# ssc4onnx
Checker with simple ONNX model structure. **S**imple **S**tructure **C**hecker for **ONNX**.
https://github.com/PINTO0309/simple-onnx-processing-tools
[](https://pepy.tech/project/ssc4onnx)  [](https://pypi.org/project/ssc4onnx/) [](https://github.com/PINTO0309/ssc4onnx/actions?query=workflow%3ACodeQL)
<p align="center">
<img src="https://user-images.githubusercontent.com/33194443/170718388-a30d9c72-be08-4d13-b3e6-d089fe3f93da.png" />
</p>
# Key concept
- Analyzes and displays the structure of huge size models that cannot be displayed by Netron.
## 1. Setup
### 1-1. HostPC
```bash
### option
$ echo export PATH="~/.local/bin:$PATH" >> ~/.bashrc \
&& source ~/.bashrc
### run
$ pip install -U onnx rich onnxruntime \
&& pip install -U ssc4onnx \
&& python -m pip install onnx_graphsurgeon \
--index-url https://pypi.ngc.nvidia.com
```
### 1-2. Docker
https://github.com/PINTO0309/simple-onnx-processing-tools#docker
## 2. CLI Usage
```
$ ssc4onnx -h
usage:
ssc4onnx [-h]
-if INPUT_ONNX_FILE_PATH
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.
```
## 3. In-script Usage
```python
>>> 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
) -> Tuple[Dict[str, int], int]
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.
Returns
-------
op_num: Dict[str, int]
Num of every op
model_size: int
Model byte size
```
## 4. CLI Execution
```bash
$ ssc4onnx -if deqflow_b_things_opset12_192x320.onnx
```
## 5. In-script Execution
```python
from ssc4onnx import structure_check
structure_check(
input_onnx_file_path="deqflow_b_things_opset12_192x320.onnx",
)
```
## 6. Sample
https://github.com/PINTO0309/ssc4onnx/releases/download/1.0.6/deqflow_b_things_opset12_192x320.onnx
https://github.com/PINTO0309/ssc4onnx/assets/33194443/fd6a4aa2-9ed5-492b-82ae-1f8306af5119
## 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
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"description": "# ssc4onnx\nChecker with simple ONNX model structure. **S**imple **S**tructure **C**hecker for **ONNX**.\n\nhttps://github.com/PINTO0309/simple-onnx-processing-tools\n\n[](https://pepy.tech/project/ssc4onnx)  [](https://pypi.org/project/ssc4onnx/) [](https://github.com/PINTO0309/ssc4onnx/actions?query=workflow%3ACodeQL)\n\n<p align=\"center\">\n <img src=\"https://user-images.githubusercontent.com/33194443/170718388-a30d9c72-be08-4d13-b3e6-d089fe3f93da.png\" />\n</p>\n\n# Key concept\n- Analyzes and displays the structure of huge size models that cannot be displayed by Netron.\n\n## 1. Setup\n\n### 1-1. HostPC\n```bash\n### option\n$ echo export PATH=\"~/.local/bin:$PATH\" >> ~/.bashrc \\\n&& source ~/.bashrc\n\n### run\n$ pip install -U onnx rich onnxruntime \\\n&& pip install -U ssc4onnx \\\n&& python -m pip install onnx_graphsurgeon \\\n --index-url https://pypi.ngc.nvidia.com\n```\n### 1-2. Docker\nhttps://github.com/PINTO0309/simple-onnx-processing-tools#docker\n\n## 2. CLI Usage\n```\n$ ssc4onnx -h\n\nusage:\n ssc4onnx [-h]\n -if INPUT_ONNX_FILE_PATH\n\noptional arguments:\n -h, --help\n show this help message and exit.\n\n -if INPUT_ONNX_FILE_PATH, --input_onnx_file_path INPUT_ONNX_FILE_PATH\n Input onnx file path.\n```\n\n## 3. In-script Usage\n```python\n>>> from ssc4onnx import structure_check\n>>> help(structure_check)\n\nHelp on function structure_check in module ssc4onnx.onnx_structure_check:\n\nstructure_check(\n input_onnx_file_path: Union[str, NoneType] = '',\n onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None\n) -> Tuple[Dict[str, int], int]\n\n Parameters\n ----------\n input_onnx_file_path: Optional[str]\n Input onnx file path.\n Either input_onnx_file_path or onnx_graph must be specified.\n Default: ''\n\n onnx_graph: Optional[onnx.ModelProto]\n onnx.ModelProto.\n Either input_onnx_file_path or onnx_graph must be specified.\n onnx_graph If specified, ignore input_onnx_file_path and process onnx_graph.\n\n Returns\n -------\n op_num: Dict[str, int]\n Num of every op\n model_size: int\n Model byte size\n```\n\n## 4. CLI Execution\n```bash\n$ ssc4onnx -if deqflow_b_things_opset12_192x320.onnx\n```\n\n## 5. In-script Execution\n```python\nfrom ssc4onnx import structure_check\n\nstructure_check(\n input_onnx_file_path=\"deqflow_b_things_opset12_192x320.onnx\",\n)\n```\n\n## 6. Sample\nhttps://github.com/PINTO0309/ssc4onnx/releases/download/1.0.6/deqflow_b_things_opset12_192x320.onnx\n\nhttps://github.com/PINTO0309/ssc4onnx/assets/33194443/fd6a4aa2-9ed5-492b-82ae-1f8306af5119\n\n\n\n## 7. Reference\n1. https://github.com/onnx/onnx/blob/main/docs/Operators.md\n2. https://docs.nvidia.com/deeplearning/tensorrt/onnx-graphsurgeon/docs/index.html\n3. https://github.com/NVIDIA/TensorRT/tree/main/tools/onnx-graphsurgeon\n4. https://github.com/PINTO0309/simple-onnx-processing-tools\n5. https://github.com/PINTO0309/PINTO_model_zoo\n\n## 8. Issues\nhttps://github.com/PINTO0309/simple-onnx-processing-tools/issues\n",
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