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support ascend310 infer of Resnext101

This commit is contained in:
zhou_lili 2021-06-22 16:10:48 +08:00
parent 08f7e99c9e
commit 6e3b0a4e20
11 changed files with 624 additions and 6 deletions
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52
model_zoo/official/cv/resnext101/README_CN.md
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@ -103,15 +103,17 @@ ResNeXt是ResNet网络的改进版本，比ResNet的网络多了块多了cardina
.
└─resnext101-64x4d-mindspore
├─README.md
├─ascend310_infer #310推理依赖的应用
├─scripts
├─run_standalone_train.sh # 启动Ascend单机训练（单卡）
├─run_distribute_train.sh # 启动Ascend分布式训练（8卡）
├─run_standalone_train_for_gpu.sh # 启动GPU单机训练（单卡）
├─run_distribute_train_for_gpu.sh # 启动GPU分布式训练（8卡）
├─run_infer_310.sh # 启动Ascend310推理
└─run_eval.sh # 启动评估
├─src
├─backbone
├─_init_.py # 初始化
├─_init_.py # 初始化
├─resnext.py # ResNeXt101骨干
├─utils
├─_init_.py # 初始化
@ -134,8 +136,11 @@ ResNeXt是ResNet网络的改进版本，比ResNet的网络多了块多了cardina
│ ├──device_adapter.py # 设备配置
│ ├──local_adapter.py # 本地设备配置
│ ├──moxing_adapter.py # modelarts设备配置
├──create_imagenet2012_label.py # 转换推理数据
├──default_config.yaml # 参数配置
├──eval.py # 评估网络
├──export.py # 转换ckpt至MINDIR格式
├──postprogress.py # 310推理后处理
├──train.py # 训练网络
├──mindspore_hub_conf.py # MindSpore Hub接口
```
@ -191,13 +196,37 @@ python eval.py --data_path ~/imagenet/val/ --platform Ascend --checkpoint_file_p
bash scripts/run_eval.sh DEVICE_ID DATA_PATH CHECKPOINT_FILE_PATH DEVICE_TARGET
```
## [推理过程](#contents)
### 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。
## 模型导出
```shell
python export.py --device_target [PLATFORM] --checkpoint_file_path [CKPT_PATH] --file_format [EXPORT_FORMAT]
```
`EXPORT_FORMAT` 可选 ["AIR", "ONNX", "MINDIR"].
`checkpoint_file_path` 参数为必填项
`device_target` 可选 ["Ascend", "GPU"]
`file_format` 可选 ["AIR", "MINDIR"]
```shell
#Ascend310 推理
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
```
`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。
```log
Total data:50000, top1 accuracy:0.79858, top5 accuracy:0.94716
```
## 高级设置
@ -251,10 +280,25 @@ python export.py --device_target [PLATFORM] --checkpoint_file_path [CKPT_PATH] -
| **epochs** | Top1/Top5 |
| :--------: | :-----------: |
| 150 | 79.56%(TOP1)/94.68%(TOP5) |
| 150 | 79.56%(TOP1)/94.68%(TOP5) |
#### 训练性能结果
| **NPUs** | train performance |
| :------: | :---------------: |
| 1 | 196.33image/sec |
| 1 | 196.33image/sec |
### 310 推理性能
#### ResNeXt101 on ImageNet
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | ResNeXt101 |
| Resource | Ascend 310; OS Euler2.8 |
| Uploaded Date | 22/06/2021 (month/day/year) |
| MindSpore Version | 1.2.0 |
| Dataset | ImageNet |
| batch_size | 1 |
| outputs | Accuracy |
| Accuracy | TOP1: 79.85%, TOP5: 94.71% |

14
model_zoo/official/cv/resnext101/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)

18
model_zoo/official/cv/resnext101/ascend310_infer/build.sh Normal file
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@ -0,0 +1,18 @@
#!/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.
# ============================================================================
cmake . -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
make

33
model_zoo/official/cv/resnext101/ascend310_infer/inc/utils.h Normal file
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@ -0,0 +1,33 @@
/**
* 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"
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);
#endif

157
model_zoo/official/cv/resnext101/ascend310_infer/src/main.cc Normal file
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@ -0,0 +1,157 @@
/**
* 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 "../inc/utils.h"
#include "include/dataset/execute.h"
#include "include/dataset/transforms.h"
#include "include/dataset/vision.h"
#include "include/dataset/vision_ascend.h"
#include "include/api/types.h"
#include "include/api/model.h"
#include "include/api/serialization.h"
#include "include/api/context.h"
using mindspore::Serialization;
using mindspore::Model;
using mindspore::Context;
using mindspore::Status;
using mindspore::ModelType;
using mindspore::Graph;
using mindspore::GraphCell;
using mindspore::kSuccess;
using mindspore::MSTensor;
using mindspore::DataType;
using mindspore::dataset::Execute;
using mindspore::dataset::TensorTransform;
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(model_path, "", "model path");
DEFINE_string(dataset, "ImageNet", "dataset: ImageNet");
DEFINE_string(dataset_path, ".", "dataset path");
DEFINE_int32(device_id, 0, "device id");
int main(int argc, char **argv) {
gflags::ParseCommandLineFlags(&argc, &argv, true);
if (RealPath(FLAGS_model_path).empty()) {
std::cout << "Invalid model" << std::endl;
return 1;
}
std::transform(FLAGS_dataset.begin(), FLAGS_dataset.end(), FLAGS_dataset.begin(), ::tolower);
auto context = std::make_shared<Context>();
auto ascend310_info = std::make_shared<mindspore::Ascend310DeviceInfo>();
ascend310_info->SetDeviceID(FLAGS_device_id);
context->MutableDeviceInfo().push_back(ascend310_info);
Graph graph;
Status ret = Serialization::Load(FLAGS_model_path, ModelType::kMindIR, &graph);
if (ret != kSuccess) {
std::cout << "Load model failed." << std::endl;
return 1;
}
Model model;
ret = model.Build(GraphCell(graph), context);
if (ret != kSuccess) {
std::cout << "ERROR: Build failed." << std::endl;
return 1;
}
std::vector<MSTensor> modelInputs = model.GetInputs();
auto all_files = GetAllFiles(FLAGS_dataset_path);
if (all_files.empty()) {
std::cout << "ERROR: no input data." << std::endl;
return 1;
}
std::shared_ptr<TensorTransform> decode(new Decode());
std::shared_ptr<TensorTransform> resize(new Resize({256, 256}));
std::shared_ptr<TensorTransform> centerCrop(new CenterCrop({224, 224}));
std::shared_ptr<TensorTransform> normImageNet(new Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375}));
std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
mindspore::dataset::Execute transformImageNet({decode, resize, centerCrop, normImageNet, hwc2chw});
std::map<double, double> costTime_map;
size_t size = all_files.size();
for (size_t i = 0; i < size; ++i) {
struct timeval start;
struct timeval end;
double startTime_ms;
double endTime_ms;
std::vector<MSTensor> inputs;
std::vector<MSTensor> outputs;
std::cout << "Start predict input files:" << all_files[i] << std::endl;
mindspore::MSTensor image = ReadFileToTensor(all_files[i]);
if (FLAGS_dataset.compare("imagenet") == 0) {
transformImageNet(image, &image);
} else {
std::cout << "unsupported dataset ...";
return 1;
}
inputs.emplace_back(modelInputs[0].Name(), modelInputs[0].DataType(), modelInputs[0].Shape(),
image.Data().get(), image.DataSize());
gettimeofday(&start, NULL);
model.Predict(inputs, &outputs);
gettimeofday(&end, NULL);
startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
WriteResult(all_files[i], outputs);
}
double average = 0.0;
int infer_cnt = 0;
char tmpCh[256] = {0};
for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
double diff = 0.0;
diff = iter->second - iter->first;
average += diff;
infer_cnt++;
}
average = average/infer_cnt;
snprintf(tmpCh, sizeof(tmpCh), "NN inference cost average time: %4.3f ms of infer_count %d\n", average, infer_cnt);
std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
file_stream << tmpCh;
file_stream.close();
costTime_map.clear();
return 0;
}

145
model_zoo/official/cv/resnext101/ascend310_infer/src/utils.cc Normal file
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@ -0,0 +1,145 @@
/**
* 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 dirName) {
struct dirent *filename;
DIR *dir = OpenDir(dirName);
if (dir == nullptr) {
return {};
}
std::vector<std::string> dirs;
std::vector<std::string> files;
while ((filename = readdir(dir)) != nullptr) {
std::string dName = std::string(filename->d_name);
if (dName == "." || dName == "..") {
continue;
} else if (filename->d_type == DT_DIR) {
dirs.emplace_back(std::string(dirName) + "/" + filename->d_name);
} else if (filename->d_type == DT_REG) {
files.emplace_back(std::string(dirName) + "/" + filename->d_name);
} else {
continue;
}
}
for (auto d : dirs) {
dir = OpenDir(d);
while ((filename = readdir(dir)) != nullptr) {
std::string dName = std::string(filename->d_name);
if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
continue;
}
files.emplace_back(std::string(d) + "/" + filename->d_name);
}
}
std::sort(files.begin(), files.end());
for (auto &f : files) {
std::cout << "image file: " << f << std::endl;
}
return files;
}
int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
std::string homePath = "./result_Files";
for (size_t i = 0; i < outputs.size(); ++i) {
size_t outputSize;
std::shared_ptr<const void> netOutput;
netOutput = outputs[i].Data();
outputSize = outputs[i].DataSize();
int pos = imageFile.rfind('/');
std::string fileName(imageFile, pos + 1);
fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
std::string outFileName = homePath + "/" + fileName;
FILE *outputFile = fopen(outFileName.c_str(), "wb");
fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
fclose(outputFile);
outputFile = nullptr;
}
return 0;
}
mindspore::MSTensor ReadFileToTensor(const std::string &file) {
if (file.empty()) {
std::cout << "Pointer file is nullptr" << std::endl;
return mindspore::MSTensor();
}
std::ifstream ifs(file);
if (!ifs.good()) {
std::cout << "File: " << file << " is not exist" << std::endl;
return mindspore::MSTensor();
}
if (!ifs.is_open()) {
std::cout << "File: " << file << "open failed" << std::endl;
return mindspore::MSTensor();
}
ifs.seekg(0, std::ios::end);
size_t size = ifs.tellg();
mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
ifs.seekg(0, std::ios::beg);
ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
ifs.close();
return buffer;
}
DIR *OpenDir(std::string_view dirName) {
if (dirName.empty()) {
std::cout << " dirName is null ! " << std::endl;
return nullptr;
}
std::string realPath = RealPath(dirName);
struct stat s;
lstat(realPath.c_str(), &s);
if (!S_ISDIR(s.st_mode)) {
std::cout << "dirName is not a valid directory !" << std::endl;
return nullptr;
}
DIR *dir;
dir = opendir(realPath.c_str());
if (dir == nullptr) {
std::cout << "Can not open dir " << dirName << std::endl;
return nullptr;
}
std::cout << "Successfully opened the dir " << dirName << std::endl;
return dir;
}
std::string RealPath(std::string_view path) {
char realPathMem[PATH_MAX] = {0};
char *realPathRet = nullptr;
realPathRet = realpath(path.data(), realPathMem);
if (realPathRet == nullptr) {
std::cout << "File: " << path << " is not exist.";
return "";
}
std::string realPath(realPathMem);
std::cout << path << " realpath is: " << realPath << std::endl;
return realPath;
}

48
model_zoo/official/cv/resnext101/create_imagenet2012_label.py Normal file
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@ -0,0 +1,48 @@
# 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.
# ============================================================================
"""create_imagenet2012_label"""
import os
import json
import argparse
parser = argparse.ArgumentParser(description="resnet imagenet2012 label")
parser.add_argument("--img_path", type=str, required=True, help="imagenet2012 file path.")
args = parser.parse_args()
def create_label(file_path):
print("[WARNING] Create imagenet label. Currently only use for Imagenet2012!")
dirs = os.listdir(file_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(file_path, file_dir))
for f in files:
img_label[f] = i
total += len(files)
with open("imagenet_label.json", "w+") as label:
json.dump(img_label, label)
print("[INFO] Completed! Total {} data.".format(total))
if __name__ == '__main__':
create_label(args.img_path)

2
model_zoo/official/cv/resnext101/default_config.yaml
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@ -49,7 +49,7 @@ device_id: 0
width: 224
height: 224
file_name: "resnext101"
file_format: "AIR"
file_format: "MINDIR"
---
# Help description for each configuration

14
model_zoo/official/cv/resnext101/export.py
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@ -15,11 +15,23 @@
"""
resnext export mindir.
"""
import argparse
import numpy as np
from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
from src.model_utils.config import config
from src.image_classification import get_network
parser = argparse.ArgumentParser(description='checkpoint export')
parser.add_argument("--device_id", type=int, default=0, help="Device id")
parser.add_argument("--batch_size", type=int, default=1, help="batch size")
parser.add_argument("--checkpoint_file_path", type=str, required=True, help="Checkpoint file path.")
parser.add_argument('--width', type=int, default=224, help='input width')
parser.add_argument('--height', type=int, default=224, help='input height')
parser.add_argument("--file_name", type=str, default="resnext101", help="output file name.")
parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
parser.add_argument("--device_target", type=str, default="Ascend",
choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
if config.device_target == "Ascend":
@ -28,7 +40,7 @@ if config.device_target == "Ascend":
if __name__ == '__main__':
net = get_network(num_classes=config.num_classes, platform=config.device_target)
param_dict = load_checkpoint(config.checkpoint_file_path)
param_dict = load_checkpoint(args.checkpoint_file_path)
load_param_into_net(net, param_dict)
input_shp = [config.batch_size, 3, config.height, config.width]
input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))

48
model_zoo/official/cv/resnext101/postprocess.py Normal file
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@ -0,0 +1,48 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""post process for 310 inference"""
import os
import json
import argparse
import numpy as np
batch_size = 1
parser = argparse.ArgumentParser(description="resnet inference")
parser.add_argument("--result_path", type=str, required=True, help="result files path.")
parser.add_argument("--label_path", type=str, required=True, help="image file path.")
args = parser.parse_args()
def get_result(result_path, label_path):
files = os.listdir(result_path)
with open(label_path, "r") as label:
labels = json.load(label)
top1 = 0
top5 = 0
total_data = len(files)
for file in files:
img_ids_name = file.split('_0.')[0]
data_path = os.path.join(result_path, img_ids_name + "_0.bin")
result = np.fromfile(data_path, dtype=np.float16).reshape(1, 1000) #reshape(batch_size, num_classes)
predict = np.argsort(-result[0], axis=-1)
if labels[img_ids_name+".JPEG"] == predict[0]:
top1 += 1
if labels[img_ids_name+".JPEG"] in predict[:5]:
top5 += 1
print(f"Total data: {total_data}, top1 accuracy: {top1/total_data}, top5 accuracy: {top5/total_data}.")
if __name__ == '__main__':
get_result(args.result_path, args.label_path)

99
model_zoo/official/cv/resnext101/scripts/run_infer_310.sh Normal file
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@ -0,0 +1,99 @@
#!/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 || $# -gt 3 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
data_path=$(get_real_path $2)
device_id=0
if [ $# == 3 ]; then
device_id=$3
fi
echo "mindir name: "$model
echo "dataset path: "$data_path
echo "device id: "$device_id
export ASCEND_HOME=/usr/local/Ascend/
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
else
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
fi
function compile_app()
{
cd ../ascend310_infer/ || exit
if [ -f "Makefile" ]; then
make clean
fi
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/main --model_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
}
function cal_acc()
{
python3.7 ../create_imagenet2012_label.py --img_path=$data_path
python3.7 ../postprocess.py --result_path=./result_Files --label_path=./imagenet_label.json &> acc.log &
}
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