Davide Sapienza
101 lines
4.0 KiB
Markdown
101 lines
4.0 KiB
Markdown
# tkDNN export weights
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## Index
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- [How to export weights](#how-to-export-weights)
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- [1)Export weights from darknet](#1export-weights-from-darknet)
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- [2)Export weights for DLA34 and ResNet101](#2export-weights-for-dla34-and-resnet101)
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- [3)Export weights for CenterNet](#3export-weights-for-centernet)
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- [4)Export weights for MobileNetSSD](#4export-weights-for-mobilenetssd)
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- [5)Export weights for CenterTrack](#5export-weights-for-centertrack)
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- [Darknet Parser](#darknet-parser)
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## How to export weights
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Weights are essential for any network to run inference. For each test a folder organized as follow is needed (in the build folder):
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```
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test_nn
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|---- layers/ (folder containing a binary file for each layer with the corresponding wieghts and bias)
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|---- debug/ (folder containing a binary file for each layer with the corresponding outputs)
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```
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Therefore, once the weights have been exported, the folders layers and debug should be placed in the corresponding test.
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### 1)Export weights from darknet
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To export weights for NNs that are defined in darknet framework, use [this](https://git.hipert.unimore.it/fgatti/darknet.git) fork of darknet and follow these steps to obtain a correct debug and layers folder, ready for tkDNN.
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```
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git clone https://git.hipert.unimore.it/fgatti/darknet.git
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cd darknet
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make
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mkdir layers debug
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./darknet export <path-to-cfg-file> <path-to-weights> layers
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```
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N.b. Use compilation with CPU (leave GPU=0 in Makefile) if you also want debug.
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### 2)Export weights for DLA34 and ResNet101
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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.
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Create Anaconda environment and activate it:
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```
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conda env create -f file_name.yml
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source activate env_name
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python <script name>
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```
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### 3)Export weights for CenterNet
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To get the weights needed to run Centernet tests use [this](https://github.com/sapienzadavide/CenterNet.git) fork of the original Centernet.
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```
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git clone https://github.com/sapienzadavide/CenterNet.git
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```
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* follow the instruction in the README.md and INSTALL.md
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```
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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
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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
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```
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### 4)Export weights for MobileNetSSD
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To get the weights needed to run Mobilenet tests use [this](https://github.com/mive93/pytorch-ssd) fork of a Pytorch implementation of SSD network.
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```
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git clone https://github.com/mive93/pytorch-ssd
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cd pytorch-ssd
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conda env create -f env_mobv2ssd.yml
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python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
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```
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### 5)Export weights for CenterTrack
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To get the weights needed to run CenterTrack tests use [this](https://github.com/sapienzadavide/CenterTrack.git) fork of the original CenterTrack.
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```
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git clone https://github.com/sapienzadavide/CenterTrack.git
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```
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* follow the instruction in the README.md and INSTALL.md
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```
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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
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```
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## Darknet Parser
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tkDNN implement and easy parser for darknet cfg files, a network can be converted with *tk::dnn::darknetParser*:
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```
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// example of parsing yolo4
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tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names");
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net->print();
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```
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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.
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<details>
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<summary>Supported layers</summary>
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convolutional
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maxpool
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avgpool
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shortcut
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upsample
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route
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reorg
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region
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yolo
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</details>
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<details>
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<summary>Supported activations</summary>
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relu
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leaky
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mish
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logistic
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</details> |