(
        self, point_clouds, noise=None, labels=None, copy=True, init_shuffle=True
    )
Construct a DataSet.
        Args:
            init_shuffle, shuffle data before first epoch has been reached.
        Output:
            original_pclouds, labels, (None or Feed)
Construct a DataSet.
        Args:
            init_shuffle, shuffle data before first epoch has been reached.
        Output:
            original_pclouds, labels, (None or Feed)
Construct a DataSet . Args : init_shuffle shuffle data before first epoch has been reached . Output : original_pclouds labels ( None or Feed )
def __init__(
        self, point_clouds, noise=None, labels=None, copy=True, init_shuffle=True
    ):
        """Construct a DataSet.
        Args:
            init_shuffle, shuffle data before first epoch has been reached.
        Output:
            original_pclouds, labels, (None or Feed)
        """

        self.num_examples = point_clouds.shape[0]
        self.n_points = point_clouds.shape[1]

        if labels is not None:
            assert (
                point_clouds.shape[0] == labels.shape[0]
            ), "points.shape: %s labels.shape: %s" % (point_clouds.shape, labels.shape)
            if copy:
                self.labels = labels.copy()
            else:
                self.labels = labels

        else:
            self.labels = np.ones(self.num_examples, dtype=np.int8)

        if noise is not None:
            assert type(noise) is np.ndarray
            if copy:
                self.noisy_point_clouds = noise.copy()
            else:
                self.noisy_point_clouds = noise
        else:
            self.noisy_point_clouds = None

        if copy:
            self.point_clouds = point_clouds.copy()
        else:
            self.point_clouds = point_clouds

        self.epochs_completed = 0
        self._index_in_epoch = 0
        if init_shuffle:
            self.shuffle_data()
def __init__ ( self , point_clouds , noise = None , labels = None , copy = True , init_shuffle = True ) : self . num_examples = point_clouds . shape [ 0 ] self . n_points = point_clouds . shape [ 1 ] if labels is not None : assert ( point_clouds . shape [ 0 ] == labels . shape [ 0 ] ) , "points.shape: %s labels.shape: %s" % ( point_clouds . shape , labels . shape ) if copy : self . labels = labels . copy ( ) else : self . labels = labels else : self . labels = np . ones ( self . num_examples , dtype = np . int8 ) if noise is not None : assert type ( noise ) is np . ndarray if copy : self . noisy_point_clouds = noise . copy ( ) else : self . noisy_point_clouds = noise else : self . noisy_point_clouds = None if copy : self . point_clouds = point_clouds . copy ( ) else : self . point_clouds = point_clouds self . epochs_completed = 0 self . _index_in_epoch = 0 if init_shuffle : self . shuffle_data ( )