[Fix] Refactor Point RCNN and fix it.

configs/_base_/models/point_rcnn.py

@@ -1,5 +1,6 @@
 model = dict(
     type='PointRCNN',
+    data_preprocessor=dict(type='Det3DDataPreprocessor'),
     backbone=dict(
         type='PointNet2SAMSG',
         in_channels=4,
@@ -34,14 +35,14 @@
             cls_linear_channels=(256, 256),
             reg_linear_channels=(256, 256)),
         cls_loss=dict(
-            type='FocalLoss',
+            type='mmdet.FocalLoss',
             use_sigmoid=True,
             reduction='sum',
             gamma=2.0,
             alpha=0.25,
             loss_weight=1.0),
         bbox_loss=dict(
-            type='SmoothL1Loss',
+            type='mmdet.SmoothL1Loss',
             beta=1.0 / 9.0,
             reduction='sum',
             loss_weight=1.0),
@@ -55,12 +56,22 @@
                                                             1.73]])),
     roi_head=dict(
         type='PointRCNNRoIHead',
-        point_roi_extractor=dict(
+        bbox_roi_extractor=dict(
             type='Single3DRoIPointExtractor',
             roi_layer=dict(type='RoIPointPool3d', num_sampled_points=512)),
         bbox_head=dict(
             type='PointRCNNBboxHead',
             num_classes=1,
+            loss_bbox=dict(
+                type='mmdet.SmoothL1Loss',
+                beta=1.0 / 9.0,
+                reduction='sum',
+                loss_weight=1.0),
+            loss_cls=dict(
+                type='mmdet.CrossEntropyLoss',
+                use_sigmoid=True,
+                reduction='sum',
+                loss_weight=1.0),
             pred_layer_cfg=dict(
                 in_channels=512,
                 cls_conv_channels=(256, 256),
@@ -79,31 +90,34 @@
     train_cfg=dict(
         pos_distance_thr=10.0,
         rpn=dict(
-            nms_cfg=dict(
-                use_rotate_nms=True, iou_thr=0.8, nms_pre=9000, nms_post=512),
-            score_thr=None),
+            rpn_proposal=dict(
+                use_rotate_nms=True,
+                score_thr=None,
+                iou_thr=0.8,
+                nms_pre=9000,
+                nms_post=512)),
         rcnn=dict(
             assigner=[
-                dict(  # for Car
-                    type='MaxIoUAssigner',
+                dict(  # for Pedestrian
+                    type='Max3DIoUAssigner',
                     iou_calculator=dict(
                         type='BboxOverlaps3D', coordinate='lidar'),
                     pos_iou_thr=0.55,
                     neg_iou_thr=0.55,
                     min_pos_iou=0.55,
                     ignore_iof_thr=-1,
                     match_low_quality=False),
-                dict(  # for Pedestrian
-                    type='MaxIoUAssigner',
+                dict(  # for Cyclist
+                    type='Max3DIoUAssigner',
                     iou_calculator=dict(
                         type='BboxOverlaps3D', coordinate='lidar'),
                     pos_iou_thr=0.55,
                     neg_iou_thr=0.55,
                     min_pos_iou=0.55,
                     ignore_iof_thr=-1,
                     match_low_quality=False),
-                dict(  # for Cyclist
-                    type='MaxIoUAssigner',
+                dict(  # for Car
+                    type='Max3DIoUAssigner',
                     iou_calculator=dict(
                         type='BboxOverlaps3D', coordinate='lidar'),
                     pos_iou_thr=0.55,
@@ -126,6 +140,9 @@
     test_cfg=dict(
         rpn=dict(
             nms_cfg=dict(
-                use_rotate_nms=True, iou_thr=0.85, nms_pre=9000, nms_post=512),
-            score_thr=None),
+                use_rotate_nms=True,
+                iou_thr=0.85,
+                nms_pre=9000,
+                nms_post=512,
+                score_thr=None)),
         rcnn=dict(use_rotate_nms=True, nms_thr=0.1, score_thr=0.1)))

configs/point_rcnn/point-rcnn_8xb2_kitti-3d-3class.py

@@ -6,7 +6,8 @@
 # dataset settings
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
-class_names = ['Car', 'Pedestrian', 'Cyclist']
+class_names = ['Pedestrian', 'Cyclist', 'Car']
+metainfo = dict(CLASSES=class_names)
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 input_modality = dict(use_lidar=True, use_camera=False)
 
@@ -42,8 +43,9 @@
     dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='PointSample', num_points=16384, sample_range=40.0),
     dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
 ]
 test_pipeline = [
     dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
@@ -61,41 +63,67 @@
             dict(type='RandomFlip3D'),
             dict(
                 type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(type='PointSample', num_points=16384, sample_range=40.0),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
+            dict(type='PointSample', num_points=16384, sample_range=40.0)
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
-
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    dataset=dict(
         type='RepeatDataset',
         times=2,
-        dataset=dict(pipeline=train_pipeline, classes=class_names)),
-    val=dict(pipeline=test_pipeline, classes=class_names),
-    test=dict(pipeline=test_pipeline, classes=class_names))
+        dataset=dict(pipeline=train_pipeline, metainfo=metainfo)))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
 
-# optimizer
 lr = 0.001  # max learning rate
-optimizer = dict(lr=lr, betas=(0.95, 0.85))
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=80)
-evaluation = dict(interval=2)
-# yapf:disable
-log_config = dict(
-    interval=30,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
+optim_wrapper = dict(optimizer=dict(lr=lr, betas=(0.95, 0.85)))
+train_cfg = dict(by_epoch=True, max_epochs=80, val_interval=2)
+
 # Default setting for scaling LR automatically
 #   - `enable` means enable scaling LR automatically
 #       or not by default.
 #   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
 auto_scale_lr = dict(enable=False, base_batch_size=16)
+param_scheduler = [
+    # learning rate scheduler
+    # During the first 35 epochs, learning rate increases from 0 to lr * 10
+    # during the next 45 epochs, learning rate decreases from lr * 10 to
+    # lr * 1e-4
+    dict(
+        type='CosineAnnealingLR',
+        T_max=35,
+        eta_min=lr * 10,
+        begin=0,
+        end=35,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    dict(
+        type='CosineAnnealingLR',
+        T_max=45,
+        eta_min=lr * 1e-4,
+        begin=35,
+        end=80,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    # momentum scheduler
+    # During the first 35 epochs, momentum increases from 0 to 0.85 / 0.95
+    # during the next 45 epochs, momentum increases from 0.85 / 0.95 to 1
+    dict(
+        type='CosineAnnealingMomentum',
+        T_max=35,
+        eta_min=0.85 / 0.95,
+        begin=0,
+        end=35,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    dict(
+        type='CosineAnnealingMomentum',
+        T_max=45,
+        eta_min=1,
+        begin=35,
+        end=80,
+        by_epoch=True,
+        convert_to_iter_based=True)
+]

docs/en/advanced_guides/customize_models.md

@@ -365,7 +365,7 @@ class PartAggregationROIHead(Base3DRoIHead):
 
         Args:
             feats_dict (dict): Contains features from the first stage.
-            rpn_results_list (List[:obj:`InstancesData`]): Detection results
+            rpn_results_list (List[:obj:`InstanceData`]): Detection results
                 of rpn head.
             batch_data_samples (List[:obj:`Det3DDataSample`]): The Data
                 samples. It usually includes information such as
@@ -412,7 +412,7 @@ class PartAggregationROIHead(Base3DRoIHead):
             voxel_dict (dict): Contains information of voxels.
             batch_input_metas (list[dict], Optional): Batch image meta info.
                 Defaults to None.
-            rpn_results_list (List[:obj:`InstancesData`]): Detection results
+            rpn_results_list (List[:obj:`InstanceData`]): Detection results
                 of rpn head.
             test_cfg (Config): Test config.
 
@@ -438,7 +438,7 @@ class PartAggregationROIHead(Base3DRoIHead):
 
         Args:
             feats_dict (dict): Contains features from the first stage.
-            rpn_results_list (List[:obj:`InstancesData`]): Detection results
+            rpn_results_list (List[:obj:`InstanceData`]): Detection results
                 of rpn head.
             batch_data_samples (List[:obj:`Det3DDataSample`]): The Data
                 samples. It usually includes information such as

mmdet3d/models/dense_heads/base_3d_dense_head.py

@@ -204,7 +204,7 @@ def predict_by_feat(self,
             score_factors (list[Tensor], optional): Score factor for
                 all scale level, each is a 4D-tensor, has shape
                 (batch_size, num_priors * 1, H, W). Defaults to None.
-            batch_input_metas (list[dict], Optional): Batch image meta info.
+            batch_input_metas (list[dict], Optional): Batch inputs meta info.
                 Defaults to None.
             cfg (ConfigDict, optional): Test / postprocessing
                 configuration, if None, test_cfg would be used.

mmdet3d/models/dense_heads/parta2_rpn_head.py

@@ -183,8 +183,7 @@ def _predict_by_feat_single(self,
         result = self.class_agnostic_nms(mlvl_bboxes, mlvl_bboxes_for_nms,
                                          mlvl_max_scores, mlvl_label_pred,
                                          mlvl_cls_score, mlvl_dir_scores,
-                                         score_thr, cfg.nms_post, cfg,
-                                         input_meta)
+                                         score_thr, cfg, input_meta)
         return result
 
     def loss_and_predict(self,
@@ -275,7 +274,7 @@ def class_agnostic_nms(self, mlvl_bboxes: Tensor,
                            mlvl_bboxes_for_nms: Tensor,
                            mlvl_max_scores: Tensor, mlvl_label_pred: Tensor,
                            mlvl_cls_score: Tensor, mlvl_dir_scores: Tensor,
-                           score_thr: int, max_num: int, cfg: ConfigDict,
+                           score_thr: int, cfg: ConfigDict,
                            input_meta: dict) -> Dict:
         """Class agnostic nms for single batch.
 
@@ -291,7 +290,6 @@ def class_agnostic_nms(self, mlvl_bboxes: Tensor,
             mlvl_dir_scores (torch.Tensor): Direction scores of
                 Multi-level bbox.
             score_thr (int): Score threshold.
-            max_num (int): Max number of bboxes after nms.
             cfg (:obj:`ConfigDict`): Training or testing config.
             input_meta (dict): Contain pcd and img's meta info.
 
@@ -339,9 +337,9 @@ def class_agnostic_nms(self, mlvl_bboxes: Tensor,
             scores = torch.cat(scores, dim=0)
             cls_scores = torch.cat(cls_scores, dim=0)
             labels = torch.cat(labels, dim=0)
-            if bboxes.shape[0] > max_num:
+            if bboxes.shape[0] > cfg.nms_post:
                 _, inds = scores.sort(descending=True)
-                inds = inds[:max_num]
+                inds = inds[:cfg.nms_post]
                 bboxes = bboxes[inds, :]
                 labels = labels[inds]
                 scores = scores[inds]