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model = dict(
type='MinkUNet',
data_preprocessor=dict(
type='Det3DDataPreprocessor',
voxel=True,
voxel_type='minkunet',
batch_first=False,
max_voxels=80000,
voxel_layer=dict(
max_num_points=-1,
point_cloud_range=[-100, -100, -20, 100, 100, 20],
voxel_size=[0.05, 0.05, 0.05],
max_voxels=(-1, -1))),
backbone=dict(
type='SPVCNNBackbone',
in_channels=4,
num_stages=4,
base_channels=32,
encoder_channels=[32, 64, 128, 256],
encoder_blocks=[2, 2, 2, 2],
decoder_channels=[256, 128, 96, 96],
decoder_blocks=[2, 2, 2, 2],
block_type='basic',
sparseconv_backend='torchsparse',
drop_ratio=0.3),
decode_head=dict(
type='MinkUNetHead',
channels=96,
num_classes=19,
dropout_ratio=0,
loss_decode=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True),
ignore_index=19),
train_cfg=dict(),
test_cfg=dict())
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