From 507e091607796b484a29d2e70a862f1431360b6e Mon Sep 17 00:00:00 2001 From: chenxuyi Date: Sat, 12 Oct 2019 17:51:15 +0800 Subject: [PATCH] use infer_classifier instead of predict_classifier --- README.md | 18 +-- README.zh.md | 21 ++- classify_infer.py => infer_classifyer.py | 12 +- predict_classifier.py | 172 ----------------------- 4 files changed, 23 insertions(+), 200 deletions(-) rename classify_infer.py => infer_classifyer.py (96%) delete mode 100644 predict_classifier.py diff --git a/README.md b/README.md index d7dc9bf..77b4639 100644 --- a/README.md +++ b/README.md @@ -979,17 +979,13 @@ when finished running this script, `cls_emb.npy` and `top_layer_emb.npy `will b Take classification tasks for example, here is the script for batch prediction: ``` -python -u predict_classifier.py \ - --use_cuda true \ - --batch_size 32 \ - --vocab_path ${MODEL_PATH}/vocab.txt \ - --init_checkpoint "./checkpoints/step_100" \ - --do_lower_case true \ - --max_seq_len 128 \ - --ernie_config_path ${MODEL_PATH}/ernie_config.json \ - --do_predict true \ - --predict_set ${TASK_DATA_PATH}/lcqmc/test.tsv \ - --num_labels 2 +python -u infer_classifyer.py \ + --ernie_config_path ${MODEL_PATH}/ernie_config.json \ + --init_checkpoint "./checkpoints/step_100" \ + --save_inference_model_path ./saved_model \ + --predict_set ${TASK_DATA_PATH}/xnli/test.tsv \ + --vocab_path ${MODEL_PATH}/vocab.txt + --num_labels 3 ``` Argument `init_checkpoint` is the path of the model, `predict_set` is the path of test file, `num_labels` is the number of target labels. diff --git a/README.zh.md b/README.zh.md index 14a0691..4106e0d 100644 --- a/README.zh.md +++ b/README.zh.md @@ -935,7 +935,7 @@ ERNIE提供了通过数据蒸馏从而达到模型压缩、加速的开发套件 完成finetune之后只需几步操作即可生成inference\_model, PaddlePaddle可以在生产环境中加载生成的预测模型并进行高效地预测。 ### 生成inference\_model -运行`classify_infer.py`或者`predict_classifier.py` 脚本时通过指定 `--save_inference_model_path` 便可生成 inference_model 到指定位置。 +运行`infer_classifyer.py` 脚本时通过指定 `--save_inference_model_path` 便可生成 inference_model 到指定位置。 如果您采用 `propeller` 完成finetune，则 `BestInferenceExporter` 会在finetune过程中根据预测指标，挑最好的模型生成 inference_model . 使用 `propeller` 完成finetune的流程请参考 `propeller_xnli_demo.ipynb` @@ -985,17 +985,14 @@ python -u ernie_encoder.py \ 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 ${MODEL_PATH}/vocab.txt \ - --init_checkpoint "./checkpoints/step_100" \ - --do_lower_case true \ - --max_seq_len 128 \ - --ernie_config_path ${MODEL_PATH}/ernie_config.json \ - --do_predict true \ - --predict_set ${TASK_DATA_PATH}/lcqmc/test.tsv \ - --num_labels 2 +python -u infer_classifyer.py \ + --ernie_config_path ${MODEL_PATH}/ernie_config.json \ + --init_checkpoint "./checkpoints/step_100" \ + --save_inference_model_path ./saved_model \ + --predict_set ${TASK_DATA_PATH}/xnli/test.tsv \ + --vocab_path ${MODEL_PATH}/vocab.txt + --num_labels 3 + ``` 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; diff --git a/classify_infer.py b/infer_classifyer.py similarity index 96% rename from classify_infer.py rename to infer_classifyer.py index 3aedce4..ae6114d 100644 --- a/classify_infer.py +++ b/infer_classifyer.py @@ -22,6 +22,7 @@ import os import time import argparse import numpy as np +import logging import multiprocessing # NOTE(paddle-dev): All of these flags should be @@ -40,7 +41,7 @@ from reader.task_reader import ClassifyReader from model.ernie import ErnieConfig from finetune.classifier import create_model -from utils.args import print_arguments, check_cuda, prepare_logger +from utils.args import print_arguments, check_cuda, prepare_logger, ArgumentGroup from utils.init import init_pretraining_params from finetune_args import parser @@ -129,6 +130,9 @@ def main(args): if not args.use_cuda: log.info("disable gpu") config.disable_gpu() + else: + log.info("using gpu") + config.enable_use_gpu(1024) # Create PaddlePredictor predictor = create_paddle_predictor(config) @@ -158,12 +162,10 @@ def main(args): # parse outputs output = outputs[0] - log.info(output.name) output_data = output.data.float_data() - #assert len(output_data) == args.num_labels * args.batch_size - batch_result = np.array(output_data).reshape((-1, args.num_labels)) + batch_result = np.array(output_data).reshape(output.shape) for single_example_probs in batch_result: - log.info("{} example\t{}".format(index, single_example_probs)) + print('\t'.join(map(str, single_example_probs.tolist()))) index += 1 log.info("qps:{}\ttotal_time:{}\ttotal_example:{}\tbatch_size:{}".format(index/total_time, total_time, index, args.batch_size)) diff --git a/predict_classifier.py b/predict_classifier.py deleted file mode 100644 index 67c219a..0000000 --- a/predict_classifier.py +++ /dev/null @@ -1,172 +0,0 @@ -# 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 - -# NOTE(paddle-dev): All of these flags should be -# set before `import paddle`. Otherwise, it would -# not take any effect. -os.environ['FLAGS_eager_delete_tensor_gb'] = '0' # enable gc - -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 ernie model config.") -model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.") -model_g.add_arg("save_inference_model_path", str, "inference_model", "If set, save the inference model to this path.") -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") -model_g.add_arg("ernie_version", str, "1.0", "ernie_version") - -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, - is_inference=True) - - 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_classify=True, - is_prediction=True, - ernie_version=args.ernie_version) - - 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!") - - assert args.save_inference_model_path, "args save_inference_model_path should be set for prediction" - _, ckpt_dir = os.path.split(args.init_checkpoint.rstrip('/')) - dir_name = ckpt_dir + '_inference_model' - model_path = os.path.join(args.save_inference_model_path, dir_name) - print("save inference model to %s" % model_path) - fluid.io.save_inference_model( - model_path, - feed_target_names, [probs], - exe, - main_program=predict_prog) - - print("load inference model from %s" % model_path) - infer_program, feed_target_names, probs = fluid.io.load_inference_model( - model_path, exe) - - src_ids = feed_target_names[0] - sent_ids = feed_target_names[1] - pos_ids = feed_target_names[2] - input_mask = feed_target_names[3] - if args.ernie_version == "2.0": - task_ids = feed_target_names[4] - - predict_data_generator = reader.data_generator( - input_file=args.predict_set, - batch_size=args.batch_size, - epoch=1, - shuffle=False) - - print("-------------- prediction results --------------") - np.set_printoptions(precision=4, suppress=True) - index = 0 - for sample in predict_data_generator(): - src_ids_data = sample[0] - sent_ids_data = sample[1] - pos_ids_data = sample[2] - task_ids_data = sample[3] - input_mask_data = sample[4] - if args.ernie_version == "1.0": - output = exe.run( - infer_program, - feed={src_ids: src_ids_data, - sent_ids: sent_ids_data, - pos_ids: pos_ids_data, - input_mask: input_mask_data}, - fetch_list=probs) - elif args.ernie_version == "2.0": - output = exe.run( - infer_program, - feed={src_ids: src_ids_data, - sent_ids: sent_ids_data, - pos_ids: pos_ids_data, - task_ids: task_ids_data, - input_mask: input_mask_data}, - fetch_list=probs) - else: - raise ValueError("ernie_version must be 1.0 or 2.0") - - for single_result in output[0]: - print("example_index:{}\t{}".format(index, single_result)) - index += 1 - -if __name__ == '__main__': - print_arguments(args) - main(args) -- GitLab