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_base_ = [
'../_base_/datasets/semantickitti.py', '../_base_/models/spvcnn.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)
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