core-bioimage-io-python

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

install with conda (for more details on conda environments, checkout the conda docs)
```
conda install -c conda-forge bioimageio.core pytorch
```

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

$ 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

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 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

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

🐍 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.

Logging level

bioimageio.spec and bioimageio.core use loguru for logging, hence the logging level may be controlled with the LOGURU_LEVEL environment variable.

Model Specification

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

Changelog

0.6.9

improve bioimageio command line interface (details in #157)
- add predict command
- package command input path is now required

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

bioimage-io/core-bioimage-io-python

core-bioimage-io-python

Get started

Installation

Via Mamba/Conda

Via pip

Set up Development Environment

💻 Use the Command Line Interface

CLI inputs from file

🐍 Use in Python

Logging level

Model Specification

Changelog

0.6.9

0.6.8

0.6.7

0.6.6

0.6.5

0.6.4

0.6.3

0.6.2

0.6.1

0.6.0

0.5.10