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Running
on
Zero
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_base_ = ["../_base_/default_runtime.py"]
# misc custom setting
batch_size = 8 # bs: total bs in all gpus
mix_prob = 0
empty_cache = False
enable_amp = True
# model settings
model = dict(
type="DefaultSegmentor",
backbone=dict(type="MinkUNet34C", in_channels=4, out_channels=19),
criteria=[
dict(
type="CrossEntropyLoss",
weight=[
3.1557,
8.7029,
7.8281,
6.1354,
6.3161,
7.9937,
8.9704,
10.1922,
1.6155,
4.2187,
1.9385,
5.5455,
2.0198,
2.6261,
1.3212,
5.1102,
2.5492,
5.8585,
7.3929,
],
loss_weight=1.0,
ignore_index=-1,
)
],
)
# scheduler settings
epoch = 50
eval_epoch = 50
optimizer = dict(type="AdamW", lr=0.002, weight_decay=0.005)
scheduler = dict(
type="OneCycleLR",
max_lr=optimizer["lr"],
pct_start=0.04,
anneal_strategy="cos",
div_factor=10.0,
final_div_factor=100.0,
)
# dataset settings
dataset_type = "SemanticKITTIDataset"
data_root = "data/semantic_kitti"
ignore_index = -1
names = [
"car",
"bicycle",
"motorcycle",
"truck",
"other-vehicle",
"person",
"bicyclist",
"motorcyclist",
"road",
"parking",
"sidewalk",
"other-ground",
"building",
"fence",
"vegetation",
"trunk",
"terrain",
"pole",
"traffic-sign",
]
data = dict(
num_classes=19,
ignore_index=ignore_index,
names=names,
train=dict(
type=dataset_type,
split="train",
data_root=data_root,
transform=[
# dict(type="RandomDropout", dropout_ratio=0.2, dropout_application_ratio=0.2),
# dict(type="RandomRotateTargetAngle", angle=(1/2, 1, 3/2), center=[0, 0, 0], axis="z", p=0.75),
dict(type="RandomRotate", angle=[-1, 1], axis="z", center=[0, 0, 0], p=0.5),
# dict(type="RandomRotate", angle=[-1/6, 1/6], axis="x", p=0.5),
# dict(type="RandomRotate", angle=[-1/6, 1/6], axis="y", p=0.5),
dict(type="PointClip", point_cloud_range=(-35.2, -35.2, -4, 35.2, 35.2, 2)),
dict(type="RandomScale", scale=[0.9, 1.1]),
# dict(type="RandomShift", shift=[0.2, 0.2, 0.2]),
dict(type="RandomFlip", p=0.5),
dict(type="RandomJitter", sigma=0.005, clip=0.02),
# dict(type="ElasticDistortion", distortion_params=[[0.2, 0.4], [0.8, 1.6]]),
dict(
type="GridSample",
grid_size=0.05,
hash_type="fnv",
mode="train",
keys=("coord", "strength", "segment"),
return_grid_coord=True,
),
# dict(type="SphereCrop", point_max=1000000, mode="random"),
# dict(type="CenterShift", apply_z=False),
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord", "grid_coord", "segment"),
feat_keys=("coord", "strength"),
),
],
test_mode=False,
ignore_index=ignore_index,
),
val=dict(
type=dataset_type,
split="val",
data_root=data_root,
transform=[
dict(type="PointClip", point_cloud_range=(-35.2, -35.2, -4, 35.2, 35.2, 2)),
dict(
type="GridSample",
grid_size=0.05,
hash_type="fnv",
mode="train",
keys=("coord", "strength", "segment"),
return_grid_coord=True,
),
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord", "grid_coord", "segment"),
feat_keys=("coord", "strength"),
),
],
test_mode=False,
ignore_index=ignore_index,
),
test=dict(
type=dataset_type,
split="val",
data_root=data_root,
transform=[
dict(type="PointClip", point_cloud_range=(-35.2, -35.2, -4, 35.2, 35.2, 2)),
],
test_mode=True,
test_cfg=dict(
voxelize=dict(
type="GridSample",
grid_size=0.05,
hash_type="fnv",
mode="test",
return_grid_coord=True,
keys=("coord", "strength"),
),
crop=None,
post_transform=[
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord", "grid_coord", "index"),
feat_keys=("coord", "strength"),
),
],
aug_transform=[
[
dict(
type="RandomRotateTargetAngle",
angle=[0],
axis="z",
center=[0, 0, 0],
p=1,
)
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1 / 2],
axis="z",
center=[0, 0, 0],
p=1,
)
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1],
axis="z",
center=[0, 0, 0],
p=1,
)
],
[
dict(
type="RandomRotateTargetAngle",
angle=[3 / 2],
axis="z",
center=[0, 0, 0],
p=1,
)
],
],
),
ignore_index=ignore_index,
),
)
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