nni/algorithms/nas/pytorch/classic_nas/mutator.py [124:221]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def sample_search(self): """ See :meth:`sample_final`. """ return self.sample_final() def sample_final(self): """ Convert the chosen arch and apply it on model. """ assert set(self._chosen_arch.keys()) == set(self._search_space.keys()), \ "Unmatched keys, expected keys '{}' from search space, found '{}'.".format(self._search_space.keys(), self._chosen_arch.keys()) result = dict() for mutable in self.mutables: if isinstance(mutable, (LayerChoice, InputChoice)): assert mutable.key in self._chosen_arch, \ "Expected '{}' in chosen arch, but not found.".format(mutable.key) data = self._chosen_arch[mutable.key] assert isinstance(data, dict) and "_value" in data and "_idx" in data, \ "'{}' is not a valid choice.".format(data) if isinstance(mutable, LayerChoice): result[mutable.key] = self._sample_layer_choice(mutable, data["_idx"], data["_value"], self._search_space[mutable.key]["_value"]) elif isinstance(mutable, InputChoice): result[mutable.key] = self._sample_input_choice(mutable, data["_idx"], data["_value"], self._search_space[mutable.key]["_value"]) elif isinstance(mutable, MutableScope): logger.info("Mutable scope '%s' is skipped during parsing choices.", mutable.key) else: raise TypeError("Unsupported mutable type: '%s'." % type(mutable)) return result def _standalone_generate_chosen(self): """ Generate the chosen architecture for standalone mode, i.e., choose the first one(s) for LayerChoice and InputChoice. :: { key_name: {"_value": "conv1", "_idx": 0} } { key_name: {"_value": ["in1"], "_idx": [0]} } Returns ------- dict the chosen architecture """ chosen_arch = {} for key, val in self._search_space.items(): if val["_type"] == LAYER_CHOICE: choices = val["_value"] chosen_arch[key] = {"_value": choices[0], "_idx": 0} elif val["_type"] == INPUT_CHOICE: choices = val["_value"]["candidates"] n_chosen = val["_value"]["n_chosen"] if n_chosen is None: n_chosen = len(choices) chosen_arch[key] = {"_value": choices[:n_chosen], "_idx": list(range(n_chosen))} else: raise ValueError("Unknown key '%s' and value '%s'." % (key, val)) return chosen_arch def _generate_search_space(self): """ Generate search space from mutables. Here is the search space format: :: { key_name: {"_type": "layer_choice", "_value": ["conv1", "conv2"]} } { key_name: {"_type": "input_choice", "_value": {"candidates": ["in1", "in2"], "n_chosen": 1}} } Returns ------- dict the generated search space """ search_space = {} for mutable in self.mutables: # for now we only generate flattened search space if isinstance(mutable, LayerChoice): key = mutable.key val = mutable.names search_space[key] = {"_type": LAYER_CHOICE, "_value": val} elif isinstance(mutable, InputChoice): key = mutable.key search_space[key] = {"_type": INPUT_CHOICE, "_value": {"candidates": mutable.choose_from, "n_chosen": mutable.n_chosen}} elif isinstance(mutable, MutableScope): logger.info("Mutable scope '%s' is skipped during generating search space.", mutable.key) else: raise TypeError("Unsupported mutable type: '%s'." % type(mutable)) return search_space def _dump_search_space(self, file_path): with open(file_path, "w") as ss_file: json.dump(self._search_space, ss_file, sort_keys=True, indent=2) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - nni/algorithms/nas/tensorflow/classic_nas/mutator.py [120:217]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def sample_search(self): """ See :meth:`sample_final`. """ return self.sample_final() def sample_final(self): """ Convert the chosen arch and apply it on model. """ assert set(self._chosen_arch.keys()) == set(self._search_space.keys()), \ "Unmatched keys, expected keys '{}' from search space, found '{}'.".format(self._search_space.keys(), self._chosen_arch.keys()) result = dict() for mutable in self.mutables: if isinstance(mutable, (LayerChoice, InputChoice)): assert mutable.key in self._chosen_arch, \ "Expected '{}' in chosen arch, but not found.".format(mutable.key) data = self._chosen_arch[mutable.key] assert isinstance(data, dict) and "_value" in data and "_idx" in data, \ "'{}' is not a valid choice.".format(data) if isinstance(mutable, LayerChoice): result[mutable.key] = self._sample_layer_choice(mutable, data["_idx"], data["_value"], self._search_space[mutable.key]["_value"]) elif isinstance(mutable, InputChoice): result[mutable.key] = self._sample_input_choice(mutable, data["_idx"], data["_value"], self._search_space[mutable.key]["_value"]) elif isinstance(mutable, MutableScope): logger.info("Mutable scope '%s' is skipped during parsing choices.", mutable.key) else: raise TypeError("Unsupported mutable type: '%s'." % type(mutable)) return result def _standalone_generate_chosen(self): """ Generate the chosen architecture for standalone mode, i.e., choose the first one(s) for LayerChoice and InputChoice. :: { key_name: {"_value": "conv1", "_idx": 0} } { key_name: {"_value": ["in1"], "_idx": [0]} } Returns ------- dict the chosen architecture """ chosen_arch = {} for key, val in self._search_space.items(): if val["_type"] == LAYER_CHOICE: choices = val["_value"] chosen_arch[key] = {"_value": choices[0], "_idx": 0} elif val["_type"] == INPUT_CHOICE: choices = val["_value"]["candidates"] n_chosen = val["_value"]["n_chosen"] if n_chosen is None: n_chosen = len(choices) chosen_arch[key] = {"_value": choices[:n_chosen], "_idx": list(range(n_chosen))} else: raise ValueError("Unknown key '%s' and value '%s'." % (key, val)) return chosen_arch def _generate_search_space(self): """ Generate search space from mutables. Here is the search space format: :: { key_name: {"_type": "layer_choice", "_value": ["conv1", "conv2"]} } { key_name: {"_type": "input_choice", "_value": {"candidates": ["in1", "in2"], "n_chosen": 1}} } Returns ------- dict the generated search space """ search_space = {} for mutable in self.mutables: # for now we only generate flattened search space if isinstance(mutable, LayerChoice): key = mutable.key val = mutable.names search_space[key] = {"_type": LAYER_CHOICE, "_value": val} elif isinstance(mutable, InputChoice): key = mutable.key search_space[key] = {"_type": INPUT_CHOICE, "_value": {"candidates": mutable.choose_from, "n_chosen": mutable.n_chosen}} elif isinstance(mutable, MutableScope): logger.info("Mutable scope '%s' is skipped during generating search space.", mutable.key) else: raise TypeError("Unsupported mutable type: '%s'." % type(mutable)) return search_space def _dump_search_space(self, file_path): with open(file_path, "w") as ss_file: json.dump(self._search_space, ss_file, sort_keys=True, indent=2) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -