Python chainer.training.extensions.observe_lr() Examples

The following are 5 code examples of chainer.training.extensions.observe_lr(). 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 chainer.training.extensions , or try the search function .
Example #1
Source File: train_ch.py    From imgclsmob with MIT License 4 votes vote down vote up 
def prepare_trainer(net,
                    optimizer_name,
                    lr,
                    momentum,
                    num_epochs,
                    train_data,
                    val_data,
                    logging_dir_path,
                    use_gpus):
    if optimizer_name == "sgd":
        optimizer = chainer.optimizers.MomentumSGD(lr=lr, momentum=momentum)
    elif optimizer_name == "nag":
        optimizer = chainer.optimizers.NesterovAG(lr=lr, momentum=momentum)
    else:
        raise Exception("Unsupported optimizer: {}".format(optimizer_name))
    optimizer.setup(net)

    # devices = tuple(range(num_gpus)) if num_gpus > 0 else (-1, )
    devices = (0,) if use_gpus else (-1,)

    updater = training.updaters.StandardUpdater(
        iterator=train_data["iterator"],
        optimizer=optimizer,
        device=devices[0])
    trainer = training.Trainer(
        updater=updater,
        stop_trigger=(num_epochs, "epoch"),
        out=logging_dir_path)

    val_interval = 100000, "iteration"
    log_interval = 1000, "iteration"

    trainer.extend(
        extension=extensions.Evaluator(
            iterator=val_data["iterator"],
            target=net,
            device=devices[0]),
        trigger=val_interval)
    trainer.extend(extensions.dump_graph("main/loss"))
    trainer.extend(extensions.snapshot(), trigger=val_interval)
    trainer.extend(
        extensions.snapshot_object(
            net,
            "model_iter_{.updater.iteration}"),
        trigger=val_interval)
    trainer.extend(extensions.LogReport(trigger=log_interval))
    trainer.extend(extensions.observe_lr(), trigger=log_interval)
    trainer.extend(
        extensions.PrintReport([
            "epoch", "iteration", "main/loss", "validation/main/loss", "main/accuracy", "validation/main/accuracy",
            "lr"]),
        trigger=log_interval)
    trainer.extend(extensions.ProgressBar(update_interval=10))

    return trainer
Example #2
Source File: train_ch_cifar.py    From imgclsmob with MIT License 4 votes vote down vote up 
def prepare_trainer(net,
                    optimizer_name,
                    lr,
                    momentum,
                    num_epochs,
                    train_iter,
                    val_iter,
                    logging_dir_path,
                    num_gpus=0):
    if optimizer_name == "sgd":
        optimizer = chainer.optimizers.MomentumSGD(lr=lr, momentum=momentum)
    elif optimizer_name == "nag":
        optimizer = chainer.optimizers.NesterovAG(lr=lr, momentum=momentum)
    else:
        raise Exception('Unsupported optimizer: {}'.format(optimizer_name))
    optimizer.setup(net)

    # devices = tuple(range(num_gpus)) if num_gpus > 0 else (-1, )
    devices = (0,) if num_gpus > 0 else (-1,)

    updater = training.updaters.StandardUpdater(
        iterator=train_iter,
        optimizer=optimizer,
        device=devices[0])
    trainer = training.Trainer(
        updater=updater,
        stop_trigger=(num_epochs, 'epoch'),
        out=logging_dir_path)

    val_interval = 100000, 'iteration'
    log_interval = 1000, 'iteration'

    trainer.extend(
        extension=extensions.Evaluator(
            val_iter,
            net,
            device=devices[0]),
        trigger=val_interval)
    trainer.extend(extensions.dump_graph('main/loss'))
    trainer.extend(extensions.snapshot(), trigger=val_interval)
    trainer.extend(
        extensions.snapshot_object(
            net,
            'model_iter_{.updater.iteration}'),
        trigger=val_interval)
    trainer.extend(extensions.LogReport(trigger=log_interval))
    trainer.extend(extensions.observe_lr(), trigger=log_interval)
    trainer.extend(
        extensions.PrintReport([
            'epoch', 'iteration', 'main/loss', 'validation/main/loss', 'main/accuracy', 'validation/main/accuracy',
            'lr']),
        trigger=log_interval)
    trainer.extend(extensions.ProgressBar(update_interval=10))

    return trainer
Example #3
Source File: train_ch_in1k.py    From imgclsmob with MIT License 4 votes vote down vote up 
def prepare_trainer(net,
                    optimizer_name,
                    lr,
                    momentum,
                    num_epochs,
                    train_iter,
                    val_iter,
                    logging_dir_path,
                    num_gpus=0):
    if optimizer_name == "sgd":
        optimizer = chainer.optimizers.MomentumSGD(lr=lr, momentum=momentum)
    elif optimizer_name == "nag":
        optimizer = chainer.optimizers.NesterovAG(lr=lr, momentum=momentum)
    else:
        raise Exception('Unsupported optimizer: {}'.format(optimizer_name))
    optimizer.setup(net)

    # devices = tuple(range(num_gpus)) if num_gpus > 0 else (-1, )
    devices = (0,) if num_gpus > 0 else (-1,)

    updater = training.updaters.StandardUpdater(
        iterator=train_iter,
        optimizer=optimizer,
        device=devices[0])
    trainer = training.Trainer(
        updater=updater,
        stop_trigger=(num_epochs, 'epoch'),
        out=logging_dir_path)

    val_interval = 100000, 'iteration'
    log_interval = 1000, 'iteration'

    trainer.extend(
        extension=extensions.Evaluator(
            val_iter,
            net,
            device=devices[0]),
        trigger=val_interval)
    trainer.extend(extensions.dump_graph('main/loss'))
    trainer.extend(extensions.snapshot(), trigger=val_interval)
    trainer.extend(
        extensions.snapshot_object(
            net,
            'model_iter_{.updater.iteration}'),
        trigger=val_interval)
    trainer.extend(extensions.LogReport(trigger=log_interval))
    trainer.extend(extensions.observe_lr(), trigger=log_interval)
    trainer.extend(
        extensions.PrintReport([
            'epoch', 'iteration', 'main/loss', 'validation/main/loss', 'main/accuracy', 'validation/main/accuracy',
            'lr']),
        trigger=log_interval)
    trainer.extend(extensions.ProgressBar(update_interval=10))

    return trainer
Example #4
Source File: train.py    From models with MIT License 4 votes vote down vote up 
def train_one_epoch(model, train_data, lr, gpu, batchsize, out):
    train_model = PixelwiseSoftmaxClassifier(model)
    if gpu >= 0:
        # Make a specified GPU current
        chainer.cuda.get_device_from_id(gpu).use()
        train_model.to_gpu()  # Copy the model to the GPU
    log_trigger = (0.1, 'epoch')
    validation_trigger = (1, 'epoch')
    end_trigger = (1, 'epoch')

    train_data = TransformDataset(
        train_data, ('img', 'label_map'), SimpleDoesItTransform(model.mean))
    val = VOCSemanticSegmentationWithBboxDataset(
        split='val').slice[:, ['img', 'label_map']]

    # Iterator
    train_iter = iterators.MultiprocessIterator(train_data, batchsize)
    val_iter = iterators.MultiprocessIterator(
        val, 1, shuffle=False, repeat=False, shared_mem=100000000)

    # Optimizer
    optimizer = optimizers.MomentumSGD(lr=lr, momentum=0.9)
    optimizer.setup(train_model)
    optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(rate=0.0001))

    # Updater
    updater = training.updaters.StandardUpdater(
        train_iter, optimizer, device=gpu)

    # Trainer
    trainer = training.Trainer(updater, end_trigger, out=out)

    trainer.extend(extensions.LogReport(trigger=log_trigger))
    trainer.extend(extensions.observe_lr(), trigger=log_trigger)
    trainer.extend(extensions.dump_graph('main/loss'))

    if extensions.PlotReport.available():
        trainer.extend(extensions.PlotReport(
            ['main/loss'], x_key='iteration',
            file_name='loss.png'))
        trainer.extend(extensions.PlotReport(
            ['validation/main/miou'], x_key='iteration',
            file_name='miou.png'))

    trainer.extend(extensions.snapshot_object(
        model, filename='snapshot.npy'),
        trigger=end_trigger)
    trainer.extend(extensions.PrintReport(
        ['epoch', 'iteration', 'elapsed_time', 'lr',
         'main/loss', 'validation/main/miou',
         'validation/main/mean_class_accuracy',
         'validation/main/pixel_accuracy']),
        trigger=log_trigger)
    trainer.extend(extensions.ProgressBar(update_interval=10))

    trainer.extend(
        SemanticSegmentationEvaluator(
            val_iter, model,
            voc_semantic_segmentation_label_names),
        trigger=validation_trigger)
    trainer.run()
Example #5
Source File: train.py    From portrait_matting with GNU General Public License v3.0 4 votes vote down vote up 
def register_extensions(trainer, model, test_iter, args):
    if args.mode.startswith('seg'):
        # Max accuracy
        best_trigger = training.triggers.BestValueTrigger(
            'validation/main/accuracy', lambda a, b: a < b, (1, 'epoch'))
    elif args.mode.startswith('mat'):
        # Min loss
        best_trigger = training.triggers.BestValueTrigger(
            'validation/main/loss', lambda a, b: a > b, (1, 'epoch'))
    else:
        logger.error('Invalid training mode')

    # Segmentation extensions
    trainer.extend(
        custom_extensions.PortraitVisEvaluator(
            test_iter, model, device=args.gpus[0],
            converter=select_converter(args.mode),
            filename='vis_epoch={epoch}_idx={index}.jpg',
            mode=args.mode
        ), trigger=(1, 'epoch'))

    # Basic extensions
    trainer.extend(extensions.dump_graph('main/loss'))
    trainer.extend(extensions.LogReport(trigger=(200, 'iteration')))
    trainer.extend(extensions.ProgressBar(update_interval=20))
    trainer.extend(extensions.PrintReport(
        ['epoch', 'main/loss', 'validation/main/loss', 'main/accuracy',
         'validation/main/accuracy', 'lr', 'elapsed_time']))
    trainer.extend(extensions.observe_lr(), trigger=(200, 'iteration'))

    # Snapshots
    trainer.extend(extensions.snapshot(
        filename='snapshot_epoch_{.updater.epoch}'
    ), trigger=(5, 'epoch'))
    trainer.extend(extensions.snapshot_object(
        model, filename='model_best'
    ), trigger=best_trigger)

    # ChainerUI extensions
    trainer.extend(chainerui.extensions.CommandsExtension())
    chainerui.utils.save_args(args, args.out)

    # Plotting extensions
    if extensions.PlotReport.available():
        trainer.extend(
            extensions.PlotReport(
                ['main/loss', 'validation/main/loss'],
                'epoch', file_name='loss.png'))
        trainer.extend(
            extensions.PlotReport(
                ['main/accuracy', 'validation/main/accuracy'],
                'epoch', file_name='accuracy.png'))