def METHOD_NAME(self, imgs, im_info):
    inputs = {}
    im_shape = []
    scale_factor = []
    if len(imgs) == 1:
        image = np.array((imgs[0], )).astype('float32')
        im_shape = np.array((im_info[0]['im_shape'], )).astype('float32')
        scale_factor = np.array(
            (im_info[0]['scale_factor'], )).astype('float32')
        inputs = dict(
            im_shape=im_shape, image=image, scale_factor=scale_factor)
        outputs = [inputs[key] for key in self.input_names]
        return outputs
    for e in im_info:
        im_shape.append(np.array((e['im_shape'], )).astype('float32'))
        scale_factor.append(
            np.array((e['scale_factor'], )).astype('float32'))
    inputs['im_shape'] = np.concatenate(im_shape, axis=0)
    inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
    imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
    max_shape_h = max([e[0] for e in imgs_shape])
    max_shape_w = max([e[1] for e in imgs_shape])
    padding_imgs = []
    for img in imgs:
        im_c, im_h, im_w = img.shape[:]
        padding_im = np.zeros(
            (im_c, max_shape_h, max_shape_w), dtype=np.float32)
        padding_im[:, :im_h, :im_w] = img
        padding_imgs.append(padding_im)
    inputs['image'] = np.stack(padding_imgs, axis=0)
    outputs = [inputs[key] for key in self.input_names]
    return outputs