[Enhance] upgrade PointPillars performace on dev branch

configs/_base_/models/hv_pointpillars_secfpn_kitti.py

@@ -34,12 +34,13 @@
         in_channels=384,
         feat_channels=384,
         use_direction_classifier=True,
+        assign_per_class=True,
         anchor_generator=dict(
-            type='Anchor3DRangeGenerator',
+            type='AlignedAnchor3DRangeGenerator',
             ranges=[
-                [0, -39.68, -0.6, 70.4, 39.68, -0.6],
-                [0, -39.68, -0.6, 70.4, 39.68, -0.6],
-                [0, -39.68, -1.78, 70.4, 39.68, -1.78],
+                [0, -39.68, -0.6, 69.12, 39.68, -0.6],
+                [0, -39.68, -0.6, 69.12, 39.68, -0.6],
+                [0, -39.68, -1.78, 69.12, 39.68, -1.78],
             ],
             sizes=[[0.8, 0.6, 1.73], [1.76, 0.6, 1.73], [3.9, 1.6, 1.56]],
             rotations=[0, 1.57],

configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py

@@ -15,21 +15,15 @@
     rate=1.0,
     prepare=dict(
         filter_by_difficulty=[-1],
-        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
+        filter_by_min_points=dict(Car=5, Pedestrian=5, Cyclist=5)),
     classes=class_names,
-    sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10))
+    sample_groups=dict(Car=15, Pedestrian=15, Cyclist=15))
 
 # PointPillars uses different augmentation hyper parameters
 train_pipeline = [
     dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
     dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
-    dict(type='ObjectSample', db_sampler=db_sampler),
-    dict(
-        type='ObjectNoise',
-        num_try=100,
-        translation_std=[0.25, 0.25, 0.25],
-        global_rot_range=[0.0, 0.0],
-        rot_range=[-0.15707963267, 0.15707963267]),
+    dict(type='ObjectSample', db_sampler=db_sampler, use_ground_plane=False),
     dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScaleTrans',

mmdet3d/datasets/kitti_dataset.py

@@ -152,12 +152,33 @@ def get_ann_info(self, index):
                 - gt_bboxes (np.ndarray): 2D ground truth bboxes.
                 - gt_labels (np.ndarray): Labels of ground truths.
                 - gt_names (list[str]): Class names of ground truths.
+                - difficulty (int): Difficulty defined by KITTI.
+                    0, 1, 2 represent xxxxx respectively.
         """
         # Use index to get the annos, thus the evalhook could also use this api
         info = self.data_infos[index]
         rect = info['calib']['R0_rect'].astype(np.float32)
         Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
 
+        if 'plane' in info:
+            # convert ground plane to velodyne coordinates
+            reverse = np.linalg.inv(rect @ Trv2c)
+
+            (plane_norm_cam,
+             plane_off_cam) = (info['plane'][:3],
+                               -info['plane'][:3] * info['plane'][3])
+            plane_norm_lidar = \
+                (reverse[:3, :3] @ plane_norm_cam[:, None])[:, 0]
+            plane_off_lidar = (
+                reverse[:3, :3] @ plane_off_cam[:, None][:, 0] +
+                reverse[:3, 3])
+            plane_lidar = np.zeros_like(plane_norm_lidar, shape=(4, ))
+            plane_lidar[:3] = plane_norm_lidar
+            plane_lidar[3] = -plane_norm_lidar.T @ plane_off_lidar
+        else:
+            plane_l = None
+
+        difficulty = info['annos']['difficulty']
         annos = info['annos']
         # we need other objects to avoid collision when sample
         annos = self.remove_dontcare(annos)
@@ -191,7 +212,9 @@ def get_ann_info(self, index):
             gt_labels_3d=gt_labels_3d,
             bboxes=gt_bboxes,
             labels=gt_labels,
-            gt_names=gt_names)
+            gt_names=gt_names,
+            plane=plane_l,
+            difficulty=difficulty)
         return anns_results
 
     def drop_arrays_by_name(self, gt_names, used_classes):

mmdet3d/datasets/pipelines/dbsampler.py

@@ -188,7 +188,7 @@ def filter_by_min_points(db_infos, min_gt_points_dict):
                 db_infos[name] = filtered_infos
         return db_infos
 
-    def sample_all(self, gt_bboxes, gt_labels, img=None):
+    def sample_all(self, gt_bboxes, gt_labels, img=None, ground_plane=None):
         """Sampling all categories of bboxes.
 
         Args:
@@ -263,6 +263,15 @@ def sample_all(self, gt_bboxes, gt_labels, img=None):
 
             gt_labels = np.array([self.cat2label[s['name']] for s in sampled],
                                  dtype=np.long)
+
+            if ground_plane is not None:
+                xyz = sampled_gt_bboxes[:, :3]
+                dz = (ground_plane[:3][None, :] *
+                      xyz).sum(-1) + ground_plane[3]
+                sampled_gt_bboxes[:, 2] -= dz
+                for i, s_points in enumerate(s_points_list):
+                    s_points.tensor[:, 2].sub_(dz[i])
+
             ret = {
                 'gt_labels_3d':
                 gt_labels,

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -263,14 +263,17 @@ class ObjectSample(object):
         sample_2d (bool): Whether to also paste 2D image patch to the images
             This should be true when applying multi-modality cut-and-paste.
             Defaults to False.
+        use_ground_plane (bool): Whether to use gound plane to adjust the
+            3D labels.
     """
 
-    def __init__(self, db_sampler, sample_2d=False):
+    def __init__(self, db_sampler, sample_2d=False, use_ground_plane=False):
         self.sampler_cfg = db_sampler
         self.sample_2d = sample_2d
         if 'type' not in db_sampler.keys():
             db_sampler['type'] = 'DataBaseSampler'
         self.db_sampler = build_from_cfg(db_sampler, OBJECTSAMPLERS)
+        self.use_ground_plane = use_ground_plane
 
     @staticmethod
     def remove_points_in_boxes(points, boxes):
@@ -301,6 +304,11 @@ def __call__(self, input_dict):
         gt_bboxes_3d = input_dict['gt_bboxes_3d']
         gt_labels_3d = input_dict['gt_labels_3d']
 
+        if self.use_ground_plane and 'plane' in input_dict['ann_info']:
+            ground_plane = input_dict['ann_info']['plane']
+            input_dict['plane'] = ground_plane
+        else:
+            ground_plane = None
         # change to float for blending operation
         points = input_dict['points']
         if self.sample_2d:
@@ -314,7 +322,10 @@ def __call__(self, input_dict):
                 img=img)
         else:
             sampled_dict = self.db_sampler.sample_all(
-                gt_bboxes_3d.tensor.numpy(), gt_labels_3d, img=None)
+                gt_bboxes_3d.tensor.numpy(),
+                gt_labels_3d,
+                img=None,
+                ground_plane=ground_plane)
 
         if sampled_dict is not None:
             sampled_gt_bboxes_3d = sampled_dict['gt_bboxes_3d']

mmdet3d/models/voxel_encoders/pillar_encoder.py

@@ -15,7 +15,6 @@ class PillarFeatureNet(nn.Module):
 
     The network prepares the pillar features and performs forward pass
     through PFNLayers.
-
     Args:
         in_channels (int, optional): Number of input features,
             either x, y, z or x, y, z, r. Defaults to 4.
@@ -54,7 +53,7 @@ def __init__(self,
         if with_cluster_center:
             in_channels += 3
         if with_voxel_center:
-            in_channels += 2
+            in_channels += 3
         if with_distance:
             in_channels += 1
         self._with_distance = with_distance
@@ -84,8 +83,10 @@ def __init__(self,
         # Need pillar (voxel) size and x/y offset in order to calculate offset
         self.vx = voxel_size[0]
         self.vy = voxel_size[1]
+        self.vz = voxel_size[2]
         self.x_offset = self.vx / 2 + point_cloud_range[0]
         self.y_offset = self.vy / 2 + point_cloud_range[1]
+        self.z_offset = self.vz / 2 + point_cloud_range[2]
         self.point_cloud_range = point_cloud_range
 
     @force_fp32(out_fp16=True)
@@ -97,7 +98,6 @@ def forward(self, features, num_points, coors):
                 (N, M, C).
             num_points (torch.Tensor): Number of points in each pillar.
             coors (torch.Tensor): Coordinates of each voxel.
-
         Returns:
             torch.Tensor: Features of pillars.
         """
@@ -114,21 +114,27 @@ def forward(self, features, num_points, coors):
         dtype = features.dtype
         if self._with_voxel_center:
             if not self.legacy:
-                f_center = torch.zeros_like(features[:, :, :2])
+                f_center = torch.zeros_like(features[:, :, :3])
                 f_center[:, :, 0] = features[:, :, 0] - (
                     coors[:, 3].to(dtype).unsqueeze(1) * self.vx +
                     self.x_offset)
                 f_center[:, :, 1] = features[:, :, 1] - (
                     coors[:, 2].to(dtype).unsqueeze(1) * self.vy +
                     self.y_offset)
+                f_center[:, :, 2] = features[:, :, 2] - (
+                    coors[:, 1].to(dtype).unsqueeze(1) * self.vz +
+                    self.z_offset)
             else:
-                f_center = features[:, :, :2]
+                f_center = features[:, :, :3]
                 f_center[:, :, 0] = f_center[:, :, 0] - (
                     coors[:, 3].type_as(features).unsqueeze(1) * self.vx +
                     self.x_offset)
                 f_center[:, :, 1] = f_center[:, :, 1] - (
                     coors[:, 2].type_as(features).unsqueeze(1) * self.vy +
                     self.y_offset)
+                f_center[:, :, 2] = f_center[:, :, 2] - (
+                    coors[:, 1].type_as(features).unsqueeze(1) * self.vz +
+                    self.z_offset)
             features_ls.append(f_center)
 
         if self._with_distance:
@@ -177,6 +183,8 @@ class DynamicPillarFeatureNet(PillarFeatureNet):
             Defaults to dict(type='BN1d', eps=1e-3, momentum=0.01).
         mode (str, optional): The mode to gather point features. Options are
             'max' or 'avg'. Defaults to 'max'.
+        legacy (bool, optional): Whether to use the new behavior or
+            the original behavior. Defaults to True.
     """
 
     def __init__(self,
@@ -188,7 +196,8 @@ def __init__(self,
                  voxel_size=(0.2, 0.2, 4),
                  point_cloud_range=(0, -40, -3, 70.4, 40, 1),
                  norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
-                 mode='max'):
+                 mode='max',
+                 legacy=True):
         super(DynamicPillarFeatureNet, self).__init__(
             in_channels,
             feat_channels,
@@ -198,7 +207,8 @@ def __init__(self,
             voxel_size=voxel_size,
             point_cloud_range=point_cloud_range,
             norm_cfg=norm_cfg,
-            mode=mode)
+            mode=mode,
+            legacy=legacy)
         self.fp16_enabled = False
         feat_channels = [self.in_channels] + list(feat_channels)
         pfn_layers = []

setup.cfg

@@ -8,7 +8,7 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmdet,mmseg,mmdet3d
-known_third_party = cv2,imageio,indoor3d_util,load_scannet_data,lyft_dataset_sdk,m2r,matplotlib,mmcv,nuimages,numba,numpy,nuscenes,pandas,plyfile,pycocotools,pyquaternion,pytest,pytorch_sphinx_theme,recommonmark,requests,scannet_utils,scipy,seaborn,shapely,skimage,tensorflow,terminaltables,torch,trimesh,ts,waymo_open_dataset
+known_third_party = cv2,imageio,indoor3d_util,load_scannet_data,lyft_dataset_sdk,m2r,matplotlib,mmcv,nuimages,numba,numpy,nuscenes,pandas,plyfile,pycocotools,pyquaternion,pytest,pytorch_sphinx_theme,recommonmark,requests,scannet_utils,scipy,seaborn,shapely,skimage,sphinx,tensorflow,terminaltables,torch,trimesh,ts,waymo_open_dataset
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY