emvollmer / tufsegm_api

Deepaas API for thermal urban feature segmentation model repo

tufsegm_api

Deepaas API for thermal urban feature segmentation (TUFSeg). This code makes use of the TUFSeg model repo.

To facilitate setting up, the bash script setting_up_deployment.sh can be run to install everything automatically:

wget https://raw.githubusercontent.com/emvollmer/tufsegm_api/master/setting_up_deployment.sh
source setting_up_deployment.sh

This takes care of all required installations and finishes by running deepaas.

The associated Docker container for this module can be found in https://github.com/emvollmer/DEEP-OC-tufsegm_api.

Project structure

├── Jenkinsfile             <- Describes basic Jenkins CI/CD pipeline
├── LICENSE                 <- License file
├── README.md               <- The top-level README for developers using this project.
├── VERSION                 <- Version file indicating the version of the model
│
├── tufsegm_api
│   ├── README.md           <- Instructions on model integration with DEEPaaS.
│   ├── __init__.py         <- Makes tufsegm_api a Python module, contains main functions
│   ├── utils.py            <- Helper functions
│   └── config.py           <- Module to define CONSTANTS used across the AI-model python package
│
├── api                     <- API subpackage for the integration with DEEP API
│   ├── __init__.py         <- Makes api a Python module, includes API interface methods
│   ├── config.py           <- API module for loading configuration from environment
│   ├── responses.py        <- API module with parsers for method responses
│   ├── schemas.py          <- API module with definition of method arguments
│   └── utils.py            <- API module with utility functions
│
├── data                    <- Data subpackage for the integration with DEEP API
│
├── docs                   <- A default Sphinx project; see sphinx-doc.org for details
│
├── models                 <- Folder to which models are automatically stored 
|                             (if not on nextcloud)
│
├── notebooks              <- Jupyter notebooks
│
├── references             <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports                <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures            <- Generated graphics and figures to be used in reporting
│
├── requirements-dev.txt    <- Requirements file to install development tools
├── requirements-test.txt   <- Requirements file to install testing tools
├── requirements.txt        <- Requirements file to run the API and models
│
├── pyproject.toml         <- Makes project pip installable (pip install -e .)
│
├── tests                   <- Scripts to perform code testing
│   ├── configurations      <- Folder to store the configuration files for DEEPaaS server
│   ├── conftest.py         <- Pytest configuration file (Not to be modified in principle)
│   ├── data                <- Folder to store the data for testing
│   ├── models              <- Folder to store the models for testing
│   ├── test_deepaas.py     <- Test file for DEEPaaS API server requirements (Start, etc.)
│   ├── test_metadata       <- Tests folder for model metadata requirements
│   ├── test_predictions    <- Tests folder for model predictions requirements
│   └── test_training       <- Tests folder for model training requirements
│
└── tox.ini                <- tox file with settings for running tox; see tox.testrun.org

Model Integration with DEEPaaS

The folder tufsegm_api is designed to contain code with which to accesss the model code from the submodule TUFSeg. The folder tufsegm_api contains an __init__.py file conserving the already defined methods.

Methods in tufsegm_api are used by the subpackage api to define the API interface. See the project structure section for more information about the api folder. API and CLI arguments and responses are adapted in api.schemas and api.responses.

Testing

Testing process is automated by tox library. Tests are implemented following pytest framework. Fixtures and parametrization are placed inside conftest.py files while assertion tests are located on test_*.py files.

- tests/data: Contains testing data (sample images).
- tests/models: Contains a dummy model.
- tests/test_metadata: Tests for get_metadata functionality.
- tests/test_predictions: Tests for inference functionality.
- tests/test_training: Tests for training functionality.

The dummy model is of the smallest possible size, but still comparatively large. For this reason, the tests take a bit of time to run.

Running the tests with tox:

$ pip install -r requirements-dev.txt
$ tox

Running the tests with pytest:

$ pip install -r requirements-test.txt
$ python -m pytest --numprocesses=auto --dist=loadscope tests

or for more detailled information

$ python -m pytest tests