A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.

Related tags

Deep Learning python models onnx
Overview

sne4onnx

A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want. Simple Network Extraction for ONNX.

https://github.com/PINTO0309/simple-onnx-processing-tools

Downloads GitHub PyPI CodeQL

Key concept

  • If INPUT OP name and OUTPUT OP name are specified, the onnx graph within the range of the specified OP name is extracted and .onnx is generated.
  • Change backend to onnx.utils.Extractor.extract_model so that onnx.ModelProto can be specified as input.

1. Setup

1-1. HostPC

### option
$ echo export PATH="~/.local/bin:$PATH" >> ~/.bashrc \
&& source ~/.bashrc

### run
$ pip install -U onnx \
&& pip install -U sne4onnx

1-2. Docker

### docker pull
$ docker pull pinto0309/sne4onnx:latest

### docker build
$ docker build -t pinto0309/sne4onnx:latest .

### docker run
$ docker run --rm -it -v `pwd`:/workdir pinto0309/sne4onnx:latest
$ cd /workdir

2. CLI Usage

$ sne4onnx -h

usage:
    sne4onnx [-h]
    --input_onnx_file_path INPUT_ONNX_FILE_PATH
    --input_op_names INPUT_OP_NAMES
    --output_op_names OUTPUT_OP_NAMES
    [--output_onnx_file_path OUTPUT_ONNX_FILE_PATH]

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

  --input_onnx_file_path INPUT_ONNX_FILE_PATH
        Input onnx file path.

  --input_op_names INPUT_OP_NAMES
        List of OP names to specify for the input layer of the model.
        Specify the name of the OP, separated by commas.
        e.g. --input_op_names aaa,bbb,ccc

  --output_op_names OUTPUT_OP_NAMES
        List of OP names to specify for the output layer of the model.
        Specify the name of the OP, separated by commas.
        e.g. --output_op_names ddd,eee,fff

  --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
        Output onnx file path. If not specified, extracted.onnx is output.

3. In-script Usage

$ python
>>> from sne4onnx import extraction
>>> help(extraction)

Help on function extraction in module sne4onnx.onnx_network_extraction:

extraction(
    input_op_names: List[str],
    output_op_names: List[str],
    input_onnx_file_path: Union[str, NoneType] = '',
    onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None,
    output_onnx_file_path: Union[str, NoneType] = ''
) -> onnx.onnx_ml_pb2.ModelProto

    Parameters
    ----------
    input_op_names: List[str]
        List of OP names to specify for the input layer of the model.
        Specify the name of the OP, separated by commas.
        e.g. ['aaa','bbb','ccc']

    output_op_names: List[str]
        List of OP names to specify for the output layer of the model.
        Specify the name of the OP, separated by commas.
        e.g. ['ddd','eee','fff']

    input_onnx_file_path: Optional[str]
        Input onnx file path.
        Either input_onnx_file_path or onnx_graph must be specified.
        onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph.

    onnx_graph: Optional[onnx.ModelProto]
        onnx.ModelProto.
        Either input_onnx_file_path or onnx_graph must be specified.
        onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph.

    output_onnx_file_path: Optional[str]
        Output onnx file path.
        If not specified, .onnx is not output.
        Default: ''

    Returns
    -------
    extracted_graph: onnx.ModelProto
        Extracted onnx ModelProto

4. CLI Execution

$ sne4onnx \
--input_onnx_file_path input.onnx \
--input_op_names aaa,bbb,ccc \
--output_op_names ddd,eee,fff \
--output_onnx_file_path output.onnx

5. In-script Execution

5-1. Use ONNX files

from sne4onnx import extraction

extracted_graph = extraction(
  input_op_names=['aaa', 'bbb', 'ccc'],
  output_op_names=['ddd', 'eee', 'fff'],
  input_onnx_file_path='input.onnx',
  output_onnx_file_path='output.onnx',
)

5-2. Use onnx.ModelProto

from sne4onnx import extraction

extracted_graph = extraction(
  input_op_names=['aaa', 'bbb', 'ccc'],
  output_op_names=['ddd', 'eee', 'fff'],
  onnx_graph=graph,
  output_onnx_file_path='output.onnx',
)

6. Samples

6-1. Pre-extraction

image image image

6-2. Extraction

$ sne4onnx \
--input_onnx_file_path hitnet_sf_finalpass_720x1280.onnx \
--input_op_names 0,1 \
--output_op_names 497,785 \
--output_onnx_file_path hitnet_sf_finalpass_720x960_head.onnx

6-3. Extracted

image image image

7. Reference

  1. https://github.com/onnx/onnx/blob/main/docs/PythonAPIOverview.md
  2. https://docs.nvidia.com/deeplearning/tensorrt/onnx-graphsurgeon/docs/index.html
  3. https://github.com/NVIDIA/TensorRT/tree/main/tools/onnx-graphsurgeon
  4. https://github.com/PINTO0309/snd4onnx
  5. https://github.com/PINTO0309/scs4onnx
  6. https://github.com/PINTO0309/snc4onnx
  7. https://github.com/PINTO0309/sog4onnx
  8. https://github.com/PINTO0309/PINTO_model_zoo

8. Issues

https://github.com/PINTO0309/simple-onnx-processing-tools/issues

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Comments
  • Significantly faster processing

    Significantly faster processing

    1. https://github.com/PINTO0309/simple-onnx-processing-tools/issues/2
    2. Eliminate comma-separated specifications for input_op_names and output_op_names
    3. Add non_verbose option
    opened by PINTO0309 0
Releases(1.0.10)

  • 1.0.10(Sep 7, 2022)

    • Add short form parameter
    $ sne4onnx -h

usage:
    sne4onnx [-h]
    -if INPUT_ONNX_FILE_PATH
    -ion INPUT_OP_NAMES
    -oon OUTPUT_OP_NAMES
    [-of OUTPUT_ONNX_FILE_PATH]
    [-n]

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

  -if INPUT_ONNX_FILE_PATH, --input_onnx_file_path INPUT_ONNX_FILE_PATH
    Input onnx file path.

  -ion INPUT_OP_NAMES [INPUT_OP_NAMES ...], --input_op_names INPUT_OP_NAMES [INPUT_OP_NAMES ...]
    List of OP names to specify for the input layer of the model.
    e.g. --input_op_names aaa bbb ccc

  -oon OUTPUT_OP_NAMES [OUTPUT_OP_NAMES ...], --output_op_names OUTPUT_OP_NAMES [OUTPUT_OP_NAMES ...]
    List of OP names to specify for the output layer of the model.
    e.g. --output_op_names ddd eee fff

  -of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
    Output onnx file path. If not specified, extracted.onnx is output.

  -n, --non_verbose
    Do not show all information logs. Only error logs are displayed.
    Source code(tar.gz)
    Source code(zip)

  • 1.0.9(Jun 7, 2022)

  • 1.0.8(May 25, 2022)

  • 1.0.7(May 8, 2022)

  • 1.0.6(May 8, 2022)

    1. Fix: https://github.com/PINTO0309/simple-onnx-processing-tools/issues/2
    2. Eliminate comma-separated specifications for input_op_names and output_op_names
    3. Add non_verbose option
    Source code(tar.gz)
    Source code(zip)

  • 1.0.5(Apr 11, 2022)

  • 1.0.4(Apr 11, 2022)

  • 1.0.3(Apr 11, 2022)

  • 1.0.2(Apr 10, 2022)

  • 1.0.1(Apr 7, 2022)

  • 1.0.0(Apr 7, 2022)

Owner
Katsuya Hyodo
Hobby programmer. Intel Software Innovator Program member.
Katsuya Hyodo
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