def get_model(self, stage: str) -> _Model:
model_params = self._config["model_params"]
model = MODELS.get_from_params(**model_params)
model = self._preprocess_model_for_stage(stage, model)
model = self._postprocess_model_for_stage(stage, model)
return model
def _get_model(**params):
key_value_flag = params.pop("_key_value", False)
if key_value_flag:
model = {}
for key, params_ in params.items():
model[key] = ConfigExperiment._get_model(**params_)
else:
model = MODELS.get_from_params(**params)
return model
def _get_model(**params):
key_value_flag = params.pop("_key_value", False)
if key_value_flag:
model = {}
for model_key, model_params in params.items():
model[model_key] = ConfigExperiment._get_model( # noqa: WPS437
**model_params)
model = nn.ModuleDict(model)
else:
model = MODELS.get_from_params(**params)
return model
def get_model(self, stage: str) -> _Model:
model_params = self._config["model_params"]
fp16 = model_params.pop("fp16", False)
model = MODELS.get_from_params(**model_params)
if fp16:
utils.assert_fp16_available()
model = Fp16Wrap(model)
model = self._preprocess_model_for_stage(stage, model)
model = self._postprocess_model_for_stage(stage, model)
return model
def _get_model(**params):
key_value_flag = params.pop("_key_value", False)
# todo registry init
dc_init_flag = params.pop("_dcgan_initialize", False)
if key_value_flag:
model = {}
for key, params_ in params.items():
model[key] = Experiment._get_model(**params_)
model = nn.ModuleDict(model)
else:
model = MODELS.get_from_params(**params)
if dc_init_flag:
model.apply(dcgan_weights_init)
return model
