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PaddleOCR
未验证 提交 1bbf6e6a 编写于 作者: M MissPenguin 提交者： GitHub
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Merge pull request #4683 from cuicheng01/dygraph 

Add tipc lite multi-predictor & arm_gpu_opencl chains
上级 830f9e74 dbdb6afb
隐藏空白更改
内联 并排
Showing with 408 addition and 84 deletion
deploy/lite/ocr_db_crnn.cc
浏览文件 @ 1bbf6e6a
......@@ -172,7 +172,10 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
cv::Mat resize_img;
int index = 0;
std::vector<double> time_info = {0, 0, 0};
for (int i = boxes.size() - 1; i >= 0; i--) {
auto preprocess_start = std::chrono::steady_clock::now();
crop_img = GetRotateCropImage(srcimg, boxes[i]);
if (use_direction_classify >= 1) {
crop_img = RunClsModel(crop_img, predictor_cls);
......@@ -191,7 +194,9 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
auto *data0 = input_tensor0->mutable_data<float>();
NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
auto preprocess_end = std::chrono::steady_clock::now();
//// Run CRNN predictor
auto inference_start = std::chrono::steady_clock::now();
predictor_crnn->Run();
// Get output and run postprocess
......@@ -199,8 +204,10 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
std::move(predictor_crnn->GetOutput(0)));
auto *predict_batch = output_tensor0->data<float>();
auto predict_shape = output_tensor0->shape();
auto inference_end = std::chrono::steady_clock::now();
// ctc decode
auto postprocess_start = std::chrono::steady_clock::now();
std::string str_res;
int argmax_idx;
int last_index = 0;
......@@ -224,7 +231,20 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
score /= count;
rec_text.push_back(str_res);
rec_text_score.push_back(score);
auto postprocess_end = std::chrono::steady_clock::now();
std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
time_info[0] += double(preprocess_diff.count() * 1000);
std::chrono::duration<float> inference_diff = inference_end - inference_start;
time_info[1] += double(inference_diff.count() * 1000);
std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
time_info[2] += double(postprocess_diff.count() * 1000);
}
times->push_back(time_info[0]);
times->push_back(time_info[1]);
times->push_back(time_info[2]);
}
std::vector<std::vector<std::vector<int>>>
......@@ -312,7 +332,6 @@ std::shared_ptr<PaddlePredictor> loadModel(std::string model_file, int num_threa
config.set_model_from_file(model_file);
config.set_threads(num_threads);
std::shared_ptr<PaddlePredictor> predictor =
CreatePaddlePredictor<MobileConfig>(config);
return predictor;
......@@ -434,6 +453,9 @@ void system(char **argv){
auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads));
auto cls_predictor = loadModel(cls_model_file, std::stoi(num_threads));
std::vector<double> det_time_info = {0, 0, 0};
std::vector<double> rec_time_info = {0, 0, 0};
for (int i = 0; i < cv_all_img_names.size(); ++i) {
std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
......@@ -459,8 +481,38 @@ void system(char **argv){
//// print recognized text
for (int i = 0; i < rec_text.size(); i++) {
std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
<< std::endl;
<< std::endl;
}
det_time_info[0] += det_times[0];
det_time_info[1] += det_times[1];
det_time_info[2] += det_times[2];
rec_time_info[0] += rec_times[0];
rec_time_info[1] += rec_times[1];
rec_time_info[2] += rec_times[2];
}
if (strcmp(argv[12], "True") == 0) {
AutoLogger autolog_det(det_model_file,
runtime_device,
std::stoi(num_threads),
std::stoi(batchsize),
"dynamic",
precision,
det_time_info,
cv_all_img_names.size());
AutoLogger autolog_rec(rec_model_file,
runtime_device,
std::stoi(num_threads),
std::stoi(batchsize),
"dynamic",
precision,
rec_time_info,
cv_all_img_names.size());
autolog_det.report();
std::cout << std::endl;
autolog_rec.report();
}
}
......@@ -503,15 +555,15 @@ void det(int argc, char **argv) {
auto img_vis = Visualization(srcimg, boxes);
std::cout << boxes.size() << " bboxes have detected:" << std::endl;
// for (int i=0; i<boxes.size(); i++){
// std::cout << "The " << i << " box:" << std::endl;
// for (int j=0; j<4; j++){
// for (int k=0; k<2; k++){
// std::cout << boxes[i][j][k] << "\t";
// }
// }
// std::cout << std::endl;
// }
for (int i=0; i<boxes.size(); i++){
std::cout << "The " << i << " box:" << std::endl;
for (int j=0; j<4; j++){
for (int k=0; k<2; k++){
std::cout << boxes[i][j][k] << "\t";
}
}
std::cout << std::endl;
}
time_info[0] += times[0];
time_info[1] += times[1];
time_info[2] += times[2];
......@@ -585,6 +637,9 @@ void rec(int argc, char **argv) {
std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
<< std::endl;
}
time_info[0] += times[0];
time_info[1] += times[1];
time_info[2] += times[2];
}
// TODO: support autolog
if (strcmp(argv[9], "True") == 0) {
......
test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
浏览文件 @ 1bbf6e6a
===========================lite_params===========================
inference:./ocr_db_crnn det
infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
runtime_device:ARM_CPU
det_infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
null:null
null:null
--cpu_threads:1|4
--det_batch_size:1
--rec_batch_size:1
--system_batch_size:1
null:null
--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
--config_dir:./config.txt
--rec_dict_dir:./ppocr_keys_v1.txt
null:null
--benchmark:True
test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt 0 → 100644
浏览文件 @ 1bbf6e6a
===========================lite_params===========================
inference:./ocr_db_crnn det
runtime_device:ARM_GPU_OPENCL
det_infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
null:null
null:null
--cpu_threads:1|4
--det_batch_size:1
null:null
--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
--config_dir:./config.txt
null:null
--benchmark:True
test_tipc/configs/ppocr_system_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt 0 → 100644
浏览文件 @ 1bbf6e6a
===========================lite_params===========================
inference:./ocr_db_crnn system
runtime_device:ARM_CPU
det_infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
rec_infer_model:ch_PP-OCRv2_rec_infer|ch_PP-OCRv2_rec_slim_quant_infer
cls_infer_model:ch_ppocr_mobile_v2.0_cls_infer|ch_ppocr_mobile_v2.0_cls_slim_infer
--cpu_threads:1|4
--det_batch_size:1
--rec_batch_size:1
--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
--config_dir:./config.txt
--rec_dict_dir:./ppocr_keys_v1.txt
--benchmark:True
test_tipc/configs/ppocr_system_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt 0 → 100644
浏览文件 @ 1bbf6e6a
===========================lite_params===========================
inference:./ocr_db_crnn system
runtime_device:ARM_GPU_OPENCL
det_infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
rec_infer_model:ch_PP-OCRv2_rec_infer|ch_PP-OCRv2_rec_slim_quant_infer
cls_infer_model:ch_ppocr_mobile_v2.0_cls_infer|ch_ppocr_mobile_v2.0_cls_slim_infer
--cpu_threads:1|4
--det_batch_size:1
--rec_batch_size:1
--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
--config_dir:./config.txt
--rec_dict_dir:./ppocr_keys_v1.txt
--benchmark:True
test_tipc/docs/test_lite_arm_cpu_cpp.md → test_tipc/docs/test_lite_arm_cpp.md
浏览文件 @ 1bbf6e6a
# Lite\_arm\_cpu\_cpp预测功能测试
# Lite\_arm\_cpp预测功能测试
Lite\_arm\_cpu\_cpp预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`，可以在ARM CPU上基于Lite预测库测试模型的C++推理功能。
Lite\_arm\_cpp预测功能测试的主程序为`test_lite_arm_cpp.sh`，可以在ARM上基于Lite预测库测试模型的C++推理功能。
## 1. 测试结论汇总
......@@ -10,12 +10,13 @@ Lite\_arm\_cpu\_cpp预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`
- 模型类型：包括正常模型（FP32）和量化模型（INT8）
- batch-size：包括1和4
- threads：包括1和4
- predictor数量：包括多predictor预测和单predictor预测
- predictor数量：包括单predictor预测和多predictor预测
- 预测库来源：包括下载方式和编译方式
- 测试硬件：ARM\_CPU/ARM\_GPU_OPENCL
| 模型类型 | batch-size | threads | predictor数量 | 预测库来源 |
| :----: | :----: | :----: | :----: | :----: |
| 正常模型/量化模型 | 1 | 1/4 | 1 | 下载方式 |
| 模型类型 | batch-size | threads | predictor数量 | 预测库来源 | 测试硬件 |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 正常模型/量化模型 | 1 | 1/4 | 单/多 | 下载方式 | ARM\_CPU/ARM\_GPU_OPENCL |
## 2. 测试流程
......@@ -23,21 +24,40 @@ Lite\_arm\_cpu\_cpp预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`
### 2.1 功能测试
先运行`prepare_lite.sh`，运行后会在当前路径下生成`test_lite.tar`，其中包含了测试数据、测试模型和用于预测的可执行文件。将`test_lite.tar`上传到被测试的手机上，在手机的终端解压该文件，进入`test_lite`目录中，然后运行`test_lite_arm_cpu_cpp.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。
先运行`prepare_lite_cpp.sh`，运行后会在当前路径下生成`test_lite.tar`，其中包含了测试数据、测试模型和用于预测的可执行文件。将`test_lite.tar`上传到被测试的手机上，在手机的终端解压该文件，进入`test_lite`目录中，然后运行`test_lite_arm_cpp.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。
#### 2.1.1 基于ARM\_CPU测试
```shell
# 数据和模型准备
bash test_tipc/prepare_lite.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
# 手机端测试:
bash test_lite_arm_cpu_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
```
**注意**：由于运行该项目需要bash等命令，传统的adb方式不能很好的安装。所以此处推荐通在手机上开启虚拟终端的方式连接电脑，连接方式可以参考[安卓手机termux连接电脑](./termux_for_android.md)。
#### 2.1.2 基于ARM\_GPU\_OPENCL测试
```shell
# 数据和模型准备
bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt
# 手机端测试:
bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt
```
**注意**：
1.由于运行该项目需要bash等命令，传统的adb方式不能很好的安装。所以此处推荐通在手机上开启虚拟终端的方式连接电脑，连接方式可以参考[安卓手机termux连接电脑](./termux_for_android.md)。
2.如果测试文本检测和识别完整的pipeline，在执行`prepare_lite_cpp.sh`时，配置文件需替换为`test_tipc/configs/ppocr_system_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt`。在手机端测试阶段，配置文件同样修改为该文件。
#### 运行结果
### 2.2 运行结果
各测试的运行情况会打印在 `./output/` 中：
运行成功时会输出：
......
test_tipc/prepare_lite.sh → test_tipc/prepare_lite_cpp.sh
浏览文件 @ 1bbf6e6a
......@@ -6,22 +6,59 @@ dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
IFS=$'\n'
lite_model_list=$(func_parser_value "${lines[2]}")
inference_cmd=$(func_parser_value "${lines[1]}")
DEVICE=$(func_parser_value "${lines[2]}")
det_lite_model_list=$(func_parser_value "${lines[3]}")
rec_lite_model_list=$(func_parser_value "${lines[4]}")
cls_lite_model_list=$(func_parser_value "${lines[5]}")
if [[ $inference_cmd =~ "det" ]];then
lite_model_list=${det_lite_model_list}
elif [[ $inference_cmd =~ "rec" ]];then
lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
elif [[ $inference_cmd =~ "system" ]];then
lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
else
echo "inference_cmd is wrong, please check."
exit 1
fi
if [ ${DEVICE} = "ARM_CPU" ];then
valid_targets="arm"
paddlelite_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz"
end_index="66"
elif [ ${DEVICE} = "ARM_GPU_OPENCL" ];then
valid_targets="opencl"
paddlelite_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.armv8.clang.with_exception.with_extra.with_cv.opencl.tar.gz"
end_index="71"
else
echo "DEVICE only suport ARM_CPU, ARM_GPU_OPENCL."
exit 2
fi
# prepare lite .nb model
pip install paddlelite==2.9
pip install paddlelite==2.10-rc
current_dir=${PWD}
IFS="|"
model_path=./inference_models
for model in ${lite_model_list[*]}; do
inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
if [[ $model =~ "PP-OCRv2" ]];then
inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
elif [[ $model =~ "v2.0" ]];then
inference_model_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/${model}.tar
else
echo "Model is wrong, please check."
exit 3
fi
inference_model=${inference_model_url##*/}
wget -nc -P ${model_path} ${inference_model_url}
cd ${model_path} && tar -xf ${inference_model} && cd ../
model_dir=${model_path}/${inference_model%.*}
model_file=${model_dir}/inference.pdmodel
param_file=${model_dir}/inference.pdiparams
paddle_lite_opt --model_dir=${model_dir} --model_file=${model_file} --param_file=${param_file} --valid_targets=arm --optimize_out=${model_dir}_opt
paddle_lite_opt --model_dir=${model_dir} --model_file=${model_file} --param_file=${param_file} --valid_targets=${valid_targets} --optimize_out=${model_dir}_opt
done
# prepare test data
......@@ -35,18 +72,19 @@ cd ./inference_models && tar -xf ${inference_model} && cd ../
cd ./test_data && tar -xf ${data_file} && rm ${data_file} && cd ../
# prepare lite env
paddlelite_url=https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz
paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
paddlelite_file=${paddlelite_zipfile:0:66}
paddlelite_file=${paddlelite_zipfile:0:${end_index}}
wget ${paddlelite_url} && tar -xf ${paddlelite_zipfile}
mkdir -p ${paddlelite_file}/demo/cxx/ocr/test_lite
cp -r ${model_path}/*_opt.nb test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
cp -r ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
cp ${FILENAME} test_tipc/test_lite_arm_cpu_cpp.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
cp ${FILENAME} test_tipc/test_lite_arm_cpp.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
cd ${paddlelite_file}/demo/cxx/ocr/
git clone https://github.com/cuicheng01/AutoLog.git
# make
make -j
sleep 1
make -j
......
test_tipc/readme.md
浏览文件 @ 1bbf6e6a
......@@ -81,10 +81,11 @@ test_tipc/
├── cpp_ppocr_det_mobile_results_fp16.txt # 预存的mobile版ppocr检测模型c++预测的fp16精度的结果
├── ...
├── prepare.sh # 完成test_*.sh运行所需要的数据和模型下载
├── prepare_lite_cpp.sh # 完成手机端test_*.sh运行所需要的数据、模型、可执行文件
├── test_train_inference_python.sh # 测试python训练预测的主程序
├── test_inference_cpp.sh # 测试c++预测的主程序
├── test_serving.sh # 测试serving部署预测的主程序
├── test_lite_arm_cpu_cpp.sh # 测试lite在arm_cpu上部署的C++预测的主程序
├── test_lite_arm_cpp.sh # 测试lite在arm上部署的C++预测的主程序
├── compare_results.py # 用于对比log中的预测结果与results中的预存结果精度误差是否在限定范围内
└── readme.md # 使用文档
```
......@@ -123,5 +124,5 @@ test_tipc/
[test_train_inference_python 使用](docs/test_train_inference_python.md)
[test_inference_cpp 使用](docs/test_inference_cpp.md)
[test_serving 使用](docs/test_serving.md)
[test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md)
[test_lite_arm_cpp 使用](docs/test_lite_arm_cpp.md)
[test_paddle2onnx 使用](docs/test_paddle2onnx.md)
test_tipc/test_lite_arm_cpp.sh 0 → 100644
浏览文件 @ 1bbf6e6a
#!/bin/bash
source ./common_func.sh
export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
FILENAME=$1
dataline=$(cat $FILENAME)
# parser params
IFS=$'\n'
lines=(${dataline})
# parser lite inference
inference_cmd=$(func_parser_value "${lines[1]}")
runtime_device=$(func_parser_value "${lines[2]}")
det_model_list=$(func_parser_value "${lines[3]}")
rec_model_list=$(func_parser_value "${lines[4]}")
cls_model_list=$(func_parser_value "${lines[5]}")
cpu_threads_list=$(func_parser_value "${lines[6]}")
det_batch_size_list=$(func_parser_value "${lines[7]}")
rec_batch_size_list=$(func_parser_value "${lines[8]}")
infer_img_dir_list=$(func_parser_value "${lines[9]}")
config_dir=$(func_parser_value "${lines[10]}")
rec_dict_dir=$(func_parser_value "${lines[11]}")
benchmark_value=$(func_parser_value "${lines[12]}")
if [[ $inference_cmd =~ "det" ]]; then
lite_model_list=${det_lite_model_list}
elif [[ $inference_cmd =~ "rec" ]]; then
lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
elif [[ $inference_cmd =~ "system" ]]; then
lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
else
echo "inference_cmd is wrong, please check."
exit 1
fi
LOG_PATH="./output"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results.log"
function func_test_det(){
IFS='|'
_script=$1
_det_model=$2
_log_path=$3
_img_dir=$4
_config=$5
if [[ $_det_model =~ "slim" ]]; then
precision="INT8"
else
precision="FP32"
fi
# lite inference
for num_threads in ${cpu_threads_list[*]}; do
for det_batchsize in ${det_batch_size_list[*]}; do
_save_log_path="${_log_path}/lite_${_det_model}_runtime_device_${runtime_device}_precision_${precision}_det_batchsize_${det_batchsize}_threads_${num_threads}.log"
command="${_script} ${_det_model} ${runtime_device} ${precision} ${num_threads} ${det_batchsize} ${_img_dir} ${_config} ${benchmark_value} > ${_save_log_path} 2>&1"
eval ${command}
status_check $? "${command}" "${status_log}"
done
done
}
function func_test_rec(){
IFS='|'
_script=$1
_rec_model=$2
_cls_model=$3
_log_path=$4
_img_dir=$5
_config=$6
_rec_dict_dir=$7
if [[ $_det_model =~ "slim" ]]; then
_precision="INT8"
else
_precision="FP32"
fi
# lite inference
for num_threads in ${cpu_threads_list[*]}; do
for rec_batchsize in ${rec_batch_size_list[*]}; do
_save_log_path="${_log_path}/lite_${_rec_model}_${cls_model}_runtime_device_${runtime_device}_precision_${_precision}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
command="${_script} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${rec_batchsize} ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
eval ${command}
status_check $? "${command}" "${status_log}"
done
done
}
function func_test_system(){
IFS='|'
_script=$1
_det_model=$2
_rec_model=$3
_cls_model=$4
_log_path=$5
_img_dir=$6
_config=$7
_rec_dict_dir=$8
if [[ $_det_model =~ "slim" ]]; then
_precision="INT8"
else
_precision="FP32"
fi
# lite inference
for num_threads in ${cpu_threads_list[*]}; do
for det_batchsize in ${det_batch_size_list[*]}; do
for rec_batchsize in ${rec_batch_size_list[*]}; do
_save_log_path="${_log_path}/lite_${_det_model}_${_rec_model}_${_cls_model}_runtime_device_${runtime_device}_precision_${_precision}_det_batchsize_${det_batchsize}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
command="${_script} ${_det_model} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${det_batchsize} ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
eval ${command}
status_check $? "${command}" "${status_log}"
done
done
done
}
echo "################### run test ###################"
if [[ $inference_cmd =~ "det" ]]; then
IFS="|"
det_model_list=(${det_model_list[*]})
for i in {0..1}; do
#run lite inference
for img_dir in ${infer_img_dir_list[*]}; do
func_test_det "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}"
done
done
elif [[ $inference_cmd =~ "rec" ]]; then
IFS="|"
rec_model_list=(${rec_model_list[*]})
cls_model_list=(${cls_model_list[*]})
for i in {0..1}; do
#run lite inference
for img_dir in ${infer_img_dir_list[*]}; do
func_test_rec "${inference_cmd}" "${rec_model}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${rec_dict_dir}" "${config_dir}"
done
done
elif [[ $inference_cmd =~ "system" ]]; then
IFS="|"
det_model_list=(${det_model_list[*]})
rec_model_list=(${rec_model_list[*]})
cls_model_list=(${cls_model_list[*]})
for i in {0..1}; do
#run lite inference
for img_dir in ${infer_img_dir_list[*]}; do
func_test_system "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${rec_model_list[i]}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}" "${rec_dict_dir}"
done
done
fi
test_tipc/test_lite_arm_cpu_cpp.sh 已删除 100644 → 0
浏览文件 @ 830f9e74
#!/bin/bash
source ./common_func.sh
export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
FILENAME=$1
dataline=$(cat $FILENAME)
# parser params
IFS=$'\n'
lines=(${dataline})
# parser lite inference
lite_inference_cmd=$(func_parser_value "${lines[1]}")
lite_model_dir_list=$(func_parser_value "${lines[2]}")
runtime_device=$(func_parser_value "${lines[3]}")
lite_cpu_threads_list=$(func_parser_value "${lines[4]}")
lite_batch_size_list=$(func_parser_value "${lines[5]}")
lite_infer_img_dir_list=$(func_parser_value "${lines[8]}")
lite_config_dir=$(func_parser_value "${lines[9]}")
lite_rec_dict_dir=$(func_parser_value "${lines[10]}")
lite_benchmark_value=$(func_parser_value "${lines[11]}")
LOG_PATH="./output"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results.log"
function func_lite(){
IFS='|'
_script=$1
_lite_model=$2
_log_path=$3
_img_dir=$4
_config=$5
if [[ $lite_model =~ "slim" ]]; then
precision="INT8"
else
precision="FP32"
fi
# lite inference
for num_threads in ${lite_cpu_threads_list[*]}; do
for batchsize in ${lite_batch_size_list[*]}; do
_save_log_path="${_log_path}/lite_${_lite_model}_runtime_device_${runtime_device}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}.log"
command="${_script} ${_lite_model} ${runtime_device} ${precision} ${num_threads} ${batchsize} ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
eval ${command}
status_check $? "${command}" "${status_log}"
done
done
}
echo "################### run test ###################"
IFS="|"
for lite_model in ${lite_model_dir_list[*]}; do
#run lite inference
for img_dir in ${lite_infer_img_dir_list[*]}; do
func_lite "${lite_inference_cmd}" "${lite_model}_opt.nb" "${LOG_PATH}" "${img_dir}" "${lite_config_dir}"
done
done
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