code: import os
  import sys
  import time
  import random
  import string
  import argparse
  
  import torch
  import torch.backends.cudnn as cudnn
  import torch.nn.init as init
  import torch.optim as optim
  import torch.utils.data
  import numpy as np
  
  from utils import CTCLabelConverter, CTCLabelConverterForBaiduWarpctc, AttnLabelConverter, Averager
  from dataset import hierarchical_dataset, AlignCollate, Batch_Balanced_Dataset
  from model import Model
  from test import validation
  
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  
  
  def train(opt):
      """ dataset preparation """
      if not opt.data_filtering_off:
          print('Filtering the images containing characters which are not in opt.character')
          print('Filtering the images whose label is longer than opt.batch_max_length')
          # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130
  
      opt.select_data = opt.select_data.split('-')
      opt.batch_ratio = opt.batch_ratio.split('-')
      train_dataset = Batch_Balanced_Dataset(opt)
  
      log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a')
      AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)
      valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt)
      valid_loader = torch.utils.data.DataLoader(
          valid_dataset, batch_size=opt.batch_size,
          shuffle=True,  # 'True' to check training progress with validation function.
          num_workers=int(opt.workers),
          collate_fn=AlignCollate_valid, pin_memory=True)
      log.write(valid_dataset_log)
      print('-' * 80)
      log.write('-' * 80 + '\n')
      log.close()
  
      """ model configuration """
      if 'CTC' in opt.Prediction:
          if opt.baiduCTC:
              converter = CTCLabelConverterForBaiduWarpctc(opt.character)
          else:
              converter = CTCLabelConverter(opt.character)
      else:
          converter = AttnLabelConverter(opt.character)
      opt.num_class = len(converter.character)
  
      if opt.rgb:
          opt.input_channel = 3
      model = Model(opt)
      print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel,
            opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction,
            opt.SequenceModeling, opt.Prediction)
  
      # weight initialization
      for name, param in model.named_parameters():
          if 'localization_fc2' in name:
              print(f'Skip {name} as it is already initialized')
              continue
          try:
              if 'bias' in name:
                  init.constant_(param, 0.0)
              elif 'weight' in name:
                  init.kaiming_normal_(param)
          except Exception as e:  # for batchnorm.
              if 'weight' in name:
                  param.data.fill_(1)
              continue
  
      # data parallel for multi-GPU
      model = torch.nn.DataParallel(model).to(device)
      model.train()
      if opt.saved_model != '':
          print(f'loading pretrained model from {opt.saved_model}')
          if opt.FT:
              model.load_state_dict(torch.load(opt.saved_model), strict=False)
          else:
              model.load_state_dict(torch.load(opt.saved_model))
      print("Model:")
      print(model)
  
      """ setup loss """
      if 'CTC' in opt.Prediction:
          if opt.baiduCTC:
              # need to install warpctc. see our guideline.
              from warpctc_pytorch import CTCLoss
              criterion = CTCLoss()
          else:
              criterion = torch.nn.CTCLoss(zero_infinity=True).to(device)
      else:
          criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device)  # ignore [GO] token = ignore index 0
      # loss averager
      loss_avg = Averager()
  
      # filter that only require gradient decent
      filtered_parameters = []
      params_num = []
      for p in filter(lambda p: p.requires_grad, model.parameters()):
          filtered_parameters.append(p)
          params_num.append(np.prod(p.size()))
      print('Trainable params num : ', sum(params_num))
      # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())]
  
      # setup optimizer
      if opt.adam:
          optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999))
      else:
          optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps)
      print("Optimizer:")
      print(optimizer)
  
      """ final options """
      # print(opt)
      with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file:
          opt_log = '------------ Options -------------\n'
          args = vars(opt)
          for k, v in args.items():
              opt_log += f'{str(k)}: {str(v)}\n'
          opt_log += '---------------------------------------\n'
          print(opt_log)
          opt_file.write(opt_log)
  
      """ start training """
      start_iter = 0
      if opt.saved_model != '':
          try:
              start_iter = int(opt.saved_model.split('_')[-1].split('.')[0])
              print(f'continue to train, start_iter: {start_iter}')
          except:
              pass
  
      start_time = time.time()
      best_accuracy = -1
      best_norm_ED = -1
      iteration = start_iter
  
      while (True):
          # train part
          image_tensors, labels = train_dataset.get_batch()
          image = image_tensors.to(device)
          text, length = converter.encode(labels, batch_max_length=opt.batch_max_length)
          batch_size = image.size(0)
  
          if 'CTC' in opt.Prediction:
              preds = model(image, text)
              preds_size = torch.IntTensor([preds.size(1)] * batch_size)
              if opt.baiduCTC:
                  preds = preds.permute(1, 0, 2)  # to use CTCLoss format
                  cost = criterion(preds, text, preds_size, length) / batch_size
              else:
                  preds = preds.log_softmax(2).permute(1, 0, 2)
                  cost = criterion(preds, text, preds_size, length)
  
          else:
              preds = model(image, text[:, :-1])  # align with Attention.forward
              target = text[:, 1:]  # without [GO] Symbol
              cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1))
  
          model.zero_grad()
          cost.backward()
          torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)  # gradient clipping with 5 (Default)
          optimizer.step()
  
          loss_avg.add(cost)
  
          # validation part
          if (
                  iteration + 1) % opt.valInterval == 0 or iteration == 0:  # To see training progress, we also conduct validation when 'iteration == 0'
              elapsed_time = time.time() - start_time
              # for log
              with open(f'./saved_models/{opt.exp_name}/log_train.txt', 'a') as log:
                  model.eval()
                  with torch.no_grad():
                      valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation(
                          model, criterion, valid_loader, converter, opt)
                  model.train()
  
                  # training loss and validation loss
                  loss_log = f'[{iteration + 1}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}'
                  loss_avg.reset()
  
                  current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}'
  
                  # keep best accuracy model (on valid dataset)
                  if current_accuracy > best_accuracy:
                      best_accuracy = current_accuracy
                      torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/best_accuracy.pth')
                  if current_norm_ED > best_norm_ED:
                      best_norm_ED = current_norm_ED
                      torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/best_norm_ED.pth')
                  best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}'
  
                  loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}'
                  print(loss_model_log)
                  log.write(loss_model_log + '\n')
  
                  # show some predicted results
                  dashed_line = '-' * 80
                  head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F'
                  predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n'
                  for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]):
                      if 'Attn' in opt.Prediction:
                          gt = gt[:gt.find('[s]')]
                          pred = pred[:pred.find('[s]')]
  
                      predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n'
                  predicted_result_log += f'{dashed_line}'
                  print(predicted_result_log)
                  log.write(predicted_result_log + '\n')
  
          # save model per 1e+5 iter.
          if (iteration + 1) % 1e+5 == 0:
              torch.save(
                  model.state_dict(), f'./saved_models/{opt.exp_name}/iter_{iteration + 1}.pth')
  
          if (iteration + 1) == opt.num_iter:
              print('end the training')
              sys.exit()
          iteration += 1
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--exp_name', help='Where to store logs and models')
      parser.add_argument('--train_data', required=True, help='path to training dataset', default="D:/data/data_lmdb_release/training")
      parser.add_argument('--valid_data', required=True, help='path to validation dataset', default="D:/data/data_lmdb_release/validation")
      parser.add_argument('--manualSeed', type=int, default=1111, help='for random seed setting')
      parser.add_argument('--workers', type=int, help='number of data loading workers', default=4)
      parser.add_argument('--batch_size', type=int, default=192, help='input batch size')
      parser.add_argument('--num_iter', type=int, default=300000, help='number of iterations to train for')
      parser.add_argument('--valInterval', type=int, default=2000, help='Interval between each validation')
      parser.add_argument('--saved_model', default='', help="path to model to continue training")
      parser.add_argument('--FT', action='store_true', help='whether to do fine-tuning')
      parser.add_argument('--adam', action='store_true', help='Whether to use adam (default is Adadelta)')
      parser.add_argument('--lr', type=float, default=1, help='learning rate, default=1.0 for Adadelta')
      parser.add_argument('--beta1', type=float, default=0.9, help='beta1 for adam. default=0.9')
      parser.add_argument('--rho', type=float, default=0.95, help='decay rate rho for Adadelta. default=0.95')
      parser.add_argument('--eps', type=float, default=1e-8, help='eps for Adadelta. default=1e-8')
      parser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping value. default=5')
      parser.add_argument('--baiduCTC', action='store_true', help='for data_filtering_off mode')
      """ Data processing """
      parser.add_argument('--select_data', type=str, default='MJ-ST',
                          help='select training data (default is MJ-ST, which means MJ and ST used as training data)')
      parser.add_argument('--batch_ratio', type=str, default='0.5-0.5',
                          help='assign ratio for each selected data in the batch')
      parser.add_argument('--total_data_usage_ratio', type=str, default='1.0',
                          help='total data usage ratio, this ratio is multiplied to total number of data.')
      parser.add_argument('--batch_max_length', type=int, default=25, help='maximum-label-length')
      parser.add_argument('--imgH', type=int, default=32, help='the height of the input image')
      parser.add_argument('--imgW', type=int, default=100, help='the width of the input image')
      parser.add_argument('--rgb', action='store_true', help='use rgb input')
      parser.add_argument('--character', type=str,
                          default='0123456789abcdefghijklmnopqrstuvwxyz', help='character label')
      parser.add_argument('--sensitive', action='store_true', help='for sensitive character mode')
      parser.add_argument('--PAD', action='store_true', help='whether to keep ratio then pad for image resize')
      parser.add_argument('--data_filtering_off', action='store_true', help='for data_filtering_off mode')
      """ Model Architecture """
      parser.add_argument('--Transformation', type=str, required=True, help='Transformation stage. None|TPS')
      parser.add_argument('--FeatureExtraction', type=str, required=True,
                          help='FeatureExtraction stage. VGG|RCNN|ResNet')
      parser.add_argument('--SequenceModeling', type=str, required=True, help='SequenceModeling stage. None|BiLSTM')
      parser.add_argument('--Prediction', type=str, required=True, help='Prediction stage. CTC|Attn')
      parser.add_argument('--num_fiducial', type=int, default=20, help='number of fiducial points of TPS-STN')
      parser.add_argument('--input_channel', type=int, default=1,
                          help='the number of input channel of Feature extractor')
      parser.add_argument('--output_channel', type=int, default=512,
                          help='the number of output channel of Feature extractor')
      parser.add_argument('--hidden_size', type=int, default=256, help='the size of the LSTM hidden state')
  
      opt = parser.parse_args()
  
      if not opt.exp_name:
          opt.exp_name = f'{opt.Transformation}-{opt.FeatureExtraction}-{opt.SequenceModeling}-{opt.Prediction}'
          opt.exp_name += f'-Seed{opt.manualSeed}'
          # print(opt.exp_name)
  
      os.makedirs(f'./saved_models/{opt.exp_name}', exist_ok=True)
  
      """ vocab / character number configuration """
      if opt.sensitive:
          # opt.character += 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
          opt.character = string.printable[:-6]  # same with ASTER setting (use 94 char).
  
      """ Seed and GPU setting """
      # print("Random Seed: ", opt.manualSeed)
      random.seed(opt.manualSeed)
      np.random.seed(opt.manualSeed)
      torch.manual_seed(opt.manualSeed)
      torch.cuda.manual_seed(opt.manualSeed)
  
      cudnn.benchmark = True
      cudnn.deterministic = True
      opt.num_gpu = torch.cuda.device_count()
      # print('device count', opt.num_gpu)
      if opt.num_gpu > 1:
          print('------ Use multi-GPU setting ------')
          print('if you stuck too long time with multi-GPU setting, try to set --workers 0')
          # check multi-GPU issue https://github.com/clovaai/deep-text-recognition-benchmark/issues/1
          opt.workers = opt.workers * opt.num_gpu
          opt.batch_size = opt.batch_size * opt.num_gpu
  
          """ previous version
          print('To equlize batch stats to 1-GPU setting, the batch_size is multiplied with num_gpu and multiplied batch_size is ', opt.batch_size)
          opt.batch_size = opt.batch_size * opt.num_gpu
          print('To equalize the number of epochs to 1-GPU setting, num_iter is divided with num_gpu by default.')
          If you dont care about it, just commnet out these line.)
          opt.num_iter = int(opt.num_iter / opt.num_gpu)
          """
  
      train(opt)
apis: torch.nn.init.kaiming_normal_ torch.optim.Adadelta torch.nn.init.constant_ torch.load torch.cuda.manual_seed torch.manual_seed torch.no_grad numpy.random.seed torch.cuda.device_count torch.nn.CrossEntropyLoss torch.optim.Adam torch.cuda.is_available torch.nn.DataParallel torch.nn.CTCLoss