diff --git a/.jenkins/check/config/filter_cpplint.txt b/.jenkins/check/config/filter_cpplint.txt
index 3eec859698b2c117d2f72f58c0e3cd7b84a4ec57..4f6882da6ff7a62153becd93da8f483188299a6f 100644
--- a/.jenkins/check/config/filter_cpplint.txt
+++ b/.jenkins/check/config/filter_cpplint.txt
@@ -26,4 +26,8 @@
"models/research/cv/FaceAttribute/infer/mxbase/faceattribute/FaceAttribute.cpp" "runtime/references"
"models/official/cv/lenet/infer/mxbase/LenetOpencv.h" "runtime/references"
-"models/official/cv/lenet/infer/mxbase/main_opencv.cpp" "runtime/references"
\ No newline at end of file
+"models/official/cv/lenet/infer/mxbase/main_opencv.cpp" "runtime/references"
+
+"models/research/cv/squeezenet1_1/infer/mxbase/Squeezenet1_1ClassifyOpencv.h" "runtime/references"
+"models/research/cv/squeezenet1_1/infer/mxbase/main_opencv.cpp" "runtime/references"
+"models/research/cv/squeezenet1_1/infer/mxbase/Squeezenet1_1ClassifyOpencv.cpp" "runtime/references"
\ No newline at end of file
diff --git a/research/cv/squeezenet1_1/eval.py b/research/cv/squeezenet1_1/eval.py
index 2ff0adcdb0f7a47c41512c9fe94067bf36462bf2..507f62fa5505ee4429b8005646cd139c236b0fb2 100644
--- a/research/cv/squeezenet1_1/eval.py
+++ b/research/cv/squeezenet1_1/eval.py
@@ -24,7 +24,7 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.CrossEntropySmooth import CrossEntropySmooth
from src.squeezenet import SqueezeNet as squeezenet
from src.dataset import create_dataset_imagenet as create_dataset
-from src.config import config
+from src.config import config_imagenet as config
local_data_url = '/cache/data'
local_ckpt_url = '/cache/ckpt.ckpt'
diff --git a/research/cv/squeezenet1_1/infer/convert/aipp.config b/research/cv/squeezenet1_1/infer/convert/aipp.config
new file mode 100644
index 0000000000000000000000000000000000000000..6190dbba99dd10434886a3b4112d046d2b9deeb1
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/convert/aipp.config
@@ -0,0 +1,15 @@
+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/research/cv/squeezenet1_1/infer/convert/convert_om.sh b/research/cv/squeezenet1_1/infer/convert/convert_om.sh
new file mode 100644
index 0000000000000000000000000000000000000000..60265c96935e42aa7dda96574a8ca3f2fc095de4
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/convert/convert_om.sh
@@ -0,0 +1,30 @@
+#!/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.
+# ============================================================================
+
+model_path=$1
+output_model_name=$2
+aipp_cfg=$3
+
+/usr/local/Ascend/atc/bin/atc \
+--model=$model_path \
+--framework=1 \
+--output=$output_model_name \
+--input_format=NCHW --input_shape="actual_input_1:1,3,227,227" \
+--enable_small_channel=1 \
+--log=error \
+--soc_version=Ascend310 \
+--insert_op_conf=$aipp_cfg \
+--output_type=FP32
\ No newline at end of file
diff --git a/research/cv/squeezenet1_1/infer/data/config/squeezenet.cfg b/research/cv/squeezenet1_1/infer/data/config/squeezenet.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..581fc76d3d75445323ea9a387f7152a72bedd1d3
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/data/config/squeezenet.cfg
@@ -0,0 +1,3 @@
+CLASS_NUM=1000
+SOFTMAX=false
+TOP_K=5
diff --git a/research/cv/squeezenet1_1/infer/docker_start_infer.sh b/research/cv/squeezenet1_1/infer/docker_start_infer.sh
new file mode 100644
index 0000000000000000000000000000000000000000..be2ff92dc7ec4e3b4ca583df0e4bac1d8efa05d5
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/docker_start_infer.sh
@@ -0,0 +1,58 @@
+#!/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
+model_dir=$2
+data_dir=$3
+
+
+function show_help() {
+ echo "Usage: docker_start.sh docker_image model_dir data_dir"
+}
+
+function param_check() {
+ if [ -z "${docker_image}" ]; then
+ echo "please input docker_image"
+ show_help
+ exit 1
+ fi
+
+ if [ -z "${model_dir}" ]; then
+ echo "please input model_dir"
+ show_help
+ exit 1
+ fi
+
+ if [ -z "${data_dir}" ]; then
+ echo "please input data_dir"
+ 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 ${model_dir}:${model_dir} \
+ -v ${data_dir}:${data_dir} \
+ ${docker_image} \
+ /bin/bash
diff --git a/research/cv/squeezenet1_1/infer/mxbase/CMakeLists.txt b/research/cv/squeezenet1_1/infer/mxbase/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..31383d92278973e8c00c16f65f2db818967b0c17
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/mxbase/CMakeLists.txt
@@ -0,0 +1,57 @@
+cmake_minimum_required(VERSION 3.14.0)
+project(squeezenet1_1)
+set(TARGET squeezenet1_1)
+add_definitions(-DENABLE_DVPP_INTERFACE)
+add_compile_options(-std=c++11 -fPIE -fstack-protector-all -fPIC -Wall
+-Dgoogle=mindxsdk_private -D_GLIBCXX_USE_CXX11_ABI=0)
+add_link_options(-Wl,-z,relro,-z,now,-z,noexecstack -pie)
+
+
+
+# Check environment variable
+if(NOT DEFINED ENV{MX_SDK_HOME})
+ message(FATAL_ERROR "please define environment variable:MX_SDK_HOME")
+endif()
+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)
+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(${TARGET} main_opencv.cpp Squeezenet1_1ClassifyOpencv.cpp)
+
+target_link_libraries(${TARGET} glog cpprest mxbase resnet50postprocess opencv_world stdc++fs)
+
+install(TARGETS ${TARGET} RUNTIME DESTINATION ${PROJECT_SOURCE_DIR}/)
diff --git a/research/cv/squeezenet1_1/infer/mxbase/Squeezenet1_1ClassifyOpencv.cpp b/research/cv/squeezenet1_1/infer/mxbase/Squeezenet1_1ClassifyOpencv.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..6b047d4d1c96f0bd349fa581131f94d8663986c6
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/mxbase/Squeezenet1_1ClassifyOpencv.cpp
@@ -0,0 +1,244 @@
+/*
+ * 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 <memory>
+#include <map>
+#include "MxBase/DeviceManager/DeviceManager.h"
+#include "MxBase/Log/Log.h"
+#include "Squeezenet1_1ClassifyOpencv.h"
+
+APP_ERROR Squeezenet1_1ClassifyOpencv::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 << "Squeezenet1_1PostProcess init failed, ret=" << ret << ".";
+ return ret;
+ }
+ return APP_ERR_OK;
+}
+
+APP_ERROR Squeezenet1_1ClassifyOpencv::DeInit() {
+ dvppWrapper_->DeInit();
+ model_->DeInit();
+ post_->DeInit();
+ MxBase::DeviceManager::GetInstance()->DestroyDevices();
+ return APP_ERR_OK;
+}
+
+APP_ERROR Squeezenet1_1ClassifyOpencv::ReadImage(const std::string &imgPath, cv::Mat &imageMat) {
+ imageMat = cv::imread(imgPath, cv::IMREAD_COLOR);
+ return APP_ERR_OK;
+}
+
+APP_ERROR Squeezenet1_1ClassifyOpencv::ResizeImage(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(resizeWidth, resizeHeight));
+ return APP_ERR_OK;
+}
+
+APP_ERROR Squeezenet1_1ClassifyOpencv::CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase &tensorBase) {
+ const uint32_t dataSize = imageMat.cols * imageMat.rows * MxBase::YUV444_RGB_WIDTH_NU;
+ LogInfo << "image size after crop" << imageMat.cols << " " << imageMat.rows;
+ 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 = {imageMat.rows * MxBase::YUV444_RGB_WIDTH_NU, static_cast<uint32_t>(imageMat.cols)};
+ tensorBase = MxBase::TensorBase(memoryDataDst, false, shape, MxBase::TENSOR_DTYPE_UINT8);
+ return APP_ERR_OK;
+}
+APP_ERROR Squeezenet1_1ClassifyOpencv::Crop(const cv::Mat &srcImageMat, cv::Mat &dstImageMat) {
+ static cv::Rect rectOfImg(14.5, 14.5, 227, 227);
+ dstImageMat = srcImageMat(rectOfImg).clone();
+ return APP_ERR_OK;
+}
+
+APP_ERROR Squeezenet1_1ClassifyOpencv::Inference(const std::vector<MxBase::TensorBase> &inputs,
+ std::vector<MxBase::TensorBase> &outputs) {
+ uint32_t first = inputs[0].GetShape()[MxBase::VECTOR_FIRST_INDEX];
+ uint32_t second = inputs[0].GetShape()[MxBase::VECTOR_SECOND_INDEX];
+ uint32_t third = inputs[0].GetShape()[MxBase::VECTOR_THIRD_INDEX];
+ uint32_t fourth = inputs[0].GetShape()[MxBase::VECTOR_FOURTH_INDEX];
+ std::cout << "++ inputs: " << inputs.size() << " " << first << " "
+ << second << " " << third << " " << fourth << std::endl;
+
+ 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(); // save time
+ inferCostTimeMilliSec += costMs;
+ if (ret != APP_ERR_OK) {
+ LogError << "ModelInference failed, ret=" << ret << ".";
+ return ret;
+ }
+ return APP_ERR_OK;
+}
+
+APP_ERROR Squeezenet1_1ClassifyOpencv::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 Squeezenet1_1ClassifyOpencv::SaveResult(const std::string &imgPath,
+ const std::vector<std::vector<MxBase::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 Squeezenet1_1ClassifyOpencv::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;
+ }
+ cv::Mat resizeImage;
+ ret = ResizeImage(imageMat, resizeImage);
+ if (ret != APP_ERR_OK) {
+ LogError << "Resize failed, ret=" << ret << ".";
+ return ret;
+ }
+
+ cv::Mat cropImage;
+ ret = Crop(resizeImage, cropImage);
+ if (ret != APP_ERR_OK) {
+ LogError << "Crop failed, ret=" << ret << ".";
+ return ret;
+ }
+
+ std::vector<MxBase::TensorBase> inputs = {};
+ std::vector<MxBase::TensorBase> outputs = {};
+
+ MxBase::TensorBase tensorBase;
+ ret = CVMatToTensorBase(cropImage, tensorBase);
+ if (ret != APP_ERR_OK) {
+ LogError << "CVMatToTensorBase 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(); // save time
+ inferCostTimeMilliSec += costMs;
+ 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 << "Save infer results into file failed. ret = " << ret << ".";
+ return ret;
+ }
+ return APP_ERR_OK;
+}
diff --git a/research/cv/squeezenet1_1/infer/mxbase/Squeezenet1_1ClassifyOpencv.h b/research/cv/squeezenet1_1/infer/mxbase/Squeezenet1_1ClassifyOpencv.h
new file mode 100644
index 0000000000000000000000000000000000000000..d964920d5d356291ce7bb79c92a7ea505c240661
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/mxbase/Squeezenet1_1ClassifyOpencv.h
@@ -0,0 +1,67 @@
+/*
+ * 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_Squeezenet1_1CLASSIFYOPENCV_H
+#define MXBASE_Squeezenet1_1CLASSIFYOPENCV_H
+#include <string>
+#include <vector>
+#include <memory>
+#include <opencv2/opencv.hpp>
+#include "MxBase/DvppWrapper/DvppWrapper.h"
+#include "MxBase/ModelInfer/ModelInferenceProcessor.h"
+#include "MxBase/Tensor/TensorContext/TensorContext.h"
+#include "ClassPostProcessors/Resnet50PostProcess.h"
+
+struct InitParam {
+ uint32_t deviceId;
+ std::string labelPath;
+ uint32_t classNum;
+ uint32_t topk;
+ bool softmax;
+ bool checkTensor;
+ std::string modelPath;
+};
+
+class Squeezenet1_1ClassifyOpencv {
+ public:
+ APP_ERROR Init(const InitParam &initParam);
+ APP_ERROR DeInit();
+ APP_ERROR ReadImage(const std::string &imgPath, cv::Mat &imageMat);
+ APP_ERROR ResizeImage(const cv::Mat &srcImageMat, cv::Mat &dstImageMat);
+ APP_ERROR CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase &tensorBase);
+ APP_ERROR Crop(const cv::Mat &srcImageMat, cv::Mat &dstImageMat);
+ 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);
+ // get infer time
+ double GetInferCostMilliSec() const {return inferCostTimeMilliSec;}
+
+ private:
+ 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;
+ // infer time
+ double inferCostTimeMilliSec = 0.0;
+};
+
+#endif
diff --git a/research/cv/squeezenet1_1/infer/mxbase/build.sh b/research/cv/squeezenet1_1/infer/mxbase/build.sh
new file mode 100644
index 0000000000000000000000000000000000000000..34574a368e211d7858747ca1c0844d0663d6f1e7
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/mxbase/build.sh
@@ -0,0 +1,54 @@
+#!/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
+ 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_squeezenet1_1()
+{
+ cd $path_cur
+ rm -rf build
+ mkdir -p build
+ cd build
+ cmake ..
+ make
+ ret=$?
+ if [ ${ret} -ne 0 ]; then
+ echo "Failed to build squeezenet1_1."
+ exit ${ret}
+ fi
+}
+
+check_env
+build_squeezenet1_1
\ No newline at end of file
diff --git a/research/cv/squeezenet1_1/infer/mxbase/main_opencv.cpp b/research/cv/squeezenet1_1/infer/mxbase/main_opencv.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..85a34b4337132ee2f3c051285018630a050782d5
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/mxbase/main_opencv.cpp
@@ -0,0 +1,89 @@
+/*
+ * 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 "Squeezenet1_1ClassifyOpencv.h"
+#include <dirent.h>
+#include "MxBase/Log/Log.h"
+
+
+namespace {
+const uint32_t CLASS_NUM = 1000;
+} // namespace
+
+APP_ERROR ScanImages(const std::string &path, std::vector<std::string> &imgFiles) {
+ DIR *dirPtr = opendir(path.c_str());
+ if (dirPtr == nullptr) {
+ LogError << "opendir failed. dir:" << path;
+ return APP_ERR_INTERNAL_ERROR;
+ }
+ dirent *direntPtr = nullptr;
+ while ((direntPtr = readdir(dirPtr)) != nullptr) {
+ std::string fileName = direntPtr->d_name;
+ if (fileName == "." || fileName == "..") {
+ continue;
+ }
+
+ imgFiles.emplace_back(path + "/" + fileName);
+ }
+ closedir(dirPtr);
+ return APP_ERR_OK;
+}
+
+
+int main(int argc, char* argv[]) {
+ if (argc <= 1) {
+ LogWarn << "Please input image path, such as './squeezenet1_1 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/models/squeezenet.om";
+ auto squeezenet1_1 = std::make_shared<Squeezenet1_1ClassifyOpencv>();
+ APP_ERROR ret = squeezenet1_1->Init(initParam);
+ if (ret != APP_ERR_OK) {
+ LogError << "Squeezenet1_1Classify init failed, ret=" << ret << ".";
+ return ret;
+ }
+
+ std::string imgPath = argv[1];
+ std::vector<std::string> imgFilePaths;
+ ret = ScanImages(imgPath, imgFilePaths);
+ if (ret != APP_ERR_OK) {
+ squeezenet1_1->DeInit();
+ return ret;
+ }
+ auto startTime = std::chrono::high_resolution_clock::now();
+ for (auto &imgFile : imgFilePaths) {
+ ret = squeezenet1_1->Process(imgFile);
+ if (ret != APP_ERR_OK) {
+ LogError << "Squeezenet1_1Classify process failed, ret=" << ret << ".";
+ squeezenet1_1->DeInit();
+ return ret;
+ }
+ }
+ auto endTime = std::chrono::high_resolution_clock::now();
+ squeezenet1_1->DeInit();
+ double costMilliSecs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
+ double fps = 1000.0 * imgFilePaths.size() / squeezenet1_1->GetInferCostMilliSec();
+ LogInfo << "[Process Delay] cost: " << costMilliSecs << " ms\tfps: " << fps << " imgs/sec";
+ return APP_ERR_OK;
+}
diff --git a/research/cv/squeezenet1_1/infer/sdk/classification_task_metric.py b/research/cv/squeezenet1_1/infer/sdk/classification_task_metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b4977db3faeedaff57e02ef58f03c11ff7b7877
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/sdk/classification_task_metric.py
@@ -0,0 +1,172 @@
+# 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.
+# ============================================================================
+"""calculation accuracy"""
+import os
+import sys
+import json
+import numpy as np
+
+np.set_printoptions(threshold=sys.maxsize)
+
+LABEL_FILE = "HiAI_label.json"
+
+def gen_file_name(img_name):
+ full_name = img_name.split('/')[-1]
+ return os.path.splitext(full_name)
+
+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)
+ ret = gt["image"]["annotations"][0]["category_id"]
+ img_gt_dict[gen_file_name(gtfile)] = ret
+ 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_lab = temp[1]
+ img_gt_dict[img_name] = img_lab
+ 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:
+ data = f.readline()
+ temp = data.strip().split(" ")
+ n_label = len(temp)
+ data_vec = np.zeros((n_label), dtype=np.float32)
+ in_type = ''
+ color = ''
+ if n_label == 0:
+ in_type = f.readline()
+ color = f.readline()
+ else:
+ for ind, cls_ind in enumerate(temp):
+ data_vec[ind] = np.int32(cls_ind)
+ return data_vec, n_label, in_type, color
+
+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)
+ ret = load_statistical_predict_result(filepath)
+ prediction = ret[0]
+ n_labels = ret[1]
+
+ gt = img_gt_dict[img_name]
+ if n_labels == 1000:
+ real_label = int(gt)
+ elif n_labels == 1001:
+ real_label = int(gt) + 1
+ else:
+ real_label = int(gt)
+
+ res_cnt = min(len(prediction), topn)
+ for i in range(res_cnt):
+ if str(real_label) == str(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)}])
+ if count == 0:
+ accuracy = 0
+ else:
+ accuracy = np.cumsum(count_hit) / count
+ 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)
+ writer.close()
+
+
+if __name__ == '__main__':
+ try:
+ # 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
+ json_file_name = sys.argv[4]
+ except IndexError:
+ 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, json_file_name,
+ img_label_dict, topn=5)
diff --git a/research/cv/squeezenet1_1/infer/sdk/main_squeezenet.py b/research/cv/squeezenet1_1/infer/sdk/main_squeezenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad873e539c3eeab2664fc79ff5f1a748d19adb48
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/sdk/main_squeezenet.py
@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+# 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.
+# ============================================================================
+"""infer squeezenet"""
+import os
+import sys
+import json
+import datetime
+from StreamManagerApi import StreamManagerApi, MxDataInput
+
+if __name__ == '__main__':
+ # 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))
+ exit()
+
+ # create streams by pipeline config file
+ with open("squeezenet.pipeline", 'rb') as f:
+ pipeline_str = f.read()
+ ret = stream_manager_api.CreateMultipleStreams(pipeline_str)
+
+ if ret != 0:
+ print("Failed to create Stream, ret=%s" % str(ret))
+ exit()
+
+ # 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)
+ file_list.sort()
+ if not os.path.exists(res_dir_name):
+ os.makedirs(res_dir_name)
+ for file_name in file_list:
+ print(file_name)
+ file_path = dir_name + file_name
+ if file_name.lower().endswith((".JPEG", ".jpeg", "JPG", "jpg")):
+ portion = os.path.splitext(file_name)
+ with open(file_path, 'rb') as f:
+ data_input.data = f.read()
+ else:
+ continue
+
+ empty_data = []
+
+ stream_name = b'im_squeezenet1_1'
+ in_plugin_id = 0
+ uniqueId = stream_manager_api.SendData(stream_name, in_plugin_id, data_input)
+ if uniqueId < 0:
+ print("Failed to send data to stream.")
+ exit()
+ # Obtain the inference result by specifying stream_name and uniqueId.
+ start_time = datetime.datetime.now()
+ infer_result = stream_manager_api.GetResult(stream_name, uniqueId)
+ 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()))
+ exit()
+ # print the infer result
+ print(infer_result.data.decode())
+
+ load_dict = json.loads(infer_result.data.decode())
+ print(load_dict)
+ 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['MxpiClass']
+
+ with open(res_dir_name + "/" + file_name[:-5] + '_1.txt', 'w') as f_write:
+ list1 = [str(item.get("classId")) + " " for item in res_vec]
+ f_write.writelines(list1)
+ f_write.write('\n')
+
+ # destroy streams
+ stream_manager_api.DestroyAllStreams()
diff --git a/research/cv/squeezenet1_1/infer/sdk/run.sh b/research/cv/squeezenet1_1/infer/sdk/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..270b7cd59b3e8f5fc05b551aea19c08933a82c7f
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/sdk/run.sh
@@ -0,0 +1,33 @@
+#!/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] $1\033[1;37m" ; }
+
+export LD_LIBRARY_PATH=${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
+
+python3.7 main_squeezenet.py $image_path $result_dir
+exit 0
diff --git a/research/cv/squeezenet1_1/infer/sdk/squeezenet.pipeline b/research/cv/squeezenet1_1/infer/sdk/squeezenet.pipeline
new file mode 100644
index 0000000000000000000000000000000000000000..bc4ba0810c83352c9f7d37df17979eba5805e989
--- /dev/null
+++ b/research/cv/squeezenet1_1/infer/sdk/squeezenet.pipeline
@@ -0,0 +1,73 @@
+{
+ "im_squeezenet1_1": {
+ "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": "256",
+ "resizeWidth": "256"
+ },
+ "factory": "mxpi_imageresize",
+ "next": "mxpi_opencvcentercrop0"
+ },
+ "mxpi_opencvcentercrop0": {
+ "props": {
+ "dataSource": "mxpi_imageresize0",
+ "cropHeight": "227",
+ "cropWidth": "227"
+ },
+ "factory": "mxpi_opencvcentercrop",
+ "next": "mxpi_tensorinfer0"
+ },
+ "mxpi_tensorinfer0": {
+ "props": {
+ "dataSource": "mxpi_opencvcentercrop0",
+ "modelPath": "../data/models/squeezenet.om",
+ "waitingTime": "2000",
+ "outputDeviceId": "-1"
+ },
+ "factory": "mxpi_tensorinfer",
+ "next": "mxpi_classpostprocessor0"
+ },
+ "mxpi_classpostprocessor0": {
+ "props": {
+ "dataSource": "mxpi_tensorinfer0",
+ "postProcessConfigPath": "../data/config/squeezenet.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/research/cv/squeezenet1_1/modelArts/train_on_modelarts.py b/research/cv/squeezenet1_1/modelArts/train_on_modelarts.py
new file mode 100644
index 0000000000000000000000000000000000000000..441e96f18b9bdd5933a138f55f2d8708ce2cc1f9
--- /dev/null
+++ b/research/cv/squeezenet1_1/modelArts/train_on_modelarts.py
@@ -0,0 +1,218 @@
+# 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.
+# ============================================================================
+"""train squeezenet."""
+import ast
+import os
+import argparse
+import glob
+import numpy as np
+
+from mindspore import context
+from mindspore import Tensor
+from mindspore import export
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.model import Model
+from mindspore.context import ParallelMode
+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 load_checkpoint, load_param_into_net
+from mindspore.common import set_seed
+from mindspore.nn.metrics import Accuracy
+from mindspore.communication.management import init
+from src.lr_generator import get_lr
+from src.CrossEntropySmooth import CrossEntropySmooth
+from src.squeezenet import SqueezeNet as squeezenet
+
+parser = argparse.ArgumentParser(description='SqueezeNet1_1')
+parser.add_argument('--net', type=str, default='squeezenet', help='Model.')
+parser.add_argument('--dataset', type=str, default='imagenet', help='Dataset.')
+parser.add_argument('--run_cloudbrain', type=ast.literal_eval, default=False,
+ help='Whether it is running on CloudBrain platform.')
+parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
+parser.add_argument('--device_num', type=int, default=1, help='Device num.')
+parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
+parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
+parser.add_argument('--pre_trained', type=str, default="None", help='Pretrained checkpoint path')
+parser.add_argument('--data_url', type=str, default="None", help='Datapath')
+parser.add_argument('--train_url', type=str, default="None", help='Train output path')
+parser.add_argument('--num_classes', type=int, default="1000", help="classes")
+parser.add_argument('--epoch_size', type=int, default="200", help="epoch_size")
+parser.add_argument('--batch_size', type=int, default="32", help="batch_size")
+args_opt = parser.parse_args()
+
+local_data_url = '/cache/data'
+local_train_url = '/cache/ckpt'
+local_pretrain_url = '/cache/preckpt.ckpt'
+
+set_seed(1)
+
+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 frozen_to_air(network, args):
+ paramdict = load_checkpoint(args.get("ckpt_file"))
+ load_param_into_net(network, paramdict)
+ input_arr = Tensor(np.zeros([args.get("batch_size"), 3, args.get("height"), args.get("width")], np.float32))
+ export(network, input_arr, file_name=args.get("file_name"), file_format=args.get("file_format"))
+
+if __name__ == '__main__':
+
+ target = args_opt.device_target
+ if args_opt.device_target != "Ascend":
+ raise ValueError("Unsupported device target.")
+
+ # init context
+ if args_opt.run_distribute:
+ device_num = int(os.getenv("RANK_SIZE"))
+ device_id = int(os.getenv("DEVICE_ID"))
+ context.set_context(mode=context.GRAPH_MODE,
+ device_target=target)
+ context.set_context(device_id=device_id,
+ enable_auto_mixed_precision=True)
+ context.set_auto_parallel_context(
+ device_num=device_num,
+ parallel_mode=ParallelMode.DATA_PARALLEL,
+ gradients_mean=True)
+ init()
+ local_data_url = os.path.join(local_data_url, str(device_id))
+
+ else:
+ device_id = 0
+ context.set_context(mode=context.GRAPH_MODE,
+ device_target=target)
+
+ # create dataset
+ if args_opt.dataset == "cifar10":
+ from src.config import config_cifar as config
+ from src.dataset import create_dataset_cifar as create_dataset
+ else:
+ from src.config import config_imagenet as config
+ from src.dataset import create_dataset_imagenet as create_dataset
+
+ if args_opt.run_cloudbrain:
+ import moxing as mox
+ mox.file.copy_parallel(args_opt.data_url, local_data_url)
+ dataset = create_dataset(dataset_path=local_data_url,
+ do_train=True,
+ repeat_num=1,
+ batch_size=args_opt.batch_size,
+ target=target,
+ run_distribute=args_opt.run_distribute)
+
+
+ step_size = dataset.get_dataset_size()
+
+ # define net
+ net = squeezenet(num_classes=args_opt.num_classes)
+
+ # load checkpoint
+ if args_opt.pre_trained != "None":
+ if args_opt.run_cloudbrain:
+ dir_path = os.path.dirname(os.path.abspath(__file__))
+ ckpt_name = args_opt.pre_trained[2:]
+ ckpt_path = os.path.join(dir_path, ckpt_name)
+ print(ckpt_path)
+ param_dict = load_checkpoint(ckpt_path)
+ filter_list = [x.name for x in net.final_conv.get_parameters()]
+ filter_checkpoint_parameter_by_list(param_dict, filter_list)
+ load_param_into_net(net, param_dict)
+
+
+ # init lr
+ lr = get_lr(lr_init=config.lr_init,
+ lr_end=config.lr_end,
+ lr_max=config.lr_max,
+ total_epochs=args_opt.epoch_size,
+ warmup_epochs=config.warmup_epochs,
+ pretrain_epochs=config.pretrain_epoch_size,
+ steps_per_epoch=step_size,
+ lr_decay_mode=config.lr_decay_mode)
+ lr = Tensor(lr)
+
+ # define loss
+ if args_opt.dataset == "imagenet":
+ 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=args_opt.num_classes)
+ else:
+ loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+
+ # define opt, model
+ loss_scale = FixedLossScaleManager(config.loss_scale,
+ drop_overflow_update=False)
+ opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
+ lr,
+ config.momentum,
+ config.weight_decay,
+ config.loss_scale,
+ use_nesterov=True)
+ model = Model(net,
+ loss_fn=loss,
+ optimizer=opt,
+ loss_scale_manager=loss_scale,
+ metrics={'acc': Accuracy()},
+ amp_level="O2",
+ keep_batchnorm_fp32=False)
+
+ # define callbacks
+ time_cb = TimeMonitor(data_size=step_size)
+ loss_cb = LossMonitor()
+ cb = [time_cb, loss_cb]
+ if config.save_checkpoint and device_id == 0:
+ config_ck = CheckpointConfig(
+ save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
+ keep_checkpoint_max=config.keep_checkpoint_max)
+ ckpt_cb = ModelCheckpoint(prefix=args_opt.net,
+ directory=local_train_url,
+ config=config_ck)
+ cb += [ckpt_cb]
+
+ # train model
+ model.train(args_opt.epoch_size - config.pretrain_epoch_size,
+ dataset,
+ callbacks=cb)
+ if device_id == 0:
+ ckpt_list = glob.glob("/cache/ckpt/squeezenet*.ckpt")
+
+ if not ckpt_list:
+ print("ckpt file not generated.")
+
+ ckpt_list.sort(key=os.path.getmtime)
+ ckpt_model = ckpt_list[-1]
+ print("checkpoint path", ckpt_model)
+
+ net = squeezenet(args_opt.num_classes)
+
+ frozen_to_air_args = {'ckpt_file': ckpt_model,
+ 'batch_size': 1,
+ 'height': 227,
+ 'width': 227,
+ 'file_name': '/cache/ckpt/squeezenet',
+ 'file_format': 'AIR'}
+ frozen_to_air(net, frozen_to_air_args)
+
+ if args_opt.run_cloudbrain:
+ mox.file.copy_parallel(local_train_url, args_opt.train_url)
diff --git a/research/cv/squeezenet1_1/scripts/docker_start.sh b/research/cv/squeezenet1_1/scripts/docker_start.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a033c3e3d5f6d0032397f027c2e2d9427429fa3d
--- /dev/null
+++ b/research/cv/squeezenet1_1/scripts/docker_start.sh
@@ -0,0 +1,35 @@
+#!/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.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
diff --git a/research/cv/squeezenet1_1/src/config.py b/research/cv/squeezenet1_1/src/config.py
index 33515aa14be07e9cae53fa7ff8231773d986473b..5ae4b114ce158b8b4d2414adc0ed79807160533d 100644
--- a/research/cv/squeezenet1_1/src/config.py
+++ b/research/cv/squeezenet1_1/src/config.py
@@ -18,7 +18,7 @@ network config setting, will be used in train.py and eval.py
from easydict import EasyDict as ed
# config for squeezenet, imagenet
-config = ed({
+config_imagenet = ed({
"class_num": 1000,
"batch_size": 32,
"loss_scale": 1024,
@@ -38,3 +38,23 @@ config = ed({
"lr_end": 0,
"lr_max": 0.01
})
+
+# config for squeezenet, cifar10
+config_cifar = ed({
+ "class_num": 10,
+ "batch_size": 32,
+ "loss_scale": 1024,
+ "momentum": 0.9,
+ "weight_decay": 1e-4,
+ "epoch_size": 120,
+ "pretrain_epoch_size": 0,
+ "save_checkpoint": True,
+ "save_checkpoint_epochs": 1,
+ "keep_checkpoint_max": 10,
+ "save_checkpoint_path": "./",
+ "warmup_epochs": 5,
+ "lr_decay_mode": "poly",
+ "lr_init": 0,
+ "lr_end": 0,
+ "lr_max": 0.01
+})
diff --git a/research/cv/squeezenet1_1/src/dataset.py b/research/cv/squeezenet1_1/src/dataset.py
index 476d80383ab3d6cf5816e1e7d9ba2e221cd12bc3..81bf48ced9b8329d2ec735917480ea0f6c68b05d 100644
--- a/research/cv/squeezenet1_1/src/dataset.py
+++ b/research/cv/squeezenet1_1/src/dataset.py
@@ -13,7 +13,7 @@
# limitations under the License.
# ============================================================================
"""
-create train or eval dataset.
+create train or eval dataset of imagenet and cifar10.
"""
import os
import mindspore.common.dtype as mstype
@@ -102,3 +102,78 @@ def create_dataset_imagenet(dataset_path,
data_set = data_set.repeat(repeat_num)
return data_set
+
+def create_dataset_cifar(dataset_path,
+ do_train,
+ repeat_num=1,
+ batch_size=32,
+ target="Ascend",
+ run_distribute=False):
+ """
+ create a train or evaluate cifar10 dataset
+ Args:
+ dataset_path(string): the path of dataset.
+ do_train(bool): whether dataset is used for train or eval.
+ repeat_num(int): the repeat times of dataset. Default: 1
+ batch_size(int): the batch size of dataset. Default: 32
+ target(str): the device target. Default: Ascend
+
+ Returns:
+ dataset
+ """
+ if target == "Ascend":
+ if run_distribute:
+ device_num = int(os.getenv("RANK_SIZE"))
+ device_id = int(os.getenv("DEVICE_ID"))
+ else:
+ device_num = 1
+ else:
+ raise ValueError("Unsupported device target.")
+
+ if device_num == 1:
+ data_set = ds.Cifar10Dataset(dataset_path,
+ num_parallel_workers=8,
+ shuffle=True)
+ else:
+ data_set = ds.Cifar10Dataset(dataset_path,
+ num_parallel_workers=8,
+ shuffle=True,
+ num_shards=device_num,
+ shard_id=device_id)
+
+ # define map operations
+ if do_train:
+ trans = [
+ C.RandomCrop((32, 32), (4, 4, 4, 4)),
+ C.RandomHorizontalFlip(prob=0.5),
+ C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
+ C.Resize((227, 227)),
+ C.Rescale(1.0 / 255.0, 0.0),
+ C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+ C.CutOut(112),
+ C.HWC2CHW()
+ ]
+ else:
+ trans = [
+ C.Resize((227, 227)),
+ C.Rescale(1.0 / 255.0, 0.0),
+ C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+ C.HWC2CHW()
+ ]
+
+ type_cast_op = C2.TypeCast(mstype.int32)
+
+ data_set = data_set.map(operations=type_cast_op,
+ input_columns="label",
+ num_parallel_workers=8)
+ data_set = data_set.map(operations=trans,
+ input_columns="image",
+ num_parallel_workers=8)
+
+ # apply batch operations
+ data_set = data_set.batch(batch_size, drop_remainder=True)
+
+ # apply dataset repeat operation
+ data_set = data_set.repeat(repeat_num)
+
+ return data_set
diff --git a/research/cv/squeezenet1_1/train.py b/research/cv/squeezenet1_1/train.py
index 9e4fdf3730cbdd87b526e34e2507c6297f067d20..fd4e72b9591b6cde4ad03130b0fc89c2e12dc809 100644
--- a/research/cv/squeezenet1_1/train.py
+++ b/research/cv/squeezenet1_1/train.py
@@ -31,7 +31,7 @@ from mindspore.communication.management import init, get_rank
from src.lr_generator import get_lr
from src.CrossEntropySmooth import CrossEntropySmooth
from src.squeezenet import SqueezeNet as squeezenet
-from src.config import config
+from src.config import config_imagenet as config
from src.dataset import create_dataset_imagenet as create_dataset
parser = argparse.ArgumentParser(description='SqueezeNet1_1')