text: import os
  import cv2
  import torch
  import argparse
  import numpy as np
  from tqdm import tqdm
  from torch.nn import functional as F
  import warnings
  import _thread
  
  warnings.filterwarnings("ignore")
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  if torch.cuda.is_available():
      torch.set_grad_enabled(False)
      torch.backends.cudnn.enabled = True
      torch.backends.cudnn.benchmark = True
  
  parser = argparse.ArgumentParser(description='Interpolation for a pair of images')
  parser.add_argument('--video', dest='video', required=True)
  parser.add_argument('--skip', dest='skip', action='store_true', help='whether to remove static frames before processing')
  parser.add_argument('--fps', dest='fps', type=int, default=None)
  parser.add_argument('--png', dest='png', action='store_true', help='whether to output png format outputs')
  parser.add_argument('--ext', dest='ext', type=str, default='mp4', help='output video extension')
  parser.add_argument('--exp', dest='exp', type=int, default=1, help='interpolation exponent (default: 1)')
  args = parser.parse_args()
  assert (args.exp in [1, 2, 3])
  args.times = 2 ** args.exp
  
  from model.RIFE import Model
  model = Model()
  model.load_model('./train_log')
  model.eval()
  model.device()
  
  videoCapture = cv2.VideoCapture(args.video)
  fps = np.round(videoCapture.get(cv2.CAP_PROP_FPS))
  success, frame = videoCapture.read()
  h, w, _ = frame.shape
  if args.fps is None:
      args.fps = fps * args.times
  fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
  video_path_wo_ext, ext = os.path.splitext(args.video)
  if args.png:
      if not os.path.exists('output'):
          os.mkdir('output')
      vid_out = None
  else:
      vid_out = cv2.VideoWriter('{}_{}X_{}fps.{}'.format(video_path_wo_ext, args.times, int(np.round(args.fps)), args.ext), fourcc, args.fps, (w, h))
      
  cnt = 0
  skip_frame = 1
  buffer = []
  
  def write_frame(i0, infs, i1, p, user_args):
      global skip_frame, cnt    
      for i in range(i0.shape[0]):
          # A video transition occurs.
          if p[i] > 0.2:
              if user_args.exp > 1:
                  infs = [i0[i] for _ in range(len(infs) - 1)]
                  infs[-1] = i1[-1]
              else:
                  infs = [i0[i] for _ in range(len(infs))]
          
          # Result was too similar to previous frame, skip if given.
          if p[i] < 5e-3 and user_args.skip:
              if skip_frame % 100 == 0:
                  print("Warning: Your video has {} static frames, "
                        "skipping them may change the duration of the generated video.".format(skip_frame))
              skip_frame += 1
              continue
          
          # Write results.        
          buffer.append(i0[i])
          for inf in infs:
              buffer.append(inf[i])
  
  def clear_buffer(user_args, buffer):
      global cnt
      for i in buffer:
          if user_args.png:
              cv2.imwrite('output/{:0>7d}.png'.format(cnt), i)
              cnt += 1
          else:
              vid_out.write(i)
  
  def make_inference(model, I0, I1, exp):
      middle = model.inference(I0, I1)
      if exp == 1:
          return [middle]
      first_half = make_inference(model, I0, middle, exp=exp - 1)
      second_half = make_inference(model, middle, I1, exp=exp - 1)
      return [*first_half, middle, *second_half]
  
  
  ph = ((h - 1) // 32 + 1) * 32
  pw = ((w - 1) // 32 + 1) * 32
  padding = (0, pw - w, 0, ph - h)
  tot_frame = videoCapture.get(cv2.CAP_PROP_FRAME_COUNT)
  print('{}.{}, {} frames in total, {}FPS to {}FPS'.format(video_path_wo_ext, args.ext, tot_frame, fps, args.fps))
  pbar = tqdm(total=tot_frame)
  img_list = [frame]
  while success:
      success, frame = videoCapture.read()
      if success:
          img_list.append(frame)
      if len(img_list) == 5 or (not success and len(img_list) > 1):
          imgs = torch.from_numpy(np.transpose(img_list, (0, 3, 1, 2))).to(device, non_blocking=True).float() / 255.
          I0 = imgs[:-1]
          I1 = imgs[1:]
          p = (F.interpolate(I0, (16, 16), mode='bilinear', align_corners=False)
               - F.interpolate(I1, (16, 16), mode='bilinear', align_corners=False)).abs()
          I0 = F.pad(I0, padding)
          I1 = F.pad(I1, padding)
          inferences = make_inference(model, I0, I1, exp=args.exp)
  
          I0 = np.array(img_list[:-1])
          I1 = np.array(img_list[1:])
          inferences = list(map(lambda x: ((x[:, :, :h, :w] * 255.).byte().cpu().detach().numpy().transpose(0, 2, 3, 1)), inferences))
          
          write_frame(I0, inferences, I1, p.mean(3).mean(2).mean(1), args)
          pbar.update(4)
          img_list = img_list[-1:]
      if len(buffer) > 100:
          _thread.start_new_thread(clear_buffer, (args, buffer))
          buffer = []
  _thread.start_new_thread(clear_buffer, (args, buffer))
  pbar.close()
  if not vid_out is None:
      vid_out.release()
  