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ONNX-Tensorflow Command Line Interface

Available commands:

  • convert

More information: onnx-tf -h

usage: onnx-tf [-h] {convert}

ONNX-Tensorflow Command Line Interface

positional arguments:
  {convert}   Available commands.

optional arguments:
  -h, --help  show this help message and exit

Usage:

Convert:

From ONNX to Tensorflow:

onnx-tf convert -i /path/to/input.onnx -o /path/to/output

More information: onnx-tf convert -h

usage: onnx-tf [-h] --infile INFILE --outdir OUTDIR [--extdatadir EXTDATADIR]
               [--device DEVICE] [--strict STRICT]
               [--logging_level LOGGING_LEVEL] [--auto_cast AUTO_CAST]

This is the converter for converting protocol buffer between tf and onnx.

optional arguments:
  -h, --help            show this help message and exit
  --infile INFILE, -i INFILE
                        Input file path.
  --outdir OUTDIR, -o OUTDIR
                        Output directory.
  --extdatadir EXTDATADIR, -e EXTDATADIR
                        External input data file directory.

backend arguments (onnx -> tf):
  --device DEVICE       The device to execute this model on. It can be either
                        CPU (default) or CUDA. (from onnx_tf.backend.prepare)
  --strict STRICT       Whether to enforce semantic equivalence between the
                        original model and the converted tensorflow model,
                        defaults to True (yes, enforce semantic equivalence).
                        Changing to False is strongly discouraged. Currently,
                        the strict flag only affects the behavior of MaxPool
                        and AveragePool ops. (from onnx_tf.backend.prepare)
  --logging_level LOGGING_LEVEL
                        The logging level, default is INFO. Change it to DEBUG
                        to see more conversion details or to WARNING to see
                        less (from onnx_tf.backend.prepare)
  --auto_cast AUTO_CAST
                        Whether to auto cast data types that might lose
                        precision for the tensors with types not natively
                        supported by Tensorflow, default is False (from
                        onnx_tf.backend.prepare)