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Running
on
Zero
_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="PointTransformer-Seg50", | |
in_channels=6, | |
num_classes=13, | |
), | |
criteria=[dict(type="CrossEntropyLoss", loss_weight=1.0, ignore_index=-1)], | |
) | |
# scheduler settings | |
epoch = 3000 | |
optimizer = dict(type="AdamW", lr=0.006, weight_decay=0.05) | |
scheduler = dict(type="MultiStepLR", milestones=[0.6, 0.8], gamma=0.1) | |
# dataset settings | |
dataset_type = "S3DISDataset" | |
data_root = "data/s3dis" | |
data = dict( | |
num_classes=13, | |
ignore_index=-1, | |
names=[ | |
"ceiling", | |
"floor", | |
"wall", | |
"beam", | |
"column", | |
"window", | |
"door", | |
"table", | |
"chair", | |
"sofa", | |
"bookcase", | |
"board", | |
"clutter", | |
], | |
train=dict( | |
type=dataset_type, | |
split=("Area_1", "Area_2", "Area_3", "Area_4", "Area_6"), | |
data_root=data_root, | |
transform=[ | |
dict(type="CenterShift", apply_z=True), | |
# 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 / 64, 1 / 64], axis="x", p=0.5), | |
# dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="y", p=0.5), | |
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="ChromaticAutoContrast", p=0.2, blend_factor=None), | |
dict(type="ChromaticTranslation", p=0.95, ratio=0.05), | |
dict(type="ChromaticJitter", p=0.95, std=0.05), | |
# dict(type="HueSaturationTranslation", hue_max=0.2, saturation_max=0.2), | |
# dict(type="RandomColorDrop", p=0.2, color_augment=0.0), | |
dict( | |
type="GridSample", | |
grid_size=0.04, | |
hash_type="fnv", | |
mode="train", | |
keys=("coord", "color", "segment"), | |
return_grid_coord=True, | |
), | |
dict(type="SphereCrop", point_max=100000, mode="random"), | |
dict(type="CenterShift", apply_z=False), | |
dict(type="NormalizeColor"), | |
# dict(type="ShufflePoint"), | |
dict(type="ToTensor"), | |
dict( | |
type="Collect", | |
keys=("coord", "grid_coord", "segment"), | |
feat_keys=["coord", "color"], | |
), | |
], | |
test_mode=False, | |
), | |
val=dict( | |
type=dataset_type, | |
split="Area_5", | |
data_root=data_root, | |
transform=[ | |
dict(type="CenterShift", apply_z=True), | |
dict( | |
type="Copy", | |
keys_dict={"coord": "origin_coord", "segment": "origin_segment"}, | |
), | |
dict( | |
type="GridSample", | |
grid_size=0.04, | |
hash_type="fnv", | |
mode="train", | |
keys=("coord", "color", "segment"), | |
return_grid_coord=True, | |
), | |
dict(type="CenterShift", apply_z=False), | |
dict(type="NormalizeColor"), | |
dict(type="ToTensor"), | |
dict( | |
type="Collect", | |
keys=("coord", "grid_coord", "segment"), | |
offset_keys_dict=dict(offset="coord"), | |
feat_keys=["coord", "color"], | |
), | |
], | |
test_mode=False, | |
), | |
test=dict( | |
type=dataset_type, | |
split="Area_5", | |
data_root=data_root, | |
transform=[dict(type="CenterShift", apply_z=True), dict(type="NormalizeColor")], | |
test_mode=True, | |
test_cfg=dict( | |
voxelize=dict( | |
type="GridSample", | |
grid_size=0.04, | |
hash_type="fnv", | |
mode="test", | |
keys=("coord", "color"), | |
return_grid_coord=True, | |
), | |
crop=None, | |
post_transform=[ | |
dict(type="CenterShift", apply_z=False), | |
dict(type="ToTensor"), | |
dict( | |
type="Collect", | |
keys=("coord", "grid_coord", "index"), | |
feat_keys=("coord", "color"), | |
), | |
], | |
aug_transform=[ | |
[dict(type="RandomScale", scale=[0.9, 0.9])], | |
[dict(type="RandomScale", scale=[0.95, 0.95])], | |
[dict(type="RandomScale", scale=[1, 1])], | |
[dict(type="RandomScale", scale=[1.05, 1.05])], | |
[dict(type="RandomScale", scale=[1.1, 1.1])], | |
[ | |
dict(type="RandomScale", scale=[0.9, 0.9]), | |
dict(type="RandomFlip", p=1), | |
], | |
[ | |
dict(type="RandomScale", scale=[0.95, 0.95]), | |
dict(type="RandomFlip", p=1), | |
], | |
[dict(type="RandomScale", scale=[1, 1]), dict(type="RandomFlip", p=1)], | |
[ | |
dict(type="RandomScale", scale=[1.05, 1.05]), | |
dict(type="RandomFlip", p=1), | |
], | |
[ | |
dict(type="RandomScale", scale=[1.1, 1.1]), | |
dict(type="RandomFlip", p=1), | |
], | |
], | |
), | |
), | |
) | |