Paddle Where is HW Benchmark ?

ppcbkaq5  于 2022-10-25  发布在  其他
关注(0)|答案(3)|浏览(170)

问题描述 Please describe your issue

Hello.
Inference tests were performed in Desktop and NVIDIA xavier NX.
But i can’t compare inference result, because i don't have Reference information.

HW Specification, Paddle Install Option, Inference result as below.
Is Inference-Result appropriate?

Please tell me average-result.
Thank you.

/*******************************************************************************************************************************************/
[ Desktop ]
Ubuntu 18.04
CPU : AMD Ryzen 5 5600G with Radeon Graphics 3.90 GHz
RAM : 32GB
GPU : RTX3060

-Install PP-
conda create -n PPDet python=3.9
conda activate PPDet
conda install paddlepaddle-gpu==2.2.2 cudatoolkit=11.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge

-Install PP-Detection-
conda activate PPDet
cd ~ && mkdir -p PPDet_Git
cd PPDet_Git && git clone https://github.com/PaddlePaddle/PaddleDetection.git
cd PaddleDetection
python3 -m pip install cython
python3 -m pip install cpython
python3 -m pip install numpy
python3 -m pip install -r requirements.txt
python3 setup.py install

-Model Export-
python3 tools/export_model.py -c /home/k/PPDet_Git/PaddleDetection/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml -o weights= https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams

python3 tools/export_model.py -c /home/k/PPDet_Git/PaddleDetection/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml -o weights= https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams

python3 tools/export_model.py -c /home/k/PPDet_Git/PaddleDetection/configs/picodet/picodet_xs_320_coco_lcnet.yml -o weights= https://paddledet.bj.bcebos.com/models/picodet_xs_320_coco_lcnet.pdparams

/*******************************************************************************************************************************************/
[ Jetson xavier NX ]
Ubuntu 18.04

-Install PP-
cd ~ && git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
git checkout release/2.2
sudo mkdir -p build_cuda && cd build_cuda

sudo cmake ..
-DWITH_NV_JETSON=ON
-DWITH_GPU=ON
-DCMAKE_CUDA_COMPILER=/usr/local/cuda-10.2/bin/nvcc
-DCMAKE_CUDA_ARCHITECTURES=72
-DCUDA_ARCH_NAME=All
-DWITH_NCCL=OFF
-DWITH_MKL=OFF
-DWITH_MKLDNN=OFF
-DWITH_PYTHON=ON
-DPY_VERSION=3.6
-DWITH_XBYAK=OFF
-DON_INFER=ON
-DWITH_TESTING=OFF
-DWITH_CONTRIB=OFF
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_CXX_FLAGS='-Wno-error -w'
..

-Install PP-Detection-
cd ~ && mkdir -p PPDet_Git
cd PPDet_Git && git clone https://github.com/PaddlePaddle/PaddleDetection.git
cd PPDet_Git && cd PaddleDetection
python3 -m pip install -r requirements.txt
sudo python3 setup.py install

-NVIDIA xavier NX mode-
sudo nvpmodel -m 0

-Model Export-
python3 tools/export_model.py -c /home/k/PPDet_Git/PaddleDetection/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml -o weights= https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams

python3 tools/export_model.py -c /home/k/PPDet_Git/PaddleDetection/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml -o weights= https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams

python3 tools/export_model.py -c /home/k/PPDet_Git/PaddleDetection/configs/picodet/picodet_xs_320_coco_lcnet.yml -o weights= https://paddledet.bj.bcebos.com/models/picodet_xs_320_coco_lcnet.pdparams

/*******************************************************************************************************************************************/
[ Inference Result ]

[ Model 1, ppyolo_r50vd_dcn_2x_coco]
python3 deploy/python/infer.py --model_dir=./output_inference/ppyolo_r50vd_dcn_2x_coco --image_file=./demo/000000014439_640x640.jpg --device=

  • Desktop : --device=GPU

total_time(ms): 1367.5, img_num: 1
average latency time(ms): 1367.50, QPS: 0.731261
preprocess_time(ms): 898.40, inference_time(ms): 469.10, postprocess_time(ms): 0.00

  • Desktop : --device=CPU

total_time(ms): 3085.9, img_num: 1
average latency time(ms): 3085.90, QPS: 0.324055
preprocess_time(ms): 20.30, inference_time(ms): 3065.60, postprocess_time(ms): 0.00

  • Jetson xavier NX : --device=GPU

total_time(ms): 5659.900000000001, img_num: 1
average latency time(ms): 5659.90, QPS: 0.176682
preprocess_time(ms): 2882.60, inference_time(ms): 2776.70, postprocess_time(ms): 0.60

  • Jetson xavier NX : --device=CPU

total_time(ms): 8196.5, img_num: 1
average latency time(ms): 8196.50, QPS: 0.122003
preprocess_time(ms): 89.00, inference_time(ms): 8107.40, postprocess_time(ms): 0.10

[ Model 2, ppyoloe_crn_s_300e_coco]

  • python3 deploy/python/infer.py --model_dir=./output_inference/ppyoloe_crn_s_300e_coco --image_file=./demo/000000014439_640x640.jpg --device=
  • Desktop : --device=GPU

total_time(ms): 1489.3000000000002, img_num: 1
average latency time(ms): 1489.30, QPS: 0.671456
preprocess_time(ms): 969.20, inference_time(ms): 520.10, postprocess_time(ms): 0.00

  • Desktop : --device=CPU

total_time(ms): 675.2, img_num: 1
average latency time(ms): 675.20, QPS: 1.481043
preprocess_time(ms): 25.80, inference_time(ms): 649.40, postprocess_time(ms): 0.00

  • Jetson xavier NX : --device=GPU

total_time(ms): 4869.799999999999, img_num: 1
average latency time(ms): 4869.80, QPS: 0.205347
preprocess_time(ms): 3322.50, inference_time(ms): 1547.20, postprocess_time(ms): 0.10

  • Jetson xavier NX : --device=CPU

total_time(ms): 65947.8, img_num: 1
average latency time(ms): 65947.80, QPS: 0.015164
preprocess_time(ms): 68.20, inference_time(ms): 65879.40, postprocess_time(ms): 0.20

[ Model 3, picodet_xs_320_coco_lcnet]

  • python3 deploy/python/infer.py --model_dir=./output_inference/picodet_xs_320_coco_lcnet --image_file=./demo/000000014439_640x640.jpg --device=
  • Desktop : --device=GPU

total_time(ms): 1494.8999999999999, img_num: 1
average latency time(ms): 1494.90, QPS: 0.668941
preprocess_time(ms): 959.80, inference_time(ms): 535.10, postprocess_time(ms): 0.00

  • Desktop : --device=CPU

total_time(ms): 84.4, img_num: 1
average latency time(ms): 84.40, QPS: 11.848341
preprocess_time(ms): 11.00, inference_time(ms): 73.40, postprocess_time(ms): 0.00

  • Jetson xavier NX : --device=GPU

total_time(ms): 5147.599999999999, img_num: 1
average latency time(ms): 5147.60, QPS: 0.194265
preprocess_time(ms): 3291.60, inference_time(ms): 1855.90, postprocess_time(ms): 0.10

  • Jetson xavier NX : --device=CPU

total_time(ms): 307.59999999999997, img_num: 1
average latency time(ms): 307.60, QPS: 3.250975
preprocess_time(ms): 29.20, inference_time(ms): 278.30, postprocess_time(ms): 0.10

Paddle

3条答案

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soat7uwm

soat7uwm1#

您好，我们已经收到了您的问题，会安排技术人员尽快解答您的问题，请耐心等待。请您再次检查是否提供了清晰的问题描述、复现代码、环境&版本、报错信息等。同时，您也可以通过查看 官网API文档 、 常见问题 、 历史Issue 、 AI社区 来寻求解答。祝您生活愉快～

Hi! We've received your issue and please be patient to get responded. We will arrange technicians to answer your questions as soon as possible. Please make sure that you have posted enough message to demo your request. You may also check out the API ， FAQ ， Github Issue and AI community to get the answer.Have a nice day!

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axr492tv

axr492tv2#

Thanks. Do you include the warmup time in your benchmarks? Usually, the GPU warmup time is much higher than the CPU. Furthermore, both the GPU/CPU preprocesses run on the CPU, so it's unreasonable that the GPU preprocess time is too much higher than the CPU。

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fcy6dtqo

fcy6dtqo3#

@liyancas Thank you for answer.
From your answer, i understand preprocess of GPU warmup.

So now, my question is below.
[1] 'Inference_time(ms)' is reasonable ? with my HW Specification and '--device=CPU' option.
[2] Should i ask benchmark data with similar my HW Specification ?

I can't find Paddle HW benchmark page.
Thank you.

赞(0）分享  回复(0）举报  2022-10-25
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