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!15753 bert & lstm & gru & textcnn 310 inference 

From: @zhangxiaoxiao16
Reviewed-by: @oacjiewen,@c_34
Signed-off-by: @c_34
This commit is contained in:
mindspore-ci-bot 2021-05-08 17:11:33 +08:00 committed by Gitee
parent 9e687f5d04 4f10876822
commit b510dbd74a
40 changed files with 2553 additions and 2 deletions
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2
model_zoo/official/cv/yolov3_darknet53/README.md
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@ -349,7 +349,7 @@ The ckpt_file parameter is required,
#### Usage
Before performing inference, the air file must bu exported by export.py.
Current batch_Size can only be set to 1. Because the DVPP hardware is used for processing, the picture must comply with the JPEG encoding format, Otherwise, an error will be reported.
Current batch_Size can only be set to 1. Because the DVPP hardware is used for processing, the picture must comply with the JPEG encoding format, Otherwise, an error will be reported. For example, the COCO_val2014_000000320612.jpg in coco2014 dataset needs to be deleted.
```shell
# Ascend310 inference

2
model_zoo/official/cv/yolov3_darknet53/README_CN.md
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@ -351,7 +351,7 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [
### 用法
在执行推理之前，需要通过export.py导出mindir文件。
目前仅可处理batch_Size为1，由于使用了DVPP硬件进行图片处理，因此图片必须满足JPEG编码格式，否则将会报错。
目前仅可处理batch_Size为1，由于使用了DVPP硬件进行图片处理，因此图片必须满足JPEG编码格式，否则将会报错。比如coco2014数据集中的COCO_val2014_000000320612.jpg需要删除。
```shell
# Ascend310 推理

34
model_zoo/official/nlp/bert/README.md
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@ -29,6 +29,8 @@
- [evaluation on cluener dataset when running on Ascend](#evaluation-on-cluener-dataset-when-running-on-ascend)
- [evaluation on msra dataset when running on Ascend](#evaluation-on-msra-dataset-when-running-on-ascend)
- [evaluation on squad v1.1 dataset when running on Ascend](#evaluation-on-squad-v11-dataset-when-running-on-ascend)
- [Export MindIR](#export-mindir)
- [Inference Process](#inference-process)
- [Model Description](#model-description)
- [Performance](#performance)
- [Pretraining Performance](#pretraining-performance)
@ -587,6 +589,38 @@ The result will be as follows:
{"exact_match": 80.3878923040233284, "f1": 87.6902384023850329}
```
### [Export MindIR](#contents)
```shell
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```
The ckpt_file parameter is required,
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
### [Inference Process](#contents)
#### Usage
Before performing inference, the mindir file must be exported by export.py. Input files must be in bin format.
```shell
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [LABEL_PATH] [DATA_FILE_PATH] [DATASET_FORMAT] [SCHEMA_PATH] [USE_CRF] [NEED_PREPROCESS] [DEVICE_ID]
```
`NEED_PREPROCESS` means weather need preprocess or not, it's value is 'y' or 'n'.
`USE_CRF` is mandatory, and must choose from [true|false], it's case-insensitive.
`DEVICE_ID` is optional, default value is 0.
#### result
Inference result is saved in current path, you can find result in acc.log file.
```eval log
F1 0.931243
```
## [Model Description](#contents)
## [Performance](#contents)

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model_zoo/official/nlp/bert/README_CN.md
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@ -30,6 +30,10 @@
- [Ascend处理器上运行后评估cluener数据集](#ascend处理器上运行后评估cluener数据集)
- [Ascend处理器上运行后评估msra数据集](#ascend处理器上运行后评估msra数据集)
- [Ascend处理器上运行后评估squad v1.1数据集](#ascend处理器上运行后评估squad-v11数据集)
- [导出mindir模型](#导出mindir模型)
- [推理过程](#推理过程)
- [用法](#用法-2)
- [结果](#结果-2)
- [模型描述](#模型描述)
- [性能](#性能)
- [预训练性能](#预训练性能)
@ -550,6 +554,37 @@ bash scripts/squad.sh
{"exact_match": 80.3878923040233284, "f1": 87.6902384023850329}
```
## 导出mindir模型
```shell
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```
参数`ckpt_file` 是必需的，`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。
## 推理过程
### 用法
在执行推理之前，需要通过export.py导出mindir文件。输入数据文件为bin格式。
```shell
# Ascend310 推理
bash run_infer_310.sh [MINDIR_PATH] [LABEL_PATH] [DATA_FILE_PATH] [DATASET_FORMAT] [SCHEMA_PATH] [USE_CRF] [NEED_PREPROCESS] [DEVICE_ID]
```
`NEED_PREPROCESS` 为必选项, 在[y|n]中取值，表示数据是否预处理为bin格式。
`USE_CRF` 为必选项, 在 [true|false]中取值，大小写不敏感。
`DEVICE_ID` 可选，默认值为 0。
### 结果
推理结果保存在当前路径，可在acc.log中看到最终精度结果。
```eval log
F1 0.931243
```
## 模型描述
## 性能

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model_zoo/official/nlp/bert/ascend310_infer/CMakeLists.txt Normal file
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@ -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)

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model_zoo/official/nlp/bert/ascend310_infer/build.sh Normal file
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@ -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

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model_zoo/official/nlp/bert/ascend310_infer/inc/utils.h Normal file
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@ -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

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model_zoo/official/nlp/bert/ascend310_infer/src/main.cc Normal file
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@ -0,0 +1,148 @@
/**
* 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/execute.h"
#include "include/dataset/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_string(input3_path, ".", "input3 path");
DEFINE_bool(use_crf, true, "use crf or not");
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);
auto input3_files = GetAllFiles(FLAGS_input3_path);
if (input0_files.empty() || input1_files.empty() || input2_files.empty() || input3_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());
if (FLAGS_use_crf) {
auto input3 = ReadFileToTensor(input3_files[i]);
inputs.emplace_back(model_inputs[3].Name(), model_inputs[3].DataType(), model_inputs[3].Shape(),
input3.Data().get(), input3.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;
}

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model_zoo/official/nlp/bert/ascend310_infer/src/utils.cc Normal file
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@ -0,0 +1,129 @@
/**
* 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;
}

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model_zoo/official/nlp/bert/postprocess.py Normal file
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# 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 script.
'''
import os
import argparse
import numpy as np
from mindspore import Tensor
from src.finetune_eval_config import bert_net_cfg
from src.assessment_method import Accuracy, F1, MCC, Spearman_Correlation
from run_ner import eval_result_print
parser = argparse.ArgumentParser(description="postprocess")
parser.add_argument("--batch_size", type=int, default=1, help="Eval batch size, default is 1")
parser.add_argument("--label_dir", type=str, default="", help="label data dir")
parser.add_argument("--assessment_method", type=str, default="BF1", choices=["BF1", "clue_benchmark", "MF1"],
help="assessment_method include: [BF1, clue_benchmark, MF1], default is BF1")
parser.add_argument("--result_dir", type=str, default="./result_Files", help="infer result Files")
parser.add_argument("--use_crf", type=str, default="false", choices=["true", "false"],
help="Use crf, default is false")
args, _ = parser.parse_known_args()
if __name__ == "__main__":
num_class = 41
assessment_method = args.assessment_method.lower()
use_crf = args.use_crf
if assessment_method == "accuracy":
callback = Accuracy()
elif assessment_method == "bf1":
callback = F1((use_crf.lower() == "true"), num_class)
elif assessment_method == "mf1":
callback = F1((use_crf.lower() == "true"), num_labels=num_class, mode="MultiLabel")
elif assessment_method == "mcc":
callback = MCC()
elif assessment_method == "spearman_correlation":
callback = Spearman_Correlation()
else:
raise ValueError("Assessment method not supported, support: [accuracy, f1, mcc, spearman_correlation]")
file_name = os.listdir(args.label_dir)
for f in file_name:
if use_crf.lower() == "true":
logits = ()
for j in range(bert_net_cfg.seq_length):
f_name = f.split('.')[0] + '_' + str(j) + '.bin'
data_tmp = np.fromfile(os.path.join(args.result_dir, f_name), np.int32)
data_tmp = data_tmp.reshape(args.batch_size, num_class + 2)
logits += ((Tensor(data_tmp),),)
f_name = f.split('.')[0] + '_' + str(bert_net_cfg.seq_length) + '.bin'
data_tmp = np.fromfile(os.path.join(args.result_dir, f_name), np.int32).tolist()
data_tmp = Tensor(data_tmp)
logits = (logits, data_tmp)
else:
f_name = os.path.join(args.result_dir, f.split('.')[0] + '_0.bin')
logits = np.fromfile(f_name, np.float32).reshape(bert_net_cfg.seq_length * args.batch_size, num_class)
logits = Tensor(logits)
label_ids = np.fromfile(os.path.join(args.label_dir, f), np.int32)
label_ids = Tensor(label_ids.reshape(args.batch_size, bert_net_cfg.seq_length))
callback.update(logits, label_ids)
print("==============================================================")
eval_result_print(assessment_method, callback)
print("==============================================================")

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model_zoo/official/nlp/bert/preprocess.py Normal file
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# 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.
# ============================================================================
'''
Bert preprocess script.
'''
import os
import argparse
from src.dataset import create_ner_dataset
def parse_args():
"""set and check parameters."""
parser = argparse.ArgumentParser(description="bert preprocess")
parser.add_argument("--assessment_method", type=str, default="BF1", choices=["BF1", "clue_benchmark", "MF1"],
help="assessment_method include: [BF1, clue_benchmark, MF1], default is BF1")
parser.add_argument("--do_eval", type=str, default="false", choices=["true", "false"],
help="Eable eval, default is false")
parser.add_argument("--use_crf", type=str, default="false", choices=["true", "false"],
help="Use crf, default is false")
parser.add_argument("--eval_data_shuffle", type=str, default="false", choices=["true", "false"],
help="Enable eval data shuffle, default is false")
parser.add_argument("--eval_batch_size", type=int, default=1, help="Eval batch size, default is 1")
parser.add_argument("--vocab_file_path", type=str, default="", help="Vocab file path, used in clue benchmark")
parser.add_argument("--label_file_path", type=str, default="", help="label file path, used in clue benchmark")
parser.add_argument("--eval_data_file_path", type=str, default="",
help="Data path, it is better to use absolute path")
parser.add_argument("--dataset_format", type=str, default="mindrecord", choices=["mindrecord", "tfrecord"],
help="Dataset format, support mindrecord or tfrecord")
parser.add_argument("--schema_file_path", type=str, default="",
help="Schema path, it is better to use absolute path")
parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
args_opt = parser.parse_args()
if args_opt.do_eval.lower() == "true" and args_opt.eval_data_file_path == "":
raise ValueError("'eval_data_file_path' must be set when do evaluation task")
if args_opt.assessment_method.lower() == "clue_benchmark" and args_opt.vocab_file_path == "":
raise ValueError("'vocab_file_path' must be set to do clue benchmark")
if args_opt.use_crf.lower() == "true" and args_opt.label_file_path == "":
raise ValueError("'label_file_path' must be set to use crf")
if args_opt.assessment_method.lower() == "clue_benchmark" and args_opt.label_file_path == "":
raise ValueError("'label_file_path' must be set to do clue benchmark")
if args_opt.assessment_method.lower() == "clue_benchmark":
args_opt.eval_batch_size = 1
return args_opt
if __name__ == "__main__":
args = parse_args()
assessment_method = args.assessment_method.lower()
if args.do_eval.lower() == "true":
ds = create_ner_dataset(batch_size=args.eval_batch_size, repeat_count=1,
assessment_method=assessment_method, data_file_path=args.eval_data_file_path,
schema_file_path=args.schema_file_path, dataset_format=args.dataset_format,
do_shuffle=(args.eval_data_shuffle.lower() == "true"), drop_remainder=False)
ids_path = os.path.join(args.result_path, "00_data")
mask_path = os.path.join(args.result_path, "01_data")
token_path = os.path.join(args.result_path, "02_data")
label_path = os.path.join(args.result_path, "03_data")
os.makedirs(ids_path)
os.makedirs(mask_path)
os.makedirs(token_path)
os.makedirs(label_path)
for idx, data in enumerate(ds.create_dict_iterator(output_numpy=True, num_epochs=1)):
input_ids = data["input_ids"]
input_mask = data["input_mask"]
token_type_id = data["segment_ids"]
label_ids = data["label_ids"]
file_name = "cluener_bs" + str(args.eval_batch_size) + "_" + str(idx) + ".bin"
ids_file_path = os.path.join(ids_path, file_name)
input_ids.tofile(ids_file_path)
mask_file_path = os.path.join(mask_path, file_name)
input_mask.tofile(mask_file_path)
token_file_path = os.path.join(token_path, file_name)
token_type_id.tofile(token_file_path)
label_file_path = os.path.join(label_path, file_name)
label_ids.tofile(label_file_path)
print("=" * 20, "export bin files finished", "=" * 20)

138
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@ -0,0 +1,138 @@
#!/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 7 || $# -gt 8 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [LABEL_PATH] [DATA_FILE_PATH] [DATASET_FORMAT] [SCHEMA_PATH] [USE_CRF] [NEED_PREPROCESS] [DEVICE_ID]
USE_CRF is mandatory, and must choose from [true|false], it's case-insensitive
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
label_file_path=$(get_real_path $2)
eval_data_file_path=$(get_real_path $3)
dataset_format=$4
schema_file_path=$(get_real_path $5)
net_type=${6,,}
if [ $net_type == 'true' ]; then
echo "downstream: CRF"
elif [ $net_type == 'false' ]; then
echo "downstream: NER"
else
echo "[USE_CRF]:true or false"
exit 1
fi
if [ "$7" == "y" ] || [ "$7" == "n" ];then
need_preprocess=$7
else
echo "weather need preprocess or not, it's value must be in [y, n]"
exit 1
fi
device_id=0
if [ $# == 8 ]; then
device_id=$8
fi
echo "mindir name: "$model
echo "label_file_path: "$label_file_path
echo "eval_data_file_path: "$eval_data_file_path
echo "dataset_format: "$dataset_format
echo "schema_file_path: "$schema_file_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=$ASCEND_HOME/opp
fi
function preprocess_data()
{
if [ -d preprocess_Result ]; then
rm -rf ./preprocess_Result
fi
mkdir preprocess_Result
python3.7 ../preprocess.py --use_crf=$net_type --do_eval=true --label_file_path=$label_file_path --eval_data_file_path=$eval_data_file_path --dataset_format=$dataset_format --schema_file_path=$schema_file_path --result_path=./preprocess_Result/
}
function compile_app()
{
cd ../ascend310_infer || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_data --input1_path=./preprocess_Result/01_data --input2_path=./preprocess_Result/02_data --input3_path=./preprocess_Result/03_data --use_crf=$net_type --device_id=$device_id &> infer.log
}
function cal_acc()
{
python3.7 ../postprocess.py --result_path=./result_Files --label_dir=./preprocess_Result/03_data --use_crf=$net_type &> 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

39
model_zoo/official/nlp/gru/README.md
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@ -12,6 +12,8 @@
- [Configuration File](#configuration-file)
- [Training Process](#training-process)
- [Inference Process](#inference-process)
- [Export MindIR](#export-mindir)
- [Inference Process](#inference-process)
- [Model Description](#model-description)
- [Performance](#performance)
- [Training Performance](#training-performance)
@ -219,6 +221,43 @@ Parameters for both training and evaluation can be set in config.py. All the dat
Note: The `DATASET_PATH` is path to mindrecord. eg. train: /dataset_path/multi30k_train_mindrecord_0 eval: /dataset_path/multi30k_test_mindrecord
## [Export MindIR](#contents)
```shell
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```
The ckpt_file parameter is required,
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
## [Inference Process](#contents)
### Usage
Before performing inference, the mindir file must be exported by export.py. Input files must be in bin format.
```shell
# Ascend310 inference
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, default value is 0.
### result
we will get target.txt and output.txt.Then we can use scripts/parse_output.sh to get the translation.
``` bash
sh parse_output.sh target.txt output.txt /path/vocab.en
```
After parse output, we will get target.txt.forbleu and output.txt.forbleu.To calculate BLEU score, you may use this [perl script](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/multi-bleu.perl) and run following command to get the BLEU score.
```bash
perl multi-bleu.perl target.txt.forbleu < output.txt.forbleu
```
# [Model Description](#content)
## [Performance](#content)

14
model_zoo/official/nlp/gru/ascend310_infer/CMakeLists.txt Normal file
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@ -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)

29
model_zoo/official/nlp/gru/ascend310_infer/build.sh Normal file
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@ -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

32
model_zoo/official/nlp/gru/ascend310_infer/inc/utils.h Normal file
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@ -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

135
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@ -0,0 +1,135 @@
/**
* 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/execute.h"
#include "include/dataset/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_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);
if (input0_files.empty() || input1_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]);
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());
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;
}

129
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@ -0,0 +1,129 @@
/**
* 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;
}

50
model_zoo/official/nlp/gru/postprocess.py Normal file
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@ -0,0 +1,50 @@
# 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 script.
'''
import os
import argparse
import numpy as np
from src.config import config
parser = argparse.ArgumentParser(description="postprocess")
parser.add_argument("--label_dir", type=str, default="", help="label data dir")
parser.add_argument("--result_dir", type=str, default="./result_Files", help="infer result Files")
args, _ = parser.parse_known_args()
if __name__ == "__main__":
file_name = os.listdir(args.label_dir)
predictions = []
target_sents = []
for f in file_name:
target_ids = np.fromfile(os.path.join(args.label_dir, f), np.int32)
target_sents.append(target_ids.reshape(config.eval_batch_size, config.max_length))
predicted_ids = np.fromfile(os.path.join(args.result_dir, f.split('.')[0] + '_0.bin'), np.int32)
predictions.append(predicted_ids.reshape(config.eval_batch_size, config.max_length - 1))
f_output = open(config.output_file, 'w')
f_target = open(config.target_file, 'w')
for batch_out, true_sentence in zip(predictions, target_sents):
for i in range(config.eval_batch_size):
target_ids = [str(x) for x in true_sentence[i].tolist()]
f_target.write(" ".join(target_ids) + "\n")
token_ids = [str(x) for x in batch_out[i].tolist()]
f_output.write(" ".join(token_ids) + "\n")
f_output.close()
f_target.close()

46
model_zoo/official/nlp/gru/preprocess.py Normal file
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@ -0,0 +1,46 @@
# 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.
# ============================================================================
"""GRU preprocess script."""
import os
import argparse
from src.dataset import create_gru_dataset
from src.config import config
parser = argparse.ArgumentParser(description='GRU preprocess')
parser.add_argument("--dataset_path", type=str, default="",
help="Dataset path, default: f`sns.")
parser.add_argument('--device_num', type=int, default=1, help='Use device nums, default is 1')
parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
args = parser.parse_args()
if __name__ == "__main__":
mindrecord_file = args.dataset_path
if not os.path.exists(mindrecord_file):
print("dataset file {} not exists, please check!".format(mindrecord_file))
raise ValueError(mindrecord_file)
dataset = create_gru_dataset(epoch_count=config.num_epochs, batch_size=config.eval_batch_size, \
dataset_path=mindrecord_file, rank_size=args.device_num, rank_id=0, do_shuffle=False, is_training=False)
source_ids_path = os.path.join(args.result_path, "00_data")
target_ids_path = os.path.join(args.result_path, "01_data")
os.makedirs(source_ids_path)
os.makedirs(target_ids_path)
for i, data in enumerate(dataset.create_dict_iterator(output_numpy=True, num_epochs=1)):
file_name = "gru_bs" + str(config.eval_batch_size) + "_" + str(i) + ".bin"
data["source_ids"].tofile(os.path.join(source_ids_path, file_name))
data["target_ids"].tofile(os.path.join(target_ids_path, file_name))
print("="*20, "export bin files finished", "="*20)

122
model_zoo/official/nlp/gru/scripts/run_infer_310.sh Normal file
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@ -0,0 +1,122 @@
#!/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=$ASCEND_HOME/opp
fi
function preprocess_data()
{
if [ -d preprocess_Result ]; then
rm -rf ./preprocess_Result
fi
mkdir preprocess_Result
python3.7 ../preprocess.py --dataset_path=$dataset_path --result_path=./preprocess_Result/
}
function compile_app()
{
cd ../ascend310_infer || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_data --input1_path=./preprocess_Result/01_data --device_id=$device_id &> infer.log
}
function cal_acc()
{
python3.7 ../postprocess.py --result_dir=./result_Files --label_dir=./preprocess_Result/01_data &> 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

30
model_zoo/official/nlp/lstm/README.md
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@ -12,6 +12,8 @@
- [Dataset Preparation](#dataset-preparation)
- [Training Process](#training-process)
- [Evaluation Process](#evaluation-process)
- [Export MindIR](#export-mindir)
- [Inference Process](#inference-process)
- [Model Description](#model-description)
- [Performance](#performance)
- [Training Performance](#training-performance)
@ -277,6 +279,34 @@ Ascend:
bash run_eval_cpu.sh ./aclimdb ./glove_dir lstm-20_390.ckpt
```
## [Export MindIR](#contents)
```shell
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```
The ckpt_file parameter is required,
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
## [Inference Process](#contents)
### Usage
Before performing inference, the mindir file must be exported by export.py. Input files must be in bin format.
```shell
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_TARGET] [DEVICE_ID]
```
`DEVICE_TARGET` must choose from ['GPU', 'CPU', 'Ascend']
`NEED_PREPROCESS` means weather need preprocess or not, it's value is 'y' or 'n'
`DEVICE_ID` is optional, default value is 0.
#### result
Inference result is saved in current path, you can find result in acc.log file.
# [Model Description](#contents)
## [Performance](#contents)

31
model_zoo/official/nlp/lstm/README_CN.md
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@ -17,6 +17,10 @@
- [准备数据集](#准备数据集)
- [训练过程](#训练过程)
- [评估过程](#评估过程)
- [导出mindir模型](#导出mindir模型)
- [推理过程](#推理过程)
- [用法](#用法-2)
- [结果](#结果-2)
- [模型描述](#模型描述)
- [性能](#性能)
- [训练性能](#训练性能)
@ -282,6 +286,33 @@ Ascend:
bash run_eval_cpu.sh 0 ./aclimdb ./glove_dir lstm-20_390.ckpt
```
## 导出mindir模型
```shell
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```
参数`ckpt_file` 是必需的，`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。
## 推理过程
### 用法
在执行推理之前，需要通过export.py导出mindir文件。输入文件为bin格式。
```shell
# Ascend310 推理
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_TARGET] [DEVICE_ID]
```
`DEVICE_TARGET` 可选值范围为：['GPU', 'CPU', 'Ascend']
`NEED_PREPROCESS` 表示数据是否需要预处理，可选值范围为：'y' 或者 'n'
`DEVICE_ID` 可选, 默认值为0.
### 结果
推理结果保存在当前路径，可在acc.log中看到最终精度结果。
# 模型描述
## 性能

14
model_zoo/official/nlp/lstm/ascend310_infer/CMakeLists.txt Normal file
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@ -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)

29
model_zoo/official/nlp/lstm/ascend310_infer/build.sh Normal file
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@ -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

32
model_zoo/official/nlp/lstm/ascend310_infer/inc/utils.h Normal file
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@ -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

131
model_zoo/official/nlp/lstm/ascend310_infer/src/main.cc Normal file
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@ -0,0 +1,131 @@
/**
* 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/execute.h"
#include "include/dataset/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_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);
if (input0_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]);
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
input0.Data().get(), input0.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;
}

129
model_zoo/official/nlp/lstm/ascend310_infer/src/utils.cc Normal file
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@ -0,0 +1,129 @@
/**
* 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;
}

47
model_zoo/official/nlp/lstm/postprocess.py Normal file
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@ -0,0 +1,47 @@
# 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.
# ============================================================================
"""
#################lstm postprocess########################
"""
import argparse
import os
import numpy as np
from mindspore.nn import Accuracy
from src.config import lstm_cfg, lstm_cfg_ascend
parser = argparse.ArgumentParser(description='LSTM Postprocess')
parser.add_argument('--label_dir', type=str, default='', help='label data directory.')
parser.add_argument('--result_dir', type=str, default="./result_Files",
help='infer result dir.')
parser.add_argument('--device_target', type=str, default="Ascend", choices=['GPU', 'CPU', 'Ascend'],
help='the target device to run, support "GPU", "CPU". Default: "Ascend".')
args, _ = parser.parse_known_args()
if __name__ == '__main__':
metrics = Accuracy()
rst_path = args.result_dir
labels = np.load(args.label_dir)
if args.device_target == 'Ascend':
cfg = lstm_cfg_ascend
else:
cfg = lstm_cfg
for i in range(len(os.listdir(rst_path))):
file_name = os.path.join(rst_path, "LSTM_data_bs" + str(cfg.batch_size) + '_' + str(i) + '_0.bin')
output = np.fromfile(file_name, np.float32).reshape(cfg.batch_size, cfg.num_classes)
metrics.update(output, labels[i])
print("result of Accuracy is: ", metrics.eval())

49
model_zoo/official/nlp/lstm/preprocess.py Normal file
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@ -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.
# ============================================================================
"""
##############preprocess#################
"""
import argparse
import os
import numpy as np
from src.config import lstm_cfg, lstm_cfg_ascend
from src.dataset import lstm_create_dataset
parser = argparse.ArgumentParser(description='preprocess')
parser.add_argument('--preprocess_path', type=str, default="./preprocess",
help='path where the pre-process data is stored.')
parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
parser.add_argument('--device_target', type=str, default="Ascend", choices=['GPU', 'CPU', 'Ascend'],
help='the target device to run, support "GPU", "CPU". Default: "Ascend".')
args = parser.parse_args()
if __name__ == '__main__':
if args.device_target == 'Ascend':
cfg = lstm_cfg_ascend
else:
cfg = lstm_cfg
dataset = lstm_create_dataset(args.preprocess_path, cfg.batch_size, training=False)
img_path = os.path.join(args.result_path, "00_data")
os.makedirs(img_path)
label_list = []
for i, data in enumerate(dataset.create_dict_iterator(output_numpy=True)):
file_name = "LSTM_data_bs" + str(cfg.batch_size) + "_" + str(i) + ".bin"
file_path = img_path + "/" + file_name
data['feature'].tofile(file_path)
label_list.append(data['label'])
np.save(args.result_path + "label_ids.npy", label_list)
print("="*20, "export bin files finished", "="*20)

130
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@ -0,0 +1,130 @@
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [[ $# -lt 4 || $# -gt 5 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_TARGET] [DEVICE_ID]
DEVICE_TARGET must choose from ['GPU', 'CPU', 'Ascend']
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${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
if [ "$4" == "GPU" ] || [ "$4" == "CPU" ] || [ "$4" == "Ascend" ];then
device_target=$4
else
echo "device_target must be in ['GPU', 'CPU', 'Ascend']"
exit 1
fi
device_id=0
if [ $# == 5 ]; then
device_id=$5
fi
echo "mindir name: "$model
echo "dataset path: "$dataset_path
echo "need preprocess: "$need_preprocess
echo "device_target: "$device_target
echo "device id: "$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=$ASCEND_HOME/opp
fi
function preprocess_data()
{
if [ -d preprocess_Result ]; then
rm -rf ./preprocess_Result
fi
mkdir preprocess_Result
python3.7 ../preprocess.py --preprocess_path=$dataset_path --result_path=./preprocess_Result/ --device_target=$device_target
}
function compile_app()
{
cd ../ascend310_infer || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_data --device_id=$device_id &> infer.log
}
function cal_acc()
{
python3.7 ../postprocess.py --result_dir=./result_Files --label_dir=./preprocess_Result/label_ids.npy --device_target=$device_target &> 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

35
model_zoo/official/nlp/textcnn/README.md
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@ -10,6 +10,8 @@
- [Script Parameters](#script-parameters)
- [Training Process](#training-process)
- [Evaluation Process](#evaluation-process)
- [Export MindIR](#export-mindir)
- [Inference Process](#inference-process)
- [Model Description](#model-description)
- [Performance](#performance)
- [ModelZoo Homepage](#modelzoo-homepage)
@ -186,6 +188,39 @@ For more configuration details, please refer the script `config.py`.
accuracy: {'acc': 0.7971428571428572}
```
## [Export MindIR](#contents)
```shell
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```
The ckpt_file parameter is required,
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
## [Inference Process](#contents)
### Usage
Before performing inference, the mindir file must be exported by export.py. Input files must be in bin format.
```shell
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DATASET_NAME] [NEED_PREPROCESS] [DEVICE_ID]
```
`DATASET_NAME` must choose from ['MR', 'SUBJ', 'SST2']
`NEED_PREPROCESS` means weather need preprocess or not, it's value is 'y' or 'n'."
`DEVICE_ID` is optional, default value is 0.
### result
Inference result is saved in current path, you can find result in acc.log file.
```python
# grep "accuracy: " acc.log
accuracy: 0.7971428571428572
```
# [Model Description](#contents)
## [Performance](#contents)

14
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@ -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)

29
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@ -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

32
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@ -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

129
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@ -0,0 +1,129 @@
/**
* 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/execute.h"
#include "include/dataset/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_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);
if (input0_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]);
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
input0.Data().get(), input0.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;
}

129
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@ -0,0 +1,129 @@
/**
* 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;
}

50
model_zoo/official/nlp/textcnn/postprocess.py Normal file
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@ -0,0 +1,50 @@
# 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 argparse
import numpy as np
from mindspore.nn.metrics import Accuracy
from src.config import cfg_mr, cfg_subj, cfg_sst2
parser = argparse.ArgumentParser(description='postprocess')
parser.add_argument('--label_dir', type=str, default="", help='label data dir')
parser.add_argument('--result_dir', type=str, default="", help="infer result dir")
parser.add_argument('--dataset', type=str, default="MR", choices=['MR', 'SUBJ', 'SST2'])
args = parser.parse_args()
if __name__ == '__main__':
if args.dataset == 'MR':
cfg = cfg_mr
elif args.dataset == 'SUBJ':
cfg = cfg_subj
elif args.dataset == 'SST2':
cfg = cfg_sst2
file_prefix = 'textcnn_bs' + str(cfg.batch_size) + '_'
metric = Accuracy()
metric.clear()
label_list = np.load(args.label_dir, allow_pickle=True)
for idx, label in enumerate(label_list):
pred = np.fromfile(os.path.join(args.result_dir, file_prefix + str(idx) + '_0.bin'), np.float32)
pred = pred.reshape(cfg.batch_size, int(pred.shape[0]/cfg.batch_size))
metric.update(pred, label)
accuracy = metric.eval()
print("accuracy: ", accuracy)

51
model_zoo/official/nlp/textcnn/preprocess.py Normal file
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@ -0,0 +1,51 @@
# 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 textcnn example on movie review#################
"""
import argparse
import os
import numpy as np
from src.config import cfg_mr, cfg_subj, cfg_sst2
from src.dataset import MovieReview, SST2, Subjectivity
parser = argparse.ArgumentParser(description='TextCNN')
parser.add_argument('--dataset', type=str, default="MR", choices=['MR', 'SUBJ', 'SST2'])
parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
args_opt = parser.parse_args()
if __name__ == '__main__':
if args_opt.dataset == 'MR':
cfg = cfg_mr
instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
elif args_opt.dataset == 'SUBJ':
cfg = cfg_subj
instance = Subjectivity(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
elif args_opt.dataset == 'SST2':
cfg = cfg_sst2
instance = SST2(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
dataset = instance.create_test_dataset(batch_size=cfg.batch_size)
img_path = os.path.join(args_opt.result_path, "00_data")
os.makedirs(img_path)
label_list = []
for i, data in enumerate(dataset.create_dict_iterator(output_numpy=True)):
file_name = "textcnn_bs" + str(cfg.batch_size) + "_" + str(i) + ".bin"
file_path = img_path + "/" + file_name
data['data'].tofile(file_path)
label_list.append(data['label'])
np.save(args_opt.result_path + "label_ids.npy", label_list)
print("="*20, "export bin files finished", "="*20)

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model_zoo/official/nlp/textcnn/scripts/run_infer_310.sh Normal file
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@ -0,0 +1,128 @@
#!/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 || $# -gt 4 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_NAME] [NEED_PREPROCESS] [DEVICE_ID]
DATASET_NAME must choose from ['MR', 'SUBJ', 'SST2']
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
if [ "$2" == "MR" ] || [ "$2" == "SUBJ" ] || [ "$2" == "SST2" ];then
dataset_name=$2
else
echo "image process mode must be in [MR, SUBJ, SST2]"
exit 1
fi
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 name: "$dataset_name
echo "need preprocess: "$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=$ASCEND_HOME/opp
fi
function preprocess_data()
{
if [ -d preprocess_Result ]; then
rm -rf ./preprocess_Result
fi
mkdir preprocess_Result
python3.7 ../preprocess.py --dataset=$dataset_name --result_path=./preprocess_Result/
}
function compile_app()
{
cd ../ascend310_infer || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_data --device_id=$device_id &> infer.log
}
function cal_acc()
{
python3.7 ../postprocess.py --result_dir=./result_Files --label_dir=./preprocess_Result/label_ids.npy --dataset=$dataset_name &> 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