Why set_rectangle pads an extra stride by default?

[Mactarvish]

Hi, I'm reading data process code in ultralytics/data/base.py, set_rectangle is shown below:

    def set_rectangle(self):
        """Sets the shape of bounding boxes for YOLO detections as rectangles."""
        bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int)  # batch index
        nb = bi[-1] + 1  # number of batches

        s = np.array([x.pop("shape") for x in self.labels])  # hw
        ar = s[:, 0] / s[:, 1]  # aspect ratio
        irect = ar.argsort()
        self.im_files = [self.im_files[i] for i in irect]
        self.labels = [self.labels[i] for i in irect]
        ar = ar[irect]

        # Set training image shapes
        shapes = [[1, 1]] * nb
        for i in range(nb):
            ari = ar[bi == i]
            mini, maxi = ari.min(), ari.max()
            if maxi < 1: # 全是横着的
                shapes[i] = [maxi, 1]
            elif mini > 1: # 全是竖着的
                shapes[i] = [1, 1 / mini]
        # 按照5入6更入的方式进行补边，多补至少一个stride
        self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride
        self.batch = bi  # batch index of image

As my understanding this function is used for padding images in a batch according to their ratio so as to concat them and save GPU memory.
My confusion is this line:

        self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride

self.pad is default set to 0.5, so that if self.imgsz / self.stride >= 0.5, the batch will be extraly pad a stride (e.g. if np.array(shapes) * self.imgsz / self.stride is 6.5, then np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) will be 8), is this redundant? Seems set self.pad to 0 is enuogh.