module cli.onnx_code#

Short summary#

module mlprodict.cli.onnx_code

Command line to check einsum scenarios.

source on GitHub

Functions#

function

truncated documentation

dynamic_doc

Generates the documentation for ONNX operators.

onnx_code

Exports an ONNX graph into a python code creating the same graph.

plot_onnx

Plots an ONNX graph on the standard output.

Documentation#

Command line to check einsum scenarios.

source on GitHub

mlprodict.cli.onnx_code.dynamic_doc(verbose=0, fLOG=<built-in function print>)#

Generates the documentation for ONNX operators.

Parameters:

  • verbose – displays the list of operator

  • fLOG – logging function

source on GitHub

mlprodict.cli.onnx_code.onnx_code(filename, format='onnx', output=None, verbose=0, name=None, opset=None, fLOG=<built-in function print>)#

Exports an ONNX graph into a python code creating the same graph.

Parameters:

  • filename – onnx file

  • format – format to export too (onnx, tf2onnx, numpy)

  • output – output file to produce or None to print it on stdout

  • verbose – verbosity level

  • name – rewrite the graph name

  • opset – overwrite the opset (may not works depending on the format)

  • fLOG – logging function

Exports an ONNX graph into a python code creating the same graph.

The command converts an ONNX graph into a python code generating the same graph. The python code may use onnx syntax, numpy syntax or tf2onnx syntax.

Example:

python -m mlprodict onnx_code --filename="something.onnx" --format=onnx

<<<

python -m mlprodict onnx_code --help

>>>

usage: onnx_code [-h] [-f FILENAME] [-fo FORMAT] [-o OUTPUT] [-v VERBOSE]
                 [-n NAME] [-op OPSET]

Exports an ONNX graph into a python code creating the same graph.

optional arguments:
  -h, --help            show this help message and exit
  -f FILENAME, --filename FILENAME
                        onnx file (default: None)
  -fo FORMAT, --format FORMAT
                        format to export too (`onnx`, `tf2onnx`, `numpy`)
                        (default: onnx)
  -o OUTPUT, --output OUTPUT
                        output file to produce or None to print it on stdout
                        (default: )
  -v VERBOSE, --verbose VERBOSE
                        verbosity level (default: 0)
  -n NAME, --name NAME  rewrite the graph name (default: )
  -op OPSET, --opset OPSET
                        overwrite the opset (may not works depending on the
                        format) (default: )

source on GitHub

mlprodict.cli.onnx_code.plot_onnx(filename, format='onnx', verbose=0, output=None, fLOG=<built-in function print>)#

Plots an ONNX graph on the standard output.

Parameters:

  • filename – onnx file

  • format – format to export too (simple, tree, dot, io, mat, raw)

  • output – output file to produce or None to print it on stdout

  • verbose – verbosity level

  • fLOG – logging function

Plots an ONNX graph as text

The command shows the ONNX graphs as a text on the standard output.

Example:

python -m mlprodict plot_onnx --filename="something.onnx" --format=simple

<<<

python -m mlprodict plot_onnx --help

>>>

usage: plot_onnx [-h] [-f FILENAME] [-fo FORMAT] [-v VERBOSE] [-o OUTPUT]

Plots an ONNX graph on the standard output.

optional arguments:
  -h, --help            show this help message and exit
  -f FILENAME, --filename FILENAME
                        onnx file (default: None)
  -fo FORMAT, --format FORMAT
                        format to export too (`simple`, `tree`, `dot`, `io`,
                        `mat`, `raw`) (default: onnx)
  -v VERBOSE, --verbose VERBOSE
                        verbosity level (default: 0)
  -o OUTPUT, --output OUTPUT
                        output file to produce or None to print it on stdout
                        (default: )

source on GitHub