Python model.NetworkCIFAR方法代碼示例

本文整理匯總了Python中model.NetworkCIFAR方法的典型用法代碼示例。如果您正苦於以下問題：Python model.NetworkCIFAR方法的具體用法？Python model.NetworkCIFAR怎麽用？Python model.NetworkCIFAR使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類model的用法示例。

在下文中一共展示了model.NetworkCIFAR方法的9個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: main

# 需要導入模塊: import model [as 別名]
# 或者: from model import NetworkCIFAR [as 別名]
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  np.random.seed(args.seed)
  torch.cuda.set_device(args.gpu)
  cudnn.benchmark = True
  torch.manual_seed(args.seed)
  cudnn.enabled=True
  torch.cuda.manual_seed(args.seed)
  logging.info('gpu device = %d' % args.gpu)
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()
  utils.load(model, args.model_path)

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()

  _, test_transform = utils._data_transforms_cifar10(args)
  test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)

  test_queue = torch.utils.data.DataLoader(
      test_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)

  model.drop_path_prob = args.drop_path_prob
  test_acc, test_obj = infer(test_queue, model, criterion)
  logging.info('test_acc %f', test_acc)

開發者ID:kcyu2014，項目名稱:eval-nas，代碼行數:35，代碼來源:test.py

示例2: main

# 需要導入模塊: import model [as 別名]
# 或者: from model import NetworkCIFAR [as 別名]
def main():
    if not torch.cuda.is_available():
        logging.info('no gpu device available')
        sys.exit(1)

    np.random.seed(args.seed)
    torch.cuda.set_device(args.gpu)
    cudnn.benchmark = True
    torch.manual_seed(args.seed)
    cudnn.enabled=True
    torch.cuda.manual_seed(args.seed)
    logging.info('gpu device = %d' % args.gpu)
    logging.info("args = %s", args)

    genotype = eval("genotypes.%s" % args.arch)
    model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
    model = model.cuda()
    utils.load(model, args.model_path)

    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

    criterion = nn.CrossEntropyLoss()
    criterion = criterion.cuda()

    _, test_transform = utils.data_transforms_cifar10(args)
    test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)

    test_queue = torch.utils.data.DataLoader(
            test_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)

    model.drop_path_prob = args.drop_path_prob
    test_acc, test_obj = infer(test_queue, model, criterion)
    logging.info('test_acc %f', test_acc)

開發者ID:antoyang，項目名稱:NAS-Benchmark，代碼行數:35，代碼來源:test.py

示例3: main

# 需要導入模塊: import model [as 別名]
# 或者: from model import NetworkCIFAR [as 別名]
def main():
    image_shape = [3, image_size, image_size]
    devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
    devices_num = len(devices.split(","))
    logging.info("args = %s", args)
    genotype = eval("genotypes.%s" % args.arch)
    model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
                    args.auxiliary, genotype)

    steps_one_epoch = math.ceil(dataset_train_size /
                                (devices_num * args.batch_size))
    train(model, args, image_shape, steps_one_epoch)

開發者ID:PaddlePaddle，項目名稱:AutoDL，代碼行數:14，代碼來源:train_mixup.py

示例4: main

# 需要導入模塊: import model [as 別名]
# 或者: from model import NetworkCIFAR [as 別名]
def main():
    image_shape = [3, image_size, image_size]
    devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
    devices_num = len(devices.split(","))
    logging.info("args = %s", args)
    genotype = eval("genotypes.%s" % args.arch)
    model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
                    args.auxiliary, genotype)
    test(model, args, image_shape)

開發者ID:PaddlePaddle，項目名稱:AutoDL，代碼行數:11，代碼來源:test_mixup.py

示例5: main

# 需要導入模塊: import model [as 別名]
# 或者: from model import NetworkCIFAR [as 別名]
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  np.random.seed(args.seed)
  torch.cuda.set_device(args.gpu)
  cudnn.benchmark = True
  torch.manual_seed(args.seed)
  cudnn.enabled=True
  torch.cuda.manual_seed(args.seed)
  logging.info('gpu device = %d' % args.gpu)
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()
  utils.load(model, args.model_path)

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()

  _, test_transform = utils._data_transforms_cifar10(args)
  test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)

  test_queue = torch.utils.data.DataLoader(
      test_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)

  model.drop_path_prob = args.drop_path_prob
  with torch.no_grad():
    test_acc, test_obj = infer(test_queue, model, criterion)
  logging.info('test_acc %f', test_acc)

開發者ID:lightaime，項目名稱:sgas，代碼行數:36，代碼來源:test.py

示例6: main

# 需要導入模塊: import model [as 別名]
# 或者: from model import NetworkCIFAR [as 別名]
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  np.random.seed(args.seed)
  torch.cuda.set_device(args.gpu)
  cudnn.benchmark = True
  torch.manual_seed(args.seed)
  cudnn.enabled=True
  torch.cuda.manual_seed(args.seed)
  logging.info('gpu device = %d' % args.gpu)
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()
  optimizer = torch.optim.SGD(
    model.parameters(),
    args.learning_rate,
    momentum=args.momentum,
    weight_decay=args.weight_decay
  )

  train_transform, valid_transform = utils._data_transforms_cifar10(args)
  train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
  valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)

  train_queue = torch.utils.data.DataLoader(
    train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=2)

  valid_queue = torch.utils.data.DataLoader(
    valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)

  scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))

  for epoch in range(args.epochs):
    scheduler.step()
    logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
    model.drop_path_prob = args.drop_path_prob * epoch / args.epochs

    train_acc, train_obj = train(train_queue, model, criterion, optimizer)
    logging.info('train_acc %f', train_acc)

    valid_acc, valid_obj = infer(valid_queue, model, criterion)
    logging.info('valid_acc %f', valid_acc)

    utils.save(model, os.path.join(args.save, 'weights.pt'))

開發者ID:kcyu2014，項目名稱:eval-nas，代碼行數:55，代碼來源:train.py

示例7: main

# 需要導入模塊: import model [as 別名]
# 或者: from model import NetworkCIFAR [as 別名]
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  np.random.seed(args.seed)
  torch.cuda.set_device(args.gpu)
  cudnn.benchmark = True
  torch.manual_seed(args.seed)
  cudnn.enabled=True
  torch.cuda.manual_seed(args.seed)
  logging.info('gpu device = %d' % args.gpu)
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()
  optimizer = torch.optim.SGD(
      model.parameters(),
      args.learning_rate,
      momentum=args.momentum,
      weight_decay=args.weight_decay
      )

  train_transform, valid_transform = utils._data_transforms_cifar10(args)
  train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
  valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)

  train_queue = torch.utils.data.DataLoader(
      train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=2)

  valid_queue = torch.utils.data.DataLoader(
      valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)

  scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))

  for epoch in range(args.epochs):
    scheduler.step()
    logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
    model.drop_path_prob = args.drop_path_prob * epoch / args.epochs

    train_acc, train_obj = train(train_queue, model, criterion, optimizer)
    logging.info('train_acc %f', train_acc)

    valid_acc, valid_obj = infer(valid_queue, model, criterion)
    logging.info('valid_acc %f', valid_acc)

    utils.save(model, os.path.join(args.save, 'weights.pt'))

開發者ID:quark0，項目名稱:darts，代碼行數:55，代碼來源:train.py

示例8: main

# 需要導入模塊: import model [as 別名]
# 或者: from model import NetworkCIFAR [as 別名]
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  torch.cuda.set_device(args.gpu)
  cudnn.enabled=True
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  if args.dataset in LARGE_DATASETS:
    model = NetworkLarge(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
  else:
    model = Network(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()
  utils.load(model, args.model_path)

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()

  _, test_transform = utils.data_transforms(args.dataset,args.cutout,args.cutout_length)
  if args.dataset=="CIFAR100":
    test_data = dset.CIFAR100(root=args.data, train=False, download=True, transform=test_transform)
  elif args.dataset=="CIFAR10":
    test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)
  elif args.dataset=="sport8":
    dset_cls = dset.ImageFolder
    val_path = '%s/Sport8/test' %args.data
    test_data = dset_cls(root=val_path, transform=test_transform)
  elif args.dataset=="mit67":
    dset_cls = dset.ImageFolder
    val_path = '%s/MIT67/test' %args.data
    test_data = dset_cls(root=val_path, transform=test_transform)
  elif args.dataset == "flowers102":
    dset_cls = dset.ImageFolder
    val_path = '%s/flowers102/test' % args.tmp_data_dir
    test_data = dset_cls(root=val_path, transform=test_transform)
  test_queue = torch.utils.data.DataLoader(
      test_data, batch_size=args.batch_size, shuffle=False, pin_memory=False, num_workers=2)

  model.drop_path_prob = 0.0
  test_acc, test_obj = infer(test_queue, model, criterion)
  logging.info('Test_acc %f', test_acc)

開發者ID:antoyang，項目名稱:NAS-Benchmark，代碼行數:47，代碼來源:test.py

示例9: main

# 需要導入模塊: import model [as 別名]
# 或者: from model import NetworkCIFAR [as 別名]
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  np.random.seed(args.seed)
  torch.cuda.set_device(args.gpu)
  cudnn.benchmark = True
  torch.manual_seed(args.seed)
  cudnn.enabled=True
  torch.cuda.manual_seed(args.seed)
  logging.info('gpu device = %d' % args.gpu)
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()
  optimizer = torch.optim.SGD(
      model.parameters(),
      args.learning_rate,
      momentum=args.momentum,
      weight_decay=args.weight_decay
      )

  train_transform, valid_transform = utils._data_transforms_cifar10(args)
  train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
  valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)

  train_queue = torch.utils.data.DataLoader(
      train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=2)

  valid_queue = torch.utils.data.DataLoader(
      valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)

  scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))

  best_val_acc = 0.
  for epoch in range(args.epochs):
    scheduler.step()
    logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
    model.drop_path_prob = args.drop_path_prob * epoch / args.epochs

    train_acc, train_obj = train(train_queue, model, criterion, optimizer)
    logging.info('train_acc %f', train_acc)

    with torch.no_grad():
      valid_acc, valid_obj = infer(valid_queue, model, criterion)
      if valid_acc > best_val_acc:
        best_val_acc = valid_acc
        utils.save(model, os.path.join(args.save, 'best_weights.pt'))
      logging.info('valid_acc %f\tbest_val_acc %f', valid_acc, best_val_acc)

    utils.save(model, os.path.join(args.save, 'weights.pt'))

開發者ID:lightaime，項目名稱:sgas，代碼行數:60，代碼來源:train.py

注：本文中的model.NetworkCIFAR方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。