[Fix] Fix ImVoxelNet and KittiMetric & refactor configs of ImVoxelNet (#1843)

* ImVoxelNet inherits from Base3DDetector

* fix typo sample_id

* replace add_pred_to_datasample with add_pred_to_datasample

* refactor imvoxelnet config

configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py

+_base_ = [
+    '../_base_/schedules/mmdet-schedule-1x.py', '../_base_/default_runtime.py'
+]
+
 model = dict(
     type='ImVoxelNet',
     data_preprocessor=dict(
@@ -151,7 +155,8 @@ test_evaluator = val_evaluator
 # optimizer
 optim_wrapper = dict(
     type='OptimWrapper',
-    optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001),
+    optimizer=dict(
+        _delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001),
     paramwise_cfg=dict(
         custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}),
     clip_grad=dict(max_norm=35., norm_type=2))
@@ -166,30 +171,7 @@ param_scheduler = [
 ]
 
 # hooks
-default_hooks = dict(
-    timer=dict(type='IterTimerHook'),
-    logger=dict(type='LoggerHook', interval=50),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=1),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-)
-
-# training schedule for 2x
-train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
-val_cfg = dict(type='ValLoop')
-test_cfg = dict(type='TestLoop')
+default_hooks = dict(checkpoint=dict(type='CheckpointHook', max_keep_ckpts=1))
 
 # runtime
-default_scope = 'mmdet3d'
-
-env_cfg = dict(
-    cudnn_benchmark=False,
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
-    dist_cfg=dict(backend='nccl'),
-)
-
-log_level = 'INFO'
-load_from = None
-resume = False
-dist_params = dict(backend='nccl')
 find_unused_parameters = True  # only 1 of 4 FPN outputs is used

mmdet3d/evaluation/metrics/kitti_metric.py

@@ -575,7 +575,7 @@ class KittiMetric(BaseMetric):
         box_preds = box_dict['bboxes_3d']
         scores = box_dict['scores_3d']
         labels = box_dict['labels_3d']
-        sample_idx = info['sample_id']
+        sample_idx = info['sample_idx']
         box_preds.limit_yaw(offset=0.5, period=np.pi * 2)
 
         if len(box_preds) == 0:

mmdet3d/models/detectors/base.py

@@ -89,7 +89,7 @@ class Base3DDetector(BaseDetector):
             raise RuntimeError(f'Invalid mode "{mode}". '
                                'Only supports loss, predict and tensor mode')
 
-    def convert_to_datasample(
+    def add_pred_to_datasample(
         self,
         data_samples: SampleList,
         data_instances_3d: OptInstanceList = None,

mmdet3d/models/detectors/fcos_mono3d.py

@@ -95,6 +95,7 @@ class FCOSMono3D(SingleStageMono3DDetector):
         x = self.extract_feat(batch_inputs_dict)
         results_list, results_list_2d = self.bbox_head.predict(
             x, batch_data_samples, rescale=rescale)
-        predictions = self.convert_to_datasample(batch_data_samples,
-                                                 results_list, results_list_2d)
+        predictions = self.add_pred_to_datasample(batch_data_samples,
+                                                  results_list,
+                                                  results_list_2d)
         return predictions

mmdet3d/models/detectors/groupfree3dnet.py

@@ -82,6 +82,6 @@ class GroupFree3DNet(SingleStage3DDetector):
         points = batch_inputs_dict['points']
         results_list = self.bbox_head.predict(points, x, batch_data_samples,
                                               **kwargs)
-        predictions = self.convert_to_datasample(batch_data_samples,
-                                                 results_list)
+        predictions = self.add_pred_to_datasample(batch_data_samples,
+                                                  results_list)
         return predictions

mmdet3d/models/detectors/h3dnet.py

@@ -154,4 +154,4 @@ class H3DNet(TwoStage3DDetector):
             feats_dict,
             batch_data_samples,
             suffix='_optimized')
-        return self.convert_to_datasample(batch_data_samples, results_list)
+        return self.add_pred_to_datasample(batch_data_samples, results_list)

mmdet3d/models/detectors/imvotenet.py

@@ -433,7 +433,7 @@ class ImVoteNet(Base3DDetector):
         if points is None:
             assert imgs is not None
             results_2d = self.predict_img_only(imgs, batch_data_samples)
-            return self.convert_to_datasample(
+            return self.add_pred_to_datasample(
                 batch_data_samples, data_instances_2d=results_2d)
 
         else:
@@ -488,7 +488,7 @@ class ImVoteNet(Base3DDetector):
                 batch_data_samples,
                 rescale=True)
 
-            return self.convert_to_datasample(batch_data_samples, results_3d)
+            return self.add_pred_to_datasample(batch_data_samples, results_3d)
 
     def predict_img_only(self,
                          imgs: Tensor,

mmdet3d/models/detectors/imvoxelnet.py

@@ -3,15 +3,15 @@ from typing import List, Tuple, Union
 
 import torch
 
+from mmdet3d.models.detectors import Base3DDetector
 from mmdet3d.models.layers.fusion_layers.point_fusion import point_sample
 from mmdet3d.registry import MODELS, TASK_UTILS
 from mmdet3d.structures.det3d_data_sample import SampleList
-from mmdet3d.utils import ConfigType, InstanceList, OptConfigType
-from mmdet.models.detectors import BaseDetector
+from mmdet3d.utils import ConfigType, OptConfigType
 
 
 @MODELS.register_module()
-class ImVoxelNet(BaseDetector):
+class ImVoxelNet(Base3DDetector):
     r"""`ImVoxelNet <https://arxiv.org/abs/2106.01178>`_.
 
     Args:
@@ -57,31 +57,6 @@ class ImVoxelNet(BaseDetector):
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg
 
-    def convert_to_datasample(self, data_samples: SampleList,
-                              data_instances: InstanceList) -> SampleList:
-        """ Convert results list to `Det3DDataSample`.
-        Args:
-            inputs (list[:obj:`Det3DDataSample`]): The input data.
-            data_instances (list[:obj:`InstanceData`]): 3D Detection
-                results of each image.
-        Returns:
-            list[:obj:`Det3DDataSample`]: 3D Detection results of the
-            input images. Each Det3DDataSample usually contain
-            'pred_instances_3d'. And the ``pred_instances_3d`` usually
-            contains following keys.
-
-                - scores_3d (Tensor): Classification scores, has a shape
-                    (num_instance, )
-                - labels_3d (Tensor): Labels of bboxes, has a shape
-                    (num_instances, ).
-                - bboxes_3d (Tensor): Contains a tensor with shape
-                    (num_instances, C) where C >=7.
-            """
-        for data_sample, pred_instances_3d in zip(data_samples,
-                                                  data_instances):
-            data_sample.pred_instances_3d = pred_instances_3d
-        return data_samples
-
     def extract_feat(self, batch_inputs_dict: dict,
                      batch_data_samples: SampleList):
         """Extract 3d features from the backbone -> fpn -> 3d projection.
@@ -185,8 +160,8 @@ class ImVoxelNet(BaseDetector):
         """
         x = self.extract_feat(batch_inputs_dict, batch_data_samples)
         results_list = self.bbox_head.predict(x, batch_data_samples, **kwargs)
-        predictions = self.convert_to_datasample(batch_data_samples,
-                                                 results_list)
+        predictions = self.add_pred_to_datasample(batch_data_samples,
+                                                  results_list)
         return predictions
 
     def _forward(self, batch_inputs_dict: dict, batch_data_samples: SampleList,

mmdet3d/models/detectors/mvx_two_stage.py

@@ -401,7 +401,7 @@ class MVXTwoStageDetector(Base3DDetector):
         else:
             results_list_2d = None
 
-        detsamples = self.convert_to_datasample(batch_data_samples,
-                                                results_list_3d,
-                                                results_list_2d)
+        detsamples = self.add_pred_to_datasample(batch_data_samples,
+                                                 results_list_3d,
+                                                 results_list_2d)
         return detsamples

mmdet3d/models/detectors/single_stage.py

@@ -108,8 +108,8 @@ class SingleStage3DDetector(Base3DDetector):
         """
         x = self.extract_feat(batch_inputs_dict)
         results_list = self.bbox_head.predict(x, batch_data_samples, **kwargs)
-        predictions = self.convert_to_datasample(batch_data_samples,
-                                                 results_list)
+        predictions = self.add_pred_to_datasample(batch_data_samples,
+                                                  results_list)
         return predictions
 
     def _forward(self,

mmdet3d/models/detectors/single_stage_mono3d.py

@@ -18,7 +18,7 @@ class SingleStageMono3DDetector(SingleStageDetector):
     boxes on the output features of the backbone+neck.
     """
 
-    def convert_to_datasample(
+    def add_pred_to_datasample(
         self,
         data_samples: SampleList,
         data_instances_3d: OptInstanceList = None,

mmdet3d/models/detectors/two_stage.py

@@ -161,8 +161,8 @@ class TwoStage3DDetector(Base3DDetector):
                                              batch_data_samples)
 
         # connvert to Det3DDataSample
-        results_list = self.convert_to_datasample(batch_data_samples,
-                                                  results_list)
+        results_list = self.add_pred_to_datasample(batch_data_samples,
+                                                   results_list)
 
         return results_list
 

mmdet3d/models/detectors/votenet.py

@@ -99,8 +99,8 @@ class VoteNet(SingleStage3DDetector):
         points = batch_inputs_dict['points']
         results_list = self.bbox_head.predict(points, feats_dict,
                                               batch_data_samples, **kwargs)
-        data_3d_samples = self.convert_to_datasample(batch_data_samples,
-                                                     results_list)
+        data_3d_samples = self.add_pred_to_datasample(batch_data_samples,
+                                                      results_list)
         return data_3d_samples
 
     def aug_test(self, aug_inputs_list: List[dict],
@@ -143,6 +143,6 @@ class VoteNet(SingleStage3DDetector):
                                                   self.bbox_head.test_cfg)
 
         merged_results = InstanceData(**merged_results_dict)
-        data_3d_samples = self.convert_to_datasample(batch_data_samples,
-                                                     [merged_results])
+        data_3d_samples = self.add_pred_to_datasample(batch_data_samples,
+                                                      [merged_results])
         return data_3d_samples