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| # dataset settings | |
| dataset_type = 'S3DISSegDataset' | |
| data_root = './data/s3dis/' | |
| class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door', | |
| 'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter') | |
| 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]), | |
| 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=tuple(range(len(class_names))), | |
| max_cat_id=13), | |
| 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='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=True, | |
| load_dim=6, | |
| use_dim=[0, 1, 2, 3, 4, 5]), | |
| dict(type='NormalizePointsColor', color_mean=None), | |
| 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=True, | |
| load_dim=6, | |
| use_dim=[0, 1, 2, 3, 4, 5]), | |
| 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=tuple(range(len(class_names))), | |
| max_cat_id=13), | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| with_label=False, | |
| class_names=class_names), | |
| dict(type='Collect3D', keys=['points', 'pts_semantic_mask']) | |
| ] | |
| data = dict( | |
| samples_per_gpu=8, | |
| workers_per_gpu=4, | |
| # train on area 1, 2, 3, 4, 6 | |
| # test on area 5 | |
| train=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_files=[ | |
| data_root + f's3dis_infos_Area_{i}.pkl' for i in train_area | |
| ], | |
| pipeline=train_pipeline, | |
| classes=class_names, | |
| test_mode=False, | |
| ignore_index=len(class_names), | |
| scene_idxs=[ | |
| data_root + f'seg_info/Area_{i}_resampled_scene_idxs.npy' | |
| for i in train_area | |
| ]), | |
| val=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_files=data_root + f's3dis_infos_Area_{test_area}.pkl', | |
| pipeline=test_pipeline, | |
| classes=class_names, | |
| test_mode=True, | |
| ignore_index=len(class_names), | |
| scene_idxs=data_root + | |
| f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'), | |
| test=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_files=data_root + f's3dis_infos_Area_{test_area}.pkl', | |
| pipeline=test_pipeline, | |
| classes=class_names, | |
| test_mode=True, | |
| ignore_index=len(class_names))) | |
| evaluation = dict(pipeline=eval_pipeline) | |