# 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 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
optimizer = dict(type='AdamW', lr=1e-3, 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-1),
#    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,
#)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=10,
    warmup_ratio=1.0 / 1000,
    step=[8, 15, 23])
momentum_config = None

# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=24)

# 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 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
optimizer = dict(type='AdamW', lr=1e-3, 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-1),
#    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,
#)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=10,
    warmup_ratio=1.0 / 1000,
    step=[8, 15, 23])
momentum_config = None

# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=24)

# 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 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 # max_norm=10 is better for SECOND #lr_config = dict( #    policy='cyclic', #    target_ratio=(10, 1e-1), #    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