code: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  
  '''
  This is the implement of standard training on GTSRB dataset.
  
  Copyright (c) Yiming Li, 2020
  '''
  
  
  from __future__ import print_function
  
  import argparse
  import os
  import shutil
  import time
  import random
  import torch
  import torch.nn as nn
  import torch.nn.parallel
  import torch.backends.cudnn as cudnn
  import torch.optim as optim
  import torch.utils.data as data
  import torchvision.transforms as transforms
  import gtsrb_dataset as dataset
  from model import *
  from utils import Bar, Logger, AverageMeter, accuracy, mkdir_p, savefig
  
  
  
  parser = argparse.ArgumentParser(description='PyTorch GTSRB')
  # Datasets
  parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
                      help='number of data loading workers (default: 2)')
  # Optimization options
  parser.add_argument('--epochs', default=30, type=int, metavar='N',
                      help='number of total epochs to run')
  parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                      help='manual epoch number (useful on restarts)')
  parser.add_argument('--train-batch', default=128, type=int, metavar='N',
                      help='train batchsize')
  parser.add_argument('--test-batch', default=128, type=int, metavar='N',
                      help='test batchsize')
  parser.add_argument('--lr', '--learning-rate', default=0.01, type=float,
                      metavar='LR', help='initial learning rate')
  parser.add_argument('--drop', '--dropout', default=0, type=float,
                      metavar='Dropout', help='Dropout ratio')
  parser.add_argument('--schedule', type=int, nargs='+', default=[20],
                          help='Decrease learning rate at these epochs.')
  parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.')
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                      help='momentum')
  parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
                      metavar='W', help='weight decay (default: 1e-4)')
  # Checkpoints
  parser.add_argument('-c', '--checkpoint', default='checkpoint/benign', type=str, metavar='PATH',
                      help='path to save checkpoint (default: checkpoint)')
  parser.add_argument('--resume', default='', type=str, metavar='PATH',
                      help='path to latest checkpoint (default: none)')
  # Miscs
  parser.add_argument('--manualSeed', type=int, default=1, help='manual seed')
  parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                      help='evaluate model on validation set')
  #Device options
  parser.add_argument('--gpu-id', default='0', type=str,
                      help='id(s) for CUDA_VISIBLE_DEVICES')
  
  
  
  args = parser.parse_args()
  state = {k: v for k, v in args._get_kwargs()}
  
  # Use CUDA
  os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
  use_cuda = torch.cuda.is_available()
  
  
  # Random seed
  if args.manualSeed is None:
      args.manualSeed = random.randint(1, 10000)
  random.seed(args.manualSeed)
  torch.manual_seed(args.manualSeed)
  if use_cuda:
      torch.cuda.manual_seed_all(args.manualSeed)
  
  best_acc = 0  # best test accuracy
  
  
  
  
  def main():
      global best_acc
      start_epoch = args.start_epoch  # start from epoch 0 or last checkpoint epoch
  
      if not os.path.isdir(args.checkpoint):
          mkdir_p(args.checkpoint)
  
      # Dataset preprocessing
      title = 'GTSRB'
  
      print('==> Preparing GTSRB dataset')
      transform = transforms.Compose([
          transforms.Resize((32, 32)),
          transforms.ToTensor()
      ])
  
      # Create Datasets
      trainset = dataset.GTSRB(
          root_dir='./data', train=True, transform=transform)
      testset = dataset.GTSRB(
          root_dir='./data', train=False, transform=transform)
  
      # Load Datasets
      trainloader = torch.utils.data.DataLoader(
          trainset, batch_size=args.train_batch, shuffle=True, num_workers=args.workers)
      testloader = torch.utils.data.DataLoader(
          testset, batch_size=args.test_batch, shuffle=False, num_workers=args.workers)
  
      # Model
      model = vgg19_bn()
      model = torch.nn.DataParallel(model).cuda()
      cudnn.benchmark = True
      print('Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
      criterion = nn.CrossEntropyLoss()
      optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
  
      # Resume
      if args.resume:
          # Load checkpoint.
          print('==> Resuming from checkpoint..')
          assert os.path.isfile(args.resume), 'Error: no checkpoint directory found!'
          args.checkpoint = os.path.dirname(args.resume)
          checkpoint = torch.load(args.resume)
          best_acc = checkpoint['best_acc']
          start_epoch = checkpoint['epoch']
          model.load_state_dict(checkpoint['state_dict'])
          optimizer.load_state_dict(checkpoint['optimizer'])
          logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
      else:
          logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
          logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.'])
  
      if args.evaluate:
          print('\nEvaluation only')
          test_loss, test_acc = test(testloader, model, criterion, start_epoch, use_cuda)
          print(' Test Loss:  %.8f, Test Acc:  %.2f' % (test_loss, test_acc))
          return
  
      # Train and val
      for epoch in range(start_epoch, args.epochs):
          adjust_learning_rate(optimizer, epoch)
  
          print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, state['lr']))
  
          train_loss, train_acc = train(args, model, trainloader, criterion, optimizer, epoch, use_cuda)
          test_loss, test_acc = test(testloader, model, criterion, epoch, use_cuda)
  
          # append logger file
          logger.append([state['lr'], train_loss, test_loss, train_acc, test_acc])
  
          # save model
          is_best = test_acc > best_acc
          best_acc = max(test_acc, best_acc)
          save_checkpoint({
                  'epoch': epoch + 1,
                  'state_dict': model.state_dict(),
                  'acc': test_acc,
                  'best_acc': best_acc,
                  'optimizer' : optimizer.state_dict(),
              }, is_best, checkpoint=args.checkpoint)
  
      logger.close()
      logger.plot()
      savefig(os.path.join(args.checkpoint, 'log.eps'))
  
      print('Best acc:')
      print(best_acc)
  
  
  
  
  
  def train(args, model, trainloader, criterion, optimizer, epoch, use_cuda):
      # switch to train mode
      model.train()
  
      batch_time = AverageMeter()
      data_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
      end = time.time()
  
      bar = Bar('Processing', max=len(trainloader))
      
      for batch_idx, (image, target) in enumerate(trainloader):
          # measure data loading time
          data_time.update(time.time() - end)
          if use_cuda:
              image, target = image.cuda(), target.cuda()
          
  
          # compute loss and do SGD step
          outputs = model(image)
          loss = criterion(outputs, target)
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          # measure train accuracy and record loss
          prec1, prec5 = accuracy(outputs.data, target.data, topk=(1, 5))
          losses.update(loss.item(), image.size(0))
          top1.update(prec1.item(), image.size(0))
          top5.update(prec5.item(), image.size(0))
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          # plot progress
          bar.suffix  = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
                      batch=batch_idx + 1,
                      size=len(trainloader),
                      data=data_time.avg,
                      bt=batch_time.avg,
                      total=bar.elapsed_td,
                      eta=bar.eta_td,
                      loss=losses.avg,
                      top1=top1.avg,
                      top5=top5.avg,
                      )
          bar.next()
      bar.finish()
      return (losses.avg, top1.avg)
  
  
  
  
  def test(testloader, model, criterion, epoch, use_cuda):
      global best_acc
  
      batch_time = AverageMeter()
      data_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to evaluate mode
      model.eval()
  
      end = time.time()
      bar = Bar('Processing', max=len(testloader))
      for batch_idx, (inputs, targets) in enumerate(testloader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          if use_cuda:
              inputs, targets = inputs.cuda(), targets.cuda()
  
          # compute output
          outputs = model(inputs)
          loss = criterion(outputs, targets)
  
          # measure accuracy and record standard loss
          prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
          losses.update(loss.item(), inputs.size(0))
          top1.update(prec1.item(), inputs.size(0))
          top5.update(prec5.item(), inputs.size(0))
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          # plot progress
          bar.suffix  = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
                      batch=batch_idx + 1,
                      size=len(testloader),
                      data=data_time.avg,
                      bt=batch_time.avg,
                      total=bar.elapsed_td,
                      eta=bar.eta_td,
                      loss=losses.avg,
                      top1=top1.avg,
                      top5=top5.avg,
                      )
          bar.next()
      bar.finish()
      return (losses.avg, top1.avg)
  
  
  def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar'):
      filepath = os.path.join(checkpoint, filename)
      torch.save(state, filepath)
      if is_best:
          shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar'))
  
  
  
  
  def adjust_learning_rate(optimizer, epoch):
      global state
      if epoch in args.schedule:
          state['lr'] *= args.gamma
          for param_group in optimizer.param_groups:
              param_group['lr'] = state['lr']
  
  
  
  if __name__ == '__main__':
      main()
  
apis: torch.utils.data.DataLoader torch.cuda.manual_seed_all torch.load torch.manual_seed torch.save torch.nn.CrossEntropyLoss torch.cuda.is_available torch.nn.DataParallel