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Sense-GVT / Fast-BEV

Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline
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3090的测试推理速度极慢 #42

Open AndrewJSong opened 1 year ago

AndrewJSong commented 1 year ago

尝试跑了一下test，不管是m0还是m5，速度大概都不到1帧/s tools/test.py configs/fastbev/exp/paper/fastbev_m5_r50_s512x1408_v250x250x6_c256_d6_f4.py work_dirs/fastbev/exp/paper/fastbev_m5_r50_s512x1408_v250x250x6_c256_d6_f4/epoch_20.pth --eval bbox

[>> ] 331/6019, 0.5 task/s, elapsed: 636s, ETA: 10922s

CUDA_VISIBLE_DEVICES=3 python -m torch.distributed.launch --nproc_per_node=1 --master_port=29503 tools/test.py configs/fastbev/exp/paper/fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f4.py work_dirs/fastbev/exp/paper/fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f4/epoch_20.pth --eval bbox --launcher="pytorch"

load checkpoint from local path: work_dirs/fastbev/exp/paper/fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f4/epoch_20.pth [> ] 140/6019, 0.9 task/s, elapsed: 161s, ETA: 675

确定是用GPU跑的，显卡有被调用。

环境 sys.platform: linux Python: 3.8.8 (default, Feb 24 2021, 21:46:12) [GCC 7.3.0] CUDA available: True GPU 0: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Build cuda_11.1.TC455_06.29190527_0 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.8.1 PyTorch compiling details: PyTorch built with:

TorchVision: 0.9.1 OpenCV: 4.7.0 MMCV: 1.4.0 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.1 MMDetection: 2.14.0 MMSegmentation: 0.14.1 MMDetection3D: 0.16.0+12f1931

Ignite616 commented 1 year ago

I also have the similar question

Ignite616 commented 11 months ago

尝试跑了一下test，不管是m0还是m5，速度大概都不到1帧/s tools/test.py configs/fastbev/exp/paper/fastbev_m5_r50_s512x1408_v250x250x6_c256_d6_f4.py work_dirs/fastbev/exp/paper/fastbev_m5_r50_s512x1408_v250x250x6_c256_d6_f4/epoch_20.pth --eval bbox

[>> ] 331/6019, 0.5 task/s, elapsed: 636s, ETA: 10922s

CUDA_VISIBLE_DEVICES=3 python -m torch.distributed.launch --nproc_per_node=1 --master_port=29503 tools/test.py configs/fastbev/exp/paper/fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f4.py work_dirs/fastbev/exp/paper/fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f4/epoch_20.pth --eval bbox --launcher="pytorch"

load checkpoint from local path: work_dirs/fastbev/exp/paper/fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f4/epoch_20.pth [> ] 140/6019, 0.9 task/s, elapsed: 161s, ETA: 675

确定是用GPU跑的，显卡有被调用。

环境 sys.platform: linux Python: 3.8.8 (default, Feb 24 2021, 21:46:12) [GCC 7.3.0] CUDA available: True GPU 0: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Build cuda_11.1.TC455_06.29190527_0 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.8.1 PyTorch compiling details: PyTorch built with:

  • GCC 7.3
  • C++ Version: 201402
  • Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  • Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
  • 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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37
  • 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 -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=1.8.1, 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: 0.9.1 OpenCV: 4.7.0 MMCV: 1.4.0 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.1 MMDetection: 2.14.0 MMSegmentation: 0.14.1 MMDetection3D: 0.16.0+12f1931

Is your reasoning speed fast now?

GeLink9999 commented 7 months ago

have you solved this problem @AndrewJSong ? Can you show the fastbev_post_trt_decode.log when convert onnx to plan using build_trt_engine.sh? I have similar issue on ORIN:

20231128-171158