def _save_model_desc(self):
search_space = SearchSpace()
codec = Codec(self.cfg.codec, search_space)
pareto_front_df = pd.read_csv(
FileOps.join_path(self.result_path, "pareto_front.csv"))
codes = pareto_front_df['Code']
for i in range(len(codes)):
search_desc = Config()
search_desc.custom = deepcopy(search_space.search_space.custom)
search_desc.modules = deepcopy(search_space.search_space.modules)
code = codes.loc[i]
search_desc.custom.code = code
search_desc.custom.method = 'full'
codec.decode(search_desc.custom)
self.trainer.output_model_desc(i, search_desc)
def _save_model_desc(self):
"""Save final model desc of NAS."""
pf_file = FileOps.join_path(self.trainer.local_output_path,
self.trainer.step_name, "pareto_front.csv")
if not FileOps.exists(pf_file):
return
with open(pf_file, "r") as file:
pf = pd.read_csv(file)
pareto_fronts = pf["encoding"].tolist()
search_space = SearchSpace()
codec = QuantCodec('QuantCodec', search_space)
for i, pareto_front in enumerate(pareto_fronts):
pareto_front = [int(x) for x in pareto_front[1:-1].split(',')]
model_desc = Config()
model_desc.modules = search_space.search_space.modules
model_desc.backbone = codec.decode(pareto_front)._desc.backbone
self.trainer.output_model_desc(i, model_desc)
