!19675 add fat-deepffm 310 master

Merge pull request !19675 from four_WW/ffm_master_310_3

model_zoo/research/recommend/Fat-DeepFFM/GetDatasetBinary.py

@@ -0,0 +1,56 @@
+# 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 argparse
+import os
+
+from src.config import ModelConfig
+from src.dataset import get_mindrecord_dataset
+
+parser = argparse.ArgumentParser(description='CTR Prediction')
+parser.add_argument('--dataset_path', type=str, default="../data/mindrecord", help='Dataset path')
+parser.add_argument('--dataset_binary_path', type=str, default="../ascend310/CriteoBinary", help='Checkpoint path')
+
+args = parser.parse_args()
+
+def generate_bin():
+    '''generate bin files'''
+    config = ModelConfig()
+    batch_size = config.batch_size
+    ds = get_mindrecord_dataset(args.dataset_path, train_mode=False)
+    batch_ids_path = os.path.join(args.dataset_binary_path, "batch_dense")
+    batch_wts_path = os.path.join(args.dataset_binary_path, "batch_spare")
+    labels_path = os.path.join(args.dataset_binary_path, "batch_labels")
+
+    os.makedirs(batch_ids_path)
+    os.makedirs(batch_wts_path)
+    os.makedirs(labels_path)
+
+    for i, data in enumerate(ds.create_dict_iterator(output_numpy=True)):
+        file_name = "criteo_bs" + str(batch_size) + "_" + str(i) + ".bin"
+        batch_dense = data['cats_vals']
+        batch_dense.tofile(os.path.join(batch_ids_path, file_name))
+
+        batch_spare = data['num_vals']
+        batch_spare.tofile(os.path.join(batch_wts_path, file_name))
+
+        labels = data['label']
+        labels.tofile(os.path.join(labels_path, file_name))
+
+    print("=" * 20, "export bin files finished", "=" * 20)
+
+
+if __name__ == '__main__':
+    generate_bin()

model_zoo/research/recommend/Fat-DeepFFM/README.md

@@ -105,6 +105,7 @@ Fat - DeepFFM consists of three parts. The FFM component is a factorization mach
 .
 └─Fat-deepffm
   ├─README.md
+  ├─asecend310                        # C++ running module
   ├─scripts
     ├─run_alone_train.sh              # launch standalone training(1p) in Ascend
     ├─run_distribute_train.sh         # launch distributed training(8p) in Ascend
@@ -117,6 +118,8 @@ Fat - DeepFFM consists of three parts. The FFM component is a factorization mach
     ├─metrics.py                      # verify the model
     ├─dataset.py                      # create dataset for deepfm
   ├─eval.py                           # eval net
+  ├─eval310.py                        # infer 310 net
+  ├─GetDatasetBinary.py               # get binary dataset
   ├─export.py                         # export net
   └─train.py                          # train net
 ```
@@ -237,18 +240,19 @@ Before performing inference, the mindir file must be exported by `export.py` scr
 
 ```shell
 # Ascend310 inference
-bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+bash scripts/run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_ID]
 ```
 
 - `NEED_PREPROCESS` means weather need preprocess or not, it's value is 'y' or 'n'.
 - `DEVICE_ID` is optional, default value is 0.
+- `DATASET_PATH` is path that contains the mindrecord dataset.
 
 ### result
 
 Inference result is saved in current path, you can find result like this in acc.log file.
 
 ```bash
-'AUC': 0.8091001899667086
+'AUC': 0.8088441692761583
 ```
 
 # [Model Description](#contents)

model_zoo/research/recommend/Fat-DeepFFM/ascend310/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/recommend/Fat-DeepFFM/ascend310/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/recommend/Fat-DeepFFM/ascend310/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/research/recommend/Fat-DeepFFM/ascend310/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/minddata/dataset/include/execute.h"
+#include "include/minddata/dataset/include/vision.h"
+#include "inc/utils.h"
+
+using mindspore::Context;
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Status;
+using mindspore::MSTensor;
+using mindspore::dataset::Execute;
+using mindspore::ModelType;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(input0_path, ".", "input0 path");
+DEFINE_string(input1_path, ".", "input1 path");
+DEFINE_string(input2_path, ".", "input2 path");
+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;
+  }
+
+  std::vector<MSTensor> model_inputs = model.GetInputs();
+  if (model_inputs.empty()) {
+    std::cout << "Invalid model, inputs is empty." << std::endl;
+    return 1;
+  }
+
+  auto input0_files = GetAllFiles(FLAGS_input0_path);
+  auto input1_files = GetAllFiles(FLAGS_input1_path);
+  auto input2_files = GetAllFiles(FLAGS_input2_path);
+
+  if (input0_files.empty() || input1_files.empty() || input2_files.empty()) {
+    std::cout << "ERROR: input data empty." << std::endl;
+    return 1;
+  }
+
+  std::map<double, double> costTime_map;
+  size_t size = input0_files.size();
+
+  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:" << input0_files[i] << std::endl;
+
+    auto input0 = ReadFileToTensor(input0_files[i]);
+    auto input1 = ReadFileToTensor(input1_files[i]);
+    auto input2 = ReadFileToTensor(input2_files[i]);
+    inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
+                        input0.Data().get(), input0.DataSize());
+    inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
+                        input1.Data().get(), input1.DataSize());
+    inputs.emplace_back(model_inputs[2].Name(), model_inputs[2].DataType(), model_inputs[2].Shape(),
+                        input2.Data().get(), input2.DataSize());
+
+    gettimeofday(&start, nullptr);
+    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);
+  }
+  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;
+}

model_zoo/research/recommend/Fat-DeepFFM/ascend310/src/utils.cc

@@ -0,0 +1,130 @@
+/**
+ * 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_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;
+}
+

model_zoo/research/recommend/Fat-DeepFFM/eval310.py

@@ -0,0 +1,57 @@
+# 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 argparse
+import os
+
+import numpy as np
+from mindspore import Tensor
+
+from src.config import ModelConfig
+from src.metrics import AUCMetric
+
+parser = argparse.ArgumentParser(description='CTR Prediction')
+parser.add_argument('--result_path', type=str, default="./result_Files", help='Dataset path')
+parser.add_argument('--label_path', type=str, default="./CriteoBinary/batch_labels", help='Checkpoint path')
+args = parser.parse_args()
+
+
+def get_acc():
+    ''' get accuracy '''
+    config = ModelConfig()
+    batch_size = config.batch_size
+    auc_metric = AUCMetric()
+    files = os.listdir(args.label_path)
+
+
+    for f in files:
+        rst_file = os.path.join(args.result_path, f.split('.')[0] + '_0.bin')
+        label_file = os.path.join(args.label_path, f)
+
+        logit = Tensor(np.fromfile(rst_file, np.float32).reshape(batch_size, 1))
+        label = Tensor(np.fromfile(label_file, np.float32).reshape(batch_size, 1))
+
+        res = []
+        res.append(logit)
+        res.append(logit)
+        res.append(label)
+
+        auc_metric.update(*res)
+    auc = auc_metric.eval()
+    print("auc : {}".format(auc))
+
+
+if __name__ == '__main__':
+    get_acc()

model_zoo/research/recommend/Fat-DeepFFM/scripts/run_infer_310.sh

@@ -0,0 +1,118 @@
+#!/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 || $# -gt 4 ]]; then
+  echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+    NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+  exit 1
+fi
+
+get_real_path() {
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+model=$(get_real_path $1)
+dataset_path=$(get_real_path $2)
+
+if [ "$3" == "y" ] || [ "$3" == "n" ]; then
+  need_preprocess=$3
+else
+  echo "weather need preprocess or not, it's value must be in [y, n]"
+  exit 1
+fi
+
+device_id=0
+if [ $# == 4 ]; then
+  device_id=$4
+fi
+
+echo "mindir name: "$model
+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/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+  export LD_LIBRARY_PATH=/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=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+  export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+  export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+  export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+  export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+  export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function preprocess_data() {
+  if [ -d CriteoBinary ]; then
+    rm -rf CriteoBinary
+  fi
+  mkdir CriteoBinary
+  python3.7 ./GetDatasetBinary.py --dataset_path=$dataset_path --result_path=./CriteoBinary/
+}
+
+function compile_app() {
+  cd ./ascend310 || 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/out/main --mindir_path=$model --input0_path=./CriteoBinary/batch_dense --input1_path=./CriteoBinary/batch_spare --input2_path=./CriteoBinary/batch_labels --device_id=$device_id &>infer.log
+
+}
+
+function cal_acc() {
+  python3.7 ./eval310.py --result_path=./result_Files --label_path=./CriteoBinary/batch_labels &>acc.log
+}
+
+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