def METHOD_NAME(self, v_path):
    outputs = []
    capture = cv2.VideoCapture(v_path)
    self.fps = capture.get(cv2.CAP_PROP_FPS)
    self.ori_height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
    self.ori_width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
    self.ori_size = (self.ori_width, self.ori_height)
    self.resize_ratio = min(self.test_size[0] / self.ori_size[0],
                            self.test_size[1] / self.ori_size[1])
    self.device = next(self.model.parameters()).device
    frame_idx = 0
    while capture.isOpened():
        ret, frame = capture.read()
        if not ret:
            break
        if frame_idx == 0:
            short_imgs_queue = [
                frame.copy() for _ in range(self.short_cfg['frame_num'])
            ]
            long_imgs_queue = [
                frame.copy() for _ in range(self.long_cfg['frame_num'])
            ]
            short_imgs_queue = [
                cv2.resize(
                    x, self.test_size,
                    interpolation=cv2.INTER_LINEAR).astype(np.uint8)
                for x in short_imgs_queue
            ]
            long_imgs_queue = [
                cv2.resize(
                    x, self.test_size,
                    interpolation=cv2.INTER_LINEAR).astype(np.uint8)
                for x in long_imgs_queue
            ]
            short_imgs_queue = [
                self.preproc(x, None,
                             (self.test_size[1], self.test_size[0]))[0]
                for x in short_imgs_queue
            ]
            long_imgs_queue = [
                self.preproc(x, None,
                             (self.test_size[1], self.test_size[0]))[0]
                for x in long_imgs_queue
            ]
        else:
            long_imgs_queue = long_imgs_queue[1:] + short_imgs_queue[:]
            short_imgs_queue = [
                frame.copy() for _ in range(self.short_cfg['frame_num'])
            ]
            short_imgs_queue = [
                cv2.resize(
                    x, self.test_size,
                    interpolation=cv2.INTER_LINEAR).astype(np.uint8)
                for x in short_imgs_queue
            ]
            short_imgs_queue = [
                self.preproc(x, None,
                             (self.test_size[1], self.test_size[0]))[0]
                for x in short_imgs_queue
            ]
        short_img = np.concatenate(short_imgs_queue, axis=0)
        long_img = np.concatenate(long_imgs_queue, axis=0)
        short_img = torch.from_numpy(short_img).unsqueeze(0)
        long_img = torch.from_numpy(long_img).unsqueeze(0)
        short_img = short_img.to(self.device)
        long_img = long_img.to(self.device)
        output = self.model((short_img, long_img))
        output = self.postprocess(output)
        output += (timestamp_format(seconds=frame_idx / self.fps), )
        outputs.append(output)
        frame_idx += 1
    return outputs