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Merge pull request #381 from bioimage-io/update_notebooks
Update notebooks
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,227 +1,4 @@ | ||
| import sys | ||
| from pathlib import Path | ||
| from typing import List, Optional, Union | ||
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| import fire | ||
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| 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 | ||
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| 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,), | ||
| ) | ||
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| 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) | ||
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| 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} | ||
| """ | ||
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| # 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) | ||
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| # # 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 | ||
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| # prediction.predict_image( | ||
| # model_rdf, inputs, outputs, padding, tiling, None if weight_format is None else weight_format.value, devices | ||
| # ) | ||
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| # predict_image.__doc__ = prediction.predict_image.__doc__ | ||
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| # @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] | ||
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| # 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) | ||
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| # # 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, | ||
| # ) | ||
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| # predict_images.__doc__ = prediction.predict_images.__doc__ | ||
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| # if torch_converter is not None: | ||
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| # @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) | ||
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| # convert_torch_weights_to_onnx.__doc__ = torch_converter.convert_weights_to_onnx.__doc__ | ||
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| # @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) | ||
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| # convert_torch_weights_to_torchscript.__doc__ = torch_converter.convert_weights_to_torchscript.__doc__ | ||
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| # if keras_converter is not None: | ||
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| # @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) | ||
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| # convert_keras_weights_to_tensorflow.__doc__ = ( | ||
| # keras_converter.convert_weights_to_tensorflow_saved_model_bundle.__doc__ | ||
| # ) | ||
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| def main(): | ||
| fire.Fire(Bioimageio, name="bioimageio") | ||
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| from bioimageio.core.commands import main | ||
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| if __name__ == "__main__": | ||
| main() |
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