Python utils._data_transforms_cifar10方法代码示例

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def build_cifar10(model_state_dict=None, optimizer_state_dict=None, **kwargs):
    epoch = kwargs.pop('epoch')
    ratio = kwargs.pop('ratio')
    train_transform, valid_transform = utils._data_transforms_cifar10(args.child_cutout_size)
    train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
    valid_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=valid_transform)

    num_train = len(train_data)
    assert num_train == len(valid_data)
    indices = list(range(num_train))    
    split = int(np.floor(ratio * num_train))
    np.random.shuffle(indices)

    train_queue = torch.utils.data.DataLoader(
        train_data, batch_size=args.child_batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
        pin_memory=True, num_workers=16)
    valid_queue = torch.utils.data.DataLoader(
        valid_data, batch_size=args.child_eval_batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
        pin_memory=True, num_workers=16)
    
    model = NASWSNetworkCIFAR(args, 10, args.child_layers, args.child_nodes, args.child_channels, args.child_keep_prob, args.child_drop_path_keep_prob,
                       args.child_use_aux_head, args.steps)
    model = model.cuda()
    train_criterion = nn.CrossEntropyLoss().cuda()
    eval_criterion = nn.CrossEntropyLoss().cuda()
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

    optimizer = torch.optim.SGD(
        model.parameters(),
        args.child_lr_max,
        momentum=0.9,
        weight_decay=args.child_l2_reg,
    )
    if model_state_dict is not None:
        model.load_state_dict(model_state_dict)
    if optimizer_state_dict is not None:
        optimizer.load_state_dict(optimizer_state_dict)
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.child_epochs, args.child_lr_min, epoch)
    return train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def build_cifar100(model_state_dict=None, optimizer_state_dict=None, **kwargs):
    epoch = kwargs.pop('epoch')
    ratio = kwargs.pop('ratio')
    train_transform, valid_transform = utils._data_transforms_cifar10(args.cutout_size)
    train_data = dset.CIFAR100(root=args.data, train=True, download=True, transform=train_transform)
    valid_data = dset.CIFAR100(root=args.data, train=True, download=True, transform=valid_transform)

    num_train = len(train_data)
    assert num_train == len(valid_data)
    indices = list(range(num_train))    
    split = int(np.floor(ratio * num_train))
    np.random.shuffle(indices)

    train_queue = torch.utils.data.DataLoader(
        train_data, batch_size=args.child_batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
        pin_memory=True, num_workers=16)
    valid_queue = torch.utils.data.DataLoader(
        valid_data, batch_size=args.child_eval_batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
        pin_memory=True, num_workers=16)
    
    model = NASWSNetworkCIFAR(args, 100, args.child_layers, args.child_nodes, args.child_channels, args.child_keep_prob, args.child_drop_path_keep_prob,
                       args.child_use_aux_head, args.steps)
    model = model.cuda()
    train_criterion = nn.CrossEntropyLoss().cuda()
    eval_criterion = nn.CrossEntropyLoss().cuda()
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

    optimizer = torch.optim.SGD(
        model.parameters(),
        args.child_lr_max,
        momentum=0.9,
        weight_decay=args.child_l2_reg,
    )
    if model_state_dict is not None:
        model.load_state_dict(model_state_dict)
    if optimizer_state_dict is not None:
        optimizer.load_state_dict(optimizer_state_dict)
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.child_epochs, args.child_lr_min, epoch)
    return train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def build_cifar10(model_state_dict, optimizer_state_dict, **kwargs):
    epoch = kwargs.pop('epoch')

    train_transform, valid_transform = utils._data_transforms_cifar10(args.cutout_size)
    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=16)
    valid_queue = torch.utils.data.DataLoader(
        valid_data, batch_size=args.eval_batch_size, shuffle=False, pin_memory=True, num_workers=16)
    
    model = NASNetworkCIFAR(args, 10, args.layers, args.nodes, args.channels, args.keep_prob, args.drop_path_keep_prob,
                       args.use_aux_head, args.steps, args.arch)
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
    logging.info("multi adds = %fM", model.multi_adds / 1000000)
    if model_state_dict is not None:
        model.load_state_dict(model_state_dict)
    
    if torch.cuda.device_count() > 1:
        logging.info("Use %d %s", torch.cuda.device_count(), "GPUs !")
        model = nn.DataParallel(model)
    model = model.cuda()

    train_criterion = nn.CrossEntropyLoss().cuda()
    eval_criterion = nn.CrossEntropyLoss().cuda()

    optimizer = torch.optim.SGD(
        model.parameters(),
        args.lr_max,
        momentum=0.9,
        weight_decay=args.l2_reg,
    )
    if optimizer_state_dict is not None:
        optimizer.load_state_dict(optimizer_state_dict)

    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs), args.lr_min, epoch)
    return train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def build_cifar100(model_state_dict, optimizer_state_dict, **kwargs):
    epoch = kwargs.pop('epoch')

    train_transform, valid_transform = utils._data_transforms_cifar10(args.cutout_size)
    train_data = dset.CIFAR100(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=16)
    valid_queue = torch.utils.data.DataLoader(
        valid_data, batch_size=args.eval_batch_size, shuffle=False, pin_memory=True, num_workers=16)
    
    model = NASNetworkCIFAR(args, 100, args.layers, args.nodes, args.channels, args.keep_prob, args.drop_path_keep_prob,
                       args.use_aux_head, args.steps, args.arch)
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
    logging.info("multi adds = %fM", model.multi_adds / 1000000)
    if model_state_dict is not None:
        model.load_state_dict(model_state_dict)

    if torch.cuda.device_count() > 1:
        logging.info("Use %d %s", torch.cuda.device_count(), "GPUs !")
        model = nn.DataParallel(model)
    model = model.cuda()

    train_criterion = nn.CrossEntropyLoss().cuda()
    eval_criterion = nn.CrossEntropyLoss().cuda()
    
    optimizer = torch.optim.SGD(
        model.parameters(),
        args.lr_max,
        momentum=0.9,
        weight_decay=args.l2_reg,
    )
    
    if optimizer_state_dict is not None:
        optimizer.load_state_dict(optimizer_state_dict)

    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs), args.lr_min, epoch)
    return train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def build_cifar10(model_state_dict, optimizer_state_dict, **kwargs):
    epoch = kwargs.pop('epoch')

    train_transform, valid_transform = utils._data_transforms_cifar10(args.cutout_size, args.autoaugment)
    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=16)
    valid_queue = torch.utils.data.DataLoader(
        valid_data, batch_size=args.eval_batch_size, shuffle=False, pin_memory=True, num_workers=16)

    model = NASNetworkCIFAR(args, 10, args.layers, args.nodes, args.channels, args.keep_prob, args.drop_path_keep_prob,
                       args.use_aux_head, args.steps, args.arch)
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
    if model_state_dict is not None:
        model.load_state_dict(model_state_dict)
    
    if torch.cuda.device_count() > 1:
        logging.info("Use %d %s", torch.cuda.device_count(), "GPUs !")
        model = nn.DataParallel(model)
    model = model.cuda()

    train_criterion = nn.CrossEntropyLoss().cuda()
    eval_criterion = nn.CrossEntropyLoss().cuda()

    optimizer = torch.optim.SGD(
        model.parameters(),
        args.lr_max,
        momentum=0.9,
        weight_decay=args.l2_reg,
    )
    if optimizer_state_dict is not None:
        optimizer.load_state_dict(optimizer_state_dict)

    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs), args.lr_min, epoch)
    return train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def build_cifar10(model_state_dict=None, optimizer_state_dict=None, **kwargs):
    epoch = kwargs.pop('epoch')
    ratio = kwargs.pop('ratio')
    train_transform, valid_transform = utils._data_transforms_cifar10(args.child_cutout_size)
    train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
    valid_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=valid_transform)
    
    num_train = len(train_data)
    assert num_train == len(valid_data)
    indices = list(range(num_train)) 
    split = int(np.floor(ratio * num_train))
    np.random.shuffle(indices)

    train_queue = torch.utils.data.DataLoader(
        train_data, batch_size=args.child_batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
        pin_memory=True, num_workers=16)
    valid_queue = torch.utils.data.DataLoader(
        valid_data, batch_size=args.child_eval_batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
        pin_memory=True, num_workers=16)
    
    model = NASWSNetworkCIFAR(10, args.child_layers, args.child_nodes, args.child_channels, args.child_keep_prob, args.child_drop_path_keep_prob,
                       args.child_use_aux_head, args.steps)
    model = model.cuda()
    train_criterion = nn.CrossEntropyLoss().cuda()
    eval_criterion = nn.CrossEntropyLoss().cuda()
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

    optimizer = torch.optim.SGD(
        model.parameters(),
        args.child_lr_max,
        momentum=0.9,
        weight_decay=args.child_l2_reg,
    )
    if model_state_dict is not None:
        model.load_state_dict(model_state_dict)
    if optimizer_state_dict is not None:
        optimizer.load_state_dict(optimizer_state_dict)
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.child_epochs, args.child_lr_min, epoch)
    return train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def build_cifar100(model_state_dict=None, optimizer_state_dict=None, **kwargs):
    epoch = kwargs.pop('epoch')
    ratio = kwargs.pop('ratio')
    train_transform, valid_transform = utils._data_transforms_cifar10(args.cutout_size)
    train_data = dset.CIFAR100(root=args.data, train=True, download=True, transform=train_transform)
    valid_data = dset.CIFAR100(root=args.data, train=True, download=True, transform=valid_transform)

    num_train = len(train_data)
    assert num_train == len(valid_data)
    indices = list(range(num_train))    
    split = int(np.floor(ratio * num_train))
    np.random.shuffle(indices)

    train_queue = torch.utils.data.DataLoader(
        train_data, batch_size=args.child_batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
        pin_memory=True, num_workers=16)
    valid_queue = torch.utils.data.DataLoader(
        valid_data, batch_size=args.child_eval_batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
        pin_memory=True, num_workers=16)
    
    model = NASWSNetworkCIFAR(100, args.child_layers, args.child_nodes, args.child_channels, args.child_keep_prob, args.child_drop_path_keep_prob,
                       args.child_use_aux_head, args.steps)
    model = model.cuda()
    train_criterion = nn.CrossEntropyLoss().cuda()
    eval_criterion = nn.CrossEntropyLoss().cuda()
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

    optimizer = torch.optim.SGD(
        model.parameters(),
        args.child_lr_max,
        momentum=0.9,
        weight_decay=args.child_l2_reg,
    )
    if model_state_dict is not None:
        model.load_state_dict(model_state_dict)
    if optimizer_state_dict is not None:
        optimizer.load_state_dict(optimizer_state_dict)
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.child_epochs, args.child_lr_min, epoch)
    return train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def build_cifar10(model_state_dict, optimizer_state_dict, **kwargs):
    epoch = kwargs.pop('epoch')

    train_transform, valid_transform = utils._data_transforms_cifar10(args.cutout_size)
    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=16)
    valid_queue = torch.utils.data.DataLoader(
        valid_data, batch_size=args.eval_batch_size, shuffle=False, pin_memory=True, num_workers=16)

    model = NASNetworkCIFAR(args, 10, args.layers, args.nodes, args.channels, args.keep_prob, args.drop_path_keep_prob,
                       args.use_aux_head, args.steps, args.arch)
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
    if model_state_dict is not None:
        model.load_state_dict(model_state_dict)
    
    if torch.cuda.device_count() > 1:
        logging.info("Use %d %s", torch.cuda.device_count(), "GPUs !")
        model = nn.DataParallel(model)
    model = model.cuda()

    train_criterion = nn.CrossEntropyLoss().cuda()
    eval_criterion = nn.CrossEntropyLoss().cuda()

    optimizer = torch.optim.SGD(
        model.parameters(),
        args.lr_max,
        momentum=0.9,
        weight_decay=args.l2_reg,
    )
    if optimizer_state_dict is not None:
        optimizer.load_state_dict(optimizer_state_dict)

    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs), args.lr_min, epoch)
    return train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
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) 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def get_train_val_loaders(self):
        if self.args.dataset == 'cifar10':
            train_transform, valid_transform = utils._data_transforms_cifar10(self.args)
            train_data = dset.CIFAR10(
                root=self.args.data, train=True, download=True, transform=train_transform)
            valid_data = dset.CIFAR10(
                root=self.args.data, train=False, download=True, transform=valid_transform)
        elif self.args.dataset == 'cifar100':
            train_transform, valid_transform = utils._data_transforms_cifar100(self.args)
            train_data = dset.CIFAR100(
                root=self.args.data, train=True, download=True, transform=train_transform)
            valid_data = dset.CIFAR100(
                root=self.args.data, train=False, download=True, transform=valid_transform)
        elif self.args.dataset == 'svhn':
            train_transform, valid_transform = utils._data_transforms_svhn(self.args)
            train_data = dset.SVHN(
                root=self.args.data, split='train', download=True, transform=train_transform)
            valid_data = dset.SVHN(
                root=self.args.data, split='test', download=True, transform=valid_transform)

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

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

        return train_queue, valid_queue, train_transform, valid_transform 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def get_train_val_loaders(self):
        if self.args.dataset == 'cifar10':
            train_transform, valid_transform = utils._data_transforms_cifar10(self.args)
            train_data = dset.CIFAR10(root=self.args.data, train=True, download=True, transform=train_transform)
        elif self.args.dataset == 'cifar100':
            train_transform, valid_transform = utils._data_transforms_cifar100(self.args)
            train_data = dset.CIFAR100(root=self.args.data, train=True, download=True, transform=train_transform)
        elif self.args.dataset == 'svhn':
            train_transform, valid_transform = utils._data_transforms_svhn(self.args)
            train_data = dset.SVHN(root=self.args.data, split='train', download=True, transform=train_transform)

        num_train = len(train_data)
        indices = list(range(num_train))
        split = int(np.floor(self.args.train_portion * num_train))

        train_queue = torch.utils.data.DataLoader(
            train_data, batch_size=self.args.batch_size,
            sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
            pin_memory=True, num_workers=2)

        valid_queue = torch.utils.data.DataLoader(
            train_data, batch_size=self.args.batch_size,
            sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
            pin_memory=True, num_workers=2)

        return train_queue, valid_queue, train_transform, valid_transform 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
def build_cifar100(model_state_dict, optimizer_state_dict, **kwargs):
    epoch = kwargs.pop('epoch')

    train_transform, valid_transform = utils._data_transforms_cifar10(args.cutout_size)
    train_data = dset.CIFAR100(root=args.data, train=True, download=True, transform=train_transform)
    valid_data = dset.CIFAR100(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=16)
    valid_queue = torch.utils.data.DataLoader(
        valid_data, batch_size=args.eval_batch_size, shuffle=False, pin_memory=True, num_workers=16)
    
    model = NASNetworkCIFAR(args, 100, args.layers, args.nodes, args.channels, args.keep_prob, args.drop_path_keep_prob,
                       args.use_aux_head, args.steps, args.arch)
    logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
    logging.info("multi adds = %fM", model.multi_adds / 1000000)
    if model_state_dict is not None:
        model.load_state_dict(model_state_dict)

    if torch.cuda.device_count() > 1:
        logging.info("Use %d %s", torch.cuda.device_count(), "GPUs !")
        model = nn.DataParallel(model)
    model = model.cuda()

    train_criterion = nn.CrossEntropyLoss().cuda()
    eval_criterion = nn.CrossEntropyLoss().cuda()
    
    optimizer = torch.optim.SGD(
        model.parameters(),
        args.lr_max,
        momentum=0.9,
        weight_decay=args.l2_reg,
    )
    
    if optimizer_state_dict is not None:
        optimizer.load_state_dict(optimizer_state_dict)

    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs), args.lr_min, epoch)
    return train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
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) 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
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')) 

# 需要导入模块: from torch import utils [as 别名]
# 或者: from torch.utils import _data_transforms_cifar10 [as 别名]
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'))