projects/TR3D/configs/tr3d_1xb16_scannet-3d-18class.py

_base_ = ['./tr3d.py', 'mmdet3d::_base_/datasets/scannet-3d.py']
custom_imports = dict(imports=['projects.TR3D.tr3d'])

model = dict(
    bbox_head=dict(
        label2level=[0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0]))

train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='DEPTH',
        shift_height=False,
        use_color=True,
        load_dim=6,
        use_dim=[0, 1, 2, 3, 4, 5]),
    dict(type='LoadAnnotations3D'),
    dict(type='GlobalAlignment', rotation_axis=2),
    # We do not sample 100k points for ScanNet, as very few scenes have
    # significantly more then 100k points. So we sample 33 to 100% of them.
    dict(type='TR3DPointSample', num_points=0.33),
    dict(
        type='RandomFlip3D',
        sync_2d=False,
        flip_ratio_bev_horizontal=0.5,
        flip_ratio_bev_vertical=0.5),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.02, 0.02],
        scale_ratio_range=[0.9, 1.1],
        translation_std=[0.1, 0.1, 0.1],
        shift_height=False),
    dict(type='NormalizePointsColor', color_mean=None),
    dict(
        type='Pack3DDetInputs',
        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='DEPTH',
        shift_height=False,
        use_color=True,
        load_dim=6,
        use_dim=[0, 1, 2, 3, 4, 5]),
    dict(type='GlobalAlignment', rotation_axis=2),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        pts_scale_ratio=1,
        flip=False,
        transforms=[
            # We do not sample 100k points for ScanNet, as very few scenes have
            # significantly more then 100k points. So it doesn't affect
            # inference time and we can accept all points.
            # dict(type='PointSample', num_points=100000),
            dict(type='NormalizePointsColor', color_mean=None),
        ]),
    dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
    batch_size=16,
    num_workers=8,
    dataset=dict(
        type='RepeatDataset',
        times=15,
        dataset=dict(pipeline=train_pipeline, filter_empty_gt=False)))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader