core-bioimage-io-python

Python specific core utilities for bioimage.io resources (in particular models).

Installation

Via Mamba/Conda

The bioimageio.core package can be installed from conda-forge via

mamba install -c conda-forge bioimageio.core

If you do not install any additional deep learning libraries, you will only be able to use general convenience functionality, but not any functionality for model prediction. To install additional deep learning libraries use:

• Pytorch/Torchscript:

  CPU installation (if you don't have an nvidia graphics card):

  mamba install -c pytorch -c conda-forge bioimageio.core pytorch torchvision cpuonly

  GPU installation (for cuda 11.6, please choose the appropriate cuda version for your system):

  mamba install -c pytorch -c nvidia -c conda-forge bioimageio.core pytorch torchvision pytorch-cuda=11.8

  Note that the pytorch installation instructions may change in the future. For the latest instructions please refer to pytorch.org.

• Tensorflow

  Currently only CPU version supported

  mamba install -c conda-forge bioimageio.core tensorflow

• ONNXRuntime

  Currently only cpu version supported

  mamba install -c conda-forge bioimageio.core onnxruntime

Via pip

The package is also available via pip (e.g. with recommended extras onnx and pytorch):

pip install "bioimageio.core[onnx,pytorch]"

Set up Development Environment

To set up a development conda environment run the following commands:

mamba env create -f dev/env.yaml
mamba activate core
pip install -e . --no-deps

There are different environment files available that only install tensorflow or pytorch as dependencies.

💻 Use the Command Line Interface

bioimageio.core installs a command line interface (CLI) 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 <MODEL>

Run prediction for an image stored on disc:

bioimageio predict-image <MODEL> --inputs <INPUT> --outputs <OUTPUT>

Run prediction for multiple images stored on disc:

bioimagei predict-images -m <MODEL> -i <INPUT_PATTERN> - o <OUTPUT_FOLDER>

<INPUT_PATTERN> is a glob pattern to select the desired images, e.g. /path/to/my/images/*.tif.

🐍 Use in Python

bioimageio.core is a python package that implements prediction with bioimageio models including standardized pre- and postprocessing operations. These models are described by---and can be loaded with---the bioimageio.spec package.

In addition bioimageio.core provides functionality to convert model weight formats.

To get an overview of this functionality, check out these example notebooks:

• model creation/loading with bioimageio.spec

and the developer documentation.

Model Specification

The model specification and its validation tools can be found at https://github.com/bioimage-io/spec-bioimage-io.

Changelog

0.6.8

• testing model inference will now check all weight formats (previously only the first one for which model adapter creation succeeded had been checked)
• fix predict with blocking (Thanks @thodkatz)

0.6.7

• predict() argument inputs may be sample

0.6.6

• add aliases to match previous API more closely

0.6.5

• improve adapter error messages

0.6.4

• add bioimageio validate-format command
• improve error messages and display of command results

0.6.3

• Fix #386
• (in model inference testing) stop assuming model inputs are tileable

0.6.2

• Fix #384

0.6.1

• Fix #378 (with #379)*

0.6.0

• add compatibility with new bioimageio.spec 0.5 (0.5.2post1)
• improve interfaces

0.5.10

• Fix critical bug in predict with tiling