pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class.py

_base_ = [
    '../_base_/datasets/scannet_seg-3d-20class.py',
    '../_base_/models/pointnet2_ssg.py',
    '../_base_/schedules/seg_cosine_200e.py', '../_base_/default_runtime.py'
]

# dataset settings
# in this setting, we only use xyz as network input
# so we need to re-write all the data pipeline
dataset_type = 'ScanNetSegDataset'
data_root = './data/scannet/'
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
               'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
               'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink',
               'bathtub', 'otherfurniture')
num_points = 8192
train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='DEPTH',
        shift_height=False,
        use_color=False,
        load_dim=6,
        use_dim=[0, 1, 2]),  # only load xyz coordinates
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_mask_3d=False,
        with_seg_3d=True),
    dict(
        type='PointSegClassMapping',
        valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28,
                       33, 34, 36, 39),
        max_cat_id=40),
    dict(
        type='IndoorPatchPointSample',
        num_points=num_points,
        block_size=1.5,
        ignore_index=len(class_names),
        use_normalized_coord=False,
        enlarge_size=0.2,
        min_unique_num=None),
    dict(type='DefaultFormatBundle3D', class_names=class_names),
    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='DEPTH',
        shift_height=False,
        use_color=False,
        load_dim=6,
        use_dim=[0, 1, 2]),
    dict(
        # a wrapper in order to successfully call test function
        # actually we don't perform test-time-aug
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        pts_scale_ratio=1,
        flip=False,
        transforms=[
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[0, 0],
                scale_ratio_range=[1., 1.],
                translation_std=[0, 0, 0]),
            dict(
                type='RandomFlip3D',
                sync_2d=False,
                flip_ratio_bev_horizontal=0.0,
                flip_ratio_bev_vertical=0.0),
            dict(
                type='DefaultFormatBundle3D',
                class_names=class_names,
                with_label=False),
            dict(type='Collect3D', keys=['points'])
        ])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
# we need to load gt seg_mask!
eval_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='DEPTH',
        shift_height=False,
        use_color=False,
        load_dim=6,
        use_dim=[0, 1, 2]),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_mask_3d=False,
        with_seg_3d=True),
    dict(
        type='PointSegClassMapping',
        valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28,
                       33, 34, 36, 39),
        max_cat_id=40),
    dict(
        type='DefaultFormatBundle3D',
        with_label=False,
        class_names=class_names),
    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]

data = dict(
    samples_per_gpu=16,
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'scannet_infos_train.pkl',
        pipeline=train_pipeline,
        classes=class_names,
        test_mode=False,
        ignore_index=len(class_names),
        scene_idxs=data_root + 'seg_info/train_resampled_scene_idxs.npy'),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'scannet_infos_val.pkl',
        pipeline=test_pipeline,
        classes=class_names,
        test_mode=True,
        ignore_index=len(class_names)),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'scannet_infos_val.pkl',
        pipeline=test_pipeline,
        classes=class_names,
        test_mode=True,
        ignore_index=len(class_names)))

evaluation = dict(pipeline=eval_pipeline, interval=5)

# model settings
model = dict(
    backbone=dict(in_channels=3),  # only [xyz]
    decode_head=dict(
        num_classes=20,
        ignore_index=20,
        # `class_weight` is generated in data pre-processing, saved in
        # `data/scannet/seg_info/train_label_weight.npy`
        # you can copy paste the values here, or input the file path as
        # `class_weight=data/scannet/seg_info/train_label_weight.npy`
        loss_decode=dict(class_weight=[
            2.389689, 2.7215734, 4.5944676, 4.8543367, 4.096086, 4.907941,
            4.690836, 4.512031, 4.623311, 4.9242644, 5.358117, 5.360071,
            5.019636, 4.967126, 5.3502126, 5.4023647, 5.4027233, 5.4169416,
            5.3954206, 4.6971426
        ])),
    test_cfg=dict(
        num_points=8192,
        block_size=1.5,
        sample_rate=0.5,
        use_normalized_coord=False,
        batch_size=24))

# runtime settings
checkpoint_config = dict(interval=5)