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_base_ = ["../_base_/default_runtime.py"]
# misc custom setting
batch_size = 12 # bs: total bs in all gpus
mix_prob = 0.8
empty_cache = False
enable_amp = True
# model settings
model = dict(
type="DefaultSegmentor",
backbone=dict(
type="SpUNet-v1m1",
in_channels=4,
num_classes=22,
channels=(32, 64, 128, 256, 256, 128, 96, 96),
layers=(2, 3, 4, 6, 2, 2, 2, 2),
),
criteria=[
dict(type="CrossEntropyLoss", loss_weight=1.0, ignore_index=-1),
dict(type="LovaszLoss", mode="multiclass", 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 = "WaymoDataset"
data_root = "data/waymo"
ignore_index = -1
names = [
"Car",
"Truck",
"Bus",
# Other small vehicles (e.g. pedicab) and large vehicles (e.g. construction vehicles, RV, limo, tram).
"Other Vehicle",
"Motorcyclist",
"Bicyclist",
"Pedestrian",
"Sign",
"Traffic Light",
# Lamp post, traffic sign pole etc.
"Pole",
# Construction cone/pole.
"Construction Cone",
"Bicycle",
"Motorcycle",
"Building",
# Bushes, tree branches, tall grasses, flowers etc.
"Vegetation",
"Tree Trunk",
# Curb on the edge of roads. This does not include road boundaries if there’s no curb.
"Curb",
# Surface a vehicle could drive on. This includes the driveway connecting
# parking lot and road over a section of sidewalk.
"Road",
# Marking on the road that’s specifically for defining lanes such as
# single/double white/yellow lines.
"Lane Marker",
# Marking on the road other than lane markers, bumps, cateyes, railtracks etc.
"Other Ground",
# Most horizontal surface that’s not drivable, e.g. grassy hill, pedestrian walkway stairs etc.
"Walkable",
# Nicely paved walkable surface when pedestrians most likely to walk on.
"Sidewalk",
]
data = dict(
num_classes=22,
ignore_index=ignore_index,
names=names,
train=dict(
type=dataset_type,
split="training",
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=(-75.2, -75.2, -4, 75.2, 75.2, 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="validation",
data_root=data_root,
transform=[
dict(type="PointClip", point_cloud_range=(-75.2, -75.2, -4, 75.2, 75.2, 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="validation",
data_root=data_root,
transform=[
dict(type="PointClip", point_cloud_range=(-75.2, -75.2, -4, 75.2, 75.2, 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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