[Fix] Fix bugs in voxelization and preprocessors (#2300)

* fix_voxel

* update

mmdet3d/models/data_preprocessors/data_preprocessor.py

@@ -388,17 +388,25 @@ class Det3DDataPreprocessor(DetDataPreprocessor):
                 rho = torch.sqrt(res[:, 0]**2 + res[:, 1]**2)
                 phi = torch.atan2(res[:, 1], res[:, 0])
                 polar_res = torch.stack((rho, phi, res[:, 2]), dim=-1)
-                # Currently we only support PyTorch >= 1.9.0, and will
-                # implement it in voxel_layer soon for better compatibility
                 min_bound = polar_res.new_tensor(
                     self.voxel_layer.point_cloud_range[:3])
                 max_bound = polar_res.new_tensor(
                     self.voxel_layer.point_cloud_range[3:])
-                polar_res = torch.clamp(polar_res, min_bound, max_bound)
+                try:  # only support PyTorch >= 1.9.0
+                    polar_res_clamp = torch.clamp(polar_res, min_bound,
+                                                  max_bound)
+                except TypeError:
+                    polar_res_clamp = polar_res.clone()
+                    for coor_idx in range(3):
+                        polar_res_clamp[:, coor_idx][
+                            polar_res[:, coor_idx] >
+                            max_bound[coor_idx]] = max_bound[coor_idx]
+                        polar_res_clamp[:, coor_idx][
+                            polar_res[:, coor_idx] <
+                            min_bound[coor_idx]] = min_bound[coor_idx]
                 res_coors = torch.floor(
-                    (polar_res - min_bound) /
-                    polar_res.new_tensor(self.voxel_layer.voxel_size)).int()
-                if self.training:
+                    (polar_res_clamp - min_bound) / polar_res_clamp.new_tensor(
+                        self.voxel_layer.voxel_size)).int()
                 self.get_voxel_seg(res_coors, data_sample)
                 res_coors = F.pad(res_coors, (1, 0), mode='constant', value=i)
                 res_voxels = torch.cat((polar_res, res[:, :2], res[:, 3:]),
@@ -423,6 +431,8 @@ class Det3DDataPreprocessor(DetDataPreprocessor):
             data_sample: (:obj:`Det3DDataSample`): The annotation data of
                 every samples. Add voxel-wise annotation forsegmentation.
         """
+
+        if self.training:
             pts_semantic_mask = data_sample.gt_pts_seg.pts_semantic_mask
             voxel_semantic_mask, _, point2voxel_map = dynamic_scatter_3d(
                 F.one_hot(pts_semantic_mask.long()).float(), res_coors, 'mean',
@@ -430,3 +440,8 @@ class Det3DDataPreprocessor(DetDataPreprocessor):
             voxel_semantic_mask = torch.argmax(voxel_semantic_mask, dim=-1)
             data_sample.gt_pts_seg.voxel_semantic_mask = voxel_semantic_mask
             data_sample.gt_pts_seg.point2voxel_map = point2voxel_map
+        else:
+            pseudo_tensor = res_coors.new_ones([res_coors.shape[0], 1]).float()
+            _, _, point2voxel_map = dynamic_scatter_3d(pseudo_tensor,
+                                                       res_coors, 'mean', True)
+            data_sample.gt_pts_seg.point2voxel_map = point2voxel_map

mmdet3d/models/voxel_encoders/voxel_encoder.py

@@ -508,8 +508,8 @@ class SegVFE(nn.Module):
             Defaults to None.
         grid_shape (tuple[float]): The grid shape of voxelization.
             Defaults to (480, 360, 32).
-        point_cloud_range (tuple[float]): The range of points
-            or voxels. Defaults to (0, -40, -3, 70.4, 40, 1).
+        point_cloud_range (tuple[float]): The range of points or voxels.
+            Defaults to (0, -3.14159265359, -4, 50, 3.14159265359, 2).
         norm_cfg (dict): Config dict of normalization layers.
         mode (str): The mode when pooling features of points
             inside a voxel. Available options include 'max' and 'avg'.
@@ -528,8 +528,8 @@ class SegVFE(nn.Module):
                  with_voxel_center: bool = False,
                  voxel_size: Optional[Sequence[float]] = None,
                  grid_shape: Sequence[float] = (480, 360, 32),
-                 point_cloud_range: Sequence[float] = (0, -180, -4, 50, 180,
-                                                       2),
+                 point_cloud_range: Sequence[float] = (0, -3.14159265359, -4,
+                                                       50, 3.14159265359, 2),
                  norm_cfg: dict = dict(type='BN1d', eps=1e-5, momentum=0.1),
                  mode: bool = 'max',
                  with_pre_norm: bool = True,
@@ -567,8 +567,8 @@ class SegVFE(nn.Module):
 
         # Need pillar (voxel) size and x/y offset in order to calculate offset
         self.vx = self.voxel_size[0]
-        self.vy = self.voxel_size[0]
-        self.vz = self.voxel_size[0]
+        self.vy = self.voxel_size[1]
+        self.vz = self.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]
@@ -589,14 +589,13 @@ class SegVFE(nn.Module):
                         nn.Linear(in_filters, out_filters), norm_layer,
                         nn.ReLU(inplace=True)))
         self.vfe_layers = nn.ModuleList(vfe_layers)
-        self.num_vfe = len(vfe_layers)
         self.vfe_scatter = DynamicScatter(self.voxel_size,
                                           self.point_cloud_range,
                                           (mode != 'max'))
         self.compression_layers = None
         if feat_compression is not None:
-            self.compression_layers = nn.Linear(feat_channels[-1],
-                                                feat_compression)
+            self.compression_layers = nn.Sequential(
+                nn.Linear(feat_channels[-1], feat_compression), nn.ReLU())
 
     def forward(self, features: Tensor, coors: Tensor, *args,
                 **kwargs) -> Tuple[Tensor]:
@@ -617,24 +616,22 @@ class SegVFE(nn.Module):
         if self._with_voxel_center:
             f_center = features.new_zeros(size=(features.size(0), 3))
             f_center[:, 0] = features[:, 0] - (
-                coors[:, 3].type_as(features) * self.vx + self.x_offset)
+                coors[:, 1].type_as(features) * self.vx + self.x_offset)
             f_center[:, 1] = features[:, 1] - (
                 coors[:, 2].type_as(features) * self.vy + self.y_offset)
             f_center[:, 2] = features[:, 2] - (
-                coors[:, 1].type_as(features) * self.vz + self.z_offset)
+                coors[:, 3].type_as(features) * self.vz + self.z_offset)
             features_ls.append(f_center)
 
         # Combine together feature decorations
         features = torch.cat(features_ls[::-1], dim=-1)
-
         if self.pre_norm is not None:
             features = self.pre_norm(features)
 
         point_feats = []
-        for i, vfe in enumerate(self.vfe_layers):
+        for vfe in self.vfe_layers:
             features = vfe(features)
             point_feats.append(features)
-            if i == self.num_vfe - 1:
         voxel_feats, voxel_coors = self.vfe_scatter(features, coors)
 
         if self.compression_layers is not None: