forked from mindspore-Ecosystem/mindspore
ncf && textrcnn && bgcf && tinybert 310 infer
modified: official/recommend/ncf/preprocess.py
This commit is contained in:
unknown
parent
ad165deb15
commit
3df895f477
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@ -16,6 +16,10 @@
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- [Training](#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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- [Description of random situation](#description-of-random-situation)
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@ -244,6 +248,38 @@ Parameters for both training and evaluation can be set in config.py.
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sedp_@10:0.01926, sedp_@20:0.01547, nov_@10:7.60851, nov_@20:7.81969
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```
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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_NAME] [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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recall_@10:0.10383, recall_@20:0.15524, ndcg_@10:0.07503, ndcg_@20:0.09249,
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sedp_@10:0.01926, sedp_@20:0.01547, nov_@10:7.60851, nov_@20:7.81969
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```
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## [Model Description](#contents)
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### [Performance](#contents)
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@ -17,6 +17,10 @@
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- [训练](#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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- [随机情况说明](#随机情况说明)
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@ -271,6 +275,38 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
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```
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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 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATASET_NAME] [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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recall_@10:0.10383, recall_@20:0.15524, ndcg_@10:0.07503, ndcg_@20:0.09249,
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sedp_@10:0.01926, sedp_@20:0.01547, nov_@10:7.60851, nov_@20:7.81969
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```
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## 模型描述
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### 训练性能
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@ -0,0 +1,14 @@
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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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@ -0,0 +1,29 @@
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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 +1,160 @@
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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_string(input3_path, ".", "input3 path");
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DEFINE_string(input4_path, ".", "input4 path");
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DEFINE_string(input5_path, ".", "input5 path");
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DEFINE_string(input6_path, ".", "input6 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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auto input3_files = GetAllFiles(FLAGS_input3_path);
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auto input4_files = GetAllFiles(FLAGS_input4_path);
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auto input5_files = GetAllFiles(FLAGS_input5_path);
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auto input6_files = GetAllFiles(FLAGS_input6_path);
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if (input0_files.empty() || input1_files.empty() || input2_files.empty() || input3_files.empty()
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|| input4_files.empty() || input5_files.empty() || input6_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, endTimeMs;
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std::vector<MSTensor> inputs, 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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auto input3 = ReadFileToTensor(input3_files[i]);
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auto input4 = ReadFileToTensor(input4_files[i]);
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auto input5 = ReadFileToTensor(input5_files[i]);
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auto input6 = ReadFileToTensor(input6_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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inputs.emplace_back(model_inputs[3].Name(), model_inputs[3].DataType(), model_inputs[3].Shape(),
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input3.Data().get(), input3.DataSize());
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inputs.emplace_back(model_inputs[4].Name(), model_inputs[4].DataType(), model_inputs[4].Shape(),
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input4.Data().get(), input4.DataSize());
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inputs.emplace_back(model_inputs[5].Name(), model_inputs[5].DataType(), model_inputs[5].Shape(),
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input5.Data().get(), input5.DataSize());
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inputs.emplace_back(model_inputs[6].Name(), model_inputs[6].DataType(), model_inputs[6].Shape(),
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input6.Data().get(), input6.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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@ -0,0 +1,129 @@
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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.
|
||||
* See the License for the specific language governing permissions and
|
||||
* 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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}
|
||||
|
||||
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 +1,75 @@
|
|||
# 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 src.metrics import BGCFEvaluate
|
||||
from src.dataset import load_graph
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--dataset", type=str, default="Beauty", help="choose which dataset")
|
||||
parser.add_argument("--datapath", type=str, default="./scripts/data_mr", help="minddata path")
|
||||
parser.add_argument('--input_dim', type=int, default=64, choices=[64, 128],
|
||||
help="user and item embedding dimension")
|
||||
parser.add_argument('--Ks', type=list, default=[5, 10, 20, 100], help="top K")
|
||||
parser.add_argument('--workers', type=int, default=8, help="number of process to generate data")
|
||||
parser.add_argument("--result_path", type=str, default="./result_Files", help="result path")
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
def get_acc():
|
||||
"""calculate accuracy"""
|
||||
train_graph, test_graph, _ = load_graph(args.datapath)
|
||||
|
||||
num_user = train_graph.graph_info()["node_num"][0]
|
||||
num_item = train_graph.graph_info()["node_num"][1]
|
||||
input_dim = args.input_dim
|
||||
user_reps = np.zeros([num_user, input_dim * 3])
|
||||
item_reps = np.zeros([num_item, input_dim * 3])
|
||||
|
||||
for i in range(50):
|
||||
sub_folder = os.path.join(args.result_path, 'result_Files_' + str(i))
|
||||
user_rep = np.fromfile(os.path.join(sub_folder, 'amazon-beauty_0.bin'), np.float16)
|
||||
user_rep = user_rep.reshape(num_user, input_dim * 3)
|
||||
item_rep = np.fromfile(os.path.join(sub_folder, 'amazon-beauty_1.bin'), np.float16)
|
||||
item_rep = item_rep.reshape(num_item, input_dim * 3)
|
||||
|
||||
user_reps += user_rep
|
||||
item_reps += item_rep
|
||||
user_reps /= 50
|
||||
item_reps /= 50
|
||||
|
||||
eval_class = BGCFEvaluate(args, train_graph, test_graph, args.Ks)
|
||||
|
||||
test_recall_bgcf, test_ndcg_bgcf, \
|
||||
test_sedp, test_nov = eval_class.eval_with_rep(user_reps, item_reps, args)
|
||||
|
||||
print('recall_@10:%.5f, recall_@20:%.5f, ndcg_@10:%.5f, ndcg_@20:%.5f, '
|
||||
'sedp_@10:%.5f, sedp_@20:%.5f, nov_@10:%.5f, nov_@20:%.5f\n' % (test_recall_bgcf[1],
|
||||
test_recall_bgcf[2],
|
||||
test_ndcg_bgcf[1],
|
||||
test_ndcg_bgcf[2],
|
||||
test_sedp[0],
|
||||
test_sedp[1],
|
||||
test_nov[1],
|
||||
test_nov[2]))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
get_acc()
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
# 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
|
||||
import numpy as np
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.common import dtype as mstype
|
||||
from src.utils import convert_item_id
|
||||
from src.dataset import TestGraphDataset, load_graph
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--dataset", type=str, default="Beauty", help="choose which dataset")
|
||||
parser.add_argument("--datapath", type=str, default="./scripts/data_mr", help="minddata path")
|
||||
parser.add_argument("--num_neg", type=int, default=10, help="negative sampling rate ")
|
||||
parser.add_argument("--raw_neighs", type=int, default=40, help="num of sampling neighbors in raw graph")
|
||||
parser.add_argument("--gnew_neighs", type=int, default=20, help="num of sampling neighbors in sample graph")
|
||||
parser.add_argument("--result_path", type=str, default="./preprocess_Result/", help="result path")
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
|
||||
def get_bin():
|
||||
"""generate bin files."""
|
||||
train_graph, _, sampled_graph_list = load_graph(args.datapath)
|
||||
test_graph_dataset = TestGraphDataset(train_graph, sampled_graph_list, num_samples=args.raw_neighs,
|
||||
num_bgcn_neigh=args.gnew_neighs,
|
||||
num_neg=args.num_neg)
|
||||
|
||||
num_user = train_graph.graph_info()["node_num"][0]
|
||||
num_item = train_graph.graph_info()["node_num"][1]
|
||||
|
||||
for i in range(50):
|
||||
data_path = os.path.join(args.result_path, "data_" + str(i))
|
||||
users_path = os.path.join(data_path, "00_users")
|
||||
os.makedirs(users_path)
|
||||
items_path = os.path.join(data_path, "01_items")
|
||||
os.makedirs(items_path)
|
||||
neg_items_path = os.path.join(data_path, "02_neg_items")
|
||||
os.makedirs(neg_items_path)
|
||||
u_test_neighs_path = os.path.join(data_path, "03_u_test_neighs")
|
||||
os.makedirs(u_test_neighs_path)
|
||||
u_test_gnew_neighs_path = os.path.join(data_path, "04_u_test_gnew_neighs")
|
||||
os.makedirs(u_test_gnew_neighs_path)
|
||||
i_test_neighs_path = os.path.join(data_path, "05_i_test_neighs")
|
||||
os.makedirs(i_test_neighs_path)
|
||||
i_test_gnew_neighs_path = os.path.join(data_path, "06_i_test_gnew_neighs")
|
||||
os.makedirs(i_test_gnew_neighs_path)
|
||||
|
||||
test_graph_dataset.random_select_sampled_graph()
|
||||
u_test_neighs, u_test_gnew_neighs = test_graph_dataset.get_user_sapmled_neighbor()
|
||||
i_test_neighs, i_test_gnew_neighs = test_graph_dataset.get_item_sampled_neighbor()
|
||||
|
||||
u_test_neighs = Tensor(convert_item_id(u_test_neighs, num_user), mstype.int32)
|
||||
u_test_gnew_neighs = Tensor(convert_item_id(u_test_gnew_neighs, num_user), mstype.int32)
|
||||
i_test_neighs = Tensor(i_test_neighs, mstype.int32)
|
||||
i_test_gnew_neighs = Tensor(i_test_gnew_neighs, mstype.int32)
|
||||
|
||||
users = Tensor(np.arange(num_user).reshape(-1,), mstype.int32)
|
||||
items = Tensor(np.arange(num_item).reshape(-1,), mstype.int32)
|
||||
neg_items = Tensor(np.arange(num_item).reshape(-1, 1), mstype.int32)
|
||||
|
||||
file_name = 'amazon-beauty.bin'
|
||||
users.asnumpy().tofile(os.path.join(users_path, file_name))
|
||||
items.asnumpy().tofile(os.path.join(items_path, file_name))
|
||||
neg_items.asnumpy().tofile(os.path.join(neg_items_path, file_name))
|
||||
u_test_neighs.asnumpy().tofile(os.path.join(u_test_neighs_path, file_name))
|
||||
u_test_gnew_neighs.asnumpy().tofile(os.path.join(u_test_gnew_neighs_path, file_name))
|
||||
i_test_neighs.asnumpy().tofile(os.path.join(i_test_neighs_path, file_name))
|
||||
i_test_gnew_neighs.asnumpy().tofile(os.path.join(i_test_gnew_neighs_path, file_name))
|
||||
print("=" * 20, "export bin files finished.", "=" * 20)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
get_bin()
|
||||
|
|
@ -0,0 +1,128 @@
|
|||
#!/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 || $# -gt 4 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_NAME] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${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/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$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=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/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/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function preprocess_data()
|
||||
{
|
||||
if [ -d preprocess_Result ]; then
|
||||
rm -rf ./preprocess_Result
|
||||
fi
|
||||
mkdir preprocess_Result
|
||||
python3.7 ../preprocess.py --datapath=$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
|
||||
rm -rf ./result_Files_all infer.log
|
||||
mkdir result_Files_all
|
||||
mkdir time_Result
|
||||
|
||||
for i in {0..49}
|
||||
do
|
||||
mkdir result_Files
|
||||
../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/data_$i/00_users --input1_path=./preprocess_Result/data_$i/01_items --input2_path=./preprocess_Result/data_$i/02_neg_items --input3_path=./preprocess_Result/data_$i/03_u_test_neighs --input4_path=./preprocess_Result/data_$i/04_u_test_gnew_neighs --input5_path=./preprocess_Result/data_$i/05_i_test_neighs --input6_path=./preprocess_Result/data_$i/06_i_test_gnew_neighs --device_id=$device_id &>> infer.log
|
||||
mv result_Files result_Files_all/result_Files_$i
|
||||
done
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --result_path=./result_Files_all --datapath=$dataset_path &> 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
|
||||
|
|
@ -26,6 +26,10 @@
|
|||
- [evaluation on SST-2 dataset](#evaluation-on-sst-2-dataset)
|
||||
- [evaluation on MNLI dataset](#evaluation-on-mnli-dataset)
|
||||
- [evaluation on QNLI dataset](#evaluation-on-qnli-dataset)
|
||||
- [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)
|
||||
|
|
@ -409,6 +413,39 @@ The best acc is 0.891176
|
|||
...
|
||||
```
|
||||
|
||||
## 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] [SCHEMA_DIR] [DATASET_TYPE] [TASK_NAME] [ASSESSMENT_METHOD] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
```
|
||||
|
||||
- `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
|
||||
=================================================================
|
||||
============== acc is 0.8862132352941177
|
||||
=================================================================
|
||||
```
|
||||
|
||||
## [Model Description](#contents)
|
||||
|
||||
## [Performance](#contents)
|
||||
|
|
|
|||
|
|
@ -29,6 +29,10 @@
|
|||
- [基于SST-2数据集进行评估](#基于sst-2数据集进行评估)
|
||||
- [基于MNLI数据集进行评估](#基于mnli数据集进行评估)
|
||||
- [基于QNLI数据集进行评估](#基于qnli数据集进行评估)
|
||||
- [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)
|
||||
- [评估性能](#评估性能)
|
||||
|
|
@ -410,6 +414,39 @@ The best acc is 0.891176
|
|||
...
|
||||
```
|
||||
|
||||
## 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 inference
|
||||
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [SCHEMA_DIR] [DATASET_TYPE] [TASK_NAME] [ASSESSMENT_METHOD] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
```
|
||||
|
||||
- `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。
|
||||
|
||||
### 结果
|
||||
|
||||
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
|
||||
=================================================================
|
||||
============== acc is 0.8862132352941177
|
||||
=================================================================
|
||||
```
|
||||
|
||||
## 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} -O0 -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 +1,142 @@
|
|||
/**
|
||||
* 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);
|
||||
ascend310->SetPrecisionMode("allow_fp32_to_fp16");
|
||||
ascend310->SetOpSelectImplMode("high_precision");
|
||||
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 +1,103 @@
|
|||
# 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.assessment_method import Accuracy, F1
|
||||
from src.td_config import eval_cfg
|
||||
|
||||
parser = argparse.ArgumentParser(description='postprocess')
|
||||
parser.add_argument("--task_name", type=str, default="", choices=["SST-2", "QNLI", "MNLI", "TNEWS", "CLUENER"],
|
||||
help="The name of the task to train.")
|
||||
parser.add_argument("--assessment_method", type=str, default="accuracy", choices=["accuracy", "bf1", "mf1"],
|
||||
help="assessment_method include: [accuracy, bf1, mf1], default is accuracy")
|
||||
parser.add_argument("--result_path", type=str, default="./result_Files", help="result path")
|
||||
parser.add_argument("--label_path", type=str, default="./preprocess_Result/label_ids.npy", help="label path")
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
|
||||
DEFAULT_NUM_LABELS = 2
|
||||
DEFAULT_SEQ_LENGTH = 128
|
||||
task_params = {"SST-2": {"num_labels": 2, "seq_length": 64},
|
||||
"QNLI": {"num_labels": 2, "seq_length": 128},
|
||||
"MNLI": {"num_labels": 3, "seq_length": 128},
|
||||
"TNEWS": {"num_labels": 15, "seq_length": 128},
|
||||
"CLUENER": {"num_labels": 43, "seq_length": 128}}
|
||||
|
||||
|
||||
class Task:
|
||||
"""
|
||||
Encapsulation class of get the task parameter.
|
||||
"""
|
||||
def __init__(self, task_name):
|
||||
self.task_name = task_name
|
||||
|
||||
@property
|
||||
def num_labels(self):
|
||||
if self.task_name in task_params and "num_labels" in task_params[self.task_name]:
|
||||
return task_params[self.task_name]["num_labels"]
|
||||
return DEFAULT_NUM_LABELS
|
||||
|
||||
@property
|
||||
def seq_length(self):
|
||||
if self.task_name in task_params and "seq_length" in task_params[self.task_name]:
|
||||
return task_params[self.task_name]["seq_length"]
|
||||
return DEFAULT_SEQ_LENGTH
|
||||
task = Task(args_opt.task_name)
|
||||
|
||||
def eval_result_print(assessment_method="accuracy", callback=None):
|
||||
"""print eval result"""
|
||||
if assessment_method == "accuracy":
|
||||
print("============== acc is {}".format(callback.acc_num / callback.total_num))
|
||||
elif assessment_method == "bf1":
|
||||
print("Precision {:.6f} ".format(callback.TP / (callback.TP + callback.FP)))
|
||||
print("Recall {:.6f} ".format(callback.TP / (callback.TP + callback.FN)))
|
||||
print("F1 {:.6f} ".format(2 * callback.TP / (2 * callback.TP + callback.FP + callback.FN)))
|
||||
elif assessment_method == "mf1":
|
||||
print("F1 {:.6f} ".format(callback.eval()))
|
||||
else:
|
||||
raise ValueError("Assessment method not supported, support: [accuracy, f1]")
|
||||
|
||||
def get_acc():
|
||||
"""
|
||||
calculate accuracy
|
||||
"""
|
||||
if args_opt.assessment_method == "accuracy":
|
||||
callback = Accuracy()
|
||||
elif args_opt.assessment_method == "bf1":
|
||||
callback = F1(num_labels=task.num_labels)
|
||||
elif args_opt.assessment_method == "mf1":
|
||||
callback = F1(num_labels=task.num_labels, mode="MultiLabel")
|
||||
else:
|
||||
raise ValueError("Assessment method not supported, support: [accuracy, f1]")
|
||||
labels = np.load(args_opt.label_path)
|
||||
file_num = len(os.listdir(args_opt.result_path))
|
||||
for i in range(file_num):
|
||||
f_name = "tinybert_bs" + str(eval_cfg.batch_size) + "_" + str(i) + "_0.bin"
|
||||
logits = np.fromfile(os.path.join(args_opt.result_path, f_name), np.float32)
|
||||
logits = logits.reshape(eval_cfg.batch_size, task.num_labels)
|
||||
label_ids = labels[i]
|
||||
callback.update(Tensor(logits), Tensor(label_ids))
|
||||
print("==============================================================")
|
||||
eval_result_print(args_opt.assessment_method, callback)
|
||||
print("==============================================================")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
get_acc()
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
# 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
|
||||
import numpy as np
|
||||
from src.td_config import eval_cfg
|
||||
from src.dataset import create_tinybert_dataset, DataType
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser(description='preprocess')
|
||||
parser.add_argument("--eval_data_dir", type=str, default="", help="Data path, it is better to use absolute path")
|
||||
parser.add_argument("--schema_dir", type=str, default="", help="Schema path, it is better to use absolute path")
|
||||
parser.add_argument("--dataset_type", type=str, default="tfrecord",
|
||||
help="dataset type tfrecord/mindrecord, default is tfrecord")
|
||||
parser.add_argument("--result_path", type=str, default="./preprocess_Result/", help="result path")
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
if args_opt.dataset_type == "tfrecord":
|
||||
dataset_type = DataType.TFRECORD
|
||||
elif args_opt.dataset_type == "mindrecord":
|
||||
dataset_type = DataType.MINDRECORD
|
||||
else:
|
||||
raise Exception("dataset format is not supported yet")
|
||||
|
||||
def get_bin():
|
||||
"""
|
||||
generate bin files.
|
||||
"""
|
||||
input_ids_path = os.path.join(args_opt.result_path, "00_input_ids")
|
||||
token_type_id_path = os.path.join(args_opt.result_path, "01_token_type_id")
|
||||
input_mask_path = os.path.join(args_opt.result_path, "02_input_mask")
|
||||
label_ids_path = os.path.join(args_opt.result_path, "label_ids.npy")
|
||||
|
||||
os.makedirs(input_ids_path)
|
||||
os.makedirs(token_type_id_path)
|
||||
os.makedirs(input_mask_path)
|
||||
|
||||
eval_dataset = create_tinybert_dataset('td', batch_size=eval_cfg.batch_size,
|
||||
device_num=1, rank=0, do_shuffle="false",
|
||||
data_dir=args_opt.eval_data_dir,
|
||||
schema_dir=args_opt.schema_dir,
|
||||
data_type=dataset_type)
|
||||
columns_list = ["input_ids", "input_mask", "segment_ids", "label_ids"]
|
||||
label_list = []
|
||||
for j, data in enumerate(eval_dataset.create_dict_iterator(output_numpy=True, num_epochs=1)):
|
||||
file_name = "tinybert_bs" + str(eval_cfg.batch_size) + "_" + str(j) + ".bin"
|
||||
input_data = []
|
||||
for i in columns_list:
|
||||
input_data.append(data[i])
|
||||
input_ids, input_mask, token_type_id, label_ids = input_data
|
||||
input_ids.tofile(os.path.join(input_ids_path, file_name))
|
||||
input_mask.tofile(os.path.join(input_mask_path, file_name))
|
||||
token_type_id.tofile(os.path.join(token_type_id_path, file_name))
|
||||
label_list.append(label_ids)
|
||||
np.save(label_ids_path, label_list)
|
||||
print("=" * 20, 'export files finished', "=" * 20)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
get_bin()
|
||||
|
|
@ -0,0 +1,131 @@
|
|||
#!/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 7 || $# -gt 8 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [SCHEMA_DIR] [DATASET_TYPE] [TASK_NAME] [ASSESSMENT_METHOD] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
dataset_path=$(get_real_path $2)
|
||||
schema_dir=$(get_real_path $3)
|
||||
dataset_type=$4
|
||||
task_name=$5
|
||||
assessment_method=$6
|
||||
|
||||
|
||||
if [ "$7" == "y" ] || [ "$7" == "n" ];then
|
||||
need_preprocess=$7
|
||||
else
|
||||
echo "weather need preprocess or not, it's value must be in [y, n]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
device_id=0
|
||||
if [ $# == 8 ]; then
|
||||
device_id=$8
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
echo "dataset path: "$dataset_path
|
||||
echo "schema dir: "$schema_dir
|
||||
echo "dataset_type: "$dataset_type
|
||||
echo "task_name: "$task_name
|
||||
echo "assessment_method: "$assessment_method
|
||||
echo "need preprocess: "$need_preprocess
|
||||
echo "device id: "$device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$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=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/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/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function preprocess_data()
|
||||
{
|
||||
if [ -d preprocess_Result ]; then
|
||||
rm -rf ./preprocess_Result
|
||||
fi
|
||||
mkdir preprocess_Result
|
||||
python3.7 ../preprocess.py --eval_data_dir=$dataset_path --schema_dir=$schema_dir --dataset_type=$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=$model --input0_path=./preprocess_Result/00_input_ids --input1_path=./preprocess_Result/01_token_type_id --input2_path=./preprocess_Result/02_input_mask --device_id=$device_id &> infer.log
|
||||
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --task_name=$task_name --assessment_method=$assessment_method --result_path=./result_Files --label_path=./preprocess_Result/label_ids.npy &> 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
|
||||
|
|
@ -15,6 +15,10 @@
|
|||
- [Distributed Training](#distributed-training)
|
||||
- [Evaluation Process](#evaluation-process)
|
||||
- [Evaluation](#evaluation)
|
||||
- [Inference Process](#inference-process)
|
||||
- [Export MindIR](#export-mindir)
|
||||
- [Infer on Ascend310](#infer-on-ascend310)
|
||||
- [result](#result)
|
||||
- [Model Description](#model-description)
|
||||
- [Performance](#performance)
|
||||
- [Evaluation Performance](#evaluation-performance)
|
||||
|
|
@ -220,6 +224,37 @@ Parameters for both training and evaluation can be set in config.py.
|
|||
HR:0.6846,NDCG:0.410
|
||||
```
|
||||
|
||||
## 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] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
```
|
||||
|
||||
- `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.
|
||||
|
||||
```grep "accuracy: " acc.log
|
||||
HR:0.6846,NDCG:0.410
|
||||
```
|
||||
|
||||
# [Model Description](#contents)
|
||||
|
||||
## [Performance](#contents)
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
}
|
||||
|
|
@ -28,9 +28,15 @@ checkpoint_file_path: "./checkpoint/NCF-14_19418.ckpt"
|
|||
|
||||
# Export options
|
||||
device_id: 0
|
||||
ckpt_file: ""
|
||||
file_name: ""
|
||||
file_format: ""
|
||||
ckpt_file: "./checkpoint/NCF-25_19418.ckpt"
|
||||
file_name: "ncf"
|
||||
file_format: "MINDIR"
|
||||
|
||||
# Preprocess options
|
||||
pre_result_path: "./preprocess_Result"
|
||||
|
||||
# Postprocess options
|
||||
post_result_path: "./result_Files"
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -51,4 +57,6 @@ layers: "The sizes of hidden layers for MLP"
|
|||
num_factors: "The Embedding size of MF model."
|
||||
checkpoint_path: "The location of the checkpoint file."
|
||||
eval_file_name: "Eval output file."
|
||||
checkpoint_file_path: "The location of the checkpoint file."
|
||||
checkpoint_file_path: "The location of the checkpoint file."
|
||||
pre_result_path: "saving dataset to numpy format path."
|
||||
post_result_path: "inference result path."
|
||||
|
|
|
|||
|
|
@ -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 numpy as np
|
||||
|
||||
from mindspore import Tensor
|
||||
from src.metrics import NCFMetric
|
||||
from model_utils.config import config
|
||||
|
||||
def get_acc():
|
||||
"""calculate accuracy"""
|
||||
if not os.path.exists(config.output_path):
|
||||
os.makedirs(config.output_path)
|
||||
|
||||
ncf_metric = NCFMetric()
|
||||
rst_path = config.post_result_path
|
||||
file_num = len(os.listdir(rst_path))//3
|
||||
bs = config.eval_batch_size
|
||||
for i in range(file_num):
|
||||
indice_name = os.path.join(rst_path, "ncf_bs" + str(bs) + "_" + str(i) + "_0.bin")
|
||||
item_name = os.path.join(rst_path, "ncf_bs" + str(bs) + "_" + str(i) + "_1.bin")
|
||||
weight_name = os.path.join(rst_path, "ncf_bs" + str(bs) + "_" + str(i) + "_2.bin")
|
||||
|
||||
batch_indices = np.fromfile(indice_name, np.int32).reshape(1600, 10)
|
||||
batch_items = np.fromfile(item_name, np.int32).reshape(1600, 100)
|
||||
metric_weights = np.fromfile(weight_name, bool)
|
||||
ncf_metric.update(Tensor(batch_indices), Tensor(batch_items), Tensor(metric_weights))
|
||||
|
||||
out = ncf_metric.eval()
|
||||
|
||||
eval_file_path = os.path.join(config.output_path, config.eval_file_name)
|
||||
eval_file = open(eval_file_path, "a+")
|
||||
eval_file.write("EvalCallBack: HR = {}, NDCG = {}\n".format(out[0], out[1]))
|
||||
eval_file.close()
|
||||
print("EvalCallBack: HR = {}, NDCG = {}".format(out[0], out[1]))
|
||||
print("=" * 100 + "Eval Finish!" + "=" * 100)
|
||||
|
||||
if __name__ == '__main__':
|
||||
get_acc()
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
# 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
|
||||
|
||||
from src.dataset import create_dataset
|
||||
from model_utils.config import config
|
||||
|
||||
def get_bin():
|
||||
"""generate bin files"""
|
||||
ds_eval, _, _ = create_dataset(test_train=False, data_dir=config.data_path,
|
||||
dataset=config.dataset, train_epochs=0,
|
||||
eval_batch_size=config.eval_batch_size)
|
||||
bs = config.eval_batch_size
|
||||
user_folder = os.path.join(config.pre_result_path, "00_user")
|
||||
os.makedirs(user_folder)
|
||||
item_folder = os.path.join(config.pre_result_path, "01_item")
|
||||
os.makedirs(item_folder)
|
||||
mask_folder = os.path.join(config.pre_result_path, "02_mask")
|
||||
os.makedirs(mask_folder)
|
||||
|
||||
for i, dataset in enumerate(ds_eval.create_tuple_iterator(output_numpy=True)):
|
||||
users, items, masks = dataset
|
||||
file_name = "ncf_bs" + str(bs) + "_" + str(i) + ".bin"
|
||||
users_path = os.path.join(user_folder, file_name)
|
||||
users.tofile(users_path)
|
||||
items_path = os.path.join(item_folder, file_name)
|
||||
items.tofile(items_path)
|
||||
masks_path = os.path.join(mask_folder, file_name)
|
||||
masks.tofile(masks_path)
|
||||
print("=" * 20, "Export bin files success", "=" * 20)
|
||||
|
||||
if __name__ == '__main__':
|
||||
get_bin()
|
||||
|
|
@ -0,0 +1,120 @@
|
|||
#!/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 2 || $# -gt 3 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
|
||||
if [ "$2" == "y" ] || [ "$2" == "n" ];then
|
||||
need_preprocess=$2
|
||||
else
|
||||
echo "weather need preprocess or not, it's value must be in [y, n]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
device_id=0
|
||||
if [ $# == 3 ]; then
|
||||
device_id=$3
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
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=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/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/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function preprocess_data()
|
||||
{
|
||||
if [ -d preprocess_Result ]; then
|
||||
rm -rf ./preprocess_Result
|
||||
fi
|
||||
mkdir preprocess_Result
|
||||
python3.7 ../preprocess.py
|
||||
}
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
|
||||
../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_user --input1_path=./preprocess_Result/01_item --input2_path=./preprocess_Result/02_mask --device_id=$device_id &> infer.log
|
||||
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py &> 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 [ $? -ne 0 ]; then
|
||||
echo "calculate accuracy failed"
|
||||
exit 1
|
||||
fi
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
cmake_minimum_required(VERSION 3.14.1)
|
||||
project(Ascend310Infer)
|
||||
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
|
||||
set(CMAKE_CXX_FLAGS "${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 +1,130 @@
|
|||
/**
|
||||
* 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_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);
|
||||
|
||||
if (input0_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]);
|
||||
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
|
||||
input0.Data().get(), input0.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 +1,44 @@
|
|||
# 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.nn.metrics import Accuracy
|
||||
from src.config import textrcnn_cfg as cfg
|
||||
|
||||
parser = argparse.ArgumentParser(description='postprocess')
|
||||
parser.add_argument('--label_path', type=str, default="./preprocess_Result/label_ids.npy")
|
||||
parser.add_argument('--result_path', type=str, default="./result_Files")
|
||||
args = parser.parse_args()
|
||||
|
||||
def get_acc():
|
||||
'''calculate accuracy'''
|
||||
metric = Accuracy()
|
||||
metric.clear()
|
||||
label_list = np.load(args.label_path, allow_pickle=True)
|
||||
file_num = len(os.listdir(args.result_path))
|
||||
|
||||
for i in range(file_num):
|
||||
f_name = "textcrnn_bs" + str(cfg.batch_size) + "_" + str(i) + "_0.bin"
|
||||
pred = np.fromfile(os.path.join(args.result_path, f_name), np.float16)
|
||||
pred = pred.reshape(cfg.batch_size, int(pred.shape[0]/cfg.batch_size))
|
||||
metric.update(pred, label_list[i])
|
||||
acc = metric.eval()
|
||||
print("============== Accuracy:{} ==============".format(acc))
|
||||
|
||||
if __name__ == '__main__':
|
||||
get_acc()
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
# 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
|
||||
import numpy as np
|
||||
|
||||
from src.config import textrcnn_cfg as cfg
|
||||
from src.dataset import create_dataset
|
||||
|
||||
parser = argparse.ArgumentParser(description='preprocess')
|
||||
parser.add_argument('--result_path', type=str, default="./preprocess_Result")
|
||||
args = parser.parse_args()
|
||||
|
||||
def get_bin():
|
||||
'''generate bin files.'''
|
||||
ds_eval = create_dataset(cfg.preprocess_path, cfg.batch_size, False)
|
||||
img_path = os.path.join(args.result_path, "00_feature")
|
||||
os.makedirs(img_path)
|
||||
label_list = []
|
||||
|
||||
for i, data in enumerate(ds_eval.create_dict_iterator(output_numpy=True)):
|
||||
file_name = "textcrnn_bs" + str(cfg.batch_size) + "_" + str(i) + ".bin"
|
||||
file_path = os.path.join(img_path, file_name)
|
||||
|
||||
data["feature"].tofile(file_path)
|
||||
label_list.append(data["label"])
|
||||
|
||||
np.save(os.path.join(args.result_path, "label_ids.npy"), label_list)
|
||||
print("=" * 20, "bin files finished", "=" * 20)
|
||||
|
||||
if __name__ == '__main__':
|
||||
get_bin()
|
||||
|
|
@ -8,6 +8,10 @@
|
|||
- [Environment Requirements](#environment-requirements)
|
||||
- [Quick Start](#quick-start)
|
||||
- [Script Description](#script-description)
|
||||
- [Inference Process](#inference-process)
|
||||
- [Export MindIR](#export-mindir)
|
||||
- [Infer on Ascend310](#infer-on-ascend310)
|
||||
- [result](#result)
|
||||
- [ModelZoo Homepage](#modelzoo-homepage)
|
||||
|
||||
## [TextRCNN Description](#contents)
|
||||
|
|
@ -138,6 +142,37 @@ Parameters for both training and evaluation can be set in config.py
|
|||
| Accuracy | 0.78 | 0.78 |
|
||||
| Speed | 35ms/step | 77ms/step |
|
||||
|
||||
## 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] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
```
|
||||
|
||||
- `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
|
||||
============== Accuracy:{} ============== 0.8008
|
||||
```
|
||||
|
||||
## [ModelZoo Homepage](#contents)
|
||||
|
||||
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
|
||||
|
|
|
|||
|
|
@ -0,0 +1,120 @@
|
|||
#!/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 2 || $# -gt 3 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [NEED_PREPROCESS] [DEVICE_ID]
|
||||
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
|
||||
if [ "$2" == "y" ] || [ "$2" == "n" ];then
|
||||
need_preprocess=$2
|
||||
else
|
||||
echo "weather need preprocess or not, it's value must be in [y, n]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
device_id=0
|
||||
if [ $# == 3 ]; then
|
||||
device_id=$3
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
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=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/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/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function preprocess_data()
|
||||
{
|
||||
if [ -d preprocess_Result ]; then
|
||||
rm -rf ./preprocess_Result
|
||||
fi
|
||||
mkdir preprocess_Result
|
||||
python3.7 ../preprocess.py --result_path=./preprocess_Result
|
||||
}
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
|
||||
../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_feature --device_id=$device_id &> infer.log
|
||||
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --label_path=./preprocess_Result/label_ids.npy --result_path=./result_Files &> 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