cylinder3d_4xb4_3x_semantickitti.py

_base_ = [
    '../_base_/datasets/semantickitti.py', '../_base_/models/cylinder3d.py',
    '../_base_/default_runtime.py'
]

# optimizer
# This schedule is mainly used by models on nuScenes dataset
lr = 0.001
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=lr, weight_decay=0.01))

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=36, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

# learning rate
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='MultiStepLR',
        begin=0,
        end=36,
        by_epoch=True,
        milestones=[30],
        gamma=0.1)
]

train_dataloader = dict(batch_size=4, )

# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (8 GPUs) x (4 samples per GPU).
# auto_scale_lr = dict(enable=False, base_batch_size=32)

default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=5))