ABFS - Automatic Building Footprint Segmentation
Videos: Project Overview, Liquid Cooling Upgrade
Article: Data Science from Concept to Production: A Case Study of ABFS
Installation
Installation Instructions
For this project, we'll use python 3.6.8. Go ahead and install `pyenv` if you don't already have it. ``` # * Install pyenv: https://github.com/pyenv/pyenv-installer # * Add init commands to bash profile (bashrc, etc.) # * Source shell before continuing # Install proper version pyenv install 3.6.8 ``` Within the project directory, go ahead and setup a new virtual environment. ``` pyenv virtualenv 3.6.8 abfs-env pyenv activate abfs-env ``` For `GDAL`, you'll need to install it separately through Homebrew/APT before installing the remaining requirements. ``` # Numpy must be installed BEFORE gdal (https://gis.stackexchange.com/a/274328) pip install numpy # On macOS: brew install gdal # On Debian/Ubuntu: sudo apt-get install libgdal-dev pip install \ --global-option=build_ext \ --global-option="-I/usr/include/gdal" GDAL==`gdal-config --version` ``` Now, go ahead and install the remaining dependencies. ``` pip install -r requirements.txt ``` For this program, you'll also need to decide whether to use a GPU-based backend. ``` # With CUDA-based GPU: pip install tensorflow-gpu # Without GPU: pip install tensorflow ``` With these packages now available, install the command line utility. ``` python setup.py install ``` Verify it is installed properly by running the CLI. ``` abfs ``` If this returns an error about the command not being found, you may have to prepend the current python binary. ``` python -m abfsUsage
The entire program is operated from the command line utility. Here are some examples.
❯ abfs -h
usage: abfs [-h] {serve,train,tune,export,evaluate} ...
positional arguments:
{serve,train,tune,export,evaluate}
serve Serve model as API
train Train a neural network
tune Tune the tolerance parameter on the validation set
export Export keras model
evaluate Evaluate keras model based on test data
optional arguments:
-h, --help show this help message and exitTrain
``` ❯ abfs train -h Using TensorFlow backend. usage: abfs train [-h] [-lr LEARNING_RATE] [-s SIZE] [-e EPOCHS] [-b BATCH_SIZE] [-mb MAX_BATCHES] [-gpus GPU_COUNT] optional arguments: -h, --help show this help message and exit -lr LEARNING_RATE, --learning-rate LEARNING_RATE -s SIZE, --size SIZE Size of image -e EPOCHS, --epochs EPOCHS -b BATCH_SIZE, --batch-size BATCH_SIZE Number of examples per batch -mb MAX_BATCHES, --max-batches MAX_BATCHES Maximum batches per epoch -gpus GPU_COUNT, --gpu-count GPU_COUNT ❯ abfs train -lr 0.02 --batch-size 8 --epochs 150 -gpus 2 ... ```Tune (Includes Graph Output)
``` ❯ abfs tune -h usage: abfs tune [-h] [-w WEIGHTS_PATH] [-e MAX_EXAMPLES] [-s SIZE] [-gpus GPU_COUNT] optional arguments: -h, --help show this help message and exit -w WEIGHTS_PATH, --weights-path WEIGHTS_PATH Path to hdf5 model weights -e MAX_EXAMPLES, --max-examples MAX_EXAMPLES Max number of examples to validate against -s SIZE, --size SIZE Size of image -gpus GPU_COUNT, --gpu-count GPU_COUNT ❯ abfs tune -w checkpoints/Export
``` ❯ abfs export -h Using TensorFlow backend. usage: abfs export [-h] [-s SIZE] [-o OUTPUT] optional arguments: -h, --help show this help message and exit -s SIZE, --size SIZE Size of image -o OUTPUT, --output OUTPUT ❯ abfs export -s 512 -o architecture Using TensorFlow backend. Save to "models/architecture.json" ```Evaluate
``` ❯ abfs evaluate -h Using TensorFlow backend. usage: abfs evaluate [-h] [-w WEIGHTS_PATH] [-b BATCH_SIZE] [-s SIZE] optional arguments: -h, --help show this help message and exit -w WEIGHTS_PATH, --weights-path WEIGHTS_PATH Path to hdf5 model weights -b BATCH_SIZE, --batch-size BATCH_SIZE Number of examples per batch -s SIZE, --size SIZE Size of image ❯ abfs evaluate -w checkpoints/unet-d82jd2-0020-0.19.hdf5 ... Loading weights from "checkpoints/unet-d82jd2-0020-0.19.hdf5" Results: [('loss', 0.1882165691484708), ... ```Serve
``` ❯ abfs serve -h Using TensorFlow backend. usage: abfs serve [-h] [-w WEIGHTS_PATH] [-m MODEL_PATH] [-a ADDRESS] [-p PORT] optional arguments: -h, --help show this help message and exit -w WEIGHTS_PATH, --weights-path WEIGHTS_PATH Path to hdf5 model weights -m MODEL_PATH, --model-path MODEL_PATH Path to keras model JSON -a ADDRESS, --address ADDRESS Address to bind server to -p PORT, --port PORT Port for server to listen on ❯ abfs serve \ --weights-path checkpoints/unet-d82jd2-0020-0.19.hdf5 \ --model-path models/unet-d82jd2.json \ --mapbox-api-keyAuthor
R. Christian Di Lorenzo. MIT License. (About Me)