s3dis-seg.py

# For S3DIS seg we usually do 13-class segmentation
class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
               'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter')
metainfo = dict(classes=class_names)
dataset_type = 'S3DISSegDataset'
data_root = 'data/s3dis/'
input_modality = dict(use_lidar=True, use_camera=False)
data_prefix = dict(
    pts='points',
    pts_instance_mask='instance_mask',
    pts_semantic_mask='semantic_mask')

# 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/s3dis/'

# 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

num_points = 4096
train_area = [1, 2, 3, 4, 6]
test_area = 5
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],
        backend_args=backend_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_mask_3d=False,
        with_seg_3d=True,
        backend_args=backend_args),
    dict(type='PointSegClassMapping'),
    dict(
        type='IndoorPatchPointSample',
        num_points=num_points,
        block_size=1.0,
        ignore_index=len(class_names),
        use_normalized_coord=True,
        enlarge_size=0.2,
        min_unique_num=None),
    dict(type='NormalizePointsColor', color_mean=None),
    dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
]
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],
        backend_args=backend_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_mask_3d=False,
        with_seg_3d=True,
        backend_args=backend_args),
    dict(type='NormalizePointsColor', color_mean=None),
    dict(type='Pack3DDetInputs', 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=True,
        load_dim=6,
        use_dim=[0, 1, 2, 3, 4, 5],
        backend_args=backend_args),
    dict(type='NormalizePointsColor', color_mean=None),
    dict(type='Pack3DDetInputs', keys=['points'])
]
tta_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='DEPTH',
        shift_height=False,
        use_color=True,
        load_dim=6,
        use_dim=[0, 1, 2, 3, 4, 5],
        backend_args=backend_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_mask_3d=False,
        with_seg_3d=True,
        backend_args=backend_args),
    dict(type='NormalizePointsColor', color_mean=None),
    dict(
        type='TestTimeAug',
        transforms=[[
            dict(
                type='RandomFlip3D',
                sync_2d=False,
                flip_ratio_bev_horizontal=0.,
                flip_ratio_bev_vertical=0.)
        ], [dict(type='Pack3DDetInputs', keys=['points'])]])
]

# train on area 1, 2, 3, 4, 6
# test on area 5
train_dataloader = dict(
    batch_size=8,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
        metainfo=metainfo,
        data_prefix=data_prefix,
        pipeline=train_pipeline,
        modality=input_modality,
        ignore_index=len(class_names),
        scene_idxs=[
            f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
        ],
        test_mode=False,
        backend_args=backend_args))
test_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_files=f's3dis_infos_Area_{test_area}.pkl',
        metainfo=metainfo,
        data_prefix=data_prefix,
        pipeline=test_pipeline,
        modality=input_modality,
        ignore_index=len(class_names),
        scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy',
        test_mode=True,
        backend_args=backend_args))
val_dataloader = test_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')

tta_model = dict(type='Seg3DTTAModel')