you need to download rknn-tookit2,and install rknn-tookit2 on your ubuntu(x86-64,amd64)
You need to download model from PySOT Model Zoo.
cd to rootPath(pysot_rknn)
python rknn/siamrpn_alex_dwxcorr.py --config experiments/siamrpn_alex_dwxcorr/config.yaml --snapshot experiments/siamrpn_alex_dwxcorr/model.pth --video demo/bag.avi
The file siamrpn_alex_dwxcorr.py can export rknn model, and test the result on your ubuntu(x86-64,amd64).
Detail about siamrpn_alex_dwxcorr model.
backbone of exemplar.
Target img:
input size: torch.Size([1, 3, 127, 127])
outputsize: torch.Size([1, 256, 6, 6])
backbone of instance.
Original img:
input size: torch.Size([1, 3, 287, 287])
outputsize: torch.Size([1, 256, 26, 26])
rpn Head
rpn head input size: [z_f,x_f]
z_f shape: torch.Size([1, 256, 6, 6])
x_f shape: torch.Size([1, 256, 26, 26])
output size: output[[cls],[loc]]
cls shape: (1, 10, 21, 21)
loc shape: (1, 20, 21, 21)
z_f is the output of backbone of exemplar, x_f is the output of backbone of instance
Install rknn-toolkit-lite2 on your rk device, I run the demo on RK3588.
Copy folders and files on the projict to your rk device, and your need to maintain the structure of the project.
pysot_rknn(root path)
demo/
tools/
rknn/
experiments/
pysot/
python tools/runRKNNLite.py
The mode inference speed on rk3588 is about 10 FPS.