diff --git a/ERNIE/README.md b/ERNIE/README.md index a07b8ddb518dece8af331ea46ae8d3933dd88ad8..e68b7bdcda66aacf043a0af2992fcc103d43c947 100644 --- a/ERNIE/README.md +++ b/ERNIE/README.md @@ -295,3 +295,25 @@ python -u ernir_encoder.py \ #### 如何获取输入句子中每个 token 经过 ERNIE 编码后的 Embedding 表示？ [解决方案同上](#如何获取输入句子经过-ERNIE-编码后的-Embedding-表示?) + +#### 如何利用 finetune 得到的模型对新数据进行批量预测？ + +我们以分类任务为例，给出了分类任务进行批量预测的脚本, 使用示例如下: + +``` +python -u predict_classifier.py \ + --use_cuda true \ + --batch_size 32 \ + --vocab_path config/vocab.txt \ + --init_checkpoint "./checkpoints/step_100" \ + --do_lower_case true \ + --max_seq_len 128 \ + --ernie_config_path config/ernie_config.json \ + --do_predict true \ + --predict_set ${TASK_DATA_PATH}/lcqmc/test.tsv \ + --num_labels 2 +``` + +实际使用时，需要通过 `init_checkpoint` 指定预测用的模型，通过 `predict_set` 指定待预测的数据文件，通过 `num_labels` 配置分类的类别数目; + +**Note**: predict_set 的数据格式与 dev_set 和 test_set 的数据格式完全一致，是由 text_a、text_b(可选) 、label 组成的2列/3列 tsv 文件，predict_set 中的 label 列起到占位符的作用，全部置 0 即可; diff --git a/ERNIE/predict_classifier.py b/ERNIE/predict_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..924d4fa7ec805cb991f4ff7c1571d3d8b73e1e6f --- /dev/null +++ b/ERNIE/predict_classifier.py @@ -0,0 +1,128 @@ +# 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. +"""Load classifier's checkpoint to do prediction or save inference model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import time +import argparse +import numpy as np +import multiprocessing +import paddle.fluid as fluid + +from reader.task_reader import ClassifyReader +from model.ernie import ErnieConfig +from finetune.classifier import create_model + +from utils.args import ArgumentGroup, print_arguments +from utils.init import init_pretraining_params +from finetune_args import parser + +# yapf: disable +parser = argparse.ArgumentParser(__doc__) +model_g = ArgumentGroup(parser, "model", "options to init, resume and save model.") +model_g.add_arg("ernie_config_path", str, None, "Path to the json file for bert model config.") +model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.") +model_g.add_arg("use_fp16", bool, False, "Whether to resume parameters from fp16 checkpoint.") +model_g.add_arg("num_labels", int, 2, "num labels for classify") + +data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options.") +data_g.add_arg("predict_set", str, None, "Predict set file") +data_g.add_arg("vocab_path", str, None, "Vocabulary path.") +data_g.add_arg("label_map_config", str, None, "Label_map_config json file.") +data_g.add_arg("max_seq_len", int, 128, "Number of words of the longest seqence.") +data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.") +data_g.add_arg("do_lower_case", bool, True, + "Whether to lower case the input text. Should be True for uncased models and False for cased models.") + +run_type_g = ArgumentGroup(parser, "run_type", "running type options.") +run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.") +run_type_g.add_arg("do_prediction", bool, True, "Whether to do prediction on test set.") + +args = parser.parse_args() +# yapf: enable. + +def main(args): + ernie_config = ErnieConfig(args.ernie_config_path) + ernie_config.print_config() + + reader = ClassifyReader( + vocab_path=args.vocab_path, + label_map_config=args.label_map_config, + max_seq_len=args.max_seq_len, + do_lower_case=args.do_lower_case, + in_tokens=False) + + predict_prog = fluid.Program() + predict_startup = fluid.Program() + with fluid.program_guard(predict_prog, predict_startup): + with fluid.unique_name.guard(): + predict_pyreader, probs, feed_target_names = create_model( + args, + pyreader_name='predict_reader', + ernie_config=ernie_config, + is_prediction=True) + + predict_prog = predict_prog.clone(for_test=True) + + if args.use_cuda: + place = fluid.CUDAPlace(0) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + + place = fluid.CUDAPlace(0) if args.use_cuda == True else fluid.CPUPlace() + exe = fluid.Executor(place) + exe.run(predict_startup) + + if args.init_checkpoint: + init_pretraining_params(exe, args.init_checkpoint, predict_prog) + else: + raise ValueError("args 'init_checkpoint' should be set for prediction!") + + predict_exe = fluid.Executor(place) + + predict_data_generator = reader.data_generator( + input_file=args.predict_set, + batch_size=args.batch_size, + epoch=1, + shuffle=False) + + predict_pyreader.decorate_tensor_provider(predict_data_generator) + + predict_pyreader.start() + all_results = [] + time_begin = time.time() + while True: + try: + results = predict_exe.run(program=predict_prog, fetch_list=[probs.name]) + all_results.extend(results[0]) + except fluid.core.EOFException: + predict_pyreader.reset() + break + time_end = time.time() + + np.set_printoptions(precision=4, suppress=True) + print("-------------- prediction results --------------") + for index, result in enumerate(all_results): + print(str(index) + '\t{}'.format(result)) + + +if __name__ == '__main__': + print_arguments(args) + main(args)