!1100 [浙江大学][高校贡献][Mindspore][resnetv2_101]-高性能预训练模型提交+精度达标

Merge pull request !1100 from Minhao Lu/master

research/cv/resnetv2/Dockerfile

+ARG FROM_IMAGE_NAME
+FROM ${FROM_IMAGE_NAME}
+
+COPY requirements.txt .
+RUN pip3.7 install -r requirements.txt

research/cv/resnetv2/infer/resnetv2_101/convert/aipp.config

+aipp_op {
+    aipp_mode : static
+    input_format : RGB888_U8
+    csc_switch : false
+    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
+}

research/cv/resnetv2/infer/resnetv2_101/convert/convert_om.sh

+#!/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.
+
+model_path=$1
+output_model_name=$2
+
+/usr/local/Ascend/atc/bin/atc \
+    --model=$model_path \
+    --framework=1 \
+    --output=$output_model_name \
+    --input_format=NCHW --input_shape="actual_input_1:1,3,32,32" \
+    --enable_small_channel=1 \
+    --log=error \
+    --soc_version=Ascend310 \
+    --insert_op_conf=./aipp.config

research/cv/resnetv2/infer/resnetv2_101/data/config/resnetv2.cfg

+CLASS_NUM=10
+SOFTMAX=false
+TOP_K=5

research/cv/resnetv2/infer/resnetv2_101/data/config/resnetv2.pipeline

+{
+    "im_resnetv2": {
+        "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": "32",
+                "resizeWidth": "32"
+            },
+            "factory": "mxpi_imageresize",
+            "next": "mxpi_tensorinfer0"
+        },
+        "mxpi_tensorinfer0": {
+            "props": {
+                "dataSource": "mxpi_imageresize0",
+                "modelPath": "../data/model/resnetv2.om",
+                "waitingTime": "1",
+                "outputDeviceId": "-1"
+            },
+            "factory": "mxpi_tensorinfer",
+            "next": "mxpi_classpostprocessor0"
+        },
+        "mxpi_classpostprocessor0": {
+            "props": {
+                "dataSource": "mxpi_tensorinfer0",
+                "postProcessConfigPath": "../data/config/resnetv2.cfg",
+                "labelPath": "../data/config/cifar10.names",
+                "postProcessLibPath": "/usr/local/sdk_home/mxManufacture/lib/modelpostprocessors/libresnet50postprocess.so"
+            },
+            "factory": "mxpi_classpostprocessor",
+            "next": "mxpi_dataserialize0"
+        },
+        "mxpi_dataserialize0": {
+            "props": {
+                "outputDataKeys": "mxpi_classpostprocessor0"
+            },
+            "factory": "mxpi_dataserialize",
+            "next": "appsink0"
+        },
+        "appsink0": {
+            "props": {
+                "blocksize": "4096000"
+            },
+            "factory": "appsink"
+        }
+    }
+}

research/cv/resnetv2/infer/resnetv2_101/data/images/cifar10.py

+#!/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.
+""" cifar10.py """
+
+import os
+import cv2
+import numpy as np
+
+loc_1 = './train_cifar10/'
+loc_2 = './test_cifar10/'
+
+
+def unpickle(file_name):
+    import pickle
+    with open(file_name, 'rb') as fo:
+        dict_res = pickle.load(fo, encoding='bytes')
+    return dict_res
+
+
+def convert_train_data(file_dir):
+    """ ./train_cifar10/ """
+    if not os.path.exists(loc_1):
+        os.mkdir(loc_1)
+    for i in range(1, 6):
+        data_name = os.path.join(file_dir, 'data_batch_' + str(i))
+        data_dict = unpickle(data_name)
+        print('{} is processing'.format(data_name))
+        for j in range(10000):
+            img = np.reshape(data_dict[b'data'][j], (3, 32, 32))
+            img = np.transpose(img, (1, 2, 0))
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            img_name = "%s%s%s.jpg" % (loc_1, str(data_dict[b'labels'][j]), str((i) * 10000 + j))
+            cv2.imwrite(img_name, img)
+        print('{} is done'.format(data_name))
+
+
+def convert_test_data(file_dir):
+    """ ./test_cifar10/ && test_label.txt """
+    if not os.path.exists(loc_2):
+        os.mkdir(loc_2)
+    test_data_name = file_dir + '/test_batch'
+    print('{} is processing'.format(test_data_name))
+    test_dict = unpickle(test_data_name)
+    for m in range(10000):
+        img = np.reshape(test_dict[b'data'][m], (3, 32, 32))
+        img = np.transpose(img, (1, 2, 0))
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img_name = '%s%s%s%s' % (loc_2, str(test_dict[b'labels'][m]), str(10000 + m), '.jpg')
+        img_label = "%s%s.jpg" % (str(test_dict[b'labels'][m]), str(10000 + m))
+        cv2.imwrite(img_name, img)
+        with open("test_label.txt", "a") as f:
+            f.write(img_label + " " * 10 + str(test_dict[b'labels'][m]))
+            f.write("\n")
+    print("{} is done".format(test_data_name))
+
+
+def cifar10_img():
+    """
+    from    ./cifar-10-batches-py
+    to      ./train_cifar10/ ./test_cifar10/ ./test_label.txt
+    """
+    file_dir = './cifar-10-batches-py'
+    convert_train_data(file_dir)
+    convert_test_data(file_dir)
+    print('Finish transforming to image')
+
+
+if __name__ == '__main__':
+    cifar10_img()

research/cv/resnetv2/infer/resnetv2_101/docker_start_infer.sh

+#!/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=$3
+
+
+function show_help() {
+    echo "Usage: docker_start.sh docker_image data_dir"
+}
+
+function param_check() {
+    if [ -z "${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

research/cv/resnetv2/infer/resnetv2_101/mxbase/CMakeLists.txt

+cmake_minimum_required(VERSION 3.14.0)
+project(resnetv2)
+set(TARGET resnetv2)
+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)
+
+if(DEFINED ENV{MXSDK_OPENSOURCE_DIR})
+    set(OPENSOURCE_DIR $ENV{MXSDK_OPENSOURCE_DIR})
+else()
+    set(OPENSOURCE_DIR ${MXBASE_ROOT_DIR}/opensource)
+endif()
+
+include_directories(${ACL_INC_DIR})
+include_directories(${OPENSOURCE_DIR}/include)
+include_directories(${OPENSOURCE_DIR}/include/opencv4)
+
+include_directories(${MXBASE_INC})
+include_directories(${MXBASE_POST_PROCESS_DIR})
+link_directories(${ACL_LIB_DIR})
+link_directories(${OPENSOURCE_DIR}/lib)
+link_directories(${MXBASE_LIB_DIR})
+link_directories(${MXBASE_POST_LIB_DIR})
+
+add_executable(${TARGET} src/main.cpp src/Resnetv2.cpp)
+
+target_link_libraries(${TARGET} glog cpprest mxbase resnet50postprocess opencv_world)
+
+install(TARGETS ${TARGET} RUNTIME DESTINATION ${PROJECT_SOURCE_DIR}/)

research/cv/resnetv2/infer/resnetv2_101/mxbase/build.sh

+#!/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_HOME}" ]; then
+        export ASCEND_HOME=/usr/local/Ascend/
+        echo "Set ASCEND_HOME to the default value: ${ASCEND_HOME}"
+    else
+        echo "ASCEND_HOME is set to ${ASCEND_HOME} by user"
+    fi
+
+    if [ ! "${ASCEND_VERSION}" ]; then
+        export ASCEND_VERSION=nnrt/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()
+{
+    cd $path_cur
+    rm -rf build
+    mkdir -p build
+    cd build
+    cmake ..
+    make
+    ret=$?
+    if [ ${ret} -ne 0 ]; then
+        echo "Failed to build resnetv2."
+        exit ${ret}
+    fi
+    make install
+}
+
+check_env
+build

research/cv/resnetv2/infer/resnetv2_101/mxbase/src/Resnetv2.cpp

+/*
+ * 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 <map>
+#include <memory>
+#include <string>
+#include <vector>
+#include "Resnetv2.h"
+#include "MxBase/DeviceManager/DeviceManager.h"
+#include "MxBase/Log/Log.h"
+
+namespace {
+    const uint32_t YUV_BYTE_NU = 3;
+    const uint32_t YUV_BYTE_DE = 2;
+    const uint32_t VPC_H_ALIGN = 2;
+    const uint32_t MAX_LENGTH = 128;
+}
+
+APP_ERROR Resnetv2::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;
+    }
+    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 << "Resnetv2PostProcess init failed, ret = " << ret << ".";
+        return ret;
+    }
+
+    return APP_ERR_OK;
+}
+
+APP_ERROR Resnetv2::DeInit() {
+    model_->DeInit();
+    post_->DeInit();
+    MxBase::DeviceManager::GetInstance()->DestroyDevices();
+    return APP_ERR_OK;
+}
+
+APP_ERROR Resnetv2::ReadImage(const std::string &imgPath, cv::Mat *imageMat) {
+    *imageMat = cv::imread(imgPath, cv::IMREAD_COLOR);
+    LogInfo << "image size: " << imageMat->size();
+    return APP_ERR_OK;
+}
+
+APP_ERROR Resnetv2::ResizeImage(cv::Mat *imageMat) {
+    static constexpr uint32_t resizeHeight = 32;
+    static constexpr uint32_t resizeWidth = 32;
+    cv::resize(*imageMat, *imageMat, cv::Size(resizeWidth, resizeHeight));
+    return APP_ERR_OK;
+}
+
+APP_ERROR Resnetv2::CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase *tensorBase) {
+    uint32_t batchSize = modelDesc_.inputTensors[0].tensorDims[0];
+    const uint32_t dataSize =  imageMat.cols *  imageMat.rows * MxBase::YUV444_RGB_WIDTH_NU * batchSize;
+    LogInfo << "image size after crop: [" << imageMat.cols << " x " << 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 * batchSize,
+        static_cast<uint32_t>(imageMat.cols)};
+    *tensorBase = MxBase::TensorBase(memoryDataDst, false, shape, MxBase::TENSOR_DTYPE_UINT8);
+    return APP_ERR_OK;
+}
+
+APP_ERROR Resnetv2::Inference(const std::vector<MxBase::TensorBase> &inputs,
+                                      std::vector<MxBase::TensorBase> *outputs) {
+    auto dtypes = model_->GetOutputDataType();
+    for (size_t i = 0; i < modelDesc_.outputTensors.size(); ++i) {
+        std::vector<uint32_t> shape = {};
+        for (size_t j = 0; j < modelDesc_.outputTensors[i].tensorDims.size(); ++j) {
+            shape.push_back((uint32_t)modelDesc_.outputTensors[i].tensorDims[j]);
+        }
+        MxBase::TensorBase tensor(shape, dtypes[i],  MxBase::MemoryData::MemoryType::MEMORY_DEVICE, deviceId_);
+        APP_ERROR ret = MxBase::TensorBase::TensorBaseMalloc(tensor);
+        if (ret != APP_ERR_OK) {
+            LogError << "TensorBaseMalloc failed, ret = " << ret << ".";
+            return ret;
+        }
+        outputs->push_back(tensor);
+    }
+    MxBase::DynamicInfo dynamicInfo = {};
+    dynamicInfo.dynamicType = MxBase::DynamicType::STATIC_BATCH;
+    auto startTime = std::chrono::high_resolution_clock::now();
+    APP_ERROR ret = model_->ModelInference(inputs, *outputs, dynamicInfo);
+    auto endTime = std::chrono::high_resolution_clock::now();
+    double costMs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
+    inferCostTimeMilliSec += costMs;
+    if (ret != APP_ERR_OK) {
+        LogError << "ModelInference failed, ret = " << ret << ".";
+        return ret;
+    }
+    return APP_ERR_OK;
+}
+
+APP_ERROR Resnetv2::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 Resnetv2::SaveResult(const std::string &imgPath, const std::string &resPath,
+                                        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 = resPath + "/" + 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 Resnetv2::Process(const std::string &imgPath, const std::string &resPath) {
+    cv::Mat imageMat;
+    APP_ERROR ret = ReadImage(imgPath, &imageMat);
+    if (ret != APP_ERR_OK) {
+        LogError << "ReadImage failed, ret = " << ret << ".";
+        return ret;
+    }
+    ret = ResizeImage(&imageMat);
+    if (ret != APP_ERR_OK) {
+        LogError << "ResizeImage failed, ret = " << ret << ".";
+        return ret;
+    }
+    std::vector<MxBase::TensorBase> inputs = {};
+    std::vector<MxBase::TensorBase> outputs = {};
+    MxBase::TensorBase tensorBase;
+    ret = CVMatToTensorBase(imageMat, &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();
+    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, resPath, BatchClsInfos);
+    if (ret != APP_ERR_OK) {
+        LogError << "Save infer results into file failed. ret = " << ret << ".";
+        return ret;
+    }
+    return APP_ERR_OK;
+}

research/cv/resnetv2/infer/resnetv2_101/mxbase/src/Resnetv2.h

+/*
+ * 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 <string>
+#include <vector>
+#include <memory>
+#ifndef MxBase_ALEXNET_H
+#define MxBase_ALEXNET_H
+#include <opencv2/opencv.hpp>
+
+#include "MxBase/DvppWrapper/DvppWrapper.h"
+#include "MxBase/ModelInfer/ModelInferenceProcessor.h"
+#include "MxBase/postprocess/include/ClassPostProcessors/Resnet50PostProcess.h"
+#include "MxBase/Tensor/TensorContext/TensorContext.h"
+
+
+struct InitParam {
+    uint32_t deviceId;
+    std::string labelPath;
+    uint32_t classNum;
+    uint32_t topk;
+    bool softmax;
+    bool checkTensor;
+    std::string modelPath;
+};
+
+struct ImageShape {
+    uint32_t width;
+    uint32_t height;
+};
+
+class Resnetv2 {
+ public:
+     APP_ERROR Init(const InitParam &initParam);
+     APP_ERROR DeInit();
+     APP_ERROR ReadImage(const std::string &imgPath, cv::Mat *imageMat);
+     APP_ERROR ResizeImage(cv::Mat *imageMat);
+     APP_ERROR CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase *tensorBase);
+     APP_ERROR Inference(const std::vector<MxBase::TensorBase> &inputs, std::vector<MxBase::TensorBase> *outputs);
+     APP_ERROR PostProcess(const std::vector<MxBase::TensorBase> &inputs,
+                              std::vector<std::vector<MxBase::ClassInfo>> *clsInfos);
+     APP_ERROR Process(const std::string &imgPath, const std::string &resPath);
+     double GetInferCostMilliSec() const {return inferCostTimeMilliSec;}
+ private:
+     APP_ERROR SaveResult(const std::string &imgPath, const std::string &resPath,
+            const std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos);
+ private:
+     std::shared_ptr<MxBase::ModelInferenceProcessor> model_;
+     std::shared_ptr<MxBase::Resnet50PostProcess> post_;
+     MxBase::ModelDesc modelDesc_;
+     uint32_t deviceId_ = 0;
+     double inferCostTimeMilliSec = 0.0;
+};
+#endif

research/cv/resnetv2/infer/resnetv2_101/mxbase/src/main.cpp

+/*
+ * 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 <dirent.h>
+#include "Resnetv2.h"
+#include "MxBase/Log/Log.h"
+
+
+namespace {
+    const uint32_t CLASS_NUM = 10;
+}
+
+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 << path.c_str();
+        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);
+    }
+    LogInfo << "opendir ok. dir:";
+    closedir(dirPtr);
+    return APP_ERR_OK;
+}
+
+int main(int argc, char* argv[]) {
+    if (argc <= 2) {
+        LogWarn << "Please input image path and result path, such as './resnetv2 image_dir res_dir'";
+        return APP_ERR_OK;
+    }
+
+    InitParam initParam = {};
+    initParam.deviceId = 0;
+    initParam.classNum = CLASS_NUM;
+    initParam.labelPath = "../data/config/cifar10.names";
+    initParam.topk = 5;
+    initParam.softmax = false;
+    initParam.checkTensor = true;
+    initParam.modelPath = "../data/model/resnetv2.om";
+    auto resnetv2 = std::make_shared<Resnetv2>();
+    APP_ERROR ret = resnetv2->Init(initParam);
+    if (ret != APP_ERR_OK) {
+        resnetv2->DeInit();
+        LogError << "Resnetv2Classify 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) {
+        return ret;
+    }
+    std::string resPath = argv[2];
+    auto startTime = std::chrono::high_resolution_clock::now();
+    for (auto &imgFile : imgFilePaths) {
+        ret = resnetv2->Process(imgFile, resPath);
+        if (ret != APP_ERR_OK) {
+            LogError << "Resnetv2Classify process failed, ret = " << ret << ".";
+            resnetv2->DeInit();
+            return ret;
+        }
+    }
+    auto endTime = std::chrono::high_resolution_clock::now();
+    resnetv2->DeInit();
+    double costMilliSecs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
+    double fps = 1000.0 * imgFilePaths.size() / resnetv2->GetInferCostMilliSec();
+    LogInfo << "[Process Delay] cost:" << costMilliSecs << " ms\tfps: " << fps << "imgs/sec";
+    return APP_ERR_OK;
+}
+

research/cv/resnetv2/infer/resnetv2_101/sdk/classification_task_metric.py

+#!/usr/bin/env python
+
+# 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.py """
+
+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)
+                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_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:
+        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):
+    """ create_visualization_statistical_result """
+    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, indent=4)
+    writer.close()
+
+def run():
+    """ run """
+    if len(sys.argv) == 5:
+        #  Target file folder 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)
+
+if __name__ == '__main__':
+    run()

research/cv/resnetv2/infer/resnetv2_101/sdk/main.py

+# 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.
+""" main.py """
+
+import datetime
+import json
+import os
+import sys
+from StreamManagerApi import StreamManagerApi, MxDataInput
+
+def info(msg):
+    nowtime = datetime.datetime.now().isoformat()
+    print("[INFO][%s %d %s] %s" %(nowtime, os.getpid(), __file__, msg))
+
+def warn(msg):
+    nowtime = datetime.datetime.now().isoformat()
+    print("\033[33m[WARN][%s %d %s] %s\033[0m" %(nowtime, os.getpid(), __file__, msg))
+
+def err(msg):
+    nowtime = datetime.datetime.now().isoformat()
+    print("\033[31m[ERROR][%s %d %s] %s\033[0m" %(nowtime, os.getpid(), __file__, msg))
+
+if __name__ == '__main__':
+    # init stream manager
+    stream_manager_api = StreamManagerApi()
+    ret = stream_manager_api.InitManager()
+    if ret != 0:
+        err("Failed to init Stream manager, ret=%s" % str(ret))
+        exit()
+
+    # create streams by pipeline config file
+    with open("../data/config/resnetv2.pipeline", 'rb') as f:
+        pipelineStr = f.read()
+    ret = stream_manager_api.CreateMultipleStreams(pipelineStr)
+
+    if ret != 0:
+        err("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)
+    if not os.path.exists(res_dir_name):
+        os.makedirs(res_dir_name)
+
+    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()
+        info("Read data from %s" % file_path)
+
+        empty_data = []
+        stream_name = b'im_resnetv2'
+        in_plugin_id = 0
+
+        start_time = datetime.datetime.now()
+
+        unique_id = stream_manager_api.SendData(stream_name, in_plugin_id,
+                                                data_input)
+        if unique_id < 0:
+            err("Failed to send data to stream.")
+            exit()
+        # Obtain the inference result by specifying streamName and uniqueId.
+        infer_result = stream_manager_api.GetResult(stream_name, unique_id)
+        end_time = datetime.datetime.now()
+        info('sdk run time: {}us'.format((end_time - start_time).microseconds))
+        if infer_result.errorCode != 0:
+            err("GetResultWithUniqueId error. errorCode=%d, errorMsg=%s" %
+                (infer_result.errorCode, infer_result.data.decode()))
+            exit()
+        # print the infer result
+        infer_res = infer_result.data.decode()
+        info("process img: {}, infer result: {}".format(file_name, infer_res))
+        load_dict = json.loads(infer_result.data.decode())
+        if load_dict.get('MxpiClass') is None:
+            with open(res_dir_name + "/" + file_name.split('.')[0] + '.txt',
+                      'w') as f_write:
+                f_write.write("")
+            continue
+        res_vec = load_dict.get('MxpiClass')
+        with open(res_dir_name + "/" + file_name.split('.')[0] + '_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()

research/cv/resnetv2/infer/resnetv2_101/sdk/run.sh

+#!/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.py $image_path  $result_dir
+exit 0

research/cv/resnetv2/modelarts/resnetv2_101/train_start.py

+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 3.0 (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 resnetv2."""
+
+import argparse
+import os
+import numpy as np
+
+from mindspore.nn import Momentum
+from mindspore import Model, Tensor, load_checkpoint, load_param_into_net, export, context
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.nn import SoftmaxCrossEntropyWithLogits
+from mindspore.common import set_seed
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
+
+# should find /src
+
+from src.lr_generator import get_lr
+from src.CrossEntropySmooth import CrossEntropySmooth
+
+parser = argparse.ArgumentParser('mindspore resnetv2 training')
+
+parser.add_argument('--net', type=str, default='resnetv2_50',
+                    help='Resnetv2 Model, resnetv2_50, resnetv2_101, resnetv2_152')
+parser.add_argument('--dataset', type=str, default='cifar10',
+                    help='Dataset, cifar10, imagenet2012')
+parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
+                    help='device where the code will be implemented (default: Ascend)')
+parser.add_argument('--train_url', type=str, required=True, default='',
+                    help='where training ckpts saved')
+parser.add_argument('--data_url', type=str, required=True, default='',
+                    help='path of dataset')
+
+# train
+parser.add_argument('--pre_trained', type=str, default=None, help='pretrained checkpoint path')
+parser.add_argument('--epoch_size', type=int, default=None, help='epochs')
+parser.add_argument('--lr_init', type=float, default=None, help='base learning rate')
+
+# export
+parser.add_argument('--width', type=int, default=32, help='input width')
+parser.add_argument('--height', type=int, default=32, help='input height')
+parser.add_argument('--file_name', type=str, default='resnetv2', help='output air file name')
+parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
+
+args, _ = parser.parse_known_args()
+
+# import net
+if args.net == "resnetv2_50":
+    from src.resnetv2 import PreActResNet50 as resnetv2
+elif args.net == 'resnetv2_101':
+    from src.resnetv2 import PreActResNet101 as resnetv2
+elif args.net == 'resnetv2_152':
+    from src.resnetv2 import PreActResNet152 as resnetv2
+else:
+    raise ValueError("network is not support.")
+
+# import dataset and config
+if args.dataset == "cifar10":
+    from src.dataset import create_dataset1 as create_dataset
+    from src.config import config1 as config
+elif args.dataset == "cifar100":
+    from src.dataset import create_dataset2 as create_dataset
+    from src.config import config2 as config
+elif args.dataset == 'imagenet2012':
+    from src.dataset import create_dataset3 as create_dataset
+    from src.config import config3 as config
+else:
+    raise ValueError("dataset is not support.")
+
+def _train():
+    """ train """
+    print("============== Starting Training ==============")
+    target = args.device_target
+
+    # init context
+    context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
+
+    # create dataset
+    dataset = create_dataset(dataset_path=args.data_url, do_train=True, repeat_num=1,
+                             batch_size=config.batch_size, target=target)
+    step_size = dataset.get_dataset_size()
+
+    # define net
+    epoch_size = args.epoch_size if args.epoch_size else config.epoch_size
+    net = resnetv2(config.class_num, config.low_memory)
+
+    # init weight
+    if args.pre_trained:
+        param_dict = load_checkpoint(args.pre_trained)
+        load_param_into_net(net, param_dict)
+
+    # init lr
+    lr_init = args.lr_init if args.lr_init else config.lr_init
+    lr = get_lr(lr_init=lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
+                warmup_epochs=config.warmup_epochs, total_epochs=epoch_size, steps_per_epoch=step_size,
+                lr_decay_mode=config.lr_decay_mode)
+    lr = Tensor(lr)
+
+    # define loss, opt, model
+    if args.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")
+    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)
+    model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
+
+    # define callbacks
+    time_cb = TimeMonitor(data_size=step_size)
+    loss_cb = LossMonitor()
+
+    config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
+                                 keep_checkpoint_max=config.keep_checkpoint_max)
+    ckpt_save_dir = args.train_url if args.train_url else config.save_checkpoint_path
+    ckpoint_cb = ModelCheckpoint(prefix=f"train_{args.net}_{args.dataset}",
+                                 directory=ckpt_save_dir, config=config_ck)
+
+    # train
+    callbacks = [time_cb, loss_cb, ckpoint_cb]
+    model.train(epoch_size, dataset, callbacks=callbacks)
+
+def _get_last_ckpt(ckpt_dir):
+    """ get ckpt """
+    ckpt_files = [(os.stat(os.path.join(ckpt_dir, ckpt_file)).st_ctime, 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, max(ckpt_files)[1])
+
+def _export_air():
+    """ export air """
+    print("============== Starting Exporting ==============")
+    ckpt_file = _get_last_ckpt(args.train_url)
+    if not ckpt_file:
+        return
+
+    net = resnetv2(config.class_num)
+    param_dict = load_checkpoint(ckpt_file)
+    load_param_into_net(net, param_dict)
+
+    input_arr = Tensor(np.zeros([config.batch_size, 3, args.height, args.width], np.float32))
+    export(net, input_arr, file_name=os.path.join(args.train_url, args.file_name), file_format=args.file_format)
+
+if __name__ == '__main__':
+    set_seed(1)
+    _train()
+    _export_air()

research/cv/resnetv2/scripts/docker_start.sh

+#!/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
\ No newline at end of file