README.md

Pruning

How to Run

1. train base model with imagenet100 dataset

   • Train Base Model (resnet18)

2. Pruning and export pruned onnx

   python onnx_export_qat.py
   

   • fine tuning

3. generate tensorrt model

   python onnx2trt.py
   

• fp16 pruned flops 80% (fine tuning)
  Gpu Mem: 130M
  [TRT_E] Test Top-1 Accuracy: 82.76%
  [TRT_E] Test Top-5 Accuracy: 96.42%
  [TRT_E] 10000 iterations time: 6.3565 [sec]
  [TRT_E] Average FPS: 1573.20 [fps]
  [TRT_E] Average inference time: 0.64 [msec]

Reference

• TensorRT-Model-Optimizer