add vgg19 310 infer master

research/cv/vgg19/README.md

@@ -257,6 +257,14 @@ python eval.py --config_path=[YAML_CONFIG_PATH] --device_target="GPU" --dataset=
     ├── vgg19
         ├── README.md                             // descriptions about vgg
         ├── README_CN.md                          // descriptions about vgg with Chinese
+        ├── ascend310_infer
+        ├── ├── CMakeLists.txt                    // CMake
+        ├── ├── build.sh                          // shell file of build
+        ├── ├── inc
+        ├── ├── ├── utils.h                       // utils.h
+        ├── ├── src
+        ├── ├── ├── main.cc                       // main file
+        ├── ├── ├── utils.cc                      // utils.cc
         ├── model_utils
         │   ├── __init__.py                       // init file
         │   ├── config.py                         // Parse arguments
@@ -447,7 +455,7 @@ python train.py  --config_path=/dir_to_code/imagenet2012_config.yaml --device_ta
 
 ```bash
 # distributed training(4p)
-bash scripts/run_distribute_train_gpu.sh /path/ImageNet2012/train"
+bash scripts/run_distribute_train_gpu.sh /path/ImageNet2012/train
 ```
 
 ### [Evaluation Process](#contents)
@@ -501,7 +509,8 @@ Current batch_Size for imagenet2012 dataset can only be set to 1.
 Inference result is saved in current path, you can find result like this in acc.log file.
 
 ```bash
-'acc': 0.92
+'top1 acc': 0.748
+'top5 acc': 0.922
 ```
 
 ## [Model Description](#contents)
@@ -545,4 +554,4 @@ In dataset.py, we set the seed inside “create_dataset" function. We also use r
 
 ## [ModelZoo Homepage](#contents)  
 
-Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).  
+Please check the official [homepage](https://gitee.com/mindspore/models/tree/master/).  

research/cv/vgg19/README_CN.md

@@ -272,16 +272,25 @@ python eval.py --config_path=[YAML_CONFIG_PATH] --device_target="GPU" --dataset=
     ├── vgg19
         ├── README.md                             // VGG 相关说明
         ├── README_CN.md                          // VGG 相关中文说明
+        ├── ascend310_infer
+        ├── ├── CMakeLists.txt                    // CMake文件
+        ├── ├── build.sh                          // build脚本文件
+        ├── ├── inc
+        ├── ├── ├── utils.h                       // utils类头文件
+        ├── ├── src
+        ├── ├── ├── main.cc                       // 主文件
+        ├── ├── ├── utils.cc                      // utils类实现
         ├── model_utils
-            ├── __init__.py                 // 初始化文件
-            ├── config.py                   // 参数配置
-            ├── device_adapter.py           // ModelArts的设备适配器
-            ├── local_adapter.py            // 本地适配器
-            └── moxing_adapter.py           // ModelArts的模型适配器
+        ├── ├── __init__.py                 // 初始化文件
+        ├── ├── config.py                   // 参数配置
+        ├── ├── device_adapter.py           // ModelArts的设备适配器
+        ├── ├── local_adapter.py            // 本地适配器
+        ├── └── moxing_adapter.py           // ModelArts的模型适配器
         ├── scripts
         │   ├── run_distribute_train.sh           // Ascend 分布式训练shell脚本
         │   ├── run_distribute_train_gpu.sh       // GPU 分布式训练shell脚本
         │   ├── run_eval.sh                       // Ascend 验证shell脚本
+        │   ├── run_infer_310.sh                  // Ascend 310推理脚本
         ├── src
         │   ├── utils
         │   │   ├── logging.py                    // 日志格式设置
@@ -463,7 +472,7 @@ python train.py --config_path=/dir_to_code/imagenet2012_config.yaml --device_tar
 
 ```bash
 # 分布式训练（8p）
-bash scripts/run_distribute_train_gpu.sh /path/ImageNet2012/train"
+bash scripts/run_distribute_train_gpu.sh /path/ImageNet2012/train
 ```
 
 ### 评估过程
@@ -517,7 +526,8 @@ python export.py --config_path [YMAL_CONFIG_PATH] --ckpt_file [CKPT_PATH] --file
 推理结果保存在脚本执行的当前路径，你可以在acc.log中看到以下精度计算结果。
 
 ```bash
-'acc': 0.92
+'top1 acc': 0.748
+'top5 acc': 0.922
 ```
 
 ## 模型描述
@@ -562,4 +572,4 @@ dataset.py中设置了“create_dataset”函数内的种子，同时还使用
 
 ## ModelZoo主页
 
-请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。  
+请浏览官网[主页](https://gitee.com/mindspore/models/tree/master/)。  

research/cv/vgg19/ascend310_infer/CMakeLists.txt

+cmake_minimum_required(VERSION 3.14.1)
+project(Ascend310Infer)
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${MINDSPORE_PATH})
+include_directories(${MINDSPORE_PATH}/include)
+include_directories(${PROJECT_SRC_ROOT})
+find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
+file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
+
+add_executable(main src/main.cc src/utils.cc)
+target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)

research/cv/vgg19/ascend310_infer/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.
+# ============================================================================
+if [ -d out ]; then
+    rm -rf out
+fi
+
+mkdir out
+cd out || exit
+
+if [ -f "Makefile" ]; then
+  make clean
+fi
+
+cmake .. \
+    -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

research/cv/vgg19/ascend310_infer/inc/utils.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.
+ */
+
+#ifndef MINDSPORE_INFERENCE_UTILS_H_
+#define MINDSPORE_INFERENCE_UTILS_H_
+
+#include <sys/stat.h>
+#include <dirent.h>
+#include <vector>
+#include <string>
+#include <memory>
+#include "include/api/types.h"
+
+std::vector<std::string> GetAllFiles(std::string_view dirName);
+DIR *OpenDir(std::string_view dirName);
+std::string RealPath(std::string_view path);
+mindspore::MSTensor ReadFileToTensor(const std::string &file);
+int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
+std::vector<std::string> GetAllFiles(std::string dir_name);
+std::vector<std::vector<std::string>> GetAllInputData(std::string dir_name);
+
+#endif

research/cv/vgg19/ascend310_infer/src/main.cc

+/**
+ * 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 <sys/time.h>
+#include <gflags/gflags.h>
+#include <dirent.h>
+#include <iostream>
+#include <string>
+#include <algorithm>
+#include <iosfwd>
+#include <vector>
+#include <fstream>
+#include <sstream>
+
+#include "include/api/model.h"
+#include "include/api/context.h"
+#include "include/api/types.h"
+#include "include/api/serialization.h"
+#include "include/dataset/vision_ascend.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/transforms.h"
+#include "include/dataset/vision.h"
+#include "inc/utils.h"
+
+using mindspore::Context;
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Status;
+using mindspore::ModelType;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::MSTensor;
+using mindspore::dataset::Execute;
+using mindspore::dataset::vision::Decode;
+using mindspore::dataset::vision::Resize;
+using mindspore::dataset::vision::CenterCrop;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::HWC2CHW;
+
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(dataset_name, "cifar10", "['cifar10', 'imagenet2012']");
+DEFINE_string(input0_path, ".", "input0 path");
+DEFINE_int32(device_id, 0, "device id");
+
+int load_model(Model *model, std::vector<MSTensor> *model_inputs, std::string mindir_path, int device_id) {
+  if (RealPath(mindir_path).empty()) {
+    std::cout << "Invalid mindir" << std::endl;
+    return 1;
+  }
+
+  auto context = std::make_shared<Context>();
+  auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
+  ascend310->SetDeviceID(device_id);
+  context->MutableDeviceInfo().push_back(ascend310);
+  mindspore::Graph graph;
+  Serialization::Load(mindir_path, ModelType::kMindIR, &graph);
+
+  Status ret = model->Build(GraphCell(graph), context);
+  if (ret != kSuccess) {
+    std::cout << "ERROR: Build failed." << std::endl;
+    return 1;
+  }
+
+  *model_inputs = model->GetInputs();
+  if (model_inputs->empty()) {
+    std::cout << "Invalid model, inputs is empty." << std::endl;
+    return 1;
+  }
+  return 0;
+}
+
+int main(int argc, char **argv) {
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+
+  Model model;
+  std::vector<MSTensor> model_inputs;
+  load_model(&model, &model_inputs, FLAGS_mindir_path, FLAGS_device_id);
+
+  std::map<double, double> costTime_map;
+  struct timeval start = {0};
+  struct timeval end = {0};
+  double startTimeMs;
+  double endTimeMs;
+
+  if (FLAGS_dataset_name == "cifar10") {
+    auto input0_files = GetAllFiles(FLAGS_input0_path);
+    if (input0_files.empty()) {
+      std::cout << "ERROR: no input data." << std::endl;
+      return 1;
+    }
+    size_t size = input0_files.size();
+    for (size_t i = 0; i < size; ++i) {
+      std::vector<MSTensor> inputs;
+      std::vector<MSTensor> outputs;
+      std::cout << "Start predict input files:" << input0_files[i] <<std::endl;
+      auto input0 = ReadFileToTensor(input0_files[i]);
+      inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
+                          input0.Data().get(), input0.DataSize());
+
+      gettimeofday(&start, nullptr);
+      Status ret = model.Predict(inputs, &outputs);
+      gettimeofday(&end, nullptr);
+      if (ret != kSuccess) {
+        std::cout << "Predict " << input0_files[i] << " failed." << std::endl;
+        return 1;
+      }
+      startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+      endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+      costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
+      WriteResult(input0_files[i], outputs);
+    }
+  } else {
+    auto input0_files = GetAllInputData(FLAGS_input0_path);
+    if (input0_files.empty()) {
+      std::cout << "ERROR: no input data." << std::endl;
+      return 1;
+    }
+    size_t size = input0_files.size();
+    for (size_t i = 0; i < size; ++i) {
+      for (size_t j = 0; j < input0_files[i].size(); ++j) {
+        std::vector<MSTensor> inputs;
+        std::vector<MSTensor> outputs;
+        std::cout << "Start predict input files:" << input0_files[i][j] <<std::endl;
+        auto decode = Decode();
+        auto resize = Resize({256, 256});
+        auto centercrop = CenterCrop({224, 224});
+        auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375});
+        auto hwc2chw = HWC2CHW();
+
+        Execute SingleOp({decode, resize, centercrop, normalize, hwc2chw});
+        auto imgDvpp = std::make_shared<MSTensor>();
+        SingleOp(ReadFileToTensor(input0_files[i][j]), imgDvpp.get());
+        inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
+                            imgDvpp->Data().get(), imgDvpp->DataSize());
+      gettimeofday(&start, nullptr);
+      Status ret = model.Predict(inputs, &outputs);
+      gettimeofday(&end, nullptr);
+      if (ret != kSuccess) {
+        std::cout << "Predict " << input0_files[i][j] << " failed." << std::endl;
+        return 1;
+      }
+      startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+      endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+      costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
+      WriteResult(input0_files[i][j], outputs);
+      }
+    }
+  }
+  double average = 0.0;
+  int inferCount = 0;
+
+  for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+    double diff = 0.0;
+    diff = iter->second - iter->first;
+    average += diff;
+    inferCount++;
+  }
+  average = average / inferCount;
+  std::stringstream timeCost;
+  timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
+  std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
+  std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
+  std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
+  fileStream << timeCost.str();
+  fileStream.close();
+  costTime_map.clear();
+  return 0;
+}

research/cv/vgg19/ascend310_infer/src/utils.cc

+/**
+ * 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 <fstream>
+#include <algorithm>
+#include <iostream>
+#include "inc/utils.h"
+
+using mindspore::MSTensor;
+using mindspore::DataType;
+
+
+std::vector<std::vector<std::string>> GetAllInputData(std::string dir_name) {
+  std::vector<std::vector<std::string>> ret;
+
+  DIR *dir = OpenDir(dir_name);
+  if (dir == nullptr) {
+    return {};
+  }
+  struct dirent *filename;
+  /* read all the files in the dir ~ */
+  std::vector<std::string> sub_dirs;
+  while ((filename = readdir(dir)) != nullptr) {
+    std::string d_name = std::string(filename->d_name);
+    // get rid of "." and ".."
+    if (d_name == "." || d_name == ".." || d_name.empty()) {
+      continue;
+    }
+    std::string dir_path = RealPath(std::string(dir_name) + "/" + filename->d_name);
+    struct stat s;
+    lstat(dir_path.c_str(), &s);
+    if (!S_ISDIR(s.st_mode)) {
+      continue;
+    }
+
+    sub_dirs.emplace_back(dir_path);
+  }
+  std::sort(sub_dirs.begin(), sub_dirs.end());
+
+  (void)std::transform(sub_dirs.begin(), sub_dirs.end(), std::back_inserter(ret),
+                       [](const std::string &d) { return GetAllFiles(d); });
+
+  return ret;
+}
+
+
+std::vector<std::string> GetAllFiles(std::string dir_name) {
+  struct dirent *filename;
+  DIR *dir = OpenDir(dir_name);
+  if (dir == nullptr) {
+    return {};
+  }
+
+  std::vector<std::string> res;
+  while ((filename = readdir(dir)) != nullptr) {
+    std::string d_name = std::string(filename->d_name);
+    if (d_name == "." || d_name == ".." || d_name.size() <= 3) {
+      continue;
+    }
+    res.emplace_back(std::string(dir_name) + "/" + filename->d_name);
+  }
+  std::sort(res.begin(), res.end());
+
+  return res;
+}
+
+
+std::vector<std::string> GetAllFiles(std::string_view dirName) {
+  struct dirent *filename;
+  DIR *dir = OpenDir(dirName);
+  if (dir == nullptr) {
+    return {};
+  }
+  std::vector<std::string> res;
+  while ((filename = readdir(dir)) != nullptr) {
+    std::string dName = std::string(filename->d_name);
+    if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
+      continue;
+    }
+    res.emplace_back(std::string(dirName) + "/" + filename->d_name);
+  }
+  std::sort(res.begin(), res.end());
+  for (auto &f : res) {
+    std::cout << "image file: " << f << std::endl;
+  }
+  return res;
+}
+
+
+int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
+  std::string homePath = "./result_Files";
+  for (size_t i = 0; i < outputs.size(); ++i) {
+    size_t outputSize;
+    std::shared_ptr<const void> netOutput;
+    netOutput = outputs[i].Data();
+    outputSize = outputs[i].DataSize();
+    int pos = imageFile.rfind('/');
+    std::string fileName(imageFile, pos + 1);
+    fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
+    std::string outFileName = homePath + "/" + fileName;
+    FILE *outputFile = fopen(outFileName.c_str(), "wb");
+    fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
+    fclose(outputFile);
+    outputFile = nullptr;
+  }
+  return 0;
+}
+
+mindspore::MSTensor ReadFileToTensor(const std::string &file) {
+  if (file.empty()) {
+    std::cout << "Pointer file is nullptr" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  std::ifstream ifs(file);
+  if (!ifs.good()) {
+    std::cout << "File: " << file << " is not exist" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  if (!ifs.is_open()) {
+    std::cout << "File: " << file << "open failed" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  ifs.seekg(0, std::ios::end);
+  size_t size = ifs.tellg();
+  mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
+
+  ifs.seekg(0, std::ios::beg);
+  ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
+  ifs.close();
+
+  return buffer;
+}
+
+
+DIR *OpenDir(std::string_view dirName) {
+  if (dirName.empty()) {
+    std::cout << " dirName is null ! " << std::endl;
+    return nullptr;
+  }
+  std::string realPath = RealPath(dirName);
+  struct stat s;
+  lstat(realPath.c_str(), &s);
+  if (!S_ISDIR(s.st_mode)) {
+    std::cout << "dirName is not a valid directory !" << std::endl;
+    return nullptr;
+  }
+  DIR *dir;
+  dir = opendir(realPath.c_str());
+  if (dir == nullptr) {
+    std::cout << "Can not open dir " << dirName << std::endl;
+    return nullptr;
+  }
+  std::cout << "Successfully opened the dir " << dirName << std::endl;
+  return dir;
+}
+
+std::string RealPath(std::string_view path) {
+  char realPathMem[PATH_MAX] = {0};
+  char *realPathRet = nullptr;
+  realPathRet = realpath(path.data(), realPathMem);
+  if (realPathRet == nullptr) {
+    std::cout << "File: " << path << " is not exist.";
+    return "";
+  }
+
+  std::string realPath(realPathMem);
+  std::cout << path << " realpath is: " << realPath << std::endl;
+  return realPath;
+}

research/cv/vgg19/export.py

@@ -34,7 +34,7 @@ def modelarts_pre_process():
 
 @moxing_wrapper(pre_process=modelarts_pre_process)
 def run_export():
-    """export model in mindir/air/onnx"""
+    '''run_export function.'''
     config.image_size = list(map(int, config.image_size.split(',')))
 
     context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)

research/cv/vgg19/mindspore_hub_conf.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """hub config."""
 from src.vgg import vgg19 as VGG19
-
+from model_utils.moxing_adapter import config
 
 def vgg19(*args, **kwargs):
     return VGG19(*args, **kwargs)
@@ -22,5 +22,5 @@ def vgg19(*args, **kwargs):
 
 def create_network(name, *args, **kwargs):
     if name == "vgg19":
-        return vgg19(*args, **kwargs)
+        return vgg19(args=config, *args, **kwargs)
     raise NotImplementedError(f"{name} is not implemented in the repo")

research/cv/vgg19/preprocess.py

@@ -22,7 +22,7 @@ from model_utils.moxing_adapter import config
 
 
 def create_label(result_path, dir_path):
-    """create label for dataset"""
+    '''create_label function'''
     print("[WARNING] Create imagenet label. Currently only use for Imagenet2012!")
     dirs = os.listdir(dir_path)
     file_list = []

research/cv/vgg19/requirements.txt

-easydict
 numpy
 pillow
+pyyaml

research/cv/vgg19/scripts/run_infer_310.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 [[ $# -lt 4 || $# -gt 5 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_NAME] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+    DATASET_NAME can choose from ['cifar10', 'imagenet2012'].
+    NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+if [ $2 == 'cifar10' ] || [ $2 == 'imagenet2012' ]; then
+  dataset_name=$2
+else
+  echo "DATASET_NAME can choose from ['cifar10', 'imagenet2012']"
+  exit 1
+fi
+config_path=$(get_real_path "../${dataset_name}_config.yaml")
+echo "config path is : ${config_path}"
+
+dataset_path=$(get_real_path $3)
+
+if [ "$4" == "y" ] || [ "$4" == "n" ];then
+    need_preprocess=$4
+else
+  echo "weather need preprocess or not, it's value must be in [y, n]"
+  exit 1
+fi
+
+device_id=0
+if [ $# == 5 ]; then
+    device_id=$5
+fi
+
+echo "mindir name: "$model
+echo "dataset name: "$dataset_name
+echo "dataset path: "$dataset_path
+echo "need preprocess: "$need_preprocess
+echo "device id: "$device_id
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+    export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+    export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
+    export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+    export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+    export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    if [ -d preprocess_Result ]; then
+        rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+    python3.7 ../preprocess.py --config_path=$config_path --dataset=$dataset_name --data_dir=$dataset_path --result_path=./preprocess_Result/
+}
+
+function compile_app()
+{
+    cd ../ascend310_infer/ || exit
+    bash build.sh &> build.log
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+
+    if [ "$dataset_name" == "cifar10" ]; then
+        ../ascend310_infer/out/main --mindir_path=$model --dataset_name=$dataset_name --input0_path=./preprocess_Result/00_data --device_id=$device_id  &> infer.log
+    else
+        ../ascend310_infer/out/main --mindir_path=$model --dataset_name=$dataset_name --input0_path=$dataset_path --device_id=$device_id  &> infer.log
+    fi
+}
+
+function cal_acc()
+{
+    if [ "$dataset_name" == "cifar10" ]; then
+        python3.7 ../postprocess.py --config_path=$config_path --result_dir=./result_Files --label_dir=./preprocess_Result/cifar10_label_ids.npy --dataset_name=$dataset_name  &> acc.log
+    else
+        python3.7 ../postprocess.py --config_path=$config_path --result_dir=./result_Files --label_dir=./preprocess_Result/imagenet_label.json --dataset_name=$dataset_name  &> acc.log
+    fi
+}
+
+if [ $need_preprocess == "y" ]; then
+    preprocess_data
+    if [ $? -ne 0 ]; then
+        echo "preprocess dataset failed"
+        exit 1
+    fi
+fi
+compile_app
+if [ $? -ne 0 ]; then
+    echo "compile app code failed"
+    exit 1
+fi
+infer
+if [ $? -ne 0 ]; then
+    echo " execute inference failed"
+    exit 1
+fi
+cal_acc
+if [ $? -ne 0 ]; then
+    echo "calculate accuracy failed"
+    exit 1
+fi
\ No newline at end of file

research/cv/vgg19/src/dataset.py

@@ -16,11 +16,11 @@
 dataset processing.
 """
 import os
+from PIL import Image, ImageFile
 from mindspore.common import dtype as mstype
 import mindspore.dataset as de
 import mindspore.dataset.transforms.c_transforms as C
 import mindspore.dataset.vision.c_transforms as vision
-from PIL import Image, ImageFile
 from src.utils.sampler import DistributedSampler
 
 ImageFile.LOAD_TRUNCATED_IMAGES = True

research/cv/vgg19/src/vgg.py

@@ -127,9 +127,9 @@ cfg = {
 }
 
 
-def vgg19(num_classes=1000, args=None, phase="train"):
+def vgg19(num_classes=1000, args=None, phase="train", **kwargs):
     """
-    Get Vgg19 neural network with batch normalization.
+    Get Vgg19 neural network with Batch Normalization.
 
     Args:
         num_classes (int): Class numbers. Default: 1000.
@@ -137,14 +137,10 @@ def vgg19(num_classes=1000, args=None, phase="train"):
         phase(str): train or test mode.
 
     Returns:
-        Cell, cell instance of Vgg19 neural network with batch normalization.
+        Cell, cell instance of Vgg19 neural network with Batch Normalization.
 
     Examples:
-        >>> vgg19(num_classes=1000, args=args)
+        >>> vgg19(num_classes=1000, args=args, **kwargs)
     """
-
-    if args is None:
-        from .config import cifar_cfg
-        args = cifar_cfg
-    net = Vgg(cfg['19'], num_classes=num_classes, args=args, batch_norm=args.batch_norm, phase=phase)
+    net = Vgg(cfg['19'], num_classes=num_classes, args=args, batch_norm=args.batch_norm, phase=phase, **kwargs)
     return net