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_base_ = [
"../_base_/default_runtime.py",
"../_base_/dataset/scannetpp.py",
]
# misc custom setting
batch_size = 24 # bs: total bs in all gpus
num_worker = 48
mix_prob = 0.8
empty_cache = False
enable_amp = True
find_unused_parameters = True
# trainer
train = dict(
type="MultiDatasetTrainer",
)
# model settings
model = dict(
type="PPT-v1m2",
backbone=dict(
type="PT-v3m1",
in_channels=6,
order=("z", "z-trans", "hilbert", "hilbert-trans"),
stride=(2, 2, 2, 2),
enc_depths=(3, 3, 3, 6, 3),
enc_channels=(48, 96, 192, 384, 512),
enc_num_head=(3, 6, 12, 24, 32),
enc_patch_size=(1024, 1024, 1024, 1024, 1024),
dec_depths=(3, 3, 3, 3),
dec_channels=(64, 96, 192, 384),
dec_num_head=(4, 6, 12, 24),
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=True,
pdnorm_ln=True,
pdnorm_decouple=True,
pdnorm_adaptive=False,
pdnorm_affine=True,
pdnorm_conditions=("ScanNet", "ScanNet++", "S3DIS", "Structured3D"),
),
criteria=[
dict(type="CrossEntropyLoss", loss_weight=1.0, ignore_index=-1),
dict(type="LovaszLoss", mode="multiclass", loss_weight=1.0, ignore_index=-1),
],
backbone_out_channels=64,
context_channels=256,
conditions=("ScanNet", "ScanNet++", "S3DIS", "Structured3D"),
num_classes=(200, 100, 13, 25),
)
# scheduler settings
epoch = 100
optimizer = dict(type="AdamW", lr=0.005, weight_decay=0.05)
scheduler = dict(
type="OneCycleLR",
max_lr=[0.005, 0.0005],
pct_start=0.05,
anneal_strategy="cos",
div_factor=10.0,
final_div_factor=1000.0,
)
param_dicts = [dict(keyword="block", lr=0.0005)]
# dataset settings
data = dict(
num_classes=100,
ignore_index=-1,
train=dict(
type="ConcatDataset",
datasets=[
# Structured3D
dict(
type="Structured3DDataset",
split=["train", "val", "test"],
data_root="data/structured3d",
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=0.02,
hash_type="fnv",
mode="train",
return_grid_coord=True,
),
dict(type="SphereCrop", sample_rate=0.8, mode="random"),
dict(type="SphereCrop", point_max=204800, mode="random"),
dict(type="CenterShift", apply_z=False),
dict(type="NormalizeColor"),
# dict(type="ShufflePoint"),
dict(type="Add", keys_dict={"condition": "Structured3D"}),
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord", "grid_coord", "segment", "condition"),
feat_keys=("color", "normal"),
),
],
test_mode=False,
loop=2, # sampling weight
),
# ScanNet
dict(
type="ScanNet200Dataset",
split=["train", "val"],
data_root="data/scannet",
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=0.02,
hash_type="fnv",
mode="train",
return_grid_coord=True,
),
dict(type="SphereCrop", point_max=204800, mode="random"),
dict(type="CenterShift", apply_z=False),
dict(type="NormalizeColor"),
dict(type="ShufflePoint"),
dict(type="Add", keys_dict={"condition": "ScanNet"}),
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord", "grid_coord", "segment", "condition"),
feat_keys=("color", "normal"),
),
],
test_mode=False,
loop=1, # sampling weight
),
# S3DIS
# dict(
# type="S3DISDataset",
# split=("Area_1", "Area_2", "Area_3", "Area_4", "Area_6"),
# data_root="data/s3dis",
# 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=0.02,
# hash_type="fnv",
# mode="train",
# return_grid_coord=True,
# ),
# dict(type="SphereCrop", sample_rate=0.6, mode="random"),
# dict(type="SphereCrop", point_max=204800, mode="random"),
# dict(type="CenterShift", apply_z=False),
# dict(type="NormalizeColor"),
# dict(type="ShufflePoint"),
# dict(type="Add", keys_dict={"condition": "S3DIS"}),
# dict(type="ToTensor"),
# dict(
# type="Collect",
# keys=("coord", "grid_coord", "segment", "condition"),
# feat_keys=("color", "normal"),
# ),
# ],
# test_mode=False,
# loop=1, # sampling weight
# ),
dict(
type="ScanNetPPDataset",
split="train_grid1mm_chunk6x6_stride3x3",
data_root="data/scannetpp",
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=0.02,
hash_type="fnv",
mode="train",
return_grid_coord=True,
),
dict(type="SphereCrop", point_max=204800, mode="random"),
dict(type="CenterShift", apply_z=False),
dict(type="NormalizeColor"),
# dict(type="ShufflePoint"),
dict(type="Add", keys_dict={"condition": "ScanNet++"}),
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord", "grid_coord", "segment", "condition"),
feat_keys=("color", "normal"),
),
],
test_mode=False,
),
],
),
val=dict(
type="ScanNetPPDataset",
split="val",
data_root="data/scannetpp",
transform=[
dict(type="CenterShift", apply_z=True),
dict(
type="GridSample",
grid_size=0.02,
hash_type="fnv",
mode="train",
return_grid_coord=True,
),
dict(type="CenterShift", apply_z=False),
dict(type="NormalizeColor"),
dict(type="ToTensor"),
dict(type="Add", keys_dict={"condition": "ScanNet++"}),
dict(
type="Collect",
keys=("coord", "grid_coord", "segment", "condition"),
feat_keys=("color", "normal"),
),
],
test_mode=False,
),
test=dict(
type="ScanNetPPDataset",
split="val",
data_root="data/scannetpp",
transform=[
dict(type="CenterShift", apply_z=True),
dict(type="NormalizeColor"),
dict(type="Copy", keys_dict={"segment": "origin_segment"}),
dict(
type="GridSample",
grid_size=0.01,
hash_type="fnv",
mode="train",
keys=("coord", "color", "normal", "segment"),
return_inverse=True,
),
],
test_mode=True,
test_cfg=dict(
voxelize=dict(
type="GridSample",
grid_size=0.02,
hash_type="fnv",
mode="test",
keys=("coord", "color", "normal"),
return_grid_coord=True,
),
crop=None,
post_transform=[
dict(type="CenterShift", apply_z=False),
dict(type="Add", keys_dict={"condition": "ScanNet++"}),
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord", "grid_coord", "index", "condition"),
feat_keys=("color", "normal"),
),
],
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,
)
],
[
dict(
type="RandomRotateTargetAngle",
angle=[0],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[0.95, 0.95]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1 / 2],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[0.95, 0.95]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[0.95, 0.95]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[3 / 2],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[0.95, 0.95]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[0],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[1.05, 1.05]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1 / 2],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[1.05, 1.05]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[1.05, 1.05]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[3 / 2],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[1.05, 1.05]),
],
[dict(type="RandomFlip", p=1)],
],
),
),
)
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