Python optimizers.get_optimizer() Examples

The following are 4 code examples of optimizers.get_optimizer(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module optimizers , or try the search function .
Example #1
Source File: train.py    From kaggle-hpa with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def run(config):
    train_dir = config.train.dir

    model = get_model(config).cuda()
    criterion = get_loss(config)
    optimizer = get_optimizer(config, model.parameters())

    checkpoint = utils.checkpoint.get_initial_checkpoint(config)
    if checkpoint is not None:
        last_epoch, step = utils.checkpoint.load_checkpoint(model, optimizer, checkpoint)
    else:
        last_epoch, step = -1, -1

    print('from checkpoint: {} last epoch:{}'.format(checkpoint, last_epoch))
    scheduler = get_scheduler(config, optimizer, last_epoch)

    dataloaders = {split:get_dataloader(config, split, get_transform(config, split))
                   for split in ['train', 'val']}

    writer = SummaryWriter(config.train.dir)
    train(config, model, dataloaders, criterion, optimizer, scheduler,
          writer, last_epoch+1)
Example #2
Source File: train.py    From kaggle-humpback with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def run(config):
    train_dir = config.train.dir

    task = get_task(config)
    optimizer = get_optimizer(config, task.get_model().parameters())

    checkpoint = utils.checkpoint.get_initial_checkpoint(config)
    if checkpoint is not None:
        last_epoch, step = utils.checkpoint.load_checkpoint(task.get_model(),
                                                            optimizer,
                                                            checkpoint)
    else:
        last_epoch, step = -1, -1

    print('from checkpoint: {} last epoch:{}'.format(checkpoint, last_epoch))
    scheduler = get_scheduler(config, optimizer, last_epoch)

    preprocess_opt = task.get_preprocess_opt()
    dataloaders = {split:get_dataloader(config, split,
                                        get_transform(config, split,
                                                      **preprocess_opt))
                   for split in ['train', 'dev']}

    writer = SummaryWriter(config.train.dir)
    train(config, task, dataloaders, optimizer, scheduler,
          writer, last_epoch+1)
Example #3
Source File: aux_model.py    From self-supervised-da with MIT License 5 votes vote down vote up 
def __init__(self, args, logger):
        self.args = args
        self.logger = logger
        self.writer = SummaryWriter(args.log_dir)
        cudnn.enabled = True

        # set up model
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = get_aux_net(args.network.arch)(aux_classes=args.aux_classes + 1, classes=args.n_classes)
        self.model = self.model.to(self.device)

        if args.mode == 'train':
            # set up optimizer, lr scheduler and loss functions
            optimizer = get_optimizer(self.args.training.optimizer)
            optimizer_params = {k: v for k, v in self.args.training.optimizer.items() if k != "name"}
            self.optimizer = optimizer(self.model.parameters(), **optimizer_params)
            self.scheduler = get_scheduler(self.optimizer, self.args.training.lr_scheduler)

            self.class_loss_func = nn.CrossEntropyLoss()

            self.start_iter = 0

            # resume
            if args.training.resume:
                self.load(args.model_dir + '/' + args.training.resume)

            cudnn.benchmark = True

        elif args.mode == 'val':
            self.load(os.path.join(args.model_dir, args.validation.model))
        else:
            self.load(os.path.join(args.model_dir, args.testing.model))
Example #4
Source File: augmentation_search.py    From kaggle-hpa with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def search_once(config, policy):
    model = get_model(config).cuda()
    criterion = get_loss(config)
    optimizer = get_optimizer(config, model.parameters())
    scheduler = get_scheduler(config, optimizer, -1)

    transforms = {'train': get_transform(config, 'train', params={'policies': policy}),
                  'val': get_transform(config, 'val')}
    dataloaders = {split:get_dataloader(config, split, transforms[split])
                   for split in ['train', 'val']}

    score_dict = train(config, model, dataloaders, criterion, optimizer, scheduler, None, 0)
    return score_dict['f1_mavg']