From e3c05625e49acf8c4210d3bdc10a9995dfb972aa Mon Sep 17 00:00:00 2001 From: unknown Date: Wed, 21 Apr 2021 14:30:29 +0800 Subject: [PATCH] yolov3_darknet53 & resnet18 310 inference new file: yolov3_resnet18/ascend310_infer/src/main.cc --- .../official/cv/yolov3_darknet53/README.md | 48 +++++ .../official/cv/yolov3_darknet53/README_CN.md | 47 ++++ .../ascend310_infer/CMakeLists.txt | 14 ++ .../yolov3_darknet53/ascend310_infer/aipp.cfg | 26 +++ .../yolov3_darknet53/ascend310_infer/build.sh | 29 +++ .../ascend310_infer/inc/utils.h | 32 +++ .../ascend310_infer/src/main.cc | 136 ++++++++++++ .../ascend310_infer/src/utils.cc | 129 +++++++++++ .../cv/yolov3_darknet53/postprocess.py | 63 ++++++ .../yolov3_darknet53/scripts/run_infer_310.sh | 100 +++++++++ .../official/cv/yolov3_resnet18/README.md | 36 ++++ .../official/cv/yolov3_resnet18/README_CN.md | 35 +++ .../ascend310_infer/CMakeLists.txt | 14 ++ .../yolov3_resnet18/ascend310_infer/build.sh | 29 +++ .../ascend310_infer/inc/utils.h | 32 +++ .../ascend310_infer/src/main.cc | 201 ++++++++++++++++++ .../ascend310_infer/src/utils.cc | 129 +++++++++++ .../official/cv/yolov3_resnet18/export.py | 13 +- .../cv/yolov3_resnet18/postprocess.py | 60 ++++++ .../yolov3_resnet18/scripts/run_infer_310.sh | 98 +++++++++ 20 files changed, 1266 insertions(+), 5 deletions(-) create mode 100644 model_zoo/official/cv/yolov3_darknet53/ascend310_infer/CMakeLists.txt create mode 100644 model_zoo/official/cv/yolov3_darknet53/ascend310_infer/aipp.cfg create mode 100644 model_zoo/official/cv/yolov3_darknet53/ascend310_infer/build.sh create mode 100644 model_zoo/official/cv/yolov3_darknet53/ascend310_infer/inc/utils.h create mode 100644 model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/main.cc create mode 100644 model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/utils.cc create mode 100644 model_zoo/official/cv/yolov3_darknet53/postprocess.py create mode 100644 model_zoo/official/cv/yolov3_darknet53/scripts/run_infer_310.sh create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_infer/CMakeLists.txt create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_infer/build.sh create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_infer/inc/utils.h create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_infer/src/main.cc create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_infer/src/utils.cc create mode 100644 model_zoo/official/cv/yolov3_resnet18/postprocess.py create mode 100644 model_zoo/official/cv/yolov3_resnet18/scripts/run_infer_310.sh diff --git a/model_zoo/official/cv/yolov3_darknet53/README.md b/model_zoo/official/cv/yolov3_darknet53/README.md index ea9443d0d9..2d9025dfd2 100644 --- a/model_zoo/official/cv/yolov3_darknet53/README.md +++ b/model_zoo/official/cv/yolov3_darknet53/README.md @@ -14,6 +14,8 @@ - [Distributed Training](#distributed-training) - [Evaluation Process](#evaluation-process) - [Evaluation](#evaluation) + - [Export MindIR](#export-mindir) + - [Inference Process](#inference-process) - [Model Description](#model-description) - [Performance](#performance) - [Evaluation Performance](#evaluation-performance) @@ -331,6 +333,52 @@ This the standard format from `pycocotools`, you can refer to [cocodataset](http Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551 ``` +### [Export MindIR](#contents) + +Currently, batchsize can only set to 1. + +```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"] + +### [Inference Process](#contents) + +#### Usage + +Before performing inference, the air file must bu exported by export.py. +Current batch_Size can only be set to 1. Because the DVPP hardware is used for processing, the picture must comply with the JPEG encoding format, Otherwise, an error will be reported. + +```shell +# Ascend310 inference +bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID] +``` + +`DEVICE_ID` is optional, default value is 0. + +#### result + +Inference result is saved in current path, you can find result in acc.log file. + +```eval log +# acc.log +=============coco eval reulst========= + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.322 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.259 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551 +``` + ## [Model Description](#contents) ### [Performance](#contents) diff --git a/model_zoo/official/cv/yolov3_darknet53/README_CN.md b/model_zoo/official/cv/yolov3_darknet53/README_CN.md index e733346743..bdff3e07ff 100644 --- a/model_zoo/official/cv/yolov3_darknet53/README_CN.md +++ b/model_zoo/official/cv/yolov3_darknet53/README_CN.md @@ -16,6 +16,10 @@ - [分布式训练](#分布式训练) - [评估过程](#评估过程) - [评估](#评估) + - [导出mindir模型](#导出mindir模型) + - [推理过程](#推理过程) + - [用法](#用法-2) + - [结果](#结果-2) - [模型描述](#模型描述) - [性能](#性能) - [评估性能](#评估性能) @@ -334,6 +338,49 @@ sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551 ``` +## 导出mindir模型 + +```shell +python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] +``` + +参数`ckpt_file` 是必需的，`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。 + +## 推理过程 + +### 用法 + +在执行推理之前，需要通过export.py导出mindir文件。 +目前仅可处理batch_Size为1，由于使用了DVPP硬件进行图片处理，因此图片必须满足JPEG编码格式，否则将会报错。 + +```shell +# Ascend310 推理 +bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID] +``` + +`DEVICE_ID` 可选，默认值为 0。 + +### 结果 + +推理结果保存在当前路径，可在acc.log中看到最终精度结果。 + +```eval log +# acc.log +=============coco eval reulst========= + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.322 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.259 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551 +``` + # 模型描述 ## 性能 diff --git a/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/CMakeLists.txt b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/CMakeLists.txt new file mode 100644 index 0000000000..ee3c854473 --- /dev/null +++ b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/CMakeLists.txt @@ -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) diff --git a/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/aipp.cfg b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/aipp.cfg new file mode 100644 index 0000000000..363d5d36fd --- /dev/null +++ b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/aipp.cfg @@ -0,0 +1,26 @@ +aipp_op { + aipp_mode : static + input_format : YUV420SP_U8 + related_input_rank : 0 + csc_switch : true + rbuv_swap_switch : false + matrix_r0c0 : 256 + matrix_r0c1 : 0 + matrix_r0c2 : 359 + matrix_r1c0 : 256 + matrix_r1c1 : -88 + matrix_r1c2 : -183 + matrix_r2c0 : 256 + matrix_r2c1 : 454 + matrix_r2c2 : 0 + input_bias_0 : 0 + input_bias_1 : 128 + input_bias_2 : 128 + + mean_chn_0 : 124 + mean_chn_1 : 117 + mean_chn_2 : 104 + var_reci_chn_0 : 0.0171247538316637 + var_reci_chn_1 : 0.0175070028011204 + var_reci_chn_2 : 0.0174291938997821 +} \ No newline at end of file diff --git a/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/build.sh b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/build.sh new file mode 100644 index 0000000000..285514e19f --- /dev/null +++ b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/build.sh @@ -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 diff --git a/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/inc/utils.h b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/inc/utils.h new file mode 100644 index 0000000000..efebe03a8c --- /dev/null +++ b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/inc/utils.h @@ -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 +#include +#include +#include +#include +#include "include/api/types.h" + +std::vector 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 &outputs); +#endif diff --git a/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/main.cc b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/main.cc new file mode 100644 index 0000000000..bbe1a8ac38 --- /dev/null +++ b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/main.cc @@ -0,0 +1,136 @@ +/** + * 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 +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "include/api/model.h" +#include "include/api/context.h" +#include "include/api/types.h" +#include "include/api/serialization.h" +#include "include/minddata/dataset/include/vision_ascend.h" +#include "include/minddata/dataset/include/execute.h" +#include "include/minddata/dataset/include/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; +using mindspore::dataset::vision::DvppDecodeResizeJpeg; + +DEFINE_string(mindir_path, "", "mindir path"); +DEFINE_string(dataset_path, ".", "dataset path"); +DEFINE_int32(device_id, 0, "device id"); +DEFINE_string(aipp_path, "./aipp.cfg", "aipp path"); + + +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(); + auto ascend310 = std::make_shared(); + ascend310->SetDeviceID(FLAGS_device_id); + context->MutableDeviceInfo().push_back(ascend310); + mindspore::Graph graph; + Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph); + ascend310->SetInsertOpConfigPath(FLAGS_aipp_path); + + Model model; + Status ret = model.Build(GraphCell(graph), context); + if (ret != kSuccess) { + std::cout << "ERROR: Build failed." << std::endl; + return 1; + } + + std::vector model_inputs = model.GetInputs(); + if (model_inputs.empty()) { + std::cout << "Invalid model, inputs is empty." << std::endl; + return 1; + } + + auto all_files = GetAllFiles(FLAGS_dataset_path); + if (all_files.empty()) { + std::cout << "ERROR: no input data." << std::endl; + return 1; + } + + std::map costTime_map; + size_t size = all_files.size(); + + for (size_t i = 0; i < size; ++i) { + struct timeval start = {0}; + struct timeval end = {0}; + double startTimeMs; + double endTimeMs; + std::vector inputs; + std::vector outputs; + std::cout << "Start predict input files:" << all_files[i] << std::endl; + + Execute resize_op(std::shared_ptr(new DvppDecodeResizeJpeg({416, 416}))); + auto imgDvpp = std::make_shared(); + resize_op(ReadFileToTensor(all_files[i]), imgDvpp.get()); + inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(), + imgDvpp->Data().get(), imgDvpp->DataSize()); + + gettimeofday(&start, nullptr); + ret = model.Predict(inputs, &outputs); + gettimeofday(&end, nullptr); + if (ret != kSuccess) { + std::cout << "Predict " << all_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(startTimeMs, endTimeMs)); + WriteResult(all_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; +} diff --git a/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/utils.cc b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/utils.cc new file mode 100644 index 0000000000..c947e4d5f4 --- /dev/null +++ b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/utils.cc @@ -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 +#include +#include +#include "inc/utils.h" + +using mindspore::MSTensor; +using mindspore::DataType; + +std::vector GetAllFiles(std::string_view dirName) { + struct dirent *filename; + DIR *dir = OpenDir(dirName); + if (dir == nullptr) { + return {}; + } + std::vector 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 &outputs) { + std::string homePath = "./result_Files"; + for (size_t i = 0; i < outputs.size(); ++i) { + size_t outputSize; + std::shared_ptr 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(size)}, nullptr, size); + + ifs.seekg(0, std::ios::beg); + ifs.read(reinterpret_cast(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; +} diff --git a/model_zoo/official/cv/yolov3_darknet53/postprocess.py b/model_zoo/official/cv/yolov3_darknet53/postprocess.py new file mode 100644 index 0000000000..5c0e8679c5 --- /dev/null +++ b/model_zoo/official/cv/yolov3_darknet53/postprocess.py @@ -0,0 +1,63 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""YoloV3 postprocess.""" +import os +import argparse +import datetime +import numpy as np +from PIL import Image +from eval import DetectionEngine + +def get_img_size(file_name): + img = Image.open(file_name) + return img.size + +parser = argparse.ArgumentParser('YoloV3 postprocess') +parser.add_argument('--result_path', type=str, required=True, help='result files path.') +parser.add_argument('--img_path', type=str, required=True, help='train data dir.') +parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu') +parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS') +parser.add_argument('--annFile', type=str, default='', help='path to annotation') +parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes') +parser.add_argument('--log_path', type=str, default='outputs/', help='inference result save location') + +args, _ = parser.parse_known_args() + +if __name__ == "__main__": + args.outputs_dir = os.path.join(args.log_path, + datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S')) + if not os.path.exists(args.outputs_dir): + os.makedirs(args.outputs_dir) + + detection = DetectionEngine(args) + bs = args.per_batch_size + + f_list = os.listdir(args.img_path) + for f in f_list: + image_size = get_img_size(os.path.join(args.img_path, f)) + f = f.split('.')[0] + output_big = np.fromfile(os.path.join(args.result_path, f + '_0.bin'), np.float32).reshape(bs, 13, 13, 3, 85) + output_me = np.fromfile(os.path.join(args.result_path, f + '_1.bin'), np.float32).reshape(bs, 26, 26, 3, 85) + output_small = np.fromfile(os.path.join(args.result_path, f + '_2.bin'), np.float32).reshape(bs, 52, 52, 3, 85) + image_id = [int(f.split('_')[-1])] + image_shape = [[image_size[0], image_size[1]]] + + detection.detect([output_small, output_me, output_big], bs, image_shape, image_id) + + detection.do_nms_for_results() + result_file_path = detection.write_result() + eval_result = detection.get_eval_result() + + print('\n=============coco eval result=========\n' + eval_result) diff --git a/model_zoo/official/cv/yolov3_darknet53/scripts/run_infer_310.sh b/model_zoo/official/cv/yolov3_darknet53/scripts/run_infer_310.sh new file mode 100644 index 0000000000..54f5e7a8c6 --- /dev/null +++ b/model_zoo/official/cv/yolov3_darknet53/scripts/run_infer_310.sh @@ -0,0 +1,100 @@ +#!/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 || $# -gt 4 ]]; then + echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID] + DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive + DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero" +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} +model=$(get_real_path $1) +data_path=$(get_real_path $2) +anno_path=$(get_real_path $3) + +device_id=0 +if [ $# == 4 ]; then + device_id=$4 +fi + +echo "mindir name: "$model +echo "dataset path: "$data_path +echo "annotation path: "$anno_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 --aipp_path=../ascend310_infer/aipp.cfg &> infer.log + +} + +function cal_acc() +{ + python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --annFile=$anno_path &> 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 \ No newline at end of file diff --git a/model_zoo/official/cv/yolov3_resnet18/README.md b/model_zoo/official/cv/yolov3_resnet18/README.md index a8e7320f2a..17116d034a 100644 --- a/model_zoo/official/cv/yolov3_resnet18/README.md +++ b/model_zoo/official/cv/yolov3_resnet18/README.md @@ -12,6 +12,8 @@ - [Training](#training) - [Evaluation Process](#evaluation-process) - [Evaluation](#evaluation) + - [Export MindIR](#export-mindir) + - [Inference Process](#inference-process) - [Model Description](#model-description) - [Performance](#performance) - [Evaluation Performance](#evaluation-performance) @@ -193,6 +195,40 @@ You will get the precision and recall value of each class: Note the precision and recall values are results of two-classification(person and face) used our own annotations with coco2017. +## [Export MindIR](#contents) + +Currently, batchsize can only set to 1. + +```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"] + +## [Inference Process](#contents) + +### Usage + +Before performing inference, the mindir file must be exported by export.py. +Current batch_Size can only be set to 1. Images to be processed needs to be copied to the to-be-processed folder based on the annotation file. + +```shell +# Ascend310 inference +bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID] +``` + +`DEVICE_ID` is optional, default value is 0. + +### result + +Inference result is saved in current path, you can find result in acc.log file. + + ```bash + class 0 precision is 88.18%, recall is 66.00% + class 1 precision is 85.34%, recall is 79.13% + ``` + # [Model Description](#contents) ## [Performance](#contents) diff --git a/model_zoo/official/cv/yolov3_resnet18/README_CN.md b/model_zoo/official/cv/yolov3_resnet18/README_CN.md index 91b9ae9fd4..3af48ba4ea 100644 --- a/model_zoo/official/cv/yolov3_resnet18/README_CN.md +++ b/model_zoo/official/cv/yolov3_resnet18/README_CN.md @@ -15,6 +15,10 @@ - [Ascend上训练](#ascend上训练) - [评估过程](#评估过程) - [Ascend评估](#ascend评估) + - [导出mindir模型](#导出mindir模型) + - [推理过程](#推理过程) + - [用法](#用法-2) + - [结果](#结果-2) - [模型描述](#模型描述) - [性能](#性能) - [评估性能](#评估性能) @@ -194,6 +198,37 @@ YOLOv3整体网络架构如下： 注意精度和召回值是使用我们自己的标注和COCO 2017的两种分类（人与脸）的结果。 +## 导出mindir模型 + +```shell +python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] +``` + +参数`ckpt_file` 是必需的，`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。 + +## 推理过程 + +### 用法 + +在执行推理之前，需要通过export.py导出mindir文件。 +目前仅可处理batch_Size为1，且图片需要根据关联的标签文件导出至待处理文件夹。 + +```shell +# Ascend310 推理 +bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID] +``` + +`DEVICE_ID` 可选，默认值为 0。 + +### 结果 + +推理结果保存在当前路径，可在acc.log中看到最终精度结果。 + + ```bash + class 0 precision is 88.18%, recall is 66.00% + class 1 precision is 85.34%, recall is 79.13% + ``` + # 模型描述 ## 性能 diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/CMakeLists.txt b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/CMakeLists.txt new file mode 100644 index 0000000000..ee3c854473 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/CMakeLists.txt @@ -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) diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/build.sh b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/build.sh new file mode 100644 index 0000000000..285514e19f --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/build.sh @@ -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 diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/inc/utils.h b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/inc/utils.h new file mode 100644 index 0000000000..efebe03a8c --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/inc/utils.h @@ -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 +#include +#include +#include +#include +#include "include/api/types.h" + +std::vector 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 &outputs); +#endif diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/src/main.cc b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/src/main.cc new file mode 100644 index 0000000000..2b0c72775e --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/src/main.cc @@ -0,0 +1,201 @@ +/** + * 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 +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "include/api/model.h" +#include "include/api/context.h" +#include "include/api/types.h" +#include "include/api/serialization.h" +#include "include/minddata/dataset/include/execute.h" +#include "include/minddata/dataset/include/vision.h" +#include "inc/utils.h" + +using mindspore::Context; +using mindspore::Serialization; +using mindspore::Model; +using mindspore::Status; +using mindspore::ModelType; +using mindspore::GraphCell; +using mindspore::kSuccess; +using mindspore::MSTensor; +using mindspore::DataType; +using mindspore::dataset::Execute; +using mindspore::dataset::TensorTransform; +using mindspore::dataset::vision::Resize; +using mindspore::dataset::vision::Pad; +using mindspore::dataset::vision::HWC2CHW; +using mindspore::dataset::vision::Normalize; +using mindspore::dataset::vision::Decode; +using mindspore::dataset::InterpolationMode; + +DEFINE_string(mindir_path, "", "mindir path"); +DEFINE_string(dataset_path, ".", "dataset path"); +DEFINE_int32(device_id, 0, "device id"); + +int PadImage(const MSTensor &input, MSTensor *output) { + std::shared_ptr normalize(new Normalize({0, 0, 0}, + {255, 255, 255})); + Execute composeNormalize({normalize}); + std::vector shape = input.Shape(); + auto imgResize = MSTensor(); + auto imgPad = MSTensor(); + const int IMAGEWIDTH = 352; + const int IMAGEHEIGHT = 640; + float widthScale, heightScale; + widthScale = static_cast(IMAGEWIDTH) / shape[0]; + heightScale = static_cast(IMAGEHEIGHT) / shape[1]; + int widthSize, heightSize; + if (widthScale < heightScale) { + widthSize = shape[0]*widthScale; + heightSize = shape[1]*widthScale; + } else { + widthSize = shape[0]*heightScale; + heightSize = shape[1]*heightScale; + } + std::shared_ptr resize(new Resize({widthSize, heightSize}, InterpolationMode::kArea)); + Execute composeResize({resize}); + Status ret = composeResize(input, &imgResize); + if (ret != kSuccess) { + std::cout << "ERROR: Resize failed." << std::endl; + return 1; + } + int padH = IMAGEHEIGHT - heightSize; + int padW = IMAGEWIDTH - widthSize; + int padHH = padH / 2; + int padWH = padW / 2; + std::shared_ptr pad(new Pad({padHH, padWH, (padH - padHH), (padW - padWH)}, {128})); + Execute composePad({pad}); + ret = composePad(imgResize, &imgPad); + if (ret != kSuccess) { + std::cout << "ERROR: Pad failed." << std::endl; + return 1; + } + ret = composeNormalize(imgPad, output); + if (ret != kSuccess) { + std::cout << "ERROR: Normalize failed." << std::endl; + return 1; + } + return 0; +} + +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(); + auto ascend310 = std::make_shared(); + 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 model_inputs = model.GetInputs(); + if (model_inputs.empty()) { + std::cout << "Invalid model, inputs is empty." << std::endl; + return 1; + } + auto all_files = GetAllFiles(FLAGS_dataset_path); + if (all_files.empty()) { + std::cout << "ERROR: no input data." << std::endl; + return 1; + } + + std::map costTime_map; + size_t size = all_files.size(); + std::shared_ptr decode(new Decode()); + Execute composeDecode({decode}); + std::shared_ptr hwc2chw(new HWC2CHW()); + Execute composeTranspose({hwc2chw}); + + for (size_t i = 0; i < size; ++i) { + struct timeval start = {0}; + struct timeval end = {0}; + double startTimeMs; + double endTimeMs; + std::vector inputs; + std::vector outputs; + std::cout << "Start predict input files:" << all_files[i] << std::endl; + auto imgDecode = MSTensor(); + auto image = ReadFileToTensor(all_files[i]); + ret = composeDecode(image, &imgDecode); + if (ret != kSuccess) { + std::cout << "ERROR: Decode failed." << std::endl; + return 1; + } + auto imgPad = MSTensor(); + PadImage(imgDecode, &imgPad); + auto img = MSTensor(); + composeTranspose(imgPad, &img); + float imgInfo[2]; + imgInfo[0] = imgDecode.Shape()[0]; + imgInfo[1] = imgDecode.Shape()[1]; + MSTensor imgShape("imgShape", DataType::kNumberTypeFloat32, std::vector{1, 2}, imgInfo, 8); + + inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(), + img.Data().get(), img.DataSize()); + inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(), + imgShape.Data().get(), imgShape.DataSize()); + + gettimeofday(&start, nullptr); + ret = model.Predict(inputs, &outputs); + gettimeofday(&end, nullptr); + if (ret != kSuccess) { + std::cout << "Predict " << all_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(startTimeMs, endTimeMs)); + WriteResult(all_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; +} diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/src/utils.cc b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/src/utils.cc new file mode 100644 index 0000000000..c947e4d5f4 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_infer/src/utils.cc @@ -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 +#include +#include +#include "inc/utils.h" + +using mindspore::MSTensor; +using mindspore::DataType; + +std::vector GetAllFiles(std::string_view dirName) { + struct dirent *filename; + DIR *dir = OpenDir(dirName); + if (dir == nullptr) { + return {}; + } + std::vector 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 &outputs) { + std::string homePath = "./result_Files"; + for (size_t i = 0; i < outputs.size(); ++i) { + size_t outputSize; + std::shared_ptr 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(size)}, nullptr, size); + + ifs.seekg(0, std::ios::beg); + ifs.read(reinterpret_cast(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; +} diff --git a/model_zoo/official/cv/yolov3_resnet18/export.py b/model_zoo/official/cv/yolov3_resnet18/export.py index 6f402a77b4..abe0c71cfa 100644 --- a/model_zoo/official/cv/yolov3_resnet18/export.py +++ b/model_zoo/official/cv/yolov3_resnet18/export.py @@ -19,7 +19,7 @@ import mindspore as ms from mindspore import context, Tensor from mindspore.train.serialization import export, load_checkpoint, load_param_into_net -from src.yolov3 import yolov3_resnet18 +from src.yolov3 import yolov3_resnet18, YoloWithEval from src.config import ConfigYOLOV3ResNet18 parser = argparse.ArgumentParser(description='yolov3_resnet18 export') @@ -38,14 +38,17 @@ if args.device_target == "Ascend": if __name__ == "__main__": config = ConfigYOLOV3ResNet18() - network = yolov3_resnet18(config) + net = yolov3_resnet18(config) + eval_net = YoloWithEval(net, config) param_dict = load_checkpoint(args.ckpt_file) - load_param_into_net(network, param_dict) + load_param_into_net(eval_net, param_dict) - network.set_train(False) + eval_net.set_train(False) shape = [args.batch_size, 3] + config.img_shape input_data = Tensor(np.zeros(shape), ms.float32) + input_shape = Tensor(np.zeros([1, 2]), ms.float32) + inputs = (input_data, input_shape) - export(network, input_data, file_name=args.file_name, file_format=args.file_format) + export(eval_net, *inputs, file_name=args.file_name, file_format=args.file_format) diff --git a/model_zoo/official/cv/yolov3_resnet18/postprocess.py b/model_zoo/official/cv/yolov3_resnet18/postprocess.py new file mode 100644 index 0000000000..b8ec48a12f --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/postprocess.py @@ -0,0 +1,60 @@ +# 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 for yolov3-resnet18""" +import os +import argparse +import numpy as np +from src.config import ConfigYOLOV3ResNet18 +from src.utils import metrics + +parser = argparse.ArgumentParser(description='Yolov3 postprocess') +parser.add_argument("--batchsize", type=int, default=1, help="batchsize.") +parser.add_argument("--anno_path", type=str, required=True, help="Annotation path.") +parser.add_argument("--result_path", type=str, required=True, help="result files path.") +args = parser.parse_args() + +if __name__ == '__main__': + config = ConfigYOLOV3ResNet18() + batchsize = args.batchsize + + anno_dict = {} + for line in open(args.anno_path): + line_list = line.split(' ') + line_list[0] = line_list[0].split('/')[-1] + anno_dict[line_list[0]] = line_list[1:] + + pred_data = [] + for key in anno_dict: + result0 = os.path.join(args.result_path, key.split('.')[0] + '_0.bin') + result1 = os.path.join(args.result_path, key.split('.')[0] + '_1.bin') + output0 = np.fromfile(result0, np.float32).reshape(batchsize, 13860, 4) + output1 = np.fromfile(result1, np.float32).reshape(batchsize, 13860, 2) + + anno_list = [] + for v in anno_dict[key]: + v_list = v.split(',') + anno_list.append(v_list) + annotation = np.array(anno_list, np.int64) + + for batch_idx in range(batchsize): + pred_data.append({"boxes": output0[batch_idx], + "box_scores": output1[batch_idx], + "annotation": annotation}) + + precisions, recalls = metrics(pred_data) + print("\n========================================\n") + for i in range(config.num_classes): + print("class {} precision is {:.2f}%, recall is {:.2f}%".format(i, precisions[i] * 100, recalls[i] * 100)) diff --git a/model_zoo/official/cv/yolov3_resnet18/scripts/run_infer_310.sh b/model_zoo/official/cv/yolov3_resnet18/scripts/run_infer_310.sh new file mode 100644 index 0000000000..14f406f228 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/scripts/run_infer_310.sh @@ -0,0 +1,98 @@ +#!/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 || $# -gt 4 ]]; then + echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID] + DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero" +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} +model=$(get_real_path $1) +data_path=$(get_real_path $2) +anno_path=$(get_real_path $3) + +device_id=0 +if [ $# == 4 ]; then + device_id=$4 +fi + +echo "mindir name: "$model +echo "dataset path: "$data_path +echo "annotation path: "$anno_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() +{ + python3.7 ../postprocess.py --result_path=./result_Files --anno_path=$anno_path &> 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 \ No newline at end of file