Repository for playing the computer vision apps: People analytics on Raspberry Pi.
- RasberryPi 4 Model B here, RAM: 4 GB and Processor 4-core @ 1.5 GHz
- microSD Card 64 GB
- 5M USB Retractable Clip 120 Degrees WebCam Web Wide-angle Camera Laptop U7 Mini or Raspi Camera
- Ubuntu Desktop 20.10 aarch64 64 bit, install on RasberriPi 4
- PyTorch: torch 1.6.0 aarch64 and torchvision 0.7.0 aarch64
- Python min. ver. 3.6 (3.8 recommended)
- Install packages
$ sudo apt install build-essential make cmake git python3-pip libatlas-base-dev
$ sudo apt install libssl-dev
$ sudo apt install libopenblas-dev libblas-dev m4 python3-yaml
$ sudo apt install libomp-dev- make swap space to 2048 MB
$ free -h
$ sudo swapoff -a
$ sudo dd if=/dev/zero of=/swapfile bs=1M count=2048
$ sudo mkswap /swapfile
$ sudo swapon /swapfile
$ free -h- Install torch 1.6.0
$ pip3 install torch-1.6.0a0+b31f58d-cp38-cp38-linux_aarch64.whlplay-with-torch/
├── config/
│ ├── config.json - holds configuration for training
│ └── parse_config.py - class to handle config file and cli options
│
├── docker/
│ ├── Dockerfile
│ └── requirements.txt
│
├── data/ - default directory for storing input data
│
├── docs/ - for documentation
│ └── play-with-torch.tex
│
├── models/ - models, losses, and metrics
│ ├── model.py
│ ├── metric.py
│ └── loss.py
│
├── samples/
│
├── saved/
│ ├── checkpoints/
│ ├── traced_model/
│ ├── models/ - trained models are saved here
│ └── logs/ - default logdir for tensorboard and logging output
│
├── site
├── templates/ - for serving model on Flask
│ └── index.html
├── tests/
├── utils/ - small utility functions
│ ├── data/
│ └── ...
│
├── inference.py - main script to inference model
├── README.md
├── trace_model.py - main script to convert model
└── train.py - main script to start training
Run inference
$ git clone https://github.com/mheriyanto/play-with-torch.git
$ cd play-with-torch/
$ python3 inference.py video --config config/nanodet-m.yml --model saved/models/nanodet_m.ckpt --path video.mp4Convert model
$ python3 trace_model.py --cfg_path config/nanodet-m.yml --model_path saved/models/nanodet_m.ckpt --input_shape 320,320Training
$ python3 train.py config/nanodet_custom_xml_dataset.yml- Implement Unit-Test: Test-Driven Development (TDD)