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!16329 310 inference for retinanet 

From: @yuzhenhua666
Reviewed-by: @oacjiewen,@c_34
Signed-off-by: @c_34
This commit is contained in:
mindspore-ci-bot 2021-05-18 17:28:59 +08:00 committed by Gitee
parent 26c73b4182 2cbbebf0ea
commit f73888899c
9 changed files with 5139 additions and 0 deletions
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70
model_zoo/official/cv/retinanet/README_CN.md
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@ -17,6 +17,13 @@
- [用法](#usage) - [用法](#usage)
- [运行](#running) - [运行](#running)
- [结果](#outcome) - [结果](#outcome)
- [模型导出](#模型导出)
- [用法](#usage)
- [运行](#running)
- [推理过程](#推理过程)
- [用法](#usage)
- [运行](#running)
- [结果](#outcome)
- [模型说明](#模型说明) - [模型说明](#模型说明)
- [性能](#性能) - [性能](#性能)
- [训练性能](#训练性能) - [训练性能](#训练性能)
@ -73,9 +80,11 @@ MSCOCO2017
. .
└─Retinanet └─Retinanet
├─README.md ├─README.md
├─ascend310_infer # 实现310推理源代码
├─scripts ├─scripts
├─run_single_train.sh # 使用Ascend环境单卡训练 ├─run_single_train.sh # 使用Ascend环境单卡训练
├─run_distribute_train.sh # 使用Ascend环境八卡并行训练 ├─run_distribute_train.sh # 使用Ascend环境八卡并行训练
├─run_infer_310.sh # Ascend推理shell脚本
├─run_eval.sh # 使用Ascend环境运行推理脚本 ├─run_eval.sh # 使用Ascend环境运行推理脚本
├─src ├─src
├─config.py # 参数配置 ├─config.py # 参数配置
@ -87,6 +96,8 @@ MSCOCO2017
├─box_utils.py # 先验框设置 ├─box_utils.py # 先验框设置
├─_init_.py # 初始化 ├─_init_.py # 初始化
├─train.py # 网络训练脚本 ├─train.py # 网络训练脚本
├─export.py # 导出 AIR,MINDIR模型的脚本
├─postprogress.py # 310推理后处理脚本
└─eval.py # 网络推理脚本 └─eval.py # 网络推理脚本
``` ```
@ -251,6 +262,65 @@ sh scripts/run_eval.sh coco 0
mAP: 0.34747137754625645 mAP: 0.34747137754625645
``` ```
### [模型导出](#content)
#### <span id="usage">用法</span>
导出模型前要修改config.py文件中的checkpoint_path配置项，值为checkpoint的路径。
```shell
python export.py --run_platform [RUN_PLATFORM] --file_format[EXPORT_FORMAT]
```
`EXPORT_FORMAT` 可选 ["AIR", "MINDIR"]
#### <span id="running">运行</span>
```运行
python export.py --run_platform ascend --file_format MINDIR
```
### [推理过程](#content)
#### <span id="usage">用法</span>
在推理之前需要在昇腾910环境上完成模型的导出。推理时要将iscrowd为true的图片排除掉。在ascend310_infer目录下保存了去排除后的图片id。
还需要修改config.py文件中的coco_root、val_data_type、instances_set配置项，值分别取coco数据集的目录，推理所用数据集的目录名称，推理完成后计算精度用的annotation文件，instances_set是用val_data_type拼接起来的，要保证文件正确并且存在。
```shell
# Ascend310 inference
sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
```
#### <span id="running">运行</span>
```运行
bash run_infer_310.sh ./retinanet.mindir ./dataset/coco2017/val2017 ./image_id.txt 0
```
#### <span id="outcome">结果</span>
推理的结果保存在当前目录下，在acc.log日志文件中可以找到类似以下的结果。
```mAP
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.350
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.509
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.385
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.368
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.509
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.303
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.413
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.415
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.155
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.435
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.608
========================================
mAP: 0.3499478734634595
```
## [模型说明](#content) ## [模型说明](#content)
### [性能](#content) ### [性能](#content)

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model_zoo/official/cv/retinanet/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/retinanet/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
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/retinanet/ascend310_infer/image_id.txt Normal file
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model_zoo/official/cv/retinanet/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);
#endif

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model_zoo/official/cv/retinanet/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 "../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::Normalize;
using mindspore::dataset::vision::HWC2CHW;
DEFINE_string(model_path, "", "model path");
DEFINE_string(dataset_path, ".", "dataset path");
DEFINE_int32(device_id, 0, "device id");
DEFINE_string(precision_mode, "allow_fp32_to_fp16", "precision mode");
DEFINE_string(op_select_impl_mode, "high_precision", "op impl mode");
DEFINE_string(buffer_optimize_mode, "off_optimize", "buffer optimize mode");
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;
}
auto context = std::make_shared<Context>();
auto ascend310_info = std::make_shared<mindspore::Ascend310DeviceInfo>();
ascend310_info->SetDeviceID(FLAGS_device_id);
ascend310_info->SetPrecisionMode(FLAGS_precision_mode);
ascend310_info->SetOpSelectImplMode(FLAGS_op_select_impl_mode);
ascend310_info->SetBufferOptimizeMode(FLAGS_buffer_optimize_mode);
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;
}
auto decode = Decode();
auto resize = Resize({600, 600});
auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375});
auto hwc2chw = HWC2CHW();
mindspore::dataset::Execute transform({decode, resize, normalize, 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]);
transform(image, &image);
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;
}

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model_zoo/official/cv/retinanet/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 "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('.'), '_' + 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/retinanet/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.
# ============================================================================
"""Evaluation for retinanet"""
import os
import argparse
import numpy as np
from PIL import Image
from src.coco_eval import metrics
parser = argparse.ArgumentParser(description='retinanet evaluation')
parser.add_argument("--result_path", type=str, required=True, help="result file path.")
parser.add_argument("--img_path", type=str, required=True, help="image file path.")
parser.add_argument("--img_id_file", type=str, required=True, help="image id file.")
args = parser.parse_args()
def get_pred(result_path, img_id):
boxes_file = os.path.join(result_path, img_id + '_0.bin')
scores_file = os.path.join(result_path, img_id + '_1.bin')
boxes = np.fromfile(boxes_file, dtype=np.float32).reshape(67995, 4)
scores = np.fromfile(scores_file, dtype=np.float32).reshape(67995, 81)
return boxes, scores
def get_img_size(file_name):
img = Image.open(file_name)
return img.size
def get_img_id(img_id_file):
f = open(img_id_file)
lines = f.readlines()
ids = []
for line in lines:
ids.append(int(line))
return ids
def cal_acc(result_path, img_path, img_id_file):
ids = get_img_id(img_id_file)
imgs = os.listdir(img_path)
pred_data = []
for img in imgs:
img_id = img.split('.')[0]
if int(img_id) not in ids:
continue
boxes, box_scores = get_pred(result_path, img_id)
w, h = get_img_size(os.path.join(img_path, img))
img_shape = np.array((h, w), dtype=np.float32)
pred_data.append({"boxes": boxes,
"box_scores": box_scores,
"img_id": int(img_id),
"image_shape": img_shape})
mAP = metrics(pred_data)
print(f"mAP: {mAP}")
if __name__ == '__main__':
cal_acc(args.result_path, args.img_path, args.img_id_file)

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model_zoo/official/cv/retinanet/scripts/run_infer_310.sh Executable 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 || $# -gt 3 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${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 $model
echo $data_path
echo $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
if [ -f "Makefile" ]; then
make clean
fi
sh build.sh &> build.log
if [ $? -ne 0 ]; then
echo "compile app code failed"
exit 1
fi
cd - || exit
}
function infer()
{
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/out/main --model_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
if [ $? -ne 0 ]; then
echo "execute inference failed"
exit 1
fi
}
function cal_acc()
{
python ../postprocess.py --result_path=result_Files --img_path=$data_path --img_id_file=../ascend310_infer/image_id.txt &> acc.log
if [ $? -ne 0 ]; then
echo "calculate accuracy failed"
exit 1
fi
}
compile_app
infer
cal_acc