Class LoadOccGTFromFile

projects/mmdet3d_plugin/datasets/pipelines/loading.py:7–37

Load multi channel images from a list of separate channel files. Expects results['img_filename'] to be a list of filenames. note that we read image in BGR style to align with opencv.imread Args: to_float32 (bool): Whether to convert the img to float32. Defaults

from mmdet3d.core.points import BasePoints
from mmdet.datasets.builder import PIPELINES


@PIPELINES.register_module()
class LoadPointsFromMultiSweepsWithPadding(object):
    """Load points from multiple sweeps. WILL PAD POINTS DIM TO LOAD DIM
    This is usually used for nuScenes dataset to utilize previous sweeps.
    Args:
        sweeps_num (int): Number of sweeps. Defaults to 10.
        load_dim (int): Dimension number of the loaded points. Defaults to 5.
        use_dim (list[int]): Which dimension to use. Defaults to [0, 1, 2, 4].
        file_client_args (dict): Config dict of file clients, refer to
            https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
            for more details. Defaults to dict(backend='disk').
        pad_empty_sweeps (bool): Whether to repeat keyframe when
            sweeps is empty. Defaults to False.
        remove_close (bool): Whether to remove close points.
            Defaults to False.
        test_mode (bool): If test_model=True used for testing, it will not
            randomly sample sweeps but select the nearest N frames.
            Defaults to False.
    """

    def __init__(self,
                 sweeps_num=10,
                 load_dim=5,
                 use_dim=[0, 1, 2, 4],
                 file_client_args=dict(backend='disk'),
                 pad_empty_sweeps=False,
                 remove_close=False,
                 test_mode=False):
        self.load_dim = load_dim
        self.sweeps_num = sweeps_num
        self.use_dim = use_dim
        self.file_client_args = file_client_args.copy()