Python specific core utilities for bioimage.io resources (in particular DL models).
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:
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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 ...
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✔️ 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 ...
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✔️ 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
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run prediction on your data
-
display the
bioimageio-predictcommand help to get an overview:$ bioimageio predict --help ...
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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)
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create an example and run prediction locally!
$ bioimageio predict impartial-shrimp --example=True ...
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🛈 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)
The bioimageio.core package can be installed from conda-forge via
conda install -c conda-forge bioimageio.coreIf 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:
The package is also available via pip
(e.g. with recommended extras onnx and pytorch):
pip install "bioimageio.core[onnx,pytorch]"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.
Here you find the bioimageio.core documentation.
- 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
bioimageio.core installs a command line interface (CLI) for testing models and other functionality.
You can list all the available commands via:
bioimageioFor examples see Get started.
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.jsonTo set up a development conda environment run the following commands:
conda env create -f dev/env.yaml
conda activate core
pip install -e . --no-depsThere are different environment files available that only install tensorflow or pytorch as dependencies, see dev folder.
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")See changelog.md
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