repo_description: Classification with PyTorch.
repo_topics: cifar10 cifar100 classification densenet imagenet preresnet pytorch resnet resnext wide-residual-networks wrn
code: '''
  Training script for ImageNet
  Copyright (c) Wei YANG, 2017
  '''
  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 torchvision.datasets as datasets
  import torchvision.models as models
  import models.imagenet as customized_models
  
  from utils import Bar, Logger, AverageMeter, accuracy, mkdir_p, savefig
  
  # Models
  default_model_names = sorted(name for name in models.__dict__
      if name.islower() and not name.startswith("__")
      and callable(models.__dict__[name]))
  
  customized_models_names = sorted(name for name in customized_models.__dict__
      if name.islower() and not name.startswith("__")
      and callable(customized_models.__dict__[name]))
  
  for name in customized_models.__dict__:
      if name.islower() and not name.startswith("__") and callable(customized_models.__dict__[name]):
          models.__dict__[name] = customized_models.__dict__[name]
  
  model_names = default_model_names + customized_models_names
  
  # Parse arguments
  parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
  
  # Datasets
  parser.add_argument('-d', '--data', default='path to dataset', type=str)
  parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                      help='number of data loading workers (default: 4)')
  # Optimization options
  parser.add_argument('--epochs', default=90, 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=256, type=int, metavar='N',
                      help='train batchsize (default: 256)')
  parser.add_argument('--test-batch', default=200, type=int, metavar='N',
                      help='test batchsize (default: 200)')
  parser.add_argument('--lr', '--learning-rate', default=0.1, 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=[150, 225],
                          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=1e-4, type=float,
                      metavar='W', help='weight decay (default: 1e-4)')
  # Checkpoints
  parser.add_argument('-c', '--checkpoint', default='checkpoint', 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)')
  # Architecture
  parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
                      choices=model_names,
                      help='model architecture: ' +
                          ' | '.join(model_names) +
                          ' (default: resnet18)')
  parser.add_argument('--depth', type=int, default=29, help='Model depth.')
  parser.add_argument('--cardinality', type=int, default=32, help='ResNet cardinality (group).')
  parser.add_argument('--base-width', type=int, default=4, help='ResNet base width.')
  parser.add_argument('--widen-factor', type=int, default=4, help='Widen factor. 4 -> 64, 8 -> 128, ...')
  # Miscs
  parser.add_argument('--manualSeed', type=int, help='manual seed')
  parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                      help='evaluate model on validation set')
  parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                      help='use pre-trained model')
  #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)
  
      # Data loading code
      traindir = os.path.join(args.data, 'train')
      valdir = os.path.join(args.data, 'val')
      normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                       std=[0.229, 0.224, 0.225])
  
      train_loader = torch.utils.data.DataLoader(
          datasets.ImageFolder(traindir, transforms.Compose([
              transforms.RandomSizedCrop(224),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              normalize,
          ])),
          batch_size=args.train_batch, shuffle=True,
          num_workers=args.workers, pin_memory=True)
  
      val_loader = torch.utils.data.DataLoader(
          datasets.ImageFolder(valdir, transforms.Compose([
              transforms.Scale(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              normalize,
          ])),
          batch_size=args.test_batch, shuffle=False,
          num_workers=args.workers, pin_memory=True)
  
      # create model
      if args.pretrained:
          print("=> using pre-trained model '{}'".format(args.arch))
          model = models.__dict__[args.arch](pretrained=True)
      elif args.arch.startswith('resnext'):
          model = models.__dict__[args.arch](
                      baseWidth=args.base_width,
                      cardinality=args.cardinality,
                  )
      else:
          print("=> creating model '{}'".format(args.arch))
          model = models.__dict__[args.arch]()
  
      if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
          model.features = torch.nn.DataParallel(model.features)
          model.cuda()
      else:
          model = torch.nn.DataParallel(model).cuda()
  
      cudnn.benchmark = True
      print('    Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
  
      # define loss function (criterion) and optimizer
      criterion = nn.CrossEntropyLoss().cuda()
      optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
  
      # Resume
      title = 'ImageNet-' + args.arch
      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(val_loader, 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(train_loader, model, criterion, optimizer, epoch, use_cuda)
          test_loss, test_acc = test(val_loader, 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(train_loader, model, 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(train_loader))
      for batch_idx, (inputs, targets) in enumerate(train_loader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          if use_cuda:
              inputs, targets = inputs.cuda(), targets.cuda(async=True)
          inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)
  
          # compute output
          outputs = model(inputs)
          loss = criterion(outputs, targets)
  
          # measure accuracy and record loss
          prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
          losses.update(loss.data[0], inputs.size(0))
          top1.update(prec1[0], inputs.size(0))
          top5.update(prec5[0], inputs.size(0))
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          # 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(train_loader),
                      data=data_time.val,
                      bt=batch_time.val,
                      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(val_loader, 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(val_loader))
      for batch_idx, (inputs, targets) in enumerate(val_loader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          if use_cuda:
              inputs, targets = inputs.cuda(), targets.cuda()
          inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)
  
          # compute output
          outputs = model(inputs)
          loss = criterion(outputs, targets)
  
          # measure accuracy and record loss
          prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
          losses.update(loss.data[0], inputs.size(0))
          top1.update(prec1[0], inputs.size(0))
          top5.update(prec5[0], 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(val_loader),
                      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()
  