ssc4onnx 1.0.1

Checker with simple ONNX model structure. Simple Structure Checker for ONNX.

ssc4onnx

Checker with simple ONNX model structure. Simple Structure Checker for ONNX.

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

Key concept

• Analyzes and displays the structure of huge size models that cannot be displayed by Netron.

1. Setup

1-1. HostPC

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

### run
$ pip install -U onnx rich \
&& pip install -U ssc4onnx

1-2. Docker

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

2. CLI Usage

$ ssc4onnx -h

usage:
    ssc4onnx [-h]
    --input_onnx_file_path INPUT_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.

3. In-script Usage

>>> from ssc4onnx import structure_check
>>> help(structure_check)

Help on function structure_check in module ssc4onnx.onnx_structure_check:

structure_check(
    input_onnx_file_path: Union[str, NoneType] = '',
    onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None
) -> None

    Parameters
    ----------
    input_onnx_file_path: Optional[str]
        Input onnx file path.
        Either input_onnx_file_path or onnx_graph must be specified.
        Default: ''

    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.

4. CLI Execution

$ ssc4onnx --input_onnx_file_path deqflow_b_things_opset12_192x320.onnx

5. In-script Execution

from ssc4onnx import structure_check

structure_check(
  input_onnx_file_path="deqflow_b_things_opset12_192x320.onnx",
)

6. Sample

┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ OP Type                ┃ OPs        ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ Add                    │ 3907       │
│ AveragePool            │ 3          │
│ Cast                   │ 2652       │
│ Concat                 │ 1983       │
│ Constant               │ 14992      │
│ ConstantOfShape        │ 1350       │
│ Conv                   │ 710        │
│ Div                    │ 1107       │
│ Einsum                 │ 353        │
│ Equal                  │ 1240       │
│ Expand                 │ 1662       │
│ Floor                  │ 416        │
│ Gather                 │ 1411       │
│ GatherElements         │ 832        │
│ Greater                │ 832        │
│ InstanceNormalization  │ 15         │
│ Less                   │ 832        │
│ MatMul                 │ 1          │
│ Mul                    │ 5267       │
│ Neg                    │ 206        │
│ Not                    │ 102        │
│ Pad                    │ 212        │
│ Range                  │ 206        │
│ ReduceSum              │ 14         │
│ Relu                   │ 352        │
│ Reshape                │ 2410       │
│ ScatterND              │ 102        │
│ Shape                  │ 1556       │
│ Sigmoid                │ 208        │
│ Slice                  │ 620        │
│ Softmax                │ 1          │
│ Split                  │ 208        │
│ Sqrt                   │ 13         │
│ Sub                    │ 2446       │
│ Tanh                   │ 104        │
│ Tile                   │ 2          │
│ Transpose              │ 317        │
│ Unsqueeze              │ 3866       │
│ Where                  │ 2904       │
│ ---------------------- │ ---------- │
│ Total number of OPs    │ 55414      │
│ ====================== │ ========== │
│ Model Size             │ 37.2MiB    │
└────────────────────────┴────────────┘
INFO: file: deqflow_b_things_opset12_192x320.onnx
INFO: input_name.1: input1 shape: [1, 3, 192, 320] dtype: float32
INFO: input_name.2: input2 shape: [1, 3, 192, 320] dtype: float32
INFO: output_name.1: flow_up shape: [1, 2, 192, 320] dtype: float32

7. Reference

1. https://github.com/onnx/onnx/blob/main/docs/Operators.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/simple-onnx-processing-tools
5. https://github.com/PINTO0309/PINTO_model_zoo

8. Issues

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