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!17518 mobilenetv1 add 310 infer 

From: @zeyangao
Reviewed-by: @wuxuejian,@c_34
Signed-off-by: @c_34
This commit is contained in:
mindspore-ci-bot 2021-06-05 16:04:17 +08:00 committed by Gitee
parent 0281ad714a 5274de6c47
commit fcf9e16207
9 changed files with 754 additions and 1 deletions
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36
model_zoo/official/cv/mobilenetv1/README.md
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@ -1,4 +1,4 @@
# Mobilenet_V1
# Mobilenet_V1
- [Mobilenet_V1](#mobilenet_v1)
- [MobileNetV1 Description](#mobilenetv1-description)
@ -161,6 +161,40 @@ Inference result will be stored in the example path, you can find result like th
result: {'top_5_accuracy': 0.9010016025641026, 'top_1_accuracy': 0.7128004807692307} ckpt=./train_parallel0/ckpt_0/mobilenetv1-90_1251.ckpt
```
## Inference Process
### [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"]
### Infer on Ascend310
Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
Current batch_Size for imagenet2012 dataset can only be set to 1.
```shell
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
```
- `MINDIR_PATH` specifies path of used "MINDIR" OR "AIR" model.
- `DATASET_PATH` specifies path of cifar10 datasets
- `DEVICE_ID` is optional, default value is 0.
### Result
Inference result is saved in current path, you can find result like this in acc.log file.
```bash
'top1 acc': 0.71966
'top5 acc': 0.90424
```
## Model description
### [Performance](#contents)

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model_zoo/official/cv/mobilenetv1/ascend310_infer/CMakeLists.txt Normal file
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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/cv/mobilenetv1/ascend310_infer/build.sh Normal file
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#!/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/cv/mobilenetv1/ascend310_infer/inc/utils.h 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.
*/
#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);
std::vector<std::string> GetAllFiles(std::string dir_name);
std::vector<std::vector<std::string>> GetAllInputData(std::string dir_name);
#endif

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model_zoo/official/cv/mobilenetv1/ascend310_infer/src/main.cc 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.
*/
#include <sys/time.h>
#include <gflags/gflags.h>
#include <dirent.h>
#include <iostream>
#include <string>
#include <algorithm>
#include <iosfwd>
#include <vector>
#include <fstream>
#include <sstream>
#include "include/api/model.h"
#include "include/api/context.h"
#include "include/api/types.h"
#include "include/api/serialization.h"
#include "include/dataset/vision_ascend.h"
#include "include/dataset/execute.h"
#include "include/dataset/transforms.h"
#include "include/dataset/vision.h"
#include "inc/utils.h"
using mindspore::Context;
using mindspore::Serialization;
using mindspore::Model;
using mindspore::Status;
using mindspore::ModelType;
using mindspore::GraphCell;
using mindspore::kSuccess;
using mindspore::MSTensor;
using mindspore::dataset::Execute;
using mindspore::dataset::vision::Decode;
using mindspore::dataset::vision::Resize;
using mindspore::dataset::vision::CenterCrop;
using mindspore::dataset::vision::Normalize;
using mindspore::dataset::vision::HWC2CHW;
DEFINE_string(mindir_path, "", "mindir path");
DEFINE_string(dataset_name, "imagenet2012", "['cifar10', 'imagenet2012']");
DEFINE_string(input0_path, ".", "input0 path");
DEFINE_int32(device_id, 0, "device id");
int load_model(Model *model, std::vector<MSTensor> *model_inputs, std::string mindir_path, int device_id) {
if (RealPath(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(device_id);
context->MutableDeviceInfo().push_back(ascend310);
mindspore::Graph graph;
Serialization::Load(mindir_path, ModelType::kMindIR, &graph);
Status ret = model->Build(GraphCell(graph), context);
if (ret != kSuccess) {
std::cout << "ERROR: Build failed." << std::endl;
return 1;
}
*model_inputs = model->GetInputs();
if (model_inputs->empty()) {
std::cout << "Invalid model, inputs is empty." << std::endl;
return 1;
}
return 0;
}
int main(int argc, char **argv) {
gflags::ParseCommandLineFlags(&argc, &argv, true);
Model model;
std::vector<MSTensor> model_inputs;
load_model(&model, &model_inputs, FLAGS_mindir_path, FLAGS_device_id);
std::map<double, double> costTime_map;
struct timeval start = {0};
struct timeval end = {0};
double startTimeMs;
double endTimeMs;
if (FLAGS_dataset_name == "cifar10") {
auto input0_files = GetAllFiles(FLAGS_input0_path);
if (input0_files.empty()) {
std::cout << "ERROR: no input data." << std::endl;
return 1;
}
size_t size = input0_files.size();
for (size_t i = 0; i < size; ++i) {
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);
Status 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);
}
} else {
auto input0_files = GetAllInputData(FLAGS_input0_path);
if (input0_files.empty()) {
std::cout << "ERROR: no input data." << std::endl;
return 1;
}
size_t size = input0_files.size();
for (size_t i = 0; i < size; ++i) {
for (size_t j = 0; j < input0_files[i].size(); ++j) {
std::vector<MSTensor> inputs;
std::vector<MSTensor> outputs;
std::cout << "Start predict input files:" << input0_files[i][j] <<std::endl;
auto decode = Decode();
auto resize = Resize({256});
auto centercrop = CenterCrop({224, 224});
auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375});
auto hwc2chw = HWC2CHW();
Execute SingleOp({decode, resize, centercrop, normalize, hwc2chw});
auto imgDvpp = std::make_shared<MSTensor>();
SingleOp(ReadFileToTensor(input0_files[i][j]), imgDvpp.get());
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
imgDvpp->Data().get(), imgDvpp->DataSize());
gettimeofday(&start, nullptr);
Status ret = model.Predict(inputs, &outputs);
gettimeofday(&end, nullptr);
if (ret != kSuccess) {
std::cout << "Predict " << input0_files[i][j] << " 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][j], 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/cv/mobilenetv1/ascend310_infer/src/utils.cc 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.
*/
#include <fstream>
#include <algorithm>
#include <iostream>
#include "inc/utils.h"
using mindspore::MSTensor;
using mindspore::DataType;
std::vector<std::vector<std::string>> GetAllInputData(std::string dir_name) {
std::vector<std::vector<std::string>> ret;
DIR *dir = OpenDir(dir_name);
if (dir == nullptr) {
return {};
}
struct dirent *filename;
/* read all the files in the dir ~ */
std::vector<std::string> sub_dirs;
while ((filename = readdir(dir)) != nullptr) {
std::string d_name = std::string(filename->d_name);
// get rid of "." and ".."
if (d_name == "." || d_name == ".." || d_name.empty()) {
continue;
}
std::string dir_path = RealPath(std::string(dir_name) + "/" + filename->d_name);
struct stat s;
lstat(dir_path.c_str(), &s);
if (!S_ISDIR(s.st_mode)) {
continue;
}
sub_dirs.emplace_back(dir_path);
}
std::sort(sub_dirs.begin(), sub_dirs.end());
(void)std::transform(sub_dirs.begin(), sub_dirs.end(), std::back_inserter(ret),
[](const std::string &d) { return GetAllFiles(d); });
return ret;
}
std::vector<std::string> GetAllFiles(std::string dir_name) {
struct dirent *filename;
DIR *dir = OpenDir(dir_name);
if (dir == nullptr) {
return {};
}
std::vector<std::string> res;
while ((filename = readdir(dir)) != nullptr) {
std::string d_name = std::string(filename->d_name);
if (d_name == "." || d_name == ".." || d_name.size() <= 3) {
continue;
}
res.emplace_back(std::string(dir_name) + "/" + filename->d_name);
}
std::sort(res.begin(), res.end());
return res;
}
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/cv/mobilenetv1/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
#
# less 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 for 310 inference"""
import os
import json
import argparse
import numpy as np
from mindspore.nn import Top1CategoricalAccuracy, Top5CategoricalAccuracy
parser = argparse.ArgumentParser(description="postprocess")
parser.add_argument("--result_dir", type=str, required=True, help="result files path.")
parser.add_argument("--label_dir", type=str, required=True, help="image file path.")
parser.add_argument('--dataset_name', type=str, choices=["cifar10", "imagenet2012"], default="imagenet2012")
args = parser.parse_args()
def calcul_acc(lab, preds):
return sum(1 for x, y in zip(lab, preds) if x == y) / len(lab)
if __name__ == '__main__':
batch_size = 1
top1_acc = Top1CategoricalAccuracy()
rst_path = args.result_dir
label_list = []
pred_list = []
if args.dataset_name == "cifar10":
from src.config import config1 as cfg
labels = np.load(args.label_dir, allow_pickle=True)
for idx, label in enumerate(labels):
f_name = os.path.join(rst_path, "mobilenetv1_data_bs" + str(cfg.batch_size) + "_" + str(idx) + "_0.bin")
pred = np.fromfile(f_name, np.float32)
pred = pred.reshape(cfg.batch_size, int(pred.shape[0] / cfg.batch_size))
top1_acc.update(pred, labels[idx])
print("acc: ", top1_acc.eval())
else:
from src.config import config2 as cfg
top5_acc = Top5CategoricalAccuracy()
file_list = os.listdir(rst_path)
with open(args.label_dir, "r") as label:
labels = json.load(label)
for f in file_list:
label = f.split("_0.bin")[0] + ".JPEG"
label_list.append(labels[label])
pred = np.fromfile(os.path.join(rst_path, f), np.float32)
pred = pred.reshape(batch_size, int(pred.shape[0] / batch_size))
top1_acc.update(pred, [labels[label],])
top5_acc.update(pred, [labels[label],])
print("Top1 acc: ", top1_acc.eval())
print("Top5 acc: ", top5_acc.eval())

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model_zoo/official/cv/mobilenetv1/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.
# ============================================================================
"""preprocess"""
import os
import argparse
import json
import numpy as np
from src.dataset import create_dataset1
def create_label(result_path, dir_path):
print("[WARNING] Create imagenet label. Currently only use for Imagenet2012!")
dirs = os.listdir(dir_path)
file_list = []
for file in dirs:
file_list.append(file)
file_list = sorted(file_list)
total = 0
img_label = {}
for i, file_dir in enumerate(file_list):
files = os.listdir(os.path.join(dir_path, file_dir))
for f in files:
img_label[f] = i
total += len(files)
json_file = os.path.join(result_path, "imagenet_label.json")
with open(json_file, "w+") as label:
json.dump(img_label, label)
print("[INFO] Completed! Total {} data.".format(total))
parser = argparse.ArgumentParser('preprocess')
parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="imagenet2012")
parser.add_argument('--data_path', type=str, default='', help='eval data dir')
parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
args = parser.parse_args()
if args.dataset == "cifar10":
from src.config import config1 as cfg
else:
from src.config import config2 as cfg
args.per_batch_size = cfg.batch_size
#args.image_size = list(map(int, cfg.image_size.split(',')))
if __name__ == "__main__":
if args.dataset == "cifar10":
dataset = create_dataset1(args.data_path, False, args.per_batch_size)
img_path = os.path.join(args.result_path, "00_data")
os.makedirs(img_path)
label_list = []
for idx, data in enumerate(dataset.create_dict_iterator(output_numpy=True)):
file_name = "mobilenetv1_data_bs" + str(args.per_batch_size) + "_" + str(idx) + ".bin"
file_path = os.path.join(img_path, file_name)
data["image"].tofile(file_path)
label_list.append(data["label"])
np.save(os.path.join(args.result_path, "cifar10_label_ids.npy"), label_list)
print("=" * 20, "export bin files finished", "=" * 20)
else:
create_label(args.result_path, args.data_path)

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model_zoo/official/cv/mobilenetv1/scripts/run_infer_310.sh Normal file
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#!/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 2 || $# -gt 3 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
dataset_path=$(get_real_path $2)
dataset_name="imagenet2012"
DVPP="CPU"
device_id=0
if [ $# == 3 ]; then
device_id=$3
fi
echo "mindir name: "$model
echo "dataset path: "$dataset_path
echo "device id: "$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=$ASCEND_HOME/opp
fi
export SLOG_PRINT_to_STDOUT=0
export GLOG_v=2
export DUMP_GE_GRAPH=2
export ASCEND_HOME=/usr/local/Ascend
export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/toolkit/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/lib/:/usr/local/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:/usr/local/Ascend/toolkit/lib64:$LD_LIBRARY_PATH
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages
export PATH=/usr/local/python375/bin:$PATH
export NPU_HOST_LIB=/usr/local/Ascend/acllib/lib64/stub
export ASCEND_OPP_PATH=/usr/local/Ascend/opp
export ASCEND_AICPU_PATH=/usr/local/Ascend
export LD_LIBRARY_PATH=/usr/local/lib64/:$LD_LIBRARY_PATH
function preprocess_data()
{
if [ -d preprocess_Result ]; then
rm -rf ./preprocess_Result
fi
mkdir preprocess_Result
python3.7 ../preprocess.py --dataset=$dataset_name --data_path=$dataset_path --result_path=./preprocess_Result/
}
function compile_app()
{
cd ../ascend310_infer/ || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
if [ "$DVPP" == "DVPP" ]; then
../ascend310_infer/out/main --mindir_path=$model --dataset_name=$dataset_name --input0_path=$dataset_path --device_id=$device_id --cpu_dvpp=../ascend310_infer/aipp.cfg --image_height=256 --image_width=256 &> infer.log
else
../ascend310_infer/out/main --mindir_path=$model --dataset_name=$dataset_name --input0_path=$dataset_path --device_id=$device_id &> infer.log
fi
}
function cal_acc()
{
python3.7 ../postprocess.py --result_dir=./result_Files --label_dir=./preprocess_Result/imagenet_label.json &> acc.log
}
preprocess_data
if [ $? -ne 0 ]; then
echo "preprocess dataset failed"
exit 1
fi
compile_app
if [ $? -ne 0 ]; then
echo "compile app code failed"
exit 1
fi
infer
if [ $? -ne 0 ]; then
echo " execute inference failed"
exit 1
fi
cal_acc
if [ $? -ne 0 ]; then
echo "calculate accuracy failed"
exit 1
fi