PyTorch-Onnx-Tensorrt

Test yolov3-trt on jetson nano

Requirements

1. Python 3
2. OpenCV
3. PyTorch
4. Onnx 1.4.1
5. Tensorrt
6. Mkdir yolov3_onnx，yolov3-416.trt放在yolov3_onnx测试

Downloading YoloV3 Configs and Weights

mkdir cfg
cd cfg 
wget https://raw.githubusercontent.com/pjreddie/darknet/f86901f6177dfc6116360a13cc06ab680e0c86b0/cfg/yolov3.cfg

mkdir weights
cd weights
wget https://pjreddie.com/media/files/yolov3.weights

Editing Config File

Inorder to Run the model in Pytorch or creating Onnx / Tensorrt File for different Input image Sizes ( 416, 608, 960 etc), you need to edit the Batch Size and Input image size in the config file - net info section.

batch=1
width=416
height=416

Generating the Onnx File

python3 create_onnx.py --reso 416

Generating the Tensorrt File

python3 create_trt_engine.py --onnx_file yolov3.onnx 

Creating the Tensorrt engine takes some time. So have some patience.

Test the YOLOv3 TensorRT engine with the "dog.jpg" image.(jetson nano run 2.55 FPS)

python3 trt_yolov3.py --model yolov3-416
                        --image --filename ${HOME}/Pictures/dog.jpg

Run the "trt_yolov3.py" demo program.(jetson nano run 3.18 FPS)

python3 trt_yolov3.py --usb --vid 0 --width 1280 --height 720   (or 640x480)

evaluating mAP of the optimized YOLOv3 engine (jetson nano coco map@IOU=0.5 → 61.6%)

python3 eval_yolov3.py --model yolov3-416 

相关链接

• https://github.com/jkjung-avt/tensorrt_demos#yolov3
• https://github.com/Rapternmn/PyTorch-Onnx-Tensorrt