8212e735fd187936161e64026eb46c599e1937d4,dlpy/network.py,Network,load_weights_from_file_with_labels,#Network#Any#Any#Any#Any#Any#Any#,1033

Before Change

        """
        from dlpy.model_conversion.model_conversion_utils import query_action_parm

        cas_lib_name, file_name, tmp_caslib = caslibify(self.conn, path, task="load")

        has_gpu_model,act_parms = query_action_parm(self.conn, "dlImportModelWeights", "deepLearn", "gpuModel")
        if (not has_gpu_model) and use_gpu:
            raise DLPyError("A GPU model was specified, but your Viya installation does not support"

After Change

        """
        from dlpy.model_conversion.model_conversion_utils import query_action_parm

        with caslibify_context(self.conn, path, task = "load") as (cas_lib_name, file_name):
            has_gpu_model,act_parms = query_action_parm(self.conn, "dlImportModelWeights", "deepLearn", "gpuModel")
            if (not has_gpu_model) and use_gpu:
                raise DLPyError("A GPU model was specified, but your Viya installation does not support"
                                "importing GPU models.")

            if label_file_name:
                from dlpy.utils import get_user_defined_labels_table
                label_table = get_user_defined_labels_table(self.conn, label_file_name, label_length)
            else:
                from dlpy.utils import get_imagenet_labels_table
                label_table = get_imagenet_labels_table(self.conn, label_length)

            if data_spec:

                has_data_spec = query_action_parm(self.conn, "dlImportModelWeights", "deepLearn", "gpuModel")

                if has_data_spec:
                    // run action with dataSpec option
                    if has_gpu_model:
                        with sw.option_context(print_messages = False):
                            rt = self._retrieve_("deeplearn.dlimportmodelweights",
                                                 model=self.model_table,
                                                 modelWeights=dict(replace=True, name=self.model_name + "_weights"),
                                                 dataSpecs=data_spec,
                                                 gpuModel=use_gpu,
                                                 formatType=format_type, weightFilePath=file_name, caslib=cas_lib_name,
                                                 labelTable=label_table)
                    else:
                        with sw.option_context(print_messages = False):
                            rt = self._retrieve_("deeplearn.dlimportmodelweights",
                                                 model=self.model_table,
                                                 modelWeights=dict(replace=True, name=self.model_name + "_weights"),
                                                 dataSpecs=data_spec,
                                                 formatType=format_type, weightFilePath=file_name, caslib=cas_lib_name,
                                                 labelTable=label_table)
                else:
                    if has_gpu_model:
                        with sw.option_context(print_messages = False):
                            rt = self._retrieve_("deeplearn.dlimportmodelweights", model=self.model_table,
                                                 modelWeights=dict(replace=True, name=self.model_name + "_weights"),
                                                 formatType=format_type, weightFilePath=file_name, caslib=cas_lib_name,
                                                 gpuModel=use_gpu,
                                                 labelTable=label_table)
                    else:
                        with sw.option_context(print_messages = False):
                            rt = self._retrieve_("deeplearn.dlimportmodelweights", model=self.model_table,
                                                 modelWeights=dict(replace=True, name=self.model_name + "_weights"),
                                                 formatType=format_type,
                                                 weightFilePath=file_name,
                                                 caslib=cas_lib_name,
                                                 labelTable=label_table)

                // handle error or create necessary attributes
                if rt.severity > 1:
                    for msg in rt.messages:
                        print(msg)
                    raise DLPyError("Cannot import model weights, there seems to be a problem.")

                // create attributes if necessary
                if not has_data_spec:
                    from dlpy.attribute_utils import create_extended_attributes
                    create_extended_attributes(self.conn, self.model_name, self.layers, data_spec)

            else:
                print("NOTE: no dataspec(s) provided - creating image classification model.")
                if has_gpu_model:
                    self._retrieve_("deeplearn.dlimportmodelweights", model=self.model_table,
                                    modelWeights=dict(replace=True, name=self.model_name + "_weights"),
                                    formatType=format_type, weightFilePath=file_name, caslib=cas_lib_name,
                                    gpuModel=use_gpu,
                                    labelTable=label_table,
                                    )
                else:
                    self._retrieve_("deeplearn.dlimportmodelweights", model=self.model_table,
                                    modelWeights=dict(replace=True, name=self.model_name + "_weights"),
                                    formatType=format_type, weightFilePath=file_name, caslib=cas_lib_name,
                                    labelTable=label_table,
                                    )

        self.set_weights(self.model_name + "_weights")

    def load_weights_from_table(self, path):
        """

Instances