deeppavlov/core/commands/infer.py
def build_model(config: Union[str, Path, dict], mode: str = 'infer',
                load_trained: bool = False, download: bool = False,
                serialized: Optional[bytes] = None) -> Chainer:
    """Build and return the model described in corresponding configuration file."""
    config = parse_config(config)

    if serialized:
        serialized: list = pickle.loads(serialized)

    if download:
        deep_download(config)

    import_packages(config.get('metadata', {}).get('imports', []))

    model_config = config['chainer']

    model = Chainer(model_config['in'], model_config['out'], model_config.get('in_y'))

    for component_config in model_config['pipe']:
        if load_trained and ('fit_on' in component_config or 'in_y' in component_config):
            try:
                component_config['load_path'] = component_config['save_path']
            except KeyError:
                log.warning('No "save_path" parameter for the {} component, so "load_path" will not be renewed'
                            .format(component_config.get('class_name', component_config.get('ref', 'UNKNOWN'))))

        if serialized and 'in' in component_config:
            component_serialized = serialized.pop(0)
        else:
            component_serialized = None

        component = from_params(component_config, mode=mode, serialized=component_serialized)

        if 'in' in component_config:
            c_in = component_config['in']
            c_out = component_config['out']
            in_y = component_config.get('in_y', None)
            main = component_config.get('main', False)
            model.append(component, c_in, c_out, in_y, main)

    return model
def build_model(config: Union[str, Path, dict], mode: str = 'infer',
                load_trained: bool = False, download: bool = False,
                serialized: Optional[bytes] = None) -> Chainer:
    """Build and return the model described in corresponding configuration file."""
    config = parse_config(config)

    if serialized:
        serialized: list = pickle.loads(serialized)

    if download:
        deep_download(config)

    import_packages(config.get('metadata', {}).get('imports', []))

    model_config = config['chainer']

    model = Chainer(model_config['in'], model_config['out'], model_config.get('in_y'))

    for component_config in model_config['pipe']:
        if load_trained and ('fit_on' in component_config or 'in_y' in component_config):
            try:
                component_config['load_path'] = component_config['save_path']
            except KeyError:
                log.warning('No "save_path" parameter for the {} component, so "load_path" will not be renewed'
                            .format(component_config.get('class_name', component_config.get('ref', 'UNKNOWN'))))

        if serialized and 'in' in component_config:
            component_serialized = serialized.pop(0)
        else:
            component_serialized = None

        component = from_params(component_config, mode=mode, serialized=component_serialized)

        if 'in' in component_config:
            c_in = component_config['in']
            c_out = component_config['out']
            in_y = component_config.get('in_y', None)
            main = component_config.get('main', False)
            model.append(component, c_in, c_out, in_y, main)

    return model
Build and return the model described in corresponding configuration file.