YOLOV5 inference solution in DeepStream and TensorRT

This repo provides sample codes to deploy YOLOV5 models in DeepStream or stand-alone TensorRT sample on Nvidia devices.

• DeepStream sample
• TensorRT sample
• Appendix

DeepStream sample

In this section, we will walk through the steps to run YOLOV5 model using DeepStream with CPU NMS.

Export the ultralytics YOLOV5 model to ONNX with TRT decode 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 the patch into yolov5 folder
git clone https://github.com/NVIDIA-AI-IOT/yolov5_gpu_optimization.git
cp yolov5_gpu_optimization/0001-Enable-onnx-export-with-decode-plugin.patch yolov5_gpu_optimization/requirement_export.txt yolov5/
cd yolov5
git checkout a80dd66efe0bc7fe3772f259260d5b7278aab42f
git am 0001-Enable-onnx-export-with-decode-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

Prepare the library for DeepStream inference.

You could start from nvcr.io/nvidia/deepstream:6.1.1-devel container for inference.

Then go to the deepstream sample directory.

cd deepstream-sample

Compile the plugin and deepstream parser:

• On x86:

  nvcc -Xcompiler -fPIC -shared -o yolov5_decode.so ./yoloForward_nc.cu ./yoloPlugins.cpp ./nvdsparsebbox_Yolo.cpp -isystem /usr/include/x86_64-linux-gnu/ -L /usr/lib/x86_64-linux-gnu/ -I /opt/nvidia/deepstream/deepstream/sources/includes -lnvinfer 
  

• On Jetson device:

  nvcc -Xcompiler -fPIC -shared -o yolov5_decode.so ./yoloForward_nc.cu ./yoloPlugins.cpp ./nvdsparsebbox_Yolo.cpp -isystem /usr/include/aarch64-linux-gnu/ -L /usr/lib/aarch64-linux-gnu/ -I /opt/nvidia/deepstream/deepstream/sources/includes -lnvinfer 
  

Run inference

You could place the exported onnx models to deepstream-sample

cp yolov5/yolov5s.onnx yolov5_gpu_optimization/deepstream-sample/

Then you could run the model pre-defined configs.

• Run inference with saving inferened video:

  deepstream-app -c config/deepstream_app_config_save_video.txt 
  

• Run inference without display

  deepstream-app -c config/deepstream_app_config.txt 
  

• Run inference with 8 streams and batch_size=8 and without display

  deepstream-app -c config/deepstream_app_config_8s.txt 
  

Performance summary:

The performance test is conducted on T4 with nvcr.io/nvidia/deepstream:6.1.1-devel

Model	Input Size	Device	precision	1 stream bs=1	4 streams bs=4	8 streams bs=8
yolov5n	3x640x640	T4	FP16	640	980	988
yolov5m	3x640x640	T4	FP16	220	270	277

TensorRT sample

In this section, we will walk through the steps to run YOLOV5 model using GPU NMS with stand-alone inference script.

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 -r yolov5_gpu_optimization/0001-Enable-onnx-export-with-batchNMS-plugin.patch yolov5_gpu_optimization/requirement_export.txt 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 tensorrt-sample
pip install -r requirement_infer.txt
apt update && apt install -y libgl1-mesa-glx 

Build plugin library by following the previous steps.

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. 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

Eavaluation in INT8 mode

To run int8 inference or evaluation, you need to install TensorRT above 8.4. You could start from nvcr.io/nvidia/tensorrt:22.07-py3

Following command is to run evaluation in int8 precision (and calibration cache will be saved into the path specify by --calib_cache):

# 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 

Notes: The calibration algorithm for YOLOV5 is IInt8MinMaxCalibrator instead of IInt8EntropyCalibrator2. So if you want to play with trtexec with the saved calibration cache, you have to change the first line of cache from MinMaxCalibration to EntropyCalibration2.

Misc for TensorRT sample

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	Xavier NX	10560	16	4	1.38
4	Xavier NX	10560	8	4	1.08

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

DeepStream deployment:

Users can intergrate the YOLOV5 with BatchedNMS plugin into DeepStream following deepstream_tao_apps

Appendix:

YOLOV5 with different activation:

We conducted experiments with different activations for pursing better trade-off between mAP and performance on TensorRT.

You can change the activation of YOLOV5 model in yolov5/models/common.py:

class Conv(nn.Module):
    # Standard convolution
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super().__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        # self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
        self.act = nn.ReLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def forward_fuse(self, x):
        return self.act(self.conv(x))

YOLOV5s experiments results so far:

Activation type	[email protected]	V100 --best FPS (bs = 32)	A10 --best FPS (bs=32)
swish (baseline)	56.7%	1047	965
ReLU	54.8% (scratch) 55.7% (swish pretrained)	1177	1065
GELU	56.6%	1004	916
Leaky ReLU	55.0%	1172	892
PReLU	54.8%	1123	932

Known issue:

• int8 0% mAP in TensorRT 8.2.5: Install TensorRT above 8.4 to avoid the issue.
• TensorRT warning at the end of the execution of stand-alone tensorrt inference script: The warning won't block the inference or evaluation. You can just ignore it.