Mmdet benchmark with python

Related tags

Deep Learningmmdet_benchmark
Overview

mmdet_benchmark

本项目是为了研究 mmdet 推断性能瓶颈，并且对其进行优化。

配置与环境

机器配置

CPU：Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz
GPU：NVIDIA GeForce RTX 3080 10GB
内存：64G
硬盘：1TB NVME SSD

mmdet 环境

Python: 3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 3080
CUDA_HOME: /usr/local/cuda
NVCC: Build cuda_10.2_r440.TC440_70.29663091_0
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.9.1+cu111
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.0.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.10.1+cu111
OpenCV: 4.5.4
MMCV: 1.3.17
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.1
MMDetection: 2.19.0+

时间分析

Mask R-CNN 的推断过程包含以下几个步骤，我们在一些可能是瓶颈的位置增加了时间统计：

注意：mask post-processing 的时间包含在 roi_head 里，所以减少 mask post-processing 的时间就是在减少 roi_head 的时间。

使用标准尺寸测试（1333x800）

测试图片：

stage pre-processing backbone rpn_head mask forward mask post-processing roi_head total
inference 13.45 24.87 10.16 3.84 15.74 23.49 72.3
inference_fp16 13.53 15.98 8.34 3.36 15.74 22.97 61.4
inference_fp16_preprocess 1.75 15.91 8.21 3.33 15.61 22.69 49.03
inference_raw_mask 1.65 15.93 8.34 3.36 1.74 8.89 33.45

使用较大尺寸测试（3840x2304）

stage pre-processing backbone rpn_head mask forward mask post-processing roi_head total
inference 128.44 187.24 69.96 6.01 173.72 183.95 569.92
inference_fp16 127.28 120.10 50.30 6.80 172.42 186.81 485.04
inference_fp16_preprocess 11.02 120.20 50.18 6.82 174.62 187.07 379.00
inference_raw_mask 11.03 120.26 50.46 6.81 2.99 15.34 197.69

可视化

mmdet 原版：

加速版：

目测没有显著差异。

总结

  • 使用 wrap_fp16_model 可以节省 backbone 的时间，但是不是所有情况下的 forward 都能节省时间；
  • 使用 torchvision.transforms.functional 去做图像预处理，可以极大提升推断速度；
  • 使用 FCNMaskHeadWithRawMask，避免对 mask 进行 resize，对越大的图像加速比越高，因为 resize 到原图大小的成本很高；
  • 后续优化，需要考虑 backbone 和 rpn_head 的优化，可以使用 TensorRT 进行加速。

原理分析

fp16

把一些支持 fp16 的层使用 fp16 来推断，可以充分利用显卡的 TensorCore，加速 forward 部分的速度。

参考链接：https://zhuanlan.zhihu.com/p/375224982

在 backbone 上，时间从 24.87 降到 15.93，在大图上从 187.24 降到 120.26，提升 35% 左右。

torchvision.transforms.functional

使用 pytorch 的 resize、pad、normalize，可以利用上 GPU 而不是 CPU。我们在推断过程中，CPU 利用率始终是最高的，而 GPU 利用率几乎没有满过，所以只要能够把 CPU 的事情交给 GPU 做，就能解决瓶颈问题，减少推断时间。

由于整个过程都可以使用 GPU，所以时间从 13.45 降低到 1.65，在大图上从 128.44 降低到 11.03，提升 10 倍左右。

FCNMaskHeadWithRawMask

首先我们看看 mmdet 处理的结果格式：

可以看到，有多少个 bbox，就有多少个 segm，每个 segm 都是原图尺寸。不管是 CPU，还是内存，都需要大量的时间去处理。

然后再看看 FCNMaskHeadWithRawMask 处理的格式：

每个结果都是 28x28 的，这也是模型原始输出，所以信息量和上面是一样的。

唯一的区别是，我们在拿到结果之后，如果要可视化，需要 resize 到 bbox 的大小，参考 detect/utils_visualize.py#L36-L40

使用 FCNMaskHeadWithRawMask 可以从 15.74 降到 1.74，大图可以从 173.72 降到 2.99，也就是说，图越大，这个加速比越大。

You might also like...
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Documentation: https://mmsegmentation.readthedocs.io/ English | 简体中文 Introduction MMSegmentation is an open source semantic segmentation toolbox based

OpenMMLab 5k Dec 31, 2022
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

Introduction English | 简体中文 MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project. The m

OpenMMLab 2.7k Jan 7, 2023
OpenMMLab Pose Estimation Toolbox and Benchmark.
OpenMMLab Pose Estimation Toolbox and Benchmark.

Introduction English | 简体中文 MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the OpenMMLab project. The master b

OpenMMLab 2.8k Dec 31, 2022
[CVPR 2021 Oral] ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis
[CVPR 2021 Oral] ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis

ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis [arxiv|pdf|v

Yinan He 78 Dec 22, 2022
A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform

FedML-AI 175 Dec 1, 2022
RoboDesk A Multi-Task Reinforcement Learning Benchmark
RoboDesk A Multi-Task Reinforcement Learning Benchmark

RoboDesk A Multi-Task Reinforcement Learning Benchmark If you find this open source release useful, please reference in your paper: @misc{kannan2021ro

Google Research 66 Oct 7, 2022
[CVPR2021] UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

UAV-Human Official repository for CVPR2021: UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicle Paper arXiv Res

null 129 Jan 4, 2023
3D AffordanceNet is a 3D point cloud benchmark consisting of 23k shapes from 23 semantic object categories, annotated with 56k affordance annotations and covering 18 visual affordance categories.
3D AffordanceNet is a 3D point cloud benchmark consisting of 23k shapes from 23 semantic object categories, annotated with 56k affordance annotations and covering 18 visual affordance categories.

3D AffordanceNet This repository is the official experiment implementation of 3D AffordanceNet benchmark. 3D AffordanceNet is a 3D point cloud benchma

null 49 Dec 1, 2022
NAS Benchmark in
NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021

NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree

null 35 Jan 3, 2023
Releases(v0.2.1)
Owner
杨培文 (Yang Peiwen)
杨培文 (Yang Peiwen)
GitHub Repository
This project uses Template Matching technique for object detecting by detection of template image over base image.

Object Detection Project Using OpenCV This project uses Template Matching technique for object detecting by detection the template image over base ima

Pratham Bhatnagar 7 May 29, 2022
[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

TransFuser This repository contains the code for the CVPR 2021 paper Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. If you find our

695 Jan 05, 2023
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

37 Dec 15, 2022
Anomaly detection related books, papers, videos, and toolboxes

Anomaly Detection Learning Resources Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify

Yue Zhao 6.7k Dec 31, 2022
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (ICCV, 2021) (PyTorch) - We released the training code!

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution Kai Zhang, Jingyun Liang, Luc Van Gool, Radu Timofte Computer Vision Lab

Kai Zhang 804 Jan 08, 2023
Structure-Preserving Deraining with Residue Channel Prior Guidance (ICCV2021)

SPDNet Structure-Preserving Deraining with Residue Channel Prior Guidance (ICCV2021) Requirements Linux Platform NVIDIA GPU + CUDA CuDNN PyTorch == 0.

41 Dec 12, 2022
LSTM model trained on a small dataset of 3000 names written in PyTorch

LSTM model trained on a small dataset of 3000 names. Model generates names from model by selecting one out of top 3 letters suggested by model at a time until an EOS (End Of Sentence) character is no

Sahil Lamba 1 Dec 20, 2021
A toolkit for document-level event extraction, containing some SOTA model implementations

❤️ A Toolkit for Document-level Event Extraction with & without Triggers Hi, there 👋 . Thanks for your stay in this repo. This project aims at buildi

Tong Zhu（朱桐） 159 Dec 22, 2022
Deep deconfounded recommender (Deep-Deconf) for paper "Deep causal reasoning for recommendations"

Deep Causal Reasoning for Recommender Systems The codes are associated with the following paper: Deep Causal Reasoning for Recommendations, Yaochen Zh

Yaochen Zhu 22 Oct 15, 2022
HybVIO visual-inertial odometry and SLAM system

HybVIO A visual-inertial odometry system with an optional SLAM module. This is a research-oriented codebase, which has been published for the purposes

Spectacular AI 320 Jan 03, 2023
Original Pytorch Implementation of FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation

FLAME Original Pytorch Implementation of FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation, accepted at the 17th IEEE Internation Co

Neelabh Sinha 19 Dec 17, 2022
Audio Visual Emotion Recognition using TDA

Audio Visual Emotion Recognition using TDA RAVDESS database with two datasets analyzed: Video and Audio dataset: Audio-Dataset: https://www.kaggle.com

Combinatorial Image Analysis research group 3 May 11, 2022
Neural Re-rendering for Full-frame Video Stabilization

NeRViS: Neural Re-rendering for Full-frame Video Stabilization Project Page | Video | Paper | Google Colab Setup Setup environment for [Yu and Ramamoo

Yu-Lun Liu 9 Jun 17, 2022
Joint Channel and Weight Pruning for Model Acceleration on Mobile Devices

Joint Channel and Weight Pruning for Model Acceleration on Mobile Devices Abstract For practical deep neural network design on mobile devices, it is e

11 Dec 30, 2022
Code for paper ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization

Benjamin Biggs 29 Dec 28, 2022
End-to-End Speech Processing Toolkit

ESPnet: end-to-end speech processing toolkit system/pytorch ver. 1.3.1 1.4.0 1.5.1 1.6.0 1.7.1 1.8.1 1.9.0 ubuntu20/python3.9/pip ubuntu20/python3.8/p

ESPnet 5.9k Jan 04, 2023
ADGAN - The Implementation of paper Controllable Person Image Synthesis with Attribute-Decomposed GAN

ADGAN - The Implementation of paper Controllable Person Image Synthesis with Attribute-Decomposed GAN CVPR 2020 (Oral); Pose and Appearance Attributes Transfer;

Men Yifang 400 Dec 29, 2022
The openspoor package is intended to allow easy transformation between different geographical and topological systems commonly used in Dutch Railway

Openspoor The openspoor package is intended to allow easy transformation between different geographical and topological systems commonly used in Dutch

7 Aug 22, 2022
This example implements the end-to-end MLOps process using Vertex AI platform and Smart Analytics technology capabilities

MLOps with Vertex AI This example implements the end-to-end MLOps process using Vertex AI platform and Smart Analytics technology capabilities. The ex

Google Cloud Platform 238 Dec 21, 2022
PyTorch implementation of popular datasets and models in remote sensing

PyTorch Remote Sensing (torchrs) (WIP) PyTorch implementation of popular datasets and models in remote sensing tasks (Change Detection, Image Super Re

isaac 222 Dec 28, 2022