pytorch-onnx-tensorrt-CIFAR10
这个是入门的项目，来自于github：https://github.com/kentaroy47/pytorch-onnx-tensorrt-CIFAR10
这个项目包括：
- 训练模型
- 模型导出到ONNX
- 使用TensorRT推理
Train cifar10 networks and inference with tensorrt.
The Tensor RT inference is about 5 times faster than Pytorch on Jetson Nano :)
Also note that FP16 pytorch models are quite fast too.
how to run tensorRT inference..
- train model
python train.py
- convert the model to onnx
python onnx_export.py --model checkpoint/res18-ckpt.t7
- Tensor RT inference
python3 trt_convert.py --model checkpoint/res18-ckpt.t7
Device: Jetson Nano
Network: Resnet 18
Pytorch FP32: 46ms
Pytorch FP16: 16ms
TensorRT: 4.2ms
running on device cuda:0
input size is.. torch.Size([1, 3, 32, 32])
model set!
pytorch inference took: 0.10171955108642577
pytorch FPS is: 314.5904563893649
FP16 pytorch inference took: 0.02646958112716675
FP16 pytorch FPS is: 1208.9348843966848
exporting model to trt...
conversion completed! took: 16.530654668807983
trt inference took: 0.006784510612487793
trt FPS is: 4716.626124970569