| Name | sbi4onnx JSON |
| Version |
1.0.6
JSON |
| download |
| home_page | https://github.com/PINTO0309/sbi4onnx |
| Summary | A very simple script that only initializes the batch size of ONNX. Simple Batchsize Initialization for ONNX. |
| upload_time | 2024-04-30 05:52:07 |
| maintainer | None |
| docs_url | None |
| author | Katsuya Hyodo |
| requires_python | >=3.6 |
| license | MIT License |
| keywords |
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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# sbi4onnx
A very simple script that only initializes the batch size of ONNX. **S**imple **B**atchsize **I**nitialization for **ONNX**.
https://github.com/PINTO0309/simple-onnx-processing-tools
[](https://pepy.tech/project/sbi4onnx)  [](https://pypi.org/project/sbi4onnx/) [](https://github.com/PINTO0309/sbi4onnx/actions?query=workflow%3ACodeQL)
<p align="center">
<img src="https://user-images.githubusercontent.com/33194443/170157713-78a1b84e-caf6-4abe-92e4-c9ea51bcaacd.png" />
</p>
# Key concept
- [x] Initializes the ONNX batch size with the specified characters.
- [x] This tool is not a panacea and may fail to initialize models with very complex structures. For example, there is an ONNX that contains a `Reshape` that involves a batch size, or a `Gemm` that contains a batch output other than 1 in the output result.
- [x] A `Reshape` in a graph cannot contain more than two undefined shapes, such as `-1` or `N` or `None` or `unk_*`. Therefore, before initializing the batch size with this tool, make sure that the `Reshape` does not already contain one or more `-1` dimensions. If it already contains undefined dimensions, it may be possible to successfully initialize the batch size by pre-writing the undefined dimensions of the relevant `Reshape` to static values using **[sam4onnx](https://github.com/PINTO0309/sam4onnx)**.
## 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 --no-deps -U onnx-simplifier \
&& pip install -U sbi4onnx
```
### 1-2. Docker
https://github.com/PINTO0309/simple-onnx-processing-tools#docker
## 2. CLI Usage
```
$ sbi4onnx -h
usage:
sbi4onnx [-h]
-if INPUT_ONNX_FILE_PATH
-of OUTPUT_ONNX_FILE_PATH
-ics INITIALIZATION_CHARACTER_STRING
[-dos]
[-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.
-ics INITIALIZATION_CHARACTER_STRING, --initialization_character_string INITIALIZATION_CHARACTER_STRING
String to initialize batch size. "-1" or "N" or "xxx", etc...
Default: '-1'
-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 sbi4onnx import initialize
>>> help(initialize)
Help on function initialize in module sbi4onnx.onnx_batchsize_initialize:
initialize(
input_onnx_file_path: Union[str, NoneType] = '',
onnx_graph: Union[onnx.onnx_ml_pb2.ModelProto, NoneType] = None,
output_onnx_file_path: Union[str, NoneType] = '',
initialization_character_string: Union[str, NoneType] = '-1',
non_verbose: Union[bool, NoneType] = False,
disable_onnxsim: 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: ''
initialization_character_string: Optional[str]
String to initialize batch size. "-1" or "N" or "xxx", etc...
Default: '-1'
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
-------
changed_graph: onnx.ModelProto
Changed onnx ModelProto.
```
## 4. CLI Execution
```bash
$ sbi4onnx \
--input_onnx_file_path whenet_224x224.onnx \
--output_onnx_file_path whenet_Nx224x224.onnx \
--initialization_character_string N
$ sbi4onnx \
--input_onnx_file_path whenet_224x224.onnx \
--output_onnx_file_path whenet_Nx224x224.onnx \
--initialization_character_string -1
$ sbi4onnx \
--input_onnx_file_path whenet_224x224.onnx \
--output_onnx_file_path whenet_Nx224x224.onnx \
--initialization_character_string abcdefg
```
## 5. In-script Execution
```python
from sbi4onnx import initialize
onnx_graph = initialize(
input_onnx_file_path="whenet_224x224.onnx",
output_onnx_file_path="whenet_Nx224x224.onnx",
initialization_character_string="abcdefg",
)
# or
onnx_graph = initialize(
onnx_graph=graph,
initialization_character_string="abcdefg",
)
```
## 6. Sample
### 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
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"description": "# sbi4onnx\nA very simple script that only initializes the batch size of ONNX. **S**imple **B**atchsize **I**nitialization for **ONNX**.\n\nhttps://github.com/PINTO0309/simple-onnx-processing-tools\n\n[](https://pepy.tech/project/sbi4onnx)  [](https://pypi.org/project/sbi4onnx/) [](https://github.com/PINTO0309/sbi4onnx/actions?query=workflow%3ACodeQL)\n\n<p align=\"center\">\n <img src=\"https://user-images.githubusercontent.com/33194443/170157713-78a1b84e-caf6-4abe-92e4-c9ea51bcaacd.png\" />\n</p>\n\n# Key concept\n\n- [x] Initializes the ONNX batch size with the specified characters.\n- [x] This tool is not a panacea and may fail to initialize models with very complex structures. For example, there is an ONNX that contains a `Reshape` that involves a batch size, or a `Gemm` that contains a batch output other than 1 in the output result.\n- [x] A `Reshape` in a graph cannot contain more than two undefined shapes, such as `-1` or `N` or `None` or `unk_*`. Therefore, before initializing the batch size with this tool, make sure that the `Reshape` does not already contain one or more `-1` dimensions. If it already contains undefined dimensions, it may be possible to successfully initialize the batch size by pre-writing the undefined dimensions of the relevant `Reshape` to static values using **[sam4onnx](https://github.com/PINTO0309/sam4onnx)**.\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 --no-deps -U onnx-simplifier \\\n&& pip install -U sbi4onnx\n```\n### 1-2. Docker\nhttps://github.com/PINTO0309/simple-onnx-processing-tools#docker\n\n## 2. CLI Usage\n```\n$ sbi4onnx -h\n\nusage:\n sbi4onnx [-h]\n -if INPUT_ONNX_FILE_PATH\n -of OUTPUT_ONNX_FILE_PATH\n -ics INITIALIZATION_CHARACTER_STRING\n [-dos]\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 -ics INITIALIZATION_CHARACTER_STRING, --initialization_character_string INITIALIZATION_CHARACTER_STRING\n String to initialize batch size. \"-1\" or \"N\" or \"xxx\", etc...\n Default: '-1'\n\n -dos, --disable_onnxsim\n Suppress the execution of onnxsim on the backend and dare to leave redundant processing.\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 sbi4onnx import initialize\n>>> help(initialize)\n\nHelp on function initialize in module sbi4onnx.onnx_batchsize_initialize:\n\ninitialize(\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 initialization_character_string: Union[str, NoneType] = '-1',\n non_verbose: Union[bool, NoneType] = False,\n disable_onnxsim: 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 initialization_character_string: Optional[str]\n String to initialize batch size. \"-1\" or \"N\" or \"xxx\", etc...\n Default: '-1'\n\n disable_onnxsim: Optional[bool]\n Suppress the execution of onnxsim on the backend and dare to leave redundant processing.\n Default: False\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 changed_graph: onnx.ModelProto\n Changed onnx ModelProto.\n```\n\n## 4. CLI Execution\n```bash\n$ sbi4onnx \\\n--input_onnx_file_path whenet_224x224.onnx \\\n--output_onnx_file_path whenet_Nx224x224.onnx \\\n--initialization_character_string N\n\n$ sbi4onnx \\\n--input_onnx_file_path whenet_224x224.onnx \\\n--output_onnx_file_path whenet_Nx224x224.onnx \\\n--initialization_character_string -1\n\n$ sbi4onnx \\\n--input_onnx_file_path whenet_224x224.onnx \\\n--output_onnx_file_path whenet_Nx224x224.onnx \\\n--initialization_character_string abcdefg\n```\n\n## 5. In-script Execution\n```python\nfrom sbi4onnx import initialize\n\nonnx_graph = initialize(\n input_onnx_file_path=\"whenet_224x224.onnx\",\n output_onnx_file_path=\"whenet_Nx224x224.onnx\",\n initialization_character_string=\"abcdefg\",\n)\n\n# or\n\nonnx_graph = initialize(\n onnx_graph=graph,\n initialization_character_string=\"abcdefg\",\n)\n```\n\n## 6. Sample\n### Before\n\n\n### After\n\n\n## 7. 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