dengjian

.jenkins/check/config/filter_cpplint.txt

@@ -51,6 +51,9 @@
 
 "models/research/cv/ibnnet/infer/mxbase/src/IbnnetOpencv.h" "runtime/references"
 
+"models/official/cv/nasnet/infer/mxbase/NASNet_A_MobileClassifyOpencv.h"  "runtime/references"
+"models/official/cv/nasnet/infer/mxbase/main_opencv.cpp"    "runtime/references"
+
 "models/official/cv/shufflenetv2/infer/mxbase/ShuffleNetV2ClassifyOpencv.h"  "runtime/references"
 "models/official/cv/shufflenetv2/infer/mxbase/main_opencv.cpp"    "runtime/references"
 

official/cv/nasnet/Dockerfile

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

official/cv/nasnet/export.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """export checkpoint file into AIR MINDIR ONNX models"""
 import argparse
+import ast
 import numpy as np
 
 import mindspore as ms
@@ -33,7 +34,13 @@ if __name__ == '__main__':
     parser.add_argument("--device_target", type=str, default="Ascend",
                         choices=["Ascend", "GPU", "CPU"],
                         help="device where the code will be implemented (default: Ascend)")
+    parser.add_argument('--overwrite_config', type=ast.literal_eval, default=False,
+                        help='whether to overwrite the config according to the arguments')
+    parser.add_argument('--num_classes', type=int, default=1000, help='number of classes')
+
     args = parser.parse_args()
+    if args.overwrite_config:
+        cfg.num_classes = args.num_classes
 
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
     if args.device_target == "Ascend" or args.device_target == "GPU":

official/cv/nasnet/infer/classification_task_metric.py

+#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.
+
+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.int_(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]
+    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,
+                                            sys.argv[4], # json_file_name,
+                                            img_label_dict, topn=5)

official/cv/nasnet/infer/convert/aipp.config

+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 : 127.5
+    min_chn_1 : 127.5
+    min_chn_2 : 127.5
+    var_reci_chn_0 : 0.00784
+    var_reci_chn_1 : 0.00784
+    var_reci_chn_2 : 0.00784
+}

official/cv/nasnet/infer/convert/air2om.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.
+if [ $# != 2 ]
+then
+    echo "Usage: sh air2om.sh [INPUT_MODEL_FILE] [OUTPUT_MODEL_NAME]"
+exit 1
+fi
+
+# check the INPUT_MODEL_FILE
+if [ ! -f $1 ]
+then
+    echo "error: INPUT_MODEL_FILE=$1 is not a file"
+exit 1
+fi
+
+input_model_file=$1
+output_model_name=$2
+
+/usr/local/Ascend/atc/bin/atc \
+--model=$input_model_file \
+--framework=1 \
+--output=$output_model_name \
+--input_format=NCHW --input_shape="actual_input_1:1,3,224,224" \
+--disable_reuse_memory=0 \
+--enable_small_channel=0 \
+--log=error \
+--soc_version=Ascend310 \
+--insert_op_conf=./aipp.config

official/cv/nasnet/infer/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
+data_dir=$2
+
+function show_help() {
+    echo "Usage: docker_start.sh docker_image data_dir"
+}
+
+function param_check() {
+    if [ -z "${docker_image}" ]; then
+        echo "please input docker_image"
+        show_help
+        exit 1
+    fi
+
+    if [ -z "${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 ${data_dir}:${data_dir} \
+  ${docker_image} \
+  /bin/bash

official/cv/nasnet/infer/mxbase/CMakeLists.txt

+cmake_minimum_required(VERSION 3.14.0)
+project(nasnet_a_mobile)
+set(TARGET nasnet_a_mobile)
+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(${TARGET} main_opencv.cpp NASNet_A_MobileClassifyOpencv.cpp)
+
+target_link_libraries(${TARGET} glog cpprest mxbase resnet50postprocess opencv_world stdc++fs)
+
+install(TARGETS ${TARGET} RUNTIME DESTINATION ${PROJECT_SOURCE_DIR}/)
+

official/cv/nasnet/infer/mxbase/NASNet_A_MobileClassifyOpencv.cpp

+/*
+ * Copyright (c) 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 "NASNet_A_MobileClassifyOpencv.h"
+#include "MxBase/DeviceManager/DeviceManager.h"
+#include "MxBase/Log/Log.h"
+
+APP_ERROR NASNet_A_MobileClassifyOpencv::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 << "Resnet50PostProcess init failed, ret=" << ret << ".";
+        return ret;
+    }
+    return APP_ERR_OK;
+}
+
+APP_ERROR NASNet_A_MobileClassifyOpencv::DeInit() {
+    model_->DeInit();
+    post_->DeInit();
+    MxBase::DeviceManager::GetInstance()->DestroyDevices();
+    return APP_ERR_OK;
+}
+
+APP_ERROR NASNet_A_MobileClassifyOpencv::ReadImage(const std::string &imgPath, cv::Mat &imageMat) {
+    imageMat = cv::imread(imgPath, cv::IMREAD_COLOR);
+    return APP_ERR_OK;
+}
+
+APP_ERROR NASNet_A_MobileClassifyOpencv::ResizeImage(const cv::Mat &srcImageMat, cv::Mat &dstImageMat) {
+    static constexpr uint32_t resizeHeight = 304;
+    static constexpr uint32_t resizeWidth  = 304;
+
+    cv::resize(srcImageMat, dstImageMat, cv::Size(resizeWidth, resizeHeight));
+    return APP_ERR_OK;
+}
+
+APP_ERROR NASNet_A_MobileClassifyOpencv::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 NASNet_A_MobileClassifyOpencv::Crop(const cv::Mat &srcImageMat, cv::Mat &dstImageMat) {
+    static cv::Rect rectOfImg(40, 40, 224, 224);
+
+    dstImageMat = srcImageMat(rectOfImg).clone();
+    return APP_ERR_OK;
+}
+
+APP_ERROR NASNet_A_MobileClassifyOpencv::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();  // save time
+    inferCostTimeMilliSec += costMs;
+    if (ret != APP_ERR_OK) {
+        LogError << "ModelInference failed, ret=" << ret << ".";
+        return ret;
+    }
+    return APP_ERR_OK;
+}
+
+APP_ERROR NASNet_A_MobileClassifyOpencv::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 NASNet_A_MobileClassifyOpencv::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 NASNet_A_MobileClassifyOpencv::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;
+}

official/cv/nasnet/infer/mxbase/NASNet_A_MobileClassifyOpencv.h

+/*
+ * Copyright (c) 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_NASNET_A_MOBILECLASSIFYOPENCV_H
+#define MXBASE_NASNET_A_MOBILECLASSIFYOPENCV_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 NASNet_A_MobileClassifyOpencv {
+ 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::ModelInferenceProcessor> model_;
+     std::shared_ptr<MxBase::Resnet50PostProcess> post_;
+     MxBase::ModelDesc modelDesc_;
+     uint32_t deviceId_ = 0;
+     // infer time
+     double inferCostTimeMilliSec = 0.0;
+};
+
+#endif

official/cv/nasnet/infer/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_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_nasnet_a_mobile()
+{
+    cd $path_cur
+    rm -rf build
+    mkdir -p build
+    cd build
+    cmake ..
+    make
+    ret=$?
+    if [ ${ret} -ne 0 ]; then
+        echo "Failed to build nasnet_a_mobile."
+        exit ${ret}
+    fi
+    make install
+}
+
+check_env
+build_nasnet_a_mobile
\ No newline at end of file

official/cv/nasnet/infer/mxbase/main_opencv.cpp

+/*
+ * Copyright (c) 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 "NASNet_A_MobileClassifyOpencv.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 './imagenet/val'.";
+        return APP_ERR_OK;
+    }
+
+    InitParam initParam = {};
+    initParam.deviceId = 0;
+    initParam.classNum = CLASS_NUM;
+    initParam.labelPath = "../imagenet1000_clsidx_to_labels.names";
+    initParam.topk = 5;
+    initParam.softmax = false;
+    initParam.checkTensor = true;
+    initParam.modelPath = "../nasnet_a_mobile.om";
+    auto nasnet_a_mobile = std::make_shared<NASNet_A_MobileClassifyOpencv>();
+    APP_ERROR ret = nasnet_a_mobile->Init(initParam);
+    if (ret != APP_ERR_OK) {
+        LogError << "NASNet_A_MobileClassify 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;
+    }
+    auto startTime = std::chrono::high_resolution_clock::now();
+    for (auto &imgFile : imgFilePaths) {
+        ret = nasnet_a_mobile->Process(imgFile);
+        if (ret != APP_ERR_OK) {
+            LogError << "NASNet_A_MobileClassify process failed, ret=" << ret << ".";
+            nasnet_a_mobile->DeInit();
+            return ret;
+        }
+    }
+    auto endTime = std::chrono::high_resolution_clock::now();
+    nasnet_a_mobile->DeInit();
+    double costMilliSecs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
+    double fps = 1000.0 * imgFilePaths.size() / nasnet_a_mobile->GetInferCostMilliSec();
+    LogInfo << "[Process Delay] cost: " << costMilliSecs << " ms\tfps: " << fps << " imgs/sec";
+    return APP_ERR_OK;
+}

official/cv/nasnet/infer/mxbase/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.
+if [ $# != 1 ]
+then
+    echo "Usage: sh run.sh [DATASET_VAL_PATH]"
+exit 1
+fi
+
+# check the DATASET_VAL_PATH
+if [ ! -d $1 ]
+then
+    echo "error: DATASET_VAL_PATH=$1 is not a path"
+exit 1
+fi
+
+export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/lib/modelpostprocessors:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:${LD_LIBRARY_PATH}
+
+# run
+./nasnet_a_mobile $1

official/cv/nasnet/infer/sdk/main.py

+# coding=utf-8
+
+"""
+Copyright (c) 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.
+"""
+
+import datetime
+import json
+import os
+import sys
+
+from StreamManagerApi import StreamManagerApi
+from StreamManagerApi import MxDataInput
+
+def run():
+    # init stream manager
+    stream_manager_api = StreamManagerApi()
+    ret = stream_manager_api.InitManager()
+    if ret != 0:
+        print("Failed to init Stream manager, ret=%s" % str(ret))
+        return
+
+    # create streams by pipeline config file
+    with open("nasnet_a_mobile.pipeline", 'rb') as f:
+        pipelineStr = f.read()
+    ret = stream_manager_api.CreateMultipleStreams(pipelineStr)
+
+    if ret != 0:
+        print("Failed to create Stream, ret=%s" % str(ret))
+        return
+
+    # Construct the input of the stream
+    data_input = MxDataInput()
+
+    dir_name = sys.argv[1]
+    res_dir_name = sys.argv[2]
+    file_list = os.listdir(dir_name)
+    if not os.path.exists(res_dir_name):
+        os.makedirs(res_dir_name)
+
+    for file_name in file_list:
+        file_path = os.path.join(dir_name, file_name)
+        if not (file_name.lower().endswith(".jpg") or file_name.lower().endswith(".jpeg")):
+            continue
+
+        with open(file_path, 'rb') as f:
+            data_input.data = f.read()
+
+        stream_name = b'im_nasnet'
+        in_plugin_id = 0
+        unique_id = stream_manager_api.SendData(stream_name, in_plugin_id, data_input)
+        if unique_id < 0:
+            print("Failed to send data to stream.")
+            return
+        # Obtain the inference result by specifying streamName and uniqueId.
+        start_time = datetime.datetime.now()
+        infer_result = stream_manager_api.GetResult(stream_name, unique_id)
+        end_time = datetime.datetime.now()
+        print('sdk run time: {}'.format((end_time - start_time).microseconds))
+        if infer_result.errorCode != 0:
+            print("GetResultWithUniqueId error. errorCode=%d, errorMsg=%s" % (
+                infer_result.errorCode, infer_result.data.decode()))
+            return
+        # print the infer result
+        infer_res = infer_result.data.decode()
+        print("process img: {}, infer result: {}".format(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[:-5] + '.txt', 'w') as f_write:
+                f_write.write("")
+            continue
+        res_vec = load_dict.get('MxpiClass')
+
+        with open(res_dir_name + "/" + file_name[:-5] + '_1.txt', 'w') as f_write:
+            res_list = [str(item.get("classId")) + " " for item in res_vec]
+            f_write.writelines(res_list)
+            f_write.write('\n')
+
+    # destroy streams
+    stream_manager_api.DestroyAllStreams()
+
+if __name__ == '__main__':
+    run()

official/cv/nasnet/infer/sdk/nasnet_a_mobile.cfg

+CLASS_NUM=1000
+SOFTMAX=false
+TOP_K=5

official/cv/nasnet/infer/sdk/nasnet_a_mobile.pipeline

+{
+    "im_nasnet": {
+        "stream_config": {
+            "deviceId": "0"
+        },
+        "appsrc1": {
+            "props": {
+                "blocksize": "409600"
+            },
+            "factory": "appsrc",
+            "next": "mxpi_imagedecoder0"
+        },
+        "mxpi_imagedecoder0": {
+            "props": {
+                "handleMethod": "opencv"
+            },
+            "factory": "mxpi_imagedecoder",
+            "next": "mxpi_imageresize0"
+        },
+        "mxpi_imageresize0": {
+            "props": {
+                "handleMethod": "opencv",
+                "resizeType": "Resizer_Stretch",
+                "resizeHeight": "304",
+                "resizeWidth": "304"
+            },
+            "factory": "mxpi_imageresize",
+            "next": "mxpi_opencvcentercrop0"
+        },
+        "mxpi_opencvcentercrop0": {
+            "props": {
+                "dataSource": "mxpi_imageresize0",
+                "cropHeight": "224",
+                "cropWidth": "224"
+            },
+            "factory": "mxpi_opencvcentercrop",
+            "next": "mxpi_tensorinfer0"
+        },
+        "mxpi_tensorinfer0": {
+            "props": {
+                "dataSource": "mxpi_opencvcentercrop0",
+                "modelPath": "../nasnet_a_mobile.om",
+                "waitingTime": "2000",
+                "outputDeviceId": "-1"
+            },
+            "factory": "mxpi_tensorinfer",
+            "next": "mxpi_classpostprocessor0"
+        },
+        "mxpi_classpostprocessor0": {
+            "props": {
+                "dataSource": "mxpi_tensorinfer0",
+                "postProcessConfigPath": "nasnet_a_mobile.cfg",
+                "labelPath": "../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"
+        }
+    }
+}

official/cv/nasnet/infer/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.
+if [ $# != 2 ]
+then
+    echo "Usage: sh run.sh [IMAGE_PATH] [RESULT_DIR]"
+exit 1
+fi
+
+# check the DATASET_VAL_PATH
+if [ ! -d $1 ]
+then
+    echo "error: IMAGE_PATH=$1 is not a path"
+exit 1
+fi
+
+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
\ No newline at end of file

official/cv/nasnet/modelarts/train_start.py

+# 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 imagenet."""
+import argparse
+import ast
+import os
+import time
+from collections import OrderedDict
+import numpy as np
+
+from mindspore import Tensor
+from mindspore import context
+from mindspore.context import ParallelMode
+from mindspore.communication.management import init, get_rank, get_group_size
+from mindspore.nn.optim.rmsprop import RMSProp
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common import set_seed
+from mindspore.common import dtype as mstype
+from mindspore import export
+
+from src.config import nasnet_a_mobile_config_gpu, nasnet_a_mobile_config_ascend
+from src.dataset import create_dataset
+from src.nasnet_a_mobile import NASNetAMobileWithLoss, NASNetAMobile
+from src.lr_generator import get_lr
+
+def export_models(checkpoint_path):
+    net = NASNetAMobile(num_classes=config.num_classes, is_training=False)
+
+    file_list = []
+    for root, _, files in os.walk(checkpoint_path):
+        for file in files:
+            if os.path.splitext(file)[1] == '.ckpt':
+                file_list.append(os.path.join(root, file))
+
+    file_list.sort(key=os.path.getmtime, reverse=True)
+    exported_count = 0
+
+    for checkpoint in file_list:
+        ckpt_dict = load_checkpoint(checkpoint)
+
+        parameter_dict = OrderedDict()
+        for name in ckpt_dict:
+            new_name = name
+            if new_name.startswith("network."):
+                new_name = new_name.replace("network.", "")
+            parameter_dict[new_name] = ckpt_dict[name]
+        load_param_into_net(net, parameter_dict)
+
+        output_file = checkpoint.replace('.ckpt', '')
+        input_data = Tensor(np.zeros([1, 3, 224, 224]), mstype.float32)
+
+        if args_opt.export_mindir_model:
+            export(net, input_data, file_name=output_file, file_format="MINDIR")
+        if args_opt.export_air_model and context.get_context("device_target") == "Ascend":
+            export(net, input_data, file_name=output_file, file_format="AIR")
+        if args_opt.export_onnx_model:
+            export(net, input_data, file_name=output_file, file_format="ONNX")
+
+        print(checkpoint, 'is exported')
+
+        exported_count += 1
+        if exported_count >= args_opt.export_checkpoint_count:
+            print('exported checkpoint count =', exported_count)
+            break
+
+def filter_checkpoint_parameter_by_list(origin_dict, param_filter):
+    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
+
+if __name__ == '__main__':
+    start_time = time.time()
+
+    parser = argparse.ArgumentParser(description='image classification training')
+    parser.add_argument('--dataset_path', type=str, default='../imagenet', help='Dataset path')
+    parser.add_argument('--resume', type=str, default='',
+                        help='resume training with existed checkpoint')
+    parser.add_argument('--resume_epoch', type=int, default=1, help='Resume from which epoch')
+    parser.add_argument('--is_distributed', type=ast.literal_eval, default=False,
+                        help='distributed training')
+    parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'),
+                        help='run platform')
+
+    parser.add_argument('--device_id', type=int, default=0, help='device id(Default:0)')
+
+    parser.add_argument('--is_modelarts', type=ast.literal_eval, default=False)
+    parser.add_argument('--data_url', type=str, default=None, help='Dataset path for modelarts')
+    parser.add_argument('--train_url', type=str, default=None, help='Output path for modelarts')
+
+    parser.add_argument('--use_pynative_mode', type=ast.literal_eval, default=False,
+                        help='whether to use pynative mode for device(Default: False)')
+
+    parser.add_argument('--amp_level', type=str, default='O0', help='level for mixed precision training')
+
+    parser.add_argument('--remove_classifier_parameter', type=ast.literal_eval, default=False,
+                        help='whether to filter the classifier parameter in the checkpoint (Default: False)')
+
+    parser.add_argument('--export_mindir_model', type=ast.literal_eval, default=True,
+                        help='whether to export MINDIR model (Default: True)')
+
+    parser.add_argument('--export_air_model', type=ast.literal_eval, default=True,
+                        help='whether to export AIR model on Ascend 910 (Default: True)')
+
+    parser.add_argument('--export_onnx_model', type=ast.literal_eval, default=False,
+                        help='whether to export ONNX model (Default: False)')
+
+    parser.add_argument('--export_checkpoint_count', type=int, default=1,
+                        help='export how many checkpoints reversed from the last epoch (Default: 1)')
+
+    parser.add_argument('--overwrite_config', type=ast.literal_eval, default=False,
+                        help='whether to overwrite the config according to the arguments')
+    #when the overwrite_config == True , the following argument will be written to config
+    parser.add_argument('--epoch_size', type=int, default=600,
+                        help='Epoches for trainning(default:600)')
+    parser.add_argument('--num_classes', type=int, default=1000, help='number of classes')
+    parser.add_argument('--cutout', type=ast.literal_eval, default=False,
+                        help='whether to cutout the data for trainning(Default: False)')
+    parser.add_argument('--train_batch_size', type=int, default=32, help='batch size for training')
+    parser.add_argument('--lr_init', type=float, default=0.32, help='learning rate for training')
+
+    args_opt = parser.parse_args()
+
+    is_modelarts = args_opt.is_modelarts
+
+    if args_opt.platform == 'GPU':
+        config = nasnet_a_mobile_config_gpu
+        drop_remainder = True
+    else:
+        config = nasnet_a_mobile_config_ascend
+        drop_remainder = False
+
+    if args_opt.overwrite_config:
+        config.epoch_size = args_opt.epoch_size
+        config.num_classes = args_opt.num_classes
+        config.cutout = args_opt.cutout
+        config.train_batch_size = args_opt.train_batch_size
+        config.lr_init = args_opt.lr_init
+
+    print('epoch_size = ', config.epoch_size, ' num_classes = ', config.num_classes)
+    print('train_batch_size = ', config.train_batch_size, ' lr_init = ', config.lr_init)
+    print('cutout = ', config.cutout, ' cutout_length =', config.cutout_length)
+
+    set_seed(config.random_seed)
+
+    if args_opt.use_pynative_mode:
+        context.set_context(mode=context.PYNATIVE_MODE, device_target=args_opt.platform)
+    else:
+        context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False)
+
+    # init distributed
+    if args_opt.is_distributed:
+        init()
+
+        if args_opt.is_modelarts:
+            device_id = get_rank()
+            config.group_size = get_group_size()
+        else:
+            if args_opt.platform == 'Ascend':
+                device_id = int(os.getenv('DEVICE_ID', default='0'))
+                config.group_size = int(os.getenv('DEVICE_NUM', default='1'))
+            else:
+                device_id = get_rank()
+                config.group_size = get_group_size()
+
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          device_num=config.group_size,
+                                          gradients_mean=True)
+    else:
+        device_id = args_opt.device_id
+        config.group_size = 1
+        context.set_context(device_id=device_id)
+    rank_id = device_id
+    config.rank = rank_id
+    print('rank_id = ', rank_id, ' group_size = ', config.group_size)
+
+    resume = args_opt.resume
+    if args_opt.is_modelarts:
+        # download dataset from obs to cache
+        import moxing
+        dataset_path = '/cache/dataset'
+        if args_opt.data_url.find('/train/') > 0:
+            dataset_path += '/train/'
+        moxing.file.copy_parallel(src_url=args_opt.data_url, dst_url=dataset_path)
+
+        # download the checkpoint from obs to cache
+        if resume != '':
+            base_name = os.path.basename(resume)
+            dst_url = '/cache/checkpoint/' + base_name
+            moxing.file.copy_parallel(src_url=resume, dst_url=dst_url)
+            resume = dst_url
+
+        # the path for the output of training
+        save_checkpoint_path = '/cache/train_output/' + str(device_id) + '/'
+    else:
+        dataset_path = args_opt.dataset_path
+        save_checkpoint_path = os.path.join(config.ckpt_path, 'ckpt_' + str(config.rank) + '/')
+
+    log_filename = os.path.join(save_checkpoint_path, 'log_' + str(device_id) + '.txt')
+
+    # dataloader
+    if dataset_path.find('/train') > 0:
+        dataset_train_path = dataset_path
+    else:
+        dataset_train_path = os.path.join(dataset_path, 'train')
+        if not os.path.exists(dataset_train_path):
+            dataset_train_path = dataset_path
+
+    train_dataset = create_dataset(dataset_train_path, True, config.rank, config.group_size,
+                                   num_parallel_workers=config.work_nums,
+                                   batch_size=config.train_batch_size,
+                                   drop_remainder=drop_remainder, shuffle=True,
+                                   cutout=config.cutout, cutout_length=config.cutout_length,
+                                   image_size=config.image_size)
+    batches_per_epoch = train_dataset.get_dataset_size()
+    # network
+    net_with_loss = NASNetAMobileWithLoss(config)
+    if resume != '':
+        ckpt = load_checkpoint(resume)
+
+        print('remove_classifier_parameter = ', args_opt.remove_classifier_parameter)
+
+        if args_opt.remove_classifier_parameter:
+            filter_list = [x.name for x in net_with_loss.network.classifier.get_parameters()]
+            filter_checkpoint_parameter_by_list(ckpt, filter_list)
+
+            filter_list = [x.name for x in net_with_loss.network.aux_logits.fc.get_parameters()]
+            filter_checkpoint_parameter_by_list(ckpt, filter_list)
+
+        load_param_into_net(net_with_loss, ckpt)
+        print(resume, ' is loaded')
+
+    # learning rate schedule
+    lr = get_lr(lr_init=config.lr_init, lr_decay_rate=config.lr_decay_rate,
+                num_epoch_per_decay=config.num_epoch_per_decay, total_epochs=config.epoch_size,
+                steps_per_epoch=batches_per_epoch, is_stair=True)
+    if resume:
+        resume_epoch = args_opt.resume_epoch
+        step_num_in_epoch = train_dataset.get_dataset_size()
+        lr = lr[step_num_in_epoch * resume_epoch:]
+        # adjust the epoch_size in config so that the source code for model.train will be simplified.
+        config.epoch_size = config.epoch_size - resume_epoch
+        print('Effective epoch_size = ', config.epoch_size)
+    lr = Tensor(lr, mstype.float32)
+
+    # optimizer
+    decayed_params = []
+    no_decayed_params = []
+    for param in net_with_loss.trainable_params():
+        if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
+            decayed_params.append(param)
+        else:
+            no_decayed_params.append(param)
+    group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
+                    {'params': no_decayed_params},
+                    {'order_params': net_with_loss.trainable_params()}]
+    optimizer = RMSProp(group_params, lr, decay=config.rmsprop_decay, weight_decay=config.weight_decay,
+                        momentum=config.momentum, epsilon=config.opt_eps, loss_scale=config.loss_scale)
+
+    # high performance
+    net_with_loss.set_train()
+
+    print('amp_level = ', args_opt.amp_level)
+
+    model = Model(net_with_loss, optimizer=optimizer, amp_level=args_opt.amp_level)
+
+    print("============== Starting Training ==============")
+    loss_cb = LossMonitor(per_print_times=batches_per_epoch)
+    time_cb = TimeMonitor(data_size=batches_per_epoch)
+
+    callbacks = [loss_cb, time_cb]
+
+    config_ck = CheckpointConfig(save_checkpoint_steps=batches_per_epoch,
+                                 keep_checkpoint_max=config.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix=f"nasnet-a-mobile-rank{config.rank}",
+                                 directory=save_checkpoint_path, config=config_ck)
+    if args_opt.is_distributed and config.is_save_on_master == 1:
+        if config.rank == 0:
+            callbacks.append(ckpoint_cb)
+    else:
+        callbacks.append(ckpoint_cb)
+
+    try:
+        model.train(config.epoch_size, train_dataset, callbacks=callbacks, dataset_sink_mode=True)
+    except KeyboardInterrupt:
+        print("!!!!!!!!!!!!!! Train Failed !!!!!!!!!!!!!!!!!!!")
+    else:
+        print("============== Train Success ==================")
+
+    export_models(save_checkpoint_path)
+
+    print("data_url   = ", args_opt.data_url)
+    print("cutout = ", config.cutout, " cutout_length = ", config.cutout_length)
+    print("epoch_size = ", config.epoch_size, " train_batch_size = ", config.train_batch_size,
+          " lr_init = ", config.lr_init, " weight_decay = ", config.weight_decay)
+
+    print("time: ", (time.time() - start_time) / 3600, " hours")
+
+    fp = open(log_filename, 'at+')
+
+    print("data_url   = ", args_opt.data_url, file=fp)
+    print("cutout = ", config.cutout, " cutout_length = ", config.cutout_length, file=fp)
+    print("epoch_size = ", config.epoch_size, " train_batch_size = ", config.train_batch_size,
+          " lr_init = ", config.lr_init, " weight_decay = ", config.weight_decay, file=fp)
+
+    print("time: ", (time.time() - start_time) / 3600, file=fp)
+    fp.close()
+
+    if args_opt.is_modelarts:
+        if os.path.exists('/cache/train_output'):
+            moxing.file.copy_parallel(src_url='/cache/train_output', dst_url=args_opt.train_url)

official/cv/nasnet/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.
+
+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

official/cv/nasnet/train.py

@@ -65,7 +65,7 @@ if __name__ == '__main__':
                         help='Epoches for trainning(default:600)')
     parser.add_argument('--num_classes', type=int, default=1000, help='number of classes')
     parser.add_argument('--cutout', type=ast.literal_eval, default=False,
-                        help='whether to cutout the data for trainning(Default: True)')
+                        help='whether to cutout the data for trainning(Default: False)')
 
     args_opt = parser.parse_args()