def post_process(self, dets, meta, scale=1):
    dets = dets.detach().cpu().numpy()
    dets = dets.reshape(1, (- 1), dets.shape[2])
    dets = ctdet_post_process(dets.copy(), [meta['c']], [meta['s']], meta['out_height'], meta['out_width'], self.opt.num_classes)
    for j in range(1, (self.num_classes + 1)):
        dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape((- 1), 5)
        dets[0][j][:, :4] /= scale
    return dets[0]
def post_process(self, dets, meta, scale=1):
    dets = dets.detach().cpu().numpy()
    dets = dets.reshape(1, (- 1), dets.shape[2])
    dets = ctdet_post_process(dets.copy(), [meta['c']], [meta['s']], meta['out_height'], meta['out_width'], self.opt.num_classes)
    for j in range(1, (self.num_classes + 1)):
        dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape((- 1), 5)
        dets[0][j][:, :4] /= scale
    return dets[0]