未验证 提交 9ba97aac 编写于 作者: S shangliang Xu 提交者: GitHub

[TIPC] add ptq test shell (#6250)

* [TIPC] add ptq test shell and txt

* [TIPC] alter shell save log, test=document_fix
上级 b0fb44b0
......@@ -107,6 +107,7 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/yolov3/yolov3_
- [test_train_inference_python 使用](docs/test_train_inference_python.md) :测试基于Python的模型训练、评估、推理等基本功能,包括裁剪、量化、蒸馏。
- [test_train_fleet_inference_python 使用](./docs/test_train_fleet_inference_python.md):测试基于Python的多机多卡训练与推理等基本功能。
- [test_inference_cpp 使用](docs/test_inference_cpp.md):测试基于C++的模型推理。
- [test_serving 使用](docs/test_serving.md):测试基于Paddle Serving的服务化部署功能。
- [test_lite_arm_cpu_cpp 使用](./):测试基于Paddle-Lite的ARM CPU端c++预测部署功能。
- [test_serving 使用](docs/test_serving.md):测试基于Paddle Serving的服务化部署功能,包括Python、C++
- test_lite_arm_cpu_cpp 使用(待开发):测试基于Paddle-Lite的ARM CPU端c++预测部署功能。
- [test_paddle2onnx 使用](docs/test_paddle2onnx.md):测试Paddle2ONNX的模型转化功能,并验证正确性。
- [test_ptq_inference_python 使用](docs/test_ptq_inference_python.md):测试基于Python的离线量化功能。
......@@ -37,7 +37,7 @@ kl_quant_export:tools/post_quant.py -c configs/keypoint/tiny_pose/tinypose_128x9
##
infer_mode:kl_quant
infer_quant:True
inference:./deploy/python/infer.py
inference:./deploy/python/keypoint_infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
......
......@@ -37,7 +37,7 @@ kl_quant_export:tools/post_quant.py -c configs/keypoint/tiny_pose/tinypose_128x9
##
infer_mode:norm
infer_quant:False
inference:./deploy/python/infer.py
inference:./deploy/python/keypoint_infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
......
===========================ptq_params===========================
model_name:tinypose_128x96
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/keypoint/tinypose_128x96.pdparams
kl_quant_export:tools/post_quant.py -c configs/keypoint/tiny_pose/tinypose_128x96.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/keypoint_infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:mask_rcnn_r50_fpn_1x_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_1x_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:picodet_l_640_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/picodet_l_640_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/picodet/legacy_model/picodet_l_640_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:picodet_lcnet_1_5x_416_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_416_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/picodet/legacy_model/more_config/picodet_lcnet_1_5x_416_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:picodet_m_416_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/picodet_m_416_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/picodet/legacy_model/picodet_m_416_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:picodet_mobilenetv3_large_1x_416_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/picodet_mobilenetv3_large_1x_416_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/picodet/legacy_model/more_config/picodet_mobilenetv3_large_1x_416_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:picodet_r18_640_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/picodet_r18_640_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/picodet/legacy_model/more_config/picodet_r18_640_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:picodet_s_320_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/picodet/legacy_model/picodet_s_320_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:picodet_shufflenetv2_1x_416_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/picodet_shufflenetv2_1x_416_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/picodet/legacy_model/more_config/picodet_shufflenetv2_1x_416_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyolo_mbv3_large_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyolo/ppyolo_mbv3_large_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyolo_mbv3_small_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyolo/ppyolo_mbv3_small_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyolo_r18vd_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyolo/ppyolo_r18vd_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyolo_r50vd_dcn_1x_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyolo_tiny_650e_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyolo/ppyolo_tiny_650e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyolov2_r101vd_dcn_365e_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyolo/ppyolov2_r101vd_dcn_365e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyolov2_r50vd_dcn_365e_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyoloe_crn_l_300e_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyoloe_crn_m_300e_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyoloe_crn_s_300e_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:ppyoloe_crn_x_300e_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
===========================ptq_params===========================
model_name:yolov3_darknet53_270e_coco
python:python3.7
filename:
##
--output_dir:./output_inference
weights:https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams
kl_quant_export:tools/post_quant.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o
export_param1:null
##
inference:./deploy/python/infer.py
--device:gpu|cpu
--enable_mkldnn:False
--cpu_threads:4
--batch_size:1|2
--run_mode:paddle
--model_dir:
--image_dir:./dataset/coco/test2017/
--run_benchmark:False
null:null
\ No newline at end of file
# Linux GPU/CPU 离线量化功能测试
Linux GPU/CPU 离线量化功能测试的主程序为`test_ptq_inference_python.sh`,可以测试基于Python的离线量化功能。
## 1. 测试结论汇总
| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
| ---- | ---- |-----------| :----: | :----: | :----: |
| 量化模型 | GPU | 1/2 | int8 | - | - |
| 量化模型 | CPU | 1/2 | - | int8 | 支持 |
## 2. 测试流程
### 2.1 功能测试
先运行`prepare.sh`准备数据和模型,然后运行`test_ptq_inference_python.sh`进行测试,最终在```test_tipc/output```目录下生成`python_infer_*.log`后缀的日志文件。
```shell
bash test_tipc/prepare.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_ptq_infer_python.txt "whole_infer"
# 用法:
bash test_tipc/test_ptq_inference_python.sh ./test_tipc/configs/yolov3/yolov3_darknet53_270e_coco_train_ptq_infer_python.txt
```
#### 运行结果
各测试的运行情况会打印在 `test_tipc/output/results_ptq_python.log` 中:
运行成功时会输出:
```
Run successfully with command - yolov3_darknet53_270e_coco - python3.7 tools/post_quant.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams filename=yolov3_darknet53_270e_coco --output_dir=./output_inference !
Run successfully with command - yolov3_darknet53_270e_coco - python3.7 ./deploy/python/infer.py --device=gpu --run_mode=paddle --model_dir=./output_inference/yolov3_darknet53_270e_coco --batch_size=2 --image_dir=./dataset/coco/test2017/ --run_benchmark=False > ./test_tipc/output/yolov3_darknet53_270e_coco/whole_infer/python_infer_gpu_mode_paddle_batchsize_2.log 2>&1 !
...
```
运行失败时会输出:
```
Run failed with command - yolov3_darknet53_270e_coco - python3.7 tools/post_quant.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --slim_config configs/slim/post_quant/yolov3_darknet53_ptq.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams filename=yolov3_darknet53_270e_coco --output_dir=./output_inference!
...
```
## 3. 更多教程
本文档为功能测试用,更详细的离线量化功能使用教程请参考:[Paddle 离线量化官网教程](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/api_cn/static/quant/quantization_api.rst#quant_post_static)
......@@ -2,6 +2,7 @@
source test_tipc/utils_func.sh
FILENAME=$1
MODE="cpp_infer"
# parser model_name
dataline=$(cat ${FILENAME})
......@@ -54,7 +55,7 @@ cpp_benchmark_value=$(func_parser_value "${lines[27]}")
cpp_infer_key1=$(func_parser_key "${lines[28]}")
cpp_infer_value1=$(func_parser_value "${lines[28]}")
LOG_PATH="./test_tipc/output"
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_cpp.log"
......@@ -74,7 +75,7 @@ function func_cpp_inference(){
fi
for threads in ${cpp_cpu_threads_list[*]}; do
for batch_size in ${cpp_batch_size_list[*]}; do
_save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_fluid_batchsize_${batch_size}.log"
_save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_mode_paddle_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
......@@ -91,7 +92,7 @@ function func_cpp_inference(){
done
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
for precision in ${cpp_precision_list[*]}; do
if [[ ${precision} != "fluid" ]]; then
if [[ ${precision} != "paddle" ]]; then
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} = "trt_int8" ]]; then
continue
fi
......@@ -100,7 +101,7 @@ function func_cpp_inference(){
fi
fi
for batch_size in ${cpp_batch_size_list[*]}; do
_save_log_path="${_log_path}/cpp_infer_gpu_precision_${precision}_batchsize_${batch_size}.log"
_save_log_path="${_log_path}/cpp_infer_gpu_mode_${precision}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
......@@ -183,6 +184,7 @@ else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
eval $env
# run cpp infer
Count=0
IFS="|"
......@@ -201,9 +203,10 @@ for infer_mode in ${cpp_infer_mode_list[*]}; do
set_export_weight=$(func_set_params "${export_weight_key}" "${export_weight_value}")
set_save_export_dir=$(func_set_params "${save_export_key}" "${save_export_value}")
set_filename=$(func_set_params "${filename_key}" "${model_name}")
export_log_path="${LOG_PATH}/export.log"
export_cmd="${python} ${run_export} ${set_export_weight} ${set_filename} ${set_save_export_dir} "
echo $export_cmd
eval $export_cmd
eval "${export_cmd} > ${export_log_path} 2>&1"
status_export=$?
status_check $status_export "${export_cmd}" "${status_log}" "${model_name}"
fi
......
......@@ -2,6 +2,7 @@
source test_tipc/utils_func.sh
FILENAME=$1
MODE="paddle2onnx_infer"
# parser model_name
dataline=$(cat ${FILENAME})
......@@ -56,7 +57,7 @@ infer_image_value=$(func_parser_value "${lines[28]}")
infer_param1_key=$(func_parser_key "${lines[29]}")
infer_param1_value=$(func_parser_value "${lines[29]}")
LOG_PATH="./test_tipc/output"
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_paddle2onnx.log"
......@@ -68,7 +69,6 @@ function func_paddle2onnx_inference(){
# paddle2onnx
echo "################### run paddle2onnx ###################"
_save_log_path="${LOG_PATH}/paddle2onnx_infer_cpu.log"
set_dirname=$(func_set_params "${model_dir_key}" "${_export_model_dir}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
......@@ -76,8 +76,9 @@ function func_paddle2onnx_inference(){
set_opset_version=$(func_set_params "${opset_version_key}" "${opset_version_value}")
set_enable_onnx_checker=$(func_set_params "${enable_onnx_checker_key}" "${enable_onnx_checker_value}")
set_paddle2onnx_params1=$(func_set_params "${paddle2onnx_params1_key}" "${paddle2onnx_params1_value}")
trans_log_path="${_log_path}/trans_model.log"
trans_model_cmd="${padlle2onnx_cmd} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_save_model} ${set_opset_version} ${set_enable_onnx_checker} ${set_paddle2onnx_params1}"
eval $trans_model_cmd
eval "${trans_model_cmd} > ${trans_log_path} 2>&1"
last_status=${PIPESTATUS[0]}
status_check $last_status "${trans_model_cmd}" "${status_log}" "${model_name}"
......@@ -87,8 +88,9 @@ function func_paddle2onnx_inference(){
set_onnx_file=$(func_set_params "${onnx_file_key}" "${_export_model_dir}/${save_file_value}")
set_infer_image_file=$(func_set_params "${infer_image_key}" "${infer_image_value}")
set_infer_param1=$(func_set_params "${infer_param1_key}" "${infer_param1_value}")
infer_model_cmd="${python} ${inference_py} ${set_infer_cfg} ${set_onnx_file} ${set_infer_image_file} ${set_infer_param1} > ${_save_log_path} 2>&1 "
eval $infer_model_cmd
_save_log_path="${_log_path}/paddle2onnx_infer_cpu.log"
infer_model_cmd="${python} ${inference_py} ${set_infer_cfg} ${set_onnx_file} ${set_infer_image_file} ${set_infer_param1}"
eval "${infer_model_cmd} > ${_save_log_path} 2>&1"
last_status=${PIPESTATUS[0]}
status_check $last_status "${infer_model_cmd}" "${status_log}" "${model_name}"
}
......@@ -110,9 +112,10 @@ for infer_mode in ${infer_mode_list[*]}; do
set_save_export_dir=$(func_set_params "${save_export_key}" "${save_export_value}")
set_filename=$(func_set_params "${filename_key}" "${model_name}")
set_export_param=$(func_set_params "${export_param_key}" "${export_param_value}")
export_log_path="${LOG_PATH}/export.log"
export_cmd="${python} ${run_export} ${set_export_weight} ${set_filename} ${set_export_param} ${set_save_export_dir} "
echo $export_cmd
eval $export_cmd
eval "${export_cmd} > ${export_log_path} 2>&1"
status_export=$?
status_check $status_export "${export_cmd}" "${status_log}" "${model_name}"
fi
......
#!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
MODE="whole_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet ptq: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parser_key "${lines[3]}")
# parser export params
save_export_key=$(func_parser_key "${lines[5]}")
save_export_value=$(func_parser_value "${lines[5]}")
export_weight_key=$(func_parser_key "${lines[6]}")
export_weight_value=$(func_parser_value "${lines[6]}")
kl_quant_export=$(func_parser_value "${lines[7]}")
export_param1_key=$(func_parser_key "${lines[8]}")
export_param1_value=$(func_parser_value "${lines[8]}")
# parser infer params
inference_py=$(func_parser_value "${lines[10]}")
device_key=$(func_parser_key "${lines[11]}")
device_list=$(func_parser_value "${lines[11]}")
use_mkldnn_key=$(func_parser_key "${lines[12]}")
use_mkldnn_list=$(func_parser_value "${lines[12]}")
cpu_threads_key=$(func_parser_key "${lines[13]}")
cpu_threads_list=$(func_parser_value "${lines[13]}")
batch_size_key=$(func_parser_key "${lines[14]}")
batch_size_list=$(func_parser_value "${lines[14]}")
run_mode_key=$(func_parser_key "${lines[15]}")
run_mode_list=$(func_parser_value "${lines[15]}")
model_dir_key=$(func_parser_key "${lines[16]}")
image_dir_key=$(func_parser_key "${lines[17]}")
image_dir_value=$(func_parser_value "${lines[17]}")
run_benchmark_key=$(func_parser_key "${lines[18]}")
run_benchmark_value=$(func_parser_value "${lines[18]}")
infer_param1_key=$(func_parser_key "${lines[19]}")
infer_param1_value=$(func_parser_value "${lines[19]}")
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_ptq_python.log"
function func_ptq_inference(){
IFS='|'
_python=$1
_log_path=$2
_script=$3
_set_model_dir=$4
set_image_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
set_run_benchmark=$(func_set_params "${run_benchmark_key}" "${run_benchmark_value}")
set_infer_param1=$(func_set_params "${infer_param1_key}" "${infer_param1_value}")
# inference
for device in ${device_list[*]}; do
set_device=$(func_set_params "${device_key}" "${device}")
if [ ${device} = "cpu" ]; then
for use_mkldnn in ${use_mkldnn_list[*]}; do
set_use_mkldnn=$(func_set_params "${use_mkldnn_key}" "${use_mkldnn}")
for threads in ${cpu_threads_list[*]}; do
set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_mode_paddle_batchsize_${batch_size}.log"
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
command="${_python} ${_script} ${set_device} ${set_use_mkldnn} ${set_cpu_threads} ${_set_model_dir} ${set_batchsize} ${set_image_dir} ${set_run_benchmark} ${set_infer_param1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}" "${model_name}"
done
done
done
elif [ ${device} = "gpu" ]; then
for run_mode in ${run_mode_list[*]}; do
if [[ ${run_mode} = "paddle" ]] || [[ ${run_mode} = "trt_int8" ]]; then
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/python_infer_gpu_mode_${run_mode}_batchsize_${batch_size}.log"
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
set_run_mode=$(func_set_params "${run_mode_key}" "${run_mode}")
command="${_python} ${_script} ${set_device} ${set_run_mode} ${_set_model_dir} ${set_batchsize} ${set_image_dir} ${set_run_benchmark} ${set_infer_param1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}" "${model_name}"
done
fi
done
else
echo "Does not support hardware other than CPU and GPU Currently!"
fi
done
}
IFS="|"
# run ptq
set_export_weight=$(func_set_params "${export_weight_key}" "${export_weight_value}")
set_save_export_dir=$(func_set_params "${save_export_key}" "${save_export_value}")
set_filename=$(func_set_params "${filename_key}" "${model_name}")
export_log_path="${LOG_PATH}/export.log"
ptq_cmd="${python} ${kl_quant_export} ${set_export_weight} ${set_filename} ${set_save_export_dir}"
echo $ptq_cmd
eval "${ptq_cmd} > ${export_log_path} 2>&1"
status_export=$?
status_check $status_export "${ptq_cmd}" "${status_log}" "${model_name}"
#run inference
set_export_model_dir=$(func_set_params "${model_dir_key}" "${save_export_value}/${model_name}")
func_ptq_inference "${python}" "${LOG_PATH}" "${inference_py}" "${set_export_model_dir}"
......@@ -2,13 +2,14 @@
source test_tipc/utils_func.sh
FILENAME=$1
MODE="serving_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet serving_infer: ${model_name}"
echo "ppdet serving_cpp_infer: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parser_key "${lines[3]}")
filename_value=$(func_parser_value "${lines[3]}")
......@@ -48,7 +49,7 @@ infer_image_value=$(func_parser_value "${lines[24]}")
http_client_key1=$(func_parser_key "${lines[25]}")
http_client_value1=$(func_parser_value "${lines[25]}")
LOG_PATH="./test_tipc/output"
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_serving_cpp.log"
......@@ -67,23 +68,25 @@ function func_serving_inference(){
# inference
for gpu_ids in ${gpu_ids_value[*]}; do
if [ ${gpu_ids} = "null" ];then
_save_log_path="${_log_path}/serving_infer_cpp_cpu_batchsize_1.log"
server_log_path="${_log_path}/cpp_server_cpu.log"
client_log_path="${_log_path}/cpp_client_cpu.log"
else
_save_log_path="${_log_path}/serving_infer_cpp_gpu_batchsize_1.log"
server_log_path="${_log_path}/cpp_server_gpu.log"
client_log_path="${_log_path}/cpp_client_gpu.log"
fi
set_gpu_ids=$(func_set_params "${gpu_ids_key}" "${gpu_ids}")
# run web service
web_service_cmd="${_python} -m paddle_serving_server.serve ${_set_server_model_dir} ${set_op} ${set_port} ${set_gpu_ids} ${set_web_service_params1} &"
web_service_cmd="${_python} -m paddle_serving_server.serve ${_set_server_model_dir} ${set_op} ${set_port} ${set_gpu_ids} ${set_web_service_params1} > ${server_log_path} 2>&1 &"
eval $web_service_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
sleep 5s
# run http client
http_client_cmd="${_python} ${http_client_py} ${_set_client_model_dir} ${_set_image_file} ${set_http_client_params1} > ${_save_log_path} 2>&1 "
http_client_cmd="${_python} ${http_client_py} ${_set_client_model_dir} ${_set_image_file} ${set_http_client_params1} > ${client_log_path} 2>&1"
eval $http_client_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${http_client_cmd}" "${status_log}" "${model_name}"
eval "cat ${_save_log_path}"
eval "cat ${client_log_path}"
ps ux | grep -i ${port_value} | awk '{print $2}' | xargs kill -s 9
sleep 2s
done
......@@ -107,9 +110,10 @@ for infer_mode in ${infer_mode_list[*]}; do
set_export_weight=$(func_set_params "${export_weight_key}" "${export_weight_value}")
set_save_export_dir=$(func_set_params "${save_export_key}" "${save_export_value}")
set_filename=$(func_set_params "${filename_key}" "${model_name}")
export_log_path="${LOG_PATH}/export.log"
export_cmd="${python} ${run_export} ${set_export_weight} ${set_filename} ${set_save_export_dir} "
echo $export_cmd
eval $export_cmd
eval "${export_cmd} > ${export_log_path} 2>&1"
status_export=$?
status_check $status_export "${export_cmd}" "${status_log}" "${model_name}"
fi
......
......@@ -2,13 +2,14 @@
source test_tipc/utils_func.sh
FILENAME=$1
MODE="serving_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet serving_infer: ${model_name}"
echo "ppdet serving_python_infer: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parser_key "${lines[3]}")
filename_value=$(func_parser_value "${lines[3]}")
......@@ -44,7 +45,7 @@ infer_image_value=$(func_parser_value "${lines[22]}")
http_client_key1=$(func_parser_key "${lines[23]}")
http_client_value1=$(func_parser_value "${lines[23]}")
LOG_PATH="./test_tipc/output"
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_serving_python.log"
......@@ -61,21 +62,22 @@ function func_serving_inference(){
# inference
for opt in ${opt_use_gpu_list[*]}; do
device_type=$(func_parser_key "${opt}")
_save_log_path="${_log_path}/serving_infer_python_${device_type}_batchsize_1.log"
server_log_path="${_log_path}/python_server_${device_type}.log"
client_log_path="${_log_path}/python_client_${device_type}.log"
opt_value=$(func_parser_value "${opt}")
_set_opt=$(func_set_params "${opt_key}" "${opt_value}")
# run web service
web_service_cmd="${_python} ${_service_script} ${_set_model_dir} ${_set_opt} ${set_web_service_params1} &"
web_service_cmd="${_python} ${_service_script} ${_set_model_dir} ${_set_opt} ${set_web_service_params1} > ${server_log_path} 2>&1 &"
eval $web_service_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
sleep 5s
# run http client
http_client_cmd="${_python} ${_client_script} ${_set_image_file} ${set_http_client_params1} > ${_save_log_path} 2>&1 "
http_client_cmd="${_python} ${_client_script} ${_set_image_file} ${set_http_client_params1} > ${client_log_path} 2>&1"
eval $http_client_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${http_client_cmd}" "${status_log}" "${model_name}"
eval "cat ${_save_log_path}"
eval "cat ${client_log_path}"
ps ux | grep -E 'web_service' | awk '{print $2}' | xargs kill -s 9
sleep 2s
done
......@@ -108,9 +110,10 @@ for infer_mode in ${infer_mode_list[*]}; do
set_export_weight=$(func_set_params "${export_weight_key}" "${export_weight_value}")
set_save_export_dir=$(func_set_params "${save_export_key}" "${save_export_value}")
set_filename=$(func_set_params "${filename_key}" "${model_name}")
export_log_path="${LOG_PATH}/export.log"
export_cmd="${python} ${run_export} ${set_export_weight} ${set_filename} ${set_save_export_dir} "
echo $export_cmd
eval $export_cmd
eval "${export_cmd} > ${export_log_path} 2>&1"
status_export=$?
status_check $status_export "${export_cmd}" "${status_log}" "${model_name}"
fi
......
......@@ -92,7 +92,7 @@ benchmark_value=$(func_parser_value "${lines[49]}")
infer_key1=$(func_parser_key "${lines[50]}")
infer_value1=$(func_parser_value "${lines[50]}")
LOG_PATH="./test_tipc/output/${model_name}"
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_python.log"
......@@ -114,7 +114,7 @@ function func_inference(){
fi
for threads in ${cpu_threads_list[*]}; do
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_fluid_batchsize_${batch_size}.log"
_save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_mode_paddle_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
......@@ -131,7 +131,7 @@ function func_inference(){
done
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
for precision in ${precision_list[*]}; do
if [[ ${precision} != "fluid" ]]; then
if [[ ${precision} != "paddle" ]]; then
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} = "trt_int8" ]]; then
continue
fi
......@@ -140,7 +140,7 @@ function func_inference(){
fi
fi
for batch_size in ${batch_size_list[*]}; do
_save_log_path="${_log_path}/python_infer_gpu_precision_${precision}_batchsize_${batch_size}.log"
_save_log_path="${_log_path}/python_infer_gpu_mode_${precision}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
......@@ -276,6 +276,7 @@ else
save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
nodes="1"
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
cmd="${python} ${run_train} LearningRate.base_lr=0.0001 log_iter=1 ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_filename} ${set_train_params1} ${set_autocast}"
elif [ ${#ips} -le 15 ];then # train with multi-gpu
......@@ -290,15 +291,17 @@ else
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} log_iter=1 ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_filename} ${set_train_params1} ${set_autocast}"
fi
# run train
eval $cmd
train_log_path="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}.log"
eval "${cmd} > ${train_log_path} 2>&1"
status_check $? "${cmd}" "${status_log}" "${model_name}"
set_eval_trained_weight=$(func_set_params "${export_weight_key}" "${save_log}/${model_name}/${train_model_name}")
# run eval
if [ ${eval_py} != "null" ]; then
set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
eval_log_path="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_eval.log"
eval_cmd="${python} ${eval_py} ${set_eval_trained_weight} ${set_use_gpu} ${set_eval_params1}"
eval $eval_cmd
eval "${eval_cmd} > ${eval_log_path} 2>&1"
status_check $? "${eval_cmd}" "${status_log}" "${model_name}"
fi
# run export model
......@@ -315,8 +318,9 @@ else
eval "cp ${save_export_model_dir}/* ${save_log}/"
fi
# run export model
export_log_path="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}_export.log"
export_cmd="${python} ${run_export} ${set_export_weight} ${set_filename} ${set_save_export_dir} "
eval $export_cmd
eval "${export_cmd} > ${export_log_path} 2>&1"
status_check $? "${export_cmd}" "${status_log}" "${model_name}"
#run inference
......
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