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
