| Name | sng4onnx JSON |
| Version |
1.0.4
JSON |
| download |
| home_page | https://github.com/PINTO0309/sng4onnx |
| Summary | A simple tool that automatically generates and assigns an OP name to each OP in an old format ONNX file. |
| upload_time | 2024-05-07 05:44:30 |
| maintainer | None |
| docs_url | None |
| author | Katsuya Hyodo |
| requires_python | >=3.6 |
| license | MIT License |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# sng4onnx
A simple tool that automatically generates and assigns an OP name to each OP in an old format ONNX file.
**S**imple op **N**ame **G**enerator for **ONNX**.
https://github.com/PINTO0309/simple-onnx-processing-tools
[](https://pepy.tech/project/sng4onnx)  [](https://pypi.org/project/sng4onnx/) [](https://github.com/PINTO0309/sng4onnx/actions?query=workflow%3ACodeQL)
<p align="center">
<img src="https://user-images.githubusercontent.com/33194443/195636410-a797d847-365a-469e-8bdd-7a3abb8aa3fd.png" />
</p>
# Key concept
- [x] Automatically generates and assigns an OP name to each OP in an old format ONNX file.
## 1. Setup
### 1-1. HostPC
```bash
### 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 sng4onnx
```
### 1-2. Docker
https://github.com/PINTO0309/simple-onnx-processing-tools#docker
## 2. CLI Usage
```
$ sng4onnx -h
usage:
sng4onnx [-h]
-if INPUT_ONNX_FILE_PATH
-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.
-of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
Output onnx file path.
-n, --non_verbose
Do not show all information logs. Only error logs are displayed.
```
## 3. In-script Usage
```python
>>> from sng4onnx import generate
>>> help(generate)
Help on function generate in module sng4onnx.onnx_opname_generator:
generate(
input_onnx_file_path: Union[str, NoneType] = '',
onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None,
output_onnx_file_path: Union[str, NoneType] = '',
non_verbose: Union[bool, NoneType] = False
) -> onnx.onnx_ml_pb2.ModelProto
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.
output_onnx_file_path: Optional[str]
Output onnx file path. If not specified, no ONNX file is output.
Default: ''
non_verbose: Optional[bool]
Do not show all information logs. Only error logs are displayed.
Default: False
Returns
-------
renamed_graph: onnx.ModelProto
Renamed onnx ModelProto.
```
## 4. CLI Execution
```bash
$ sng4onnx \
--input_onnx_file_path emotion-ferplus-8.onnx \
--output_onnx_file_path emotion-ferplus-8_renamed.onnx
```
## 5. In-script Execution
```python
from sng4onnx import generate
onnx_graph = generate(
input_onnx_file_path="fusionnet_180x320.onnx",
output_onnx_file_path="fusionnet_180x320_renamed.onnx",
)
```
## 6. Sample
https://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet18-v1-7.onnx
### Before
### After
## 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
Raw data
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"description": "# sng4onnx\nA simple tool that automatically generates and assigns an OP name to each OP in an old format ONNX file. \n**S**imple op **N**ame **G**enerator for **ONNX**.\n\nhttps://github.com/PINTO0309/simple-onnx-processing-tools\n\n[](https://pepy.tech/project/sng4onnx)  [](https://pypi.org/project/sng4onnx/) [](https://github.com/PINTO0309/sng4onnx/actions?query=workflow%3ACodeQL)\n\n<p align=\"center\">\n <img src=\"https://user-images.githubusercontent.com/33194443/195636410-a797d847-365a-469e-8bdd-7a3abb8aa3fd.png\" />\n</p>\n\n# Key concept\n\n- [x] Automatically generates and assigns an OP name to each OP in an old format ONNX file.\n\n## 1. Setup\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 \\\n&& python3 -m pip install -U onnx_graphsurgeon --index-url https://pypi.ngc.nvidia.com \\\n&& pip install -U sng4onnx\n```\n### 1-2. Docker\nhttps://github.com/PINTO0309/simple-onnx-processing-tools#docker\n\n## 2. CLI Usage\n```\n$ sng4onnx -h\n\nusage:\n sng4onnx [-h]\n -if INPUT_ONNX_FILE_PATH\n -of OUTPUT_ONNX_FILE_PATH\n [-n]\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 -of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH\n Output onnx file path.\n\n -n, --non_verbose\n Do not show all information logs. Only error logs are displayed.\n```\n\n## 3. In-script Usage\n```python\n>>> from sng4onnx import generate\n>>> help(generate)\n\nHelp on function generate in module sng4onnx.onnx_opname_generator:\n\ngenerate(\n input_onnx_file_path: Union[str, NoneType] = '',\n onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None,\n output_onnx_file_path: Union[str, NoneType] = '',\n non_verbose: Union[bool, NoneType] = False\n) -> onnx.onnx_ml_pb2.ModelProto\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 output_onnx_file_path: Optional[str]\n Output onnx file path. If not specified, no ONNX file is output.\n Default: ''\n\n non_verbose: Optional[bool]\n Do not show all information logs. Only error logs are displayed.\n Default: False\n\n Returns\n -------\n renamed_graph: onnx.ModelProto\n Renamed onnx ModelProto.\n```\n\n## 4. CLI Execution\n```bash\n$ sng4onnx \\\n--input_onnx_file_path emotion-ferplus-8.onnx \\\n--output_onnx_file_path emotion-ferplus-8_renamed.onnx\n```\n\n## 5. In-script Execution\n```python\nfrom sng4onnx import generate\n\nonnx_graph = generate(\n input_onnx_file_path=\"fusionnet_180x320.onnx\",\n output_onnx_file_path=\"fusionnet_180x320_renamed.onnx\",\n)\n```\n\n## 6. Sample\nhttps://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet18-v1-7.onnx\n### Before\n\n\n### After\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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