Exemple #1
0
def create_models(dataset_dir):
    """Initialize the app (available for localhost only)

    Parameters:
        dataset_dir (:func:`str`): Path to the training set
    """
    logger.debug("Creating models...")

    if not local_exec:
        logger.error("Models can only be generated locally")
        exit(1)

    # Modify the configuration for local execution
    app_config['root'] = os.environ['ROOT']

    # Generate inline models and train classifier
    denoiser = Denoiser(app_config)

    if not exists(dataset_dir) or not isdir(dataset_dir):
        logger.error(dataset_dir+" is not a valid directory")
        exit(2)

    dataset = [join(dataset_dir, f) for f in listdir(dataset_dir)]

    denoiser.generate_models(dataset)
    logger.info("Inline models generated")

    denoiser.train(dataset)
    logger.info("Classifier trained")
Exemple #2
0
def create_models(dataset_dir):
    """Initialize the app (available for localhost only)

    Parameters:
        dataset_dir (:func:`str`): Path to the training set
    """
    logger.debug("Creating models...")

    if not local_exec:
        logger.error("Models can only be generated locally")
        exit(1)

    # Modify the configuration for local execution
    app_config['root'] = os.environ['ROOT']

    # Generate inline models and train classifier
    denoiser = Denoiser(app_config)

    if not exists(dataset_dir) or not isdir(dataset_dir):
        logger.error(dataset_dir + " is not a valid directory")
        exit(2)

    dataset = [join(dataset_dir, f) for f in listdir(dataset_dir)]

    denoiser.generate_models(dataset)
    logger.info("Inline models generated")

    denoiser.train(dataset)
    logger.info("Classifier trained")
def mae_albdiv_bn(train_data, test_data):
    """Train the MAE denoiser with albedo divide and batch norm on. Use all
    features."""
    config.ALBEDO_DIVIDE = True
    feature_list = ["normal", "albedo", "depth"]
    denoiser = Denoiser(
        train_data,
        test_data,
        adam_lr=1e-4,
        num_epochs=
        100000,  # Stupid number of epochs, early stopping will prevent reaching this
        early_stopping=True,
        kernel_predict=True,
        bn=True,
        feature_list=feature_list,
        num_layers=7,
        batch_size=64,
        loss="mae",
        model_dir="../experiments/models/mae_albdiv_bn",
        log_dir="../experiments/logs/mae_albdiv_bn")
    denoiser.buildNetwork()
    denoiser.train()
def vgg_albdiv_bn(train_data, test_data):
    """Train the vgg denoiser with albedo divide and batch norm. Use all
    features"""
    config.ALBEDO_DIVIDE = True
    feature_list = ["normal", "albedo", "depth"]
    denoiser = Denoiser(
        train_data,
        test_data,
        adam_lr=1e-3,
        num_epochs=100000,
        early_stopping=True,
        kernel_predict=True,
        bn=True,
        feature_list=feature_list,
        num_layers=7,
        batch_size=64,
        loss="vgg22",
        model_dir="../experiments/models/vgg_albdiv_bn",
        log_dir="../experiments/logs/vgg_albdiv_bn",
    )
    denoiser.buildNetwork()
    denoiser.train()