111 lines
3.6 KiB
YAML
111 lines
3.6 KiB
YAML
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DATASET: 'PandasetDataset'
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DATA_PATH: '../data/pandaset'
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POINT_CLOUD_RANGE: [-70, -40, -3, 70, 40, 1] # xmin, ymin, zmin, xmax, ymax, zmax
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DATA_SPLIT: {
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'train': train,
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'test': val
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}
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SEQUENCES: {
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'train': ['014', '050', '079', '048', '093', '091', '063', '104', '100', '092', '012', '047', '018', '006', '099', '085', '035', '041', '052', '105', '030', '113', '002', '084', '028', '119', '044', '005', '102', '034', '077', '064', '067', '058', '019', '015', '037', '095', '120', '066', '023', '071', '117', '098', '139', '038', '116', '046', '088', '089', '040', '033', '016', '024', '122', '039', '158', '069', '124', '123', '106'], # ~60% of the sequences, randomly chosen
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'val': ['045', '059', '055', '051', '020', '097', '073', '043', '003', '101', '027', '056', '011', '078', '080', '109', '042', '021', '094', '057'], # ~20% of the sequences, randomly chosen
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'test': ['074', '004', '086', '062', '068', '008', '001', '110', '053', '115', '054', '065', '017', '103', '072', '013', '029', '090', '112', '149', '070', '032'] # ~20% of the sequences, randomly chosen
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}
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# Acquisition device to consider when loading the data
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# Pandaset contains data from:
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# - a pandar64 spinning lidar
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# - a pandarGT forward facing lidar
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# To use data from:
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# - the pandar64 lidar only (default), set LIDAR_DEVICE to 0,
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# - the pandarGT lidar onlu, set it to 1
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# - both devices, set it to -1
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LIDAR_DEVICE: 0
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INFO_PATH: {
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'train': [pandaset_infos_train.pkl],
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'test': [pandaset_infos_val.pkl],
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}
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TRAINING_CATEGORIES: {
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# This maps raw dataset categories with the corresponding categories used in training
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# This map can be incomplete. In case a category is not present, the category
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# for training is the same as the raw dataset category
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'Car': 'Car',
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'Pickup Truck': 'Car',
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'Medium-sized Truck': 'Truck',
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'Semi-truck': 'Truck',
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'Towed Object': 'Other Vehicle',
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'Motorcycle': 'Motorcycle',
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'Other Vehicle - Construction Vehicle': 'Other Vehicle',
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'Other Vehicle - Uncommon': 'Other Vehicle',
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'Other Vehicle - Pedicab': 'Other Vehicle',
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'Emergency Vehicle': 'Other Vehicle',
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'Bus': 'Bus',
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'Bicycle': 'Bicycle',
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'Pedestrian': 'Pedestrian',
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'Pedestrian with Object': 'Pedestrian',
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'Animals - Other': 'Animal'
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}
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FOV_POINTS_ONLY: False
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DATA_AUGMENTOR:
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DISABLE_AUG_LIST: ['placeholder']
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AUG_CONFIG_LIST:
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# gt sampling not working at the moment
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- NAME: gt_sampling
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USE_ROAD_PLANE: False
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DB_INFO_PATH:
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- pandaset_dbinfos_train.pkl
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PREPARE: {
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filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Bicycle:5'],
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filter_by_difficulty: [-1],
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}
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SAMPLE_GROUPS: ['Car:20','Pedestrian:15', 'Bicycle:15']
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NUM_POINT_FEATURES: 4
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DATABASE_WITH_FAKELIDAR: False
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REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
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LIMIT_WHOLE_SCENE: True
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- NAME: random_world_flip
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ALONG_AXIS_LIST: ['x', 'y']
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- NAME: random_world_rotation
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WORLD_ROT_ANGLE: [-3.14159265, 3.114159265]
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- NAME: random_world_scaling
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WORLD_SCALE_RANGE: [0.95, 1.05]
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POINT_FEATURE_ENCODING: {
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encoding_type: absolute_coordinates_encoding,
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used_feature_list: ['x', 'y', 'z', 'intensity'],
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src_feature_list: ['x', 'y', 'z', 'intensity'],
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}
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DATA_PROCESSOR:
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- NAME: mask_points_and_boxes_outside_range
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REMOVE_OUTSIDE_BOXES: True
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- NAME: shuffle_points
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SHUFFLE_ENABLED: {
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'train': True,
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'test': False
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}
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- NAME: transform_points_to_voxels
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VOXEL_SIZE: [0.05, 0.05, 0.1]
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MAX_POINTS_PER_VOXEL: 5
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MAX_NUMBER_OF_VOXELS: {
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'train': 16000,
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'test': 40000
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}
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