forked from mindspore-Ecosystem/mindspore
!18454 FaceQualityAssessment add 310 infer
Merge pull request !18454 from ZeyangGAO/faceqa
This commit is contained in:
Gitee
commit
f042392842
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@ -7,6 +7,10 @@
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Running Example](#running-example)
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- [Inference Process](#inference-process)
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- [Export MindIR](#export-mindir)
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- [Infer on Ascend](#infer-on-ascend)
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- [Result](#result)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [ModelZoo Homepage](#modelzoo-homepage)
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@ -260,6 +264,56 @@ IPN of 5 keypoints:19.57019303768714
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MAE of elur:18.021210976971098
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```
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## [Inference Process](#contents)
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### [Export MindIR](#contents)
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```shell
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python export.py --pretrained [CKPT_PATH] --batch_size 1 --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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`batch_size` should be set to 1
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`pretrained` is the ckpt file path referenced
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`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
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for example, on Ascend:
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```bash
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python export.py --pretrained ./0-1_19000.ckpt --batch_size 1 --file_name faq.mindir --file_format MINDIR
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```
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### [Infer on Ascend310](#contents)
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Before performing inference, the mindir file must be exported by `export.py` script.
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Current batch_Size can only be set to 1.
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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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- `DATA_PATH` is mandatory, and must specify original data path.
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- `DEVICE_ID` is optional, default value is 0.
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for example, on Ascend:
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```bash
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cd ./scripts
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sh run_infer_310.sh ../fqa.mindir ../face_quality_dataset/ASLW2000 0
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```
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### [Result](#contents)
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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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5 keypoints average err:['3.399', '4.320', '3.927', '3.109', '3.379']
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3 eulers average err:['21.192', '15.342', '16.559']
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IPN of 5 keypoints:20.30505629501458
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MAE of elur:17.69762644062826
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```
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### Convert model
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If you want to infer the network on Ascend 310, you should convert the model to AIR:
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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} -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 +1,32 @@
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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 +1,165 @@
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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/vision_ascend.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::TensorTransform;
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using mindspore::dataset::vision::DvppDecodeResizeJpeg;
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using mindspore::dataset::vision::Resize;
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using mindspore::dataset::vision::HWC2CHW;
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using mindspore::dataset::vision::Normalize;
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using mindspore::dataset::vision::Decode;
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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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DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
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DEFINE_string(cpu_dvpp, "CPU", "cpu or dvpp process");
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DEFINE_int32(image_height, 96, "image height");
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DEFINE_int32(image_width, 96, "image width");
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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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ascend310->SetBufferOptimizeMode("off_optimize");
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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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if (FLAGS_cpu_dvpp == "DVPP") {
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if (RealPath(FLAGS_aipp_path).empty()) {
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std::cout << "Invalid aipp path" << std::endl;
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return 1;
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} else {
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ascend310->SetInsertOpConfigPath(FLAGS_aipp_path);
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}
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}
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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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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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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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if (FLAGS_cpu_dvpp == "DVPP") {
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auto resizeShape = {static_cast <uint32_t>(FLAGS_image_height), static_cast <uint32_t>(FLAGS_image_width)};
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Execute resize_op(std::shared_ptr<DvppDecodeResizeJpeg>(new DvppDecodeResizeJpeg(resizeShape)));
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auto imgDvpp = std::make_shared<MSTensor>();
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resize_op(ReadFileToTensor(all_files[i]), imgDvpp.get());
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inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(),
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imgDvpp->Data().get(), imgDvpp->DataSize());
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} else {
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std::shared_ptr<TensorTransform> decode(new Decode());
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std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
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std::shared_ptr<TensorTransform> normalize(
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new Normalize({0, 0, 0}, {255, 255, 255}));
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auto resizeShape = {FLAGS_image_height, FLAGS_image_width};
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std::shared_ptr<TensorTransform> resize(new Resize(resizeShape));
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Execute composeDecode({decode, resize, normalize, hwc2chw});
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auto img = MSTensor();
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auto image = ReadFileToTensor(all_files[i]);
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composeDecode(image, &img);
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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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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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}
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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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@ -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");
|
||||
* 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.
|
||||
*/
|
||||
|
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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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|
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std::ifstream ifs(file);
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if (!ifs.good()) {
|
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std::cout << "File: " << file << " is not exist" << std::endl;
|
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return mindspore::MSTensor();
|
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}
|
||||
|
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if (!ifs.is_open()) {
|
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std::cout << "File: " << file << "open failed" << std::endl;
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return mindspore::MSTensor();
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}
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|
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ifs.seekg(0, std::ios::end);
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size_t size = ifs.tellg();
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mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
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ifs.seekg(0, std::ios::beg);
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ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
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ifs.close();
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return buffer;
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}
|
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|
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|
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DIR *OpenDir(std::string_view dirName) {
|
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if (dirName.empty()) {
|
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std::cout << " dirName is null ! " << std::endl;
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return nullptr;
|
||||
}
|
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std::string realPath = RealPath(dirName);
|
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struct stat s;
|
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lstat(realPath.c_str(), &s);
|
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if (!S_ISDIR(s.st_mode)) {
|
||||
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 +1,174 @@
|
|||
# Copyright 2020-2021 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.
|
||||
# ============================================================================
|
||||
"""Face Quality Assessment cal acc."""
|
||||
import os
|
||||
import warnings
|
||||
import argparse
|
||||
import numpy as np
|
||||
import cv2
|
||||
from tqdm import tqdm
|
||||
|
||||
from mindspore import context
|
||||
|
||||
warnings.filterwarnings('ignore')
|
||||
|
||||
|
||||
def softmax(x):
|
||||
"""Compute softmax values for each sets of scores in x."""
|
||||
return np.exp(x) / np.sum(np.exp(x), axis=1)
|
||||
|
||||
def get_md_output(result_path, file_name):
|
||||
'''get md output'''
|
||||
eul_result_path = os.path.join(result_path, file_name + "_0.bin")
|
||||
heatmap_result_path = os.path.join(result_path, file_name + "_1.bin")
|
||||
out_eul = np.fromfile(eul_result_path, dtype=np.float32)
|
||||
heatmap = np.fromfile(heatmap_result_path, dtype=np.float32).reshape([1, 5, 48, 48])
|
||||
heatmap = heatmap[0]
|
||||
eulers = out_eul * 90
|
||||
|
||||
kps_score_sum = 0
|
||||
kp_scores = list()
|
||||
kp_coord_ori = list()
|
||||
|
||||
for i, _ in enumerate(heatmap):
|
||||
map_1 = heatmap[i].reshape(1, 48*48)
|
||||
map_1 = softmax(map_1)
|
||||
|
||||
kp_coor = map_1.argmax()
|
||||
max_response = map_1.max()
|
||||
kp_scores.append(max_response)
|
||||
kps_score_sum += min(max_response, 0.25)
|
||||
kp_coor = int((kp_coor % 48) * 2.0), int((kp_coor / 48) * 2.0)
|
||||
kp_coord_ori.append(kp_coor)
|
||||
|
||||
return kp_scores, kps_score_sum, kp_coord_ori, eulers, 1
|
||||
|
||||
|
||||
def read_gt(txt_path, x_length, y_length):
|
||||
'''read gt'''
|
||||
txt_line = open(txt_path).readline()
|
||||
eulers_txt = txt_line.strip().split(" ")[:3]
|
||||
kp_list = [[-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]]
|
||||
box_cur = txt_line.strip().split(" ")[3:]
|
||||
bndbox = []
|
||||
for index in range(len(box_cur) // 2):
|
||||
bndbox.append([box_cur[index * 2], box_cur[index * 2 + 1]])
|
||||
kp_id = -1
|
||||
for box in bndbox:
|
||||
kp_id = kp_id + 1
|
||||
x_coord = float(box[0])
|
||||
y_coord = float(box[1])
|
||||
if x_coord < 0 or y_coord < 0:
|
||||
continue
|
||||
|
||||
kp_list[kp_id][0] = int(float(x_coord) / x_length * 96)
|
||||
|
||||
kp_list[kp_id][1] = int(float(y_coord) / y_length * 96)
|
||||
|
||||
return eulers_txt, kp_list
|
||||
|
||||
|
||||
def read_img(img_path):
|
||||
img_ori = cv2.imread(img_path)
|
||||
return img_ori
|
||||
|
||||
def test_infer(args):
|
||||
'''test infer starts'''
|
||||
print('----infer----begin----')
|
||||
|
||||
result_file = './result_file.txt'
|
||||
if os.path.exists(result_file):
|
||||
os.remove(result_file)
|
||||
epoch_result = open(result_file, 'a')
|
||||
epoch_result.write('./FaceQualityAssessment' + '\n')
|
||||
|
||||
path = args.result_path
|
||||
kp_error_all = [[], [], [], [], []]
|
||||
eulers_error_all = [[], [], []]
|
||||
kp_ipn = []
|
||||
|
||||
file_list = os.listdir(path)
|
||||
for file in tqdm(file_list):
|
||||
file_name = file.split('_')[0]
|
||||
img_path = os.path.join(args.data_path, file_name + '.jpg')
|
||||
label_path = os.path.join(args.label_path, file_name + '.txt')
|
||||
img_ori = read_img(img_path)
|
||||
x_length = img_ori.shape[1]
|
||||
y_length = img_ori.shape[0]
|
||||
eulers_gt, kp_list = read_gt(label_path, x_length, y_length)
|
||||
_, _, kp_coord_ori, eulers_ori, _ = get_md_output(args.result_path, file_name)
|
||||
eulgt = list(eulers_gt)
|
||||
for euler_id, _ in enumerate(eulers_ori):
|
||||
eulori = eulers_ori[euler_id]
|
||||
eulers_error_all[euler_id].append(abs(eulori-float(eulgt[euler_id])))
|
||||
|
||||
eye01 = kp_list[0]
|
||||
eye02 = kp_list[1]
|
||||
eye_dis = 1
|
||||
cur_flag = True
|
||||
if eye01[0] < 0 or eye01[1] < 0 or eye02[0] < 0 or eye02[1] < 0:
|
||||
cur_flag = False
|
||||
else:
|
||||
eye_dis = np.sqrt(np.square(abs(eye01[0]-eye02[0]))+np.square(abs(eye01[1]-eye02[1])))
|
||||
cur_error_list = []
|
||||
for i in range(5):
|
||||
kp_coord_gt = kp_list[i]
|
||||
kp_coord_model = kp_coord_ori[i]
|
||||
if kp_coord_gt[0] != -1:
|
||||
dis = np.sqrt(np.square(
|
||||
kp_coord_gt[0] - kp_coord_model[0]) + np.square(kp_coord_gt[1] - kp_coord_model[1]))
|
||||
kp_error_all[i].append(dis)
|
||||
cur_error_list.append(dis)
|
||||
if cur_flag:
|
||||
kp_ipn.append(sum(cur_error_list)/len(cur_error_list)/eye_dis)
|
||||
|
||||
kp_ave_error = []
|
||||
for kps, _ in enumerate(kp_error_all):
|
||||
kp_ave_error.append("%.3f" % (sum(kp_error_all[kps])/len(kp_error_all[kps])))
|
||||
|
||||
euler_ave_error = []
|
||||
elur_mae = []
|
||||
for eulers, _ in enumerate(eulers_error_all):
|
||||
euler_ave_error.append("%.3f" % (sum(eulers_error_all[eulers])/len(eulers_error_all[eulers])))
|
||||
elur_mae.append((sum(eulers_error_all[eulers])/len(eulers_error_all[eulers])))
|
||||
|
||||
print(r'5 keypoints average err:'+str(kp_ave_error))
|
||||
print(r'3 eulers average err:'+str(euler_ave_error))
|
||||
print('IPN of 5 keypoints:'+str(sum(kp_ipn)/len(kp_ipn)*100))
|
||||
print('MAE of elur:'+str(sum(elur_mae)/len(elur_mae)))
|
||||
|
||||
epoch_result.write(str(sum(kp_ipn)/len(kp_ipn)*100)+'\t'+str(sum(elur_mae)/len(elur_mae))+'\t'
|
||||
+ str(kp_ave_error)+'\t'+str(euler_ave_error)+'\n')
|
||||
|
||||
print('----infer----end----')
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description='Face Quality Assessment')
|
||||
parser.add_argument('--result_path', type=str, default='', help='infer results, e.g. /result_Files')
|
||||
parser.add_argument('--data_path', type=str, default='', help='original imagess')
|
||||
parser.add_argument('--label_path', type=str, default='', help='original txt folder after preprocess')
|
||||
parser.add_argument('--device_target', type=str, choices=['Ascend', 'GPU', 'CPU'], default='Ascend',
|
||||
help='device target')
|
||||
|
||||
arg = parser.parse_args()
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=arg.device_target, save_graphs=False)
|
||||
if arg.device_target == 'Ascend':
|
||||
devid = 0
|
||||
context.set_context(device_id=devid)
|
||||
|
||||
test_infer(arg)
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
# Copyright 2020-2021 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 dataset folder."""
|
||||
import os
|
||||
import shutil
|
||||
import argparse
|
||||
|
||||
def seperate_image_label(data_path, result_path):
|
||||
'''seperate txt and jpg files as preprocess'''
|
||||
dirs = os.listdir(data_path)
|
||||
img_path = os.path.join(result_path, "image")
|
||||
label_path = os.path.join(result_path, "label")
|
||||
os.makedirs(img_path)
|
||||
os.makedirs(label_path)
|
||||
for file in dirs:
|
||||
if file != "Code":
|
||||
file_suffix = file.split('.')[1]
|
||||
if file_suffix == "jpg":
|
||||
file_path = os.path.join(data_path, file)
|
||||
save_path = os.path.join(img_path, file)
|
||||
shutil.copy(file_path, save_path)
|
||||
elif file_suffix == "txt":
|
||||
file_path = os.path.join(data_path, file)
|
||||
save_path = os.path.join(label_path, file)
|
||||
shutil.copy(file_path, save_path)
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
parser = argparse.ArgumentParser(description='Face Quality Assessment preprocess')
|
||||
parser.add_argument('--data_path', type=str, default='', help='data_path, e.g. ./face_quality_dataset/AWLW2000')
|
||||
parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='path to store preprocess')
|
||||
arg = parser.parse_args()
|
||||
seperate_image_label(data_path=arg.data_path, result_path=arg.result_path)
|
||||
|
|
@ -0,0 +1,127 @@
|
|||
#!/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 || $# -gt 3 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
|
||||
MINDIR_PATH is mandatory, and must specify mindir path used
|
||||
DATA_PATH is mandatory, and must specify dataset path used
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${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 "data 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/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=$ASCEND_HOME/opp
|
||||
fi
|
||||
export ASCEND_HOME=/usr/local/Ascend
|
||||
|
||||
export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/toolkit/bin:$PATH
|
||||
|
||||
export LD_LIBRARY_PATH=/usr/local/lib/:/usr/local/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:/usr/local/Ascend/toolkit/lib64:$LD_LIBRARY_PATH
|
||||
|
||||
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages
|
||||
|
||||
export PATH=/usr/local/python375/bin:$PATH
|
||||
export NPU_HOST_LIB=/usr/local/Ascend/acllib/lib64/stub
|
||||
export ASCEND_OPP_PATH=/usr/local/Ascend/opp
|
||||
export ASCEND_AICPU_PATH=/usr/local/Ascend
|
||||
export LD_LIBRARY_PATH=/usr/local/lib64/:$LD_LIBRARY_PATH
|
||||
|
||||
function preprocess_data()
|
||||
{
|
||||
if [ -d preprocess_Result ]; then
|
||||
rm -rf ./preprocess_Result
|
||||
fi
|
||||
mkdir preprocess_Result
|
||||
python3.7 ../preprocess.py --data_path=$data_path --result_path=./preprocess_Result/
|
||||
}
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer/ || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
|
||||
../ascend310_infer/out/main --mindir_path=$model --dataset_path=./preprocess_Result/image --cpu_dvpp='CPU' --device_id=$device_id --image_height=96 --image_width=96 &> infer.log
|
||||
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --result_path=./result_Files --data_path=./preprocess_Result/image --label_path=./preprocess_Result/label &> acc.log
|
||||
}
|
||||
|
||||
preprocess_data
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "preprocess dataset failed"
|
||||
exit 1
|
||||
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