# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """utils script""" def _load_param_into_net(model, params_dict): """ load fp32 model parameters to quantization model. Args: model: quantization model params_dict: f32 param Returns: None """ iterable_dict = { 'weight': iter([item for item in params_dict.items() if item[0].endswith('weight')]), 'bias': iter([item for item in params_dict.items() if item[0].endswith('bias')]), 'gamma': iter([item for item in params_dict.items() if item[0].endswith('gamma')]), 'beta': iter([item for item in params_dict.items() if item[0].endswith('beta')]), 'moving_mean': iter([item for item in params_dict.items() if item[0].endswith('moving_mean')]), 'moving_variance': iter( [item for item in params_dict.items() if item[0].endswith('moving_variance')]), 'minq': iter([item for item in params_dict.items() if item[0].endswith('minq')]), 'maxq': iter([item for item in params_dict.items() if item[0].endswith('maxq')]) } for name, param in model.parameters_and_names(): key_name = name.split(".")[-1] if key_name not in iterable_dict.keys(): continue value_param = next(iterable_dict[key_name], None) if value_param is not None: param.set_parameter_data(value_param[1].data) print(f'init model param {name} with checkpoint param {value_param[0]}')