diff --git a/official/cv/lenet/ascend310_infer/src/main.cc b/official/cv/lenet/ascend310_infer/src/main.cc index ae23279ccd4e8f948fd108df0bc65676e72330da..d856e39f472426d3b4deb9bb7f5dfe995b53f8af 100644 --- a/official/cv/lenet/ascend310_infer/src/main.cc +++ b/official/cv/lenet/ascend310_infer/src/main.cc @@ -31,6 +31,8 @@ #include "include/dataset/vision_ascend.h" #include "include/dataset/execute.h" #include "include/dataset/vision.h" +#include "include/dataset/vision_lite.h" + #include "inc/utils.h" using mindspore::Context; @@ -47,6 +49,8 @@ using mindspore::dataset::vision::Resize; using mindspore::dataset::vision::HWC2CHW; using mindspore::dataset::vision::Normalize; using mindspore::dataset::vision::Decode; +using mindspore::dataset::vision::Rescale; +using mindspore::dataset::vision::RGB2GRAY; DEFINE_string(mindir_path, "", "mindir path"); DEFINE_string(dataset_path, ".", "dataset path"); @@ -108,15 +112,14 @@ int main(int argc, char **argv) { inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(), imgDvpp->Data().get(), imgDvpp->DataSize()); } else { - std::shared_ptr<TensorTransform> decode(new Decode()); - std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW()); - std::shared_ptr<TensorTransform> normalize( - new Normalize({123.675, 116.28, 103.53}, {58.395, 57.120, 57.375})); + auto decode = Decode(); + auto hwc2chw = HWC2CHW(); + auto togray = RGB2GRAY(); + auto rescale_op1 = Rescale(1/255.0, 0.0); + auto rescale_op2 = Rescale(1/ 0.3081, -1 * 0.1307 / 0.3081); auto resizeShape = {FLAGS_image_height, FLAGS_image_width}; - std::shared_ptr<TensorTransform> resize(new Resize(resizeShape)); - auto resizeShape1 = {1, FLAGS_image_height}; - std::shared_ptr<TensorTransform> reshape_one_channel(new Resize(resizeShape1)); - Execute composeDecode({decode, resize, normalize, hwc2chw, reshape_one_channel}); + auto resize = Resize(resizeShape); + Execute composeDecode({decode, togray, resize, rescale_op1, rescale_op2, hwc2chw}); auto img = MSTensor(); auto image = ReadFileToTensor(all_files[i]); composeDecode(image, &img); diff --git a/research/cv/RefineNet/README.md b/research/cv/RefineNet/README.md index 4a83b074ec735e286baa20a0b7ef6956c359c44c..e9235bb6f6db9e281875cc4a7b7650a8ae5d3dfa 100644 --- a/research/cv/RefineNet/README.md +++ b/research/cv/RefineNet/README.md @@ -23,6 +23,10 @@ - [Ascend澶勭悊鍣ㄧ幆澧冭繍琛宂(#ascend澶勭悊鍣ㄧ幆澧冭繍琛�-1) - [缁撴灉](#缁撴灉-1) - [璁粌鍑嗙‘鐜嘳(#璁粌鍑嗙‘鐜�) + - [Mindir鎺ㄧ悊](#Mindir鎺ㄧ悊) + - [瀵煎嚭妯″瀷](#瀵煎嚭妯″瀷) + - [鍦ˋscend310鎵ц鎺ㄧ悊](#鍦ˋscend310鎵ц鎺ㄧ悊) + - [缁撴灉](#缁撴灉) - [妯″瀷鎻忚堪](#妯″瀷鎻忚堪) - [鎬ц兘](#鎬ц兘) - [璇勪及鎬ц兘](#璇勪及鎬ц兘) @@ -347,6 +351,35 @@ cd .. | :----------: | :-----: | :-------------: | | refinenet | 80.3 | 80.3 | +## Mindir鎺ㄧ悊 + +### [瀵煎嚭妯″瀷](#contents) + +```shell +python export.py --checkpoint [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] +``` + +- 鍙傛暟`checkpoint`涓哄繀濉」銆� +- `file_format` 蹇呴』鍦� ["AIR", "MINDIR"]涓€夋嫨銆� + +### 鍦ˋscend310鎵ц鎺ㄧ悊 + +鍦ㄦ墽琛屾帹鐞嗗墠锛宮indir鏂囦欢蹇呴』閫氳繃`export.py`鑴氭湰瀵煎嚭銆備互涓嬪睍绀轰簡浣跨敤mindir妯″瀷鎵ц鎺ㄧ悊鐨勭ず渚嬨€� +鐩墠浠呮敮鎸乥atch_size涓�1鐨勬帹鐞嗐€� + +```shell +# Ascend310 inference +bash run_infer_310.sh [MINDIR_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID] +``` + +- `DATA_ROOT` 琛ㄧず杩涘叆妯″瀷鎺ㄧ悊鏁版嵁闆嗙殑鏍圭洰褰曘€� +- `DATA_LIST` 琛ㄧず杩涘叆妯″瀷鎺ㄧ悊鏁版嵁闆嗙殑鏂囦欢鍒楄〃銆� +- `DEVICE_ID` 鍙€夛紝榛樿鍊间负0銆� + +### 缁撴灉 + +鎺ㄧ悊缁撴灉淇濆瓨鍦ㄨ剼鏈墽琛岀殑褰撳墠璺緞锛屼綘鍙互鍦╝cc.log涓湅鍒颁互涓嬬簿搴﹁绠楃粨鏋溿€� + # 妯″瀷鎻忚堪 ## 鎬ц兘 diff --git a/research/cv/RefineNet/ascend310_infer/CMakeLists.txt b/research/cv/RefineNet/ascend310_infer/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..671784a2aa030b419c494501dedb5fb27ed5f24e --- /dev/null +++ b/research/cv/RefineNet/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) \ No newline at end of file diff --git a/research/cv/RefineNet/ascend310_infer/build.sh b/research/cv/RefineNet/ascend310_infer/build.sh new file mode 100644 index 0000000000000000000000000000000000000000..c7aac7196661fcfa50c3de063336355bf3831286 --- /dev/null +++ b/research/cv/RefineNet/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="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`" +make \ No newline at end of file diff --git a/research/cv/RefineNet/ascend310_infer/inc/utils.h b/research/cv/RefineNet/ascend310_infer/inc/utils.h new file mode 100644 index 0000000000000000000000000000000000000000..8f14820084b4982caf7acfd85f6bb69e1c83b098 --- /dev/null +++ b/research/cv/RefineNet/ascend310_infer/inc/utils.h @@ -0,0 +1,34 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_INFERENCE_UTILS_H_ +#define MINDSPORE_INFERENCE_UTILS_H_ + +#include <sys/stat.h> +#include <dirent.h> +#include <vector> +#include <string> +#include <memory> +#include "include/api/types.h" + +std::vector<std::string> GetAllFiles(std::string_view dirName); +std::vector<std::string> GetImagesById(const std::string &idFile, const std::string &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::string& segstr, + const std::vector<mindspore::MSTensor> &outputs); +#endif diff --git a/research/cv/RefineNet/ascend310_infer/src/main.cc b/research/cv/RefineNet/ascend310_infer/src/main.cc new file mode 100644 index 0000000000000000000000000000000000000000..8833a7054a58397b0d29989fc0ecabed88d5797f --- /dev/null +++ b/research/cv/RefineNet/ascend310_infer/src/main.cc @@ -0,0 +1,226 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include <sys/time.h> +#include <gflags/gflags.h> +#include <dirent.h> +#include <iostream> +#include <string> +#include <algorithm> +#include <iosfwd> +#include <vector> +#include <fstream> +#include <sstream> + +#include "include/api/context.h" +#include "include/api/model.h" +#include "include/api/types.h" +#include "include/api/serialization.h" +#include "include/dataset/vision.h" +#include "include/dataset/execute.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::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::SwapRedBlue; +using mindspore::dataset::vision::Decode; +using mindspore::dataset::vision::HorizontalFlip; + + + +DEFINE_string(mindir_path, "", "mindir path"); +DEFINE_string(image_list, "", "image list"); +DEFINE_string(dataset_path, ".", "dataset path"); +DEFINE_int32(device_id, 0, "device id"); + + +int PadImage(const MSTensor& input, MSTensor* output) { + std::shared_ptr<TensorTransform> normalize(new Normalize({ 103.53, 116.28, 123.675 }, + { 57.375, 57.120, 58.395 })); + std::shared_ptr<TensorTransform> swapredblue(new SwapRedBlue()); + Execute NormalizeBgr({normalize, swapredblue}); + std::vector<int64_t> shape = input.Shape(); + auto imgResize = MSTensor(); + auto imgNormalizeBgr = MSTensor(); + int paddingSize; + const int IMAGEWIDTH = 513; + const int IMAGEHEIGHT = 513; + float widthScale, heightScale; + widthScale = static_cast<float>(IMAGEWIDTH) / shape[1]; + heightScale = static_cast<float>(IMAGEHEIGHT) / shape[0]; + Status ret; + if (widthScale < heightScale) { + int heightSize = shape[0] * widthScale; + std::shared_ptr<TensorTransform> resize(new Resize({ heightSize, IMAGEWIDTH })); + Execute composeResizeWidth({ resize }); + ret = composeResizeWidth(input, &imgResize); + if (ret != kSuccess) { + std::cout << "ERROR: Resize Width failed." << std::endl; + return 1; + } + ret = NormalizeBgr(imgResize, &imgNormalizeBgr); + if (ret != kSuccess) { + std::cout << "ERROR: Normalize and bgr transfer failed." << std::endl; + return 1; + } + paddingSize = IMAGEHEIGHT - heightSize; + std::shared_ptr<TensorTransform> pad(new Pad({ 0, 0, 0, paddingSize })); + Execute composePad({ pad }); + ret = composePad(imgNormalizeBgr, output); + if (ret != kSuccess) { + std::cout << "ERROR: Height Pad failed." << std::endl; + return 1; + } + } else { + int widthSize = shape[1] * heightScale; + std::shared_ptr<TensorTransform> resize(new Resize({ IMAGEHEIGHT, widthSize })); + Execute composeResizeHeight({ resize }); + ret = composeResizeHeight(input, &imgResize); + + if (ret != kSuccess) { + std::cout << "ERROR: Resize Height failed." << std::endl; + return 1; + } + ret = NormalizeBgr(imgResize, &imgNormalizeBgr); + + if (ret != kSuccess) { + std::cout << "ERROR: Normalize and bgr transfer failed." << std::endl; + return 1; + } + paddingSize = IMAGEWIDTH - widthSize; + std::shared_ptr<TensorTransform> pad(new Pad({ 0, 0, paddingSize, 0 })); + Execute composePad({ pad }); + ret = composePad(imgNormalizeBgr, output); + if (ret != kSuccess) { + std::cout << "ERROR: Width Pad 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<Context>(); + auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>(); + ascend310->SetDeviceID(FLAGS_device_id); + context->MutableDeviceInfo().push_back(ascend310); + mindspore::Graph graph; + Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph); + + Model model; + Status ret = model.Build(GraphCell(graph), context); + if (ret != kSuccess) { + std::cout << "ERROR: Build failed." << std::endl; + return 1; + } + std::vector<MSTensor> model_inputs = model.GetInputs(); + if (model_inputs.empty()) { + std::cout << "Invalid model, inputs is empty." << std::endl; + return 1; + } + + auto all_files = GetImagesById(FLAGS_image_list, FLAGS_dataset_path); + if (all_files.empty()) { + std::cout << "ERROR: no input data." << std::endl; + return 1; + } + + std::map<double, double> costTime_map; + size_t size = all_files.size(); + std::shared_ptr<TensorTransform> decode(new Decode()); + std::shared_ptr<TensorTransform> swapredblue(new SwapRedBlue()); + Execute DecodeBgr({decode, swapredblue}); + Execute hwc2chw(std::make_shared<HWC2CHW>()); + Execute horizonflip(std::make_shared<HorizontalFlip>()); + + for (size_t i = 0; i < size; ++i) { + struct timeval start = { 0 }, end = { 0 }; + double startTimeMs, endTimeMs; + std::vector<MSTensor> inputs, outputs, inputFlip, outputFlip; + std::cout << "Start predict input files:" << all_files[i] << std::endl; + auto image = ReadFileToTensor(all_files[i]); + auto imgDecodeBgr = MSTensor(); + ret = DecodeBgr(image, &imgDecodeBgr); + if (ret != kSuccess) { + std::cout << "ERROR: Decode and RgbToBgr failed." << std::endl; + return 1; + } + + auto imgPad = MSTensor(), img = MSTensor(), imgFlip = MSTensor(), imgtrans = MSTensor(); + PadImage(imgDecodeBgr, &imgPad); + hwc2chw(imgPad, &img); + inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(), + img.Data().get(), img.DataSize()); + horizonflip(imgPad, &imgtrans); + hwc2chw(imgtrans, &imgFlip); + inputFlip.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(), + imgFlip.Data().get(), imgFlip.DataSize()); + gettimeofday(&start, nullptr); + ret = model.Predict(inputs, &outputs); + gettimeofday(&end, nullptr); + auto retFlip = model.Predict(inputFlip, &outputFlip); + if ((ret != kSuccess) || (retFlip != 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<double, double>(startTimeMs, endTimeMs)); + int rst = WriteResult(all_files[i], "", outputs); + int rstFlip = WriteResult(all_files[i], "_Flip", outputFlip); + if ((rst != 0) || (rstFlip != 0)) { + std::cout << "write result failed." << std::endl; + return rst; + } + } + 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/research/cv/RefineNet/ascend310_infer/src/utils.cc b/research/cv/RefineNet/ascend310_infer/src/utils.cc new file mode 100644 index 0000000000000000000000000000000000000000..4685b9162eac127e66186c973382b6d41fc0e4f6 --- /dev/null +++ b/research/cv/RefineNet/ascend310_infer/src/utils.cc @@ -0,0 +1,163 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <fstream> +#include <algorithm> +#include <iostream> +#include "../inc/utils.h" + +using mindspore::MSTensor; +using mindspore::DataType; + +std::vector<std::string> GetAllFiles(std::string_view dirName) { + struct dirent *filename; + DIR *dir = OpenDir(dirName); + if (dir == nullptr) { + return {}; + } + std::vector<std::string> res; + while ((filename = readdir(dir)) != nullptr) { + std::string dName = std::string(filename->d_name); + if (dName == "." || dName == ".." || filename->d_type != DT_REG) { + continue; + } + res.emplace_back(std::string(dirName) + "/" + filename->d_name); + } + std::sort(res.begin(), res.end()); + for (auto &f : res) { + std::cout << "image file: " << f << std::endl; + } + return res; +} + +std::vector<std::string> GetImagesById(const std::string &idFile, const std::string &dirName) { + std::ifstream readFile(idFile); + std::string line; + std::vector<std::string> result; + + if (!readFile.is_open()) { + std::cout << "can not open image id txt file" << std::endl; + return result; + } + + while (getline(readFile, line)) { + std::size_t pos = line.find(" "); + std::string id = line.substr(0, pos); + result.emplace_back(dirName + "/" + id); + } + + return result; +} + + +int WriteResult(const std::string& imageFile, const std::string& segstr, const std::vector<MSTensor> &outputs) { + std::string homePath = "./result_Files"; + const int INVALID_POINTER = -1; + const int ERROR = -2; + for (size_t i = 0; i < outputs.size(); ++i) { + size_t outputSize; + std::shared_ptr<const void> netOutput; + netOutput = outputs[i].Data(); + outputSize = outputs[i].DataSize(); + int pos = imageFile.rfind('/'); + std::string fileName(imageFile, pos + 1); + fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), + '_' + std::to_string(i) + segstr + ".bin"); + std::string outFileName = homePath + "/" + fileName; + FILE * outputFile = fopen(outFileName.c_str(), "wb"); + if (outputFile == nullptr) { + std::cout << "open result file " << outFileName << " failed" << std::endl; + return INVALID_POINTER; + } + size_t size = fwrite(netOutput.get(), sizeof(char), outputSize, outputFile); + if (size != outputSize) { + fclose(outputFile); + outputFile = nullptr; + std::cout << "write result file " << outFileName << " failed, write size[" << size << + "] is smaller than output size[" << outputSize << "], maybe the disk is full." << std::endl; + return ERROR; + } + fclose(outputFile); + outputFile = nullptr; + } + return 0; +} + +MSTensor ReadFileToTensor(const std::string &file) { + if (file.empty()) { + std::cout << "Pointer file is nullptr" << std::endl; + return MSTensor(); + } + + std::ifstream ifs(file); + if (!ifs.good()) { + std::cout << "File: " << file << " is not exist" << std::endl; + return MSTensor(); + } + + if (!ifs.is_open()) { + std::cout << "File: " << file << "open failed" << std::endl; + return MSTensor(); + } + + ifs.seekg(0, std::ios::end); + size_t size = ifs.tellg(); + MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size); + + ifs.seekg(0, std::ios::beg); + ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size); + ifs.close(); + + return buffer; +} + + +DIR *OpenDir(std::string_view dirName) { + if (dirName.empty()) { + std::cout << " dirName is null ! " << std::endl; + return nullptr; + } + std::string realPath = RealPath(dirName); + struct stat s; + lstat(realPath.c_str(), &s); + if (!S_ISDIR(s.st_mode)) { + std::cout << "dirName is not a valid directory !" << std::endl; + return nullptr; + } + DIR *dir; + dir = opendir(realPath.c_str()); + if (dir == nullptr) { + std::cout << "Can not open dir " << dirName << std::endl; + return nullptr; + } + std::cout << "Successfully opened the dir " << dirName << std::endl; + return dir; +} + +std::string RealPath(std::string_view path) { + char realPathMem[PATH_MAX] = {0}; + char *realPathRet = nullptr; + realPathRet = realpath(path.data(), realPathMem); + + if (realPathRet == nullptr) { + std::cout << "File: " << path << " is not exist."; + return ""; + } + + std::string realPath(realPathMem); + std::cout << path << " realpath is: " << realPath << std::endl; + return realPath; +} diff --git a/research/cv/RefineNet/export.py b/research/cv/RefineNet/export.py index b14169ffbf80680785f26d33c88bc249bfc68cf1..47ca67d590ed66fff3c6eeb5b7b6e5807c4bb425 100644 --- a/research/cv/RefineNet/export.py +++ b/research/cv/RefineNet/export.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ -"""export AIR file.""" +"""export file.""" import argparse import numpy as np from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export @@ -24,13 +24,17 @@ if __name__ == '__main__': parser = argparse.ArgumentParser(description='checkpoint export') parser.add_argument('--checkpoint', type=str, default='', help='checkpoint of refinenet (Default: None)') parser.add_argument('--num_classes', type=int, default=21, help='the number of classes (Default: 21)') + parser.add_argument('--batch_size', type=int, default=1, help='batch size') + parser.add_argument('--crop_size', type=int, default=513, help='crop size') + parser.add_argument('--file_name', type=str, default="refinenet", help='model name') + parser.add_argument('--file_format', type=str, choices=["MINDIR", "AIR"], default="MINDIR", help='model format') args = parser.parse_args() - network = RefineNet(Bottleneck, [3, 4, 23, 3], args.num_classes) param_dict = load_checkpoint(args.checkpoint) # load the parameter into net load_param_into_net(network, param_dict) - input_data = np.random.uniform(0.0, 1.0, size=[32, 3, 513, 513]).astype(np.float32) - export(network, Tensor(input_data), file_name=args.model + '-300_11.air', file_format='AIR') + image_shape = [args.batch_size, 3, args.crop_size, args.crop_size] + input_data = np.random.uniform(0.0, 1.0, size=image_shape).astype(np.float32) + export(network, Tensor(input_data), file_name=args.file_name, file_format=args.file_format) diff --git a/research/cv/RefineNet/postprocess.py b/research/cv/RefineNet/postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..ac3a3f906693efc7546564c4b3b8f3baddbc5141 --- /dev/null +++ b/research/cv/RefineNet/postprocess.py @@ -0,0 +1,85 @@ +# 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. +# ============================================================================ +"""post process for 310 inference""" +import os +import argparse +import numpy as np +from PIL import Image +import cv2 + +parser = argparse.ArgumentParser(description="refinenet accuracy calculation") +parser.add_argument('--data_root', type=str, default='./vocaug_single', help='root path of val data') +parser.add_argument('--data_lst', type=str, default='', help='list of val data') +parser.add_argument('--crop_size', type=int, default=513, help='crop size') +parser.add_argument('--num_classes', type=int, default=21, help='number of classes') +parser.add_argument('--result_path', type=str, default='./result_Files', help='result Files path') +parser.add_argument('--flip', action='store_true', help='perform left-right flip') +args, _ = parser.parse_known_args() + +def get_img_size(file_name): + img = Image.open(file_name) + return img.size + +def get_resized_size(org_h, org_w, long_size=513): + if org_h > org_w: + new_h = long_size + new_w = int(1.0 * long_size * org_w / org_h) + else: + new_w = long_size + new_h = int(1.0 * long_size * org_h / org_w) + + return new_h, new_w + +def cal_hist(a, b, n): + k = (a >= 0) & (a < n) + return np.bincount(n * a[k].astype(np.int32) + b[k], minlength=n ** 2).reshape(n, n) + +def acc_cal(): + ''' calculate accuarcy''' + hist = np.zeros((args.num_classes, args.num_classes)) + with open(args.data_lst) as f: + img_lst = f.readlines() + + for line in enumerate(img_lst): + img_path, msk_path = line[1].strip().split(' ') + img_file_path = os.path.join(args.data_root, img_path) + org_width, org_height = get_img_size(img_file_path) + resize_h, resize_w = get_resized_size(org_height, org_width) + + result_file = os.path.join(args.result_path, os.path.basename(img_path).split('.jpg')[0] + '_0.bin') + net_out = np.fromfile(result_file, np.float32).reshape(args.num_classes, args.crop_size, args.crop_size) + if args.flip: + bin_path = os.path.basename(img_path).split('.jpg')[0] + '_0_Flip.bin' + net_out_flip = np.fromfile(os.path.join(args.result_path, bin_path), np.float32) + net_out_flip = net_out_flip.reshape(args.num_classes, args.crop_size, args.crop_size) + net_out += net_out_flip[:, :, ::-1] + + probs_ = net_out[:, :resize_h, :resize_w].transpose((1, 2, 0)) + probs_ = cv2.resize(probs_, (org_width, org_height)) + + result_msk = probs_.argmax(axis=2) + + msk_path = os.path.join(args.data_root, msk_path) + mask = cv2.imread(msk_path, cv2.IMREAD_GRAYSCALE) + + hist += cal_hist(mask.flatten(), result_msk.flatten(), args.num_classes) + + print(hist) + iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist)) + print('per-class IoU', iu) + print('mean IoU', np.nanmean(iu)) + +if __name__ == '__main__': + acc_cal() diff --git a/research/cv/RefineNet/scripts/run_infer_310.sh b/research/cv/RefineNet/scripts/run_infer_310.sh new file mode 100644 index 0000000000000000000000000000000000000000..2e0557a5e6b60452a24b199044578b52228e11e7 --- /dev/null +++ b/research/cv/RefineNet/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 || $# -gt 4 ]]; then + echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID] + DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero" +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} +model=$(get_real_path $1) +data_root=$(get_real_path $2) +data_list_path=$(get_real_path $3) + + +device_id=0 +if [ $# == 4 ]; then + device_id=$4 +fi + +echo "mindir name: "$model +echo "data root path: "$data_root +echo "data list path: "$data_list_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=$ASCEND_HOME/opp +fi + +function compile_app() +{ + cd ../ascend310_infer || exit + bash build.sh &> build.log +} + +function infer() +{ + cd - || exit + if [ -d result_Files ]; then + rm -rf ./result_Files + fi + if [ -d time_Result ]; then + rm -rf ./time_Result + fi + mkdir result_Files + mkdir time_Result + ../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_root --image_list=$data_list_path --device_id=$device_id &> infer.log +} + +function cal_acc() +{ + python ../postprocess.py --data_root=$data_root --data_lst=$data_list_path --scales=1.0 --result_path=./result_Files --flip &> 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 diff --git a/research/cv/meta-baseline/README.md b/research/cv/meta-baseline/README.md index 2ce8b0478cd0d09b30e6a336428335cc94fcc30e..80a5d3163a8a63e0a5a982f217be9c0ea7906e69 100644 --- a/research/cv/meta-baseline/README.md +++ b/research/cv/meta-baseline/README.md @@ -14,6 +14,10 @@ - [1. Training Classifier-Baseline](#1-training-classifier-baseline) - [2. Training Meta-Baseline](#2-training-meta-baseline) - [3. Test](#3-test) + - [Inference Process](#inference-process) + - [Export MindIR](#export-mindir) + - [Infer on Ascend310](#infer-on-ascend310) + - [result](#result) - [Performance](#Performance) - [Citation](#citation) @@ -200,6 +204,38 @@ python eval.py --load_encoder (dir) --num_shots 1 --root_path ./dataset/ --devic ``` +## Inference Process + +### [Export MindIR](#contents) + +```shell + +python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] + +``` + +- The `ckpt_file` parameter is required. +- `file_format` should be in ["AIR", "MINDIR"]. + +### Infer on Ascend310 + +Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model. + +```shell + +# Ascend310 inference +bash run_infer_310.sh [MINDIR_PATH] [ROOT_PATH] [NEED_PREPROCESS] [DEVICE_ID] + +``` + +- `ROOT_PATH` the root path of validation dataset. +- `NEED_PREPROCESS` means weather need preprocess or not, it's value is 'y' or 'n'. +- `DEVICE_ID` is optional, default value is 0. + +### result + +Inference result is saved in current path, you can find result like this in acc.log file. + ## [Performance](#Contents) ### Training Performance diff --git a/research/cv/meta-baseline/ascend310_infer/CMakeLists.txt b/research/cv/meta-baseline/ascend310_infer/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..ee3c85447340e0449ff2b70ed24f60a17e07b2b6 --- /dev/null +++ b/research/cv/meta-baseline/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/research/cv/meta-baseline/ascend310_infer/build.sh b/research/cv/meta-baseline/ascend310_infer/build.sh new file mode 100644 index 0000000000000000000000000000000000000000..713d7f657ddfa5f75b069351c55f8447f77c72d0 --- /dev/null +++ b/research/cv/meta-baseline/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="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`" +make diff --git a/research/cv/meta-baseline/ascend310_infer/inc/utils.h b/research/cv/meta-baseline/ascend310_infer/inc/utils.h new file mode 100644 index 0000000000000000000000000000000000000000..efebe03a8c1179f5a1f9d5f7ee07e0352a9937c6 --- /dev/null +++ b/research/cv/meta-baseline/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 <sys/stat.h> +#include <dirent.h> +#include <vector> +#include <string> +#include <memory> +#include "include/api/types.h" + +std::vector<std::string> GetAllFiles(std::string_view dirName); +DIR *OpenDir(std::string_view dirName); +std::string RealPath(std::string_view path); +mindspore::MSTensor ReadFileToTensor(const std::string &file); +int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs); +#endif diff --git a/research/cv/meta-baseline/ascend310_infer/src/main.cc b/research/cv/meta-baseline/ascend310_infer/src/main.cc new file mode 100644 index 0000000000000000000000000000000000000000..dffc0b562622287ad725b8e759a4a612d18fc5e9 --- /dev/null +++ b/research/cv/meta-baseline/ascend310_infer/src/main.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 <sys/time.h> +#include <gflags/gflags.h> +#include <dirent.h> +#include <iostream> +#include <string> +#include <algorithm> +#include <iosfwd> +#include <vector> +#include <fstream> +#include <sstream> + +#include "include/api/model.h" +#include "include/api/context.h" +#include "include/api/types.h" +#include "include/api/serialization.h" +#include "include/dataset/execute.h" +#include "include/dataset/vision.h" +#include "inc/utils.h" + +using mindspore::Context; +using mindspore::Serialization; +using mindspore::Model; +using mindspore::Status; +using mindspore::MSTensor; +using mindspore::dataset::Execute; +using mindspore::ModelType; +using mindspore::GraphCell; +using mindspore::kSuccess; + +DEFINE_string(mindir_path, "", "mindir path"); +DEFINE_string(input0_path, ".", "input0 path"); +DEFINE_int32(device_id, 0, "device id"); + +int main(int argc, char **argv) { + gflags::ParseCommandLineFlags(&argc, &argv, true); + if (RealPath(FLAGS_mindir_path).empty()) { + std::cout << "Invalid mindir" << std::endl; + return 1; + } + + auto context = std::make_shared<Context>(); + auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>(); + ascend310->SetDeviceID(FLAGS_device_id); + context->MutableDeviceInfo().push_back(ascend310); + mindspore::Graph graph; + Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph); + + Model model; + Status ret = model.Build(GraphCell(graph), context); + if (ret != kSuccess) { + std::cout << "ERROR: Build failed." << std::endl; + return 1; + } + + std::vector<MSTensor> model_inputs = model.GetInputs(); + if (model_inputs.empty()) { + std::cout << "Invalid model, inputs is empty." << std::endl; + return 1; + } + + auto input0_files = GetAllFiles(FLAGS_input0_path); + if (input0_files.empty()) { + std::cout << "ERROR: input data empty." << std::endl; + return 1; + } + + std::map<double, double> costTime_map; + size_t size = input0_files.size(); + + for (size_t i = 0; i < size; ++i) { + struct timeval start = {0}; + struct timeval end = {0}; + double startTimeMs; + double endTimeMs; + std::vector<MSTensor> inputs; + std::vector<MSTensor> outputs; + std::cout << "Start predict input files:" << input0_files[i] << std::endl; + + auto input0 = ReadFileToTensor(input0_files[i]); + inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(), + input0.Data().get(), input0.DataSize()); + + gettimeofday(&start, nullptr); + ret = model.Predict(inputs, &outputs); + gettimeofday(&end, nullptr); + if (ret != kSuccess) { + std::cout << "Predict " << input0_files[i] << " failed." << std::endl; + return 1; + } + startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000; + endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000; + costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs)); + WriteResult(input0_files[i], outputs); + } + double average = 0.0; + int inferCount = 0; + + for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) { + double diff = 0.0; + diff = iter->second - iter->first; + average += diff; + inferCount++; + } + average = average / inferCount; + std::stringstream timeCost; + timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl; + std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl; + std::string fileName = "./time_Result" + std::string("/test_perform_static.txt"); + std::ofstream fileStream(fileName.c_str(), std::ios::trunc); + fileStream << timeCost.str(); + fileStream.close(); + costTime_map.clear(); + return 0; +} diff --git a/research/cv/meta-baseline/ascend310_infer/src/utils.cc b/research/cv/meta-baseline/ascend310_infer/src/utils.cc new file mode 100644 index 0000000000000000000000000000000000000000..c947e4d5f451b90bd4728aa3a92c4cfab174f5e6 --- /dev/null +++ b/research/cv/meta-baseline/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 <fstream> +#include <algorithm> +#include <iostream> +#include "inc/utils.h" + +using mindspore::MSTensor; +using mindspore::DataType; + +std::vector<std::string> GetAllFiles(std::string_view dirName) { + struct dirent *filename; + DIR *dir = OpenDir(dirName); + if (dir == nullptr) { + return {}; + } + std::vector<std::string> res; + while ((filename = readdir(dir)) != nullptr) { + std::string dName = std::string(filename->d_name); + if (dName == "." || dName == ".." || filename->d_type != DT_REG) { + continue; + } + res.emplace_back(std::string(dirName) + "/" + filename->d_name); + } + std::sort(res.begin(), res.end()); + for (auto &f : res) { + std::cout << "image file: " << f << std::endl; + } + return res; +} + +int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) { + std::string homePath = "./result_Files"; + for (size_t i = 0; i < outputs.size(); ++i) { + size_t outputSize; + std::shared_ptr<const void> netOutput; + netOutput = outputs[i].Data(); + outputSize = outputs[i].DataSize(); + int pos = imageFile.rfind('/'); + std::string fileName(imageFile, pos + 1); + fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin"); + std::string outFileName = homePath + "/" + fileName; + FILE * outputFile = fopen(outFileName.c_str(), "wb"); + fwrite(netOutput.get(), outputSize, sizeof(char), outputFile); + fclose(outputFile); + outputFile = nullptr; + } + return 0; +} + +mindspore::MSTensor ReadFileToTensor(const std::string &file) { + if (file.empty()) { + std::cout << "Pointer file is nullptr" << std::endl; + return mindspore::MSTensor(); + } + + std::ifstream ifs(file); + if (!ifs.good()) { + std::cout << "File: " << file << " is not exist" << std::endl; + return mindspore::MSTensor(); + } + + if (!ifs.is_open()) { + std::cout << "File: " << file << "open failed" << std::endl; + return mindspore::MSTensor(); + } + + ifs.seekg(0, std::ios::end); + size_t size = ifs.tellg(); + mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size); + + ifs.seekg(0, std::ios::beg); + ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size); + ifs.close(); + + return buffer; +} + + +DIR *OpenDir(std::string_view dirName) { + if (dirName.empty()) { + std::cout << " dirName is null ! " << std::endl; + return nullptr; + } + std::string realPath = RealPath(dirName); + struct stat s; + lstat(realPath.c_str(), &s); + if (!S_ISDIR(s.st_mode)) { + std::cout << "dirName is not a valid directory !" << std::endl; + return nullptr; + } + DIR *dir; + dir = opendir(realPath.c_str()); + if (dir == nullptr) { + std::cout << "Can not open dir " << dirName << std::endl; + return nullptr; + } + std::cout << "Successfully opened the dir " << dirName << std::endl; + return dir; +} + +std::string RealPath(std::string_view path) { + char realPathMem[PATH_MAX] = {0}; + char *realPathRet = nullptr; + realPathRet = realpath(path.data(), realPathMem); + + if (realPathRet == nullptr) { + std::cout << "File: " << path << " is not exist."; + return ""; + } + + std::string realPath(realPathMem); + std::cout << path << " realpath is: " << realPath << std::endl; + return realPath; +} diff --git a/research/cv/meta-baseline/export.py b/research/cv/meta-baseline/export.py index 05998c7d68a84980c103fec148ce48bcff441152..22379f936bd11991c92fa39877d09b0ba35a24ab 100644 --- a/research/cv/meta-baseline/export.py +++ b/research/cv/meta-baseline/export.py @@ -25,7 +25,7 @@ from src.model.classifier import Classifier parser = argparse.ArgumentParser(description='meta-baseline') parser.add_argument('--device_id', type=int, default=0, help='Device id.') -parser.add_argument("--batch_size", type=int, default=128, help="batch size") +parser.add_argument("--batch_size", type=int, default=320, help="batch size") parser.add_argument('--n_classes', type=int, default=64) parser.add_argument('--ckpt_file', type=str, required=True, help='Checkpoint file path.') parser.add_argument('--file_name', type=str, default='meta_baseline', help='Output file name.') @@ -45,7 +45,7 @@ if __name__ == '__main__': assert args.ckpt_file is not None, "args.ckpt_file is None." param_dict = load_checkpoint(args.ckpt_file) load_param_into_net(network, param_dict) - + network.set_train(mode=False) img = Tensor(np.ones([args.batch_size, 3, 84, 84]), mstype.float32) - export(network, img, file_name=args.file_name, file_format=args.file_format) + export(network.encoder, img, file_name=args.file_name, file_format=args.file_format) diff --git a/research/cv/meta-baseline/postprocess.py b/research/cv/meta-baseline/postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..a55629c61c0636f17cc0f67564a3168205da2c1e --- /dev/null +++ b/research/cv/meta-baseline/postprocess.py @@ -0,0 +1,94 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +postprocess +""" +import os +import argparse +from functools import reduce +import numpy as np +import mindspore as ms +from mindspore import ops, Tensor, context +import src.util as util + +def cal_acc(args): + """ + :return: meta-baseline eval + """ + temp = 5. + n_shots = [args.num_shots] + file_num = int(len(os.listdir(args.post_result_path)) / args.num_shots) + + aves_keys = ['tl', 'ta', 'vl', 'va'] + for n_shot in n_shots: + aves_keys += ['fsa-' + str(n_shot)] + aves = {k: util.Averager() for k in aves_keys} + + label_list = np.load(os.path.join(args.pre_result_path, "label.npy"), allow_pickle=True) + shape_list = np.load(os.path.join(args.pre_result_path, "shape.npy"), allow_pickle=True) + x_shot_shape = shape_list[0] + x_query_shape = shape_list[1] + shot_shape = x_shot_shape[:-3] + query_shape = x_query_shape[:-3] + x_shot_len = reduce(lambda x, y: x*y, shot_shape) + x_query_len = reduce(lambda x, y: x*y, query_shape) + + for i, n_shot in enumerate(n_shots): + np.random.seed(0) + label_shot = label_list[i] + for j in range(file_num): + labels = Tensor(label_shot[j]) + f = os.path.join(args.post_result_path, "nshot_" + str(i) + "_" + str(j) + "_0.bin") + x_tot = Tensor(np.fromfile(f, np.float32).reshape(args.batch_size, 512)) + x_shot, x_query = x_tot[:x_shot_len], x_tot[-x_query_len:] + x_shot = x_shot.view(*shot_shape, -1) + x_query = x_query.view(*query_shape, -1) + + ########## cross-class bias ############ + bs = x_shot.shape[0] + fs = x_shot.shape[-1] + bias = x_shot.view(bs, -1, fs).mean(1) - x_query.mean(1) + x_query = x_query + ops.ExpandDims()(bias, 1) + + x_shot = x_shot.mean(axis=-2) + x_shot = ops.L2Normalize(axis=-1)(x_shot) + x_query = ops.L2Normalize(axis=-1)(x_query) + logits = ops.BatchMatMul()(x_query, x_shot.transpose(0, 2, 1)) + + logits = logits * temp + + ret = ops.Argmax()(logits) == labels.astype(ms.int32) + acc = ret.astype(ms.float32).mean() + aves['fsa-' + str(n_shot)].add(acc.asnumpy()) + + for k, v in aves.items(): + aves[k] = v.item() + for n_shot in n_shots: + key = 'fsa-' + str(n_shot) + print("epoch {}, {}-shot, val acc {:.4f}".format(str(1), n_shot, aves[key])) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--device_target', type=str, default='CPU', choices=['Ascend', 'GPU', 'CPU']) + parser.add_argument('--dataset', default='mini-imagenet') + parser.add_argument('--post_result_path', default='./result_Files') + parser.add_argument('--pre_result_path', type=str, default='./preprocess_Result') + parser.add_argument('--batch_size', type=int, default=320) + parser.add_argument('--num_shots', type=int, default=1) + args_opt = parser.parse_args() + context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False) + + cal_acc(args_opt) diff --git a/research/cv/meta-baseline/preprocess.py b/research/cv/meta-baseline/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..f770f263fb63343552800ca92b0c9c3d6fc14812 --- /dev/null +++ b/research/cv/meta-baseline/preprocess.py @@ -0,0 +1,92 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +preprocess +""" +import os +import argparse +import numpy as np +from mindspore import ops, context +import mindspore.dataset as ds +import src.util as util +from src.data.IterSamplers import CategoriesSampler +from src.data.mini_Imagenet import MiniImageNet + + +def gen_bin(args): + """ + generate binary files + """ + n_way = 5 + n_query = 15 + n_shots = [args.num_shots] + root_path = os.path.join(args.root_path, args.dataset) + testset = MiniImageNet(root_path, 'test') + + fs_loaders = [] + for n_shot in n_shots: + test_sampler = CategoriesSampler(testset.data, testset.label, n_way, n_shot + n_query, + 200, + args.ep_per_batch) + test_loader = ds.GeneratorDataset(test_sampler, ['data'], shuffle=True) + fs_loaders.append(test_loader) + + input_path = os.path.join(args.pre_result_path, "00_data") + label_path = os.path.join(args.pre_result_path, "label.npy") + shape_path = os.path.join(args.pre_result_path, "shape.npy") + if not os.path.exists(input_path): + os.makedirs(input_path) + + label_list = [] + shape_list = [] + for i, n_shot in enumerate(n_shots): + np.random.seed(0) + label_shot = [] + for j, data in enumerate(fs_loaders[i].create_dict_iterator()): + x_shot, x_query = data['data'][:, :, :n_shot], data['data'][:, :, n_shot:] + img_shape = x_query.shape[-3:] + x_query = x_query.view(args.ep_per_batch, -1, + *img_shape) # bs*(way*n_query)*3*84*84 + label = util.make_nk_label(n_way, n_query, args.ep_per_batch) # bs*(way*n_query) + if j == 0: + shape_list.append(x_shot.shape) + shape_list.append(x_query.shape) + + img_shape = x_shot.shape[-3:] + + x_shot = x_shot.view(-1, *img_shape) + x_query = x_query.view(-1, *img_shape) + input0 = ops.Concat(0)([x_shot, x_query]) + file_name = "nshot_" + str(i) + "_" + str(j) + ".bin" + input0.asnumpy().tofile(os.path.join(input_path, file_name)) + label_shot.append(label.asnumpy()) + label_list.append(label_shot) + + np.save(label_path, label_list) + np.save(shape_path, shape_list) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--root_path', default='./dataset/') + parser.add_argument('--device_target', type=str, default='CPU', choices=['Ascend', 'GPU', 'CPU']) + parser.add_argument('--dataset', default='mini-imagenet') + parser.add_argument('--ep_per_batch', type=int, default=4) + parser.add_argument('--pre_result_path', type=str, default='./preprocess_Result') + parser.add_argument('--num_shots', type=int, default=1) + + args_opt = parser.parse_args() + context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False) + gen_bin(args_opt) diff --git a/research/cv/meta-baseline/scripts/run_infer_310.sh b/research/cv/meta-baseline/scripts/run_infer_310.sh new file mode 100644 index 0000000000000000000000000000000000000000..2bc7d49a55a47fd771b2ee303ec75e89fe3b86c4 --- /dev/null +++ b/research/cv/meta-baseline/scripts/run_infer_310.sh @@ -0,0 +1,122 @@ +#!/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 || $# -gt 4 ]]; then + echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [ROOT_PATH] [NEED_PREPROCESS] [DEVICE_ID] + NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'. + DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero" +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} +model=$(get_real_path $1) +dataset_root_path=$(get_real_path $2) + +if [ "$3" == "y" ] || [ "$3" == "n" ];then + need_preprocess=$3 +else + echo "weather need preprocess or not, it's value must be in [y, n]" + exit 1 +fi + +device_id=0 +if [ $# == 4 ]; then + device_id=$4 +fi + +echo "mindir name: "$model +echo "root dataset path: "$dataset_root_path +echo "need preprocess: "$need_preprocess +echo "device id: "$device_id + +export ASCEND_HOME=/usr/local/Ascend/ +if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then + export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH + export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH + export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe + export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH + export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp +else + export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH + export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH + export ASCEND_OPP_PATH=$ASCEND_HOME/opp +fi + +function preprocess_data() +{ + if [ -d preprocess_Result ]; then + rm -rf ./preprocess_Result + fi + mkdir preprocess_Result + python ../preprocess.py --root_path $dataset_root_path +} + +function compile_app() +{ + cd ../ascend310_infer || exit + bash build.sh &> build.log +} + +function infer() +{ + cd - || exit + if [ -d result_Files ]; then + rm -rf ./result_Files + fi + if [ -d time_Result ]; then + rm -rf ./time_Result + fi + mkdir result_Files + mkdir time_Result + + ../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_data --device_id=$device_id &> infer.log + +} + +function cal_acc() +{ + python ../postprocess.py &> acc.log +} + +if [ $need_preprocess == "y" ]; then + preprocess_data + if [ $? -ne 0 ]; then + echo "preprocess dataset failed" + exit 1 + fi +fi +compile_app +if [ $? -ne 0 ]; then + echo "compile app code failed" + exit 1 +fi +infer +if [ $? -ne 0 ]; then + echo " execute inference failed" + exit 1 +fi +cal_acc +if [ $? -ne 0 ]; then + echo "calculate accuracy failed" + exit 1 +fi