What should I do if I want to train this model on my own dataset?

[KyleYueye]

I notice that for each dataset, info files (.pkl) are generated. Do you have any rules about generating these info files? Because I want to train this model on my own dataset. Thanks a lot.

[jskhu]

It depends on the dataset, but typically the pickle files contain labels, calibration, and basic metadata. For example, KITTI's train pickle file contains the following information for each training sample:

>>> import pickle
>>> import pprint
>>> pp = pprint.PrettyPrinter()
>>> with open('kitti_infos_train.pkl', 'rb') as f:
...     data = pickle.load(f)
...
>>> pp.pprint(data[0])
{'annos': {'alpha': array([-0.2]),
           'bbox': array([[712.4 , 143.  , 810.73, 307.92]], dtype=float32),
           'difficulty': array([0], dtype=int32),
           'dimensions': array([[1.2 , 1.89, 0.48]]),
           'gt_boxes_lidar': array([[ 8.73138046, -1.85591757, -0.65469939,  1.2       ,  0.48      ,
         1.89      , -1.58079633]]),
           'index': array([0], dtype=int32),
           'location': array([[1.84, 1.47, 8.41]], dtype=float32),
           'name': array(['Pedestrian'], dtype='<U10'),
           'num_points_in_gt': array([377], dtype=int32),
           'occluded': array([0.]),
           'rotation_y': array([0.01]),
           'score': array([-1.]),
           'truncated': array([0.])},
 'calib': {'P2': array([[ 7.07049316e+02,  0.00000000e+00,  6.04081421e+02,
         4.57583084e+01],
       [ 0.00000000e+00,  7.07049316e+02,  1.80506607e+02,
        -3.45415711e-01],
       [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00,
         4.98101581e-03],
       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
         1.00000000e+00]]),
           'R0_rect': array([[ 0.9999128 ,  0.01009263, -0.00851193,  0.        ],
       [-0.01012729,  0.9999406 , -0.00403767,  0.        ],
       [ 0.00847067,  0.00412352,  0.9999556 ,  0.        ],
       [ 0.        ,  0.        ,  0.        ,  1.        ]],
      dtype=float32),
           'Tr_velo_to_cam': array([[ 0.00692796, -0.99997222, -0.00275783, -0.02457729],
       [-0.00116298,  0.00274984, -0.99999553, -0.06127237],
       [ 0.99997532,  0.00693114, -0.0011439 , -0.33210289],
       [ 0.        ,  0.        ,  0.        ,  1.        ]])},
 'image': {'image_idx': '000000',
           'image_shape': array([ 370, 1224], dtype=int32)},
 'point_cloud': {'lidar_idx': '000000', 'num_features': 4}}

Unless your data is extremely different, my suggestion would be to convert your data to a KITTI-like format so you can use OpenPCDet's dataset framework easily. An example Waymo to KITTI converter is here: https://github.com/caizhongang/waymo_kitti_converter. You can probably do something similar with your dataset.

[KyleYueye]

It depends on the dataset, but typically the pickle files contain labels, calibration, and basic metadata. For example, KITTI's train pickle file contains the following information for each training sample:

>>> import pickle
>>> import pprint
>>> pp = pprint.PrettyPrinter()
>>> with open('kitti_infos_train.pkl', 'rb') as f:
...     data = pickle.load(f)
...
>>> pp.pprint(data[0])
{'annos': {'alpha': array([-0.2]),
           'bbox': array([[712.4 , 143.  , 810.73, 307.92]], dtype=float32),
           'difficulty': array([0], dtype=int32),
           'dimensions': array([[1.2 , 1.89, 0.48]]),
           'gt_boxes_lidar': array([[ 8.73138046, -1.85591757, -0.65469939,  1.2       ,  0.48      ,
         1.89      , -1.58079633]]),
           'index': array([0], dtype=int32),
           'location': array([[1.84, 1.47, 8.41]], dtype=float32),
           'name': array(['Pedestrian'], dtype='<U10'),
           'num_points_in_gt': array([377], dtype=int32),
           'occluded': array([0.]),
           'rotation_y': array([0.01]),
           'score': array([-1.]),
           'truncated': array([0.])},
 'calib': {'P2': array([[ 7.07049316e+02,  0.00000000e+00,  6.04081421e+02,
         4.57583084e+01],
       [ 0.00000000e+00,  7.07049316e+02,  1.80506607e+02,
        -3.45415711e-01],
       [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00,
         4.98101581e-03],
       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
         1.00000000e+00]]),
           'R0_rect': array([[ 0.9999128 ,  0.01009263, -0.00851193,  0.        ],
       [-0.01012729,  0.9999406 , -0.00403767,  0.        ],
       [ 0.00847067,  0.00412352,  0.9999556 ,  0.        ],
       [ 0.        ,  0.        ,  0.        ,  1.        ]],
      dtype=float32),
           'Tr_velo_to_cam': array([[ 0.00692796, -0.99997222, -0.00275783, -0.02457729],
       [-0.00116298,  0.00274984, -0.99999553, -0.06127237],
       [ 0.99997532,  0.00693114, -0.0011439 , -0.33210289],
       [ 0.        ,  0.        ,  0.        ,  1.        ]])},
 'image': {'image_idx': '000000',
           'image_shape': array([ 370, 1224], dtype=int32)},
 'point_cloud': {'lidar_idx': '000000', 'num_features': 4}}

Unless your data is extremely different, my suggestion would be to convert your data to a KITTI-like format so you can use OpenPCDet's dataset framework easily. An example Waymo to KITTI converter is here: https://github.com/caizhongang/waymo_kitti_converter. You can probably do something similar with your dataset.

Thank you