A package for running predictions using fAIr
Project description
fAIr Predictor
Run your fAIr Model Predictions anywhere !
Prerequisites
fAIr Predictor has support for GPU , CPU and tflite based devices
- Install
tensorflow-cpuortflite-runtimeaccording to your requirements
tflite-runtime support is for having very light deployment in order to run inference &
tensorflow-cpu might require installation of efficientnet
Example on Collab
# Install
!pip install fairpredictor
# Import
from predictor import predict
# Parameters for your predictions
bbox=[100.56228021333352,13.685230854641182,100.56383321235313,13.685961853747969]
model_path='checkpoint.h5'
zoom_level=20
tms_url='https://tiles.openaerialmap.org/6501a65c0906de000167e64d/0/6501a65c0906de000167e64e/{z}/{x}/{y}'
# Run your prediction
my_predictions=predict(bbox,model_path,zoom_level,tms_url)
print(my_predictions)
## Visualize your predictions
import geopandas as gpd
import matplotlib.pyplot as plt
gdf = gpd.GeoDataFrame.from_features(my_predictions)
gdf.plot()
plt.show()
Works on CPU ! Can work on serverless functions, No other dependencies to run predictions
Use raster2polygon
There is another postprocessing option that supports distance threshold between polygon for merging them , If it is useful for you install raster2polygon by :
pip install raster2polygon
Load Testing
CAUTION : Always take permission of server admin before you perform load test
In order to perform load testing we use Locust , To enable this hit following command within the root dir
-
Install locust
pip install locust -
Run locust script
locust -f locust.py
Populate your HOST and replace it with BASE URL of the Predictor URL
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