text: import torch.nn
  from utils.dataset import ijgiDataset as Dataset
  from models.sequenceencoder import LSTMSequentialEncoder
  from utils.logger import Logger, Printer, VisdomLogger
  import argparse
  from utils.snapshot import save, resume
  import os
  
  def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument("data", type=str,help="path to dataset")
      parser.add_argument('-b', "--batchsize", default=1 , type=int, help="batch size")
      parser.add_argument('-w', "--workers", default=0, type=int, help="number of dataset worker threads")
      parser.add_argument('-e', "--epochs", default=10, type=int, help="epochs to train")
      parser.add_argument('-l', "--learning_rate", default=1e-3, type=float, help="learning rate")
      parser.add_argument('-s', "--snapshot", default=None, type=str, help="load weights from snapshot")
      parser.add_argument('-c', "--checkpoint_dir", default=None, type=str, help="directory to save checkpoints")
      return parser.parse_args()
  
  def main(
      datadir,
      batchsize = 16,
      workers = 0,
      epochs = 100,
      lr = 1e-3,
      snapshot = None,
      checkpoint_dir = None
      ):
  
      traindataset = Dataset(datadir, tileids="tileids/train_fold0.tileids")
      testdataset = Dataset(datadir, tileids="tileids/test_fold0.tileids")
  
      nclasses = len(traindataset.classes)
  
      traindataloader = torch.utils.data.DataLoader(traindataset,batch_size=batchsize,shuffle=True,num_workers=workers)
      testdataloader = torch.utils.data.DataLoader(testdataset,batch_size=batchsize,shuffle=False,num_workers=workers)
  
      logger = Logger(columns=["loss"], modes=["train", "test"])
  
      vizlogger = VisdomLogger()
  
      network = LSTMSequentialEncoder(48,48,nclasses=nclasses)
  
      optimizer = torch.optim.Adam(network.parameters(), lr=lr)
      loss = torch.nn.NLLLoss()
  
      if torch.cuda.is_available():
          network = torch.nn.DataParallel(network).cuda()
          loss = loss.cuda()
  
      start_epoch = 0
  
      if snapshot is not None:
          state = resume(snapshot,model=network, optimizer=optimizer)
  
          if "epoch" in state.keys():
              start_epoch = state["epoch"]
  
          if "data" in state.keys():
              logger.resume(state["data"])
  
      for epoch in range(start_epoch, epochs):
  
          logger.update_epoch(epoch)
  
          print("\nEpoch {}".format(epoch))
          print("train")
          train_epoch(traindataloader, network, optimizer, loss, loggers=(logger,vizlogger))
          print("\ntest")
          test_epoch(testdataloader, network,loss, loggers=(logger, vizlogger))
  
          data = logger.get_data()
          vizlogger.update(data)
  
          if checkpoint_dir is not None:
              checkpoint_name = os.path.join(checkpoint_dir,"model_{:02d}.pth".format(epoch))
              save(checkpoint_name, network, optimizer, epoch=epoch, data=data)
  
  def train_epoch(dataloader, network, optimizer, loss, loggers):
      logger, vizlogger = loggers
  
      printer = Printer(N=len(dataloader))
      logger.set_mode("train")
  
      for iteration, data in enumerate(dataloader):
          optimizer.zero_grad()
  
          input, target = data
  
          if torch.cuda.is_available():
              input = input.cuda()
              target = target.cuda()
  
          output = network.forward(input)
          l = loss(output, target)
          #print(l)
          stats = {"loss":l.data.cpu().numpy()}
  
          l.backward()
          optimizer.step()
  
          printer.print(stats, iteration)
          logger.log(stats, iteration)
          vizlogger.plot_steps(logger.get_data())
  
  def test_epoch(dataloader, network, loss, loggers):
      logger, vizlogger = loggers
  
      printer = Printer(N=len(dataloader))
      logger.set_mode("test")
  
      with torch.no_grad():
          for iteration, data in enumerate(dataloader):
  
              input, target = data
  
              if torch.cuda.is_available():
                  input = input.cuda()
                  target = target.cuda()
  
              output = network.forward(input)
              l = loss(output, target)
              #print(l)
              stats = {"loss":l.data.cpu().numpy()}
  
              printer.print(stats, iteration)
              logger.log(stats, iteration)
              vizlogger.plot_steps(logger.get_data())
  
          vizlogger.plot_images(target.cpu().detach().numpy(), output.cpu().detach().numpy())
  
  if __name__ == "__main__":
  
      args = parse_args()
  
      main(
          args.data,
          batchsize=args.batchsize,
          workers=args.workers,
          epochs=args.epochs,
          lr=args.learning_rate,
          snapshot=args.snapshot,
          checkpoint_dir=args.checkpoint_dir
      )
  