code: import argparse
  import os
  import numpy as np
  from tqdm import tqdm
  from ipdb import set_trace
  from mypath import Path
  from dataloaders import make_data_loader
  from modeling.sync_batchnorm.replicate import patch_replication_callback
  from modeling.deeplab import *
  from modeling.unet import *
  from utils.loss import SegmentationLosses
  from utils.calculate_weights import calculate_weigths_labels
  from utils.lr_scheduler import LR_Scheduler
  from utils.saver import Saver
  from utils.summaries import TensorboardSummary
  from utils.metrics import Evaluator
  from dataloaders.utils import encode_segmap
  
  class Trainer(object):
      def __init__(self, args):
          self.args = args
  
          # Define Saver
          self.saver = Saver(args)
          self.saver.save_experiment_config()
          # Define Tensorboard Summary
          self.summary = TensorboardSummary(self.saver.experiment_dir)
          self.writer = self.summary.create_summary()
  
          # Define Dataloader
          kwargs = {'num_workers': args.workers, 'pin_memory': True}
          self.train_loader, self.val_loader, self.test_loader, self.nclass = make_data_loader(args, **kwargs)
  
          # Define network
          if args.model == 'deeplab':
              model = DeepLab(num_classes=self.nclass,
                          backbone=args.backbone,
                          output_stride=args.out_stride,
                          sync_bn=args.sync_bn,
                          freeze_bn=args.freeze_bn)
              model_params = [{'params': model.get_1x_lr_params(), 'lr': args.lr},
              {'params': model.get_10x_lr_params(), 'lr': args.lr * 10}]
          elif args.model == 'unet':
              model = UNet(n_channels=3,
                           n_classes=self.nclass)
              model_params = model.parameters()
  
          # Define Optimizer
          optimizer = torch.optim.SGD(model_params, lr=args.lr,
                                      momentum=args.momentum,
                                      weight_decay=args.weight_decay,
                                      nesterov=args.nesterov)
  
          # Define Criterion
          # whether to use class balanced weights
          if args.use_balanced_weights:
              classes_weights_path = os.path.join(Path.db_root_dir(args.dataset), args.dataset+'_classes_weights.npy')
              if os.path.isfile(classes_weights_path):
                  weight = np.load(classes_weights_path)
              else:
                  weight = calculate_weigths_labels(args.dataset, self.train_loader, self.nclass)
              weight = torch.from_numpy(weight.astype(np.float32))
          else:
              weight = None
          self.criterion = SegmentationLosses(weight=weight, cuda=args.cuda).build_loss(mode=args.loss_type)
          self.model, self.optimizer = model, optimizer
  
          # Define Evaluator
          self.evaluator = Evaluator(self.nclass)
          # Define lr scheduler
          self.scheduler = LR_Scheduler(args.lr_scheduler, args.lr,
                                              args.epochs, len(self.train_loader))
  
          # Using cuda
          if args.cuda:
              self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids)
              patch_replication_callback(self.model)
              self.model = self.model.cuda()
  
          # Resuming checkpoint
          self.best_pred = 0.0
          if args.resume is not None:
              if not os.path.isfile(args.resume):
                  raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume))
              checkpoint = torch.load(args.resume)
              args.start_epoch = checkpoint['epoch']
              if args.cuda:
                  self.model.module.load_state_dict(checkpoint['state_dict'])
              else:
                  self.model.load_state_dict(checkpoint['state_dict'])
              if not args.ft:
                  self.optimizer.load_state_dict(checkpoint['optimizer'])
              self.best_pred = checkpoint['best_pred']
              print("=> loaded checkpoint '{}' (epoch {})"
                    .format(args.resume, checkpoint['epoch']))
  
          # Clear start epoch if fine-tuning
          if args.ft:
              args.start_epoch = 0
  
      def training(self, epoch):
          train_loss = 0.0
          self.model.train()
          tbar = tqdm(self.train_loader)
          num_img_tr = len(self.train_loader)
          #$set_trace()
          for i, sample in enumerate(tbar):
              image, target = sample['image'], sample['label']
              if self.args.cuda:
                  image, target = image.cuda(), target.cuda()
              self.scheduler(self.optimizer, i, epoch, self.best_pred)
              self.optimizer.zero_grad()
              if self.args.model == 'deeplab':
                  output, fuse = self.model(image)
              elif self.args.model == 'unet':
                  output = self.model(image)
              #print("lalala",output.shape)
              loss = self.criterion(output, target)
              loss.backward()
              self.optimizer.step()
              train_loss += loss.item()
              tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1)))
              self.writer.add_scalar('train/total_loss_iter', loss.item(), i + num_img_tr * epoch)
  
              # Show 10 * 3 inference results each epoch
              if i % (num_img_tr // 10) == 0:
                  global_step = i + num_img_tr * epoch
                  self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step)
  
          self.writer.add_scalar('train/total_loss_epoch', train_loss, epoch)
          print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0]))
          print('Loss: %.3f' % train_loss)
  
          if self.args.no_val == 'True':
              # save checkpoint every epoch
              is_best = False
              self.saver.save_checkpoint({
                  'epoch': epoch + 1,
                  'state_dict': self.model.module.state_dict(),
                  'optimizer': self.optimizer.state_dict(),
                  'best_pred': self.best_pred,
              }, is_best)
  
  
      def validation(self, epoch):
          self.model.eval()
          self.evaluator.reset()
          tbar = tqdm(self.val_loader, desc='\r')
          test_loss = 0.0
          for i, sample in enumerate(tbar):
              image, target = sample['image'], sample['label']
              if self.args.cuda:
                  image, target = image.cuda(), target.cuda()
              with torch.no_grad():
                  if self.args.model == 'deeplab':
                      output, fuse = self.model(image)
                  else:
                      output = self.model(image)
              loss = self.criterion(output, target)
              test_loss += loss.item()
              tbar.set_description('Test loss: %.3f' % (test_loss / (i + 1)))
              pred = output.data.cpu().numpy()
              target = target.cpu().numpy()
              pred = np.argmax(pred, axis=1)
              # Add batch sample into evaluator
              self.evaluator.add_batch(target, pred)
  	
          # Fast test during the training
          Acc = self.evaluator.Pixel_Accuracy()
          Acc_class = self.evaluator.Pixel_Accuracy_Class()
          mIoU = self.evaluator.Mean_Intersection_over_Union()
          FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union()
          Jaccard = self.evaluator.Jaccard()
          Dice    = self.evaluator.Dice()
          self.writer.add_scalar('val/total_loss_epoch', test_loss, epoch)
          self.writer.add_scalar('val/mIoU', mIoU, epoch)
          self.writer.add_scalar('val/Acc', Acc, epoch)
          self.writer.add_scalar('val/Acc_class', Acc_class, epoch)
          self.writer.add_scalar('val/fwIoU', FWIoU, epoch)
          self.writer.add_scalar('val/dice', Dice, epoch)
          self.writer.add_scalar('val/jaccard', Jaccard, epoch)
          print('Validation:')
          print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0]))
          print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(Acc, Acc_class, mIoU, FWIoU))
          print('Loss: %.3f' % test_loss)
  
          new_pred = mIoU
          if new_pred > self.best_pred:
              is_best = True
              self.best_pred = new_pred
              self.saver.save_checkpoint({
                  'epoch': epoch + 1,
                  'state_dict': self.model.module.state_dict(),
                  'optimizer': self.optimizer.state_dict(),
                  'best_pred': self.best_pred,
              }, is_best)
  
  def main():
      parser = argparse.ArgumentParser(description="PyTorch DeeplabV3Plus Training")
      parser.add_argument('--model', type=str, default='deeplab',
                          choices=['deeplab', 'unet'], help='model name (default:deeplab)')
      parser.add_argument('--backbone', type=str, default='resnet',
                          choices=['resnet', 'xception', 'drn', 'mobilenet'],
                          help='backbone name (default: resnet)')
      parser.add_argument('--out-stride', type=int, default=16,
                          help='network output stride (default: 8)')
      parser.add_argument('--dataset', type=str, default='pascal',
                          choices=['pascal', 'coco', 'cityscapes', 'isic','chaos1','chaos2'],
                          help='dataset name (default: pascal)')
      parser.add_argument('--train-percent',type=float,default=0.8,help='the percent of train_dataset and total_dataset')
      parser.add_argument('--use-sbd', action='store_true', default=False,
                          help='whether to use SBD dataset (default: True)')
      parser.add_argument('--high-confidence', action='store_true', default=False,
                          help='whether use high-confidence (default: Frue)')
      parser.add_argument('--workers', type=int, default=4,
                          metavar='N', help='dataloader threads')
      parser.add_argument('--base-size', type=int, default=513,
                          help='base image size')
      parser.add_argument('--crop-size', type=int, default=256,
                          help='crop image size')
      parser.add_argument('--sync-bn', type=bool, default=None,
                          help='whether to use sync bn (default: auto)')
      parser.add_argument('--freeze-bn', type=bool, default=False,
                          help='whether to freeze bn parameters (default: False)')
      parser.add_argument('--loss-type', type=str, default='ce',
                          choices=['ce', 'focal'],
                          help='loss func type (default: ce)')
      # training hyper params
      parser.add_argument('--epochs', type=int, default=None, metavar='N',
                          help='number of epochs to train (default: auto)')
      parser.add_argument('--start_epoch', type=int, default=0,
                          metavar='N', help='start epochs (default:0)')
      parser.add_argument('--batch-size', type=int, default=None,
                          metavar='N', help='input batch size for \
                                  training (default: auto)')
      parser.add_argument('--test-batch-size', type=int, default=None,
                          metavar='N', help='input batch size for \
                                  testing (default: auto)')
      parser.add_argument('--use-balanced-weights', action='store_true', default=False,
                          help='whether to use balanced weights (default: False)')
      # optimizer params
      parser.add_argument('--lr', type=float, default=None, metavar='LR',
                          help='learning rate (default: auto)')
      parser.add_argument('--lr-scheduler', type=str, default='poly',
                          choices=['poly', 'step', 'cos'],
                          help='lr scheduler mode: (default: poly)')
      parser.add_argument('--momentum', type=float, default=0.9,
                          metavar='M', help='momentum (default: 0.9)')
      parser.add_argument('--percent', type=float, default=0.6,
                          metavar='M', help='percent of pseudo labels (default: 0.6)')
      parser.add_argument('--label-percent', type=float, default=0.6,
                          metavar='M', help='percent of pseudo labels (default: 0.6)')
      parser.add_argument('--weight-decay', type=float, default=5e-4,
                          metavar='M', help='w-decay (default: 5e-4)')
      parser.add_argument('--nesterov', action='store_true', default=False,
                          help='whether use nesterov (default: False)')
      # cuda, seed and logging
      parser.add_argument('--no-cuda', action='store_true', default=
                          False, help='disables CUDA training')
      parser.add_argument('--gpu-ids', type=str, default='0',
                          help='use which gpu to train, must be a \
                          comma-separated list of integers only (default=0)')
      parser.add_argument('--seed', type=int, default=1, metavar='S',
                          help='random seed (default: 1)')
      # checking point
      parser.add_argument('--resume', type=str, default=None,
                          help='put the path to resuming file if needed')
      parser.add_argument('--checkname', type=str, default=None,
                          help='set the checkpoint name')
      # finetuning pre-trained models
      parser.add_argument('--ft', action='store_true', default=False,
                          help='finetuning on a different dataset')
      # evaluation option
      parser.add_argument('--eval-interval', type=int, default=1,
                          help='evaluuation interval (default: 1)')
      parser.add_argument('--no-val', type=str, default=False,
                          help='skip validation during training')
  
      args = parser.parse_args()
      args.cuda = not args.no_cuda and torch.cuda.is_available()
      if args.cuda:
          try:
              args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
          except ValueError:
              raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
  
      if args.sync_bn is None:
          if args.cuda and len(args.gpu_ids) > 1:
              args.sync_bn = True
          else:
              args.sync_bn = False
  
      # default settings for epochs, batch_size and lr
      if args.epochs is None:
          epoches = {
              'coco': 30,
              'cityscapes': 200,
              'pascal': 50,
          }
          args.epochs = epoches[args.dataset.lower()]
  
      if args.batch_size is None:
          args.batch_size = 4 * len(args.gpu_ids)
  
      if args.test_batch_size is None:
          args.test_batch_size = args.batch_size
  
      if args.lr is None:
          lrs = {
              'coco': 0.1,
              'cityscapes': 0.01,
              'pascal': 0.007,
          }
          args.lr = lrs[args.dataset.lower()] / (4 * len(args.gpu_ids)) * args.batch_size
  
  
      if args.checkname is None:
          args.checkname = 'deeplab-'+str(args.backbone)
      print(args)
      #torch.manual_seed(args.seed)
      trainer = Trainer(args)
      print('Starting Epoch:', trainer.args.start_epoch)
      print('Total Epoches:', trainer.args.epochs)
      for epoch in range(trainer.args.start_epoch, trainer.args.epochs):
          trainer.training(epoch)
          if not trainer.args.no_val and epoch % args.eval_interval == (args.eval_interval - 1):
              trainer.validation(epoch)
  
      trainer.writer.close()
  
  if __name__ == "__main__":
     main()
  
apis: numpy.load numpy.argmax