text: import argparse, os
  import torch
  import random
  import torch.backends.cudnn as cudnn
  import torch.nn as nn
  import torch.optim as optim
  from torch.autograd import Variable
  from torch.utils.data import DataLoader
  from vdsr import Net
  from dataset import DatasetFromHdf5
  
  # Training settings
  parser = argparse.ArgumentParser(description="PyTorch VDSR")
  parser.add_argument("--batchSize", type=int, default=128, help="Training batch size")
  parser.add_argument("--nEpochs", type=int, default=50, help="Number of epochs to train for")
  parser.add_argument("--lr", type=float, default=0.1, help="Learning Rate. Default=0.1")
  parser.add_argument("--step", type=int, default=10, help="Sets the learning rate to the initial LR decayed by momentum every n epochs, Default: n=10")
  parser.add_argument("--cuda", action="store_true", help="Use cuda?")
  parser.add_argument("--resume", default="", type=str, help="Path to checkpoint (default: none)")
  parser.add_argument("--start-epoch", default=1, type=int, help="Manual epoch number (useful on restarts)")
  parser.add_argument("--clip", type=float, default=0.4, help="Clipping Gradients. Default=0.4")
  parser.add_argument("--threads", type=int, default=1, help="Number of threads for data loader to use, Default: 1")
  parser.add_argument("--momentum", default=0.9, type=float, help="Momentum, Default: 0.9")
  parser.add_argument("--weight-decay", "--wd", default=1e-4, type=float, help="Weight decay, Default: 1e-4")
  parser.add_argument('--pretrained', default='', type=str, help='path to pretrained model (default: none)')
  parser.add_argument("--gpus", default="0", type=str, help="gpu ids (default: 0)")
  
  def main():
      global opt, model
      opt = parser.parse_args()
      print(opt)
  
      cuda = opt.cuda
      if cuda:
          print("=> use gpu id: '{}'".format(opt.gpus))
          os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpus
          if not torch.cuda.is_available():
                  raise Exception("No GPU found or Wrong gpu id, please run without --cuda")
  
      opt.seed = random.randint(1, 10000)
      print("Random Seed: ", opt.seed)
      torch.manual_seed(opt.seed)
      if cuda:
          torch.cuda.manual_seed(opt.seed)
  
      cudnn.benchmark = True
  
      print("===> Loading datasets")
      train_set = DatasetFromHdf5("data/train.h5")
      training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)
  
      print("===> Building model")
      model = Net()
      criterion = nn.MSELoss(size_average=False)
  
      print("===> Setting GPU")
      if cuda:
          model = model.cuda()
          criterion = criterion.cuda()
  
      # optionally resume from a checkpoint
      if opt.resume:
          if os.path.isfile(opt.resume):
              print("=> loading checkpoint '{}'".format(opt.resume))
              checkpoint = torch.load(opt.resume)
              opt.start_epoch = checkpoint["epoch"] + 1
              model.load_state_dict(checkpoint["model"].state_dict())
          else:
              print("=> no checkpoint found at '{}'".format(opt.resume))
  
      # optionally copy weights from a checkpoint
      if opt.pretrained:
          if os.path.isfile(opt.pretrained):
              print("=> loading model '{}'".format(opt.pretrained))
              weights = torch.load(opt.pretrained)
              model.load_state_dict(weights['model'].state_dict())
          else:
              print("=> no model found at '{}'".format(opt.pretrained))  
  
      print("===> Setting Optimizer")
      optimizer = optim.SGD(model.parameters(), lr=opt.lr, momentum=opt.momentum, weight_decay=opt.weight_decay)
  
      print("===> Training")
      for epoch in range(opt.start_epoch, opt.nEpochs + 1):
          train(training_data_loader, optimizer, model, criterion, epoch)
          save_checkpoint(model, epoch)
  
  def adjust_learning_rate(optimizer, epoch):
      """Sets the learning rate to the initial LR decayed by 10 every 10 epochs"""
      lr = opt.lr * (0.1 ** (epoch // opt.step))
      return lr
  
  def train(training_data_loader, optimizer, model, criterion, epoch):
      lr = adjust_learning_rate(optimizer, epoch-1)
  
      for param_group in optimizer.param_groups:
          param_group["lr"] = lr
  
      print("Epoch = {}, lr = {}".format(epoch, optimizer.param_groups[0]["lr"]))
  
      model.train()
  
      for iteration, batch in enumerate(training_data_loader, 1):
          input, target = Variable(batch[0]), Variable(batch[1], requires_grad=False)
  
          if opt.cuda:
              input = input.cuda()
              target = target.cuda()
  
          loss = criterion(model(input), target)
          optimizer.zero_grad()
          loss.backward() 
          nn.utils.clip_grad_norm(model.parameters(),opt.clip) 
          optimizer.step()
  
          if iteration%100 == 0:
              print("===> Epoch[{}]({}/{}): Loss: {:.10f}".format(epoch, iteration, len(training_data_loader), loss.data[0]))
  
  def save_checkpoint(model, epoch):
      model_out_path = "checkpoint/" + "model_epoch_{}.pth".format(epoch)
      state = {"epoch": epoch ,"model": model}
      if not os.path.exists("checkpoint/"):
          os.makedirs("checkpoint/")
  
      torch.save(state, model_out_path)
  
      print("Checkpoint saved to {}".format(model_out_path))
  
  if __name__ == "__main__":
      main()