Python functionality for the bioimage model zoo
Project description
core-bioimage-io-python
Python specific core utilities for running models in the BioImage Model Zoo
Installation
Via Conda
The bioimageio.core
package supports various back-ends for running BioimageIO networks:
-
Pytorch/Torchscript:
# cpu installation (if you don't have an nvidia graphics card) conda install -c pytorch -c conda-forge bioimageio.core pytorch torchvision cpuonly # gpu installation conda install -c pytorch -c conda-forge bioimageio.core pytorch torchvision cudatoolkit
-
Tensorflow
# currently only cpu version supported conda install -c conda-forge bioimageio.core tensorflow
-
ONNXRuntime
# currently only cpu version supported conda install -c conda-forge bioimageio.core onnxruntime
Via pip
The package is also available via pip:
pip install bioimageio.core
Set up Development Environment
To set up a development conda environment run the following commands:
conda env create -f dev/environment-base.yaml
conda activate bio-core-dev
pip install -e . --no-deps
There are different environment files that only install tensorflow or pytorch as dependencies available.
Command Line
bioimageio.core
installs a command line interface for testing models and other functionality. You can list all the available commands via:
bioimageio
Check that a model adheres to the model spec:
bioimageio validate <MODEL>
Test a model (including prediction for the test input):
bioimageio test-model -m <MODEL>
Run prediction for an image stored on disc:
bioimageio predict-image -m <MODEL> -i <INPUT> -o <OUTPUT>
From python
bioimageio.core
can be used as a python library. See the notebook example/bioimageio-core-usage.ipynb for usage examples.
Model Specification
The model specification and its validation tools can be found at https://github.com/bioimage-io/spec-bioimage-io.
Project details
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