!17995 resnetv2 310 infer

Merge pull request !17995 from 张晓晓/resnetv2

model_zoo/official/cv/resnet/create_imagenet2012_label.py

@@ -6,7 +6,7 @@
 #
 # http://www.apache.org/licenses/LICENSE-2.0
 #
-# less required by applicable law or agreed to in writing, software
+# 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

model_zoo/official/cv/resnet/postprocess.py

@@ -6,7 +6,7 @@
 #
 # http://www.apache.org/licenses/LICENSE-2.0
 #
-# less required by applicable law or agreed to in writing, software
+# 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

model_zoo/research/cv/resnetv2/README_CN.md

@@ -250,6 +250,34 @@ result: {'top_5_accuracy': 0.9988982371794872, 'top_1_accuracy': 0.9502283653846
 result: {'top_1_accuracy': 0.7606515786082474, 'top_5_accuracy': 0.9271504510309279}
 ```
 
+## 推理过程
+
+### [导出MindIR](#contents)
+
+```shell
+python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
+```
+
+参数ckpt_file为必填项，
+`file_format` 必须在 ["AIR", "MINDIR"]中选择。
+
+### 在Ascend310执行推理
+
+在执行推理前，mindir文件必须通过`export.py`脚本导出。以下展示了使用mindir模型执行推理的示例。
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATASET] [DATA_PATH] [DEVICE_ID]
+```
+
+- `DATASET` 为数据集类型，如cifar10, cifar100等。
+- `DATA_PATH`为数据集路径。  
+- `DEVICE_ID` 可选，默认值为0。
+
+### 结果
+
+推理结果保存在脚本执行的当前路径，你可以在acc.log中看到精度计算结果。
+
 # 模型描述
 
 ## 性能

model_zoo/research/cv/resnetv2/ascend310_infer/CMakeLists.txt

@@ -0,0 +1,14 @@
+cmake_minimum_required(VERSION 3.14.1)
+project(Ascend310Infer)
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O2 -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)

model_zoo/research/cv/resnetv2/ascend310_infer/build.sh

@@ -0,0 +1,29 @@
+#!/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

model_zoo/research/cv/resnetv2/ascend310_infer/inc/utils.h

@@ -0,0 +1,33 @@
+/**
+ * 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"
+
+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);
+
+#endif

model_zoo/research/cv/resnetv2/ascend310_infer/src/main.cc

@@ -0,0 +1,140 @@
+/**
+ * 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::dataset::vision::Decode;
+using mindspore::dataset::vision::Resize;
+using mindspore::dataset::vision::CenterCrop;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::HWC2CHW;
+using mindspore::dataset::TensorTransform;
+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;
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(dataset_path, ".", "dataset path");
+DEFINE_string(dataset, "imagenet2012", "dataset");
+DEFINE_int32(device_id, 0, "device id");
+
+int main(int argc, char **argv) {
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    if (RealPath(FLAGS_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(FLAGS_device_id);
+    context->MutableDeviceInfo().push_back(ascend310);
+    mindspore::Graph graph;
+    Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
+    Model model;
+    Status ret = model.Build(GraphCell(graph), context);
+    if (ret != kSuccess) {
+        std::cout << "ERROR: Build failed." << std::endl;
+        return 1;
+    }
+
+    auto all_files = GetAllFiles(FLAGS_dataset_path);
+    if (all_files.empty()) {
+        std::cout << "ERROR: no input data." << std::endl;
+        return 1;
+    }
+
+    std::vector<MSTensor> modelInputs = model.GetInputs();
+    std::map<double, double> costTime_map;
+    size_t size = all_files.size();
+
+    auto decode = Decode();
+    auto resize = Resize({256});
+    auto centercrop = CenterCrop({224});
+    auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375});
+    auto hwc2chw = HWC2CHW();
+    mindspore::dataset::Execute SingleOp({decode, resize, centercrop, normalize, hwc2chw});
+
+    for (size_t i = 0; i < size; ++i) {
+        struct timeval start = {0};
+        struct timeval end = {0};
+        double startTimeMs;
+        double endTimeMs;
+        std::vector<MSTensor> inputs;
+        std::vector<MSTensor> outputs;
+        std::cout << "Start predict input files:" << all_files[i] <<std::endl;
+
+        MSTensor image = ReadFileToTensor(all_files[i]);
+        if (FLAGS_dataset == "imagenet2012") {
+            SingleOp(image, &image);
+        }
+
+        inputs.emplace_back(modelInputs[0].Name(), modelInputs[0].DataType(), modelInputs[0].Shape(),
+                            image.Data().get(), image.DataSize());
+        gettimeofday(&start, nullptr);
+        ret = model.Predict(inputs, &outputs);
+        gettimeofday(&end, nullptr);
+        if (ret != kSuccess) {
+            std::cout << "Predict " << all_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(all_files[i], outputs);
+    }
+    double average = 0.0;
+    int inferCount = 0;
+
+    for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+        average += iter->second - iter->first;
+        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;
+}

model_zoo/research/cv/resnetv2/ascend310_infer/src/utils.cc

@@ -0,0 +1,145 @@
+/**
+ * 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::string> GetAllFiles(std::string dirName) {
+    struct dirent *filename;
+    DIR *dir = OpenDir(dirName);
+    if (dir == nullptr) {
+        return {};
+    }
+    std::vector<std::string> dirs;
+    std::vector<std::string> files;
+    while ((filename = readdir(dir)) != nullptr) {
+        std::string dName = std::string(filename->d_name);
+        if (dName == "." || dName == "..") {
+            continue;
+        } else if (filename->d_type == DT_DIR) {
+            dirs.emplace_back(std::string(dirName) + "/" + filename->d_name);
+        } else if (filename->d_type == DT_REG) {
+            files.emplace_back(std::string(dirName) + "/" + filename->d_name);
+        } else {
+            continue;
+        }
+    }
+
+    for (auto d : dirs) {
+        dir = OpenDir(d);
+        while ((filename = readdir(dir)) != nullptr) {
+            std::string dName = std::string(filename->d_name);
+            if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
+                continue;
+            }
+            files.emplace_back(std::string(d) + "/" + filename->d_name);
+        }
+    }
+    std::sort(files.begin(), files.end());
+    for (auto &f : files) {
+        std::cout << "image file: " << f << std::endl;
+    }
+    return files;
+}
+
+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;
+}

model_zoo/research/cv/resnetv2/create_imagenet2012_label.py

@@ -0,0 +1,49 @@
+# 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.
+# ============================================================================
+"""create_imagenet2012_label"""
+import os
+import json
+import argparse
+
+parser = argparse.ArgumentParser(description="resnet imagenet2012 label")
+parser.add_argument("--img_path", type=str, required=True, help="imagenet2012 file path.")
+args = parser.parse_args()
+
+
+def create_label(file_path):
+    '''create imagenet2012 label'''
+    print("[WARNING] Create imagenet label. Currently only use for Imagenet2012!")
+    dirs = os.listdir(file_path)
+    file_list = []
+    for file in dirs:
+        file_list.append(file)
+    file_list = sorted(file_list)
+
+    total = 0
+    img_label = {}
+    for i, file_dir in enumerate(file_list):
+        files = os.listdir(os.path.join(file_path, file_dir))
+        for f in files:
+            img_label[f] = i
+        total += len(files)
+
+    with open("imagenet_label.json", "w+") as label:
+        json.dump(img_label, label)
+
+    print("[INFO] Completed! Total {} data.".format(total))
+
+
+if __name__ == '__main__':
+    create_label(args.img_path)

model_zoo/research/cv/resnetv2/postprocess.py

@@ -0,0 +1,84 @@
+# 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.
+# ============================================================================
+"""postprocess"""
+import os
+import json
+import argparse
+import numpy as np
+from mindspore.nn import Top1CategoricalAccuracy, Top5CategoricalAccuracy
+
+parser = argparse.ArgumentParser(description="postprocess")
+parser.add_argument("--dataset", type=str, required=True, help="dataset type.")
+parser.add_argument("--result_path", type=str, required=True, help="result files path.")
+parser.add_argument("--label_path", type=str, required=True, help="image file path.")
+args_opt = parser.parse_args()
+
+if args_opt.dataset == "cifar10":
+    from src.config import config1 as config
+elif args_opt.dataset == "cifar100":
+    from src.config import config2 as config
+elif args_opt.dataset == 'imagenet2012':
+    from src.config import config3 as config
+else:
+    raise ValueError("dataset is not support.")
+
+def cal_acc_cifar(result_path, label_path):
+    '''calculate cifar accuracy'''
+    top1_acc = Top1CategoricalAccuracy()
+    top5_acc = Top5CategoricalAccuracy()
+    result_shape = (config.batch_size, config.class_num)
+
+    file_num = len(os.listdir(result_path))
+    label_list = np.load(label_path)
+    for i in range(file_num):
+        f_name = args_opt.dataset + "_bs" + str(config.batch_size) + "_" + str(i) + "_0.bin"
+        full_file_path = os.path.join(result_path, f_name)
+        if os.path.isfile(full_file_path):
+            result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape)
+            gt_classes = label_list[i]
+
+            top1_acc.update(result, gt_classes)
+            top5_acc.update(result, gt_classes)
+    print("top1 acc: ", top1_acc.eval())
+    print("top5 acc: ", top5_acc.eval())
+
+def cal_acc_imagenet(result_path, label_path):
+    '''calculate imagenet2012 accuracy'''
+    batch_size = 1
+    files = os.listdir(result_path)
+    with open(label_path, "r") as label:
+        labels = json.load(label)
+
+    top1 = 0
+    top5 = 0
+    total_data = len(files)
+    for file in files:
+        img_ids_name = file.split('_0.')[0]
+        data_path = os.path.join(result_path, img_ids_name + "_0.bin")
+        result = np.fromfile(data_path, dtype=np.float32).reshape(batch_size, config.class_num)
+        for batch in range(batch_size):
+            predict = np.argsort(-result[batch], axis=-1)
+            if labels[img_ids_name+".JPEG"] == predict[0]:
+                top1 += 1
+            if labels[img_ids_name+".JPEG"] in predict[:5]:
+                top5 += 1
+    print(f"Total data: {total_data}, top1 accuracy: {top1/total_data}, top5 accuracy: {top5/total_data}.")
+
+
+if __name__ == '__main__':
+    if args_opt.dataset.lower() == "cifar10" or args_opt.dataset.lower() == "cifar100":
+        cal_acc_cifar(args_opt.result_path, args_opt.label_path)
+    else:
+        cal_acc_imagenet(args_opt.result_path, args_opt.label_path)

model_zoo/research/cv/resnetv2/preprocess.py

@@ -0,0 +1,59 @@
+# 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.
+# ============================================================================
+""" preprocess """
+import os
+import argparse
+import numpy as np
+
+parser = argparse.ArgumentParser(description='preprocess')
+parser.add_argument('--dataset', type=str, default='cifar10', help='Dataset, cifar10, imagenet2012')
+parser.add_argument('--dataset_path', type=str, default="../cifar-10/cifar-10-verify-bin",
+                    help='Dataset path.')
+parser.add_argument('--output_path', type=str, default="./preprocess_Result",
+                    help='preprocess Result path.')
+args_opt = parser.parse_args()
+
+# import dataset
+if args_opt.dataset == "cifar10":
+    from src.dataset import create_dataset1 as create_dataset
+    from src.config import config1 as config
+elif args_opt.dataset == "cifar100":
+    from src.dataset import create_dataset2 as create_dataset
+    from src.config import config2 as config
+else:
+    raise ValueError("dataset is not support.")
+
+
+def get_cifar_bin():
+    '''generate cifar bin files.'''
+    ds = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
+    img_path = os.path.join(args_opt.output_path, "00_img_data")
+    label_path = os.path.join(args_opt.output_path, "label.npy")
+    os.makedirs(img_path)
+    label_list = []
+
+    for i, data in enumerate(ds.create_dict_iterator(output_numpy=True)):
+        img_data = data["image"]
+        img_label = data["label"]
+
+        file_name = args_opt.dataset + "_bs" + str(config.batch_size) + "_" + str(i) + ".bin"
+        img_file_path = os.path.join(img_path, file_name)
+        img_data.tofile(img_file_path)
+        label_list.append(img_label)
+    np.save(label_path, label_list)
+    print("=" * 20, "export bin files finished", "=" * 20)
+
+if __name__ == '__main__':
+    get_cifar_bin()

model_zoo/research/cv/resnetv2/scripts/run_infer_310.sh

@@ -0,0 +1,123 @@
+#!/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 3 || $# -gt 4 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET] [DATA_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+if [ ${2,,} == 'cifar10' ] || [ ${2,,} == 'cifar100' ] || [ ${2,,} == 'imagenet2012' ]; then
+  dataset=$2
+else
+  echo "dataset must choose from [cifar10, cifar100, imagenet2012]"
+  exit 1
+fi
+
+data_path=$(get_real_path $3)
+
+device_id=0
+if [ $# == 4 ]; then
+    device_id=$4
+fi
+
+echo "mindir name: "$model
+echo "dataset path: "$data_path
+echo "dataset: "$dataset
+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=$ASCEND_HOME/opp
+fi
+
+function compile_app()
+{
+    cd ../ascend310_infer || exit
+    bash build.sh &> build.log
+}
+
+function preprocess_data()
+{
+    if [ -d preprocess_Result ]; then
+        rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+
+    python3.7 ../preprocess.py --dataset=$dataset --dataset_path=$data_path --output_path=./preprocess_Result
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+    ../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --dataset=$dataset --device_id=$device_id  &> infer.log
+}
+
+function cal_acc()
+{
+    if [ "${dataset}" == "cifar10" ] || [ "${dataset}" == "cifar100" ]; then
+        python ../postprocess.py --dataset=$dataset --label_path=./preprocess_Result/label.npy --result_path=result_Files &> acc.log
+    else
+        python3.7 ../create_imagenet2012_label.py  --img_path=$data_path
+        python3.7 ../postprocess.py --dataset=$dataset --result_path=./result_Files --label_path=./imagenet_label.json  &> acc.log
+    fi
+}
+
+if [ "${dataset}" == "cifar10" ] || [ "${dataset}" == "cifar100" ]; then
+    preprocess_data
+    data_path=./preprocess_Result/00_img_data
+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