# -*- coding: UTF-8 -*- # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # 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. from __future__ import print_function import os import six import ast import copy import tarfile import shutil import numpy as np import paddle.fluid as fluid def init_checkpoint(exe, init_checkpoint_path, main_program, skip_list = []): assert os.path.exists( init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path def existed_persitables(var): if not fluid.io.is_persistable(var): return False if var.name in skip_list: return False return os.path.exists(os.path.join(init_checkpoint_path, var.name)) fluid.io.load_vars( exe, init_checkpoint_path, main_program=main_program, predicate=existed_persitables) print("Load model from {}".format(init_checkpoint_path)) def init_pretraining_params(exe, pretraining_params_path, main_program): assert os.path.exists(pretraining_params_path ), "[%s] cann't be found." % pretraining_params_path assert os.path.exists(os.path.join(pretraining_params_path, '__palmmodel__')), "__palmmodel__ not found." print("Loading pretraining parameters from {}...".format( pretraining_params_path)) with tarfile.open(os.path.join(pretraining_params_path, '__palmmodel__'), 'r:') as f: f.extractall(os.path.join(pretraining_params_path, '.temp')) pretraining_params_path = os.path.join(pretraining_params_path, '.temp') def existed_params(var): if not isinstance(var, fluid.framework.Parameter): return False if not os.path.exists(os.path.join(pretraining_params_path, var.name)): print('Warning: {} not found in {}.'.format(var.name, pretraining_params_path)) return os.path.exists(os.path.join(pretraining_params_path, var.name)) fluid.io.load_vars( exe, pretraining_params_path, main_program=main_program, predicate=existed_params) shutil.rmtree(pretraining_params_path) print('')