!18386 ascend 310 inference for cnn

Merge pull request !18386 from 于振华/cnn_master

model_zoo/official/cv/cnn_direction_model/README.md

@@ -12,6 +12,10 @@
         - [Training](#training)
     - [Evaluation Process](#evaluation-process)
         - [Evaluation](#evaluation)
+    - [Export Process](#Export-process)
+        - [Export](#Export)
+    - [Inference Process](#Inference-process)
+        - [Inference](#Inference)
 - [Model Description](#model-description)
     - [Performance](#performance)
         - [Evaluation Performance](#evaluation-performance)
@@ -87,10 +91,12 @@ sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH]
 ├── cv
     ├── cnn_direction_model
         ├── README.md                    // descriptions about cnn_direction_model
+        ├── ascend310_infer              // application for 310 inference
         ├── requirements.txt             // packages needed
         ├── scripts
         │   ├──run_distribute_train_ascend.sh          // distributed training in ascend
         │   ├──run_standalone_eval_ascend.sh             //  evaluate in ascend
+        │   ├──run_eval.sh                               // shell script for evaluation on Ascend
         │   ├──run_standalone_train_ascend.sh          //  train standalone in ascend
         ├── src
         │   ├──dataset.py                               // creating dataset
@@ -104,6 +110,8 @@ sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH]
         ├── train.py               // training script
         ├── eval.py               //  evaluation script
         ├── default_config.yaml   //  config file
+        ├── postprogress.py       // post process for 310 inference
+        ├── export.py             // export checkpoint files into air/mindir
 ```
 
 ## [Script Parameters](#contents)
@@ -222,6 +230,36 @@ sh scripts/run_distribute_train_ascend.sh /home/rank_table.json /home/fsns/train
 # (7) Start model inference。
 ```
 
+## [Export Process](#contents)
+
+### [Export](#content)
+
+```shell
+python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
+```
+
+`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
+
+## [Inference Process](#contents)
+
+### Usage
+
+Before performing inference, we need to export model first. Air model can only be exported in Ascend 910 environment, mindir model can be exported in any environment.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+```
+
+### result
+
+Inference result is saved in current path, you can find result like this in acc.log file.
+
+```python
+top1_correct=10096, total=10202, acc=98.96%
+top1_correct=8888, total=10202, acc=87.12%
+```
+
 # [Model Description](#contents)
 
 ## [Performance](#contents)

model_zoo/official/cv/cnn_direction_model/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} -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)

model_zoo/official/cv/cnn_direction_model/ascend310_infer/build.sh

@@ -0,0 +1,23 @@
+#!/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
+  mkdir out
+fi
+cd out || exit
+cmake .. \
+    -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

model_zoo/official/cv/cnn_direction_model/ascend310_infer/inc/utils.h

@@ -0,0 +1,32 @@
+/**
+ * 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);
+#endif

model_zoo/official/cv/cnn_direction_model/ascend310_infer/src/main.cc

@@ -0,0 +1,132 @@
+/**
+ * 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 "../inc/utils.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/transforms.h"
+#include "include/dataset/vision.h"
+#include "include/dataset/vision_ascend.h"
+#include "include/api/types.h"
+#include "include/api/model.h"
+#include "include/api/serialization.h"
+#include "include/api/context.h"
+
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Context;
+using mindspore::Status;
+using mindspore::ModelType;
+using mindspore::Graph;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::MSTensor;
+
+DEFINE_string(model_path, "", "model path");
+DEFINE_string(dataset_path, ".", "dataset path");
+DEFINE_int32(device_id, 0, "device id");
+
+int main(int argc, char **argv) {
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    if (RealPath(FLAGS_model_path).empty()) {
+        std::cout << "Invalid model" << std::endl;
+        return 1;
+    }
+
+    auto context = std::make_shared<Context>();
+    auto ascend310_info = std::make_shared<mindspore::Ascend310DeviceInfo>();
+    ascend310_info->SetDeviceID(FLAGS_device_id);
+    context->MutableDeviceInfo().push_back(ascend310_info);
+
+    Graph graph;
+    Status ret = Serialization::Load(FLAGS_model_path, ModelType::kMindIR, &graph);
+    if (ret != kSuccess) {
+        std::cout << "Load model failed." << std::endl;
+        return 1;
+    }
+
+    Model model;
+    ret = model.Build(GraphCell(graph), context);
+    if (ret != kSuccess) {
+        std::cout << "ERROR: Build failed." << std::endl;
+        return 1;
+    }
+
+    std::vector<MSTensor> modelInputs = model.GetInputs();
+
+    auto all_files = GetAllFiles(FLAGS_dataset_path);
+    if (all_files.empty()) {
+        std::cout << "ERROR: no input data." << std::endl;
+        return 1;
+    }
+
+    std::map<double, double> costTime_map;
+
+    size_t size = all_files.size();
+    for (size_t i = 0; i < size; ++i) {
+        struct timeval start;
+        struct timeval end;
+        double startTime_ms;
+        double endTime_ms;
+        std::vector<MSTensor> inputs;
+        std::vector<MSTensor> outputs;
+
+        std::cout << "Start predict input files:" << all_files[i] << std::endl;
+        mindspore::MSTensor image =  ReadFileToTensor(all_files[i]);
+
+        inputs.emplace_back(modelInputs[0].Name(), modelInputs[0].DataType(), modelInputs[0].Shape(),
+                            image.Data().get(), image.DataSize());
+
+        gettimeofday(&start, NULL);
+        model.Predict(inputs, &outputs);
+        gettimeofday(&end, NULL);
+
+        startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+        endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+        costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
+        WriteResult(all_files[i], outputs);
+    }
+    double average = 0.0;
+    int infer_cnt = 0;
+    for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+        double diff = 0.0;
+        diff = iter->second - iter->first;
+        average += diff;
+        infer_cnt++;
+    }
+
+    average = average / infer_cnt;
+    std::stringstream timeCost;
+    timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << infer_cnt << std::endl;
+    std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
+
+    std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
+    std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
+    file_stream << timeCost.str();
+    file_stream.close();
+    costTime_map.clear();
+    return 0;
+}

model_zoo/official/cv/cnn_direction_model/ascend310_infer/src/utils.cc

@@ -0,0 +1,147 @@
+/**
+ * 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 "inc/utils.h"
+
+#include <fstream>
+#include <algorithm>
+#include <iostream>
+
+using mindspore::MSTensor;
+using mindspore::DataType;
+
+std::vector<std::string> GetAllFiles(std::string_view 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/official/cv/cnn_direction_model/default_config.yaml

@@ -61,6 +61,12 @@ eval_dataset_path: ""
 checkpoint_path: ""
 
 # export options
+ckpt_file: ""
+file_name: "cnn"
+file_format: "MINDIR"
+
+#310 inferenct options
+result_path: "./preprocess_Result/"
 
 ---
 # Help description for each configuration

model_zoo/official/cv/cnn_direction_model/export.py

@@ -0,0 +1,36 @@
+# 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.
+# ============================================================================
+"""export script"""
+
+import numpy as np
+import mindspore as ms
+from mindspore import Tensor, context, load_checkpoint, export
+from src.cnn_direction_model import CNNDirectionModel
+from src.model_utils.config import config
+from src.model_utils.device_adapter import get_device_id
+
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
+device_id = get_device_id()
+context.set_context(device_id=device_id)
+
+if __name__ == '__main__':
+    net = CNNDirectionModel([3, 64, 48, 48, 64], [64, 48, 48, 64, 64], [256, 64], [64, 512])
+
+    param_dict = load_checkpoint(config.ckpt_file, net=net)
+    net.set_train(False)
+
+    input_data = Tensor(np.zeros([1, 3, config.im_size_h, config.im_size_w]), ms.float32)
+
+    export(net, input_data, file_name=config.file_name, file_format=config.file_format)

model_zoo/official/cv/cnn_direction_model/postprocess.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.
+# ============================================================================
+'''post process for 310 inference'''
+import os
+import argparse
+import numpy as np
+
+parser = argparse.ArgumentParser(description='post process for cnn')
+parser.add_argument("--result_path", type=str, required=True, help="result file path")
+parser.add_argument("--label_path", type=str, required=True, help="label file")
+args = parser.parse_args()
+
+def cal_acc(result_path, label_path):
+    img_total = 0
+    top1_correct = 0
+
+    result_shape = (1, 2)
+
+    files = os.listdir(result_path)
+    for file in files:
+        full_file_path = os.path.join(result_path, file)
+        if os.path.isfile(full_file_path):
+            result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape)
+            label_file = os.path.join(label_path, file.split(".bin")[0][:-2] + ".bin")
+            gt_classes = np.fromfile(label_file, dtype=np.int32)
+
+            top1_output = np.argmax(result, (-1))
+
+            t1_correct = np.equal(top1_output, gt_classes).sum()
+            top1_correct += t1_correct
+            img_total += 1
+
+    acc1 = 100.0 * top1_correct / img_total
+    print('top1_correct={}, total={}, acc={:.2f}%'.format(top1_correct, img_total, acc1))
+
+if __name__ == "__main__":
+    cal_acc(args.result_path, args.label_path)

model_zoo/official/cv/cnn_direction_model/preprocess.py

@@ -0,0 +1,68 @@
+# 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 for cnn"""
+
+import os
+import random
+
+import numpy as np
+from src.model_utils.config import config
+from src.dataset import create_dataset_eval
+
+from mindspore import dataset as de
+
+random.seed(1)
+np.random.seed(1)
+de.config.set_seed(1)
+
+def preprocess():
+    dataset_name = config.dataset_name
+    dataset_lr, dataset_rl = create_dataset_eval(config.data_root_test +  "/" + dataset_name +
+                                                 ".mindrecord0", config=config, dataset_name=dataset_name)
+
+    lr_img_path = os.path.join(config.result_path, "lr_dataset/" + "img_data")
+    lr_label_path = os.path.join(config.result_path, "lr_dataset/" + "label")
+    os.makedirs(lr_img_path)
+    os.makedirs(lr_label_path)
+
+    for idx, data in enumerate(dataset_lr.create_dict_iterator(output_numpy=True, num_epochs=1)):
+        img_data = data["image"]
+        img_label = data["label"]
+
+        file_name = "cnn_fsns_1_" + str(idx) + ".bin"
+        img_file_path = os.path.join(lr_img_path, file_name)
+        img_data.tofile(img_file_path)
+
+        label_file_path = os.path.join(lr_label_path, file_name)
+        img_label.tofile(label_file_path)
+
+    rl_img_path = os.path.join(config.result_path, "rl_dataset/" + "img_data")
+    rl_label_path = os.path.join(config.result_path, "rl_dataset/" + "label")
+    os.makedirs(rl_img_path)
+    os.makedirs(rl_label_path)
+
+    for idx, data in enumerate(dataset_rl.create_dict_iterator(output_numpy=True, num_epochs=1)):
+        img_data = data["image"]
+        img_label = data["label"]
+
+        file_name = "cnn_fsns_1_" + str(idx) + ".bin"
+        img_file_path = os.path.join(rl_img_path, file_name)
+        img_data.tofile(img_file_path)
+
+        label_file_path = os.path.join(rl_label_path, file_name)
+        img_label.tofile(label_file_path)
+
+if __name__ == '__main__':
+    preprocess()

model_zoo/official/cv/cnn_direction_model/scripts/run_infer_310.sh

@@ -0,0 +1,119 @@
+#!/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 1 || $# -gt 2 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+
+device_id=0
+if [ $# == 2 ]; then
+    device_id=$2
+fi
+
+echo "mindir name: "$model
+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=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    if [ -d preprocess_Result ]; then
+        rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+  #  python3.7 ../preprocess.py --dataset_path=$dataset_path --result_path=./preprocess_Result/
+    python3.7 ../preprocess.py
+}
+
+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
+
+    ../ascend310_infer/out/main --model_path=$model --dataset_path=./preprocess_Result/lr_dataset/img_data --device_id=$device_id  &> lr_infer.log
+    mv result_Files result_Files_lr
+    mv time_Result time_Result_lr
+
+    mkdir result_Files
+    mkdir time_Result
+    ../ascend310_infer/out/main --model_path=$model --dataset_path=./preprocess_Result/rl_dataset/img_data --device_id=$device_id  &> rl_infer.log
+    mv result_Files result_Files_rl
+    mv time_Result time_Result_rl
+}
+
+function cal_acc()
+{
+    python3.7 ../postprocess.py --result_path=./result_Files_lr --label_path=./preprocess_Result/lr_dataset/label &> acc.log
+    python3.7 ../postprocess.py --result_path=./result_Files_rl --label_path=./preprocess_Result/rl_dataset/label &>> acc.log
+}
+
+preprocess_data
+if [ $? -ne 0 ]; then
+    echo "preprocess dataset failed"
+    exit 1
+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

model_zoo/official/cv/googlenet/export.py

@@ -33,7 +33,7 @@ if config.device_target == "Ascend":
 if __name__ == '__main__':
     net = GoogleNet(num_classes=config.num_classes)
 
-    assert config.checkpoint_path is not None, "config.checkpoint_path is None."
+    assert config.ckpt_file is not None, "config.ckpt_file is None."
     param_dict = load_checkpoint(config.ckpt_file)
     load_param_into_net(net, param_dict)
 

model_zoo/official/cv/googlenet/postprocess.py

@@ -17,7 +17,7 @@ import os
 import argparse
 import numpy as np
 
-parser = argparse.ArgumentParser(description='fasterrcnn_export')
+parser = argparse.ArgumentParser(description='postprocess for googlenet')
 parser.add_argument("--dataset", type=str, default="imagenet", help="result file path")
 parser.add_argument("--result_path", type=str, required=True, help="result file path")
 parser.add_argument("--label_file", type=str, required=True, help="label file")

model_zoo/official/cv/unet/postprocess.py

@@ -78,7 +78,7 @@ if __name__ == '__main__':
     metrics = dice_coeff()
 
     if config.dataset == "Cell_nuclei":
-        img_size = tuple(config.img_size)
+        img_size = tuple(config.image_size)
         for i, bin_name in enumerate(os.listdir('./preprocess_Result/')):
             f = bin_name.replace(".png", "")
             bin_name_softmax = f + "_0.bin"

model_zoo/official/cv/unet/preprocess.py

@@ -24,7 +24,7 @@ from src.model_utils.config import config
 def preprocess_dataset(data_dir, result_path, cross_valid_ind=1):
 
     _, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False, do_crop=config.crop,
-                                      img_size=config.img_size)
+                                      img_size=config.image_size)
 
     labels_list = []
     for i, data in enumerate(valid_dataset):