code: import torch
  from torch.autograd import Variable
  import torch.nn.functional as F
  import torchvision.transforms as transforms
  
  import torch.nn as nn
  import torch.utils.data
  import numpy as np
  from opt import opt
  
  from dataloader import VideoLoader, DetectionLoader, DetectionProcessor, DataWriter, Mscoco
  from yolo.util import write_results, dynamic_write_results
  from SPPE.src.main_fast_inference import *
  
  import ntpath
  import os
  import sys
  from tqdm import tqdm
  import time
  from fn import getTime
  import cv2
  
  from pPose_nms import pose_nms, write_json
  
  args = opt
  args.dataset = 'coco'
  if not args.sp:
      torch.multiprocessing.set_start_method('forkserver', force=True)
      torch.multiprocessing.set_sharing_strategy('file_system')
  
  if __name__ == "__main__":
      videofile = args.video
      mode = args.mode
      if not os.path.exists(args.outputpath):
          os.mkdir(args.outputpath)
      
      if not len(videofile):
          raise IOError('Error: must contain --video')
  
      # Load input video
      data_loader = VideoLoader(videofile, batchSize=args.detbatch).start()
      (fourcc,fps,frameSize) = data_loader.videoinfo()
  
      # Load detection loader
      print('Loading YOLO model..')
      sys.stdout.flush()
      det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
      det_processor = DetectionProcessor(det_loader).start()
      
      # Load pose model
      pose_dataset = Mscoco()
      if args.fast_inference:
          print('Using fast inference...')
          pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
      else:
          print('Using slow, more accurate inference...')
          pose_model = InferenNet(4 * 1 + 1, pose_dataset)
      pose_model
      pose_model.eval()
  
      runtime_profile = {
          'dt': [],
          'pt': [],
          'pn': []
      }
  
      # Data writer
      save_path = os.path.join(args.outputpath, 'AlphaPose_'+ntpath.basename(videofile).split('.')[0]+'.avi')
      writer = DataWriter(args.save_video, save_path, cv2.VideoWriter_fourcc(*'XVID'), fps, frameSize).start()
  
      im_names_desc =  tqdm(range(data_loader.length()))
      batchSize = args.posebatch
      for i in im_names_desc:
          start_time = getTime()
          with torch.no_grad():
              (inps, orig_img, im_name, boxes, scores, pt1, pt2) = det_processor.read()
              if orig_img is None:
                  break
              if boxes is None or boxes.nelement() == 0:
                  writer.save(None, None, None, None, None, orig_img, im_name.split('/')[-1])
                  continue
  
              ckpt_time, det_time = getTime(start_time)
              runtime_profile['dt'].append(det_time)
              # Pose Estimation
              
              datalen = inps.size(0)
              leftover = 0
              if (datalen) % batchSize:
                  leftover = 1
              num_batches = datalen // batchSize + leftover
              hm = []
              for j in range(num_batches):
                  inps_j = inps[j*batchSize:min((j +  1)*batchSize, datalen)]
                  hm_j = pose_model(inps_j)
                  hm.append(hm_j)
              hm = torch.cat(hm)
              ckpt_time, pose_time = getTime(ckpt_time)
              runtime_profile['pt'].append(pose_time)
  
              hm = hm.cpu().data
              writer.save(boxes, scores, hm, pt1, pt2, orig_img, im_name.split('/')[-1])
  
              ckpt_time, post_time = getTime(ckpt_time)
              runtime_profile['pn'].append(post_time)
  
          if args.profile:
              # TQDM
              im_names_desc.set_description(
              'det time: {dt:.3f} | pose time: {pt:.2f} | post processing: {pn:.4f}'.format(
                  dt=np.mean(runtime_profile['dt']), pt=np.mean(runtime_profile['pt']), pn=np.mean(runtime_profile['pn']))
              )
  
      print('===========================> Finish Model Running.')
      if (args.save_img or args.save_video) and not args.vis_fast:
          print('===========================> Rendering remaining images in the queue...')
          print('===========================> If this step takes too long, you can enable the --vis_fast flag to use fast rendering (real-time).')
      while(writer.running()):
          pass
      writer.stop()
      final_result = writer.results()
      write_json(final_result, args.outputpath)
  
apis: torch.multiprocessing.set_sharing_strategy torch.no_grad torch.multiprocessing.set_start_method torch.cat numpy.mean