Fix optimizer

mmdet3d/apis/train.py

 import torch
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import DistSamplerSeedHook, Runner
+from mmcv.runner import DistSamplerSeedHook, Runner, build_optimizer
 
 from mmdet3d.utils import get_root_logger
 from mmdet.apis.train import parse_losses
 from mmdet.core import (DistEvalHook, DistOptimizerHook, EvalHook,
-                        Fp16OptimizerHook, build_optimizer)
+                        Fp16OptimizerHook)
 from mmdet.datasets import build_dataloader, build_dataset
 
 

mmdet3d/core/optimizer/cocktail_constructor.py

+from mmcv.runner.optimizer import OPTIMIZER_BUILDERS, OPTIMIZERS
 from mmcv.utils import build_from_cfg
 
 from mmdet3d.utils import get_root_logger
-from mmdet.core.optimizer import OPTIMIZER_BUILDERS, OPTIMIZERS
 from .cocktail_optimizer import CocktailOptimizer
 
 

mmdet3d/core/optimizer/cocktail_optimizer.py

+from mmcv.runner.optimizer import OPTIMIZERS
 from torch.optim import Optimizer
 
-from mmdet.core.optimizer import OPTIMIZERS
-
 
 @OPTIMIZERS.register_module()
 class CocktailOptimizer(Optimizer):
@@ -9,6 +8,11 @@ class CocktailOptimizer(Optimizer):
 
     This optimizer applies the cocktail optimzation for multi-modality models.
 
+    Args:
+        optimizers (list[:obj:`torch.optim.Optimizer`]): The list containing
+            different optimizers that optimize different parameters
+        step_intervals (list[int]): Step intervals of each optimizer
+
     """
 
     def __init__(self, optimizers, step_intervals=None):
@@ -18,6 +22,9 @@ class CocktailOptimizer(Optimizer):
             self.param_groups += optimizer.param_groups
         if not isinstance(step_intervals, list):
             step_intervals = [1] * len(self.optimizers)
+        assert len(step_intervals) == len(optimizers), \
+            '"step_intervals" should contain the same number of intervals as' \
+            f'len(optimizers)={len(optimizers)}, got {step_intervals}'
         self.step_intervals = step_intervals
         self.num_step_updated = 0