Update mmdet3d/core/bbox/structures/base_box3d.py,...

Update mmdet3d/core/bbox/structures/base_box3d.py, mmdet3d/core/bbox/structures/lidar_box3d.py, mmdet3d/core/post_processing/box3d_nms.py files

mmdet3d/models/losses/chamfer_distance.py

@@ -14,12 +14,12 @@ def chamfer_distance(src,
     """Calculate Chamfer Distance of two sets.
 
     Args:
-        src (Tensor): Source set with shape [B, N, C] to
+        src (torch.Tensor): Source set with shape [B, N, C] to
             calculate Chamfer Distance.
-        dst (Tensor): Destination set with shape [B, M, C] to
+        dst (torch.Tensor): Destination set with shape [B, M, C] to
             calculate Chamfer Distance.
-        src_weight (Tensor or float): Weight of source loss.
-        dst_weight (Tensor or float): Weight of destination loss.
+        src_weight (torch.Tensor or float): Weight of source loss.
+        dst_weight (torch.Tensor or float): Weight of destination loss.
         criterion_mode (str): Criterion mode to calculate distance.
             The valid modes are smooth_l1, l1 or l2.
         reduction (str): Method to reduce losses.
@@ -27,12 +27,14 @@ def chamfer_distance(src,
 
     Returns:
         tuple: Source and Destination loss with indices.
-            - loss_src (Tensor): The min distance from source to destination.
-            - loss_dst (Tensor): The min distance from destination to source.
-            - indices1 (Tensor): Index the min distance point for each point
-                in source to destination.
-            - indices2 (Tensor): Index the min distance point for each point
-                in destination to source.
+            - loss_src (torch.Tensor): The min distance
+                from source to destination.
+            - loss_dst (torch.Tensor): The min distance
+                from destination to source.
+            - indices1 (torch.Tensor): Index the min distance point
+                for each point in source to destination.
+            - indices2 (torch.Tensor): Index the min distance point
+                for each point in destination to source.
     """
 
     if criterion_mode == 'smooth_l1':
@@ -106,14 +108,14 @@ class ChamferDistance(nn.Module):
         """Forward function of loss calculation.
 
         Args:
-            source (Tensor): Source set with shape [B, N, C] to
+            source (torch.Tensor): Source set with shape [B, N, C] to
                 calculate Chamfer Distance.
-            target (Tensor): Destination set with shape [B, M, C] to
+            target (torch.Tensor): Destination set with shape [B, M, C] to
                 calculate Chamfer Distance.
-            src_weight (Tensor | float, optional): Weight of source loss.
-                Defaults to 1.0.
-            dst_weight (Tensor | float, optional): Weight of destination loss.
-                Defaults to 1.0.
+            src_weight (torch.Tensor | float, optional):
+                Weight of source loss. Defaults to 1.0.
+            dst_weight (torch.Tensor | float, optional):
+                Weight of destination loss. Defaults to 1.0.
             reduction_override (str, optional): Method to reduce losses.
                 The valid reduction method are 'none', 'sum' or 'mean'.
                 Defaults to None.

mmdet3d/models/middle_encoders/sparse_unet.py

@@ -147,14 +147,14 @@ class SparseUNet(nn.Module):
         """Forward of upsample and residual block.
 
         Args:
-            x_lateral (SparseConvTensor): lateral tensor
-            x_bottom (SparseConvTensor): feature from bottom layer
+            x_lateral (:obj:`SparseConvTensor`): lateral tensor
+            x_bottom (:obj:`SparseConvTensor`): feature from bottom layer
             lateral_layer (SparseBasicBlock): convolution for lateral tensor
             merge_layer (SparseSequential): convolution for merging features
             upsample_layer (SparseSequential): convolution for upsampling
 
         Returns:
-            SparseConvTensor: upsampled feature
+            :obj:`SparseConvTensor`: upsampled feature
         """
         x = lateral_layer(x_lateral)
         x.features = torch.cat((x_bottom.features, x.features), dim=1)
@@ -169,11 +169,11 @@ class SparseUNet(nn.Module):
         """reduce channel for element-wise addition.
 
         Args:
-            x (SparseConvTensor): x.features (N, C1)
+            x (:obj:`SparseConvTensor`): x.features (N, C1)
             out_channels (int): the number of channel after reduction
 
         Returns:
-            SparseConvTensor: channel reduced feature
+            :obj:`SparseConvTensor`: channel reduced feature
         """
         features = x.features
         n, in_channels = features.shape

mmdet3d/models/model_utils/vote_module.py

@@ -66,11 +66,13 @@ class VoteModule(nn.Module):
         """forward.
 
         Args:
-            seed_points (Tensor): (B, N, 3) coordinate of the seed points.
-            seed_feats (Tensor): (B, C, N) features of the seed points.
+            seed_points (torch.Tensor): (B, N, 3) coordinate of the seed
+                points.
+            seed_feats (torch.Tensor): (B, C, N) features of the seed points.
 
         Returns:
-            tuple[Tensor]:
+            tuple[torch.Tensor]:
+
                 - vote_points: Voted xyz based on the seed points
                     with shape (B, M, 3) M=num_seed*vote_per_seed.
                 - vote_features: Voted features based on the seed points with
@@ -106,14 +108,14 @@ class VoteModule(nn.Module):
         """Calculate loss of voting module.
 
         Args:
-            seed_points (Tensor): coordinate of the seed points.
-            vote_points (Tensor): coordinate of the vote points.
-            seed_indices (Tensor): indices of seed points in raw points.
-            vote_targets_mask (Tensor): mask of valid vote targets.
-            vote_targets (Tensor): targets of votes.
+            seed_points (torch.Tensor): coordinate of the seed points.
+            vote_points (torch.Tensor): coordinate of the vote points.
+            seed_indices (torch.Tensor): indices of seed points in raw points.
+            vote_targets_mask (torch.Tensor): mask of valid vote targets.
+            vote_targets (torch.Tensor): targets of votes.
 
         Returns:
-            Tensor: weighted vote loss.
+            torch.Tensor: weighted vote loss.
         """
         batch_size, num_seed = seed_points.shape[:2]
 

mmdet3d/models/roi_heads/bbox_heads/parta2_bbox_head.py

@@ -37,7 +37,7 @@ class PartA2BboxHead(nn.Module):
             regression layers.
         roi_feat_size (int): The size of pooled roi features.
         with_corner_loss (bool): Whether to use corner loss or not.
-        bbox_coder (BaseBBoxCoder): Bbox coder for box head.
+        bbox_coder (:obj:`BaseBBoxCoder`): Bbox coder for box head.
         conv_cfg (dict): Config dict of convolutional layers
         norm_cfg (dict): Config dict of normalization layers
         loss_bbox (dict): Config dict of box regression loss.
@@ -285,15 +285,15 @@ class PartA2BboxHead(nn.Module):
         """Coumputing losses.
 
         Args:
-            cls_score (Torch.tensor): Scores of each roi.
-            bbox_pred (Torch.tensor): Predictions of bboxes.
-            rois (Torch.tensor): Roi bboxes.
-            labels (Torch.tensor): Labels of class.
-            bbox_targets (Torch.tensor): Target of positive bboxes.
-            pos_gt_bboxes (Torch.tensor): Gt of positive bboxes.
-            reg_mask (Torch.tensor): Mask for positive bboxes.
-            label_weights (Torch.tensor): Weights of class loss.
-            bbox_weights (Torch.tensor): Weights of bbox loss.
+            cls_score (torch.Tensor): Scores of each roi.
+            bbox_pred (torch.Tensor): Predictions of bboxes.
+            rois (torch.Tensor): Roi bboxes.
+            labels (torch.Tensor): Labels of class.
+            bbox_targets (torch.Tensor): Target of positive bboxes.
+            pos_gt_bboxes (torch.Tensor): Gt of positive bboxes.
+            reg_mask (torch.Tensor): Mask for positive bboxes.
+            label_weights (torch.Tensor): Weights of class loss.
+            bbox_weights (torch.Tensor): Weights of bbox loss.
 
         Returns:
             dict: Computed losses.
@@ -357,9 +357,9 @@ class PartA2BboxHead(nn.Module):
         """Generate targets.
 
         Args:
-            sampling_results (list[:obj:SamplingResult]):
+            sampling_results (list[:obj:`SamplingResult`]):
                 Sampled results from rois.
-            rcnn_train_cfg (ConfigDict): Training config of rcnn.
+            rcnn_train_cfg (:obj:`ConfigDict`): Training config of rcnn.
             concat (bool): Whether to concatenate targets between batches.
 
         Returns:
@@ -511,7 +511,7 @@ class PartA2BboxHead(nn.Module):
             class_labels (torch.Tensor): Label of classes
             class_pred (torch.Tensor): Score for nms.
             img_metas (list[dict]): Contain pcd and img's meta info.
-            cfg (ConfigDict): Testing config.
+            cfg (:obj:`ConfigDict`): Testing config.
 
         Returns:
             list[tuple]: Decoded bbox, scores and labels after nms.