From fb72d02ffb331c36bf3b884ae81674f8fc228201 Mon Sep 17 00:00:00 2001 From: FOURTH <3236961631@qq.com> Date: Thu, 8 Jul 2021 12:15:06 +0800 Subject: [PATCH] fat-deepffm infer 310 commit --- .../recommend/Fat-DeepFFM/GetDatasetBinary.py | 56 +++++++ .../research/recommend/Fat-DeepFFM/README.md | 8 +- .../Fat-DeepFFM/ascend310/CMakeLists.txt | 14 ++ .../recommend/Fat-DeepFFM/ascend310/build.sh | 29 ++++ .../Fat-DeepFFM/ascend310/inc/utils.h | 32 ++++ .../Fat-DeepFFM/ascend310/src/main.cc | 140 ++++++++++++++++++ .../Fat-DeepFFM/ascend310/src/utils.cc | 130 ++++++++++++++++ .../research/recommend/Fat-DeepFFM/eval310.py | 57 +++++++ .../Fat-DeepFFM/scripts/run_infer_310.sh | 118 +++++++++++++++ 9 files changed, 582 insertions(+), 2 deletions(-) create mode 100644 model_zoo/research/recommend/Fat-DeepFFM/GetDatasetBinary.py create mode 100644 model_zoo/research/recommend/Fat-DeepFFM/ascend310/CMakeLists.txt create mode 100644 model_zoo/research/recommend/Fat-DeepFFM/ascend310/build.sh create mode 100644 model_zoo/research/recommend/Fat-DeepFFM/ascend310/inc/utils.h create mode 100644 model_zoo/research/recommend/Fat-DeepFFM/ascend310/src/main.cc create mode 100644 model_zoo/research/recommend/Fat-DeepFFM/ascend310/src/utils.cc create mode 100644 model_zoo/research/recommend/Fat-DeepFFM/eval310.py create mode 100644 model_zoo/research/recommend/Fat-DeepFFM/scripts/run_infer_310.sh diff --git a/model_zoo/research/recommend/Fat-DeepFFM/GetDatasetBinary.py b/model_zoo/research/recommend/Fat-DeepFFM/GetDatasetBinary.py new file mode 100644 index 00000000000..0f7a1a4e903 --- /dev/null +++ b/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() diff --git a/model_zoo/research/recommend/Fat-DeepFFM/README.md b/model_zoo/research/recommend/Fat-DeepFFM/README.md index 010127e46f9..a9983f40b25 100644 --- a/model_zoo/research/recommend/Fat-DeepFFM/README.md +++ b/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) diff --git a/model_zoo/research/recommend/Fat-DeepFFM/ascend310/CMakeLists.txt b/model_zoo/research/recommend/Fat-DeepFFM/ascend310/CMakeLists.txt new file mode 100644 index 00000000000..170e6c5275e --- /dev/null +++ b/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) diff --git a/model_zoo/research/recommend/Fat-DeepFFM/ascend310/build.sh b/model_zoo/research/recommend/Fat-DeepFFM/ascend310/build.sh new file mode 100644 index 00000000000..817a8059574 --- /dev/null +++ b/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 diff --git a/model_zoo/research/recommend/Fat-DeepFFM/ascend310/inc/utils.h b/model_zoo/research/recommend/Fat-DeepFFM/ascend310/inc/utils.h new file mode 100644 index 00000000000..abeb8fcbf11 --- /dev/null +++ b/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 +#include +#include +#include +#include +#include "include/api/types.h" + +std::vector 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 &outputs); +#endif diff --git a/model_zoo/research/recommend/Fat-DeepFFM/ascend310/src/main.cc b/model_zoo/research/recommend/Fat-DeepFFM/ascend310/src/main.cc new file mode 100644 index 00000000000..b367d35bae1 --- /dev/null +++ b/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 +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#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(); + auto ascend310 = std::make_shared(); + 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 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 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 inputs; + std::vector 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(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; +} diff --git a/model_zoo/research/recommend/Fat-DeepFFM/ascend310/src/utils.cc b/model_zoo/research/recommend/Fat-DeepFFM/ascend310/src/utils.cc new file mode 100644 index 00000000000..28f816c9b08 --- /dev/null +++ b/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 +#include +#include +#include "inc/utils.h" + +using mindspore::MSTensor; +using mindspore::DataType; + +std::vector GetAllFiles(std::string_view dirName) { + struct dirent *filename; + DIR *dir = OpenDir(dirName); + if (dir == nullptr) { + return {}; + } + std::vector 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 &outputs) { + std::string homePath = "./result_Files"; + for (size_t i = 0; i < outputs.size(); ++i) { + size_t outputSize; + std::shared_ptr 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(size)}, nullptr, size); + + ifs.seekg(0, std::ios::beg); + ifs.read(reinterpret_cast(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; +} + diff --git a/model_zoo/research/recommend/Fat-DeepFFM/eval310.py b/model_zoo/research/recommend/Fat-DeepFFM/eval310.py new file mode 100644 index 00000000000..4f080165a52 --- /dev/null +++ b/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() diff --git a/model_zoo/research/recommend/Fat-DeepFFM/scripts/run_infer_310.sh b/model_zoo/research/recommend/Fat-DeepFFM/scripts/run_infer_310.sh new file mode 100644 index 00000000000..41677f1f72a --- /dev/null +++ b/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