support class-balanced sampling for nuScenes dataset

pcdet/datasets/nuscenes/nuscenes_dataset.py

@@ -16,6 +16,8 @@ class NuScenesDataset(DatasetTemplate):
         )
         self.infos = []
         self.include_nuscenes_data(self.mode)
+        if self.training and self.dataset_cfg.get('BALANCED_RESAMPLING', False):
+            self.infos = self.balanced_infos_resampling(self.infos)
 
     def include_nuscenes_data(self, mode):
         self.logger.info('Loading NuScenes dataset')
@@ -32,6 +34,40 @@ class NuScenesDataset(DatasetTemplate):
         self.infos.extend(nuscenes_infos)
         self.logger.info('Total samples for NuScenes dataset: %d' % (len(nuscenes_infos)))
 
+    def balanced_infos_resampling(self, infos):
+        """
+        Class-balanced sampling of nuScenes dataset from https://arxiv.org/abs/1908.09492
+        """
+        cls_infos = {name: [] for name in self.class_names}
+        for info in infos:
+            for name in set(info['gt_names']):
+                if name in self.class_names:
+                    cls_infos[name].append(info)
+
+        duplicated_samples = sum([len(v) for _, v in cls_infos.items()])
+        cls_dist = {k: len(v) / duplicated_samples for k, v in cls_infos.items()}
+
+        sampled_infos = []
+
+        frac = 1.0 / len(self.class_names)
+        ratios = [frac / v for v in cls_dist.values()]
+
+        for cur_cls_infos, ratio in zip(list(cls_infos.values()), ratios):
+            sampled_infos += np.random.choice(
+                cur_cls_infos, int(len(cur_cls_infos) * ratio)
+            ).tolist()
+        self.logger.info('Total samples after balanced resampling: %s' % (len(sampled_infos)))
+
+        cls_infos_new = {name: [] for name in self.class_names}
+        for info in sampled_infos:
+            for name in set(info['gt_names']):
+                if name in self.class_names:
+                    cls_infos_new[name].append(info)
+
+        cls_dist_new = {k: len(v) / len(sampled_infos) for k, v in cls_infos_new.items()}
+
+        return sampled_infos
+
     def get_sweep(self, sweep_info):
         def remove_ego_points(points, center_radius=1.0):
             mask = ~((np.abs(points[:, 0]) < center_radius) & (np.abs(points[:, 1]) < center_radius))

tools/cfgs/dataset_configs/nuscenes_dataset.yaml

@@ -17,6 +17,8 @@ INFO_PATH: {
 
 POINT_CLOUD_RANGE: [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
 
+BALANCED_RESAMPLING: True 
+
 DATA_AUGMENTOR:
     DISABLE_AUG_LIST: ['placeholder']
     AUG_CONFIG_LIST:

tools/cfgs/nuscenes_models/second_multihead.yaml

@@ -221,13 +221,13 @@ MODEL:
             MULTI_CLASSES_NMS: False
             NMS_TYPE: nms_gpu
             NMS_THRESH: 0.2
-            NMS_PRE_MAXSIZE: 4096
-            NMS_POST_MAXSIZE: 250 
+            NMS_PRE_MAXSIZE: 1000
+            NMS_POST_MAXSIZE: 100
 
 
 OPTIMIZATION:
     OPTIMIZER: adam_onecycle
-    LR: 0.003
+    LR: 0.003 
     WEIGHT_DECAY: 0.01
     MOMENTUM: 0.9