82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
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# -*- coding: UTF-8 -*-
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import time
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import sys
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import paddle.fluid as fluid
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import paddle
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import jieba.lac_small.utils as utils
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import jieba.lac_small.creator as creator
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import jieba.lac_small.reader_small as reader_small
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import numpy
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word_emb_dim=128
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grnn_hidden_dim=128
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bigru_num=2
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use_cuda=False
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basepath = os.path.abspath(__file__)
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folder = os.path.dirname(basepath)
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init_checkpoint = os.path.join(folder, "model_baseline")
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batch_size=1
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dataset = reader_small.Dataset()
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infer_program = fluid.Program()
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with fluid.program_guard(infer_program, fluid.default_startup_program()):
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with fluid.unique_name.guard():
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infer_ret = creator.create_model(dataset.vocab_size, dataset.num_labels, mode='infer')
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infer_program = infer_program.clone(for_test=True)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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utils.init_checkpoint(exe, init_checkpoint, infer_program)
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results = []
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def get_sent(str1):
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feed_data=dataset.get_vars(str1)
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a = numpy.array(feed_data).astype(numpy.int64)
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a=a.reshape(-1,1)
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c = fluid.create_lod_tensor(a, [[a.shape[0]]], place)
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words, crf_decode = exe.run(
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infer_program,
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fetch_list=[infer_ret['words'], infer_ret['crf_decode']],
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feed={"words":c, },
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return_numpy=False,
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use_program_cache=True)
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sents=[]
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sent,tag = utils.parse_result(words, crf_decode, dataset)
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sents = sents + sent
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return sents
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def get_result(str1):
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feed_data=dataset.get_vars(str1)
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a = numpy.array(feed_data).astype(numpy.int64)
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a=a.reshape(-1,1)
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c = fluid.create_lod_tensor(a, [[a.shape[0]]], place)
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words, crf_decode = exe.run(
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infer_program,
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fetch_list=[infer_ret['words'], infer_ret['crf_decode']],
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feed={"words":c, },
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return_numpy=False,
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use_program_cache=True)
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results=[]
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results += utils.parse_result(words, crf_decode, dataset)
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return results
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