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Merged
merged 4 commits into from
Apr 26, 2024
Merged

Update notebooks #381

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f13d1a8
move cli to commands submodule
FynnBe Apr 26, 2024
a182b86
improve docs
FynnBe Apr 26, 2024
1717a32
update notebooks
FynnBe Apr 26, 2024
c5b6687
add requests dep
FynnBe Apr 26, 2024
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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -104,7 +104,7 @@ 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`.

## From python
## 🐍 From python

`bioimageio.core` is a python package that implements prediction with bioimageio models
including standardized pre- and postprocessing operations.
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225 changes: 1 addition & 224 deletions bioimageio/core/__main__.py
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@@ -1,227 +1,4 @@
import sys
from pathlib import Path
from typing import List, Optional, Union

import fire

from bioimageio.core import __version__, test_description
from bioimageio.spec import save_bioimageio_package
from bioimageio.spec.collection import CollectionDescr
from bioimageio.spec.dataset import DatasetDescr
from bioimageio.spec.model import ModelDescr
from bioimageio.spec.model.v0_5 import WeightsFormat
from bioimageio.spec.notebook import NotebookDescr


class Bioimageio:
def package(
self,
source: str,
path: Path = Path("bioimageio-package.zip"),
weight_format: Optional[WeightsFormat] = None,
):
"""Package a bioimageio resource as a zip file

Args:
source: RDF source e.g. `bioimageio.yaml` or `http://example.com/rdf.yaml`
path: output path
weight-format: include only this single weight-format
"""
_ = save_bioimageio_package(
source,
output_path=path,
weights_priority_order=None if weight_format is None else (weight_format,),
)

def test(
self,
source: str,
weight_format: Optional[WeightsFormat] = None,
*,
devices: Optional[Union[str, List[str]]] = None,
decimal: int = 4,
):
"""test a bioimageio resource

Args:
source: Path or URL to the bioimageio resource description file
(bioimageio.yaml or rdf.yaml) or to a zipped resource
weight_format: (model only) The weight format to use
devices: Device(s) to use for testing
decimal: Precision for numerical comparisons
"""
summary = test_description(
source,
weight_format=None if weight_format is None else weight_format,
devices=[devices] if isinstance(devices, str) else devices,
decimal=decimal,
)
print(f"\ntesting model {source}...")
print(summary.format())
sys.exit(0 if summary.status == "passed" else 1)


Bioimageio.__doc__ = f"""
work with resources shared on bioimage.io

library versions:
bioimageio.core {__version__}
bioimageio.spec {__version__}

spec format versions:
model RDF {ModelDescr.implemented_format_version}
dataset RDF {DatasetDescr.implemented_format_version}
notebook RDF {NotebookDescr.implemented_format_version}
collection RDF {CollectionDescr.implemented_format_version}

"""

# TODO: add predict commands
# @app.command()
# def predict_image(
# model_rdf: Annotated[
# Path, typer.Argument(help="Path to the model resource description file (rdf.yaml) or zipped model.")
# ],
# inputs: Annotated[List[Path], typer.Option(help="Path(s) to the model input(s).")],
# outputs: Annotated[List[Path], typer.Option(help="Path(s) for saveing the model output(s).")],
# # NOTE: typer currently doesn't support union types, so we only support boolean here
# # padding: Optional[Union[str, bool]] = typer.Argument(
# # None, help="Padding to apply in each dimension passed as json encoded string."
# # ),
# # tiling: Optional[Union[str, bool]] = typer.Argument(
# # None, help="Padding to apply in each dimension passed as json encoded string."
# # ),
# padding: Annotated[
# Optional[bool], typer.Option(help="Whether to pad the image to a size suited for the model.")
# ] = None,
# tiling: Annotated[Optional[bool], typer.Option(help="Whether to run prediction in tiling mode.")] = None,
# weight_format: Annotated[Optional[WeightsFormatEnum], typer.Option(help="The weight format to use.")] = None,
# devices: Annotated[Optional[List[str]], typer.Option(help="Devices for running the model.")] = None,
# ):
# if isinstance(padding, str):
# padding = json.loads(padding.replace("'", '"'))
# assert isinstance(padding, dict)
# if isinstance(tiling, str):
# tiling = json.loads(tiling.replace("'", '"'))
# assert isinstance(tiling, dict)

# # this is a weird typer bug: default devices are empty tuple although they should be None
# if devices is None or len(devices) == 0:
# devices = None

# prediction.predict_image(
# model_rdf, inputs, outputs, padding, tiling, None if weight_format is None else weight_format.value, devices
# )


# predict_image.__doc__ = prediction.predict_image.__doc__


# @app.command()
# def predict_images(
# model_rdf: Annotated[
# Path, typer.Argument(help="Path to the model resource description file (rdf.yaml) or zipped model.")
# ],
# input_pattern: Annotated[str, typer.Argument(help="Glob pattern for the input images.")],
# output_folder: Annotated[str, typer.Argument(help="Folder to save the outputs.")],
# output_extension: Annotated[Optional[str], typer.Argument(help="Optional output extension.")] = None,
# # NOTE: typer currently doesn't support union types, so we only support boolean here
# # padding: Optional[Union[str, bool]] = typer.Argument(
# # None, help="Padding to apply in each dimension passed as json encoded string."
# # ),
# # tiling: Optional[Union[str, bool]] = typer.Argument(
# # None, help="Padding to apply in each dimension passed as json encoded string."
# # ),
# padding: Annotated[
# Optional[bool], typer.Option(help="Whether to pad the image to a size suited for the model.")
# ] = None,
# tiling: Annotated[Optional[bool], typer.Option(help="Whether to run prediction in tiling mode.")] = None,
# weight_format: Annotated[Optional[WeightsFormatEnum], typer.Option(help="The weight format to use.")] = None,
# devices: Annotated[Optional[List[str]], typer.Option(help="Devices for running the model.")] = None,
# ):
# input_files = glob(input_pattern)
# input_names = [os.path.split(infile)[1] for infile in input_files]
# output_files = [os.path.join(output_folder, fname) for fname in input_names]
# if output_extension is not None:
# output_files = [f"{os.path.splitext(outfile)[0]}{output_extension}" for outfile in output_files]

# if isinstance(padding, str):
# padding = json.loads(padding.replace("'", '"'))
# assert isinstance(padding, dict)
# if isinstance(tiling, str):
# tiling = json.loads(tiling.replace("'", '"'))
# assert isinstance(tiling, dict)

# # this is a weird typer bug: default devices are empty tuple although they should be None
# if len(devices) == 0:
# devices = None
# prediction.predict_images(
# model_rdf,
# input_files,
# output_files,
# padding=padding,
# tiling=tiling,
# weight_format=None if weight_format is None else weight_format.value,
# devices=devices,
# verbose=True,
# )


# predict_images.__doc__ = prediction.predict_images.__doc__


# if torch_converter is not None:

# @app.command()
# def convert_torch_weights_to_onnx(
# model_rdf: Path = typer.Argument(
# ..., help="Path to the model resource description file (rdf.yaml) or zipped model."
# ),
# output_path: Path = typer.Argument(..., help="Where to save the onnx weights."),
# opset_version: Optional[int] = typer.Argument(12, help="Onnx opset version."),
# use_tracing: bool = typer.Option(True, help="Whether to use torch.jit tracing or scripting."),
# verbose: bool = typer.Option(True, help="Verbosity"),
# ):
# ret_code = torch_converter.convert_weights_to_onnx(model_rdf, output_path, opset_version, use_tracing, verbose)
# sys.exit(ret_code)

# convert_torch_weights_to_onnx.__doc__ = torch_converter.convert_weights_to_onnx.__doc__

# @app.command()
# def convert_torch_weights_to_torchscript(
# model_rdf: Path = typer.Argument(
# ..., help="Path to the model resource description file (rdf.yaml) or zipped model."
# ),
# output_path: Path = typer.Argument(..., help="Where to save the torchscript weights."),
# use_tracing: bool = typer.Option(True, help="Whether to use torch.jit tracing or scripting."),
# ):
# torch_converter.convert_weights_to_torchscript(model_rdf, output_path, use_tracing)
# sys.exit(0)

# convert_torch_weights_to_torchscript.__doc__ = torch_converter.convert_weights_to_torchscript.__doc__


# if keras_converter is not None:

# @app.command()
# def convert_keras_weights_to_tensorflow(
# model_rdf: Annotated[
# Path, typer.Argument(help="Path to the model resource description file (rdf.yaml) or zipped model.")
# ],
# output_path: Annotated[Path, typer.Argument(help="Where to save the tensorflow weights.")],
# ):
# rd = load_description(model_rdf)
# ret_code = keras_converter.convert_weights_to_tensorflow_saved_model_bundle(rd, output_path)
# sys.exit(ret_code)

# convert_keras_weights_to_tensorflow.__doc__ = (
# keras_converter.convert_weights_to_tensorflow_saved_model_bundle.__doc__
# )


def main():
fire.Fire(Bioimageio, name="bioimageio")

from bioimageio.core.commands import main

if __name__ == "__main__":
main()
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