[Feature] Support entire PAConv and PAConvCUDA models (#783)

* add PAConv decode head

* add config files

* add paconv's correlation loss

* support reg loss in Segmentor class

* minor fix

* add augmentation to configs

* fix ed7 in cfg

* fix bug in corr loss

* enable syncbn in paconv

* rename to loss_regularization

* rename loss_reg to loss_regularize

* use SyncBN

* change weight kernels to kernel weights

* rename corr_loss to reg_loss

* minor fix

* configs fix IndoorPatchPointSample

* fix grouped points minus center error

* update transform_3d & add configs

* merge master

* fix enlarge_size bug

* refine config

* remove cfg files

* minor fix

* add comments on PAConv's ScoreNet

* refine comments

* update compatibility doc

* remove useless lines in transforms_3d

* rename with_loss_regularization to with_regularization_loss

* revert palette change

* remove xavier init from PAConv's ScoreNet

configs/_base_/models/paconv_cuda_ssg.py

@@ -0,0 +1,7 @@
+_base_ = './paconv_ssg.py'
+
+model = dict(
+ backbone=dict(
+ sa_cfg=dict(
+ type='PAConvCUDASAModule',
+ scorenet_cfg=dict(mlp_channels=[8, 16, 16]))))

configs/_base_/models/paconv_ssg.py

@@ -0,0 +1,49 @@
+# model settings
+model = dict(
+ type='EncoderDecoder3D',
+ backbone=dict(
+ type='PointNet2SASSG',
+ in_channels=9, # [xyz, rgb, normalized_xyz]
+ num_points=(1024, 256, 64, 16),
+ radius=(None, None, None, None), # use kNN instead of ball query
+ num_samples=(32, 32, 32, 32),
+ sa_channels=((32, 32, 64), (64, 64, 128), (128, 128, 256), (256, 256,
+ 512)),
+ fp_channels=(),
+ norm_cfg=dict(type='BN2d', momentum=0.1),
+ sa_cfg=dict(
+ type='PAConvSAModule',
+ pool_mod='max',
+ use_xyz=True,
+ normalize_xyz=False,
+ paconv_num_kernels=[16, 16, 16],
+ paconv_kernel_input='w_neighbor',
+ scorenet_input='w_neighbor_dist',
+ scorenet_cfg=dict(
+ mlp_channels=[16, 16, 16],
+ score_norm='softmax',
+ temp_factor=1.0,
+ last_bn=False))),
+ decode_head=dict(
+ type='PAConvHead',
+ # PAConv model's decoder takes skip connections from beckbone
+ # different from PointNet++, it also concats input features in the last
+ # level of decoder, leading to `128 + 6` as the channel number
+ fp_channels=((768, 256, 256), (384, 256, 256), (320, 256, 128),
+ (128 + 6, 128, 128, 128)),
+ channels=128,
+ dropout_ratio=0.5,
+ conv_cfg=dict(type='Conv1d'),
+ norm_cfg=dict(type='BN1d'),
+ act_cfg=dict(type='ReLU'),
+ loss_decode=dict(
+ type='CrossEntropyLoss',
+ use_sigmoid=False,
+ class_weight=None, # should be modified with dataset
+ loss_weight=1.0)),
+ # correlation loss to regularize PAConv's kernel weights
+ loss_regularization=dict(
+ type='PAConvRegularizationLoss', reduction='sum', loss_weight=10.0),
+ # model training and testing settings
+ train_cfg=dict(),
+ test_cfg=dict(mode='slide'))

docs/compatibility.md

@@ -4,6 +4,12 @@ This document provides detailed descriptions of the BC-breaking changes in MMDet
 
 ## MMDetection3D 0.16.0
 
+### Returned values of `QueryAndGroup` operation
+
+We modified the returned `grouped_xyz` value of operation `QueryAndGroup` to support PAConv segmentor. Originally, the `grouped_xyz` is centered by subtracting the grouping centers, which represents the relative positions of grouped points. Now, we didn't perform such subtraction and the returned `grouped_xyz` stands for the absolute coordinates of these points.
+
+Note that, the other returned variables of `QueryAndGroup` such as `new_features`, `unique_cnt` and `grouped_idx` are not affected.
+
 ### NuScenes coco-style data pre-processing
 
 We remove the rotation and dimension hack in the monocular 3D detection on nuScenes. Specifically, we transform the rotation and dimension of boxes defined by nuScenes devkit to the coordinate system of our `CameraInstance3DBoxes` in the pre-processing and transform them back in the post-processing. In this way, we can remove the corresponding [hack](https://github.com/open-mmlab/mmdetection3d/pull/744/files#diff-5bee5062bd84e6fa25a2fdd71353f6f283dfdc4a66a0316c3b1ca26078c978b6L165) used in the visualization tools. The modification also guarantees the correctness of all the operations based on our `CameraInstance3DBoxes` (such as NMS and flip augmentation) when training monocular 3D detectors.
@@ -15,7 +21,7 @@ The modification only influences nuScenes coco-style json files. Please re-run t
 We adopt a new pre-processing procedure for the ScanNet dataset in order to support ImVoxelNet, which is a multi-view method requiring image data. In previous versions of MMDetection3D, ScanNet dataset was only used for point cloud based 3D detection and segmentation methods. We plan adding ImVoxelNet to our model zoo, thus updating ScanNet correspondingly by adding image-related pre-processing steps. Specifically, we made these changes:
 
 - Add [script](https://github.com/open-mmlab/mmdetection3d/blob/master/data/scannet/extract_posed_images.py) for extracting RGB data.
-- Update [script](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/scannet_data_utils.py) for annotation creating. 
+- Update [script](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/scannet_data_utils.py) for annotation creating.
 - Add instructions in the documents on preparing image data.
 
 Please refer to the ScanNet [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/data/scannet/README.md/) for more details.

mmdet3d/apis/test.py

@@ -43,7 +43,7 @@ def single_gpu_test(model,
  models_3d = (Base3DDetector, Base3DSegmentor,
  SingleStageMono3DDetector)
  if isinstance(model.module, models_3d):
- model.module.show_results(data, result, out_dir)
+ model.module.show_results(data, result, out_dir=out_dir)
  # Visualize the results of MMDetection model
  # 'show_result' is MMdetection visualization API
  else:

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -928,17 +928,20 @@ class IndoorPatchPointSample(object):
  Defaults to None.
  ignore_index (int, optional): Label index that won't be used for the
  segmentation task. This is set in PointSegClassMapping as neg_cls.
+ If not None, will be used as a patch selection criterion.
  Defaults to None.
  use_normalized_coord (bool, optional): Whether to use normalized xyz as
  additional features. Defaults to False.
  num_try (int, optional): Number of times to try if the patch selected
  is invalid. Defaults to 10.
  enlarge_size (float | None, optional): Enlarge the sampled patch to
  [-block_size / 2 - enlarge_size, block_size / 2 + enlarge_size] as
- an augmentation. If None, set it as 0.01. Defaults to 0.2.
+ an augmentation. If None, set it as 0. Defaults to 0.2.
  min_unique_num (int | None, optional): Minimum number of unique points
  the sampled patch should contain. If None, use PointNet++'s method
  to judge uniqueness. Defaults to None.
+ eps (float, optional): A value added to patch boundary to guarantee
+ points coverage. Defaults to 1e-2.
 
  Note:
  This transform should only be used in the training process of point
@@ -955,14 +958,16 @@ def __init__(self,
  use_normalized_coord=False,
  num_try=10,
  enlarge_size=0.2,
- min_unique_num=None):
+ min_unique_num=None,
+ eps=1e-2):
  self.num_points = num_points
  self.block_size = block_size
  self.ignore_index = ignore_index
  self.use_normalized_coord = use_normalized_coord
  self.num_try = num_try
- self.enlarge_size = enlarge_size if enlarge_size is not None else 0.01
+ self.enlarge_size = enlarge_size if enlarge_size is not None else 0.0
  self.min_unique_num = min_unique_num
+ self.eps = eps
 
  if sample_rate is not None:
  warnings.warn(
@@ -1010,7 +1015,7 @@ def _input_generation(self, coords, patch_center, coord_max, attributes,
 
  return points
 
- def _patch_points_sampling(self, points, sem_mask, replace=None):
+ def _patch_points_sampling(self, points, sem_mask):
  """Patch points sampling.
 
  First sample a valid patch.
@@ -1019,8 +1024,6 @@ def _patch_points_sampling(self, points, sem_mask, replace=None):
  Args:
  points (:obj:`BasePoints`): 3D Points.
  sem_mask (np.ndarray): semantic segmentation mask for input points.
- replace (bool): Whether the sample is with or without replacement.
- Defaults to None.
 
  Returns:
  tuple[:obj:`BasePoints`, np.ndarray] | :obj:`BasePoints`:
@@ -1040,7 +1043,8 @@ def _patch_points_sampling(self, points, sem_mask, replace=None):
  # random sample a point as patch center
  cur_center = coords[np.random.choice(coords.shape[0])]
 
- # boundary of a patch
+ # boundary of a patch, which would be enlarged by
+ # `self.enlarge_size` as an augmentation
  cur_max = cur_center + np.array(
  [self.block_size / 2.0, self.block_size / 2.0, 0.0])
  cur_min = cur_center - np.array(
@@ -1057,14 +1061,14 @@ def _patch_points_sampling(self, points, sem_mask, replace=None):
 
  cur_coords = coords[cur_choice, :]
  cur_sem_mask = sem_mask[cur_choice]
-
- # two criterion for patch sampling, adopted from PointNet++
- # points within selected patch shoule be scattered separately
+ point_idxs = np.where(cur_choice)[0]
  mask = np.sum(
- (cur_coords >= (cur_min - 0.01)) * (cur_coords <=
- (cur_max + 0.01)),
+ (cur_coords >= (cur_min - self.eps)) * (cur_coords <=
+  (cur_max + self.eps)),
  axis=1) == 3
 
+ # two criteria for patch sampling, adopted from PointNet++
+ # 1. selected patch should contain enough unique points
  if self.min_unique_num is None:
  # use PointNet++'s method as default
  # [31, 31, 62] are just some big values used to transform
@@ -1077,9 +1081,10 @@ def _patch_points_sampling(self, points, sem_mask, replace=None):
  vidx[:, 2])
  flag1 = len(vidx) / 31.0 / 31.0 / 62.0 >= 0.02
  else:
+ # if `min_unique_num` is provided, directly compare with it
  flag1 = mask.sum() >= self.min_unique_num
 
- # selected patch should contain enough annotated points
+ # 2. selected patch should contain enough annotated points
  if self.ignore_index is None:
  flag2 = True
  else:
@@ -1089,11 +1094,19 @@ def _patch_points_sampling(self, points, sem_mask, replace=None):
  if flag1 and flag2:
  break
 
- # random sample idx
- if replace is None:
- replace = (cur_sem_mask.shape[0] < self.num_points)
- choices = np.random.choice(
- np.where(cur_choice)[0], self.num_points, replace=replace)
+ # sample idx to `self.num_points`
+ if point_idxs.size >= self.num_points:
+ # no duplicate in sub-sampling
+ choices = np.random.choice(
+ point_idxs, self.num_points, replace=False)
+ else:
+ # do not use random choice here to avoid some points not counted
+ dup = np.random.choice(point_idxs.size,
+ self.num_points - point_idxs.size)
+ idx_dup = np.concatenate(
+ [np.arange(point_idxs.size),
+ np.array(dup)], 0)
+ choices = point_idxs[idx_dup]
 
  # construct model input
  points = self._input_generation(coords[choices], cur_center, coord_max,

mmdet3d/models/decode_heads/__init__.py

@@ -1,3 +1,4 @@
+from .paconv_head import PAConvHead
 from .pointnet2_head import PointNet2Head
 
-__all__ = ['PointNet2Head']
+__all__ = ['PointNet2Head', 'PAConvHead']

mmdet3d/models/decode_heads/paconv_head.py

@@ -0,0 +1,62 @@
+from mmcv.cnn.bricks import ConvModule
+
+from mmdet.models import HEADS
+from .pointnet2_head import PointNet2Head
+
+
+@HEADS.register_module()
+class PAConvHead(PointNet2Head):
+ r"""PAConv decoder head.
+
+ Decoder head used in `PAConv <https://arxiv.org/abs/2103.14635>`_.
+ Refer to the `official code <https://github.com/CVMI-Lab/PAConv>`_.
+
+ Args:
+ fp_channels (tuple[tuple[int]]): Tuple of mlp channels in FP modules.
+ fp_norm_cfg (dict|None): Config of norm layers used in FP modules.
+ Default: dict(type='BN2d').
+ """
+
+ def __init__(self,
+ fp_channels=((768, 256, 256), (384, 256, 256),
+ (320, 256, 128), (128 + 6, 128, 128, 128)),
+ fp_norm_cfg=dict(type='BN2d'),
+ **kwargs):
+ super(PAConvHead, self).__init__(fp_channels, fp_norm_cfg, **kwargs)
+
+ # https://github.com/CVMI-Lab/PAConv/blob/main/scene_seg/model/pointnet2/pointnet2_paconv_seg.py#L53
+ # PointNet++'s decoder conv has bias while PAConv's doesn't have
+ # so we need to rebuild it here
+ self.pre_seg_conv = ConvModule(
+ fp_channels[-1][-1],
+ self.channels,
+ kernel_size=1,
+ bias=False,
+ conv_cfg=self.conv_cfg,
+ norm_cfg=self.norm_cfg,
+ act_cfg=self.act_cfg)
+
+ def forward(self, feat_dict):
+ """Forward pass.
+
+ Args:
+ feat_dict (dict): Feature dict from backbone.
+
+ Returns:
+ torch.Tensor: Segmentation map of shape [B, num_classes, N].
+ """
+ sa_xyz, sa_features = self._extract_input(feat_dict)
+
+ # PointNet++ doesn't use the first level of `sa_features` as input
+ # while PAConv inputs it through skip-connection
+ fp_feature = sa_features[-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)
+
+ output = self.pre_seg_conv(fp_feature)
+ output = self.cls_seg(output)
+
+ return output

mmdet3d/models/decode_heads/pointnet2_head.py

@@ -15,18 +15,22 @@ class PointNet2Head(Base3DDecodeHead):
 
  Args:
  fp_channels (tuple[tuple[int]]): Tuple of mlp channels in FP modules.
+ fp_norm_cfg (dict|None): Config of norm layers used in FP modules.
+ Default: dict(type='BN2d').
  """
 
  def __init__(self,
  fp_channels=((768, 256, 256), (384, 256, 256),
  (320, 256, 128), (128, 128, 128, 128)),
+ fp_norm_cfg=dict(type='BN2d'),
  **kwargs):
  super(PointNet2Head, self).__init__(**kwargs)
 
  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))
+ self.FP_modules.append(
+ PointFPModule(mlp_channels=cur_fp_mlps, norm_cfg=fp_norm_cfg))
 
  # https://github.com/charlesq34/pointnet2/blob/master/models/pointnet2_sem_seg.py#L40
  self.pre_seg_conv = ConvModule(

mmdet3d/models/losses/__init__.py

@@ -1,8 +1,10 @@
 from mmdet.models.losses import FocalLoss, SmoothL1Loss, binary_cross_entropy
 from .axis_aligned_iou_loss import AxisAlignedIoULoss, axis_aligned_iou_loss
 from .chamfer_distance import ChamferDistance, chamfer_distance
+from .paconv_regularization_loss import PAConvRegularizationLoss
 
 __all__ = [
  'FocalLoss', 'SmoothL1Loss', 'binary_cross_entropy', 'ChamferDistance',
- 'chamfer_distance', 'axis_aligned_iou_loss', 'AxisAlignedIoULoss'
+ 'chamfer_distance', 'axis_aligned_iou_loss', 'AxisAlignedIoULoss',
+ 'PAConvRegularizationLoss'
 ]