scs4onnx 1.0.0

A very simple tool that compresses the overall size of the ONNX model by aggregating duplicate constant values as much as possible. Simple Constant value Shrink for ONNX.

scs4onnx

A very simple tool that compresses the overall size of the ONNX model by aggregating duplicate constant values as much as possible. Simple Constant value Shrink for ONNX.

Key concept

1. If the same constant tensor is found by scanning the entire graph for Constant values, it is aggregated into a single constant tensor.
2. Ignore scalar values.
3. Ignore variables.

1. Setup

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

### run
$ pip install -U onnx \
&& python3 -m pip install -U onnx_graphsurgeon --index-url https://pypi.ngc.nvidia.com \
&& pip install -U scs4onnx

2. Usage

$ scs4onnx -h

usage: scs4onnx [-h] onnx_file_path {shrink,npy}

positional arguments:
  onnx_file_path  Input onnx file path.
  {shrink,npy}    Constant Value Compression Mode.
  
                  shrink: Share constant values inside the model as much as possible.
                          The model size is slightly larger because some shared constant values
                          remain inside the model, but performance is maximized.
                  npy:    Outputs constant values used repeatedly in the model to an external
                          file .npy. Instead of the smallest model body size, the file loading
                          overhead is greater.
                  Default: shrink

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

3. Execution

$ scs4onnx input.onnx shrink

4. Sample

4-1. shrink mode sample

• 297.8MB -> 67.4MB

  

4-2. npy mode sample

• 297.8MB -> 21.3MB