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简体中文 | English

Benchmark

本文档给出了PaddleVideo系列模型在各平台预测耗时benchmark。


目录

1. 视频分类模型

1.1 测试数据

我们从Kinetics-400数据集中,随机选择提供100条用于benchmark时间测试,测试数据可以点击下载。

解压后文件目录:

time-test
├── data       # 测试视频文件
└── file.list  # 文件列表

视频属性如下:

mean video time:  9.67s
mean video width:  373
mean video height:  256
mean fps:  25

1.2 测试环境

硬件环境:

  • CPU: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
  • GPU: Tesla V100 16G

软件环境:

  • Python 3.7
  • PaddlePaddle 2.3.1
  • CUDA 10.2
  • CUDNN 8.1.1
  • 各python库版本参考requirement.txt

1.3 测试结果

1.3.1 GPU推理速度一览

各模型性能数据按预测总时间排序,结果如下:

模型名称 骨干网络 配置文件 精度% 预处理时间ms 模型推理时间ms 预测总时间ms
PP-TSM MobileNetV2 pptsm_mv2_k400_videos_uniform.yaml 68.09 51.5 3.31 54.81
PP-TSM MobileNetV3 pptsm_mv3_k400_frames_uniform.yaml 69.84 51 4.34 55.34
PP-TSMv2 PP-LCNet_v2.8f pptsm_lcnet_k400_8frames_uniform.yaml 72.45 55.31 4.37 59.68
TSM R50 tsm_k400_frames.yaml 71.06 52.02 9.87 61.89
PP-TSM R50 pptsm_k400_frames_uniform.yaml 75.11 51.84 11.26 63.1
PP-TSM R101 pptsm_k400_frames_dense_r101.yaml 76.35 52.1 17.91 70.01
PP-TSMv2 PP-LCNet_v2.16f pptsm_lcnet_k400_16frames_uniform.yaml 74.38 69.4 7.55 76.95
SlowFast 4*16 slowfast.yaml 74.35 99.27 27.4 126.67
*VideoSwin B videoswin_k400_videos.yaml 82.4 95.65 117.22 212.88
MoViNet A0 movinet_k400_frame.yaml 66.62 150.36 47.24 197.60
*PP-TimeSformer base pptimesformer_k400_videos.yaml 78.87 299.48 133.41 432.90
*TimeSformer base timesformer_k400_videos.yaml 77.29 301.54 136.12 437.67
TSN R50 tsn_k400_frames.yaml 69.81 794.30 168.70 963.00
PP-TSN R50 pptsn_k400_frames.yaml 75.06 837.75 175.12 1012.87
  • 注:带*表示该模型未使用tensorRT进行预测加速。
  • TSN预测时采用TenCrop,比TSM采用的CenterCrop更加耗时。TSN如果使用CenterCrop,则速度稍优于TSM,但精度会低3.5个点。

1.3.2 CPU推理速度一览

各模型性能数据按预测总时间排序,结果如下:

模型名称 骨干网络 配置文件 精度% 预处理时间ms 模型推理时间ms 预测总时间ms
PP-TSM MobileNetV2 pptsm_mv2_k400_videos_uniform.yaml 68.09 52.62 137.03 189.65
PP-TSM MobileNetV3 pptsm_mv3_k400_frames_uniform.yaml 69.84 53.44 139.13 192.58
PP-TSMv2 PP-LCNet_v2.8f pptsm_lcnet_k400_8frames_uniform.yaml 72.45 53.37 189.62 242.99
PP-TSMv2 PP-LCNet_v2.16f pptsm_lcnet_k400_16frames_uniform.yaml 74.38 68.07 388.64 456.71
SlowFast 4*16 slowfast.yaml 74.35 110.04 1201.36 1311.41
TSM R50 tsm_k400_frames.yaml 71.06 52.47 1302.49 1354.96
PP-TSM R50 pptsm_k400_frames_uniform.yaml 75.11 52.26 1354.21 1406.48
*MoViNet A0 movinet_k400_frame.yaml 66.62 148.30 1290.46 1438.76
PP-TSM R101 pptsm_k400_frames_dense_r101.yaml 76.35 52.50 2236.94 2289.45
PP-TimeSformer base pptimesformer_k400_videos.yaml 78.87 294.89 13426.53 13721.43
TimeSformer base timesformer_k400_videos.yaml 77.29 297.33 14034.77 14332.11
TSN R50 tsn_k400_frames.yaml 69.81 860.41 18359.26 19219.68
PP-TSN R50 pptsn_k400_frames.yaml 75.06 835.86 19778.60 20614.46
*VideoSwin B videoswin_k400_videos.yaml 82.4 76.21 32983.49 33059.70
  • 注: 带*表示该模型未使用mkldnn进行预测加速。

1.4 测试方法

在进行测试之前,需要安装requirements.txt相关依赖,并且还需安装AutoLog用于记录计算时间,使用如下命令安装:

python3.7 -m pip install --upgrade pip
pip3.7 install --upgrade -r requirements.txt
python3.7 -m pip install git+https://github.com/LDOUBLEV/AutoLog

1.4.1 单个模型测试

以PP-TSM模型为例,请先参考PP-TSM文档导出推理模型,之后使用如下命令进行速度测试:

python3.7 tools/predict.py --input_file time-test/file.list \
                          --time_test_file=True \
                          --config configs/recognition/pptsm/pptsm_k400_frames_uniform.yaml \
                          --model_file inference/ppTSM/ppTSM.pdmodel \
                          --params_file inference/ppTSM/ppTSM.pdiparams \
                          --use_gpu=False \
                          --use_tensorrt=False \
                          --enable_mkldnn=True \
                          --enable_benchmark=True \
                          --disable_glog True
  • 各参数含义如下:
input_file:     指定测试文件/文件列表, 示例使用1.1小节提供的测试数据
time_test_file: 是否进行时间测试,请设为True
config:         指定模型配置文件
model_file:     指定推理文件pdmodel路径
params_file:    指定推理文件pdiparams路径
use_gpu:        是否使用GPU预测, False则使用CPU预测
use_tensorrt:   是否开启TensorRT预测
enable_mkldnn:  开启benchmark时间测试,默认设为True
disable_glog:   是否关闭推理时的日志,请设为True
  • 测试时,GPU推理使用FP32+TensorRT配置下,CPU使用mkldnn加速。运行100次,去除前3次的warmup时间,得到推理平均时间。

1.4.2 批量测试

使用以下批量测试脚本,可以方便的将性能结果进行复现:

    1. 下载预训练模型:
mkdir ckpt
cd ckpt
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.1/PPTSM/ppTSM_k400_uniform_distill.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.2/ppTSM_k400_uniform_distill_r101.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.3/ppTSM_mv2_k400.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.3/ppTSM_mv3_k400.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.3/PPTSMv2_k400_16f_dml.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.2/ppTSN_k400_8.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.2/ppTimeSformer_k400_8f_distill.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.1/TSM/TSM_k400.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.2/TSN_k400.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.2/TimeSformer_k400.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo/SlowFast/SlowFast.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.3/MoViNetA0_k400.pdparams
wget https://videotag.bj.bcebos.com/PaddleVideo-release2.2/VideoSwin_k400.pdparams
    1. 准备各模型配置参数列表文件model.list
PP-TSM_R50      configs/recognition/pptsm/pptsm_k400_frames_uniform.yaml        ckpt/ppTSM_k400_uniform_distill.pdparams ppTSM
PP-TSM_R101     configs/recognition/pptsm/pptsm_k400_frames_dense_r101.yaml     ckpt/ppTSM_k400_uniform_distill_r101.pdparams ppTSM
PP-TSM_MobileNetV2      configs/recognition/pptsm/pptsm_mv2_k400_videos_uniform.yaml    ckpt/ppTSM_mv2_k400.pdparams ppTSM
PP-TSM_MobileNetV3      configs/recognition/pptsm/pptsm_mv3_k400_frames_uniform.yaml    ckpt/ppTSM_mv3_k400.pdparams ppTSM
PP-TSMv2_PP-LCNet_v2    configs/recognition/pptsm/v2/pptsm_lcnet_k400_16frames_uniform_dml_distillation.yaml      ckpt/PPTSMv2_k400_16f_dml.pdparams ppTSMv2
PP-TSN_R50      configs/recognition/pptsn/pptsn_k400_frames.yaml        ckpt/ppTSN_k400_8.pdparams ppTSN
PP-TimeSformer_base     configs/recognition/pptimesformer/pptimesformer_k400_videos.yaml        ckpt/ppTimeSformer_k400_8f_distill.pdparams ppTimeSformer
TSM_R50 configs/recognition/tsm/tsm_k400_frames.yaml    ckpt/TSM_k400.pdparams TSM
TSN_R50 configs/recognition/tsn/tsn_k400_frames.yaml    ckpt/TSN_k400.pdparams TSN
TimeSformer_base        configs/recognition/timesformer/timesformer_k400_videos.yaml    ckpt/TimeSformer_k400.pdparams TimeSformer
SlowFast_416    configs/recognition/slowfast/slowfast.yaml      ckpt/SlowFast.pdparams SlowFast
MoViNet_A0      configs/recognition/movinet/movinet_k400_frame.yaml     ckpt/MoViNetA0_k400.pdparams MoViNet
VideoSwin_B     configs/recognition/videoswin/videoswin_k400_videos.yaml        ckpt/VideoSwin_k400.pdparams VideoSwin
    1. 批量导出模型,执行时传入model.list文件
file=$1

while read line
do
    arr=($line)
    ModelName=${arr[0]}
    ConfigFile=${arr[1]}
    ParamsPath=${arr[2]}
    echo $ModelName

    python3.7 tools/export_model.py -c $ConfigFile \
                                    -p $ParamsPath \
                                    -o inference/$ModelName
done <$file
    1. 测试时间,执行时传入model.list文件
file=$1

while read line
do
    arr=($line)
    ModelName=${arr[0]}
    ConfigFile=${arr[1]}
    ParamsPath=${arr[2]}
    Model=${arr[3]}

    python3.7 tools/predict.py --input_file ../../time-test/file.list \
                            --time_test_file=True \
                            --config $ConfigFile \
                            --model_file inference/$ModelName/$Model.pdmodel \
                            --params_file inference/$ModelName/$Model.pdiparams \
                            --use_gpu=False \
                            --use_tensorrt=False \
                            --enable_mkldnn=False \
                            --enable_benchmark=True \
                            --disable_glog True
    echo =====$ModelName END====
done <$file

2. 时序分割模型

2.1 测试环境

硬件环境:

  • 8 NVIDIA Tesla V100 (16G) GPUs
  • Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz

软件环境:

  • Python 3.7
  • PaddlePaddle2.0
  • CUDA 10.1
  • CUDNN 7.6.3
  • NCCL 2.1.15
  • GCC 8.2.0

2.2 测试结果

本仓库提供经典和热门时序动作分割模型的性能和精度对比

Model Metrics Value Flops(M) Params(M) test time(ms) bs=1 test time(ms) bs=2 inference time(ms) bs=1 inference time(ms) bs=2
MS-TCN [email protected] 38.8% 791.360 0.8 170 - 10.68 -
ASRF [email protected] 55.7% 1,283.328 1.3 190 - 16.34 -
  • 模型名称:填写模型的具体名字,比如PP-TSM
  • Metrics:填写模型测试时所用的指标,使用的数据集为breakfast
  • Value:填写Metrics指标对应的数值,一般保留小数点后两位
  • Flops(M):模型一次前向运算所需的浮点运算量,可以调用PaddleVideo/tools/summary.py脚本计算(不同模型可能需要稍作修改),保留小数点后一位,使用数据输入形状为(1, 2048, 1000)的张量测得
  • Params(M):模型参数量,和Flops一起会被脚本计算出来,保留小数点后一位
  • test time(ms) bs=1:python脚本开batchsize=1测试时,一个样本所需的耗时,保留小数点后两位。测试使用的数据集为breakfast
  • test time(ms) bs=2:python脚本开batchsize=2测试时,一个样本所需的耗时,保留小数点后两位。时序动作分割模型一般是全卷积网络,所以训练、测试和推理的batch_size都是1。测试使用的数据集为breakfast
  • inference time(ms) bs=1:推理模型用GPU(默认V100)开batchsize=1测试时,一个样本所需的耗时,保留小数点后两位。推理使用的数据集为breakfast
  • inference time(ms) bs=2:推理模型用GPU(默认V100)开batchsize=1测试时,一个样本所需的耗时,保留小数点后两位。时序动作分割模型一般是全卷积网络,所以训练、测试和推理的batch_size都是1。推理使用的数据集为breakfast