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tinydarknet add 310 infer

This commit is contained in:
chenweitao_295 2021-05-29 17:38:52 +08:00
parent 20b1e0291c
commit ca6d289e5c
10 changed files with 662 additions and 11 deletions
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64
model_zoo/official/cv/tinydarknet/README.md
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@ -14,9 +14,14 @@
- [Distributed Training](#distributed-training)
- [Evaluation Procsee](#evaluation-process)
- [Evaluation](#evaluation)
- [Inference Process](#inference-process)
- [Export MindIR](#export-mindir)
- [Infer on Ascend310](#infer-on-ascend310)
- [result](#result)
- [Model Description](#model-description)
- [Performance](#performance)
- [Training Performance](#training-performance)
- [Evaluation Performance](#evaluation-performance)
- [Inference Performance](#inference-performance)
- [ModelZoo Homepage](#modelzoo-homepage)
@ -97,12 +102,14 @@ For more details, please refer the specify script.
```bash
├── tinydarknet
├── README.md // descriptions about Tiny-Darknet in English
├── README_CN.md // descriptions about Tiny-Darknet in Chinese
├── README.md // descriptions about Tiny-Darknet in English
├── README_CN.md // descriptions about Tiny-Darknet in Chinese
├── ascend310_infer // application for 310 inference
├── scripts
├──run_standalone_train.sh // shell script for single on Ascend
├──run_distribute_train.sh // shell script for distributed on Ascend
├──run_eval.sh // shell script for evaluation on Ascend
├──run_infer_310.sh // shell script for inference on Ascend310
├── src
├─lr_scheduler //learning rate scheduler
├─__init__.py // init
@ -116,7 +123,8 @@ For more details, please refer the specify script.
├── train.py // training script
├── eval.py // evaluation script
├── export.py // export checkpoint file into air/onnx
├── mindspore_hub_conf.py // hub config
├── mindspore_hub_conf.py // hub config
├── postprocess.py // postprocess script
```
@ -235,6 +243,40 @@ For more configuration details, please refer the script config.py.
accuracy: {'top_1_accuracy': 0.5871979166666667, 'top_5_accuracy': 0.8175280448717949}
```
## [Inference process](#contents)
### Export MindIR
```shell
# Ascend310 inference
python export.py --dataset [DATASET] --file_name [FILE_NAME] --file_format [EXPORT_FORMAT]
```
The parameter does not have the ckpt_file option. Please store the ckpt file according to the path of the parameter `checkpoint_path` in `config.py`.
`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 can only be set to 1.
```shell
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [LABEL_PATH] [DVPP] [DEVICE_ID]
```
- `LABEL_PATH` label.txt path. Write a py script to sort the category under the dataset, map the file names under the categories and category sort values,Such as[file name : sort value], and write the mapping results to the labe.txt file.
- `DVPP` is mandatory, and must choose from ["DVPP", "CPU"], it's case-insensitive.The size of the picture that MobilenetV2 performs inference is [224, 224], the DVPP hardware limits the width of divisible by 16, and the height is divisible by 2. The network conforms to the standard, and the network can pre-process the image through DVPP.
- `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
'top_1_accuracy': 59.07%, 'top_5_accuracy': 81.73%
```
# [Model Description](#contents)
## [Performance](#contents)
@ -256,7 +298,7 @@ For more configuration details, please refer the script config.py.
| Parameters(M) | 4.0M |
| Scripts | [Tiny-Darknet Scripts](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/tinydarknet) |
### [Inference Performance](#contents)
### [Evaluation Performance](#contents)
| Parameters | Ascend |
| ------------------- | --------------------------- |
@ -270,6 +312,20 @@ For more configuration details, please refer the script config.py.
| Accuracy | 8 pc Top-1: 58.7%; Top-5: 81.7% |
| Model for inference | 11.6M (.ckpt file) |
### [Inference Performance](#contents)
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | TinyDarknet |
| Resource | Ascend 310; Euler2.8 |
| Uploaded Date | 29/05/2021 (month/day/year) |
| MindSpore Version | 1.2.0 |
| Dataset | ImageNet |
| batch_size | 1 |
| outputs | Accuracy |
| Accuracy | Top-1: 59.07%; Top-5: 81.73%|
| Model for inference | 10.3M(.ckpt file) |
# [ModelZoo Homepage](#contents)
Please check the official[homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

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model_zoo/official/cv/tinydarknet/README_CN.md
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@ -14,10 +14,15 @@
- [分布式训练](#分布式训练)
- [评估过程](#评估过程)
- [评估](#评估)
- [推理过程](#推理过程)
- [导出MindIR](#导出mindir)
- [在Ascend310执行推理](#在ascend310执行推理)
- [结果](#结果)
- [模型描述](#模型描述)
- [性能](#性能)
- [训练性能](#训练性能)
- [评估性能](#评估性能)
- [推理性能](#推理性能)
- [ModelZoo主页](#modelzoo主页)
# [Tiny-DarkNet描述](#目录)
@ -105,12 +110,14 @@ Tiny-DarkNet是Joseph Chet Redmon等人提出的一个16层的针对于经典的
```bash
├── tinydarknet
├── README.md // Tiny-Darknet英文说明
├── README.md // Tiny-Darknet英文说明
├── README_CN.md // Tiny-Darknet中文说明
├── ascend310_infer // 用于310推理
├── scripts
├──run_standalone_train.sh // Ascend单卡训练shell脚本
├──run_distribute_train.sh // Ascend分布式训练shell脚本
├──run_standalone_train.sh // Ascend单卡训练shell脚本
├──run_distribute_train.sh // Ascend分布式训练shell脚本
├──run_eval.sh // Ascend评估shell脚本
├──run_infer_310.sh // Ascend310推理shell脚本
├── src
├─lr_scheduler //学习率策略
├─__init__.py // 初始化文件
@ -125,6 +132,7 @@ Tiny-DarkNet是Joseph Chet Redmon等人提出的一个16层的针对于经典的
├── eval.py // 评估脚本
├── export.py // 导出checkpoint文件
├── mindspore_hub_conf.py // hub配置文件
├── postprocess.py // 310推理后处理脚本
```
@ -243,6 +251,39 @@ Tiny-DarkNet是Joseph Chet Redmon等人提出的一个16层的针对于经典的
accuracy: {'top_1_accuracy': 0.5871979166666667, 'top_5_accuracy': 0.8175280448717949}
```
## 推理过程
### 导出MindIR
```shell
python export.py --dataset [DATASET] --file_name [FILE_NAME] --file_format [EXPORT_FORMAT]
```
参数没有ckpt_file选项，ckpt文件请按照`config.py`中参数`checkpoint_path`的路径存放。
`EXPORT_FORMAT` 可选 ["AIR", "MINDIR"].
### 在Ascend310执行推理
在执行推理前，mindir文件必须通过`export.py`脚本导出。以下展示了使用mindir模型执行推理的示例。
目前仅支持batch_size为1的推理。
```shell
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [LABEL_PATH] [DVPP] [DEVICE_ID]
```
- `LABEL_PATH` label.txt存放的路径，写一个py脚本对数据集下的类别名进行排序，对类别下的文件名和类别排序值做映射，例如[文件名:排序值]，将映射结果写到labe.txt文件中。
- `DVPP` 为必填项，需要在["DVPP", "CPU"]选择，大小写均可。注意目前仅支持CPU模式。
- `DEVICE_ID` 可选，默认值为0。
### 结果
推理结果保存在脚本执行的当前路径，你可以在acc.log中看到以下精度计算结果。
```bash
'top_1_accuracy': 59.07%, 'top_5_accuracy': 81.73%
```
# [模型描述](#目录)
## [性能](#目录)
@ -278,6 +319,20 @@ Tiny-DarkNet是Joseph Chet Redmon等人提出的一个16层的针对于经典的
| 准确率 | 8卡 Top-1: 58.7%; Top-5: 81.7% |
| 推理模型 | 11.6M (.ckpt文件) |
### [推理性能](#目录)
| 参数 | Ascend |
| -------------- | ---------------------------|
| 模型版本 | TinyDarknet |
| 资源 | Ascend 310；系统 Euler2.8 |
| 上传日期 | 2021-05-29 |
| MindSpore版本 | 1.2.0 |
| 数据集 | ImageNet |
| batch_size | 1 |
| 输出 | Accuracy |
| 准确率 | Top-1: 59.07% Top-5: 81.73% |
| 推理模型 | 10.3M（.ckpt文件） |
# [ModelZoo主页](#目录)
请参考官方[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

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model_zoo/official/cv/tinydarknet/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/cv/tinydarknet/ascend310_infer/build.sh Normal file
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@ -0,0 +1,23 @@
#!/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
mkdir out
fi
cd out || exit
cmake .. \
-DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
make

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model_zoo/official/cv/tinydarknet/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/cv/tinydarknet/ascend310_infer/src/main.cc Normal file
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@ -0,0 +1,159 @@
/**
* 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/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::TensorTransform;
using mindspore::dataset::vision::Resize;
using mindspore::dataset::vision::HWC2CHW;
using mindspore::dataset::vision::Normalize;
using mindspore::dataset::vision::Decode;
using mindspore::dataset::vision::CenterCrop;
DEFINE_string(mindir_path, "", "mindir path");
DEFINE_string(dataset_path, ".", "dataset path");
DEFINE_int32(device_id, 0, "device id");
DEFINE_string(aipp_path, "../../scripts/aipp.cfg", "aipp path");
DEFINE_string(cpu_dvpp, "", "cpu or dvpp process");
DEFINE_int32(image_height, 224, "image height");
DEFINE_int32(image_width, 224, "image width");
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);
ascend310->SetBufferOptimizeMode("off_optimize");
context->MutableDeviceInfo().push_back(ascend310);
mindspore::Graph graph;
Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
if (FLAGS_cpu_dvpp == "DVPP") {
if (RealPath(FLAGS_aipp_path).empty()) {
std::cout << "Invalid aipp path" << std::endl;
return 1;
} else {
ascend310->SetInsertOpConfigPath(FLAGS_aipp_path);
}
}
Model model;
Status ret = model.Build(GraphCell(graph), context);
if (ret != kSuccess) {
std::cout << "ERROR: Build failed." << std::endl;
return 1;
}
std::shared_ptr<TensorTransform> decode(new Decode());
std::shared_ptr<TensorTransform> resize(new Resize({256}));
std::shared_ptr<TensorTransform> dvpp_resize(new Resize({256, 256}));
auto crop_size = {FLAGS_image_height, FLAGS_image_width};
std::shared_ptr<TensorTransform> center_crop(new CenterCrop(crop_size));
std::shared_ptr<TensorTransform> normalize(new Normalize({123.675, 116.28, 103.53},
{58.395, 57.120, 57.375}));
std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
Execute transform({decode, resize, center_crop, normalize, hwc2chw});
Execute dvpptransform({decode, dvpp_resize});
auto all_files = GetAllFiles(FLAGS_dataset_path);
std::map<double, double> costTime_map;
size_t size = all_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:" << all_files[i] << std::endl;
if (FLAGS_cpu_dvpp == "DVPP") {
auto imgDvpp = std::make_shared<MSTensor>();
dvpptransform(ReadFileToTensor(all_files[i]), imgDvpp.get());
inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(),
imgDvpp->Data().get(), imgDvpp->DataSize());
} else {
auto img = MSTensor();
auto image = ReadFileToTensor(all_files[i]);
transform(image, &img);
std::vector<MSTensor> model_inputs = model.GetInputs();
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
img.Data().get(), img.DataSize());
}
gettimeofday(&start, nullptr);
ret = model.Predict(inputs, &outputs);
gettimeofday(&end, nullptr);
if (ret != kSuccess) {
std::cout << "Predict " << all_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(all_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/cv/tinydarknet/ascend310_infer/src/utils.cc Normal file
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@ -0,0 +1,130 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "inc/utils.h"
#include <fstream>
#include <algorithm>
#include <iostream>
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('.'), ".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;
}

10
model_zoo/official/cv/tinydarknet/export.py
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@ -24,12 +24,15 @@ from mindspore import Tensor
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
from src.config import imagenet_cfg
from src.tinydarknet import TinydarkNet
from src.tinydarknet import TinyDarkNet
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Classification')
parser.add_argument('--dataset_name', type=str, default='imagenet', choices=['imagenet', 'cifar10'],
help='dataset name.')
parser.add_argument('--file_format', type=str, default='AIR', choices=['MINDIR', 'AIR'],
help='file format.')
parser.add_argument('--file_name', type=str, default='tinydarknet', help='output file name.')
args_opt = parser.parse_args()
if args_opt.dataset_name == 'imagenet':
@ -37,12 +40,11 @@ if __name__ == '__main__':
else:
raise ValueError("dataset is not support.")
net = TinydarkNet(num_classes=cfg.num_classes)
net = TinyDarkNet(num_classes=cfg.num_classes)
assert cfg.checkpoint_path is not None, "cfg.checkpoint_path is None."
param_dict = load_checkpoint(cfg.checkpoint_path)
load_param_into_net(net, param_dict)
input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, 224, 224]), ms.float32)
export(net, input_arr, file_name=cfg.onnx_filename, file_format="ONNX")
export(net, input_arr, file_name=cfg.air_filename, file_format="AIR")
export(net, input_arr, file_name=args_opt.file_name, file_format=args_opt.file_format)

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model_zoo/official/cv/tinydarknet/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.
# ============================================================================
"""post process for 310 inference"""
import os
import argparse
import numpy as np
parser = argparse.ArgumentParser(description='tinydarknet calcul top1 and top5 acc')
parser.add_argument("--result_path", type=str, required=True, default='', help="result file path")
parser.add_argument("--label_file", type=str, required=True, default='', help="label file")
args = parser.parse_args()
def get_top5_acc(top_arg, gt_class):
sub_count = 0
for top5, gt in zip(top_arg, gt_class):
if gt in top5:
sub_count += 1
return sub_count
def read_label(label_file):
with open(label_file, 'r') as f:
lines = f.readlines()
img_dict = {}
for line in lines:
img_id = line.split(':')[0]
label = line.split(':')[1]
img_dict[img_id] = label
return img_dict
def cal_acc(result_path, label_file):
img_label = read_label(label_file)
img_tot = 0
top1_correct = 0
top5_correct = 0
result_shape = (1, 1000)
files = os.listdir(result_path)
for file in files:
full_file_path = os.path.join(result_path, file)
if os.path.isfile(full_file_path):
result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape)
gt_classes = int(img_label[file.split('.')[0]])
top1_output = np.argmax(result, (-1))
top5_output = np.argsort(result)[:, -5:]
t1_correct = np.equal(top1_output, gt_classes).sum()
top1_correct += t1_correct
top5_correct += get_top5_acc(top5_output, [gt_classes])
img_tot += 1
acc1 = 100 * top1_correct / img_tot
acc5 = 100 * top5_correct / img_tot
print('total={}, top1_correct={}, acc={:.2f}%'.format(img_tot, top1_correct, acc1))
print('total={}, top5_correct={}, acc={:.2f}%'.format(img_tot, top5_correct, acc5))
if __name__ == '__main__':
cal_acc(args.result_path, args.label_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 4 || $# -gt 5 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [LABEL_FILE] [DVPP] [DEVICE_ID]
DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
data_path=$(get_real_path $2)
label_file=$(get_real_path $3)
DVPP=${4^^}
device_id=0
if [ $# == 5 ]; then
device_id=$5
fi
echo "mindir name: "$model
echo "dataset path: "$data_path
echo "label file: "$label_file
echo "image process mode: "$DVPP
echo "device id: "$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=$ASCEND_HOME/opp
fi
function compile_app()
{
cd ../ascend310_infer || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
if [ "$DVPP" == "DVPP" ];then
echo "TinyDarknet CURRENTLY ONLY SUPPORT CPU MODE"
exit 1
elif [ "$DVPP" == "CPU" ]; then
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --cpu_dvpp=$DVPP --device_id=$device_id --image_height=224 --image_width=224 &> infer.log
else
echo "image process mode must be in [DVPP|CPU]"
exit 1
fi
}
function cal_acc()
{
python3.7 ../postprocess.py --result_path=./result_Files --label_file=$label_file &> 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