Change cyclic_20e

configs/_base_/schedules/cyclic_20e.py

@@ -1,6 +1,6 @@
 # For nuScenes dataset, we usually evaluate the model at the end of training.
 # Since the models are trained by 24 epochs by default, we set evaluation
-# interval to be 24. Please change the interval accordingly if you do not
+# interval to be 20. Please change the interval accordingly if you do not
 # use a default schedule.
 # optimizer
 # This schedule is mainly used by models on nuScenes dataset

configs/centerpoint/centerpoint_01voxel_second_secfpn_4x8_cyclic_20e_nus.py

@@ -142,28 +142,3 @@
             box_type_3d='LiDAR')),
     val=dict(pipeline=test_pipeline, classes=class_names),
     test=dict(pipeline=test_pipeline, classes=class_names))
-
-# For nuScenes dataset, we usually evaluate the model at the end of training.
-# Since the models are trained by 24 epochs by default, we set evaluation
-# interval to be 24. Please change the interval accordingly if you do not
-# use a default schedule.
-# optimizer
-# This schedule is mainly used by models on nuScenes dataset
-optimizer = dict(type='AdamW', lr=1e-4, weight_decay=0.01)
-# max_norm=10 is better for SECOND
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-lr_config = dict(
-    policy='cyclic',
-    target_ratio=(10, 1e-4),
-    cyclic_times=1,
-    step_ratio_up=0.4,
-)
-momentum_config = dict(
-    policy='cyclic',
-    target_ratio=(0.85 / 0.95, 1),
-    cyclic_times=1,
-    step_ratio_up=0.4,
-)
-
-# runtime settings
-total_epochs = 20

configs/centerpoint/centerpoint_02pillar_second_secfpn_4x8_cyclic_20e_nus.py

@@ -141,27 +141,3 @@
             box_type_3d='LiDAR')),
     val=dict(pipeline=test_pipeline, classes=class_names),
     test=dict(pipeline=test_pipeline, classes=class_names))
-
-# For nuScenes dataset, we usually evaluate the model at the end of training.
-# Since the models are trained by 24 epochs by default, we set evaluation
-# interval to be 24. Please change the interval accordingly if you do not
-# use a default schedule.
-
-optimizer = dict(type='AdamW', lr=1e-4, weight_decay=0.01)
-# max_norm=10 is better for SECOND
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-lr_config = dict(
-    policy='cyclic',
-    target_ratio=(10, 1e-4),
-    cyclic_times=1,
-    step_ratio_up=0.4,
-)
-momentum_config = dict(
-    policy='cyclic',
-    target_ratio=(0.85 / 0.95, 1),
-    cyclic_times=1,
-    step_ratio_up=0.4,
-)
-
-# runtime settings
-total_epochs = 20