def create_compressed_model_and_algo_for_test(model: NNCFNetwork, config: NNCFConfig, dummy_forward_fn: Callable[[Module], Any] = None) \ -> Tuple[NNCFNetwork, CompressionAlgorithmController]: assert isinstance(config, NNCFConfig) NNCFConfig.validate(config) algo, model = create_compressed_model(model, config, dump_graphs=False, dummy_forward_fn=dummy_forward_fn) return model, algo
def create_nncf_model_and_single_algo_builder(model: Module, config: NNCFConfig, dummy_forward_fn: Callable[[Module], Any] = None, wrap_inputs_fn: Callable[[Tuple, Dict], Tuple[Tuple, Dict]] = None) \ -> Tuple[NNCFNetwork, PTCompressionAlgorithmController]: assert isinstance(config, NNCFConfig) NNCFConfig.validate(config) input_info_list = create_input_infos(config) scopes_without_shape_matching = config.get('scopes_without_shape_matching', []) ignored_scopes = config.get('ignored_scopes') target_scopes = config.get('target_scopes') compressed_model = NNCFNetwork( model, input_infos=input_info_list, dummy_forward_fn=dummy_forward_fn, wrap_inputs_fn=wrap_inputs_fn, ignored_scopes=ignored_scopes, target_scopes=target_scopes, scopes_without_shape_matching=scopes_without_shape_matching) algo_names = extract_algorithm_names(config) assert len(algo_names) == 1 algo_name = next(iter(algo_names)) builder_cls = PT_COMPRESSION_ALGORITHMS.get(algo_name) builder = builder_cls(config, should_init=True) return compressed_model, builder
def create_compressed_model_and_algo_for_test(model: NNCFNetwork, config: NNCFConfig, dummy_forward_fn: Callable[[Module], Any] = None, wrap_inputs_fn: Callable[[Tuple, Dict], Tuple[Tuple, Dict]] = None, resuming_state_dict: dict = None) \ -> Tuple[NNCFNetwork, CompressionAlgorithmController]: assert isinstance(config, NNCFConfig) NNCFConfig.validate(config) algo, model = create_compressed_model(model, config, dump_graphs=False, dummy_forward_fn=dummy_forward_fn, wrap_inputs_fn=wrap_inputs_fn, resuming_state_dict=resuming_state_dict) return model, algo
def create_compressed_model_and_algo_for_test(model: Module, config: NNCFConfig=None, dummy_forward_fn: Callable[[Module], Any] = None, wrap_inputs_fn: Callable[[Tuple, Dict], Tuple[Tuple, Dict]] = None, compression_state: Dict[str, Any] = None) \ -> Tuple[NNCFNetwork, PTCompressionAlgorithmController]: if config is not None: assert isinstance(config, NNCFConfig) NNCFConfig.validate(config) algo, model = create_compressed_model(model, config, dump_graphs=False, dummy_forward_fn=dummy_forward_fn, wrap_inputs_fn=wrap_inputs_fn, compression_state=compression_state) return model, algo
def create_nncf_model_and_algo_builder(model: NNCFNetwork, config: NNCFConfig, dummy_forward_fn: Callable[[Module], Any] = None, wrap_inputs_fn: Callable[[Tuple, Dict], Tuple[Tuple, Dict]] = None, resuming_state_dict: dict = None): assert isinstance(config, NNCFConfig) NNCFConfig.validate(config) input_info_list = create_input_infos(config) scopes_without_shape_matching = config.get('scopes_without_shape_matching', []) ignored_scopes = config.get('ignored_scopes') target_scopes = config.get('target_scopes') compressed_model = NNCFNetwork(model, input_infos=input_info_list, dummy_forward_fn=dummy_forward_fn, wrap_inputs_fn=wrap_inputs_fn, ignored_scopes=ignored_scopes, target_scopes=target_scopes, scopes_without_shape_matching=scopes_without_shape_matching) should_init = resuming_state_dict is None compression_algo_builder_list = create_compression_algorithm_builders(config, should_init=should_init) return compressed_model, compression_algo_builder_list