text: from __future__ import print_function
  import os
  import argparse
  import torch
  import torch.backends.cudnn as cudnn
  import numpy as np
  import time
  from data import cfg_mnet, cfg_slim, cfg_rfb
  from layers.functions.prior_box import PriorBox
  from utils.nms.py_cpu_nms import py_cpu_nms
  import cv2
  from models.retinaface import RetinaFace
  from models.net_slim import Slim
  from models.net_rfb import RFB
  from utils.box_utils import decode, decode_landm
  from utils.timer import Timer
  
  
  parser = argparse.ArgumentParser(description='Test')
  parser.add_argument('-m', '--trained_model', default='./weights/RBF_Final.pth',
                      type=str, help='Trained state_dict file path to open')
  parser.add_argument('--network', default='RFB', help='Backbone network mobile0.25 or slim or RFB')
  parser.add_argument('--origin_size', default=True, type=str, help='Whether use origin image size to evaluate')
  parser.add_argument('--long_side', default=640, help='when origin_size is false, long_side is scaled size(320 or 640 for long side)')
  parser.add_argument('--save_folder', default='./widerface_evaluate/widerface_txt/', type=str, help='Dir to save txt results')
  parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
  parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold')
  parser.add_argument('--top_k', default=5000, type=int, help='top_k')
  parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold')
  parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
  parser.add_argument('--save_image', action="store_true", default=True, help='show detection results')
  parser.add_argument('--vis_thres', default=0.6, type=float, help='visualization_threshold')
  args = parser.parse_args()
  
  
  def check_keys(model, pretrained_state_dict):
      ckpt_keys = set(pretrained_state_dict.keys())
      model_keys = set(model.state_dict().keys())
      used_pretrained_keys = model_keys & ckpt_keys
      unused_pretrained_keys = ckpt_keys - model_keys
      missing_keys = model_keys - ckpt_keys
      print('Missing keys:{}'.format(len(missing_keys)))
      print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
      print('Used keys:{}'.format(len(used_pretrained_keys)))
      assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
      return True
  
  
  def remove_prefix(state_dict, prefix):
      ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
      print('remove prefix \'{}\''.format(prefix))
      f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
      return {f(key): value for key, value in state_dict.items()}
  
  
  def load_model(model, pretrained_path, load_to_cpu):
      print('Loading pretrained model from {}'.format(pretrained_path))
      if load_to_cpu:
          pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
      else:
          device = torch.cuda.current_device()
          pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
      if "state_dict" in pretrained_dict.keys():
          pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
      else:
          pretrained_dict = remove_prefix(pretrained_dict, 'module.')
      check_keys(model, pretrained_dict)
      model.load_state_dict(pretrained_dict, strict=False)
      return model
  
  
  if __name__ == '__main__':
      torch.set_grad_enabled(False)
  
      cfg = None
      net = None
      if args.network == "mobile0.25":
          cfg = cfg_mnet
          net = RetinaFace(cfg = cfg, phase = 'test')
      elif args.network == "slim":
          cfg = cfg_slim
          net = Slim(cfg = cfg, phase = 'test')
      elif args.network == "RFB":
          cfg = cfg_rfb
          net = RFB(cfg = cfg, phase = 'test')
      else:
          print("Don't support network!")
          exit(0)
  
      net = load_model(net, args.trained_model, args.cpu)
      net.eval()
      print('Finished loading model!')
      print(net)
      cudnn.benchmark = True
      device = torch.device("cpu" if args.cpu else "cuda")
      net = net.to(device)
  
      # testing begin
      for i in range(100):
          image_path = "./img/sample.jpg"
  
          img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
          img = np.float32(img_raw)
  
          # testing scale
          target_size = args.long_side
          max_size = args.long_side
          im_shape = img.shape
          im_size_min = np.min(im_shape[0:2])
          im_size_max = np.max(im_shape[0:2])
          resize = float(target_size) / float(im_size_min)
          # prevent bigger axis from being more than max_size:
          if np.round(resize * im_size_max) > max_size:
              resize = float(max_size) / float(im_size_max)
          if args.origin_size:
              resize = 1
  
          if resize != 1:
              img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
          im_height, im_width, _ = img.shape
  
  
          scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
          img -= (104, 117, 123)
          img = img.transpose(2, 0, 1)
          img = torch.from_numpy(img).unsqueeze(0)
          img = img.to(device)
          scale = scale.to(device)
  
          tic = time.time()
          loc, conf, landms = net(img)  # forward pass
          print('net forward time: {:.4f}'.format(time.time() - tic))
  
          priorbox = PriorBox(cfg, image_size=(im_height, im_width))
          priors = priorbox.forward()
          priors = priors.to(device)
          prior_data = priors.data
          boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
          boxes = boxes * scale / resize
          boxes = boxes.cpu().numpy()
          scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
          landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
          scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
                                 img.shape[3], img.shape[2], img.shape[3], img.shape[2],
                                 img.shape[3], img.shape[2]])
          scale1 = scale1.to(device)
          landms = landms * scale1 / resize
          landms = landms.cpu().numpy()
  
          # ignore low scores
          inds = np.where(scores > args.confidence_threshold)[0]
          boxes = boxes[inds]
          landms = landms[inds]
          scores = scores[inds]
  
          # keep top-K before NMS
          order = scores.argsort()[::-1][:args.top_k]
          boxes = boxes[order]
          landms = landms[order]
          scores = scores[order]
  
          # do NMS
          dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
          keep = py_cpu_nms(dets, args.nms_threshold)
          # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
          dets = dets[keep, :]
          landms = landms[keep]
  
          # keep top-K faster NMS
          dets = dets[:args.keep_top_k, :]
          landms = landms[:args.keep_top_k, :]
  
          dets = np.concatenate((dets, landms), axis=1)
  
          # show image
          if args.save_image:
              for b in dets:
                  if b[4] < args.vis_thres:
                      continue
                  text = "{:.4f}".format(b[4])
                  b = list(map(int, b))
                  cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
                  cx = b[0]
                  cy = b[1] + 12
                  cv2.putText(img_raw, text, (cx, cy),
                              cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
  
                  # landms
  
                  cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
                  cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
                  cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
                  cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
                  cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
              # save image
  
              name = "test.jpg"
              cv2.imwrite(name, img_raw)
  
  