports

* remove coord2box and box2coord

* update dir_limit_offset

* Some minor improvements

* Removed some \s in comments

* Revert a change

* Change Box3DMode to Coord3DMode where points are converted

* Fix points_in_bbox function

* Fix Imvoxelnet config

* Revert adding a line

* Fix rotation bug when batch size is 0

* Keep sign of dir_scores as before

* Fix several comments

* Add a comment

* Fix docstring

* Add data update scripts

* Fix comments

* fix import

* refactor iou_neg_piecewise_sampler.py

* add docstring

* modify docstring
Co-authored-by: Yezhen Cong <52420115+THU17cyz@users.noreply.github.com>
Co-authored-by: THU17cyz <congyezhen71@hotmail.com>

* [Feature] Add roipooling cuda ops (#843)

* [Refactor] Main code modification for coordinate system refactor (#677)

* [Enhance] Add script for data update (#774)

* Fixed wrong config paths and fixed a bug in test

* Fixed metafile

* Coord sys refactor (main code)

* Update test_waymo_dataset.py

* Manually resolve conflict

* Removed unused lines and fixed imports

* remove coord2box and box2coord

* update dir_limit_offset

* Some minor improvements

* Removed some \s in comments

* Revert a change

* Change Box3DMode to Coord3DMode where points are converted

* Fix points_in_bbox function

* Fix Imvoxelnet config

* Revert adding a line

* Fix rotation bug when batch size is 0

* Keep sign of dir_scores as before

* Fix several comments

* Add a comment

* Fix docstring

* Add data update scripts

* Fix comments

* fix import

* add roipooling cuda ops

* add roi extractor

* add test_roi_extractor unittest

* Modify setup.py to install roipooling ops

* modify docstring

* remove enlarge bbox in roipoint pooling

* add_roipooling_ops

* modify docstring
Co-authored-by: Yezhen Cong <52420115+THU17cyz@users.noreply.github.com>
Co-authored-by: THU17cyz <congyezhen71@hotmail.com>

* [Refactor] Refactor code structure and docstrings (#803)

* refactor points_in_boxes

* Merge same functions of three boxes

* More docstring fixes and unify x/y/z size

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Remove None in function param type

* Fix unittest

* Add comments for NMS functions

* Merge methods of Points

* Add unittest

* Add optional and default value

* Fix box conversion and add unittest

* Fix comments

* Add unit test

* Indent

* Fix CI

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Add unit test for box bev

* More unit tests and refine docstrings in box_np_ops

* Fix comment

* Add deprecation warning

* support pointrcnn backbone

* add docstring

* modify docstring

* modify docstring

* modify docstring

* Update pointnet2_fp_neck.py

* add code block

* refine docstring & code

* add unittest on fp_neck

* refine unittest

* refine unittest

* refine unittest

* refine unittest

* refine unittest

* fix docstring
Co-authored-by: Yezhen Cong <52420115+THU17cyz@users.noreply.github.com>
Co-authored-by: Xi Liu <75658786+xiliu8006@users.noreply.github.com>
Co-authored-by: THU17cyz <congyezhen71@hotmail.com>
Co-authored-by: xiliu8006 <xiliu8006@gmail.com>

mmdet3d/models/backbones/pointnet2_sa_msg.py

@@ -64,7 +64,11 @@ class PointNet2SAMSG(BasePointNet):
         self.out_indices = out_indices
         assert max(out_indices) < self.num_sa
         assert len(num_points) == len(radii) == len(num_samples) == len(
-            sa_channels) == len(aggregation_channels)
+            sa_channels)
+        if aggregation_channels is not None:
+            assert len(sa_channels) == len(aggregation_channels)
+        else:
+            aggregation_channels = [None] * len(sa_channels)
 
         self.SA_modules = nn.ModuleList()
         self.aggregation_mlps = nn.ModuleList()

mmdet3d/models/dense_heads/anchor3d_head.py

@@ -81,6 +81,10 @@ class Anchor3DHead(BaseModule, AnchorTrainMixin):
         self.assign_per_class = assign_per_class
         self.dir_offset = dir_offset
         self.dir_limit_offset = dir_limit_offset
+        import warnings
+        warnings.warn(
+            'dir_offset and dir_limit_offset will be depressed and be '
+            'incorporated into box coder in the future')
         self.fp16_enabled = False
 
         # build anchor generator

mmdet3d/models/dense_heads/fcos_mono3d_head.py

@@ -212,16 +212,20 @@ class FCOSMono3DHead(AnchorFreeMono3DHead):
     @staticmethod
     def get_direction_target(reg_targets,
                              dir_offset=0,
-                             dir_limit_offset=0,
+                             dir_limit_offset=0.0,
                              num_bins=2,
                              one_hot=True):
         """Encode direction to 0 ~ num_bins-1.
 
         Args:
             reg_targets (torch.Tensor): Bbox regression targets.
-            dir_offset (int): Direction offset.
-            num_bins (int): Number of bins to divide 2*PI.
-            one_hot (bool): Whether to encode as one hot.
+            dir_offset (int, optional): Direction offset. Default to 0.
+            dir_limit_offset (float, optional): Offset to set the direction
+                range. Default to 0.0.
+            num_bins (int, optional): Number of bins to divide 2*PI.
+                Default to 2.
+            one_hot (bool, optional): Whether to encode as one hot.
+                Default to True.
 
         Returns:
             torch.Tensor: Encoded direction targets.

mmdet3d/models/detectors/mvx_two_stage.py

@@ -94,7 +94,6 @@ class MVXTwoStageDetector(Base3DDetector):
                               'key, please consider using init_cfg.')
                 self.img_roi_head.init_cfg = dict(
                     type='Pretrained', checkpoint=img_pretrained)
-
         if self.with_pts_backbone:
             if pts_pretrained is not None:
                 warnings.warn('DeprecationWarning: pretrained is a deprecated '

mmdet3d/models/necks/__init__.py

@@ -2,6 +2,9 @@
 from mmdet.models.necks.fpn import FPN
 from .dla_neck import DLANeck
 from .imvoxel_neck import OutdoorImVoxelNeck
+from .pointnet2_fp_neck import PointNetFPNeck
 from .second_fpn import SECONDFPN
 
-__all__ = ['FPN', 'SECONDFPN', 'OutdoorImVoxelNeck', 'DLANeck']
+__all__ = [
+    'FPN', 'SECONDFPN', 'OutdoorImVoxelNeck', 'PointNetFPNeck', 'DLANeck'
+]

mmdet3d/models/necks/pointnet2_fp_neck.py

+from mmcv.runner import BaseModule
+from torch import nn as nn
+
+from mmdet3d.ops import PointFPModule
+from mmdet.models import NECKS
+
+
+@NECKS.register_module()
+class PointNetFPNeck(BaseModule):
+    r"""PointNet FP Module used in PointRCNN.
+
+    Refer to the `official code <https://github.com/charlesq34/pointnet2>`_.
+
+    .. code-block:: none
+
+        sa_n ----------------------------------------
+                                                     |
+        ... ---------------------------------        |
+                                             |       |
+        sa_1 -------------                   |       |
+                          |                  |       |
+        sa_0 -> fp_0 -> fp_module ->fp_1 -> ... -> fp_module -> fp_n
+
+    sa_n including sa_xyz (torch.Tensor) and sa_features (torch.Tensor)
+    fp_n including fp_xyz (torch.Tensor) and fp_features (torch.Tensor)
+
+    Args:
+        fp_channels (tuple[tuple[int]]): Tuple of mlp channels in FP modules.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self, fp_channels, init_cfg=None):
+        super(PointNetFPNeck, self).__init__(init_cfg=init_cfg)
+
+        self.num_fp = len(fp_channels)
+        self.FP_modules = nn.ModuleList()
+        for cur_fp_mlps in fp_channels:
+            self.FP_modules.append(PointFPModule(mlp_channels=cur_fp_mlps))
+
+    def _extract_input(self, feat_dict):
+        """Extract inputs from features dictionary.
+
+        Args:
+            feat_dict (dict): Feature dict from backbone, which may contain
+                the following keys and values:
+
+                - sa_xyz (list[torch.Tensor]): Points of each sa module
+                    in shape (N, 3).
+                - sa_features (list[torch.Tensor]): Output features of
+                    each sa module in shape (N, M).
+
+        Returns:
+            list[torch.Tensor]: Coordinates of multiple levels of points.
+            list[torch.Tensor]: Features of multiple levels of points.
+        """
+        sa_xyz = feat_dict['sa_xyz']
+        sa_features = feat_dict['sa_features']
+        assert len(sa_xyz) == len(sa_features)
+
+        return sa_xyz, sa_features
+
+    def forward(self, feat_dict):
+        """Forward pass.
+
+        Args:
+            feat_dict (dict): Feature dict from backbone.
+
+        Returns:
+            dict[str, torch.Tensor]: Outputs of the Neck.
+
+                - fp_xyz (torch.Tensor): The coordinates of fp features.
+                - fp_features (torch.Tensor): The features from the last
+                    feature propogation layers.
+        """
+        sa_xyz, sa_features = self._extract_input(feat_dict)
+
+        fp_feature = sa_features[-1]
+        fp_xyz = sa_xyz[-1]
+
+        for i in range(self.num_fp):
+            # consume the points in a bottom-up manner
+            fp_feature = self.FP_modules[i](sa_xyz[-(i + 2)], sa_xyz[-(i + 1)],
+                                            sa_features[-(i + 2)], fp_feature)
+            fp_xyz = sa_xyz[-(i + 2)]
+
+        ret = dict(fp_xyz=fp_xyz, fp_features=fp_feature)
+        return ret

mmdet3d/ops/pointnet_modules/point_sa_module.py

@@ -69,6 +69,8 @@ class BasePointSAModule(nn.Module):
             self.num_point = [num_point]
         elif isinstance(num_point, list) or isinstance(num_point, tuple):
             self.num_point = num_point
+        elif num_point is None:
+            self.num_point = None
         else:
             raise NotImplementedError('Error type of num_point!')
 
@@ -78,8 +80,12 @@ class BasePointSAModule(nn.Module):
         self.fps_mod_list = fps_mod
         self.fps_sample_range_list = fps_sample_range_list
 
-        self.points_sampler = Points_Sampler(self.num_point, self.fps_mod_list,
+        if self.num_point is not None:
+            self.points_sampler = Points_Sampler(self.num_point,
+                                                 self.fps_mod_list,
                                                  self.fps_sample_range_list)
+        else:
+            self.points_sampler = None
 
         for i in range(len(radii)):
             radius = radii[i]
@@ -126,9 +132,12 @@ class BasePointSAModule(nn.Module):
         elif target_xyz is not None:
             new_xyz = target_xyz.contiguous()
         else:
+            if self.num_point is not None:
                 indices = self.points_sampler(points_xyz, features)
-            new_xyz = gather_points(xyz_flipped, indices).transpose(
-                1, 2).contiguous() if self.num_point is not None else None
+                new_xyz = gather_points(xyz_flipped,
+                                        indices).transpose(1, 2).contiguous()
+            else:
+                new_xyz = None
 
         return new_xyz, indices
 

tests/test_models/test_common_modules/test_pointnet_modules.py

@@ -108,6 +108,20 @@ def test_pointnet_sa_module_msg():
     assert new_features.shape == torch.Size([1, 48, 20])
     assert inds.shape == torch.Size([1, 20])
 
+    # test num_points = None
+    self = PointSAModuleMSG(
+        num_point=None,
+        radii=[0.2, 0.4],
+        sample_nums=[4, 8],
+        mlp_channels=[[12, 16], [12, 32]],
+        norm_cfg=dict(type='BN2d'),
+        use_xyz=False,
+        pool_mod='max').cuda()
+
+    # test forward
+    new_xyz, new_features, inds = self(xyz, features)
+    assert new_features.shape == torch.Size([1, 48, 1])
+
     # length of 'fps_mod' should be same as 'fps_sample_range_list'
     with pytest.raises(AssertionError):
         PointSAModuleMSG(

tests/test_models/test_necks/test_necks.py

@@ -59,6 +59,39 @@ def test_imvoxel_neck():
     assert outputs[0].shape == (1, 256, 248, 216)
 
 
+def test_fp_neck():
+    if not torch.cuda.is_available():
+        pytest.skip()
+
+    xyzs = [16384, 4096, 1024, 256, 64]
+    feat_channels = [1, 96, 256, 512, 1024]
+    channel_num = 5
+
+    sa_xyz = [torch.rand(3, xyzs[i], 3) for i in range(channel_num)]
+    sa_features = [
+        torch.rand(3, feat_channels[i], xyzs[i]) for i in range(channel_num)
+    ]
+
+    neck_cfg = dict(
+        type='PointNetFPNeck',
+        fp_channels=((1536, 512, 512), (768, 512, 512), (608, 256, 256),
+                     (257, 128, 128)))
+
+    neck = build_neck(neck_cfg)
+    neck.init_weights()
+
+    if torch.cuda.is_available():
+        sa_xyz = [x.cuda() for x in sa_xyz]
+        sa_features = [x.cuda() for x in sa_features]
+        neck.cuda()
+
+    feats_sa = {'sa_xyz': sa_xyz, 'sa_features': sa_features}
+    outputs = neck(feats_sa)
+    assert outputs['fp_xyz'].cpu().numpy().shape == (3, 16384, 3)
+    assert outputs['fp_features'].detach().cpu().numpy().shape == (3, 128,
+                                                                   16384)
+
+
 def test_dla_neck():
 
     s = 32