diff --git a/.jenkins/check/config/filter_cpplint.txt b/.jenkins/check/config/filter_cpplint.txt
index 41a00d12ead8264ed1f566d050ff02f76495a153..2d8fb85c63fef7a80f011c7651e672fcb630ee99 100644
--- a/.jenkins/check/config/filter_cpplint.txt
+++ b/.jenkins/check/config/filter_cpplint.txt
@@ -71,4 +71,7 @@
"models/official/cv/tinydarknet/infer/mxbase/TinydarknetClassify.h" "runtime/references"
"models/official/cv/tinydarknet/infer/mxbase/TinydarknetClassify.cpp" "runtime/references"
-"models/official/cv/tinydarknet/infer/mxbase/main.cpp" "runtime/references"
\ No newline at end of file
+"models/official/cv/tinydarknet/infer/mxbase/main.cpp" "runtime/references"
+
+"models/official/cv/resnet/infer/ResNet152/mxbase/Resnet152ClassifyOpencv.h" "runtime/references"
+"models/official/cv/resnet/infer/ResNet18/mxbase/Resnet18ClassifyOpencv.h" "runtime/references"
\ No newline at end of file
diff --git a/.jenkins/check/config/whitelizard.txt b/.jenkins/check/config/whitelizard.txt
index 3492971906d065ee2163841984d3b1620b24934f..934d28a6a7be265acac026ab25076b15c54be201 100644
--- a/.jenkins/check/config/whitelizard.txt
+++ b/.jenkins/check/config/whitelizard.txt
@@ -1,4 +1,5 @@
-# Scene1:
+
+# Scene1:
# function_name1, function_name2
# Scene2:
# file_path:function_name1, function_name2
diff --git a/official/cv/resnet/infer/ResNet152/convert/aipp.config b/official/cv/resnet/infer/ResNet152/convert/aipp.config
new file mode 100644
index 0000000000000000000000000000000000000000..84886ae1a2432b993d0990221de2f76424f679b4
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/convert/aipp.config
@@ -0,0 +1,16 @@
+aipp_op {
+ aipp_mode: static
+ input_format : RGB888_U8
+
+ rbuv_swap_switch : true
+
+ mean_chn_0 : 0
+ mean_chn_1 : 0
+ mean_chn_2 : 0
+ 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
+}
diff --git a/official/cv/resnet/infer/ResNet152/convert/convert_om.sh b/official/cv/resnet/infer/ResNet152/convert/convert_om.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4c3a5c41da72d224f72e2c205131aa5c3fa54554
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/convert/convert_om.sh
@@ -0,0 +1,47 @@
+#!/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 [ $# -ne 3 ]
+then
+ echo "Wrong parameter format."
+ echo "Usage:"
+ echo " bash $0 INPUT_AIR_PATH OUTPUT_OM_PATH_NAME"
+ echo "Example: "
+ echo " bash convert_om.sh models/0-150_1251.air models/0-150_1251.om"
+
+ exit 255
+fi
+
+air_path=$1
+aipp_cfg_path=$2
+output_model_name=$3
+
+echo "Input AIR file path: ${air_path}"
+echo "Output OM file path: ${output_model_name}"
+echo "AIPP cfg file path: ${aipp_cfg_path}"
+
+
+/usr/local/Ascend/atc/bin/atc \
+--model=$air_path \
+--framework=1 \
+--output=$output_model_name \
+--input_format=NCHW --input_shape="actual_input_1:1,3,304,304" \
+--enable_small_channel=1 \
+--log=error \
+--soc_version=Ascend310 \
+--insert_op_conf="$aipp_cfg_path" \
+--output_type=FP32
+
diff --git a/official/cv/resnet/infer/ResNet152/convert/data.py b/official/cv/resnet/infer/ResNet152/convert/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..82a3d88926f91505919f5eb0b0960796a07437b4
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/convert/data.py
@@ -0,0 +1,37 @@
+# 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.
+# ============================================================================
+"""data convert"""
+
+import sys
+
+def convert(input_path, output_path):
+ data = []
+ with open(input_path, 'r') as f:
+ for line in f.readlines():
+ data.append(line[10:-1])
+ fp = open(output_path, 'a', encoding='utf-8')
+ for name in data:
+ fp.write(name+'\n')
+
+
+if __name__ == '__main__':
+ if len(sys.argv) != 3:
+ print("Wrong parameter format.")
+ print("Usage:")
+ print(" python3 convert.py [SYN__PATH] [NAMES_OUTPUT_PATH_NAME]")
+ sys.exit()
+ input_p = sys.argv[1]
+ output_p = sys.argv[2]
+ convert(input_p, output_p)
diff --git a/official/cv/resnet/infer/ResNet152/data/config/resnet152.cfg b/official/cv/resnet/infer/ResNet152/data/config/resnet152.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..deb7ac6b5d649e06ecec772413256842341f723c
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/data/config/resnet152.cfg
@@ -0,0 +1,3 @@
+CLASS_NUM=1001
+SOFTMAX=false
+TOP_K=5
diff --git a/official/cv/resnet/infer/ResNet152/docker_start_infer.sh b/official/cv/resnet/infer/ResNet152/docker_start_infer.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1e91b6142a15e068cb3354c47a2d31c6bf7fabf9
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/docker_start_infer.sh
@@ -0,0 +1,48 @@
+#!/usr/bin/env 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.
+
+docker_image=$1
+data_path=$2
+
+function show_help() {
+ echo "Usage: docker_start.sh docker_image data_path"
+}
+
+function param_check() {
+ if [ -z "${docker_image}" ]; then
+ echo "please input docker_image"
+ show_help
+ exit 1
+ fi
+
+ if [ -z "${data_path}" ]; then
+ echo "please input data_path"
+ show_help
+ exit 1
+ fi
+}
+
+param_check
+
+docker run -it \
+ --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 ${data_path}:${data_path} \
+ ${docker_image} \
+ /bin/bash
diff --git a/official/cv/resnet/infer/ResNet152/mxbase/CMakeLists.txt b/official/cv/resnet/infer/ResNet152/mxbase/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c4f29af77aa9c4e09068e5ed689f0677096255fa
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/mxbase/CMakeLists.txt
@@ -0,0 +1,48 @@
+cmake_minimum_required(VERSION 3.14.0)
+project(resnet)
+set(TARGET resnet)
+
+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)
+
+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 ${MXBASE_ROOT_DIR}/include/MxBase/postprocess/include)
+set(OPENSOURCE_DIR ${MXBASE_ROOT_DIR}/opensource)
+
+include_directories(${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(${TARGET} main.cpp Resnet152ClassifyOpencv.cpp)
+
+target_link_libraries(${TARGET} glog cpprest mxbase resnet50postprocess opencv_world stdc++fs)
+
+install(TARGETS ${TARGET} RUNTIME DESTINATION ${PROJECT_SOURCE_DIR}/)
+
diff --git a/official/cv/resnet/infer/ResNet152/mxbase/Resnet152ClassifyOpencv.cpp b/official/cv/resnet/infer/ResNet152/mxbase/Resnet152ClassifyOpencv.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..8f6aff2caf0993f586538713c13f71a4e2a60cd6
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/mxbase/Resnet152ClassifyOpencv.cpp
@@ -0,0 +1,214 @@
+/**
+ * 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 "Resnet152ClassifyOpencv.h"
+#include "MxBase/DeviceManager/DeviceManager.h"
+#include "MxBase/Log/Log.h"
+
+using MxBase::DeviceManager;
+using MxBase::TensorBase;
+using MxBase::MemoryData;
+using MxBase::ClassInfo;
+
+namespace {
+ const uint32_t YUV_BYTE_NU = 3;
+ const uint32_t YUV_BYTE_DE = 2;
+ const uint32_t VPC_H_ALIGN = 2;
+}
+
+APP_ERROR Resnet152ClassifyOpencv::Init(const InitParam &initParam) {
+ deviceId_ = initParam.deviceId;
+ APP_ERROR ret = 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;
+ }
+ 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;
+ }
+ return APP_ERR_OK;
+}
+
+APP_ERROR Resnet152ClassifyOpencv::DeInit() {
+ model_->DeInit();
+ post_->DeInit();
+ DeviceManager::GetInstance()->DestroyDevices();
+ return APP_ERR_OK;
+}
+
+APP_ERROR Resnet152ClassifyOpencv::PreProcess(const std::string &imgPath,
+ TensorBase &tensorBase) {
+ static constexpr uint32_t resizeHeight = 304;
+ static constexpr uint32_t resizeWidth = 304;
+
+ cv::Mat imageMat = cv::imread(imgPath, cv::IMREAD_COLOR);
+ cv::resize(imageMat, imageMat, cv::Size(resizeWidth, resizeHeight));
+
+ static cv::Rect rectOfImg(40, 40, 224, 224);
+ imageMat = imageMat(rectOfImg).clone();
+
+ const uint32_t dataSize = imageMat.cols * imageMat.rows * MxBase::XRGB_WIDTH_NU;
+ LogInfo << "image size after resize" << imageMat.cols << " " << imageMat.rows;
+
+ MemoryData memoryDataDst(dataSize, MemoryData::MEMORY_DEVICE, deviceId_);
+ MemoryData memoryDataSrc(imageMat.data, dataSize, 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 = {imageMat.rows * MxBase::XRGB_WIDTH_NU, static_cast<uint32_t>(imageMat.cols)};
+ tensorBase = TensorBase(memoryDataDst, false, shape, MxBase::TENSOR_DTYPE_UINT8);
+ return APP_ERR_OK;
+}
+
+APP_ERROR Resnet152ClassifyOpencv::Inference(std::vector<TensorBase> &inputs,
+ std::vector<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]);
+ }
+ TensorBase tensor(shape, dtypes[i], MemoryData::MemoryType::MEMORY_DEVICE, deviceId_);
+ APP_ERROR ret = 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();
+ inferCostTimeMilliSec += costMs;
+ if (ret != APP_ERR_OK) {
+ LogError << "ModelInference failed, ret=" << ret << ".";
+ return ret;
+ }
+ return APP_ERR_OK;
+}
+
+APP_ERROR Resnet152ClassifyOpencv::PostProcess(std::vector<TensorBase> &inputs,
+ std::vector<std::vector<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 Resnet152ClassifyOpencv::SaveResult(const std::string &imgPath,
+ std::vector<std::vector<ClassInfo>> &batchClsInfos) {
+ LogInfo << "image path" << imgPath;
+ std::string fileName = imgPath.substr(imgPath.find_last_of("/") + 1);
+ size_t dot = fileName.find_last_of(".");
+ std::string resFileName = "result/" + fileName.substr(0, dot) + "_1.txt";
+ LogInfo << "file path for saving result" << resFileName;
+
+ std::ofstream outfile(resFileName);
+ if (outfile.fail()) {
+ LogError << "Failed to open result file: ";
+ return APP_ERR_COMM_FAILURE;
+ }
+
+ uint32_t batchIndex = 0;
+ for (auto clsInfos : batchClsInfos) {
+ std::string resultStr;
+ for (auto clsInfo : clsInfos) {
+ LogDebug << " className:" << clsInfo.className << " confidence:" << clsInfo.confidence <<
+ " classIndex:" << clsInfo.classId;
+ resultStr += std::to_string(clsInfo.classId) + " ";
+ }
+
+ outfile << resultStr << std::endl;
+ batchIndex++;
+ }
+ outfile.close();
+ return APP_ERR_OK;
+}
+
+APP_ERROR Resnet152ClassifyOpencv::Process(const std::string &imgPath) {
+ TensorBase tensorBase;
+ std::vector<TensorBase> inputs;
+ std::vector<TensorBase> outputs;
+
+ APP_ERROR ret = PreProcess(imgPath, tensorBase);
+ if (ret != APP_ERR_OK) {
+ LogError << "Convert image to TensorBase failed, ret=" << ret << ".";
+ return ret;
+ }
+
+ inputs.push_back(tensorBase);
+
+ auto startTime = std::chrono::high_resolution_clock::now();
+ ret = Inference(inputs, outputs);
+ auto endTime = std::chrono::high_resolution_clock::now();
+ double costMs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
+ inferCostTimeMilliSec += costMs;
+ if (ret != APP_ERR_OK) {
+ LogError << "Inference failed, ret=" << ret << ".";
+ return ret;
+ }
+ std::vector<std::vector<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 << "Save infer results into file failed. ret = " << ret << ".";
+ return ret;
+ }
+
+ return APP_ERR_OK;
+}
diff --git a/official/cv/resnet/infer/ResNet152/mxbase/Resnet152ClassifyOpencv.h b/official/cv/resnet/infer/ResNet152/mxbase/Resnet152ClassifyOpencv.h
new file mode 100644
index 0000000000000000000000000000000000000000..7e34a27816f8d4d742ac84c868ec4a75cb346973
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/mxbase/Resnet152ClassifyOpencv.h
@@ -0,0 +1,64 @@
+/**
+ * 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 MXBASE_RESNET152CLASSIFYOPENCV_H
+#define MXBASE_RESNET152CLASSIFYOPENCV_H
+
+#include <map>
+#include <memory>
+#include <string>
+#include <vector>
+#include <opencv2/opencv.hpp>
+#include "MxBase/DvppWrapper/DvppWrapper.h"
+#include "MxBase/ModelInfer/ModelInferenceProcessor.h"
+#include "ClassPostProcessors/Resnet50PostProcess.h"
+#include "MxBase/Tensor/TensorContext/TensorContext.h"
+
+struct InitParam {
+ uint32_t deviceId;
+ std::string labelPath;
+ uint32_t classNum;
+ uint32_t topk;
+ bool softmax;
+ bool checkTensor;
+ std::string modelPath;
+};
+
+class Resnet152ClassifyOpencv {
+ public:
+ APP_ERROR Init(const InitParam &initParam);
+ APP_ERROR DeInit();
+ APP_ERROR PreProcess(const std::string &imgPath, MxBase::TensorBase &tensorBase);
+ APP_ERROR Inference(std::vector<MxBase::TensorBase> &inputs,
+ std::vector<MxBase::TensorBase> &outputs);
+ APP_ERROR PostProcess(std::vector<MxBase::TensorBase> &inputs,
+ std::vector<std::vector<MxBase::ClassInfo>> &clsInfos);
+ APP_ERROR Process(const std::string &imgPath);
+ // get infer time
+ double GetInferCostMilliSec() const {return inferCostTimeMilliSec;}
+
+ private:
+ APP_ERROR SaveResult(const std::string &imgPath,
+ std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos);
+ std::shared_ptr<MxBase::ModelInferenceProcessor> model_;
+ std::shared_ptr<MxBase::Resnet50PostProcess> post_;
+ MxBase::ModelDesc modelDesc_;
+ uint32_t deviceId_ = 0;
+ // infer time
+ double inferCostTimeMilliSec = 0.0;
+};
+
+#endif
diff --git a/official/cv/resnet/infer/ResNet152/mxbase/build.sh b/official/cv/resnet/infer/ResNet152/mxbase/build.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1f120af92518a1d7f32f1f5a9cc70891a4c19529
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/mxbase/build.sh
@@ -0,0 +1,53 @@
+#!/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.
+
+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
+ 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
+ echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
+ else
+ echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user"
+ fi
+}
+
+function build_resnet152()
+{
+ cd "$path_cur" || exit
+ rm -rf build
+ mkdir -p build
+ cd build || exit
+ cmake ..
+ make
+ ret=$?
+ if [ ${ret} -ne 0 ]; then
+ echo "Failed to build resnet152."
+ exit ${ret}
+ fi
+ make install
+}
+
+check_env
+build_resnet152
diff --git a/official/cv/resnet/infer/ResNet152/mxbase/main.cpp b/official/cv/resnet/infer/ResNet152/mxbase/main.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..cd0c6695aa6e8ed1d5ff8b7f7da64277fea9b427
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/mxbase/main.cpp
@@ -0,0 +1,90 @@
+/*
+ * Copyright (c) 2021. Huawei Technologies Co., Ltd. All rights reserved.
+ *
+ * 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 <dirent.h>
+#include "Resnet152ClassifyOpencv.h"
+#include "MxBase/Log/Log.h"
+
+namespace {
+const uint32_t CLASS_NUM = 1001;
+}
+
+APP_ERROR ReadFilesFromPath(const std::string &path, std::vector<std::string> *files) {
+ DIR *dir = NULL;
+ struct dirent *ptr = NULL;
+
+ if ((dir=opendir(path.c_str())) == NULL) {
+ LogError << "Open dir error: " << path;
+ return APP_ERR_COMM_OPEN_FAIL;
+ }
+
+ while ((ptr=readdir(dir)) != NULL) {
+ // d_type == 8 is file
+ if (ptr->d_type == 8) {
+ files->push_back(path + ptr->d_name);
+ }
+ }
+ closedir(dir);
+ // sort ascending order
+ sort(files->begin(), files->end());
+ return APP_ERR_OK;
+}
+
+int main(int argc, char* argv[]) {
+ if (argc <= 1) {
+ LogWarn << "Please input image path, such as './resnet image_dir'.";
+ 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/resnet152_bs1_304.om";
+ auto resnet152 = std::make_shared<Resnet152ClassifyOpencv>();
+ APP_ERROR ret = resnet152->Init(initParam);
+ if (ret != APP_ERR_OK) {
+ LogError << "resnet152Classify init failed, ret=" << ret << ".";
+ return ret;
+ }
+
+ std::string inferPath = argv[1];
+ std::vector<std::string> files;
+ ret = ReadFilesFromPath(inferPath, &files);
+ if (ret != APP_ERR_OK) {
+ LogError << "Read files from path failed, ret=" << ret << ".";
+ return ret;
+ }
+
+ auto startTime = std::chrono::high_resolution_clock::now();
+ for (uint32_t i = 0; i < files.size(); i++) {
+ ret = resnet152->Process(files[i]);
+ if (ret != APP_ERR_OK) {
+ LogError << "resnet152Classify process failed, ret=" << ret << ".";
+ resnet152->DeInit();
+ return ret;
+ }
+ }
+ auto endTime = std::chrono::high_resolution_clock::now();
+ resnet152->DeInit();
+ double costMilliSecs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
+ double fps = 1000.0 * files.size() / resnet152->GetInferCostMilliSec();
+ LogInfo << "[Process Delay] cost: " << costMilliSecs << " ms\tfps: " << fps << " imgs/sec";
+ return APP_ERR_OK;
+}
diff --git a/official/cv/resnet/infer/ResNet152/mxbase/run.sh b/official/cv/resnet/infer/ResNet152/mxbase/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ace816142f9dfdb654e5eae645c94d246bb6fc36
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/mxbase/run.sh
@@ -0,0 +1,20 @@
+#!/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.
+
+export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/lib/modelpostprocessors:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:${LD_LIBRARY_PATH}
+
+# run
+./resnet ../data/image_val/
diff --git a/official/cv/resnet/infer/ResNet152/sdk/classification_task_metric.py b/official/cv/resnet/infer/ResNet152/sdk/classification_task_metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c82151c1ad0b0fd8322b8e704e670a871963c27
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/sdk/classification_task_metric.py
@@ -0,0 +1,154 @@
+#coding = utf-8
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the BSD 3-Clause License (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://opensource.org/licenses/BSD-3-Clause
+#
+# 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.
+"""classification task metric"""
+import os
+import sys
+import json
+import numpy as np
+
+
+np.set_printoptions(threshold=sys.maxsize)
+
+LABEL_FILE = "HiAI_label.json"
+
+def cre_groundtruth_dict(gtfile_path):
+ """
+ :param filename: file contains the imagename and label number
+ :return: dictionary key imagename, value is label number
+ """
+ img_gt_dict = {}
+ for gtfile in os.listdir(gtfile_path):
+ if gtfile != LABEL_FILE:
+ with open(os.path.join(gtfile_path, gtfile), 'r') as f:
+ gt = json.load(f)
+ image_name = os.path.splitext(gtfile.split('/')[-1])
+ img_gt_dict[image_name] = gt["image"]["annotations"][0]["category_id"]
+ return img_gt_dict
+
+
+def cre_groundtruth_dict_fromtxt(gtfile_path):
+ """
+ :param filename: file contains the imagename and label number
+ :return: dictionary key imagename, value is label number
+ """
+ img_gt_dict = {}
+ with open(gtfile_path, 'r')as f:
+ for line in f.readlines():
+ temp = line.strip().split(" ")
+ img_name = temp[0].split(".")[0]
+ img_gt_dict[img_name] = temp[1]
+ return img_gt_dict
+
+
+def load_statistical_predict_result(filepath):
+ """
+ function:
+ the prediction esult file data extraction
+ input:
+ result file:filepath
+ output:
+ n_label:numble of label
+ data_vec: the probabilitie of prediction in the 1000
+ :return: probabilities, numble of label, in_type, color
+ """
+ with open(filepath, 'r')as f:
+ temp = f.readline().strip().split(" ")
+ n_label = len(temp)
+ data_vec = np.zeros((len(temp)), dtype=np.float32)
+ if n_label != 0:
+ for ind, cls_ind in enumerate(temp):
+ data_vec[ind] = np.int(cls_ind)
+ return data_vec, n_label
+
+
+def create_visualization_statistical_result(prediction_file_path,
+ result_store_path, json_file_name,
+ img_gt_dict, topn=5):
+ """
+ :param prediction_file_path:
+ :param result_store_path:
+ :param json_file_name:
+ :param img_gt_dict:
+ :param topn:
+ :return:
+ """
+ writer = open(os.path.join(result_store_path, json_file_name), 'w')
+ table_dict = {}
+ table_dict["title"] = "Overall statistical evaluation"
+ table_dict["value"] = []
+
+ count = 0
+ res_cnt = 0
+ n_labels = ""
+ count_hit = np.zeros(topn)
+ for tfile_name in os.listdir(prediction_file_path):
+ count += 1
+ temp = tfile_name.split('.')[0]
+ index = temp.rfind('_')
+ img_name = temp[:index]
+ filepath = os.path.join(prediction_file_path, tfile_name)
+ prediction, n_labels = load_statistical_predict_result(filepath)
+ if n_labels == 1001:
+ real_label = int(img_gt_dict[img_name]) + 1
+ else:
+ real_label = int(img_gt_dict[img_name])
+ res_cnt = min(len(prediction), topn)
+ for i in range(res_cnt):
+ if real_label == int(prediction[i]):
+ count_hit[i] += 1
+ break
+ if 'value' not in table_dict.keys():
+ print("the item value does not exist!")
+ else:
+ table_dict["value"].extend(
+ [{"key": "Number of images", "value": str(count)},
+ {"key": "Number of classes", "value": str(n_labels)}])
+ accuracy = np.cumsum(count_hit) / count if count else 0
+ for i in range(res_cnt):
+ table_dict["value"].append({"key": "Top" + str(i + 1) + " accuracy",
+ "value": str(
+ round(accuracy[i] * 100, 2)) + '%'})
+ json.dump(table_dict, writer, indent=2)
+ writer.close()
+
+
+if __name__ == '__main__':
+ if len(sys.argv) == 5:
+ # txt file path
+ folder_davinci_target = sys.argv[1]
+ # annotation files path, "val_label.txt"
+ annotation_file_path = sys.argv[2]
+ # the path to store the results json path
+ result_json_path = sys.argv[3]
+ # result json file name
+ result_json_file_name = sys.argv[4]
+ else:
+ print("Please enter target file result folder | ground truth label file | result json file folder | "
+ "result json file name, such as ./result val_label.txt . result.json")
+ exit(1)
+
+ if not os.path.exists(folder_davinci_target):
+ print("Target file folder does not exist.")
+
+ if not os.path.exists(annotation_file_path):
+ print("Ground truth file does not exist.")
+
+ if not os.path.exists(result_json_path):
+ print("Result folder doesn't exist.")
+
+ img_label_dict = cre_groundtruth_dict_fromtxt(annotation_file_path)
+ create_visualization_statistical_result(folder_davinci_target,
+ result_json_path, result_json_file_name,
+ img_label_dict, topn=5)
diff --git a/official/cv/resnet/infer/ResNet152/sdk/main.py b/official/cv/resnet/infer/ResNet152/sdk/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c39cc78f7fc6d9dd231de1c9b2d6eebed2bd7ab
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/sdk/main.py
@@ -0,0 +1,104 @@
+# coding=utf-8
+
+# 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.
+# ============================================================================
+"""run sdk"""
+import datetime
+import json
+import os
+import sys
+
+from StreamManagerApi import StreamManagerApi
+from StreamManagerApi import MxDataInput
+
+
+def run():
+ """run sdk"""
+ # init stream manager
+ stream_manager_api = StreamManagerApi()
+ ret = stream_manager_api.InitManager()
+ if ret != 0:
+ print("Failed to init Stream manager, ret=%s" % str(ret))
+ return
+
+ # create streams by pipeline config file
+ with open("./resnet152.pipeline", 'rb') as f:
+ pipelineStr = f.read()
+ ret = stream_manager_api.CreateMultipleStreams(pipelineStr)
+
+ if ret != 0:
+ print("Failed to create Stream, ret=%s" % str(ret))
+ return
+
+ # Construct the input of the stream
+ data_input = MxDataInput()
+
+ dir_name = sys.argv[1]
+ res_dir_name = sys.argv[2]
+ file_list = os.listdir(dir_name)
+ if not os.path.exists(res_dir_name):
+ os.makedirs(res_dir_name)
+
+ image_count = 0
+ for file_name in file_list:
+ file_path = os.path.join(dir_name, file_name)
+ if not (file_name.lower().endswith(
+ ".jpg") or file_name.lower().endswith(".jpeg")):
+ continue
+
+ with open(file_path, 'rb') as f:
+ data_input.data = f.read()
+
+ stream_name = b'im_resnet152'
+ in_plugin_id = 0
+ unique_id = stream_manager_api.SendData(stream_name, in_plugin_id,
+ data_input)
+ if unique_id < 0:
+ print("Failed to send data to stream.")
+ return
+ # Obtain the inference result by specifying streamName and uniqueId.
+ start_time = datetime.datetime.now()
+ infer_result = stream_manager_api.GetResult(stream_name, unique_id)
+ end_time = datetime.datetime.now()
+ print('sdk run time: {}'.format((end_time - start_time).microseconds))
+ if infer_result.errorCode != 0:
+ print("GetResultWithUniqueId error. errorCode=%d, errorMsg=%s" % (
+ infer_result.errorCode, infer_result.data.decode()))
+ return
+ # print the infer result
+ infer_res = infer_result.data.decode()
+ print("process img{}: {}, infer result: {}".format(image_count, file_name, infer_res))
+ image_count = image_count + 1
+
+ load_dict = json.loads(infer_result.data.decode())
+ if load_dict.get('MxpiClass') is None:
+ with open(res_dir_name + "/" + file_name[:-5] + '.txt',
+ 'w') as f_write:
+ f_write.write("")
+ continue
+ res_vec = load_dict.get('MxpiClass')
+
+ with open(res_dir_name + "/" + file_name[:-5] + '_1.txt',
+ 'w') as f_write:
+ res_list = [str(item.get("classId")) + " " for item in res_vec]
+ f_write.writelines(res_list)
+ f_write.write('\n')
+
+ # destroy streams
+ stream_manager_api.DestroyAllStreams()
+
+
+if __name__ == '__main__':
+ run()
diff --git a/official/cv/resnet/infer/ResNet152/sdk/resnet152.pipeline b/official/cv/resnet/infer/ResNet152/sdk/resnet152.pipeline
new file mode 100644
index 0000000000000000000000000000000000000000..4fdf23d6e5769b2938107b93612e7b07f367af7b
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/sdk/resnet152.pipeline
@@ -0,0 +1,73 @@
+{
+ "im_resnet152": {
+ "stream_config": {
+ "deviceId": "0"
+ },
+ "appsrc1": {
+ "props": {
+ "blocksize": "409600"
+ },
+ "factory": "appsrc",
+ "next": "mxpi_imagedecoder0"
+ },
+ "mxpi_imagedecoder0": {
+ "props": {
+ "handleMethod": "opencv"
+ },
+ "factory": "mxpi_imagedecoder",
+ "next": "mxpi_imageresize0"
+ },
+ "mxpi_imageresize0": {
+ "props": {
+ "handleMethod": "opencv",
+ "resizeType": "Resizer_Stretch",
+ "resizeHeight": "304",
+ "resizeWidth": "304"
+ },
+ "factory": "mxpi_imageresize",
+ "next": "mxpi_opencvcentercrop0"
+ },
+ "mxpi_opencvcentercrop0": {
+ "props": {
+ "dataSource": "mxpi_imageresize0",
+ "cropHeight": "224",
+ "cropWidth": "224"
+ },
+ "factory": "mxpi_opencvcentercrop",
+ "next": "mxpi_tensorinfer0"
+ },
+ "mxpi_tensorinfer0": {
+ "props": {
+ "dataSource": "mxpi_opencvcentercrop0",
+ "modelPath": "../data/model/resnet152_bs1_304.om",
+ "waitingTime": "2000",
+ "outputDeviceId": "-1"
+ },
+ "factory": "mxpi_tensorinfer",
+ "next": "mxpi_classpostprocessor0"
+ },
+ "mxpi_classpostprocessor0": {
+ "props": {
+ "dataSource": "mxpi_tensorinfer0",
+ "postProcessConfigPath": "../data/config/resnet152.cfg",
+ "labelPath": "../data/config/imagenet1000_clsidx_to_labels.names",
+ "postProcessLibPath": "libresnet50postprocess.so"
+ },
+ "factory": "mxpi_classpostprocessor",
+ "next": "mxpi_dataserialize0"
+ },
+ "mxpi_dataserialize0": {
+ "props": {
+ "outputDataKeys": "mxpi_classpostprocessor0"
+ },
+ "factory": "mxpi_dataserialize",
+ "next": "appsink0"
+ },
+ "appsink0": {
+ "props": {
+ "blocksize": "4096000"
+ },
+ "factory": "appsink"
+ }
+ }
+}
diff --git a/official/cv/resnet/infer/ResNet152/sdk/run.sh b/official/cv/resnet/infer/ResNet152/sdk/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1c76b73e58c6973ecbd0ca6781cfa5da33495db0
--- /dev/null
+++ b/official/cv/resnet/infer/ResNet152/sdk/run.sh
@@ -0,0 +1,32 @@
+#!/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.
+image_path=$1
+result_dir=$2
+
+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] $1\033[1;37m" ; }
+
+export GST_PLUGIN_SCANNER=${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
+
+python3.7 main.py $image_path $result_dir
+exit 0
diff --git a/official/cv/resnet/modelarts/ResNet152/config.py b/official/cv/resnet/modelarts/ResNet152/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4e93a532be2c0e65c1a533ab1063865fbbb1fc3
--- /dev/null
+++ b/official/cv/resnet/modelarts/ResNet152/config.py
@@ -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.
+# ============================================================================
+
+"""Parse arguments"""
+
+import os
+import ast
+import argparse
+from pprint import pprint, pformat
+import yaml
+
+
+class Config:
+ """
+ Configuration namespace. Convert dictionary to members.
+ """
+ def __init__(self, cfg_dict):
+ for k, v in cfg_dict.items():
+ if isinstance(v, (list, tuple)):
+ setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
+ else:
+ setattr(self, k, Config(v) if isinstance(v, dict) else v)
+
+ def __str__(self):
+ return pformat(self.__dict__)
+
+ def __repr__(self):
+ return self.__str__()
+
+
+def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="resnet50_cifar10_config.yaml"):
+ """
+ Parse command line arguments to the configuration according to the default yaml.
+
+ Args:
+ parser: Parent parser.
+ cfg: Base configuration.
+ helper: Helper description.
+ cfg_path: Path to the default yaml config.
+ """
+ parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]",
+ parents=[parser])
+ helper = {} if helper is None else helper
+ choices = {} if choices is None else choices
+ for item in cfg:
+ if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
+ help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
+ choice = choices[item] if item in choices else None
+ if isinstance(cfg[item], bool):
+ parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice,
+ help=help_description)
+ else:
+ parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice,
+ help=help_description)
+ args = parser.parse_args()
+ return args
+
+
+def parse_yaml(yaml_path):
+ """
+ Parse the yaml config file.
+
+ Args:
+ yaml_path: Path to the yaml config.
+ """
+ with open(yaml_path, 'r') as fin:
+ try:
+ cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
+ cfgs = [x for x in cfgs]
+ if len(cfgs) == 1:
+ cfg_helper = {}
+ cfg = cfgs[0]
+ cfg_choices = {}
+ elif len(cfgs) == 2:
+ cfg, cfg_helper = cfgs
+ cfg_choices = {}
+ elif len(cfgs) == 3:
+ cfg, cfg_helper, cfg_choices = cfgs
+ else:
+ raise ValueError("At most 3 docs (config description for help, choices) are supported in config yaml")
+ print(cfg_helper)
+ except:
+ raise ValueError("Failed to parse yaml")
+ return cfg, cfg_helper, cfg_choices
+
+
+def merge(args, cfg):
+ """
+ Merge the base config from yaml file and command line arguments.
+
+ Args:
+ args: Command line arguments.
+ cfg: Base configuration.
+ """
+ args_var = vars(args)
+ for item in args_var:
+ cfg[item] = args_var[item]
+ return cfg
+
+
+def get_config():
+ """
+ Get Config according to the yaml file and cli arguments.
+ """
+ parser = argparse.ArgumentParser(description="default name", add_help=False)
+ current_dir = os.path.dirname(os.path.abspath(__file__))
+ parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, \
+ "./resnet152_cifar10_config.yaml"), help="Config file path")
+ path_args, _ = parser.parse_known_args()
+ default, helper, choices = parse_yaml(path_args.config_path)
+ args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
+ final_config = merge(args, default)
+ pprint(final_config)
+ print("Please check the above information for the configurations", flush=True)
+ return Config(final_config)
+
+config = get_config()
diff --git a/official/cv/resnet/modelarts/ResNet152/resnet152_cifar10_config.yaml b/official/cv/resnet/modelarts/ResNet152/resnet152_cifar10_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d9ce685a6f7c1fc0877e4352cfff95d0d1cea31c
--- /dev/null
+++ b/official/cv/resnet/modelarts/ResNet152/resnet152_cifar10_config.yaml
@@ -0,0 +1,105 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: True
+# Url for modelarts
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+# Path for local
+run_distribute: False
+enable_profiling: False
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path/"
+device_target: "Ascend"
+checkpoint_path: "./checkpoint/"
+checkpoint_file_path: ""
+
+# ==============================================================================
+# Training options
+optimizer: "Momentum"
+infer_label: ""
+class_num: 10
+batch_size: 32
+loss_scale: 1024
+momentum: 0.9
+weight_decay: 0.0001
+epoch_size: 2
+pretrain_epoch_size: 0
+save_checkpoint: True
+save_checkpoint_epochs: 5
+keep_checkpoint_max: 10
+save_checkpoint_path: "./"
+warmup_epochs: 0
+lr_decay_mode: "step"
+use_label_smooth: True
+label_smooth_factor: 0.1
+lr: 0.1
+lr_init: 0.0
+lr_max: 0.1
+lr_end: 0.0001
+lars_epsilon: 0.0
+lars_coefficient: 0.001
+
+net_name: "resnet152"
+dataset: "cifar10"
+device_num: 1
+pre_trained: ""
+run_eval: False
+eval_dataset_path: ""
+parameter_server: False
+filter_weight: False
+save_best_ckpt: True
+eval_start_epoch: 40
+eval_interval: 1
+enable_cache: False
+cache_session_id: ""
+mode_name: "GRAPH"
+boost_mode: "O0"
+acc_mode: "O0"
+conv_init: "XavierUniform"
+dense_init: "TruncatedNormal"
+all_reduce_fusion_config:
+ - 180
+ - 313
+train_image_size: 224
+eval_image_size: 224
+
+# Export options
+device_id: 0
+width: 224
+height: 224
+file_name: "resnet152"
+file_format: "MINDIR"
+ckpt_file: ""
+network_dataset: "resnet152_cifar10"
+
+# Retrain options
+save_graphs: False
+save_graphs_path: "./graphs"
+has_trained_epoch: 0
+has_trained_step: 0
+
+# postprocess resnet inference
+result_path: ''
+label_path: ''
+
+
+---
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Dataset url for obs"
+checkpoint_url: "The location of checkpoint for obs"
+data_path: "Dataset path for local"
+output_path: "Training output path for local"
+load_path: "The location of checkpoint for obs"
+device_target: "Target device type, available: [Ascend, GPU, CPU]"
+enable_profiling: "Whether enable profiling while training, default: False"
+num_classes: "Class for dataset"
+batch_size: "Batch size for training and evaluation"
+epoch_size: "Total training epochs."
+checkpoint_path: "The location of the checkpoint file."
+checkpoint_file_path: "The location of the checkpoint file."
+result_path: "result files path."
+label_path: "image file path."
+save_graphs: "Whether save graphs during training, default: False."
+save_graphs_path: "Path to save graphs."
diff --git a/official/cv/resnet/modelarts/ResNet152/train_start.py b/official/cv/resnet/modelarts/ResNet152/train_start.py
new file mode 100644
index 0000000000000000000000000000000000000000..800fbb1e138f186daebddecbae2c2b8bf5a57ee0
--- /dev/null
+++ b/official/cv/resnet/modelarts/ResNet152/train_start.py
@@ -0,0 +1,426 @@
+# Copyright 2020-2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""train resnet."""
+import datetime
+import glob
+import os
+import numpy as np
+import moxing as mox
+
+from mindspore import context
+from mindspore import Tensor
+from mindspore.nn.optim import Momentum, thor, LARS
+from mindspore.train.model import Model
+from mindspore.context import ParallelMode
+from mindspore.train.train_thor import ConvertModelUtils
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
+from mindspore.communication.management import init, get_rank
+from mindspore.common import set_seed
+from mindspore.parallel import set_algo_parameters
+import mindspore.nn as nn
+import mindspore.common.initializer as weight_init
+import mindspore.log as logger
+
+from modelarts.ResNet152.config import config
+
+from src.lr_generator import get_lr, warmup_cosine_annealing_lr
+from src.CrossEntropySmooth import CrossEntropySmooth
+from src.eval_callback import EvalCallBack
+from src.metric import DistAccuracy, ClassifyCorrectCell
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.device_adapter import get_rank_id, get_device_num
+from src.resnet import conv_variance_scaling_initializer
+
+
+
+set_seed(1)
+
+
+class LossCallBack(LossMonitor):
+ """
+ Monitor the loss in training.
+ If the loss in NAN or INF terminating training.
+ """
+
+ def __init__(self, has_trained_epoch=0):
+ super(LossCallBack, self).__init__()
+ self.has_trained_epoch = has_trained_epoch
+
+ def step_end(self, run_context):
+ """step end"""
+ cb_params = run_context.original_args()
+ loss = cb_params.net_outputs
+
+ if isinstance(loss, (tuple, list)):
+ if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
+ loss = loss[0]
+
+ if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
+ loss = np.mean(loss.asnumpy())
+
+ cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
+
+ if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
+ raise ValueError("epoch: {} step: {}. Invalid loss, terminating training.".format(
+ cb_params.cur_epoch_num, cur_step_in_epoch))
+ if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
+ print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num + int(self.has_trained_epoch),
+ cur_step_in_epoch, loss), flush=True)
+
+
+if config.net_name in ("resnet18", "resnet34", "resnet50", "resnet152"):
+ if config.net_name == "resnet18":
+ from src.resnet import resnet18 as resnet
+ elif config.net_name == "resnet34":
+ from src.resnet import resnet34 as resnet
+ elif config.net_name == "resnet50":
+ from src.resnet import resnet50 as resnet
+ else:
+ from src.resnet import resnet152 as resnet
+ if config.dataset == "cifar10":
+ from src.dataset import create_dataset1 as create_dataset
+ else:
+ if config.mode_name == "GRAPH":
+ from src.dataset import create_dataset2 as create_dataset
+ else:
+ from src.dataset import create_dataset_pynative as create_dataset
+elif config.net_name == "resnet101":
+ from src.resnet import resnet101 as resnet
+ from src.dataset import create_dataset3 as create_dataset
+else:
+ from src.resnet import se_resnet50 as resnet
+ from src.dataset import create_dataset4 as create_dataset
+
+
+def filter_checkpoint_parameter_by_list(origin_dict, param_filter):
+ """remove useless parameters according to filter_list"""
+ for key in list(origin_dict.keys()):
+ for name in param_filter:
+ if name in key:
+ print("Delete parameter from checkpoint: ", key)
+ del origin_dict[key]
+ break
+
+
+def apply_eval(eval_param):
+ eval_model = eval_param["model"]
+ eval_ds = eval_param["dataset"]
+ metrics_name = eval_param["metrics_name"]
+ res = eval_model.eval(eval_ds)
+ return res[metrics_name]
+
+
+def set_graph_kernel_context(run_platform, net_name):
+ if run_platform == "GPU" and net_name == "resnet101":
+ context.set_context(enable_graph_kernel=True)
+ context.set_context(graph_kernel_flags="--enable_parallel_fusion --enable_expand_ops=Conv2D")
+
+
+def set_parameter():
+ """set_parameter"""
+ target = config.device_target
+ if target == "CPU":
+ config.run_distribute = False
+
+ config.save_graphs = not config.pre_trained
+
+ # init context
+ if config.mode_name == 'GRAPH':
+ if target == "Ascend":
+ rank_save_graphs_path = os.path.join(config.save_graphs_path, "soma", str(os.getenv('DEVICE_ID')))
+ context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=config.save_graphs,
+ save_graphs_path=rank_save_graphs_path)
+ else:
+ context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=config.save_graphs)
+ set_graph_kernel_context(target, config.net_name)
+ else:
+ context.set_context(mode=context.PYNATIVE_MODE, device_target=target, save_graphs=False)
+
+ if config.parameter_server:
+ context.set_ps_context(enable_ps=True)
+ if config.run_distribute:
+ if target == "Ascend":
+ device_id = int(os.getenv('DEVICE_ID'))
+ context.set_context(device_id=device_id)
+ context.set_auto_parallel_context(device_num=config.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+ gradients_mean=True)
+ set_algo_parameters(elementwise_op_strategy_follow=True)
+ if config.net_name == "resnet50" or config.net_name == "se-resnet50":
+ if config.boost_mode not in ["O1", "O2"]:
+ context.set_auto_parallel_context(all_reduce_fusion_config=config.all_reduce_fusion_config)
+ elif config.net_name in ["resnet101", "resnet152"]:
+ context.set_auto_parallel_context(all_reduce_fusion_config=config.all_reduce_fusion_config)
+ init()
+ # GPU target
+ else:
+ init()
+ context.set_auto_parallel_context(device_num=get_device_num(),
+ parallel_mode=ParallelMode.DATA_PARALLEL,
+ gradients_mean=True)
+ if config.net_name == "resnet50":
+ context.set_auto_parallel_context(all_reduce_fusion_config=config.all_reduce_fusion_config)
+
+
+def load_pre_trained_checkpoint():
+ """
+ Load checkpoint according to pre_trained path.
+ """
+ param_dict = None
+ config.pre_trained = os.path.join(os.path.dirname(os.path.abspath(__file__)), config.pre_trained)
+ if config.pre_trained:
+ if os.path.isdir(config.pre_trained):
+ ckpt_save_dir = os.path.join(config.output_path, config.checkpoint_path, "ckpt_0")
+ ckpt_pattern = os.path.join(ckpt_save_dir, "*.ckpt")
+ ckpt_files = glob.glob(ckpt_pattern)
+ if not ckpt_files:
+ logger.warning(f"There is no ckpt file in {ckpt_save_dir}, "
+ f"pre_trained is unsupported.")
+ else:
+ ckpt_files.sort(key=os.path.getmtime, reverse=True)
+ time_stamp = datetime.datetime.now()
+ print(f"time stamp {time_stamp.strftime('%Y.%m.%d-%H:%M:%S')}"
+ f" pre trained ckpt model {ckpt_files[0]} loading",
+ flush=True)
+ param_dict = load_checkpoint(ckpt_files[0])
+ elif os.path.isfile(config.pre_trained):
+ param_dict = load_checkpoint(config.pre_trained)
+ else:
+ print(f"Invalid pre_trained {config.pre_trained} parameter.")
+ return param_dict
+
+
+def init_weight(net, param_dict):
+ """init_weight"""
+ if config.pre_trained:
+ if param_dict:
+ if param_dict.get("epoch_num") and param_dict.get("step_num"):
+ config.has_trained_epoch = int(param_dict["epoch_num"].data.asnumpy())
+ config.has_trained_step = int(param_dict["step_num"].data.asnumpy())
+ else:
+ config.has_trained_epoch = 0
+ config.has_trained_step = 0
+
+ if config.filter_weight:
+ filter_list = [x.name for x in net.end_point.get_parameters()]
+ filter_checkpoint_parameter_by_list(param_dict, filter_list)
+ load_param_into_net(net, param_dict)
+ else:
+ for _, cell in net.cells_and_names():
+ if isinstance(cell, nn.Conv2d):
+ if config.conv_init == "XavierUniform":
+ cell.weight.set_data(weight_init.initializer(weight_init.XavierUniform(),
+ cell.weight.shape,
+ cell.weight.dtype))
+ elif config.conv_init == "TruncatedNormal":
+ weight = conv_variance_scaling_initializer(cell.in_channels,
+ cell.out_channels,
+ cell.kernel_size[0])
+ cell.weight.set_data(weight)
+ if isinstance(cell, nn.Dense):
+ if config.dense_init == "TruncatedNormal":
+ cell.weight.set_data(weight_init.initializer(weight_init.TruncatedNormal(),
+ cell.weight.shape,
+ cell.weight.dtype))
+ elif config.dense_init == "RandomNormal":
+ in_channel = cell.in_channels
+ out_channel = cell.out_channels
+ weight = np.random.normal(loc=0, scale=0.01, size=out_channel * in_channel)
+ weight = Tensor(np.reshape(weight, (out_channel, in_channel)), dtype=cell.weight.dtype)
+ cell.weight.set_data(weight)
+
+
+def init_lr(step_size):
+ """init lr"""
+ if config.optimizer == "Thor":
+ from src.lr_generator import get_thor_lr
+ lr = get_thor_lr(0, config.lr_init, config.lr_decay, config.lr_end_epoch, step_size, decay_epochs=39)
+ else:
+ if config.net_name in ("resnet18", "resnet34", "resnet50", "resnet152", "se-resnet50"):
+ lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
+ warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size,
+ lr_decay_mode=config.lr_decay_mode)
+ else:
+ lr = warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size,
+ config.pretrain_epoch_size * step_size)
+ return lr
+
+
+def init_loss_scale():
+ if config.dataset == "imagenet2012":
+ if not config.use_label_smooth:
+ config.label_smooth_factor = 0.0
+ loss = CrossEntropySmooth(sparse=True, reduction="mean",
+ smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
+ else:
+ loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+ return loss
+
+
+def init_group_params(net):
+ """init group params"""
+ decayed_params = []
+ no_decayed_params = []
+ for param in net.trainable_params():
+ if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
+ decayed_params.append(param)
+ else:
+ no_decayed_params.append(param)
+
+ group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
+ {'params': no_decayed_params},
+ {'order_params': net.trainable_params()}]
+ return group_params
+
+
+def run_eval(target, model, ckpt_save_dir, cb):
+ """run_eval"""
+ if config.run_eval:
+ if config.eval_dataset_path is None or (not os.path.isdir(config.eval_dataset_path)):
+ raise ValueError("{} is not a existing path.".format(config.eval_dataset_path))
+ eval_dataset = create_dataset(dataset_path=config.eval_dataset_path, do_train=False,
+ batch_size=config.batch_size, train_image_size=config.train_image_size,
+ eval_image_size=config.eval_image_size,
+ target=target, enable_cache=config.enable_cache,
+ cache_session_id=config.cache_session_id)
+ eval_param_dict = {"model": model, "dataset": eval_dataset, "metrics_name": "acc"}
+ eval_cb = EvalCallBack(apply_eval, eval_param_dict, interval=config.eval_interval,
+ eval_start_epoch=config.eval_start_epoch, save_best_ckpt=config.save_best_ckpt,
+ ckpt_directory=ckpt_save_dir, besk_ckpt_name="best_acc.ckpt",
+ metrics_name="acc")
+ cb += [eval_cb]
+
+
+def set_save_ckpt_dir():
+ """set save ckpt dir"""
+ ckpt_save_dir = os.path.join(config.output_path, config.checkpoint_path)
+ if config.enable_modelarts and config.run_distribute:
+ ckpt_save_dir = ckpt_save_dir + "ckpt_" + str(get_rank_id()) + "/"
+ else:
+ if config.run_distribute:
+ ckpt_save_dir = ckpt_save_dir + "ckpt_" + str(get_rank()) + "/"
+ return ckpt_save_dir
+
+
+def _get_last_ckpt(ckpt_dir):
+ ckpt_files = [ckpt_file for ckpt_file in os.listdir(ckpt_dir)
+ if ckpt_file.endswith('.ckpt')]
+ if not ckpt_files:
+ print("No ckpt file found.")
+ return None
+
+ return os.path.join(ckpt_dir, sorted(ckpt_files)[-1])
+
+
+def _export_air(ckpt_dir):
+ """export air"""
+ ckpt_file = _get_last_ckpt(ckpt_dir)
+ if not ckpt_file:
+ return
+ net = resnet(config.class_num)
+ param_dict = load_checkpoint(ckpt_file)
+ load_param_into_net(net, param_dict)
+
+ input_arr = Tensor(np.zeros([1, 3, 304, 304],
+ np.float32))
+ print("Start export air.")
+ export(net, input_arr, file_name=config.file_name,
+ file_format="AIR")
+ file_name = config.file_name + ".air"
+ mox.file.copy(file_name, os.path.join(config.output_path, file_name))
+ print("Export success.")
+
+
+@moxing_wrapper()
+def train_net():
+ """train net"""
+ target = config.device_target
+ set_parameter()
+ ckpt_param_dict = load_pre_trained_checkpoint()
+ dataset = create_dataset(dataset_path=config.data_path, do_train=True, repeat_num=1,
+ batch_size=config.batch_size, train_image_size=config.train_image_size,
+ eval_image_size=config.eval_image_size, target=target,
+ distribute=config.run_distribute)
+ step_size = dataset.get_dataset_size()
+ net = resnet(class_num=config.class_num)
+ if config.parameter_server:
+ net.set_param_ps()
+
+ init_weight(net=net, param_dict=ckpt_param_dict)
+ lr = Tensor(init_lr(step_size=step_size))
+ # define opt
+ group_params = init_group_params(net)
+ opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
+ if config.optimizer == "LARS":
+ opt = LARS(opt, epsilon=config.lars_epsilon, coefficient=config.lars_coefficient,
+ lars_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'bias' not in x.name)
+ loss = init_loss_scale()
+ loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
+ dist_eval_network = ClassifyCorrectCell(net) if config.run_distribute else None
+ metrics = {"acc"}
+ if config.run_distribute:
+ metrics = {'acc': DistAccuracy(batch_size=config.batch_size, device_num=config.device_num)}
+ if (config.net_name not in ("resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "se-resnet50")) or \
+ config.parameter_server or target == "CPU":
+ ## fp32 training
+ model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics, eval_network=dist_eval_network)
+ else:
+ model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics=metrics,
+ amp_level="O2", # boost_level=config.boost_mode,
+ keep_batchnorm_fp32=False,
+ eval_network=dist_eval_network)
+
+ if config.optimizer == "Thor" and config.dataset == "imagenet2012":
+ from src.lr_generator import get_thor_damping
+ damping = get_thor_damping(0, config.damping_init, config.damping_decay, 70, step_size)
+ split_indices = [26, 53]
+ opt = thor(net, lr, Tensor(damping), config.momentum, config.weight_decay, config.loss_scale,
+ config.batch_size, split_indices=split_indices, frequency=config.frequency)
+ model = ConvertModelUtils().convert_to_thor_model(model=model, network=net, loss_fn=loss, optimizer=opt,
+ loss_scale_manager=loss_scale, metrics={'acc'},
+ amp_level="O2", keep_batchnorm_fp32=False)
+ config.run_eval = False
+ logger.warning("Thor optimizer not support evaluation while training.")
+
+ # define callbacks
+ time_cb = TimeMonitor(data_size=step_size)
+ loss_cb = LossCallBack(config.has_trained_epoch)
+ cb = [time_cb, loss_cb]
+ ckpt_save_dir = set_save_ckpt_dir()
+ if config.save_checkpoint:
+ ckpt_append_info = [{"epoch_num": config.has_trained_epoch, "step_num": config.has_trained_step}]
+ config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
+ keep_checkpoint_max=config.keep_checkpoint_max,
+ append_info=ckpt_append_info)
+ ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck)
+ cb += [ckpt_cb]
+ run_eval(target, model, ckpt_save_dir, cb)
+ # train model
+ if config.net_name == "se-resnet50":
+ config.epoch_size = config.train_epoch_size
+ dataset_sink_mode = (not config.parameter_server) and target != "CPU"
+ config.pretrain_epoch_size = config.has_trained_epoch
+ model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb,
+ sink_size=dataset.get_dataset_size(), dataset_sink_mode=dataset_sink_mode)
+
+ if config.run_eval and config.enable_cache:
+ print("Remember to shut down the cache server via \"cache_admin --stop\"")
+
+ _export_air(ckpt_save_dir)
+
+if __name__ == '__main__':
+ train_net()
diff --git a/official/cv/resnet/scripts/docker_start.sh b/official/cv/resnet/scripts/docker_start.sh
new file mode 100644
index 0000000000000000000000000000000000000000..882c28e4926bafc36e30489091b068e1edb8dd2c
--- /dev/null
+++ b/official/cv/resnet/scripts/docker_start.sh
@@ -0,0 +1,38 @@
+#!/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.
+# ============================================================================
+
+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 \
+ --privileged \
+ -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+ -v /usr/local/Ascend/add-ons/:/usr/local/Ascend/add-ons \
+ -v ${data_dir}:${data_dir} \
+ -v ${model_dir}:${model_dir} \
+ -v /root/ascend/log:/root/ascend/log ${docker_image} /bin/bash