diff --git a/model_zoo/official/cv/resnet/README.md b/model_zoo/official/cv/resnet/README.md index c5580d25bda..c2978ac5684 100644 --- a/model_zoo/official/cv/resnet/README.md +++ b/model_zoo/official/cv/resnet/README.md @@ -521,9 +521,10 @@ Current batch_Size can only be set to 1. The precision calculation process needs ```shell # Ascend310 inference -bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] +bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATA_PATH] [DEVICE_ID] ``` +- `NET_TYPE` can choose from [resnet18, se-resnet50]. - `DEVICE_ID` is optional, default value is 0. ### result diff --git a/model_zoo/official/cv/resnet/README_CN.md b/model_zoo/official/cv/resnet/README_CN.md index fcc68e1b0e2..61c0f42036a 100755 --- a/model_zoo/official/cv/resnet/README_CN.md +++ b/model_zoo/official/cv/resnet/README_CN.md @@ -487,9 +487,10 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [ ```shell # Ascend310 inference -bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] +bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATA_PATH] [DEVICE_ID] ``` +- `NET_TYPE` 选择范围：[resnet18, se-resnet50]。 - `DEVICE_ID` 可选，默认值为0。 ### 结果 diff --git a/model_zoo/official/cv/resnet/ascend310_infer/src/main.cc b/model_zoo/official/cv/resnet/ascend310_infer/src/main.cc index 8966f10c502..5b219766e66 100644 --- a/model_zoo/official/cv/resnet/ascend310_infer/src/main.cc +++ b/model_zoo/official/cv/resnet/ascend310_infer/src/main.cc @@ -53,6 +53,7 @@ using mindspore::dataset::Execute; DEFINE_string(mindir_path, "", "mindir path"); DEFINE_string(dataset_path, ".", "dataset path"); +DEFINE_string(network, "resnet18", "networktype"); DEFINE_int32(device_id, 0, "device id"); int main(int argc, char **argv) { @@ -83,19 +84,26 @@ int main(int argc, char **argv) { std::map costTime_map; size_t size = all_files.size(); - // Define transform - std::vector crop_paras = {224}; - std::vector resize_paras = {256}; - std::vector mean = {0.485 * 255, 0.456 * 255, 0.406 * 255}; - std::vector std = {0.229 * 255, 0.224 * 255, 0.225 * 255}; std::shared_ptr decode(new Decode()); - std::shared_ptr resize(new Resize(resize_paras)); - std::shared_ptr centercrop(new CenterCrop(crop_paras)); - std::shared_ptr normalize(new Normalize(mean, std)); + std::shared_ptr resize(new Resize({256})); + std::shared_ptr centercrop(new CenterCrop({224})); + std::shared_ptr normalize(new Normalize({123.675, 116.28, 103.53}, + {58.395, 57.12, 57.375})); std::shared_ptr hwc2chw(new HWC2CHW()); - std::vector> trans_list = {decode, resize, centercrop, normalize, hwc2chw}; + std::shared_ptr sr_resize(new Resize({292})); + std::shared_ptr sr_centercrop(new CenterCrop({256})); + std::shared_ptr sr_normalize(new Normalize({123.68, 116.78, 103.94}, + {1.0, 1.0, 1.0})); + + std::vector> trans_list; + + if (FLAGS_network == "se-resnet50") { + trans_list = {decode, sr_resize, sr_centercrop, sr_normalize, hwc2chw}; + } else { + trans_list = {decode, resize, centercrop, normalize, hwc2chw}; + } mindspore::dataset::Execute SingleOp(trans_list); for (size_t i = 0; i < size; ++i) { diff --git a/model_zoo/official/cv/resnet/scripts/run_infer_310.sh b/model_zoo/official/cv/resnet/scripts/run_infer_310.sh index c5586cd7fd4..31cd75f788f 100644 --- a/model_zoo/official/cv/resnet/scripts/run_infer_310.sh +++ b/model_zoo/official/cv/resnet/scripts/run_infer_310.sh @@ -14,8 +14,9 @@ # limitations under the License. # ============================================================================ -if [[ $# -lt 2 || $# -gt 3 ]]; then - echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] +if [[ $# -lt 3 || $# -gt 4 ]]; then + echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATA_PATH] [DEVICE_ID] + NET_TYPE can choose from [resnet18, se-resnet50] DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero" exit 1 fi @@ -28,15 +29,23 @@ get_real_path(){ fi } model=$(get_real_path $1) -data_path=$(get_real_path $2) +if [ $2 == 'resnet18' ] || [ $2 == 'se-resnet50' ]; then + network=$2 +else + echo "NET_TYPE can choose from [resnet18, se-resnet50]" + exit 1 +fi + +data_path=$(get_real_path $3) device_id=0 -if [ $# == 3 ]; then - device_id=$3 +if [ $# == 4 ]; then + device_id=$4 fi echo "mindir name: "$model echo "dataset path: "$data_path +echo "network: "$network echo "device id: "$device_id export ASCEND_HOME=/usr/local/Ascend/ @@ -59,7 +68,7 @@ function compile_app() if [ -f "Makefile" ]; then make clean fi - sh build.sh &> build.log + bash build.sh &> build.log } function infer() @@ -73,7 +82,7 @@ function infer() fi mkdir result_Files mkdir time_Result - ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log + ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --network=$network --device_id=$device_id &> infer.log } function cal_acc() diff --git a/model_zoo/official/cv/resnext50/README.md b/model_zoo/official/cv/resnext50/README.md index 1ad66f7ffa4..283e1585bfc 100644 --- a/model_zoo/official/cv/resnext50/README.md +++ b/model_zoo/official/cv/resnext50/README.md @@ -13,6 +13,7 @@ - [Training Process](#training-process) - [Evaluation Process](#evaluation-process) - [Model Export](#model-export) + - [Inference Process](#inference-process) - [Model Description](#model-description) - [Performance](#performance) - [Training Performance](#evaluation-performance) @@ -212,7 +213,29 @@ acc=93.88%(TOP5) python export.py --device_target [PLATFORM] --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT] ``` -`EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"] +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. Currently, only batchsize 1 is supported. + +```shell +# Ascend310 inference +bash run_infer_310.sh [MINDIR_PATH] [DATA_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. + +```log +Total data:50000, top1 accuracy:0.78462, top5 accuracy:0.94182 +``` # [Model description](#contents) diff --git a/model_zoo/official/cv/resnext50/README_CN.md b/model_zoo/official/cv/resnext50/README_CN.md index 69f71bba6c7..c9bb681dcf6 100644 --- a/model_zoo/official/cv/resnext50/README_CN.md +++ b/model_zoo/official/cv/resnext50/README_CN.md @@ -18,6 +18,9 @@ - [样例](#样例-1) - [结果](#结果) - [模型导出](#模型导出) + - [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) - [训练性能](#训练性能) @@ -216,7 +219,30 @@ acc=93.88%(TOP5) python export.py --device_target [PLATFORM] --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT] ``` -`EXPORT_FORMAT` 可选 ["AIR", "ONNX", "MINDIR"]. +`ckpt_file` 参数为必填项。 +`EXPORT_FORMAT` 可选 ["AIR", "MINDIR"]。 + +## [推理过程](#contents) + +### 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。 + +```shell +#Ascend310 推理 +bash run_infer_310.sh [MINDIR_PATH] [DATA_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。 + +```log +Total data:50000, top1 accuracy:0.78462, top5 accuracy:0.94182 +``` # 模型描述 diff --git a/model_zoo/official/cv/resnext50/ascend310_infer/inc/utils.h b/model_zoo/official/cv/resnext50/ascend310_infer/inc/utils.h new file mode 100644 index 00000000000..f8ae1e5b473 --- /dev/null +++ b/model_zoo/official/cv/resnext50/ascend310_infer/inc/utils.h @@ -0,0 +1,35 @@ +/** + * 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); +std::vector GetAllFiles(std::string dir_name); +std::vector> GetAllInputData(std::string dir_name); + +#endif diff --git a/model_zoo/official/cv/resnext50/ascend310_infer/src/CMakeLists.txt b/model_zoo/official/cv/resnext50/ascend310_infer/src/CMakeLists.txt new file mode 100644 index 00000000000..0397995b0e0 --- /dev/null +++ b/model_zoo/official/cv/resnext50/ascend310_infer/src/CMakeLists.txt @@ -0,0 +1,14 @@ +cmake_minimum_required(VERSION 3.14.1) +project(MindSporeCxxTestcase[CXX]) +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 main.cc utils.cc) +target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags) diff --git a/model_zoo/official/cv/resnext50/ascend310_infer/src/build.sh b/model_zoo/official/cv/resnext50/ascend310_infer/src/build.sh new file mode 100644 index 00000000000..7fac9cff3a9 --- /dev/null +++ b/model_zoo/official/cv/resnext50/ascend310_infer/src/build.sh @@ -0,0 +1,18 @@ +#!/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. +# ============================================================================ + +cmake . -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`" +make \ No newline at end of file diff --git a/model_zoo/official/cv/resnext50/ascend310_infer/src/main.cc b/model_zoo/official/cv/resnext50/ascend310_infer/src/main.cc new file mode 100644 index 00000000000..988923b66a6 --- /dev/null +++ b/model_zoo/official/cv/resnext50/ascend310_infer/src/main.cc @@ -0,0 +1,145 @@ +/** + * 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/dataset/vision_ascend.h" +#include "include/dataset/execute.h" +#include "include/dataset/transforms.h" +#include "include/dataset/vision.h" +#include "inc/utils.h" + +using mindspore::dataset::vision::Decode; +using mindspore::dataset::vision::Resize; +using mindspore::dataset::vision::CenterCrop; +using mindspore::dataset::vision::Normalize; +using mindspore::dataset::vision::HWC2CHW; +using mindspore::dataset::TensorTransform; +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::dataset::Execute; + + +DEFINE_string(mindir_path, "", "mindir path"); +DEFINE_string(dataset_path, ".", "dataset path"); +DEFINE_int32(device_id, 0, "device id"); + +int main(int argc, char **argv) { + gflags::ParseCommandLineFlags(&argc, &argv, true); + if (RealPath(FLAGS_mindir_path).empty()) { + std::cout << "Invalid mindir" << std::endl; + return 1; + } + + auto context = std::make_shared(); + 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; + } + + auto all_files = GetAllInputData(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()); + std::shared_ptr resize(new Resize({256})); + std::shared_ptr centercrop(new CenterCrop({224})); + std::shared_ptr normalize(new Normalize({123.675, 116.28, 103.53}, + {58.395, 57.12, 57.375})); + std::shared_ptr hwc2chw(new HWC2CHW()); + + std::vector> trans_list; + trans_list = {decode, resize, centercrop, normalize, hwc2chw}; + + mindspore::dataset::Execute SingleOp(trans_list); + + for (size_t i = 0; i < size; ++i) { + for (size_t j = 0; j < all_files[i].size(); ++j) { + 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][j] <(); + SingleOp(ReadFileToTensor(all_files[i][j]), 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][j] << " 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][j], 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/resnext50/ascend310_infer/src/utils.cc b/model_zoo/official/cv/resnext50/ascend310_infer/src/utils.cc new file mode 100644 index 00000000000..d71f388b83d --- /dev/null +++ b/model_zoo/official/cv/resnext50/ascend310_infer/src/utils.cc @@ -0,0 +1,185 @@ +/** + * 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> GetAllInputData(std::string dir_name) { + std::vector> ret; + + DIR *dir = OpenDir(dir_name); + if (dir == nullptr) { + return {}; + } + struct dirent *filename; + /* read all the files in the dir ~ */ + std::vector sub_dirs; + while ((filename = readdir(dir)) != nullptr) { + std::string d_name = std::string(filename->d_name); + // get rid of "." and ".." + if (d_name == "." || d_name == ".." || d_name.empty()) { + continue; + } + std::string dir_path = RealPath(std::string(dir_name) + "/" + filename->d_name); + struct stat s; + lstat(dir_path.c_str(), &s); + if (!S_ISDIR(s.st_mode)) { + continue; + } + + sub_dirs.emplace_back(dir_path); + } + std::sort(sub_dirs.begin(), sub_dirs.end()); + + (void)std::transform(sub_dirs.begin(), sub_dirs.end(), std::back_inserter(ret), + [](const std::string &d) { return GetAllFiles(d); }); + + return ret; +} + + +std::vector GetAllFiles(std::string dir_name) { + struct dirent *filename; + DIR *dir = OpenDir(dir_name); + if (dir == nullptr) { + return {}; + } + + std::vector res; + while ((filename = readdir(dir)) != nullptr) { + std::string d_name = std::string(filename->d_name); + if (d_name == "." || d_name == ".." || d_name.size() <= 3) { + continue; + } + res.emplace_back(std::string(dir_name) + "/" + filename->d_name); + } + std::sort(res.begin(), res.end()); + + return res; +} + + +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/resnext50/create_imagenet2012_label.py b/model_zoo/official/cv/resnext50/create_imagenet2012_label.py new file mode 100644 index 00000000000..38f6ee94284 --- /dev/null +++ b/model_zoo/official/cv/resnext50/create_imagenet2012_label.py @@ -0,0 +1,48 @@ +# 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 +# +# less 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. +# ============================================================================ +"""create_imagenet2012_label""" +import os +import json +import argparse + +parser = argparse.ArgumentParser(description="resnet imagenet2012 label") +parser.add_argument("--img_path", type=str, required=True, help="imagenet2012 file path.") +args = parser.parse_args() + + +def create_label(file_path): + print("[WARNING] Create imagenet label. Currently only use for Imagenet2012!") + dirs = os.listdir(file_path) + file_list = [] + for file in dirs: + file_list.append(file) + file_list = sorted(file_list) + + total = 0 + img_label = {} + for i, file_dir in enumerate(file_list): + files = os.listdir(os.path.join(file_path, file_dir)) + for f in files: + img_label[f] = i + total += len(files) + + with open("imagenet_label.json", "w+") as label: + json.dump(img_label, label) + + print("[INFO] Completed! Total {} data.".format(total)) + + +if __name__ == '__main__': + create_label(args.img_path) diff --git a/model_zoo/official/cv/resnext50/export.py b/model_zoo/official/cv/resnext50/export.py index 107a7fc0dcf..03b6710f232 100644 --- a/model_zoo/official/cv/resnext50/export.py +++ b/model_zoo/official/cv/resnext50/export.py @@ -1,4 +1,4 @@ -# Copyright 2020 Huawei Technologies Co., Ltd +# 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. @@ -17,6 +17,7 @@ resnext export mindir. """ import argparse import numpy as np +from mindspore.common import dtype as mstype from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export from src.config import config from src.image_classification import get_network @@ -38,10 +39,15 @@ if args.device_target == "Ascend": context.set_context(device_id=args.device_id) if __name__ == '__main__': - net = get_network(num_classes=config.num_classes, platform=args.device_target) + network = get_network(num_classes=config.num_classes, platform=args.device_target) param_dict = load_checkpoint(args.ckpt_file) - load_param_into_net(net, param_dict) + load_param_into_net(network, param_dict) + if args.device_target == "Ascend": + network.to_float(mstype.float16) + else: + auto_mixed_precision(network) + network.set_train(False) input_shp = [args.batch_size, 3, args.height, args.width] input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32)) - export(net, input_array, file_name=args.file_name, file_format=args.file_format) + export(network, input_array, file_name=args.file_name, file_format=args.file_format) diff --git a/model_zoo/official/cv/resnext50/postprocess.py b/model_zoo/official/cv/resnext50/postprocess.py new file mode 100644 index 00000000000..ce89cdcabf2 --- /dev/null +++ b/model_zoo/official/cv/resnext50/postprocess.py @@ -0,0 +1,51 @@ +# 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 +# +# less 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. +# ============================================================================ +"""post process for 310 inference""" +import os +import json +import argparse +import numpy as np +from src.config import config + +batch_size = 1 +parser = argparse.ArgumentParser(description="resnet inference") +parser.add_argument("--result_path", type=str, required=True, help="result files path.") +parser.add_argument("--label_path", type=str, required=True, help="image file path.") +args = parser.parse_args() + + +def get_result(result_path, label_path): + files = os.listdir(result_path) + with open(label_path, "r") as label: + labels = json.load(label) + + top1 = 0 + top5 = 0 + total_data = len(files) + for file in files: + img_ids_name = file.split('_0.')[0] + data_path = os.path.join(result_path, img_ids_name + "_0.bin") + result = np.fromfile(data_path, dtype=np.float16).reshape(batch_size, config.num_classes) + for batch in range(batch_size): + predict = np.argsort(-result[batch], axis=-1) + if labels[img_ids_name+".JPEG"] == predict[0]: + top1 += 1 + if labels[img_ids_name+".JPEG"] in predict[:5]: + top5 += 1 + print(f"Total data: {total_data}, top1 accuracy: {top1/total_data}, top5 accuracy: {top5/total_data}.") + + +if __name__ == '__main__': + get_result(args.result_path, args.label_path) diff --git a/model_zoo/official/cv/resnext50/scripts/run_infer_310.sh b/model_zoo/official/cv/resnext50/scripts/run_infer_310.sh new file mode 100644 index 00000000000..1cfe281313a --- /dev/null +++ b/model_zoo/official/cv/resnext50/scripts/run_infer_310.sh @@ -0,0 +1,99 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +if [[ $# -lt 2 || $# -gt 3 ]]; then + echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] + DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero" +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} +model=$(get_real_path $1) +data_path=$(get_real_path $2) + +device_id=0 +if [ $# == 3 ]; then + device_id=$3 +fi + +echo "mindir name: "$model +echo "dataset path: "$data_path +echo "device id: "$device_id + +export ASCEND_HOME=/usr/local/Ascend/ +if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then + export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH + export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe + export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH + export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp +else + export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH + export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH + export ASCEND_OPP_PATH=$ASCEND_HOME/opp +fi + +function compile_app() +{ + cd ../ascend310_infer/src/ || exit + if [ -f "Makefile" ]; then + make clean + fi + bash build.sh &> build.log +} + +function infer() +{ + cd - || exit + if [ -d result_Files ]; then + rm -rf ./result_Files + fi + if [ -d time_Result ]; then + rm -rf ./time_Result + fi + mkdir result_Files + mkdir time_Result + ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log +} + +function cal_acc() +{ + python3.7 ../create_imagenet2012_label.py --img_path=$data_path + python3.7 ../postprocess.py --result_path=./result_Files --label_path=./imagenet_label.json &> 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