Quickstart
Requirement
This project comprises a set of commands to be run at a shell command prompt. Examples used here are for a bash shell in an Ubuntu GNU/Linux environment.
Python 3.9, see the full list of dependencies in environment.yml
miniconda (highly recommended)
nvidia GPU (highly recommended)
Note
The system can be used on your workstation or cluster.
Installation
To execute scripts in this project, first create and activate your python environment with the following commands:
$ conda env create -f environment.yml
$ conda activate geo_deep_env
Note
Tested on Ubuntu 20.04 and Windows 10 using miniconda.
Running GDL
This is an example of how to run GDL for a Segmentation task on the massachusetts buildings dataset (link). GDL is using Hydra library for more information see the Configuration section or go visit their documentation.
# Clone this github repo
(geo_deep_env) $ git clone https://github.com/NRCan/geo-deep-learning.git
(geo_deep_env) $ cd geo-deep-learning
# By default the task is set to segmentation
# Creating the patches from the raw data
(geo_deep_env) $ python GDL.py mode=tiling
# Training the neural network
(geo_deep_env) $ python GDL.py mode=train
# Inference on the data
(geo_deep_env) $ python GDL.py mode=inference
This example runs with a default configuration
./config/gdl_config_template.yaml.
For further examples on configuration options or how to change the configuration
go see the Configuration documentation.