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snc4onnx

Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

You guys don't have to go out of your way to use this tool, you can use the official API and write your code as you like. I only created this tool because I didn't want to write a single line of Python code. What can be done with this tool can also be done with the official API. https://github.com/onnx/onnx/blob/main/docs/PythonAPIOverview.md#onnx-compose

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

Downloads GitHub PyPI CodeQL

<p align="center"> <img src="https://user-images.githubusercontent.com/33194443/170151148-1b33c37f-9a97-4c55-a6ae-f4abccdcfc28.png" /> </p>

1. Setup

1-1. HostPC

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

### run
$ pip install -U onnx \
&& pip install -U onnx-simplifier \
&& python3 -m pip install -U onnx_graphsurgeon --index-url https://pypi.ngc.nvidia.com \
&& pip install -U snc4onnx

1-2. Docker

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

2. CLI Usage

$ snc4onnx -h

usage:
  snc4onnx [-h]
    -if INPUT_ONNX_FILE_PATHS [INPUT_ONNX_FILE_PATHS ...]
    -sd SRCOP_DESTOP [SRCOP_DESTOP ...]
    [-opam OP_PREFIXES_AFTER_MERGING [OP_PREFIXES_AFTER_MERGING ...]]
    [-of OUTPUT_ONNX_FILE_PATH]
    [-f]
    [-dos]
    [-n]

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

  -if INPUT_ONNX_FILE_PATHS [INPUT_ONNX_FILE_PATHS ...], \
    --input_onnx_file_paths INPUT_ONNX_FILE_PATHS [INPUT_ONNX_FILE_PATHS ...]
      Input onnx file paths. At least two onnx files must be specified.

  -sd SRCOP_DESTOP [SRCOP_DESTOP ...], --srcop_destop SRCOP_DESTOP [SRCOP_DESTOP ...]
      The names of the output OP to join from and the input OP to join to are
      out1 in1 out2 in2 out3 in3 ....
      format.
      In other words, to combine model1 and model2,
      --srcop_destop model1_out1 model2_in1 model1_out2 model2_in2
      Also, --srcop_destop can be specified multiple times.
      The first --srcop_destop specifies the correspondence between model1 and model2,
      and the second --srcop_destop specifies the correspondence between
      model1 and model2 combined and model3.
      It is necessary to take into account that the prefix specified
      in op_prefixes_after_merging is
      given at the beginning of each OP name.
      e.g. To combine model1 with model2 and model3.
      --srcop_destop model1_src_op1 model2_dest_op1 model1_src_op2 model2_dest_op2 ...
      --srcop_destop combined_model1.2_src_op1 model3_dest_op1 combined_model1.2_src_op2 model3_dest_op2 ...

  -opam OP_PREFIXES_AFTER_MERGING [OP_PREFIXES_AFTER_MERGING ...], \
    --op_prefixes_after_merging OP_PREFIXES_AFTER_MERGING [OP_PREFIXES_AFTER_MERGING ...]
      Since a single ONNX file cannot contain multiple OPs with the same name,
      a prefix is added to all OPs in each input ONNX model to avoid duplication.
      Specify the same number of paths as input_onnx_file_paths.
      e.g. --op_prefixes_after_merging model1_prefix model2_prefix model3_prefix ...

  -of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
      Output onnx file path.

  -f, --output_of_onnx_file_in_the_process_of_fusion
      Output of onnx files in the process of fusion.

  -dos, --disable_onnxsim
      Suppress the execution of onnxsim on the backend and dare to leave redundant processing.

  -n, --non_verbose
      Do not show all information logs. Only error logs are displayed.

3. In-script Usage

$ python
>>> from snc4onnx import combine
>>> help(combine)

Help on function combine in module snc4onnx.onnx_network_combine:

combine(
  srcop_destop: List[str],
  op_prefixes_after_merging: Union[List[str], NoneType] = [],
  input_onnx_file_paths: Union[List[str], NoneType] = [],
  onnx_graphs: Union[List[onnx.onnx_ml_pb2.ModelProto], NoneType] = [],
  output_onnx_file_path: Union[str, NoneType] = '',
  output_of_onnx_file_in_the_process_of_fusion: Union[bool, NoneType] = False,
  disable_onnxsim: Union[bool, NoneType] = False,
  non_verbose: Union[bool, NoneType] = False
) -> onnx.onnx_ml_pb2.ModelProto

    Parameters
    ----------
    srcop_destop: List[str]
        The names of the output OP to join from and the input OP to join to are
        [["out1","in1"], ["out2","in2"], ["out3","in3"]] format.

        In other words, to combine model1 and model2,
        srcop_destop =
            [
                ["model1_out1_opname","model2_in1_opname"],
                ["model1_out2_opname","model2_in2_opname"]
            ]

        The first srcop_destop specifies the correspondence between model1 and model2,
        and the second srcop_destop specifies the correspondence
        between model1 and model2 combined and model3.
        It is necessary to take into account that the prefix specified
        in op_prefixes_after_merging is given at the beginning of each OP name.

        e.g. To combine model1 with model2 and model3.
        srcop_destop =
            [
                [
                    ["model1_src_op1","model2_dest_op1"],
                    ["model1_src_op2","model2_dest_op2"]
                ],
                [
                    ["combined_model1.2_src_op1","model3_dest_op1"],
                    ["combined_model1.2_src_op2","model3_dest_op2"]
                ],
                ...
            ]

    op_prefixes_after_merging: List[str]
        Since a single ONNX file cannot contain multiple OPs with the same name,
        a prefix is added to all OPs in each input ONNX model to avoid duplication.
        Specify the same number of paths as input_onnx_file_paths.
        e.g. op_prefixes_after_merging = ["model1_prefix","model2_prefix","model3_prefix", ...]

    input_onnx_file_paths: Optional[List[str]]
        Input onnx file paths. At least two onnx files must be specified.
        Either input_onnx_file_paths or onnx_graphs must be specified.
        onnx_graphs If specified, ignore input_onnx_file_paths and process onnx_graphs.
        e.g. input_onnx_file_paths = ["model1.onnx", "model2.onnx", "model3.onnx", ...]

    onnx_graphs: Optional[List[onnx.ModelProto]]
        List of onnx.ModelProto. At least two onnx graphs must be specified.
        Either input_onnx_file_paths or onnx_graphs must be specified.
        onnx_graphs If specified, ignore input_onnx_file_paths and process onnx_graphs.
        e.g. onnx_graphs = [graph1, graph2, graph3, ...]

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

    output_of_onnx_file_in_the_process_of_fusion: Optional[bool]
        Output of onnx files in the process of fusion.
        Default: False

    disable_onnxsim: Optional[bool]
        Suppress the execution of onnxsim on the backend and dare to leave redundant processing.
        Default: False

    non_verbose: Optional[bool]
        Do not show all information logs. Only error logs are displayed.
        Default: False

    Returns
    -------
    combined_graph: onnx.ModelProto
        Combined onnx ModelProto

4. CLI Execution

$ snc4onnx \
--input_onnx_file_paths crestereo_init_iter2_120x160.onnx crestereo_next_iter2_240x320.onnx \
--output_onnx_file_path merged.onnx \
--srcop_destop output flow_init \
--op_prefixes_after_merging init next

5. In-script Execution

5-1. ONNX files

from snc4onnx import combine

combined_graph = combine(
    srcop_destop = [
        ['output', 'flow_init']
    ],
    op_prefixes_after_merging = [
        'init',
        'next',
    ],
    input_onnx_file_paths = [
        'crestereo_init_iter2_120x160.onnx',
        'crestereo_next_iter2_240x320.onnx',
    ],
    non_verbose = True,
)

5-2. onnx.ModelProtos

from snc4onnx import combine

combined_graph = combine(
    srcop_destop = [
        ['output', 'flow_init']
    ],
    op_prefixes_after_merging = [
        'init',
        'next',
    ],
    onnx_graphs = [
        graph1,
        graph2,
        graph3,
    ],
    non_verbose = True,
)

6. Sample

6-1 INPUT <-> OUTPUT

6-2 INPUT + INPUT

7. Reference

  1. https://github.com/onnx/onnx/blob/main/docs/PythonAPIOverview.md
  2. https://github.com/PINTO0309/sne4onnx
  3. https://github.com/PINTO0309/snd4onnx
  4. https://github.com/PINTO0309/scs4onnx
  5. https://github.com/PINTO0309/sog4onnx
  6. https://github.com/PINTO0309/PINTO_model_zoo

8. Issues

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