Mmdet benchmark with python

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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。

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