# sog4onnx
Simple ONNX operation generator. **S**imple **O**peration **G**enerator for **ONNX**.
https://github.com/PINTO0309/simple-onnx-processing-tools
[](https://pepy.tech/project/sog4onnx)  [](https://pypi.org/project/sog4onnx/) [](https://github.com/PINTO0309/sog4onnx/actions?query=workflow%3ACodeQL)
<p align="center">
<img src="https://user-images.githubusercontent.com/33194443/170155206-3e771286-b5c4-4ac0-a5d7-ec9a0196cbbb.png" />
</p>
# Key concept
- [x] Variable, Constant, Operation and Attribute can be generated externally.
- [x] Allow Opset to be specified externally.
- [x] No check for consistency of Operations within the tool, as new OPs are added frequently and the definitions of existing OPs change with each new version of ONNX's Opset.
- [x] Only one OP can be defined at a time, and the goal is to generate free ONNX graphs using a combination of **[snc4onnx](https://github.com/PINTO0309/snc4onnx)**, **[sne4onnx](https://github.com/PINTO0309/sne4onnx)**, **[snd4onnx](https://github.com/PINTO0309/snd4onnx)** and **[scs4onnx](https://github.com/PINTO0309/scs4onnx)**.
- [x] List of parameters that can be specified: https://github.com/onnx/onnx/blob/main/docs/Operators.md
## 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 sog4onnx
```
### 1-2. Docker
https://github.com/PINTO0309/simple-onnx-processing-tools#docker
## 2. CLI Usage
```
$ sog4onnx -h
usage: sog4onnx [-h]
--ot OP_TYPE
--os OPSET
--ir IR_VERSION
--on OP_NAME
[-iv NAME TYPE VALUE]
[-ov NAME TYPE VALUE]
[-a NAME DTYPE VALUE]
[-of OUTPUT_ONNX_FILE_PATH]
[-n]
optional arguments:
-h, --help
show this help message and exit
-ot OP_TYPE, --op_type OP_TYPE
ONNX OP type.
https://github.com/onnx/onnx/blob/main/docs/Operators.md
-os OPSET, --opset OPSET
ONNX opset number.
-ir IR_VERSION, --ir_version IR_VERSION
ONNX ir_version number.
-on OP_NAME, --op_name OP_NAME
OP name.
-iv INPUT_VARIABLES INPUT_VARIABLES INPUT_VARIABLES, --input_variables INPUT_VARIABLES INPUT_VARIABLES INPUT_VARIABLES
input_variables can be specified multiple times.
--input_variables variable_name numpy.dtype shape
https://github.com/onnx/onnx/blob/main/docs/Operators.md
e.g.
--input_variables i1 float32 [1,3,5,5] \
--input_variables i2 int32 [1] \
--input_variables i3 float64 [1,3,224,224]
-ov OUTPUT_VARIABLES OUTPUT_VARIABLES OUTPUT_VARIABLES, --output_variables OUTPUT_VARIABLES OUTPUT_VARIABLES OUTPUT_VARIABLES
output_variables can be specified multiple times.
--output_variables variable_name numpy.dtype shape
https://github.com/onnx/onnx/blob/main/docs/Operators.md
e.g.
--output_variables o1 float32 [1,3,5,5] \
--output_variables o2 int32 [1] \
--output_variables o3 float64 [1,3,224,224]
-a ATTRIBUTES ATTRIBUTES ATTRIBUTES, --attributes ATTRIBUTES ATTRIBUTES ATTRIBUTES
attributes can be specified multiple times.
dtype is one of "float32" or "float64" or "int32" or "int64" or "str".
--attributes name dtype value
https://github.com/onnx/onnx/blob/main/docs/Operators.md
e.g.
--attributes alpha float32 1.0 \
--attributes beta float32 1.0 \
--attributes transA int32 0 \
--attributes transB int32 0
-of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
Output onnx file path.
If not specified, a file with the OP type name is generated.
e.g. op_type="Gemm" -> Gemm.onnx
-n, --non_verbose
Do not show all information logs. Only error logs are displayed.
```
## 3. In-script Usage
```python
$ python
>>> from sog4onnx import generate
>>> help(generate)
Help on function generate in module sog4onnx.onnx_operation_generator:
generate(
op_type: str,
opset: int,
ir_version: int,
op_name: str,
input_variables: dict,
output_variables: dict,
attributes: Union[dict, NoneType] = None,
output_onnx_file_path: Union[str, NoneType] = '',
non_verbose: Union[bool, NoneType] = False
) -> onnx.onnx_ml_pb2.ModelProto
Parameters
----------
op_type: str
ONNX op type.
See below for the types of OPs that can be specified.
https://github.com/onnx/onnx/blob/main/docs/Operators.md
e.g. "Add", "Div", "Gemm", ...
opset: int
ONNX opset number.
e.g. 11
ir_version: int
ONNX ir_version number.
e.g. 9
op_name: str
OP name.
input_variables: Optional[dict]
Specify input variables for the OP to be generated.
See below for the variables that can be specified.
https://github.com/onnx/onnx/blob/main/docs/Operators.md
{"input_var_name1": [numpy.dtype, shape], "input_var_name2": [dtype, shape], ...}
e.g.
input_variables = {
"name1": [np.float32, [1,224,224,3]],
"name2": [np.bool_, [0]],
...
}
output_variables: Optional[dict]
Specify output variables for the OP to be generated.
See below for the variables that can be specified.
https://github.com/onnx/onnx/blob/main/docs/Operators.md
{"output_var_name1": [numpy.dtype, shape], "output_var_name2": [dtype, shape], ...}
e.g.
output_variables = {
"name1": [np.float32, [1,224,224,3]],
"name2": [np.bool_, [0]],
...
}
attributes: Optional[dict]
Specify output attributes for the OP to be generated.
See below for the attributes that can be specified.
When specifying Tensor format values, specify an array converted to np.ndarray.
https://github.com/onnx/onnx/blob/main/docs/Operators.md
{"attr_name1": value1, "attr_name2": value2, "attr_name3": value3, ...}
e.g.
attributes = {
"alpha": 1.0,
"beta": 1.0,
"transA": 0,
"transB": 0
}
Default: None
output_onnx_file_path: Optional[str]
Output of 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
-------
single_op_graph: onnx.ModelProto
Single op onnx ModelProto
```
## 4. CLI Execution
```bash
$ sog4onnx \
--op_type Gemm \
--opset 1 \
--op_name gemm_custom1 \
--input_variables i1 float32 [1,2,3] \
--input_variables i2 float32 [1,1] \
--input_variables i3 int32 [0] \
--output_variables o1 float32 [1,2,3] \
--attributes alpha float32 1.0 \
--attributes beta float32 1.0 \
--attributes transA int32 0 \
--attributes transB int32 0
```
## 5. In-script Execution
```python
import numpy as np
from sog4onnx import generate
single_op_graph = generate(
op_type = 'Gemm',
opset = 1,
op_name = "gemm_custom1",
input_variables = {
"i1": [np.float32, [1,2,3]],
"i2": [np.float32, [1,1]],
"i3": [np.int32, [0]],
},
output_variables = {
"o1": [np.float32, [1,2,3]],
},
attributes = {
"alpha": 1.0,
"beta": 1.0,
"broadcast": 0,
"transA": 0,
"transB": 0,
},
non_verbose = True,
)
```
## 6. Sample
### 6-1. opset=1, Gemm
```bash
$ sog4onnx \
--op_type Gemm \
--opset 1 \
--op_name gemm_custom1 \
--input_variables i1 float32 [1,2,3] \
--input_variables i2 float32 [1,1] \
--input_variables i3 int32 [0] \
--output_variables o1 float32 [1,2,3] \
--attributes alpha float32 1.0 \
--attributes beta float32 1.0 \
--attributes transA int32 0 \
--attributes transB int32 0
--non_verbose
```
### 6-2. opset=11, Add
```bash
$ sog4onnx \
--op_type Add \
--opset 11 \
--op_name add_custom1 \
--input_variables i1 float32 [1,2,3] \
--input_variables i2 float32 [1,2,3] \
--output_variables o1 float32 [1,2,3] \
--non_verbose
```
### 6-3. opset=11, NonMaxSuppression
```bash
$ sog4onnx \
--op_type NonMaxSuppression \
--opset 11 \
--op_name nms_custom1 \
--input_variables boxes float32 [1,6,4] \
--input_variables scores float32 [1,1,6] \
--input_variables max_output_boxes_per_class int64 [1] \
--input_variables iou_threshold float32 [1] \
--input_variables score_threshold float32 [1] \
--output_variables selected_indices int64 [3,3] \
--attributes center_point_box int64 1
```
### 6-4. opset=11, Constant
```bash
$ sog4onnx \
--op_type Constant \
--opset 11 \
--op_name const_custom1 \
--output_variables boxes float32 [1,6,4] \
--attributes value float32 \
[[\
[0.5,0.5,1.0,1.0],\
[0.5,0.6,1.0,1.0],\
[0.5,0.4,1.0,1.0],\
[0.5,10.5,1.0,1.0],\
[0.5,10.6,1.0,1.0],\
[0.5,100.5,1.0,1.0]\
]]
```
### 6-5. opset=11, EfficientNMS_TRT (TensorRT Efficient NMS Plugin)
```bash
$ sog4onnx \
--op_type EfficientNMS_TRT \
--opset 11 \
--op_name trt_nms_efficient_std_11 \
--input_variables boxes float32 [1,3549,4] \
--input_variables scores float32 [1,3549,16] \
--attributes plugin_version str 1 \
--attributes score_threshold float32 0.25 \
--attributes iou_threshold float32 0.45 \
--attributes max_output_boxes int64 20 \
--attributes background_class int64 -1 \
--attributes score_activation bool False \
--attributes box_coding int64 0 \
--output_variables num_detections int32 [1,1] \
--output_variables detection_boxes float32 [1,20,4] \
--output_variables detection_scores float32 [1,20] \
--output_variables detection_classes int32 [1,20]
```
## 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/sne4onnx
5. https://github.com/PINTO0309/snd4onnx
6. https://github.com/PINTO0309/snc4onnx
7. https://github.com/PINTO0309/scs4onnx
8. 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": "# sog4onnx\nSimple ONNX operation generator. **S**imple **O**peration **G**enerator for **ONNX**.\n\nhttps://github.com/PINTO0309/simple-onnx-processing-tools\n\n[](https://pepy.tech/project/sog4onnx)  [](https://pypi.org/project/sog4onnx/) [](https://github.com/PINTO0309/sog4onnx/actions?query=workflow%3ACodeQL)\n\n<p align=\"center\">\n <img src=\"https://user-images.githubusercontent.com/33194443/170155206-3e771286-b5c4-4ac0-a5d7-ec9a0196cbbb.png\" />\n</p>\n\n# Key concept\n\n- [x] Variable, Constant, Operation and Attribute can be generated externally.\n- [x] Allow Opset to be specified externally.\n- [x] No check for consistency of Operations within the tool, as new OPs are added frequently and the definitions of existing OPs change with each new version of ONNX's Opset.\n- [x] Only one OP can be defined at a time, and the goal is to generate free ONNX graphs using a combination of **[snc4onnx](https://github.com/PINTO0309/snc4onnx)**, **[sne4onnx](https://github.com/PINTO0309/sne4onnx)**, **[snd4onnx](https://github.com/PINTO0309/snd4onnx)** and **[scs4onnx](https://github.com/PINTO0309/scs4onnx)**.\n- [x] List of parameters that can be specified: https://github.com/onnx/onnx/blob/main/docs/Operators.md\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 sog4onnx\n```\n### 1-2. Docker\nhttps://github.com/PINTO0309/simple-onnx-processing-tools#docker\n\n## 2. CLI Usage\n```\n$ sog4onnx -h\n\nusage: sog4onnx [-h]\n --ot OP_TYPE\n --os OPSET\n --ir IR_VERSION\n --on OP_NAME\n [-iv NAME TYPE VALUE]\n [-ov NAME TYPE VALUE]\n [-a NAME DTYPE VALUE]\n [-of OUTPUT_ONNX_FILE_PATH]\n [-n]\n\noptional arguments:\n -h, --help\n show this help message and exit\n\n -ot OP_TYPE, --op_type OP_TYPE\n ONNX OP type.\n https://github.com/onnx/onnx/blob/main/docs/Operators.md\n\n -os OPSET, --opset OPSET\n ONNX opset number.\n\n -ir IR_VERSION, --ir_version IR_VERSION\n ONNX ir_version number.\n\n -on OP_NAME, --op_name OP_NAME\n OP name.\n\n -iv INPUT_VARIABLES INPUT_VARIABLES INPUT_VARIABLES, --input_variables INPUT_VARIABLES INPUT_VARIABLES INPUT_VARIABLES\n input_variables can be specified multiple times.\n --input_variables variable_name numpy.dtype shape\n https://github.com/onnx/onnx/blob/main/docs/Operators.md\n\n e.g.\n --input_variables i1 float32 [1,3,5,5] \\\n --input_variables i2 int32 [1] \\\n --input_variables i3 float64 [1,3,224,224]\n\n -ov OUTPUT_VARIABLES OUTPUT_VARIABLES OUTPUT_VARIABLES, --output_variables OUTPUT_VARIABLES OUTPUT_VARIABLES OUTPUT_VARIABLES\n output_variables can be specified multiple times.\n --output_variables variable_name numpy.dtype shape\n https://github.com/onnx/onnx/blob/main/docs/Operators.md\n\n e.g.\n --output_variables o1 float32 [1,3,5,5] \\\n --output_variables o2 int32 [1] \\\n --output_variables o3 float64 [1,3,224,224]\n\n -a ATTRIBUTES ATTRIBUTES ATTRIBUTES, --attributes ATTRIBUTES ATTRIBUTES ATTRIBUTES\n attributes can be specified multiple times.\n dtype is one of \"float32\" or \"float64\" or \"int32\" or \"int64\" or \"str\".\n --attributes name dtype value\n https://github.com/onnx/onnx/blob/main/docs/Operators.md\n\n e.g.\n --attributes alpha float32 1.0 \\\n --attributes beta float32 1.0 \\\n --attributes transA int32 0 \\\n --attributes transB int32 0\n\n -of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH\n Output onnx file path.\n If not specified, a file with the OP type name is generated.\n\n e.g. op_type=\"Gemm\" -> Gemm.onnx\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$ python\n>>> from sog4onnx import generate\n>>> help(generate)\nHelp on function generate in module sog4onnx.onnx_operation_generator:\n\ngenerate(\n op_type: str,\n opset: int,\n ir_version: int,\n op_name: str,\n input_variables: dict,\n output_variables: dict,\n attributes: Union[dict, 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 op_type: str\n ONNX op type.\n See below for the types of OPs that can be specified.\n https://github.com/onnx/onnx/blob/main/docs/Operators.md\n\n e.g. \"Add\", \"Div\", \"Gemm\", ...\n\n opset: int\n ONNX opset number.\n\n e.g. 11\n\n ir_version: int\n ONNX ir_version number.\n\n e.g. 9\n\n op_name: str\n OP name.\n\n input_variables: Optional[dict]\n Specify input variables for the OP to be generated.\n See below for the variables that can be specified.\n https://github.com/onnx/onnx/blob/main/docs/Operators.md\n {\"input_var_name1\": [numpy.dtype, shape], \"input_var_name2\": [dtype, shape], ...}\n\n e.g.\n input_variables = {\n \"name1\": [np.float32, [1,224,224,3]],\n \"name2\": [np.bool_, [0]],\n ...\n }\n\n output_variables: Optional[dict]\n Specify output variables for the OP to be generated.\n See below for the variables that can be specified.\n https://github.com/onnx/onnx/blob/main/docs/Operators.md\n {\"output_var_name1\": [numpy.dtype, shape], \"output_var_name2\": [dtype, shape], ...}\n\n e.g.\n output_variables = {\n \"name1\": [np.float32, [1,224,224,3]],\n \"name2\": [np.bool_, [0]],\n ...\n }\n\n attributes: Optional[dict]\n Specify output attributes for the OP to be generated.\n See below for the attributes that can be specified.\n When specifying Tensor format values, specify an array converted to np.ndarray.\n https://github.com/onnx/onnx/blob/main/docs/Operators.md\n {\"attr_name1\": value1, \"attr_name2\": value2, \"attr_name3\": value3, ...}\n\n e.g.\n attributes = {\n \"alpha\": 1.0,\n \"beta\": 1.0,\n \"transA\": 0,\n \"transB\": 0\n }\n Default: None\n\n output_onnx_file_path: Optional[str]\n Output of onnx file path.\n 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 single_op_graph: onnx.ModelProto\n Single op onnx ModelProto\n```\n\n## 4. CLI Execution\n```bash\n$ sog4onnx \\\n--op_type Gemm \\\n--opset 1 \\\n--op_name gemm_custom1 \\\n--input_variables i1 float32 [1,2,3] \\\n--input_variables i2 float32 [1,1] \\\n--input_variables i3 int32 [0] \\\n--output_variables o1 float32 [1,2,3] \\\n--attributes alpha float32 1.0 \\\n--attributes beta float32 1.0 \\\n--attributes transA int32 0 \\\n--attributes transB int32 0\n```\n\n## 5. In-script Execution\n```python\nimport numpy as np\nfrom sog4onnx import generate\n\nsingle_op_graph = generate(\n op_type = 'Gemm',\n opset = 1,\n op_name = \"gemm_custom1\",\n input_variables = {\n \"i1\": [np.float32, [1,2,3]],\n \"i2\": [np.float32, [1,1]],\n \"i3\": [np.int32, [0]],\n },\n output_variables = {\n \"o1\": [np.float32, [1,2,3]],\n },\n attributes = {\n \"alpha\": 1.0,\n \"beta\": 1.0,\n \"broadcast\": 0,\n \"transA\": 0,\n \"transB\": 0,\n },\n non_verbose = True,\n)\n```\n\n## 6. Sample\n### 6-1. opset=1, Gemm\n```bash\n$ sog4onnx \\\n--op_type Gemm \\\n--opset 1 \\\n--op_name gemm_custom1 \\\n--input_variables i1 float32 [1,2,3] \\\n--input_variables i2 float32 [1,1] \\\n--input_variables i3 int32 [0] \\\n--output_variables o1 float32 [1,2,3] \\\n--attributes alpha float32 1.0 \\\n--attributes beta float32 1.0 \\\n--attributes transA int32 0 \\\n--attributes transB int32 0\n--non_verbose\n```\n\n\n\n### 6-2. opset=11, Add\n```bash\n$ sog4onnx \\\n--op_type Add \\\n--opset 11 \\\n--op_name add_custom1 \\\n--input_variables i1 float32 [1,2,3] \\\n--input_variables i2 float32 [1,2,3] \\\n--output_variables o1 float32 [1,2,3] \\\n--non_verbose\n```\n\n\n\n### 6-3. opset=11, NonMaxSuppression\n```bash\n$ sog4onnx \\\n--op_type NonMaxSuppression \\\n--opset 11 \\\n--op_name nms_custom1 \\\n--input_variables boxes float32 [1,6,4] \\\n--input_variables scores float32 [1,1,6] \\\n--input_variables max_output_boxes_per_class int64 [1] \\\n--input_variables iou_threshold float32 [1] \\\n--input_variables score_threshold float32 [1] \\\n--output_variables selected_indices int64 [3,3] \\\n--attributes center_point_box int64 1\n```\n\n\n\n### 6-4. opset=11, Constant\n```bash\n$ sog4onnx \\\n--op_type Constant \\\n--opset 11 \\\n--op_name const_custom1 \\\n--output_variables boxes float32 [1,6,4] \\\n--attributes value float32 \\\n[[\\\n[0.5,0.5,1.0,1.0],\\\n[0.5,0.6,1.0,1.0],\\\n[0.5,0.4,1.0,1.0],\\\n[0.5,10.5,1.0,1.0],\\\n[0.5,10.6,1.0,1.0],\\\n[0.5,100.5,1.0,1.0]\\\n]]\n```\n\n\n### 6-5. opset=11, EfficientNMS_TRT (TensorRT Efficient NMS Plugin)\n```bash\n$ sog4onnx \\\n--op_type EfficientNMS_TRT \\\n--opset 11 \\\n--op_name trt_nms_efficient_std_11 \\\n--input_variables boxes float32 [1,3549,4] \\\n--input_variables scores float32 [1,3549,16] \\\n--attributes plugin_version str 1 \\\n--attributes score_threshold float32 0.25 \\\n--attributes iou_threshold float32 0.45 \\\n--attributes max_output_boxes int64 20 \\\n--attributes background_class int64 -1 \\\n--attributes score_activation bool False \\\n--attributes box_coding int64 0 \\\n--output_variables num_detections int32 [1,1] \\\n--output_variables detection_boxes float32 [1,20,4] \\\n--output_variables detection_scores float32 [1,20] \\\n--output_variables detection_classes int32 [1,20]\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/sne4onnx\n5. https://github.com/PINTO0309/snd4onnx\n6. https://github.com/PINTO0309/snc4onnx\n7. https://github.com/PINTO0309/scs4onnx\n8. 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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