minkunet18_w32_torchsparse_8xb2-amp-15e_semantickitti.py

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

train_pipeline = [
    dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_seg_3d=True,
        seg_3d_dtype='np.int32',
        seg_offset=2**16,
        dataset_type='semantickitti'),
    dict(type='PointSegClassMapping'),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[0., 6.28318531],
        scale_ratio_range=[0.95, 1.05],
        translation_std=[0, 0, 0],
    ),
    dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
]

train_dataloader = dict(
    sampler=dict(seed=0), dataset=dict(pipeline=train_pipeline))

lr = 0.24
optim_wrapper = dict(
    type='AmpOptimWrapper',
    loss_scale='dynamic',
    optimizer=dict(
        type='SGD', lr=lr, weight_decay=0.0001, momentum=0.9, nesterov=True))

param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.008, by_epoch=False, begin=0, end=125),
    dict(
        type='CosineAnnealingLR',
        begin=0,
        T_max=15,
        by_epoch=True,
        eta_min=1e-5,
        convert_to_iter_based=True)
]

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=15, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=1))
randomness = dict(seed=0, deterministic=False, diff_rank_seed=True)
env_cfg = dict(cudnn_benchmark=True)