def get_kd_config(config: NNCFConfig,
kd_type='mse',
scale=1,
temperature=None) -> NNCFConfig:
if isinstance(config.get('compression', {}), dict):
config['compression'] = [config['compression']] if config.get(
'compression', None) is not None else []
kd_algo_dict = {
'algorithm': 'knowledge_distillation',
'type': kd_type,
'scale': scale
}
if temperature is not None:
kd_algo_dict['temperature'] = temperature
config['compression'].append(kd_algo_dict)
return config
def __init__(self, config: NNCFConfig, should_init: bool = True):
super().__init__(config, should_init)
self.quantize_inputs = self._algo_config.get('quantize_inputs', True)
self.quantize_outputs = self._algo_config.get('quantize_outputs',
False)
self._overflow_fix = self._algo_config.get('overflow_fix', 'enable')
self._target_device = config.get('target_device', 'ANY')
algo_config = self._get_algo_specific_config_section()
if self._target_device == 'VPU' and 'preset' in algo_config:
raise RuntimeError(
"The VPU target device does not support presets.")
self.global_quantizer_constraints = {}
self.ignored_scopes_per_group = {}
self.target_scopes_per_group = {}
self._op_names = []
for quantizer_group in QuantizerGroup:
self._parse_group_params(self._algo_config, quantizer_group)
if self.should_init:
self._parse_init_params()
self._range_initializer = None
self._bn_adaptation = None
self._quantizer_setup = None
self.hw_config = None
if self._target_device != "TRIAL":
hw_config_type = HWConfigType.from_str(
HW_CONFIG_TYPE_TARGET_DEVICE_MAP[self._target_device])
hw_config_path = TFHWConfig.get_path_to_hw_config(hw_config_type)
self.hw_config = TFHWConfig.from_json(hw_config_path)
def get_multipliers_from_config(config: NNCFConfig) -> Dict[str, float]:
algo_to_multipliers = {}
algorithms = get_config_algorithms(config)
global_multiplier = config.get('compression_lr_multiplier', 1)
for algo in algorithms:
algo_name = algo['algorithm']
algo_to_multipliers[algo_name] = algo.get('compression_lr_multiplier',
global_multiplier)
return algo_to_multipliers
def is_accuracy_aware_training(nncf_config: NNCFConfig) -> bool:
if nncf_config.get("accuracy_aware_training") is not None:
return True
return False
