forked from mindspore-Ecosystem/mindspore
psenet 310 infer
modified: psenet/ascend310_infer/src/main.cc
modified: psenet/postprocess.py
modified: psenet/scripts/run_infer_310.sh
modified: psenet/README_CN.md
This commit is contained in:
unknown
parent
b82956cefc
commit
7320e6b3f6
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@ -14,6 +14,10 @@
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- [Distributed Training](#distributed-training)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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- [Inference Process](#inference-process)
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- [Export MindIR](#export-mindir)
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- [Infer on Ascend310](#infer-on-ascend310)
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- [result](#result)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Evaluation Performance](#evaluation-performance)
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@ -180,6 +184,33 @@ sh ./script/run_eval_ascend.sh.sh
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Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean": 0.8076736279747451, "AP": 0}
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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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Current batch_Size can only be set to 1. Before running the following process, please configure the environment by following the instructions provided in [Quick start](#quick-start).
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```shell
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# Ascend310 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
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```
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- `DEVICE_ID` is optional, default value is 0.
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### result
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The `res` folder is generated in the upper-level directory. For details about the final precision calculation, see [Eval Script for ICDAR2015](#eval-script-for-icdar2015).
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# [Model Description](#contents)
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## [Performance](#contents)
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@ -16,7 +16,11 @@
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- [运行测试代码](#运行测试代码)
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- [ICDAR2015评估脚本](#icdar2015评估脚本)
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- [用法](#用法)
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- [结果](#结果)
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- [结果](#结果)
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- [推理过程](#推理过程)
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- [导出MindIR](#导出mindir)
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- [在Ascend310执行推理](#在ascend310执行推理)
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- [结果](#结果)
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- [模型描述](#模型描述)
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- [性能](#性能)
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- [评估性能](#评估性能)
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@ -178,6 +182,33 @@ sh ./script/run_eval_ascend.sh.sh
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Calculated!{"precision": 0.8147966668299853，"recall"：0.8006740491092923，"hmean"：0.8076736279747451，"AP"：0}
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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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目前仅支持batch_Size为1的推理。在执行推理前，请按照[快速入门](#快速入门)配置环境。
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```shell
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# Ascend310 推理
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bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
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```
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- `DEVICE_ID` 可选，默认值为0。
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### result
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在运行目录的上一级目录将生成`res`文件夹，最终精度计算过程，请参照[ICDAR2015评估脚本](#icdar2015评估脚本).
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# 模型描述
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## 性能
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cmake_minimum_required(VERSION 3.14.1)
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project(Ascend310Infer)
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add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -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 +1,35 @@
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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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std::vector<std::string> GetAllFiles(std::string dir_name);
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std::vector<std::vector<std::string>> GetAllInputData(std::string dir_name);
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#endif
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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::ModelType;
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using mindspore::GraphCell;
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using mindspore::kSuccess;
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using mindspore::MSTensor;
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using mindspore::dataset::Execute;
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using mindspore::dataset::vision::Decode;
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using mindspore::dataset::vision::Resize;
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using mindspore::dataset::vision::Pad;
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using mindspore::dataset::vision::Normalize;
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using mindspore::dataset::vision::HWC2CHW;
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DEFINE_string(mindir_path, "", "mindir path");
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DEFINE_string(dataset_path, ".", "dataset 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 all_files = GetAllFiles(FLAGS_dataset_path);
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if (all_files.empty()) {
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std::cout << "ERROR: no input data." << 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 = all_files.size();
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auto decode = Decode();
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Execute composeDecode(decode);
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auto resize = Resize({1920, 1920});
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auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375});
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auto hwc2chw = HWC2CHW();
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for (size_t i = 0; i < size; ++i) {
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struct timeval start = {0};
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struct timeval end = {0};
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double startTimeMs;
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double endTimeMs;
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std::vector<MSTensor> inputs;
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std::vector<MSTensor> outputs;
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std::cout << "Start predict input files:" << all_files[i] <<std::endl;
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auto imgDecode = MSTensor();
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auto image = ReadFileToTensor(all_files[i]);
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ret = composeDecode(image, &imgDecode);
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if (ret != kSuccess) {
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std::cout << "ERROR: Decode failed." << std::endl;
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return 1;
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}
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std::vector<int64_t> shape = imgDecode.Shape();
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int imgHeight = shape[0];
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int imgWidth = shape[1];
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std::vector<int> pad_size;
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if (imgWidth < imgHeight) {
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pad_size = {0, 0, (imgHeight - imgWidth), 0};
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} else {
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pad_size = {0, 0, 0, (imgWidth - imgHeight)};
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}
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auto pad = Pad(pad_size, {0});
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Execute trans_list({pad, resize, normalize, hwc2chw});
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auto img = MSTensor();
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ret = trans_list(imgDecode, &img);
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if (ret != kSuccess) {
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std::cout << "ERROR: Image transfer failed." << std::endl;
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return 1;
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}
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inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
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img.Data().get(), img.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 " << all_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(all_files[i], outputs);
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}
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double average = 0.0;
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int inferCount = 0;
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for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
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double diff = 0.0;
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diff = iter->second - iter->first;
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average += diff;
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inferCount++;
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}
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average = average / inferCount;
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std::stringstream timeCost;
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timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
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std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
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std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
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std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
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fileStream << timeCost.str();
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fileStream.close();
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costTime_map.clear();
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return 0;
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}
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
|
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* You may obtain a copy of the License at
|
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*
|
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* http://www.apache.org/licenses/LICENSE-2.0
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*
|
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* Unless required by applicable law or agreed to in writing, software
|
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* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* 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::vector<std::string>> GetAllInputData(std::string dir_name) {
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std::vector<std::vector<std::string>> ret;
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DIR *dir = OpenDir(dir_name);
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if (dir == nullptr) {
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return {};
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}
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struct dirent *filename;
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/* read all the files in the dir ~ */
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std::vector<std::string> sub_dirs;
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while ((filename = readdir(dir)) != nullptr) {
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std::string d_name = std::string(filename->d_name);
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// get rid of "." and ".."
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if (d_name == "." || d_name == ".." || d_name.empty()) {
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continue;
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}
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std::string dir_path = RealPath(std::string(dir_name) + "/" + filename->d_name);
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struct stat s;
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lstat(dir_path.c_str(), &s);
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if (!S_ISDIR(s.st_mode)) {
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continue;
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}
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sub_dirs.emplace_back(dir_path);
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}
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std::sort(sub_dirs.begin(), sub_dirs.end());
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(void)std::transform(sub_dirs.begin(), sub_dirs.end(), std::back_inserter(ret),
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[](const std::string &d) { return GetAllFiles(d); });
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return ret;
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}
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std::vector<std::string> GetAllFiles(std::string dir_name) {
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struct dirent *filename;
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DIR *dir = OpenDir(dir_name);
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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 d_name = std::string(filename->d_name);
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if (d_name == "." || d_name == ".." || d_name.size() <= 3) {
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continue;
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}
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res.emplace_back(std::string(dir_name) + "/" + filename->d_name);
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}
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std::sort(res.begin(), res.end());
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return res;
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}
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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) {
|
||||
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 +1,104 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
|
||||
import os
|
||||
import math
|
||||
import operator
|
||||
from functools import reduce
|
||||
import argparse
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
from src.config import config
|
||||
from src.ETSNET.pse import pse
|
||||
|
||||
def sort_to_clockwise(points):
|
||||
center = tuple(map(operator.truediv, reduce(lambda x, y: map(operator.add, x, y), points), [len(points)] * 2))
|
||||
clockwise_points = sorted(points, key=lambda coord: (-135 - math.degrees(
|
||||
math.atan2(*tuple(map(operator.sub, coord, center))[::-1]))) % 360, reverse=True)
|
||||
return clockwise_points
|
||||
|
||||
def write_result_as_txt(image_name, img_bboxes, path):
|
||||
if not os.path.isdir(path):
|
||||
os.makedirs(path)
|
||||
filename = os.path.join(path, 'res_{}.txt'.format(os.path.splitext(image_name)[0]))
|
||||
lines = []
|
||||
for _, img_bbox in enumerate(img_bboxes):
|
||||
img_bbox = img_bbox.reshape(-1, 2)
|
||||
img_bbox = np.array(list(sort_to_clockwise(img_bbox)))[[3, 0, 1, 2]].copy().reshape(-1)
|
||||
values = [int(v) for v in img_bbox]
|
||||
line = "%d,%d,%d,%d,%d,%d,%d,%d\n" % tuple(values)
|
||||
lines.append(line)
|
||||
with open(filename, 'w') as f:
|
||||
for line in lines:
|
||||
f.write(line)
|
||||
|
||||
def get_img(image_path):
|
||||
image = cv2.imread(image_path)
|
||||
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
|
||||
return image
|
||||
|
||||
parser = argparse.ArgumentParser(description='postprocess')
|
||||
parser.add_argument("--result_path", type=str, default="./scripts/result_Files", help='result Files path.')
|
||||
parser.add_argument("--img_path", type=str, default="", help='image files path.')
|
||||
args = parser.parse_args()
|
||||
|
||||
if __name__ == "__main__":
|
||||
if not os.path.isdir('./res/submit_ic15/'):
|
||||
os.makedirs('./res/submit_ic15/')
|
||||
if not os.path.isdir('./res/vis_ic15/'):
|
||||
os.makedirs('./res/vis_ic15/')
|
||||
|
||||
file_list = os.listdir(args.img_path)
|
||||
for k in file_list:
|
||||
if os.path.splitext(k)[-1].lower() in ['.jpg', '.jpeg', '.png']:
|
||||
img_path = os.path.join(args.img_path, k)
|
||||
img = get_img(img_path).reshape(1, 720, 1280, 3)
|
||||
img = img[0].astype(np.uint8).copy()
|
||||
img_name = os.path.split(img_path)[-1]
|
||||
|
||||
score = np.fromfile(os.path.join(args.result_path, k.split('.')[0] + '_0.bin'), np.float32)
|
||||
score = score.reshape(1, 1, config.INFER_LONG_SIZE, config.INFER_LONG_SIZE)
|
||||
kernels = np.fromfile(os.path.join(args.result_path, k.split('.')[0] + '_1.bin'), bool)
|
||||
kernels = kernels.reshape(1, config.KERNEL_NUM, config.INFER_LONG_SIZE, config.INFER_LONG_SIZE)
|
||||
score = np.squeeze(score)
|
||||
kernels = np.squeeze(kernels)
|
||||
|
||||
# post-process
|
||||
pred = pse(kernels, 5.0)
|
||||
scale = max(img.shape[:2]) * 1.0 / config.INFER_LONG_SIZE
|
||||
label = pred
|
||||
label_num = np.max(label) + 1
|
||||
bboxes = []
|
||||
|
||||
for i in range(1, label_num):
|
||||
pot = np.array(np.where(label == i)).transpose((1, 0))[:, ::-1]
|
||||
if pot.shape[0] < 600:
|
||||
continue
|
||||
|
||||
score_i = np.mean(score[label == i])
|
||||
if score_i < 0.93:
|
||||
continue
|
||||
|
||||
rect = cv2.minAreaRect(pot)
|
||||
bbox = cv2.boxPoints(rect) * scale
|
||||
bbox = bbox.astype('int32')
|
||||
cv2.drawContours(img, [bbox], 0, (0, 255, 0), 3)
|
||||
bboxes.append(bbox)
|
||||
|
||||
# save res
|
||||
cv2.imwrite('./res/vis_ic15/{}'.format(img_name), img[:, :, [2, 1, 0]].copy())
|
||||
write_result_as_txt(img_name, bboxes, './res/submit_ic15/')
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
#!/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 || $# -gt 3 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
data_path=$(get_real_path $2)
|
||||
|
||||
device_id=0
|
||||
if [ $# == 3 ]; then
|
||||
device_id=$3
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
echo "dataset path: "$data_path
|
||||
echo "device id: "$device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer/ || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
cd .. || exit
|
||||
python3.7 postprocess.py --result_path=./scripts/result_Files --img_path=$data_path &> ./scripts/acc.log
|
||||
}
|
||||
|
||||
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