code: """
  ****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
  Copyright (c) 2018 [Thomson Licensing]
  All Rights Reserved
  This program contains proprietary information which is a trade secret/business \
  secret of [Thomson Licensing] and is protected, even if unpublished, under \
  applicable Copyright laws (including French droit d'auteur) and/or may be \
  subject to one or more patent(s).
  Recipient is to retain this program in confidence and is not permitted to use \
  or make copies thereof other than as permitted in a written agreement with \
  [Thomson Licensing] unless otherwise expressly allowed by applicable laws or \
  by [Thomson Licensing] under express agreement.
  Thomson Licensing is a company of the group TECHNICOLOR
  *******************************************************************************
  This scripts permits one to reproduce training and experiments of:
      Engilberge, M., Chevallier, L., Pérez, P., & Cord, M. (2018, April).
      Finding beans in burgers: Deep semantic-visual embedding with localization.
      In Proceedings of CVPR (pp. 3984-3993)
  
  Author: Martin Engilberge
  """
  
  import argparse
  import time
  
  import numpy as np
  import torch
  
  from misc.dataset import TextDataset
  from misc.model import joint_embedding
  from misc.utils import save_obj, collate_fn_cap_padded
  from torch.utils.data import DataLoader
  
  
  device = torch.device("cuda")
  # device = torch.device("cpu") # uncomment to run with cpu
  
  
  if __name__ == '__main__':
  
      parser = argparse.ArgumentParser(description='Extract embedding representation for images')
      parser.add_argument("-p", '--path', dest="model_path", help='Path to the weights of the model to evaluate', required=True)
      parser.add_argument("-d", '--data', dest="data_path", help='path to the file containing the sentence to embed')
      parser.add_argument("-o", '--output', dest="output_path", help='path of the output file', default="./text_embedding")
      parser.add_argument("-bs", "--batch_size", help="The size of the batches", type=int, default=1)
  
      args = parser.parse_args()
  
      print("Loading model from:", args.model_path)
      checkpoint = torch.load(args.model_path, map_location=lambda storage, loc: storage)
  
      join_emb = joint_embedding(checkpoint['args_dict'])
      join_emb.load_state_dict(checkpoint["state_dict"])
  
      for param in join_emb.parameters():
          param.requires_grad = False
  
      join_emb.to(device)
      join_emb.eval()
  
      dataset = TextDataset(args.data_path)
      print("Dataset size: ", len(dataset))
  
      dataset_loader = DataLoader(dataset, batch_size=args.batch_size, num_workers=3, pin_memory=True, collate_fn=collate_fn_cap_padded)
  
      caps_enc = list()
  
      print("### Starting sentence embedding ###")
      end = time.time()
      for i, (caps, length) in enumerate(dataset_loader, 0):
  
          input_caps = caps.to(device)
  
          with torch.no_grad():
              _, output_emb = join_emb(None, input_caps, length)
  
          caps_enc.append(output_emb.cpu().data.numpy())
  
          if i % 100 == 99:
              print(str((i + 1) * args.batch_size) + "/" + str(len(dataset)) + " captions encoded - Time per batch: " + str((time.time() - end)) + "s")
  
          end = time.time()
  
      print("Processing done -> saving")
      caps_stack = np.vstack(caps_enc)
  
      save_obj(caps_stack, args.output_path)
      print("The data has been save to ", args.output_path)
  
apis: numpy.vstack torch.utils.data.DataLoader torch.load torch.no_grad torch.device