projects/CENet/configs/cenet-64x512_4xb4_semantickitti.py

_base_ = ['../../../configs/_base_/default_runtime.py']
custom_imports = dict(
    imports=['projects.CENet.cenet'], allow_failed_imports=False)

# For SemanticKitti we usually do 19-class segmentation.
# For labels_map we follow the uniform format of MMDetection & MMSegmentation
# i.e. we consider the unlabeled class as the last one, which is different
# from the original implementation of some methods e.g. Cylinder3D.
dataset_type = 'SemanticKittiDataset'
data_root = 'data/semantickitti/'
class_names = [
    'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist',
    'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building',
    'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign'
]
labels_map = {
    0: 19,  # "unlabeled"
    1: 19,  # "outlier" mapped to "unlabeled" --------------mapped
    10: 0,  # "car"
    11: 1,  # "bicycle"
    13: 4,  # "bus" mapped to "other-vehicle" --------------mapped
    15: 2,  # "motorcycle"
    16: 4,  # "on-rails" mapped to "other-vehicle" ---------mapped
    18: 3,  # "truck"
    20: 4,  # "other-vehicle"
    30: 5,  # "person"
    31: 6,  # "bicyclist"
    32: 7,  # "motorcyclist"
    40: 8,  # "road"
    44: 9,  # "parking"
    48: 10,  # "sidewalk"
    49: 11,  # "other-ground"
    50: 12,  # "building"
    51: 13,  # "fence"
    52: 19,  # "other-structure" mapped to "unlabeled" ------mapped
    60: 8,  # "lane-marking" to "road" ---------------------mapped
    70: 14,  # "vegetation"
    71: 15,  # "trunk"
    72: 16,  # "terrain"
    80: 17,  # "pole"
    81: 18,  # "traffic-sign"
    99: 19,  # "other-object" to "unlabeled" ----------------mapped
    252: 0,  # "moving-car" to "car" ------------------------mapped
    253: 6,  # "moving-bicyclist" to "bicyclist" ------------mapped
    254: 5,  # "moving-person" to "person" ------------------mapped
    255: 7,  # "moving-motorcyclist" to "motorcyclist" ------mapped
    256: 4,  # "moving-on-rails" mapped to "other-vehic------mapped
    257: 4,  # "moving-bus" mapped to "other-vehicle" -------mapped
    258: 3,  # "moving-truck" to "truck" --------------------mapped
    259: 4  # "moving-other"-vehicle to "other-vehicle"-----mapped
}

metainfo = dict(
    classes=class_names, seg_label_mapping=labels_map, max_label=259)

input_modality = dict(use_lidar=True, use_camera=False)

# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)

# data_root = 's3://openmmlab/datasets/detection3d/semantickitti/'

# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection3d/',
#          'data/': 's3://openmmlab/datasets/detection3d/'
#      }))
backend_args = None

train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=4,
        use_dim=4,
        backend_args=backend_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_seg_3d=True,
        seg_3d_dtype='np.int32',
        seg_offset=2**16,
        dataset_type='semantickitti',
        backend_args=backend_args),
    dict(type='PointSegClassMapping'),
    dict(type='PointSample', num_points=0.9),
    dict(
        type='RandomFlip3D',
        sync_2d=False,
        flip_ratio_bev_horizontal=0.5,
        flip_ratio_bev_vertical=0.5),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-3.1415929, 3.1415929],
        scale_ratio_range=[0.95, 1.05],
        translation_std=[0.1, 0.1, 0.1],
    ),
    dict(
        type='SemkittiRangeView',
        H=64,
        W=512,
        fov_up=3.0,
        fov_down=-25.0,
        means=(11.71279, -0.1023471, 0.4952, -1.0545, 0.2877),
        stds=(10.24, 12.295865, 9.4287, 0.8643, 0.1450),
        ignore_index=19),
    dict(type='Pack3DDetInputs', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=4,
        use_dim=4,
        backend_args=backend_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_seg_3d=True,
        seg_3d_dtype='np.int32',
        seg_offset=2**16,
        dataset_type='semantickitti',
        backend_args=backend_args),
    dict(type='PointSegClassMapping'),
    dict(
        type='SemkittiRangeView',
        H=64,
        W=512,
        fov_up=3.0,
        fov_down=-25.0,
        means=(11.71279, -0.1023471, 0.4952, -1.0545, 0.2877),
        stds=(10.24, 12.295865, 9.4287, 0.8643, 0.1450),
        ignore_index=19),
    dict(
        type='Pack3DDetInputs',
        keys=['img'],
        meta_keys=('proj_x', 'proj_y', 'proj_range', 'unproj_range'))
]

train_dataloader = dict(
    batch_size=4,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='semantickitti_infos_train.pkl',
        pipeline=train_pipeline,
        metainfo=metainfo,
        modality=input_modality,
        ignore_index=19,
        backend_args=backend_args))
val_dataloader = dict(
    batch_size=1,
    num_workers=1,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='semantickitti_infos_val.pkl',
        pipeline=test_pipeline,
        metainfo=metainfo,
        modality=input_modality,
        ignore_index=19,
        test_mode=True,
        backend_args=backend_args))
test_dataloader = val_dataloader

val_evaluator = dict(type='SegMetric')
test_evaluator = val_evaluator

vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

model = dict(
    type='RangeImageSegmentor',
    data_preprocessor=dict(type='Det3DDataPreprocessor'),
    backbone=dict(
        type='CENet',
        in_channels=5,
        stem_channels=128,
        num_stages=4,
        stage_blocks=(3, 4, 6, 3),
        out_channels=(128, 128, 128, 128),
        fuse_channels=(256, 128),
        strides=(1, 2, 2, 2),
        dilations=(1, 1, 1, 1),
        act_cfg=dict(type='HSwish', inplace=True)),
    decode_head=dict(
        type='RangeImageHead',
        channels=128,
        num_classes=20,
        dropout_ratio=0,
        loss_ce=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=False,
            class_weight=None,
            loss_weight=1.0),
        loss_lovasz=dict(type='LovaszLoss', loss_weight=1.5, reduction='none'),
        loss_boundary=dict(type='BoundaryLoss', loss_weight=1.0),
        conv_seg_kernel_size=1,
        ignore_index=19),
    auxiliary_head=[
        dict(
            type='RangeImageHead',
            channels=128,
            num_classes=20,
            dropout_ratio=0,
            loss_ce=dict(
                type='mmdet.CrossEntropyLoss',
                use_sigmoid=False,
                class_weight=None,
                loss_weight=1.0),
            loss_lovasz=dict(
                type='LovaszLoss', loss_weight=1.5, reduction='none'),
            loss_boundary=dict(type='BoundaryLoss', loss_weight=1.0),
            conv_seg_kernel_size=1,
            ignore_index=19,
            indices=2),
        dict(
            type='RangeImageHead',
            channels=128,
            num_classes=20,
            dropout_ratio=0,
            loss_ce=dict(
                type='mmdet.CrossEntropyLoss',
                use_sigmoid=False,
                class_weight=None,
                loss_weight=1.0),
            loss_lovasz=dict(
                type='LovaszLoss', loss_weight=1.5, reduction='none'),
            loss_boundary=dict(type='BoundaryLoss', loss_weight=1.0),
            conv_seg_kernel_size=1,
            ignore_index=19,
            indices=3),
        dict(
            type='RangeImageHead',
            channels=128,
            num_classes=20,
            dropout_ratio=0,
            loss_ce=dict(
                type='mmdet.CrossEntropyLoss',
                use_sigmoid=False,
                class_weight=None,
                loss_weight=1.0),
            loss_lovasz=dict(
                type='LovaszLoss', loss_weight=1.5, reduction='none'),
            loss_boundary=dict(type='BoundaryLoss', loss_weight=1.0),
            conv_seg_kernel_size=1,
            ignore_index=19,
            indices=4)
    ],
    train_cfg=None,
    test_cfg=dict(use_knn=True, knn=7, search=7, sigma=1.0, cutoff=2.0))

# optimizer
# This schedule is mainly used on Semantickitti dataset in segmentation task
optim_wrapper = dict(
    type='AmpOptimWrapper',
    loss_scale='dynamic',
    optimizer=dict(
        type='AdamW',
        lr=0.04,
        betas=(0.9, 0.999),
        weight_decay=(0.01),
        eps=0.000005))

param_scheduler = [
    dict(
        type='OneCycleLR',
        total_steps=50,
        by_epoch=True,
        eta_max=0.0025,
        pct_start=0.2,
        div_factor=25.0,
        final_div_factor=100.0,
        convert_to_iter_based=True)
]

# runtime settings
train_cfg = dict(by_epoch=True, max_epochs=50, val_interval=1)
val_cfg = dict()
test_cfg = dict()

# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (4 GPUs) x (4 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)

default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=1))