Weights are essential for any network to run inference. For each test a folder organized as follow is needed (in the build folder):
test_nn
|---- layers/ (folder containing a binary file for each layer with the corresponding wieghts and bias)
|---- debug/ (folder containing a binary file for each layer with the corresponding outputs)
Therefore, once the weights have been exported, the folders layers and debug should be placed in the corresponding test.
To export weights for NNs that are defined in darknet framework, use this fork of darknet and follow these steps to obtain a correct debug and layers folder, ready for tkDNN.
git clone https://git.hipert.unimore.it/fgatti/darknet.git
cd darknet
make
mkdir layers debug
./darknet export <path-to-cfg-file> <path-to-weights> layers
N.B. Use compilation with CPU (leave GPU=0 in Makefile) if you also want debug.
To get weights and outputs needed to run the tests dla34 and resnet101 use the Python script and the Anaconda environment included in the repository.
Create Anaconda environment and activate it:
conda env create -f file_name.yml
source activate env_name
python <script name>
To get the weights needed to run Centernet tests use this fork of the original Centernet.
git clone https://github.com/sapienzadavide/CenterNet.git
- follow the instruction in the README.md and INSTALL.md
python demo.py --input_res 512 --arch resdcn_101 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_resdcn101.pth --exp_wo --exp_wo_dim 512
python demo.py --input_res 512 --arch dla_34 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_dla_2x.pth --exp_wo --exp_wo_dim 512
To get the weights needed to run Mobilenet tests use this fork of a Pytorch implementation of SSD network.
git clone https://github.com/mive93/pytorch-ssd
cd pytorch-ssd
conda env create -f env_mobv2ssd.yml
python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
To get the weights needed to run CenterTrack tests use this fork of the original CenterTrack.
git clone https://github.com/sapienzadavide/CenterTrack.git
- follow the instruction in the README.md and INSTALL.md
python demo.py tracking,ddd --load_model ../models/nuScenes_3Dtracking.pth --dataset nuscenes --pre_hm --track_thresh 0.1 --demo /path/to/image/or/folder/or/video/or/webcam --test_focal_length 633 --exp_wo --exp_wo_dim 512 --input_h 512 --input_w 512
To get the weights needed to run Shelfnet tests use this fork of a Pytorch implementation of Shelfnet network.
git clone https://git.hipert.unimore.it/mverucchi/shelfnet
cd shelfnet
cd ShelfNet18_realtime
conda env create --file shelfnet_env.yml
conda activate shelfnet
mkdir layer debug
python export.py
To get the weights needed to run Shelfnet tests use this fork of a Pytorch implementation of monodepth2 network.
git clone https://github.com/perseusdg/monodepth2
cd monodepth2
mkdir models # Download the official weights and put depth.pth and encorder.pth inside this new folder
conda env create --file monodepth.yaml
conda activate monodepth2
python exporter.py # you will find the weights inside the tkDNN_bin folder
tkDNN implement and easy parser for darknet cfg files, a network can be converted with tk::dnn::darknetParser:
// example of parsing yolo4
tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names");
net->print();
All models from darknet are now parsed directly from cfg, you still need to export the weights with the described tools in the previous section.