Python models 模块，get_models() 实例源码

def load_model(name):
    '''Creates and returns an instance of the model given its class name.
    The created model has a single placeholder node for feeding images.
    '''
    # Find the model class from its name
    all_models = models.get_models()
    lut = {model.__name__: model for model in all_models}
    if name not in lut:
        print('Invalid model index. Options are:')
        # Display a list of valid model names
        for model in all_models:
            print('\t* {}'.format(model.__name__))
        return None
    NetClass = lut[name]

    # Create a placeholder for the input image
    spec = models.get_data_spec(model_class=NetClass)
    data_node = tf.placeholder(tf.float32,
                               shape=(None, spec.crop_size, spec.crop_size, spec.channels))

    # Construct and return the model
    return NetClass({'data': data_node})

def main():
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s : " +
        "%(module)s (%(lineno)s) - %(levelname)s - %(message)s")

    conf = ConfigParser(os.environ)
    conf.read(sys.argv[1])

    logging.warning('loading datasets...')
    datasets = get_data(conf)
    logging.warning('loaded these: {0}'.format(datasets.keys()))
    logging.warning('loading models...')
    models = get_models(conf)
    logging.warning('evaluating...')
    for data_type, data in datasets.iteritems():
        logging.warning('data: {0}'.format(data_type))
        r = Regression(conf)
        r.featurize_data(data, models)
        r.evaluate()

def load_model(name):
    '''Creates and returns an instance of the model given its class name.
    The created model has a single placeholder node for feeding images.
    '''
    # Find the model class from its name
    all_models = models.get_models()
    lut = {model.__name__: model for model in all_models}
    if name not in lut:
        print('Invalid model index. Options are:')
        # Display a list of valid model names
        for model in all_models:
            print('\t* {}'.format(model.__name__))
        return None
    NetClass = lut[name]

    # Create a placeholder for the input image
    spec = models.get_data_spec(model_class=NetClass)
    data_node = tf.placeholder(tf.float32,
                               shape=(None, spec.crop_size, spec.crop_size, spec.channels))

    # Construct and return the model
    return NetClass({'data': data_node})