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weight = None
resume = False
evaluate = True
test_only = False
seed = 28024989
save_path = 'exp/nuscenes/semseg-pt-v3m1-0-base'
num_worker = 16
batch_size = 12
batch_size_val = None
batch_size_test = None
epoch = 50
eval_epoch = 50
sync_bn = False
enable_amp = True
empty_cache = False
find_unused_parameters = False
mix_prob = 0.8
param_dicts = [dict(keyword='block', lr=0.0002)]
hooks = [
dict(type='CheckpointLoader'),
dict(type='IterationTimer', warmup_iter=2),
dict(type='InformationWriter'),
dict(type='SemSegEvaluator'),
dict(type='CheckpointSaver', save_freq=None),
dict(type='PreciseEvaluator', test_last=False)
]
train = dict(type='DefaultTrainer')
test = dict(type='SemSegTester', verbose=True)
model = dict(
type='DefaultSegmentorV2',
num_classes=16,
backbone_out_channels=64,
backbone=dict(
type='PT-v3m1',
in_channels=4,
order=['z', 'z-trans', 'hilbert', 'hilbert-trans'],
stride=(2, 2, 2, 2),
enc_depths=(2, 2, 2, 6, 2),
enc_channels=(32, 64, 128, 256, 512),
enc_num_head=(2, 4, 8, 16, 32),
enc_patch_size=(1024, 1024, 1024, 1024, 1024),
dec_depths=(2, 2, 2, 2),
dec_channels=(64, 64, 128, 256),
dec_num_head=(4, 4, 8, 16),
dec_patch_size=(1024, 1024, 1024, 1024),
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0,
drop_path=0.3,
shuffle_orders=True,
pre_norm=True,
enable_rpe=False,
enable_flash=True,
upcast_attention=False,
upcast_softmax=False,
cls_mode=False,
pdnorm_bn=False,
pdnorm_ln=False,
pdnorm_decouple=True,
pdnorm_adaptive=False,
pdnorm_affine=True,
pdnorm_conditions=('nuScenes', 'SemanticKITTI', 'Waymo')),
criteria=[
dict(type='CrossEntropyLoss', loss_weight=1.0, ignore_index=-1),
dict(
type='LovaszLoss',
mode='multiclass',
loss_weight=1.0,
ignore_index=-1)
])
optimizer = dict(type='AdamW', lr=0.002, weight_decay=0.005)
scheduler = dict(
type='OneCycleLR',
max_lr=[0.002, 0.0002],
pct_start=0.04,
anneal_strategy='cos',
div_factor=10.0,
final_div_factor=100.0)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes'
ignore_index = -1
names = [
'barrier', 'bicycle', 'bus', 'car', 'construction_vehicle', 'motorcycle',
'pedestrian', 'traffic_cone', 'trailer', 'truck', 'driveable_surface',
'other_flat', 'sidewalk', 'terrain', 'manmade', 'vegetation'
]
data = dict(
num_classes=16,
ignore_index=-1,
names=[
'barrier', 'bicycle', 'bus', 'car', 'construction_vehicle',
'motorcycle', 'pedestrian', 'traffic_cone', 'trailer', 'truck',
'driveable_surface', 'other_flat', 'sidewalk', 'terrain', 'manmade',
'vegetation'
],
train=dict(
type='NuScenesDataset',
split='train',
data_root='data/nuscenes',
transform=[
dict(
type='RandomRotate',
angle=[-1, 1],
axis='z',
center=[0, 0, 0],
p=0.5),
dict(type='RandomScale', scale=[0.9, 1.1]),
dict(type='RandomFlip', p=0.5),
dict(type='RandomJitter', sigma=0.005, clip=0.02),
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=-1,
loop=1),
val=dict(
type='NuScenesDataset',
split='val',
data_root='data/nuscenes',
transform=[
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=-1),
test=dict(
type='NuScenesDataset',
split='val',
data_root='data/nuscenes',
transform=[
dict(type='Copy', keys_dict=dict(segment='origin_segment')),
dict(
type='GridSample',
grid_size=0.025,
hash_type='fnv',
mode='train',
keys=('coord', 'strength', 'segment'),
return_inverse=True)
],
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=[[{
'type': 'RandomScale',
'scale': [0.9, 0.9]
}], [{
'type': 'RandomScale',
'scale': [0.95, 0.95]
}], [{
'type': 'RandomScale',
'scale': [1, 1]
}], [{
'type': 'RandomScale',
'scale': [1.05, 1.05]
}], [{
'type': 'RandomScale',
'scale': [1.1, 1.1]
}],
[{
'type': 'RandomScale',
'scale': [0.9, 0.9]
}, {
'type': 'RandomFlip',
'p': 1
}],
[{
'type': 'RandomScale',
'scale': [0.95, 0.95]
}, {
'type': 'RandomFlip',
'p': 1
}],
[{
'type': 'RandomScale',
'scale': [1, 1]
}, {
'type': 'RandomFlip',
'p': 1
}],
[{
'type': 'RandomScale',
'scale': [1.05, 1.05]
}, {
'type': 'RandomFlip',
'p': 1
}],
[{
'type': 'RandomScale',
'scale': [1.1, 1.1]
}, {
'type': 'RandomFlip',
'p': 1
}]]),
ignore_index=-1))
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