模型推理 Benchmark

PaddleX 支持统计模型推理耗时，需通过环境变量进行设置，具体如下：

PADDLE_PDX_INFER_BENCHMARK：设置为 True 时则开启 Benchmark，默认为 False；
PADDLE_PDX_INFER_BENCHMARK_WARMUP：设置 warm up，在开始测试前，使用随机数据循环迭代 n 次，默认为 0；
PADDLE_PDX_INFER_BENCHMARK_DATA_SIZE：设置随机数据的尺寸，默认为 224；
PADDLE_PDX_INFER_BENCHMARK_ITER：使用随机数据进行 Benchmark 测试的循环次数，仅当输入数据为 None 时，将使用随机数据进行测试；
PADDLE_PDX_INFER_BENCHMARK_OUTPUT：用于设置保存的目录，如 ./benchmark，默认为 None，表示不保存 Benchmark 指标；

使用示例如下：

PADDLE_PDX_INFER_BENCHMARK=True \
PADDLE_PDX_INFER_BENCHMARK_WARMUP=5 \
PADDLE_PDX_INFER_BENCHMARK_DATA_SIZE=320 \
PADDLE_PDX_INFER_BENCHMARK_ITER=10 \
PADDLE_PDX_INFER_BENCHMARK_OUTPUT=./benchmark \
python main.py \
    -c ./paddlex/configs/object_detection/PicoDet-XS.yaml \
    -o Global.mode=predict \
    -o Predict.model_dir=None \
    -o Predict.batch_size=2 \
    -o Predict.input=None

在开启 Benchmark 后，将自动打印 benchmark 指标：

+----------------+-----------------+-----------------+------------------------+
|   Component    | Total Time (ms) | Number of Calls | Avg Time Per Call (ms) |
+----------------+-----------------+-----------------+------------------------+
|    ReadCmp     |   100.20136833  |        10       |      10.02013683       |
|     Resize     |   17.05980301   |        20       |       0.85299015       |
|   Normalize    |   45.44949532   |        20       |       2.27247477       |
|   ToCHWImage   |    0.03671646   |        20       |       0.00183582       |
|    Copy2GPU    |   12.28785515   |        10       |       1.22878551       |
|     Infer      |   76.59482956   |        10       |       7.65948296       |
|    Copy2CPU    |    0.39863586   |        10       |       0.03986359       |
| DetPostProcess |    0.43916702   |        20       |       0.02195835       |
+----------------+-----------------+-----------------+------------------------+
+-------------+-----------------+---------------------+----------------------------+
|    Stage    | Total Time (ms) | Number of Instances | Avg Time Per Instance (ms) |
+-------------+-----------------+---------------------+----------------------------+
|  PreProcess |   162.74738312  |          20         |         8.13736916         |
|  Inference  |   89.28132057   |          20         |         4.46406603         |
| PostProcess |    0.43916702   |          20         |         0.02195835         |
|   End2End   |    0.27992606   |          20         |         0.01399630         |
|    WarmUp   |    5.37562728   |          5          |         1.07512546         |
+-------------+-----------------+---------------------+----------------------------+

在 Benchmark 结果中，会统计该模型全部组件（Component）的总耗时（Total Time，单位为“毫秒”）、调用次数（Number of Calls）、调用平均执行耗时（Avg Time Per Call，单位“毫秒”），以及按预热（WarmUp）、预处理（PreProcess）、模型推理（Inference）、后处理（PostProcess）和端到端（End2End）进行划分的耗时统计，包括每个阶段的总耗时（Total Time，单位为“毫秒”）、样本数（Number of Instances）和单样本平均执行耗时（Avg Time Per Instance，单位“毫秒”），同时，上述指标会保存到到本地： ./benchmark/detail.csv 和 ./benchmark/summary.csv：

Component,Total Time (ms),Number of Calls,Avg Time Per Call (ms)
ReadCmp,0.10199093818664551,10,0.01019909381866455
Resize,0.011309385299682617,20,0.0005654692649841309
Normalize,0.035140275955200195,20,0.0017570137977600097
ToCHWImage,4.744529724121094e-05,20,2.3722648620605467e-06
Copy2GPU,0.00861215591430664,10,0.000861215591430664
Infer,0.820899248123169,10,0.08208992481231689
Copy2CPU,0.006002187728881836,10,0.0006002187728881836
DetPostProcess,0.0004436969757080078,20,2.218484878540039e-05

Stage,Total Time (ms),Number of Instance,Avg Time Per Instance (ms)
PreProcess,0.14848804473876953,20,0.007424402236938477
Inference,0.8355135917663574,20,0.04177567958831787
PostProcess,0.0004436969757080078,20,2.218484878540039e-05
End2End,1.0054960250854492,20,0.05027480125427246
WarmUp,8.869974851608276,5,1.7739949703216553

benchmark.md 4.5 KB 文件历史 原始文件

模型推理 Benchmark

benchmark.md 4.5 KB

文件历史原始文件