Performance Profiling for TensorRT Library

Test Environment

  • CPU:Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz GPU:Tesla P4
  • TensorRT4.0, CUDA8.0, CUDNNV7
  • Test model ResNet50, MobileNet, ResNet101, Inception V3.

Test Targets

PaddlePaddle, Pytorch, Tensorflow

  • In test, PaddlePaddle adopts subgraph optimization to integrate TensorRT model .
  • Native implementation is used in Pytorch. Model address 1 , address 2 .
  • Test for TensorFlow contains test for native TF and TF—TRT. Test for TF—TRT hasn't reached expectation wihch will be complemented later. Model address .

ResNet50

|batch_size|PaddlePaddle(ms)|Pytorch(ms)|TensorFlow(ms)| |---|---|---|---| |1|4.64117 |16.3|10.878| |5|6.90622| 22.9 |20.62| |10|7.9758 |40.6|34.36|

MobileNet

|batch_size|PaddlePaddle(ms)|Pytorch(ms)|TensorFlow(ms)| |---|---|---|---| |1| 1.7541 | 7.8 |2.72| |5| 3.04666 | 7.8 |3.19| |10|4.19478 | 14.47 |4.25|

ResNet101

|batch_size|PaddlePaddle(ms)|Pytorch(ms)|TensorFlow(ms)| |---|---|---|---| |1|8.95767| 22.48 |18.78| |5|12.9811 | 33.88 |34.84| |10|14.1463| 61.97 |57.94|

Inception v3

|batch_size|PaddlePaddle(ms)|Pytorch(ms)|TensorFlow(ms)| |---|---|---|---| |1|15.1613 | 24.2 |19.1| |5|18.5373 | 34.8 |27.2| |10|19.2781| 54.8 |36.7|