forked from mindspore-Ecosystem/mindspore
deepfm && wide_and_deep 310 infer
modified: deepfm/README.md modified: deepfm/README_CN.md modified: deepfm/postprocess.py modified: deepfm/preprocess.py modified: wide_and_deep/postprocess.py modified: wide_and_deep/preprocess.py
This commit is contained in:
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@ -13,6 +13,10 @@
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- [Distributed Training](#distributed-training)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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- [Inference Process](#inference-process)
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- [Export MindIR](#export-mindir)
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- [Infer on Ascend310](#infer-on-ascend310)
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- [result](#result)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Evaluation Performance](#evaluation-performance)
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@ -269,6 +273,37 @@ Parameters for both training and evaluation can be set in config.py
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To do.
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## Inference Process
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### [Export MindIR](#contents)
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```shell
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python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
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```
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The ckpt_file parameter is required,
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`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
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### Infer on Ascend310
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Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
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```shell
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# Ascend310 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_ID]
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```
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- `NEED_PREPROCESS` means weather need preprocess or not, it's value is 'y' or 'n'.
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- `DEVICE_ID` is optional, default value is 0.
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### result
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Inference result is saved in current path, you can find result like this in acc.log file.
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```bash
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auc : 0.8057789065281104
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```
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# [Model Description](#contents)
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## [Performance](#contents)
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@ -16,6 +16,10 @@
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- [分布式训练](#分布式训练)
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- [评估过程](#评估过程)
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- [评估](#评估)
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- [推理过程](#推理过程)
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- [导出MindIR](#导出mindir)
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- [在Ascend310执行推理](#在ascend310执行推理)
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- [结果](#结果)
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- [模型描述](#模型描述)
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- [性能](#性能)
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- [评估性能](#评估性能)
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@ -251,6 +255,37 @@ FM和深度学习部分拥有相同的输入原样特征向量,让DeepFM能从
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- 在GPU运行时评估数据集
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待运行。
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## 推理过程
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### [导出MindIR](#contents)
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```shell
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python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
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```
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参数ckpt_file为必填项,
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`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中选择。
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### 在Ascend310执行推理
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在执行推理前,mindir文件必须通过`export.py`脚本导出。以下展示了使用minir模型执行推理的示例。
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```shell
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# Ascend310 推理
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bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_ID]
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```
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- `NEED_PREPROCESS` 表示数据是否需要预处理,取值范围为 'y' 或者 'n'。
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- `DEVICE_ID` 可选,默认值为0。
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### 结果
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推理结果保存在脚本执行的当前路径,你可以在acc.log中看到以下精度计算结果。
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```bash
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auc : 0.8057789065281104
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```
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## 模型描述
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## 性能
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cmake_minimum_required(VERSION 3.14.1)
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project(Ascend310Infer)
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add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O2 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
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set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
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option(MINDSPORE_PATH "mindspore install path" "")
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include_directories(${MINDSPORE_PATH})
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include_directories(${MINDSPORE_PATH}/include)
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include_directories(${PROJECT_SRC_ROOT})
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find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
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file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
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add_executable(main src/main.cc src/utils.cc)
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target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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if [ -d out ]; then
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rm -rf out
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fi
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mkdir out
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cd out || exit
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if [ -f "Makefile" ]; then
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make clean
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fi
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cmake .. \
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-DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
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make
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@ -0,0 +1,32 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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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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*/
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#ifndef MINDSPORE_INFERENCE_UTILS_H_
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#define MINDSPORE_INFERENCE_UTILS_H_
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#include <sys/stat.h>
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#include <dirent.h>
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#include <vector>
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#include <string>
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#include <memory>
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#include "include/api/types.h"
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std::vector<std::string> GetAllFiles(std::string_view dirName);
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DIR *OpenDir(std::string_view dirName);
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std::string RealPath(std::string_view path);
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mindspore::MSTensor ReadFileToTensor(const std::string &file);
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int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
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#endif
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@ -0,0 +1,140 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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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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*/
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#include <sys/time.h>
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#include <gflags/gflags.h>
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#include <dirent.h>
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#include <iostream>
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#include <string>
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#include <algorithm>
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#include <iosfwd>
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#include <vector>
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#include <fstream>
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#include <sstream>
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#include "include/api/model.h"
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#include "include/api/context.h"
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#include "include/api/types.h"
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#include "include/api/serialization.h"
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#include "include/dataset/execute.h"
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#include "include/dataset/vision.h"
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#include "inc/utils.h"
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using mindspore::Context;
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using mindspore::Serialization;
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using mindspore::Model;
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using mindspore::Status;
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using mindspore::MSTensor;
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using mindspore::dataset::Execute;
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using mindspore::ModelType;
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using mindspore::GraphCell;
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using mindspore::kSuccess;
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DEFINE_string(mindir_path, "", "mindir path");
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DEFINE_string(input0_path, ".", "input0 path");
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DEFINE_string(input1_path, ".", "input1 path");
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DEFINE_string(input2_path, ".", "input2 path");
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DEFINE_int32(device_id, 0, "device id");
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int main(int argc, char **argv) {
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gflags::ParseCommandLineFlags(&argc, &argv, true);
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if (RealPath(FLAGS_mindir_path).empty()) {
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std::cout << "Invalid mindir" << std::endl;
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return 1;
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}
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auto context = std::make_shared<Context>();
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auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
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ascend310->SetDeviceID(FLAGS_device_id);
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context->MutableDeviceInfo().push_back(ascend310);
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mindspore::Graph graph;
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Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
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Model model;
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Status ret = model.Build(GraphCell(graph), context);
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if (ret != kSuccess) {
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std::cout << "ERROR: Build failed." << std::endl;
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return 1;
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}
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std::vector<MSTensor> model_inputs = model.GetInputs();
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if (model_inputs.empty()) {
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std::cout << "Invalid model, inputs is empty." << std::endl;
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return 1;
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}
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auto input0_files = GetAllFiles(FLAGS_input0_path);
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auto input1_files = GetAllFiles(FLAGS_input1_path);
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auto input2_files = GetAllFiles(FLAGS_input2_path);
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if (input0_files.empty() || input1_files.empty() || input2_files.empty()) {
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std::cout << "ERROR: input data empty." << std::endl;
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return 1;
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}
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std::map<double, double> costTime_map;
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size_t size = input0_files.size();
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for (size_t i = 0; i < size; ++i) {
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struct timeval start = {0};
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struct timeval end = {0};
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double startTimeMs;
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double endTimeMs;
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std::vector<MSTensor> inputs;
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std::vector<MSTensor> outputs;
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std::cout << "Start predict input files:" << input0_files[i] << std::endl;
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auto input0 = ReadFileToTensor(input0_files[i]);
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auto input1 = ReadFileToTensor(input1_files[i]);
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auto input2 = ReadFileToTensor(input2_files[i]);
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inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
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input0.Data().get(), input0.DataSize());
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inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
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input1.Data().get(), input1.DataSize());
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inputs.emplace_back(model_inputs[2].Name(), model_inputs[2].DataType(), model_inputs[2].Shape(),
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input2.Data().get(), input2.DataSize());
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gettimeofday(&start, nullptr);
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ret = model.Predict(inputs, &outputs);
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gettimeofday(&end, nullptr);
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if (ret != kSuccess) {
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std::cout << "Predict " << input0_files[i] << " failed." << std::endl;
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return 1;
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}
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startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
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endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
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costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
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WriteResult(input0_files[i], outputs);
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}
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double average = 0.0;
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int inferCount = 0;
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for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
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double diff = 0.0;
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diff = iter->second - iter->first;
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average += diff;
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inferCount++;
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}
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average = average / inferCount;
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std::stringstream timeCost;
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timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
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std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
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std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
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std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
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fileStream << timeCost.str();
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fileStream.close();
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costTime_map.clear();
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return 0;
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}
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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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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*/
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#include <fstream>
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#include <algorithm>
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#include <iostream>
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#include "inc/utils.h"
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using mindspore::MSTensor;
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using mindspore::DataType;
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std::vector<std::string> GetAllFiles(std::string_view dirName) {
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struct dirent *filename;
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DIR *dir = OpenDir(dirName);
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if (dir == nullptr) {
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return {};
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}
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std::vector<std::string> res;
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while ((filename = readdir(dir)) != nullptr) {
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std::string dName = std::string(filename->d_name);
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if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
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continue;
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}
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res.emplace_back(std::string(dirName) + "/" + filename->d_name);
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}
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std::sort(res.begin(), res.end());
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for (auto &f : res) {
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std::cout << "image file: " << f << std::endl;
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}
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return res;
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}
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int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
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std::string homePath = "./result_Files";
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for (size_t i = 0; i < outputs.size(); ++i) {
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size_t outputSize;
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std::shared_ptr<const void> netOutput;
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netOutput = outputs[i].Data();
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outputSize = outputs[i].DataSize();
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int pos = imageFile.rfind('/');
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std::string fileName(imageFile, pos + 1);
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fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
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std::string outFileName = homePath + "/" + fileName;
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FILE * outputFile = fopen(outFileName.c_str(), "wb");
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fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
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fclose(outputFile);
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outputFile = nullptr;
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}
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return 0;
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}
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mindspore::MSTensor ReadFileToTensor(const std::string &file) {
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if (file.empty()) {
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std::cout << "Pointer file is nullptr" << std::endl;
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return mindspore::MSTensor();
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}
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std::ifstream ifs(file);
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if (!ifs.good()) {
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std::cout << "File: " << file << " is not exist" << std::endl;
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return mindspore::MSTensor();
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}
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if (!ifs.is_open()) {
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std::cout << "File: " << file << "open failed" << std::endl;
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return mindspore::MSTensor();
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}
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ifs.seekg(0, std::ios::end);
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size_t size = ifs.tellg();
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mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
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ifs.seekg(0, std::ios::beg);
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ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
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ifs.close();
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return buffer;
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}
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DIR *OpenDir(std::string_view dirName) {
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if (dirName.empty()) {
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std::cout << " dirName is null ! " << std::endl;
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return nullptr;
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}
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std::string realPath = RealPath(dirName);
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struct stat s;
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lstat(realPath.c_str(), &s);
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if (!S_ISDIR(s.st_mode)) {
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std::cout << "dirName is not a valid directory !" << std::endl;
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return nullptr;
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}
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DIR *dir;
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dir = opendir(realPath.c_str());
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if (dir == nullptr) {
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std::cout << "Can not open dir " << dirName << std::endl;
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return nullptr;
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}
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std::cout << "Successfully opened the dir " << dirName << std::endl;
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return dir;
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}
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std::string RealPath(std::string_view path) {
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char realPathMem[PATH_MAX] = {0};
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char *realPathRet = nullptr;
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realPathRet = realpath(path.data(), realPathMem);
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if (realPathRet == nullptr) {
|
||||
std::cout << "File: " << path << " is not exist.";
|
||||
return "";
|
||||
}
|
||||
|
||||
std::string realPath(realPathMem);
|
||||
std::cout << path << " realpath is: " << realPath << std::endl;
|
||||
return realPath;
|
||||
}
|
|
@ -0,0 +1,52 @@
|
|||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
"""postprocess."""
|
||||
import os
|
||||
import argparse
|
||||
import numpy as np
|
||||
from mindspore import Tensor
|
||||
from src.deepfm import AUCMetric
|
||||
from src.config import TrainConfig
|
||||
|
||||
parser = argparse.ArgumentParser(description='postprocess')
|
||||
parser.add_argument('--result_path', type=str, default="./result_Files", help='result path')
|
||||
parser.add_argument('--label_path', type=str, default=None, help='label path')
|
||||
args_opt, _ = parser.parse_known_args()
|
||||
|
||||
def get_acc():
|
||||
''' get accuracy '''
|
||||
auc_metric = AUCMetric()
|
||||
train_config = TrainConfig()
|
||||
files = os.listdir(args_opt.label_path)
|
||||
batch_size = train_config.batch_size
|
||||
|
||||
for f in files:
|
||||
rst_file = os.path.join(args_opt.result_path, f.split('.')[0] + '_0.bin')
|
||||
label_file = os.path.join(args_opt.label_path, f)
|
||||
|
||||
logit = Tensor(np.fromfile(rst_file, np.float32).reshape(batch_size, 1))
|
||||
label = Tensor(np.fromfile(label_file, np.float32).reshape(batch_size, 1))
|
||||
|
||||
res = []
|
||||
res.append(logit)
|
||||
res.append(logit)
|
||||
res.append(label)
|
||||
|
||||
auc_metric.update(*res)
|
||||
auc = auc_metric.eval()
|
||||
print("auc : {}".format(auc))
|
||||
|
||||
if __name__ == '__main__':
|
||||
get_acc()
|
|
@ -0,0 +1,57 @@
|
|||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
"""preprocess."""
|
||||
import os
|
||||
import argparse
|
||||
|
||||
from src.config import DataConfig, TrainConfig
|
||||
from src.dataset import create_dataset, DataType
|
||||
|
||||
parser = argparse.ArgumentParser(description='preprocess.')
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--result_path', type=str, default='./preprocess_Result', help='Result path')
|
||||
args_opt, _ = parser.parse_known_args()
|
||||
|
||||
def generate_bin():
|
||||
'''generate bin files'''
|
||||
data_config = DataConfig()
|
||||
train_config = TrainConfig()
|
||||
|
||||
ds = create_dataset(args_opt.dataset_path, train_mode=False,
|
||||
epochs=1, batch_size=train_config.batch_size,
|
||||
data_type=DataType(data_config.data_format))
|
||||
batch_ids_path = os.path.join(args_opt.result_path, "00_batch_ids")
|
||||
batch_wts_path = os.path.join(args_opt.result_path, "01_batch_wts")
|
||||
labels_path = os.path.join(args_opt.result_path, "02_labels")
|
||||
|
||||
os.makedirs(batch_ids_path)
|
||||
os.makedirs(batch_wts_path)
|
||||
os.makedirs(labels_path)
|
||||
|
||||
for i, data in enumerate(ds.create_dict_iterator(output_numpy=True)):
|
||||
file_name = "criteo_bs" + str(train_config.batch_size) + "_" + str(i) + ".bin"
|
||||
batch_ids = data['feat_ids']
|
||||
batch_ids.tofile(os.path.join(batch_ids_path, file_name))
|
||||
|
||||
batch_wts = data['feat_vals']
|
||||
batch_wts.tofile(os.path.join(batch_wts_path, file_name))
|
||||
|
||||
labels = data['label']
|
||||
labels.tofile(os.path.join(labels_path, file_name))
|
||||
|
||||
print("=" * 20, "export bin files finished", "=" * 20)
|
||||
|
||||
if __name__ == '__main__':
|
||||
generate_bin()
|
|
@ -0,0 +1,122 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
|
||||
if [[ $# -lt 3 || $# -gt 4 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
dataset_path=$(get_real_path $2)
|
||||
|
||||
if [ "$3" == "y" ] || [ "$3" == "n" ];then
|
||||
need_preprocess=$3
|
||||
else
|
||||
echo "weather need preprocess or not, it's value must be in [y, n]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
device_id=0
|
||||
if [ $# == 4 ]; then
|
||||
device_id=$4
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
echo "dataset path: "$dataset_path
|
||||
echo "need preprocess: "$need_preprocess
|
||||
echo "device id: "$device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function preprocess_data()
|
||||
{
|
||||
if [ -d preprocess_Result ]; then
|
||||
rm -rf ./preprocess_Result
|
||||
fi
|
||||
mkdir preprocess_Result
|
||||
python3.7 ../preprocess.py --dataset_path=$dataset_path --result_path=./preprocess_Result/
|
||||
}
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
|
||||
../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_batch_ids --input1_path=./preprocess_Result/01_batch_wts --input2_path=./preprocess_Result/02_labels --device_id=$device_id &> infer.log
|
||||
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --result_path=./result_Files --label_path=./preprocess_Result/02_labels &> acc.log
|
||||
}
|
||||
|
||||
if [ $need_preprocess == "y" ]; then
|
||||
preprocess_data
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "preprocess dataset failed"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
compile_app
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "compile app code failed"
|
||||
exit 1
|
||||
fi
|
||||
infer
|
||||
if [ $? -ne 0 ]; then
|
||||
echo " execute inference failed"
|
||||
exit 1
|
||||
fi
|
||||
cal_acc
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "calculate accuracy failed"
|
||||
exit 1
|
||||
fi
|
|
@ -19,6 +19,10 @@
|
|||
- [Distribute Training](#distribute-training)
|
||||
- [Parameter Server](#parameter-server)
|
||||
- [Evaluation Process](#evaluation-process)
|
||||
- [Inference Process](#inference-process)
|
||||
- [Export MindIR](#export-mindir)
|
||||
- [Infer on Ascend310](#infer-on-ascend310)
|
||||
- [result](#result)
|
||||
- [Model Description](#model-description)
|
||||
- [Performance](#performance)
|
||||
- [Training Performance](#training-performance)
|
||||
|
@ -317,6 +321,38 @@ To evaluate the model, command as follows:
|
|||
python eval.py
|
||||
```
|
||||
|
||||
## Inference Process
|
||||
|
||||
### [Export MindIR](#contents)
|
||||
|
||||
```shell
|
||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||
```
|
||||
|
||||
The ckpt_file parameter is required,
|
||||
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
|
||||
|
||||
### Infer on Ascend310
|
||||
|
||||
Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
|
||||
|
||||
```shell
|
||||
# Ascend310 inference
|
||||
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DATA_TYPE] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
```
|
||||
|
||||
- `DATA_TYPE` means dataset type, it's value is ['tfrecord', 'mindrecord', 'hd5'].
|
||||
- `NEED_PREPROCESS` means weather need preprocess or not, it's value is 'y' or 'n'.
|
||||
- `DEVICE_ID` is optional, default value is 0.
|
||||
|
||||
### result
|
||||
|
||||
Inference result is saved in current path, you can find result like this in acc.log file.
|
||||
|
||||
```bash
|
||||
================================================================================ auc : 0.8080494136248402
|
||||
```
|
||||
|
||||
# [Model Description](#contents)
|
||||
|
||||
## [Performance](#contents)
|
||||
|
|
|
@ -19,6 +19,10 @@
|
|||
- [分布式训练](#分布式训练)
|
||||
- [参数服务器](#参数服务器)
|
||||
- [评估过程](#评估过程)
|
||||
- [推理过程](#推理过程)
|
||||
- [导出MindIR](#导出mindir)
|
||||
- [在Ascend310执行推理](#在ascend310执行推理)
|
||||
- [结果](#结果)
|
||||
- [模型描述](#模型描述)
|
||||
- [性能](#性能)
|
||||
- [训练性能](#训练性能)
|
||||
|
@ -319,6 +323,46 @@ bash run_parameter_server_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE SERV
|
|||
python eval.py
|
||||
```
|
||||
|
||||
## [Evaluation Process](#contents)
|
||||
|
||||
To evaluate the model, command as follows:
|
||||
|
||||
```python
|
||||
python eval.py
|
||||
```
|
||||
|
||||
## 推理过程
|
||||
|
||||
### [导出MindIR](#contents)
|
||||
|
||||
```shell
|
||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||
```
|
||||
|
||||
参数ckpt_file为必填项,
|
||||
`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中选择。
|
||||
|
||||
### 在Ascend310执行推理
|
||||
|
||||
在执行推理前,mindir文件必须通过`export.py`脚本导出。以下展示了使用minir模型执行推理的示例。
|
||||
|
||||
```shell
|
||||
# Ascend310 推理
|
||||
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DATA_TYPE] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
```
|
||||
|
||||
- `DATA_TYPE` 表示数据类型, 取值范围为 ['tfrecord', 'mindrecord', 'hd5']。
|
||||
- `NEED_PREPROCESS` 表示数据是否需要预处理,取值范围为 'y' 或者 'n'。
|
||||
- `DEVICE_ID` 可选,默认值为0。
|
||||
|
||||
### result
|
||||
|
||||
推理结果保存在脚本执行的当前路径,你可以在acc.log中看到以下精度计算结果。
|
||||
|
||||
```bash
|
||||
================================================================================ auc : 0.8080494136248402
|
||||
```
|
||||
|
||||
# 模型描述
|
||||
|
||||
## 性能
|
||||
|
|
|
@ -0,0 +1,14 @@
|
|||
cmake_minimum_required(VERSION 3.14.1)
|
||||
project(Ascend310Infer)
|
||||
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
|
||||
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O2 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
|
||||
set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
|
||||
option(MINDSPORE_PATH "mindspore install path" "")
|
||||
include_directories(${MINDSPORE_PATH})
|
||||
include_directories(${MINDSPORE_PATH}/include)
|
||||
include_directories(${PROJECT_SRC_ROOT})
|
||||
find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
|
||||
file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
|
||||
|
||||
add_executable(main src/main.cc src/utils.cc)
|
||||
target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
|
|
@ -0,0 +1,29 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
if [ -d out ]; then
|
||||
rm -rf out
|
||||
fi
|
||||
|
||||
mkdir out
|
||||
cd out || exit
|
||||
|
||||
if [ -f "Makefile" ]; then
|
||||
make clean
|
||||
fi
|
||||
|
||||
cmake .. \
|
||||
-DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
|
||||
make
|
|
@ -0,0 +1,32 @@
|
|||
/**
|
||||
* Copyright 2021 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.
|
||||
*/
|
||||
|
||||
#ifndef MINDSPORE_INFERENCE_UTILS_H_
|
||||
#define MINDSPORE_INFERENCE_UTILS_H_
|
||||
|
||||
#include <sys/stat.h>
|
||||
#include <dirent.h>
|
||||
#include <vector>
|
||||
#include <string>
|
||||
#include <memory>
|
||||
#include "include/api/types.h"
|
||||
|
||||
std::vector<std::string> GetAllFiles(std::string_view dirName);
|
||||
DIR *OpenDir(std::string_view dirName);
|
||||
std::string RealPath(std::string_view path);
|
||||
mindspore::MSTensor ReadFileToTensor(const std::string &file);
|
||||
int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
|
||||
#endif
|
|
@ -0,0 +1,140 @@
|
|||
/**
|
||||
* Copyright 2021 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.
|
||||
*/
|
||||
#include <sys/time.h>
|
||||
#include <gflags/gflags.h>
|
||||
#include <dirent.h>
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
#include <algorithm>
|
||||
#include <iosfwd>
|
||||
#include <vector>
|
||||
#include <fstream>
|
||||
#include <sstream>
|
||||
|
||||
#include "include/api/model.h"
|
||||
#include "include/api/context.h"
|
||||
#include "include/api/types.h"
|
||||
#include "include/api/serialization.h"
|
||||
#include "include/dataset/execute.h"
|
||||
#include "include/dataset/vision.h"
|
||||
#include "inc/utils.h"
|
||||
|
||||
using mindspore::Context;
|
||||
using mindspore::Serialization;
|
||||
using mindspore::Model;
|
||||
using mindspore::Status;
|
||||
using mindspore::MSTensor;
|
||||
using mindspore::dataset::Execute;
|
||||
using mindspore::ModelType;
|
||||
using mindspore::GraphCell;
|
||||
using mindspore::kSuccess;
|
||||
|
||||
DEFINE_string(mindir_path, "", "mindir path");
|
||||
DEFINE_string(input0_path, ".", "input0 path");
|
||||
DEFINE_string(input1_path, ".", "input1 path");
|
||||
DEFINE_string(input2_path, ".", "input2 path");
|
||||
DEFINE_int32(device_id, 0, "device id");
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
gflags::ParseCommandLineFlags(&argc, &argv, true);
|
||||
if (RealPath(FLAGS_mindir_path).empty()) {
|
||||
std::cout << "Invalid mindir" << std::endl;
|
||||
return 1;
|
||||
}
|
||||
|
||||
auto context = std::make_shared<Context>();
|
||||
auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
|
||||
ascend310->SetDeviceID(FLAGS_device_id);
|
||||
context->MutableDeviceInfo().push_back(ascend310);
|
||||
mindspore::Graph graph;
|
||||
Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
|
||||
|
||||
Model model;
|
||||
Status ret = model.Build(GraphCell(graph), context);
|
||||
if (ret != kSuccess) {
|
||||
std::cout << "ERROR: Build failed." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
|
||||
std::vector<MSTensor> model_inputs = model.GetInputs();
|
||||
if (model_inputs.empty()) {
|
||||
std::cout << "Invalid model, inputs is empty." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
|
||||
auto input0_files = GetAllFiles(FLAGS_input0_path);
|
||||
auto input1_files = GetAllFiles(FLAGS_input1_path);
|
||||
auto input2_files = GetAllFiles(FLAGS_input2_path);
|
||||
|
||||
if (input0_files.empty() || input1_files.empty() || input2_files.empty()) {
|
||||
std::cout << "ERROR: input data empty." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
|
||||
std::map<double, double> costTime_map;
|
||||
size_t size = input0_files.size();
|
||||
|
||||
for (size_t i = 0; i < size; ++i) {
|
||||
struct timeval start = {0};
|
||||
struct timeval end = {0};
|
||||
double startTimeMs;
|
||||
double endTimeMs;
|
||||
std::vector<MSTensor> inputs;
|
||||
std::vector<MSTensor> outputs;
|
||||
std::cout << "Start predict input files:" << input0_files[i] << std::endl;
|
||||
|
||||
auto input0 = ReadFileToTensor(input0_files[i]);
|
||||
auto input1 = ReadFileToTensor(input1_files[i]);
|
||||
auto input2 = ReadFileToTensor(input2_files[i]);
|
||||
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
|
||||
input0.Data().get(), input0.DataSize());
|
||||
inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
|
||||
input1.Data().get(), input1.DataSize());
|
||||
inputs.emplace_back(model_inputs[2].Name(), model_inputs[2].DataType(), model_inputs[2].Shape(),
|
||||
input2.Data().get(), input2.DataSize());
|
||||
|
||||
gettimeofday(&start, nullptr);
|
||||
ret = model.Predict(inputs, &outputs);
|
||||
gettimeofday(&end, nullptr);
|
||||
if (ret != kSuccess) {
|
||||
std::cout << "Predict " << input0_files[i] << " failed." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
|
||||
endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
|
||||
costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
|
||||
WriteResult(input0_files[i], outputs);
|
||||
}
|
||||
double average = 0.0;
|
||||
int inferCount = 0;
|
||||
|
||||
for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
|
||||
double diff = 0.0;
|
||||
diff = iter->second - iter->first;
|
||||
average += diff;
|
||||
inferCount++;
|
||||
}
|
||||
average = average / inferCount;
|
||||
std::stringstream timeCost;
|
||||
timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
|
||||
std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
|
||||
std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
|
||||
std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
|
||||
fileStream << timeCost.str();
|
||||
fileStream.close();
|
||||
costTime_map.clear();
|
||||
return 0;
|
||||
}
|
|
@ -0,0 +1,129 @@
|
|||
/**
|
||||
* Copyright 2021 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.
|
||||
*/
|
||||
|
||||
#include <fstream>
|
||||
#include <algorithm>
|
||||
#include <iostream>
|
||||
#include "inc/utils.h"
|
||||
|
||||
using mindspore::MSTensor;
|
||||
using mindspore::DataType;
|
||||
|
||||
std::vector<std::string> GetAllFiles(std::string_view dirName) {
|
||||
struct dirent *filename;
|
||||
DIR *dir = OpenDir(dirName);
|
||||
if (dir == nullptr) {
|
||||
return {};
|
||||
}
|
||||
std::vector<std::string> res;
|
||||
while ((filename = readdir(dir)) != nullptr) {
|
||||
std::string dName = std::string(filename->d_name);
|
||||
if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
|
||||
continue;
|
||||
}
|
||||
res.emplace_back(std::string(dirName) + "/" + filename->d_name);
|
||||
}
|
||||
std::sort(res.begin(), res.end());
|
||||
for (auto &f : res) {
|
||||
std::cout << "image file: " << f << std::endl;
|
||||
}
|
||||
return res;
|
||||
}
|
||||
|
||||
int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
|
||||
std::string homePath = "./result_Files";
|
||||
for (size_t i = 0; i < outputs.size(); ++i) {
|
||||
size_t outputSize;
|
||||
std::shared_ptr<const void> netOutput;
|
||||
netOutput = outputs[i].Data();
|
||||
outputSize = outputs[i].DataSize();
|
||||
int pos = imageFile.rfind('/');
|
||||
std::string fileName(imageFile, pos + 1);
|
||||
fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
|
||||
std::string outFileName = homePath + "/" + fileName;
|
||||
FILE * outputFile = fopen(outFileName.c_str(), "wb");
|
||||
fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
|
||||
fclose(outputFile);
|
||||
outputFile = nullptr;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
mindspore::MSTensor ReadFileToTensor(const std::string &file) {
|
||||
if (file.empty()) {
|
||||
std::cout << "Pointer file is nullptr" << std::endl;
|
||||
return mindspore::MSTensor();
|
||||
}
|
||||
|
||||
std::ifstream ifs(file);
|
||||
if (!ifs.good()) {
|
||||
std::cout << "File: " << file << " is not exist" << std::endl;
|
||||
return mindspore::MSTensor();
|
||||
}
|
||||
|
||||
if (!ifs.is_open()) {
|
||||
std::cout << "File: " << file << "open failed" << std::endl;
|
||||
return mindspore::MSTensor();
|
||||
}
|
||||
|
||||
ifs.seekg(0, std::ios::end);
|
||||
size_t size = ifs.tellg();
|
||||
mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
|
||||
|
||||
ifs.seekg(0, std::ios::beg);
|
||||
ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
|
||||
ifs.close();
|
||||
|
||||
return buffer;
|
||||
}
|
||||
|
||||
|
||||
DIR *OpenDir(std::string_view dirName) {
|
||||
if (dirName.empty()) {
|
||||
std::cout << " dirName is null ! " << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
std::string realPath = RealPath(dirName);
|
||||
struct stat s;
|
||||
lstat(realPath.c_str(), &s);
|
||||
if (!S_ISDIR(s.st_mode)) {
|
||||
std::cout << "dirName is not a valid directory !" << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
DIR *dir;
|
||||
dir = opendir(realPath.c_str());
|
||||
if (dir == nullptr) {
|
||||
std::cout << "Can not open dir " << dirName << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
std::cout << "Successfully opened the dir " << dirName << std::endl;
|
||||
return dir;
|
||||
}
|
||||
|
||||
std::string RealPath(std::string_view path) {
|
||||
char realPathMem[PATH_MAX] = {0};
|
||||
char *realPathRet = nullptr;
|
||||
realPathRet = realpath(path.data(), realPathMem);
|
||||
|
||||
if (realPathRet == nullptr) {
|
||||
std::cout << "File: " << path << " is not exist.";
|
||||
return "";
|
||||
}
|
||||
|
||||
std::string realPath(realPathMem);
|
||||
std::cout << path << " realpath is: " << realPath << std::endl;
|
||||
return realPath;
|
||||
}
|
|
@ -0,0 +1,53 @@
|
|||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
"""postprocess."""
|
||||
import os
|
||||
import argparse
|
||||
import numpy as np
|
||||
from mindspore import Tensor
|
||||
from src.metrics import AUCMetric
|
||||
|
||||
parser = argparse.ArgumentParser(description='postprocess')
|
||||
parser.add_argument('--result_path', type=str, default="./result_Files", help='result path')
|
||||
parser.add_argument('--label_path', type=str, default=None, help='label path')
|
||||
parser.add_argument("--batch_size", type=int, default=16000, help="infer batch size.")
|
||||
args_opt, _ = parser.parse_known_args()
|
||||
|
||||
def get_acc():
|
||||
''' get accuracy '''
|
||||
auc_metric = AUCMetric()
|
||||
auc_metric.clear()
|
||||
batch_size = args_opt.batch_size
|
||||
|
||||
files = os.listdir(args_opt.label_path)
|
||||
|
||||
for f in files:
|
||||
rst_file = os.path.join(args_opt.result_path, f.split('.')[0] + '_0.bin')
|
||||
label_file = os.path.join(args_opt.label_path, f)
|
||||
|
||||
predict = Tensor(np.fromfile(rst_file, np.float32).reshape(batch_size, 1))
|
||||
label = Tensor(np.fromfile(label_file, np.float32).reshape(batch_size, 1))
|
||||
|
||||
res = []
|
||||
res.append(predict)
|
||||
res.append(predict)
|
||||
res.append(label)
|
||||
|
||||
auc_metric.update(*res)
|
||||
|
||||
auc_metric.eval()
|
||||
|
||||
if __name__ == '__main__':
|
||||
get_acc()
|
|
@ -0,0 +1,63 @@
|
|||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
"""preprocess."""
|
||||
import os
|
||||
import argparse
|
||||
|
||||
from src.datasets import create_dataset, DataType
|
||||
|
||||
parser = argparse.ArgumentParser(description='preprocess.')
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--result_path', type=str, default='./preprocess_Result', help='Result path')
|
||||
parser.add_argument("--batch_size", type=int, default=16000, help="infer batch size.")
|
||||
parser.add_argument("--dataset_type", type=str, default="mindrecord", choices=["tfrecord", "mindrecord", "hd5"])
|
||||
|
||||
args_opt, _ = parser.parse_known_args()
|
||||
|
||||
def generate_bin():
|
||||
'''generate bin files'''
|
||||
data_path = args_opt.dataset_path
|
||||
batch_size = args_opt.batch_size
|
||||
if args_opt.dataset_type == "tfrecord":
|
||||
dataset_type = DataType.TFRECORD
|
||||
elif args_opt.dataset_type == "mindrecord":
|
||||
dataset_type = DataType.MINDRECORD
|
||||
else:
|
||||
dataset_type = DataType.H5
|
||||
ds = create_dataset(data_path, train_mode=False, epochs=1,
|
||||
batch_size=batch_size, data_type=dataset_type)
|
||||
feat_ids_path = os.path.join(args_opt.result_path, "00_feat_ids")
|
||||
feat_vals_path = os.path.join(args_opt.result_path, "01_feat_vals")
|
||||
label_path = os.path.join(args_opt.result_path, "02_labels")
|
||||
|
||||
os.makedirs(feat_ids_path)
|
||||
os.makedirs(feat_vals_path)
|
||||
os.makedirs(label_path)
|
||||
|
||||
for i, data in enumerate(ds.create_dict_iterator(output_numpy=True)):
|
||||
file_name = "criteo_bs" + str(batch_size) + "_" + str(i) + ".bin"
|
||||
batch_ids = data['feat_ids']
|
||||
batch_ids.tofile(os.path.join(feat_ids_path, file_name))
|
||||
|
||||
batch_wts = data['feat_vals']
|
||||
batch_wts.tofile(os.path.join(feat_vals_path, file_name))
|
||||
|
||||
labels = data['label']
|
||||
labels.tofile(os.path.join(label_path, file_name))
|
||||
|
||||
print("=" * 20, "export bin files finished", "=" * 20)
|
||||
|
||||
if __name__ == '__main__':
|
||||
generate_bin()
|
|
@ -0,0 +1,125 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
|
||||
if [[ $# -lt 4 || $# -gt 5 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DATA_TYPE] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
DATA_TYPE means dataset type, it's value is ['tfrecord', 'mindrecord', 'hd5'].
|
||||
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
dataset_path=$(get_real_path $2)
|
||||
dataset_type=$3
|
||||
|
||||
if [ "$4" == "y" ] || [ "$4" == "n" ];then
|
||||
need_preprocess=$4
|
||||
else
|
||||
echo "weather need preprocess or not, it's value must be in [y, n]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
device_id=0
|
||||
if [ $# == 5 ]; then
|
||||
device_id=$5
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
echo "dataset path: "$dataset_path
|
||||
echo "data type: "$dataset_type
|
||||
echo "need preprocess: "$need_preprocess
|
||||
echo "device id: "$device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function preprocess_data()
|
||||
{
|
||||
if [ -d preprocess_Result ]; then
|
||||
rm -rf ./preprocess_Result
|
||||
fi
|
||||
mkdir preprocess_Result
|
||||
python3.7 ../preprocess.py --dataset_path=$dataset_path --dataset_type=$dataset_type --result_path=./preprocess_Result/
|
||||
}
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
|
||||
../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_feat_ids --input1_path=./preprocess_Result/01_feat_vals --input2_path=./preprocess_Result/02_labels --device_id=$device_id &> infer.log
|
||||
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --result_path=./result_Files --label_path=./preprocess_Result/02_labels &> acc.log
|
||||
}
|
||||
|
||||
if [ $need_preprocess == "y" ]; then
|
||||
preprocess_data
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "preprocess dataset failed"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
compile_app
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "compile app code failed"
|
||||
exit 1
|
||||
fi
|
||||
infer
|
||||
if [ $? -ne 0 ]; then
|
||||
echo " execute inference failed"
|
||||
exit 1
|
||||
fi
|
||||
cal_acc
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "calculate accuracy failed"
|
||||
exit 1
|
||||
fi
|
Loading…
Reference in New Issue