diff --git a/.jenkins/check/config/filter_cpplint.txt b/.jenkins/check/config/filter_cpplint.txt
index 313125d94c16be396051efe60f6418b66f0796ff..16e06fff9edbf10dc6ec8ab50f634d456512b47b 100644
--- a/.jenkins/check/config/filter_cpplint.txt
+++ b/.jenkins/check/config/filter_cpplint.txt
@@ -142,4 +142,8 @@
"models/official/audio/lpcnet/ascend310_infer/inc/lpcnet.h" "runtime/int"
"models/official/audio/lpcnet/ascend310_infer/src/main.cc" "build/include_subdir"
-"models/official/cv/simclr/infer/mxbase/SimCLRClassifyOpencv.h" "runtime/references"
\ No newline at end of file
+"models/official/cv/simclr/infer/mxbase/SimCLRClassifyOpencv.h" "runtime/references"
+
+"models/research/recommend/mmoe/infer/mxbase/src/main.cpp" "runtime/references"
+"models/research/recommend/mmoe/infer/mxbase/src/Vgg19Classify.h" "runtime/references"
+"models/research/recommend/mmoe/infer/mxbase/src/Vgg19Classify.cpp" "runtime/references"
\ No newline at end of file
diff --git a/research/cv/vgg19/infer/convert/aipp_vgg19_rgb.config b/research/cv/vgg19/infer/convert/aipp_vgg19_rgb.config
new file mode 100644
index 0000000000000000000000000000000000000000..36ab8bc868bf57a7f0cb3ac16cede0a62af8c1db
--- /dev/null
+++ b/research/cv/vgg19/infer/convert/aipp_vgg19_rgb.config
@@ -0,0 +1,13 @@
+aipp_op {
+ aipp_mode: static
+ input_format: RGB888_U8
+
+ rbuv_swap_switch: true
+
+ min_chn_0: 123.675
+ min_chn_1: 116.28
+ min_chn_2: 103.53
+ 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/research/cv/vgg19/infer/convert/atc.sh b/research/cv/vgg19/infer/convert/atc.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b64ad3281522ff269634611f7c60b5d45c4541e2
--- /dev/null
+++ b/research/cv/vgg19/infer/convert/atc.sh
@@ -0,0 +1,29 @@
+#!/usr/bin/bash
+# Copyright 2022 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.
+# ============================================================================
+
+model=$1
+output_model_name=$2
+
+atc \
+ --model=$model \
+ --framework=1 \
+ --output=$output_model_name \
+ --input_shape="input:1,224,224,3" \
+ --enable_small_channel=1 \
+ --log=error \
+ --soc_version=Ascend310 \
+ --insert_op_conf=aipp_vgg19_rgb.config
+exit 0
diff --git a/research/cv/vgg19/infer/data/config/vgg19.cfg b/research/cv/vgg19/infer/data/config/vgg19.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..aaf7e1a74e849b92811f284e466559e20246cde6
--- /dev/null
+++ b/research/cv/vgg19/infer/data/config/vgg19.cfg
@@ -0,0 +1,3 @@
+CLASS_NUM=1000
+SOFTMAX=false
+TOP_K=5
\ No newline at end of file
diff --git a/research/cv/vgg19/infer/data/config/vgg19.pipeline b/research/cv/vgg19/infer/data/config/vgg19.pipeline
new file mode 100644
index 0000000000000000000000000000000000000000..349240b136cf7c38c5f20ca1fdc8530d1afcd214
--- /dev/null
+++ b/research/cv/vgg19/infer/data/config/vgg19.pipeline
@@ -0,0 +1,80 @@
+{
+ "im_vgg19": {
+ "stream_config": {
+ "deviceId": "0"
+ },
+ "mxpi_imagedecoder0": {
+ "props": {
+ "handleMethod": "opencv"
+ },
+ "factory": "mxpi_imagedecoder",
+ "next": "mxpi_imageresize0"
+ },
+ "mxpi_imageresize0": {
+ "props": {
+ "handleMethod": "opencv",
+ "resizeHeight": "256",
+ "resizeWidth": "256",
+ "resizeType": "Resizer_Stretch"
+ },
+ "factory": "mxpi_imageresize",
+ "next": "mxpi_imagecrop0:1"
+ },
+ "mxpi_imagecrop0": {
+ "props": {
+ "dataSource": "appsrc1",
+ "dataSourceImage": "mxpi_imageresize0",
+ "handleMethod": "opencv"
+ },
+ "factory": "mxpi_imagecrop",
+ "next": "mxpi_tensorinfer0"
+ },
+ "mxpi_tensorinfer0": {
+ "props": {
+ "dataSource": "mxpi_imagecrop0",
+ "modelPath": "../data/model/vgg19.om",
+ "waitingTime": "1",
+ "outputDeviceId": "-1"
+ },
+ "factory": "mxpi_tensorinfer",
+ "next": "mxpi_classpostprocessor0"
+ },
+ "mxpi_classpostprocessor0": {
+ "props": {
+ "dataSource": "mxpi_tensorinfer0",
+ "postProcessConfigPath": "../data/config/vgg19.cfg",
+ "labelPath": "../data/config/imagenet1000_clsidx_to_labels.names",
+ "postProcessLibPath": "/usr/local/sdk_home/mxManufacture/lib/modelpostprocessors/libresnet50postprocess.so"
+ },
+ "factory": "mxpi_classpostprocessor",
+ "next": "mxpi_dataserialize0"
+ },
+ "mxpi_dataserialize0": {
+ "props": {
+ "outputDataKeys": "mxpi_classpostprocessor0"
+ },
+ "factory": "mxpi_dataserialize",
+ "next": "appsink0"
+ },
+ "appsrc1": {
+ "props": {
+ "blocksize": "409600"
+ },
+ "factory": "appsrc",
+ "next": "mxpi_imagecrop0:0"
+ },
+ "appsrc0": {
+ "props": {
+ "blocksize": "409600"
+ },
+ "factory": "appsrc",
+ "next": "mxpi_imagedecoder0"
+ },
+ "appsink0": {
+ "props": {
+ "blocksize": "4096000"
+ },
+ "factory": "appsink"
+ }
+ }
+}
\ No newline at end of file
diff --git a/research/cv/vgg19/infer/docker_start_infer.sh b/research/cv/vgg19/infer/docker_start_infer.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2678ff3f94b2b0be1bb20af554f3787f58b70aef
--- /dev/null
+++ b/research/cv/vgg19/infer/docker_start_infer.sh
@@ -0,0 +1,49 @@
+#!/usr/bin/env bash
+
+# Copyright 2022 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.
+
+docker_image=$1
+model_dir=$2
+
+
+function show_help() {
+ echo "Usage: docker_start.sh docker_image model_dir data_dir"
+}
+
+function param_check() {
+ if [ -z "${docker_image}" ]; then
+ echo "please input docker_image"
+ show_help
+ exit 1
+ fi
+
+ if [ -z "${model_dir}" ]; then
+ echo "please input model_dir"
+ show_help
+ exit 1
+ fi
+}
+
+param_check
+
+docker run -it -u root \
+ --device=/dev/davinci0 \
+ --device=/dev/davinci_manager \
+ --device=/dev/devmm_svm \
+ --device=/dev/hisi_hdc \
+ -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+ -v ${model_dir}:${model_dir} \
+ ${docker_image} \
+ /bin/bash
diff --git a/research/cv/vgg19/infer/mxbase/CMakeLists.txt b/research/cv/vgg19/infer/mxbase/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d1e10dad195d3123caebaf482ddcd75cc9c062e7
--- /dev/null
+++ b/research/cv/vgg19/infer/mxbase/CMakeLists.txt
@@ -0,0 +1,54 @@
+cmake_minimum_required(VERSION 3.10.0)
+project(vgg19)
+
+set(TARGET vgg19)
+
+add_definitions(-DENABLE_DVPP_INTERFACE)
+
+add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
+add_definitions(-Dgoogle=mindxsdk_private)
+
+add_compile_options(-std=c++11 -fPIE -fstack-protector-all -fPIC -Wall)
+add_link_options(-Wl,-z,relro,-z,now,-z,noexecstack -s -pie)
+
+# Check environment variable
+if(NOT DEFINED ENV{ASCEND_HOME})
+ message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
+endif()
+if(NOT DEFINED ENV{ASCEND_VERSION})
+ message(WARNING "please define environment variable:ASCEND_VERSION")
+endif()
+if(NOT DEFINED ENV{ARCH_PATTERN})
+ message(WARNING "please define environment variable:ARCH_PATTERN")
+endif()
+set(ACL_INC_DIR $ENV{ASCEND_HOME}/$ENV{ASCEND_VERSION}/$ENV{ARCH_PATTERN}/acllib/include)
+set(ACL_LIB_DIR $ENV{ASCEND_HOME}/$ENV{ASCEND_VERSION}/$ENV{ARCH_PATTERN}/acllib/lib64)
+include_directories($ENV{ASCEND_HOME}/ascend-toolkit/5.0.4/x86_64-linux/runtime/include)
+
+set(MXBASE_ROOT_DIR $ENV{MX_SDK_HOME})
+set(MXBASE_INC ${MXBASE_ROOT_DIR}/include)
+set(MXBASE_LIB_DIR ${MXBASE_ROOT_DIR}/lib)
+set(MXBASE_POST_LIB_DIR ${MXBASE_ROOT_DIR}/lib/modelpostprocessors)
+set(MXBASE_POST_PROCESS_DIR ${PROJECT_SOURCE_DIR}/src/include)
+if(DEFINED ENV{MXSDK_OPENSOURCE_DIR})
+ set(OPENSOURCE_DIR $ENV{MXSDK_OPENSOURCE_DIR})
+else()
+ set(OPENSOURCE_DIR ${MXBASE_ROOT_DIR}/opensource)
+endif()
+
+include_directories(${ACL_INC_DIR})
+include_directories(${OPENSOURCE_DIR}/include)
+include_directories(${OPENSOURCE_DIR}/include/opencv4)
+
+include_directories(${MXBASE_INC})
+include_directories(${MXBASE_POST_PROCESS_DIR})
+
+link_directories(${ACL_LIB_DIR})
+link_directories(${OPENSOURCE_DIR}/lib)
+link_directories(${MXBASE_LIB_DIR})
+link_directories(${MXBASE_POST_LIB_DIR})
+
+add_executable(${TARGET} ./src/main.cpp ./src/Vgg19Classify.cpp)
+target_link_libraries(${TARGET} glog cpprest mxbase resnet50postprocess opencv_world stdc++fs)
+
+install(TARGETS ${TARGET} RUNTIME DESTINATION ${PROJECT_SOURCE_DIR}/)
\ No newline at end of file
diff --git a/research/cv/vgg19/infer/mxbase/build.sh b/research/cv/vgg19/infer/mxbase/build.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0c1c5012dabc85a10ac78815096775c048d6ad36
--- /dev/null
+++ b/research/cv/vgg19/infer/mxbase/build.sh
@@ -0,0 +1,41 @@
+#!/usr/bin/bash
+path_cur=$(dirname $0)
+
+function check_env()
+{
+ # set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
+ if [ ! "${ASCEND_VERSION}" ]; then
+ export ASCEND_VERSION=ascend-toolkit/latest
+ echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
+ else
+ echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
+ fi
+
+ if [ ! "${ARCH_PATTERN}" ]; then
+ # set ARCH_PATTERN to ./ when it was not specified by user
+ export ARCH_PATTERN=./
+ echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
+ else
+ echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user"
+ fi
+}
+
+function build_vgg19()
+{
+ cd $path_cur
+ rm -rf build
+ mkdir -p build
+ cd build
+ cmake ..
+ make
+ ret=$?
+ if [ ${ret} -ne 0 ]; then
+ echo "Failed to build vgg19."
+ exit ${ret}
+ fi
+ make install
+}
+
+check_env
+build_vgg19
+exit 0
\ No newline at end of file
diff --git a/research/cv/vgg19/infer/mxbase/src/Vgg19Classify.cpp b/research/cv/vgg19/infer/mxbase/src/Vgg19Classify.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..0df9058de52e7b8a6e9c819d8ec7ad35517309fd
--- /dev/null
+++ b/research/cv/vgg19/infer/mxbase/src/Vgg19Classify.cpp
@@ -0,0 +1,230 @@
+/*
+ * Copyright 2022. 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 <unistd.h>
+#include <sys/stat.h>
+#include <map>
+#include <memory>
+#include <vector>
+#include <string>
+#include "Vgg19Classify.h"
+#include "MxBase/DeviceManager/DeviceManager.h"
+#include "MxBase/Log/Log.h"
+
+namespace {
+ const uint32_t YUV_BYTE_NU = 3;
+ const uint32_t YUV_BYTE_DE = 2;
+ const uint32_t VPC_H_ALIGN = 2;
+}
+
+APP_ERROR Vgg19Classify::Init(const InitParam &initParam) {
+ deviceId_ = initParam.deviceId;
+ APP_ERROR ret = MxBase::DeviceManager::GetInstance()->InitDevices();
+ if (ret != APP_ERR_OK) {
+ LogError << "Init devices failed, ret=" << ret << ".";
+ return ret;
+ }
+ ret = MxBase::TensorContext::GetInstance()->SetContext(initParam.deviceId);
+ if (ret != APP_ERR_OK) {
+ LogError << "Set context failed, ret=" << ret << ".";
+ return ret;
+ }
+ dvppWrapper_ = std::make_shared<MxBase::DvppWrapper>();
+ ret = dvppWrapper_->Init();
+ if (ret != APP_ERR_OK) {
+ LogError << "DvppWrapper init failed, ret=" << ret << ".";
+ return ret;
+ }
+ model_ = std::make_shared<MxBase::ModelInferenceProcessor>();
+ ret = model_->Init(initParam.modelPath, modelDesc_);
+ if (ret != APP_ERR_OK) {
+ LogError << "ModelInferenceProcessor init failed, ret=" << ret << ".";
+ return ret;
+ }
+ MxBase::ConfigData configData;
+ const std::string softmax = initParam.softmax ? "true" : "false";
+ const std::string checkTensor = initParam.checkTensor ? "true" : "false";
+
+ configData.SetJsonValue("CLASS_NUM", std::to_string(initParam.classNum));
+ configData.SetJsonValue("TOP_K", std::to_string(initParam.topk));
+ configData.SetJsonValue("SOFTMAX", softmax);
+ configData.SetJsonValue("CHECK_MODEL", checkTensor);
+
+ auto jsonStr = configData.GetCfgJson().serialize();
+ std::map<std::string, std::shared_ptr<void>> config;
+ config["postProcessConfigContent"] = std::make_shared<std::string>(jsonStr);
+ config["labelPath"] = std::make_shared<std::string>(initParam.labelPath);
+
+ post_ = std::make_shared<MxBase::Resnet50PostProcess>();
+ ret = post_->Init(config);
+ if (ret != APP_ERR_OK) {
+ LogError << "Resnet50PostProcess init failed, ret=" << ret << ".";
+ return ret;
+ }
+ tfile_.open("mx_pred_result.txt");
+ if (!tfile_) {
+ LogError << "Open result file failed.";
+ return APP_ERR_COMM_OPEN_FAIL;
+ }
+ return APP_ERR_OK;
+}
+
+APP_ERROR Vgg19Classify::DeInit() {
+ dvppWrapper_->DeInit();
+ model_->DeInit();
+ post_->DeInit();
+ MxBase::DeviceManager::GetInstance()->DestroyDevices();
+ tfile_.close();
+ return APP_ERR_OK;
+}
+
+APP_ERROR Vgg19Classify::ReadImage(const std::string &imgPath, cv::Mat &imageMat) {
+ imageMat = cv::imread(imgPath, cv::IMREAD_COLOR);
+ return APP_ERR_OK;
+}
+
+APP_ERROR Vgg19Classify::Resize(const cv::Mat &srcImageMat, cv::Mat &dstImageMat) {
+ static constexpr uint32_t resizeHeight = 256;
+ static constexpr uint32_t resizeWidth = 256;
+ cv::resize(srcImageMat, dstImageMat, cv::Size(resizeHeight, resizeWidth));
+ return APP_ERR_OK;
+}
+
+APP_ERROR Vgg19Classify::Crop(const cv::Mat &srcMat, cv::Mat &dstMat) {
+ static cv::Rect rectOfImg(16, 16, 224, 224);
+ dstMat = srcMat(rectOfImg).clone();
+ return APP_ERR_OK;
+}
+
+APP_ERROR Vgg19Classify::CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase &tensorBase) {
+ const uint32_t dataSize = imageMat.cols * imageMat.rows * imageMat.channels();
+ MxBase::MemoryData MemoryDataDst(dataSize, MxBase::MemoryData::MEMORY_DEVICE, deviceId_);
+ MxBase::MemoryData MemoryDataSrc(imageMat.data, dataSize, MxBase::MemoryData::MEMORY_HOST_MALLOC);
+ APP_ERROR ret = MxBase::MemoryHelper::MxbsMallocAndCopy(MemoryDataDst, MemoryDataSrc);
+ if (ret != APP_ERR_OK) {
+ LogError << GetError(ret) << "Memory malloc failed.";
+ return ret;
+ }
+ std::vector<uint32_t> shape = {static_cast<uint32_t>(imageMat.rows),
+ static_cast<uint32_t>(imageMat.cols), static_cast<uint32_t>(imageMat.channels())};
+ tensorBase = MxBase::TensorBase(MemoryDataDst, false, shape, MxBase::TENSOR_DTYPE_UINT8);
+ return APP_ERR_OK;
+}
+
+APP_ERROR Vgg19Classify::Inference(const std::vector<MxBase::TensorBase> &inputs,
+ std::vector<MxBase::TensorBase> &outputs) {
+ auto dtypes = model_->GetOutputDataType();
+ for (size_t i = 0; i < modelDesc_.outputTensors.size(); ++i) {
+ std::vector<uint32_t> shape = {};
+ for (size_t j = 0; j < modelDesc_.outputTensors[i].tensorDims.size(); ++j) {
+ shape.push_back((uint32_t)modelDesc_.outputTensors[i].tensorDims[j]);
+ }
+ MxBase::TensorBase tensor(shape, dtypes[i], MxBase::MemoryData::MemoryType::MEMORY_DEVICE, deviceId_);
+ APP_ERROR ret = MxBase::TensorBase::TensorBaseMalloc(tensor);
+ if (ret != APP_ERR_OK) {
+ LogError << "TensorBaseMalloc failed, ret=" << ret << ".";
+ return ret;
+ }
+ outputs.push_back(tensor);
+ }
+ MxBase::DynamicInfo dynamicInfo = {};
+ dynamicInfo.dynamicType = MxBase::DynamicType::STATIC_BATCH;
+ auto startTime = std::chrono::high_resolution_clock::now();
+ APP_ERROR ret = model_->ModelInference(inputs, outputs, dynamicInfo);
+ auto endTime = std::chrono::high_resolution_clock::now();
+ double costMs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
+ g_inferCost.push_back(costMs);
+ if (ret != APP_ERR_OK) {
+ LogError << "ModelInference failed, ret=" << ret << ".";
+ return ret;
+ }
+ return APP_ERR_OK;
+}
+
+APP_ERROR Vgg19Classify::PostProcess(const std::vector<MxBase::TensorBase> &inputs,
+ std::vector<std::vector<MxBase::ClassInfo>> &clsInfos) {
+ APP_ERROR ret = post_->Process(inputs, clsInfos);
+ if (ret != APP_ERR_OK) {
+ LogError << "Process failed, ret=" << ret << ".";
+ return ret;
+ }
+ return APP_ERR_OK;
+}
+
+APP_ERROR Vgg19Classify::Process(const std::string &imgPath) {
+ cv::Mat imageMat;
+ APP_ERROR ret = ReadImage(imgPath, imageMat);
+ if (ret != APP_ERR_OK) {
+ LogError << "ReadImage failed, ret=" << ret << ".";
+ return ret;
+ }
+ ret = Resize(imageMat, imageMat);
+ if (ret != APP_ERR_OK) {
+ LogError << "Resize failed, ret=" << ret << ".";
+ return ret;
+ }
+ cv::Mat cropImage;
+ ret = Crop(imageMat, cropImage);
+ if (ret != APP_ERR_OK) {
+ LogError << "Crop failed, ret=" << ret << ".";
+ return ret;
+ }
+ MxBase::TensorBase tensorBase;
+ ret = CVMatToTensorBase(cropImage, tensorBase);
+ if (ret != APP_ERR_OK) {
+ LogError << "CVMatToTensorBase failed, ret=" << ret << ".";
+ return ret;
+ }
+ std::vector<MxBase::TensorBase> inputs = {};
+ std::vector<MxBase::TensorBase> outputs = {};
+ inputs.push_back(tensorBase);
+ ret = Inference(inputs, outputs);
+ if (ret != APP_ERR_OK) {
+ LogError << "Inference failed, ret=" << ret << ".";
+ return ret;
+ }
+ std::vector<std::vector<MxBase::ClassInfo>> BatchClsInfos = {};
+ ret = PostProcess(outputs, BatchClsInfos);
+ if (ret != APP_ERR_OK) {
+ LogError << "PostProcess failed, ret=" << ret << ".";
+ return ret;
+ }
+ ret = SaveResult(imgPath, BatchClsInfos);
+ if (ret != APP_ERR_OK) {
+ LogError << "Export result to file failed, ret=" << ret << ".";
+ return ret;
+ }
+ return APP_ERR_OK;
+}
+
+APP_ERROR Vgg19Classify::SaveResult(const std::string &imgPath, const std::vector<std::vector<MxBase::ClassInfo>> \
+ &BatchClsInfos) {
+ uint32_t batchIndex = 0;
+ std::string fileName = imgPath.substr(imgPath.find_last_of("/") + 1);
+ size_t dot = fileName.find_last_of(".");
+ for (const auto &clsInfos : BatchClsInfos) {
+ std::string resultStr;
+ for (const auto &clsInfo : clsInfos) {
+ resultStr += std::to_string(clsInfo.classId) + ",";
+ }
+ tfile_ << fileName.substr(0, dot) << " " << resultStr << std::endl;
+ if (tfile_.fail()) {
+ LogError << "Failed to write the result to file.";
+ return APP_ERR_COMM_WRITE_FAIL;
+ }
+ batchIndex++;
+ }
+ return APP_ERR_OK;
+}
diff --git a/research/cv/vgg19/infer/mxbase/src/Vgg19Classify.h b/research/cv/vgg19/infer/mxbase/src/Vgg19Classify.h
new file mode 100644
index 0000000000000000000000000000000000000000..3edebbe666bad632faa4e680004a17cd796a36c1
--- /dev/null
+++ b/research/cv/vgg19/infer/mxbase/src/Vgg19Classify.h
@@ -0,0 +1,62 @@
+/*
+ * Copyright 2022. 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 MXBASE_VGG19CLASSIFY_H
+#define MXBASE_VGG19CLASSIFY_H
+
+#include <string>
+#include <vector>
+#include <memory>
+#include <opencv2/opencv.hpp>
+#include "MxBase/DvppWrapper/DvppWrapper.h"
+#include "MxBase/ModelInfer/ModelInferenceProcessor.h"
+#include "MxBase/postprocess/include/ClassPostProcessors/Resnet50PostProcess.h"
+#include "MxBase/Tensor/TensorContext/TensorContext.h"
+
+extern std::vector<double> g_inferCost;
+
+struct InitParam {
+ uint32_t deviceId;
+ std::string labelPath;
+ uint32_t classNum;
+ uint32_t topk;
+ bool softmax;
+ bool checkTensor;
+ std::string modelPath;
+};
+
+class Vgg19Classify {
+ public:
+ APP_ERROR Init(const InitParam &initParam);
+ APP_ERROR DeInit();
+ APP_ERROR ReadImage(const std::string &imgPath, cv::Mat &imageMat);
+ APP_ERROR Resize(const cv::Mat &srcImageMat, cv::Mat &dstImageMat);
+ APP_ERROR Crop(const cv::Mat &srcMat, cv::Mat &dstMat);
+ APP_ERROR CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase &tensorBase);
+ APP_ERROR Inference(const std::vector<MxBase::TensorBase> &inputs, std::vector<MxBase::TensorBase> &outputs);
+ APP_ERROR PostProcess(const std::vector<MxBase::TensorBase> &inputs,
+ std::vector<std::vector<MxBase::ClassInfo>> &clsInfos);
+ APP_ERROR Process(const std::string &imgPath);
+ APP_ERROR SaveResult(const std::string &imgPath, const std::vector<std::vector<MxBase::ClassInfo>> &BatchClsInfos);
+ private:
+ std::shared_ptr<MxBase::DvppWrapper> dvppWrapper_;
+ std::shared_ptr<MxBase::ModelInferenceProcessor> model_;
+ std::shared_ptr<MxBase::Resnet50PostProcess> post_;
+ MxBase::ModelDesc modelDesc_;
+ uint32_t deviceId_ = 0;
+ std::ofstream tfile_;
+};
+#endif
diff --git a/research/cv/vgg19/infer/mxbase/src/main.cpp b/research/cv/vgg19/infer/mxbase/src/main.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..c91a58003881736138d2a11931c83200f7fbc440
--- /dev/null
+++ b/research/cv/vgg19/infer/mxbase/src/main.cpp
@@ -0,0 +1,70 @@
+/*
+ * Copyright 2022. 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 <experimental/filesystem>
+#include "Vgg19Classify.h"
+#include "MxBase/Log/Log.h"
+
+namespace fs = std::experimental::filesystem;
+namespace {
+const uint32_t CLASS_NUM = 1000;
+}
+std::vector<double> g_inferCost;
+
+int main(int argc, char* argv[]) {
+ if (argc <= 1) {
+ LogWarn << "Please input image path, such as './main ../data/input 10'.";
+ return APP_ERR_OK;
+ }
+ InitParam initParam = {};
+ initParam.deviceId = 0;
+ initParam.classNum = CLASS_NUM;
+ initParam.labelPath = "../data/config/imagenet1000_clsidx_to_labels.names";
+ initParam.topk = 5;
+ initParam.softmax = false;
+ initParam.checkTensor = true;
+ initParam.modelPath = "../data/model/vgg19.om";
+ auto vgg19 = std::make_shared<Vgg19Classify>();
+ APP_ERROR ret = vgg19->Init(initParam);
+ if (ret != APP_ERR_OK) {
+ LogError << "Vgg19Classify init failed, ret=" << ret << ".";
+ return ret;
+ }
+ std::string imgDir = argv[1];
+ int limit = std::strtol(argv[2], nullptr, 0);
+ int index = 0;
+ for (auto & entry : fs::directory_iterator(imgDir)) {
+ if (index == limit) {
+ break;
+ }
+ index++;
+ LogInfo << "read image path " << entry.path();
+ ret = vgg19->Process(entry.path());
+ if (ret != APP_ERR_OK) {
+ LogError << "Vgg19Classify process failed, ret=" << ret << ".";
+ vgg19->DeInit();
+ return ret;
+ }
+ }
+ vgg19->DeInit();
+ double costSum = 0;
+ for (unsigned int i = 0; i < g_inferCost.size(); i++) {
+ costSum += g_inferCost[i];
+ }
+ LogInfo << "Infer images sum " << g_inferCost.size() << ", cost total time: " << costSum << " ms.";
+ LogInfo << "The throughput: " << g_inferCost.size() * 1000 / costSum << " images/sec.";
+ return APP_ERR_OK;
+}
diff --git a/research/cv/vgg19/infer/sdk/main.py b/research/cv/vgg19/infer/sdk/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..1262560d610272de2e4b720ad8c8c3116a6d5311
--- /dev/null
+++ b/research/cv/vgg19/infer/sdk/main.py
@@ -0,0 +1,167 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import argparse
+import glob
+import json
+import os
+from contextlib import ExitStack
+
+from StreamManagerApi import StreamManagerApi, MxDataInput, InProtobufVector, \
+ MxProtobufIn
+import MxpiDataType_pb2 as MxpiDataType
+
+
+class GlobDataLoader():
+ def __init__(self, glob_pattern, limit=None):
+ self.glob_pattern = glob_pattern
+ self.limit = limit
+ self.file_list = self.get_file_list()
+ self.cur_index = 0
+
+ def get_file_list(self):
+ return glob.iglob(self.glob_pattern)
+
+ def __iter__(self):
+ return self
+
+ def __next__(self):
+ if self.cur_index == self.limit:
+ raise StopIteration()
+ label = None
+ file_path = next(self.file_list)
+ with open(file_path, 'rb') as fd:
+ data = fd.read()
+
+ self.cur_index += 1
+ return get_file_name(file_path), label, data
+
+
+class Predictor():
+ def __init__(self, pipeline_conf, stream_name):
+ self.pipeline_conf = pipeline_conf
+ self.stream_name = stream_name
+
+ def __enter__(self):
+ self.stream_manager_api = StreamManagerApi()
+ ret = self.stream_manager_api.InitManager()
+ if ret != 0:
+ raise Exception(f"Failed to init Stream manager, ret={ret}")
+
+ # create streams by pipeline config file
+ with open(self.pipeline_conf, 'rb') as f:
+ pipeline_str = f.read()
+ ret = self.stream_manager_api.CreateMultipleStreams(pipeline_str)
+ if ret != 0:
+ raise Exception(f"Failed to create Stream, ret={ret}")
+ self.data_input = MxDataInput()
+
+ return self
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ # destroy streams
+ self.stream_manager_api.DestroyAllStreams()
+
+ def predict(self, dataset):
+ print("Start predict........")
+ print('>' * 30)
+ for name, _, data in dataset:
+ self.data_input.data = data
+ yield self._predict(name, self.data_input)
+ print("predict end.")
+ print('<' * 30)
+
+ def _predict(self, name, data):
+ plugin_id = 0
+ protobuf_data = self._predict_gen_protobuf()
+ self._predict_send_protobuf(self.stream_name, 1, protobuf_data)
+ unique_id = self._predict_send_data(self.stream_name, plugin_id, data)
+ result = self._predict_get_result(self.stream_name, unique_id)
+ return name, json.loads(result.data.decode())
+
+ def _predict_gen_protobuf(self):
+ object_list = MxpiDataType.MxpiObjectList()
+ object_vec = object_list.objectVec.add()
+ object_vec.x0 = 16
+ object_vec.y0 = 16
+ object_vec.x1 = 240
+ object_vec.y1 = 240
+
+ protobuf = MxProtobufIn()
+ protobuf.key = b'appsrc1'
+ protobuf.type = b'MxTools.MxpiObjectList'
+ protobuf.protobuf = object_list.SerializeToString()
+ protobuf_vec = InProtobufVector()
+ protobuf_vec.push_back(protobuf)
+ return protobuf_vec
+
+ def _predict_send_protobuf(self, stream_name, in_plugin_id, data):
+ self.stream_manager_api.SendProtobuf(stream_name, in_plugin_id, data)
+
+ def _predict_send_data(self, stream_name, in_plugin_id, data_input):
+ unique_id = self.stream_manager_api.SendData(stream_name, in_plugin_id,
+ data_input)
+ if unique_id < 0:
+ raise Exception("Failed to send data to stream")
+ return unique_id
+
+ def _predict_get_result(self, stream_name, unique_id):
+ result = self.stream_manager_api.GetResult(stream_name, unique_id)
+ if result.errorCode != 0:
+ raise Exception(
+ f"GetResultWithUniqueId error."
+ f"errorCode={result.errorCode}, msg={result.data.decode()}")
+ return result
+
+
+def get_file_name(file_path):
+ return os.path.splitext(os.path.basename(file_path.rstrip('/')))[0]
+
+
+def result_encode(file_name, result):
+ sep = ','
+ pred_class_ids = sep.join(
+ str(i.get('classId')) for i in result.get("MxpiClass", []))
+ return f"{file_name} {pred_class_ids}\n"
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser.add_argument('glob', help='img pth glob pattern.')
+ parser.add_argument('result_file', help='result file')
+ return parser.parse_args()
+
+
+def main():
+ pipeline_conf = "../data/config/vgg19.pipeline"
+ stream_name = b'im_vgg19'
+
+ args = parse_args()
+ result_fname = get_file_name(args.result_file)
+ pred_result_file = f"{result_fname}.txt"
+ dataset = GlobDataLoader(args.glob+"/*", limit=50000)
+ with ExitStack() as stack:
+ predictor = stack.enter_context(Predictor(pipeline_conf, stream_name))
+ result_fd = stack.enter_context(open(pred_result_file, 'w'))
+
+ for fname, pred_result in predictor.predict(dataset):
+ result_fd.write(result_encode(fname, pred_result))
+
+ print(f"success, result in {pred_result_file}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/research/cv/vgg19/infer/sdk/run.sh b/research/cv/vgg19/infer/sdk/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ec55bb5920b7c3aff03995e11fda3b9b4d4c6293
--- /dev/null
+++ b/research/cv/vgg19/infer/sdk/run.sh
@@ -0,0 +1,16 @@
+#!/usr/bin/bash
+set -e
+
+# Simple log helper functions
+info() { echo -e "\033[1;34m[INFO ][MxStream] $1\033[1;37m" ; }
+warn() { echo >&2 -e "\033[1;31m[WARN ][MxStream] $1\033[1;37m" ; }
+
+export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:${LD_LIBRARY_PATH}
+export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner
+export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins
+
+#to set PYTHONPATH, import the StreamManagerApi.py
+export PYTHONPATH=$PYTHONPATH:${MX_SDK_HOME}/python
+
+python3 main.py $1 $2
+exit 0
diff --git a/research/cv/vgg19/infer/util/task_metric.py b/research/cv/vgg19/infer/util/task_metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..e09e8ab9281d5c60ddc0ca0943032d9e8f0e33de
--- /dev/null
+++ b/research/cv/vgg19/infer/util/task_metric.py
@@ -0,0 +1,106 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import argparse
+import json
+import os
+
+import numpy as np
+
+
+def get_file_name(file_path):
+ return os.path.splitext(os.path.basename(file_path.rstrip('/')))[0]
+
+
+def load_gt(gt_file):
+ gt = {}
+ with open(gt_file, 'r') as fd:
+ for line in fd.readlines():
+ img_name, img_label_index = line.strip().split(" ", 1)
+ gt[get_file_name(img_name)] = img_label_index
+ return gt
+
+
+def load_pred(pred_file):
+ pred = {}
+ with open(pred_file, 'r') as fd:
+ for line in fd.readlines():
+ ret = line.strip().split(" ", 1)
+ if len(ret) < 2:
+ print(f"Warning: load pred, no result, line:{line}")
+ continue
+ img_name, ids = ret
+ img_name = get_file_name(img_name)
+ pred[img_name] = [x.strip() for x in ids.split(',')]
+ return pred
+
+
+def calc_accuracy(gt_map, pred_map, top_k=5):
+ hits = [0] * top_k
+ miss_match = []
+ total = 0
+ for img, preds in pred_map.items():
+ gt = gt_map.get(img)
+ if gt is None:
+ print(f"Warning: {img}'s gt is not exists.")
+ continue
+ try:
+ index = preds.index(gt, 0, top_k)
+ hits[index] += 1
+ except ValueError:
+ miss_match.append({'img': img, 'gt': gt, 'prediction': preds})
+ finally:
+ total += 1
+
+ top_k_hit = np.cumsum(hits)
+ accuracy = top_k_hit / total
+ return {
+ 'total': total,
+ 'accuracy': [acc for acc in accuracy],
+ 'miss': miss_match,
+ }
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser.add_argument('prediction', help='prediction result file')
+ parser.add_argument('gt', help='ground true result file')
+ parser.add_argument('result_json', help='metric result file')
+ parser.add_argument('top_k', help='top k', type=int)
+ return parser.parse_args()
+
+
+def main():
+ args = parse_args()
+ prediction_file = args.prediction
+ gt_file = args.gt
+ top_k = args.top_k
+ result_json = args.result_json
+
+ gt = load_gt(gt_file)
+ prediction = load_pred(prediction_file)
+ result = calc_accuracy(gt, prediction, top_k)
+ result.update({
+ 'prediction_file': prediction_file,
+ 'gt_file': gt_file,
+ })
+ with open(result_json, 'w') as fd:
+ json.dump(result, fd, indent=2)
+ print(f"\nsuccess, result in {result_json}")
+
+
+if __name__ == '__main__':
+ main()
diff --git a/research/cv/vgg19/modelarts/train_modelarts.py b/research/cv/vgg19/modelarts/train_modelarts.py
new file mode 100644
index 0000000000000000000000000000000000000000..32d5e96f8e5d4c6c017f108a61fcf13dfd045761
--- /dev/null
+++ b/research/cv/vgg19/modelarts/train_modelarts.py
@@ -0,0 +1,256 @@
+# Copyright 2022 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.
+# ============================================================================
+"""
+#################train vgg19 example on imagenet2012########################
+"""
+import datetime
+import os
+import time
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+from mindspore.communication.management import init, get_rank, get_group_size
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train.model import Model
+from mindspore.context import ParallelMode
+from mindspore.train.serialization import load_param_into_net, load_checkpoint, export
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore.common import set_seed
+import mindspore.common.dtype as mstype
+from src.dataset import vgg_create_dataset
+from src.dataset import classification_dataset
+
+from src.crossentropy import CrossEntropy
+from src.warmup_step_lr import warmup_step_lr
+from src.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
+from src.warmup_step_lr import lr_steps
+from src.utils.logging import get_logger
+from src.utils.util import get_param_groups
+from src.vgg import vgg19
+
+import numpy as np
+
+from model_utils.moxing_adapter import config
+from model_utils.moxing_adapter import moxing_wrapper
+from model_utils.device_adapter import get_device_id, get_device_num
+
+set_seed(1)
+
+
+def modelarts_pre_process():
+ '''modelarts pre process function.'''
+
+ def unzip(zip_file, save_dir):
+ import zipfile
+ s_time = time.time()
+ if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
+ zip_isexist = zipfile.is_zipfile(zip_file)
+ if zip_isexist:
+ fz = zipfile.ZipFile(zip_file, 'r')
+ data_num = len(fz.namelist())
+ print("Extract Start...")
+ print("unzip file num: {}".format(data_num))
+ data_print = int(data_num / 100) if data_num > 100 else 1
+ i = 0
+ for file in fz.namelist():
+ if i % data_print == 0:
+ print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
+ i += 1
+ fz.extract(file, save_dir)
+ print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
+ int(int(time.time() - s_time) % 60)))
+ print("Extract Done.")
+ else:
+ print("This is not zip.")
+ else:
+ print("Zip has been extracted.")
+
+ if config.need_modelarts_dataset_unzip:
+ zip_file_1 = os.path.join(config.data_url, config.modelarts_dataset_unzip_name + ".zip")
+ save_dir_1 = os.path.join(config.data_url)
+
+ sync_lock = "/tmp/unzip_sync.lock"
+
+ # Each server contains 8 devices as most.
+ if config.device_target == "GPU":
+ init()
+ device_id = get_rank()
+ device_num = get_group_size()
+ elif config.device_target == "Ascend":
+ device_id = get_device_id()
+ device_num = get_device_num()
+ else:
+ raise ValueError("Not support device_target.")
+
+ if device_id % min(device_num, 8) == 0 and not os.path.exists(sync_lock):
+ print("Zip file path: ", zip_file_1)
+ print("Unzip file save dir: ", save_dir_1)
+ unzip(zip_file_1, save_dir_1)
+ print("===Finish extract data synchronization===")
+ try:
+ os.mknod(sync_lock)
+ except IOError:
+ pass
+
+ while True:
+ if os.path.exists(sync_lock):
+ break
+ time.sleep(1)
+
+ print("Device: {}, Finish sync unzip data from {} to {}.".format(device_id, zip_file_1, save_dir_1))
+
+ config.ckpt_path = os.path.join(config.output_path, config.ckpt_path)
+
+
+def output_model(network):
+ network.set_train(False)
+ file_name = os.path.join(config.train_url, config.file_name)
+ input_arr = Tensor(np.zeros([1, 3, config.image_size[0], config.image_size[1]]), mstype.float32)
+ export(network,
+ input_arr,
+ file_name=file_name,
+ file_format=config.file_format)
+
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_train():
+ config.lr_epochs = list(map(int, config.lr_epochs.split(',')))
+ config.image_size = list(map(int, config.image_size.split(',')))
+ config.per_batch_size = config.batch_size
+
+ _enable_graph_kernel = config.device_target == "GPU"
+ context.set_context(mode=context.GRAPH_MODE,
+ enable_graph_kernel=_enable_graph_kernel, device_target=config.device_target)
+
+ config.device_id = get_device_id()
+
+ if config.is_distributed:
+ if config.device_target == "Ascend":
+ init()
+ context.set_context(device_id=config.device_id)
+ elif config.device_target == "GPU":
+ if not config.enable_modelarts:
+ init()
+ else:
+ if not config.need_modelarts_dataset_unzip:
+ init()
+ config.rank = get_rank()
+ config.group_size = get_group_size()
+ device_num = config.group_size
+ context.reset_auto_parallel_context()
+ context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+ gradients_mean=True, all_reduce_fusion_config=[2, 18])
+ else:
+ if config.device_target == "Ascend":
+ context.set_context(device_id=config.device_id)
+
+ # select for master rank save ckpt or all rank save, compatible for model parallel
+ config.rank_save_ckpt_flag = 0
+ if config.is_save_on_master:
+ if config.rank == 0:
+ config.rank_save_ckpt_flag = 1
+ else:
+ config.rank_save_ckpt_flag = 1
+
+ # logger
+ config.outputs_dir = os.path.join(config.train_url,
+ datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
+ config.logger = get_logger(config.train_url, config.rank)
+
+ if config.dataset == "cifar10":
+ dataset = vgg_create_dataset(config.data_url, config.image_size, config.per_batch_size,
+ config.rank, config.group_size)
+ else:
+ dataset = classification_dataset(config.data_url, config.image_size, config.per_batch_size,
+ config.rank, config.group_size)
+
+ batch_num = dataset.get_dataset_size()
+ config.steps_per_epoch = dataset.get_dataset_size()
+ config.logger.save_args(config)
+
+ # network
+ config.logger.important_info('start create network')
+
+ # get network and init
+ network = vgg19(config.num_classes, config)
+
+ # pre_trained
+ if config.pre_trained:
+ load_param_into_net(network, load_checkpoint(config.pre_trained))
+
+ # lr scheduler
+ if config.lr_scheduler == 'exponential':
+ lr = warmup_step_lr(config.lr,
+ config.lr_epochs,
+ config.steps_per_epoch,
+ config.warmup_epochs,
+ config.max_epoch,
+ gamma=config.lr_gamma,
+ )
+ elif config.lr_scheduler == 'cosine_annealing':
+ lr = warmup_cosine_annealing_lr(config.lr,
+ config.steps_per_epoch,
+ config.warmup_epochs,
+ config.max_epoch,
+ config.T_max,
+ config.eta_min)
+ elif config.lr_scheduler == 'step':
+ lr = lr_steps(0, lr_init=config.lr_init, lr_max=config.lr_max, warmup_epochs=config.warmup_epochs,
+ total_epochs=config.max_epoch, steps_per_epoch=batch_num)
+ else:
+ raise NotImplementedError(config.lr_scheduler)
+
+ # optimizer
+ opt = Momentum(params=get_param_groups(network),
+ learning_rate=Tensor(lr),
+ momentum=config.momentum,
+ weight_decay=config.weight_decay,
+ loss_scale=config.loss_scale)
+
+ if config.dataset == "cifar10":
+ loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+ model = Model(network, loss_fn=loss, optimizer=opt, metrics={'acc'},
+ amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
+ else:
+ if not config.label_smooth:
+ config.label_smooth_factor = 0.0
+ loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.num_classes)
+
+ loss_scale_manager = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
+ model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager, amp_level="O2")
+
+ # define callbacks
+ time_cb = TimeMonitor(data_size=batch_num)
+ loss_cb = LossMonitor(per_print_times=batch_num)
+ callbacks = [time_cb, loss_cb]
+ if config.rank_save_ckpt_flag:
+ ckpt_config = CheckpointConfig(save_checkpoint_steps=config.ckpt_interval * config.steps_per_epoch,
+ keep_checkpoint_max=config.keep_checkpoint_max)
+ save_ckpt_path = os.path.join(config.train_url, 'ckpt_' + str(config.rank) + '/')
+ ckpt_cb = ModelCheckpoint(config=ckpt_config,
+ directory=save_ckpt_path,
+ prefix='{}'.format(config.rank))
+ callbacks.append(ckpt_cb)
+
+ model.train(config.max_epoch, dataset, callbacks=callbacks)
+
+ # output model
+ output_model(network)
+
+
+if __name__ == '__main__':
+ run_train()
diff --git a/research/cv/vgg19/scripts/docker_start.sh b/research/cv/vgg19/scripts/docker_start.sh
new file mode 100644
index 0000000000000000000000000000000000000000..44f86f9101f75f333b5942915e32108f78975df1
--- /dev/null
+++ b/research/cv/vgg19/scripts/docker_start.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+# Copyright 2022 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.mitations under the License.
+
+docker_image=$1
+data_dir=$2
+model_dir=$3
+
+docker run -it --ipc=host \
+ --device=/dev/davinci0 \
+ --device=/dev/davinci1 \
+ --device=/dev/davinci2 \
+ --device=/dev/davinci3 \
+ --device=/dev/davinci4 \
+ --device=/dev/davinci5 \
+ --device=/dev/davinci6 \
+ --device=/dev/davinci7 \
+ --device=/dev/davinci_manager \
+ --device=/dev/devmm_svm --device=/dev/hisi_hdc \
+ -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+ -v /usr/local/Ascend/add-ons/:/usr/local/Ascend/add-ons/ \
+ -v ${model_dir}:${model_dir} \
+ -v ${data_dir}:${data_dir} \
+ -v /root/ascend/log:/root/ascend/log ${docker_image} /bin/bash