classicvalues / yolov5_gpu_optimization

This repository provides YOLOV5 GPU optimization sample

YOLOV5 TensorRT inference sample

Export the ultralytics YOLOV5 model to ONNX with TRT BatchNMS plugin

You could start from nvcr.io/nvidia/pytorch:22.03-py3 container for export.

git clone https://github.com/ultralytics/yolov5.git
# clone yolov5_trt_infer repo and copy files into yolov5 folder
git clone https://github.com/NVIDIA-AI-IOT/yolov5_gpu_optimization.git
cp yolov5_gpu_optimization/* yolov5/
cd yolov5
git checkout a80dd66efe0bc7fe3772f259260d5b7278aab42f
git am 0001-Enable-onnx-export-with-batchNMS-plugin.patch
pip install -r requirement_export.txt
apt update && apt install -y libgl1-mesa-glx 
python export.py --weights yolov5s.pt --include onnx --simplify --dynamic

Run with TensorRT:

For the following section, you could start from nvcr.io/nvidia/tensorrt:22.05-py3 and prepare env by:

cd yolov5
pip install -r requirement_infer.txt
apt update && apt install -y libgl1-mesa-glx 

Run inference

python yolov5_trt_inference.py --input_images_folder=</path/to/coco/images/val2017/> --output_images_folder=./coco_output --onnx=</path/to/yolov5s.onnx>

Run evaluation on COCO17 validation dataset

Square inference evaluation:

The image will be resized to 3xINPUT_SIZExINPUT_SIZE while be kept aspect ratio.

python yolov5_trt_inference.py --input_images_folder=</path/to/coco/images/val2017/> --output_images_folder=<path/to/coco_output_dir> --onnx=</path/to/yolov5s.onnx> --coco_anno=</path/to/coco/annotations/instances_val2017.json> 

Rectangular inference evaluation:

This is not real rectangular inference as in pytorch due to some dynmaic shape limitation in TensorRT. It is same to setting pad=0, rect=False, imgsz=input_size + stride in ultralytics YOLOV5.

# Default FP16 precision
python yolov5_trt_inference.py --input_images_folder=</path/to/coco/images/val2017/> --output_images_folder=<path/to/coco_output_dir> --onnx=</path/to/yolov5s.onnx> --coco_anno=</path/to/coco/annotations/instances_val2017.json> --rect

To run int8 inference or evaluation, you need to install TensorRT 8.4 in the container.

• download the TensorRT 8.4 tar ball from devzone
  • Download the tar package. (e.g: TensorRT 8.4 GA for Linux x86_64 and CUDA 11.0, 11.1, 11.2, 11.3, 11.4, 11.5, 11.6 and 11.7 TAR Package)
• extract the files and install new tensorrt python wheel:

tar zxvf TensorRT-8.4.1.5.Linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz
cd TensorRT-8.4.1.5/python
pip install tensorrt-8.4.1.5-cp38-none-linux_x86_64.whl

• set the LD_LIBRARY_PATH:

export LD_LIBRARY_PATH=</path/to/TensorRT-8.4 lib>:$LD_LIBRARY_PATH

Following command is to run evaluation in int8 precision:

# INT8 precision
python yolov5_trt_inference.py --input_images_folder=</path/to/coco/images/val2017/> --output_images_folder=<path/to/coco_output_dir> --onnx=</path/to/yolov5s.onnx> --coco_anno=</path/to/coco/annotations/instances_val2017.json> --rect --data_type=int8 --save_engine=./yolov5s_int8_maxbs16.engine  --calib_img_dir=</path/to/coco/images/val2017/> --calib_cache=yolov5s_bs16_n10.cache --n_batches=10 --batch_size=16 

Appendix

Performance&&mAP summary

Here is the performance and mAP summary. Tested on V100 16G with TensorRT 8.2.5 in rectangular inference mode.

Model	Input Size	precision	FPS bs=32	FPS bs= 1	[email protected]
yolov5n	640	FP16	1295	448	45.9%
yolov5s	640	FP16	917	378	57.1%
yolov5m	640	FP16	614	282	64%
yolov5l	640	FP16	416	202	67.3%
yolov5x	640	FP16	231	135	68.5%
yolov5n6	1280	FP16	341	160	54.2%
yolov5s6	1280	FP16	261	139	63.2%
yolov5m6	1280	FP16	155	99	68.8%
yolov5l6	1280	FP16	106	68	70.7%
yolov5x6	1280	FP16	60	45	71.9%

nbit-NMS

Users can also enable nbit-NMS by changing the scoreBits in export.py.

# Default to be 16-bit
nms_attrs["scoreBits"] = 16
# Can be changed to smaller one to boost NMS operation:
# e.g. nms_attrs["scoreBits"] = 8

performance gain:

Classes number	Device	Anchors number	Score bits	Batch size	NMS Execution time (ms)
80	A30	25200	16	32	12.1
80	A30	25200	8	32	10.0
4	Jetson NX	10560	16	4	1.38
4	Jetson NX	10560	8	4	1.08

Note: small score bits may slightly decrease the final mAP.

Known issue:

• int8 0% mAP in TensorRT 8.2.5: Install TensorRT 8.4 to avoid the issue.
• Dynamic shape inference: The dynamic shape inference will run into CUDA error with some specific shapes.
• TensorRT warning at the end of the execution: The warning won't block the inference or evaluation. You can just ignore it.