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_base_ = [
'../_base_/datasets/semantickitti.py', '../_base_/models/spvcnn.py',
'../_base_/schedules/schedule-3x.py', '../_base_/default_runtime.py'
]
model = dict(data_preprocessor=dict(max_voxels=None))
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='RandomChoice',
transforms=[
[
dict(
type='LaserMix',
num_areas=[3, 4, 5, 6],
pitch_angles=[-25, 3],
pre_transform=[
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')
],
prob=1)
],
[
dict(
type='PolarMix',
instance_classes=[0, 1, 2, 3, 4, 5, 6, 7],
swap_ratio=0.5,
rotate_paste_ratio=1.0,
pre_transform=[
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')
],
prob=1)
],
],
prob=[0.5, 0.5]),
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(dataset=dict(pipeline=train_pipeline))
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
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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