bioimageio.core 0.9.5

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

bioimageio.core

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

Get started

To get started we recommend installing bioimageio.core with conda together with a deep learning framework, e.g. pytorch, and run a few bioimageio commands to see what bioimage.core has to offer:

1. install with conda (for more details on conda environments, checkout the conda docs)

   conda install -c conda-forge bioimageio.core pytorch
   

2. test a model

   $ bioimageio test powerful-chipmunk
   ...
   

   (Click to expand output)

     ✔️                 bioimageio validation passed
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     source            https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/powerful-chipmunk/1/files/rdf.yaml
     format version    model 0.4.10
     bioimageio.spec   0.5.3post4
     bioimageio.core   0.6.8
   
   
   
     ❓   location                                     detail
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     ✔️                                                 initialized ModelDescr to describe model 0.4.10
   
     ✔️                                                 bioimageio.spec format validation model 0.4.10
     🔍   context.perform_io_checks                    True
     🔍   context.root                                 https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/powerful-chipmunk/1/files
     🔍   context.known_files.weights.pt               3bd9c518c8473f1e35abb7624f82f3aa92f1015e66fb1f6a9d08444e1f2f5698
     🔍   context.known_files.weights-torchscript.pt   4e568fd81c0ffa06ce13061327c3f673e1bac808891135badd3b0fcdacee086b
     🔍   context.warning_level                        error
   
     ✔️                                                 Reproduce test outputs from test inputs
   
     ✔️                                                 Reproduce test outputs from test inputs
   

   or

   $ bioimageio test impartial-shrimp
   ...
   

   (Click to expand output)

     ✔️                 bioimageio validation passed
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     source            https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/impartial-shrimp/1.1/files/rdf.yaml
     format version    model 0.5.3
     bioimageio.spec   0.5.3.2
     bioimageio.core   0.6.9
   
   
     ❓   location                    detail
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     ✔️                                initialized ModelDescr to describe model 0.5.3
   
   
     ✔️                                bioimageio.spec format validation model 0.5.3
   
     🔍   context.perform_io_checks   False
     🔍   context.warning_level       error
   
     ✔️                                Reproduce test outputs from test inputs (pytorch_state_dict)
   
   
     ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:
   
                                     0
   
     ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:
   
                                     0
   
     ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:
   
                                     1
   
     ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:
   
                                     1
   
     ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:
   
                                     2
   
     ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:
   
                                     2
   
     ✔️                                Reproduce test outputs from test inputs (torchscript)
   
   
     ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 0
   
   
     ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 0
   
   
     ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 1
   
   
     ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 1
   
   
     ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 2
   
   
     ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 2
   

3. run prediction on your data

• display the bioimageio-predict command help to get an overview:

  $ bioimageio predict --help
  ...
  

  (Click to expand output)

  usage: bioimageio predict [-h] [--inputs Sequence[Union[str,Annotated[Tuple[str,...],MinLenmin_length=1]]]]
                            [--outputs {str,Tuple[str,...]}] [--overwrite bool] [--blockwise bool] [--stats Path]
                            [--preview bool]
                            [--weight_format {typing.Literal['keras_hdf5','onnx','pytorch_state_dict','tensorflow_js','tensorflow_saved_model_bundle','torchscript'],any}]
                            [--example bool]
                            SOURCE
  
  bioimageio-predict - Run inference on your data with a bioimage.io model.
  
  positional arguments:
    SOURCE                Url/path to a bioimageio.yaml/rdf.yaml file
                          or a bioimage.io resource identifier, e.g. 'affable-shark'
  
  optional arguments:
    -h, --help            show this help message and exit
    --inputs Sequence[Union[str,Annotated[Tuple[str,...],MinLen(min_length=1)]]]
                          Model input sample paths (for each input tensor)
  
                          The input paths are expected to have shape...
                          - (n_samples,) or (n_samples,1) for models expecting a single input tensor
                          - (n_samples,) containing the substring '{input_id}', or
                          - (n_samples, n_model_inputs) to provide each input tensor path explicitly.
  
                          All substrings that are replaced by metadata from the model description:
                          - '{model_id}'
                          - '{input_id}'
  
                          Example inputs to process sample 'a' and 'b'
                          for a model expecting a 'raw' and a 'mask' input tensor:
                          --inputs="[["a_raw.tif","a_mask.tif"],["b_raw.tif","b_mask.tif"]]"
                          (Note that JSON double quotes need to be escaped.)
  
                          Alternatively a `bioimageio-cli.yaml` (or `bioimageio-cli.json`) file
                          may provide the arguments, e.g.:
                          ```yaml
                          inputs:
                          - [a_raw.tif, a_mask.tif]
                          - [b_raw.tif, b_mask.tif]
                          ```
  
                          `.npy` and any file extension supported by imageio are supported.
                          Aavailable formats are listed at
                          https://imageio.readthedocs.io/en/stable/formats/index.html#all-formats.
                          Some formats have additional dependencies.
  
                            (default: ('{input_id}/001.tif',))
    --outputs {str,Tuple[str,...]}
                          Model output path pattern (per output tensor)
  
                          All substrings that are replaced:
                          - '{model_id}' (from model description)
                          - '{output_id}' (from model description)
                          - '{sample_id}' (extracted from input paths)
  
                            (default: outputs_{model_id}/{output_id}/{sample_id}.tif)
    --overwrite bool      allow overwriting existing output files (default: False)
    --blockwise bool      process inputs blockwise (default: False)
    --stats Path          path to dataset statistics
                          (will be written if it does not exist,
                          but the model requires statistical dataset measures)
                            (default: dataset_statistics.json)
    --preview bool        preview which files would be processed
                          and what outputs would be generated. (default: False)
    --weight_format {typing.Literal['keras_hdf5','onnx','pytorch_state_dict','tensorflow_js','tensorflow_saved_model_bundle','torchscript'],any}
                          The weight format to use. (default: any)
    --example bool        generate and run an example
  
                          1. downloads example model inputs
                          2. creates a `{model_id}_example` folder
                          3. writes input arguments to `{model_id}_example/bioimageio-cli.yaml`
                          4. executes a preview dry-run
                          5. executes prediction with example input
  
                            (default: False)
  

• create an example and run prediction locally!

  $ bioimageio predict impartial-shrimp --example=True
  ...
  

  (Click to expand output)

  🛈 bioimageio prediction preview structure:
  {'{sample_id}': {'inputs': {'{input_id}': '<input path>'},
                  'outputs': {'{output_id}': '<output path>'}}}
  🔎 bioimageio prediction preview output:
  {'1': {'inputs': {'input0': 'impartial-shrimp_example/input0/001.tif'},
        'outputs': {'output0': 'impartial-shrimp_example/outputs/output0/1.tif'}}}
  predict with impartial-shrimp: 100%|███████████████████████████████████████████████████| 1/1 [00:21<00:00, 21.76s/sample]
  🎉 Sucessfully ran example prediction!
  To predict the example input using the CLI example config file impartial-shrimp_example\bioimageio-cli.yaml, execute `bioimageio predict` from impartial-shrimp_example:
  $ cd impartial-shrimp_example
  $ bioimageio predict "impartial-shrimp"
  
  Alternatively run the following command in the current workind directory, not the example folder:
  $ bioimageio predict --preview=False --overwrite=True --stats="impartial-shrimp_example/dataset_statistics.json" --inputs="[[\"impartial-shrimp_example/input0/001.tif\"]]" --outputs="impartial-shrimp_example/outputs/{output_id}/{sample_id}.tif" "impartial-shrimp"
  (note that a local 'bioimageio-cli.json' or 'bioimageio-cli.yaml' may interfere with this)
  

Installation

Via Conda

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

conda 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 depending on model prediction. To install additional deep learning libraries add pytorch, onnxruntime, keras or tensorflow.

Deeplearning frameworks to consider installing alongside bioimageio.core:

• Pytorch/Torchscript
• TensorFlow
• ONNXRuntime

Via pip

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

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

🐍 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.

Documentation

Here you find the bioimageio.core documentation.

Presentations

• Create a model from scratch (source)

Examples

Notebooks that save and load resource descriptions and validate their format (using bioimageio.spec, a dependency of bioimageio.core)

load_model_and_create_your_own.ipynb

dataset_creation.ipynb

Use the described resources in Python with bioimageio.core

model_usage.ipynb

💻 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

For examples see Get started.

CLI inputs from file

For convenience the command line options (not arguments) may be given in a bioimageio-cli.json or bioimageio-cli.yaml file, e.g.:

# bioimageio-cli.yaml
inputs: inputs/*_{tensor_id}.h5
outputs: outputs_{model_id}/{sample_id}_{tensor_id}.h5
overwrite: true
blockwise: true
stats: inputs/dataset_statistics.json

Set up Development Environment

To set up a development environment run the following commands:

conda create -n core python=$(grep -E '^requires-python' pyproject.toml | grep -oE '[0-9]+\.[0-9]+')
conda activate core
pip install -e .[dev,partners]

Joint development of bioimageio.spec and bioimageio.core

Assuming spec-bioimage-io is cloned to the parent folder a joint development environment can be created with the following commands:

conda create -n core python=$(grep -E '^requires-python' pyproject.toml | grep -oE '[0-9]+\.[0-9]+')
conda activate core
pip install -e .[dev,partners] -e ../spec-bioimage-io[dev]

Logging level

bioimageio.spec and bioimageio.core use loguru for logging, hence the logging level may be controlled with the LOGURU_LEVEL environment variable. The bioimageio CLI has logging enabled by default. To activate logging when using bioimageio.spec/bioimageio.core as a library, add

from loguru import logger

logger.enable("bioimageio")

Changelog

See changelog.md