Performance drop when using multiple sources

[flmello]

Performance drop when using multiple sources

[flmello]

There was a set_property overriding network-mode back to 0, instead of 2. Now, I get 4 [email protected]. 5 [email protected].

As @marcoslucianops said before, and just to summarize, the solution is:

1. Set board to MAXN: sudo nvpmodel -m 0
2. At the script: sink.set_property("sync", 0)
3. At the config file: network-mode=2, #network-mode: 0=FP32, 1=INT8, 2=FP16 mode

Number 3) is the most relevant between those three actions.

[marcoslucianops]

Try to set the board to MAXN model

sudo nvpmodel -m 0

[marcoslucianops]

#450 (comment)

Remember to set it on the config_infer_primary_yoloV5.txt file

[flmello]

My board was already at MAXN model and my sink's sync property was also set to 0.

I changed FP32 to FP16 by setting network-mode=2. However I see just a marginal change, it was running at 15.99 fps and now it is at 16.18 fps. I had understood from #450 (comment) that it should have bustered the processing, but it did not. However, the model created was model_b4_gpu0_fp32.engine and I believe it should be model_b4_gpu0_fp16.engine. I checked my script was changing network-mode to 0, but there is no set_property like that.

[marcoslucianops]

The network-mode should be set on the config_infer_primary file (according to your yolo model).

[flmello]

The network-mode should be set on the config_infer_primary file (according to your yolo model).

Yes, I am setting it to 2. Are there more properties to be set? I am using the config_infer_primary_yoloV5.txt, just like this:

[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
#0=RGB, 1=BGR, 2=Gray
model-color-format=0

onnx-file=/home/ubuntu/EdgeServer/model/yolov5s.onnx
labelfile-path=/home/ubuntu/EdgeServer/model/labels_yolov5s.txt
num-detected-classes=80

batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2

interval=0
gie-unique-id=1
process-mode=1
network-type=0
cluster-mode=2
maintain-aspect-ratio=1
symmetric-padding=1
parse-bbox-func-name=NvDsInferParseYolo
#parse-bbox-func-name=NvDsInferParseYoloCuda
custom-lib-path=/home/ubuntu/EdgeServer/nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
engine-create-func-name=NvDsInferYoloCudaEngineGet

[class-attrs-all]
nms-iou-threshold=0.45
pre-cluster-threshold=0.25
topk=300