Install pipx: To manage poetry, this is recommended as it isolates Poetry in its own virtual environment, preventing conflicts with system-wide Python packages.
# macOS
brew update
brew install pipx
pipx ensurepath
# linux (debian)
sudo apt update
sudo apt install pipx
export PATH="$HOME/.local/bin:$PATH"
# alt, not recommended
python -m pip install --user pipx
python -m pipx ensurepath
Install poetry: Restart or refresh your shell, install Poetry, and set desired Python version (>=3.11).
source ~/.bashrc
pipx install poetry
poetry env use 3.11.1
Activate venv: Set configurations, create a virtual environment, and activate it. Note: use poetry init when starting a new project from scratch, use poetry install to set up dependencies from an existing lock file.
poetry config virtualenvs.create true
poetry config virtualenvs.in-project true
poetry install
source .venv/bin/activate
To install or remove packages, respectively, use poetry add and poetry remove.
poetry add [--dev] <package_name>
poetry remove [--dev] <package_name>
Set environment variables: Set the PYTHONPATH environment variable to make local directories accessible for import in your venv, and define any other vars in a .env file; follow .env.sample
export PYTHONPATH=$(pwd):$PYTHONPATH
set -a; source .env; set +a
Get the ONNX models
In order to run survey_models.py you must download the onnx model files from this GoogleDrive link, then copy them in their respective directories.
cp ~/Downloads/v001_model.onnx src/onnx_models/age_classify_v001/v001_model.onnx
cp ~/Downloads/vit_model.onnx src/onnx_models/vit_age_classifier/vit_model.onnx
cp ~/Downloads/vit_model.onnx src/onnx_models/fareface/fareface_age.onnx
Alternatively, you could run the convert_to_onnx.py files in each directory to regenerate the respective ONNX files.
Your models directory structure should then look like this:
/onnx_models
--/age_classify_v001
----convert_to_onnx.py
----model.py
----v001_model.onnx
--/vit_age_classifier
----convert_to_onnx.py
----model.py
----vit_model.onnx
--surey_models.py
You are good to go!
python src/server/server-onnx.py
... And open Rescue Box: With the server running, register the models in the Rescue Box desktop application (localhost:5000), and use as inputs test images located in src/onnx_models/test_images/.
Results will be displayed as a JSON blob in the desktop app, and be written to a SQLite database at the project's root directory, in a table named model_output. Each run will generate the same created_at timestamp in the table (for each image and each model); to confirm results were successfully written, simply log into the venv interpreter and run the following two commands:
from src.utils.common import read_db
df = read_db("model_output", "select * from MODEL_OUTPUT order by created_at desc")
df.head()