Pointcept/configs/semantic_kitti/semseg-minkunet34c-0-base.py

_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,
    ),
)