131 lines
4.4 KiB
Markdown
131 lines
4.4 KiB
Markdown
Apart from `deploy.py`, there are other useful tools under the `tools/` directory.
|
|
|
|
## torch2onnx
|
|
|
|
This tool can be used to convert PyTorch model from OpenMMLab to ONNX.
|
|
|
|
### Usage
|
|
|
|
```bash
|
|
python tools/torch2onnx.py \
|
|
${DEPLOY_CFG} \
|
|
${MODEL_CFG} \
|
|
${CHECKPOINT} \
|
|
${INPUT_IMG} \
|
|
--work-dir ${WORK_DIR} \
|
|
--device cpu \
|
|
--log-level INFO
|
|
```
|
|
|
|
### Description of all arguments
|
|
|
|
- `deploy_cfg` : The path of the deploy config file in MMDeploy codebase.
|
|
- `model_cfg` : The path of model config file in OpenMMLab codebase.
|
|
- `checkpoint` : The path of the model checkpoint file.
|
|
- `img` : The path of the image file used to convert the model.
|
|
- `--work-dir` : Directory to save output ONNX models Default is `./work-dir`.
|
|
- `--device` : The device used for conversion. If not specified, it will be set to `cpu`.
|
|
- `--log-level` : To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
|
|
|
|
## extract
|
|
|
|
ONNX model with `Mark` nodes in it can be partitioned into multiple subgraphs. This tool can be used to extract the subgraph from the ONNX model.
|
|
|
|
### Usage
|
|
|
|
```bash
|
|
python tools/extract.py \
|
|
${INPUT_MODEL} \
|
|
${OUTPUT_MODEL} \
|
|
--start ${PARITION_START} \
|
|
--end ${PARITION_END} \
|
|
--log-level INFO
|
|
```
|
|
|
|
### Description of all arguments
|
|
|
|
- `input_model` : The path of input ONNX model. The output ONNX model will be extracted from this model.
|
|
- `output_model` : The path of output ONNX model.
|
|
- `--start` : The start point of extracted model with format `<function_name>:<input/output>`. The `function_name` comes from the decorator `@mark`.
|
|
- `--end` : The end point of extracted model with format `<function_name>:<input/output>`. The `function_name` comes from the decorator `@mark`.
|
|
- `--log-level` : To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
|
|
|
|
### Note
|
|
|
|
To support the model partition, you need to add Mark nodes in the ONNX model. The Mark node comes from the `@mark` decorator.
|
|
For example, if we have marked the `multiclass_nms` as below, we can set `end=multiclass_nms:input` to extract the subgraph before NMS.
|
|
|
|
```python
|
|
@mark('multiclass_nms', inputs=['boxes', 'scores'], outputs=['dets', 'labels'])
|
|
def multiclass_nms(*args, **kwargs):
|
|
"""Wrapper function for `_multiclass_nms`."""
|
|
```
|
|
|
|
## onnx2pplnn
|
|
|
|
This tool helps to convert an `ONNX` model to an `PPLNN` model.
|
|
|
|
### Usage
|
|
|
|
```bash
|
|
python tools/onnx2pplnn.py \
|
|
${ONNX_PATH} \
|
|
${OUTPUT_PATH} \
|
|
--device cuda:0 \
|
|
--opt-shapes [224,224] \
|
|
--log-level INFO
|
|
```
|
|
|
|
### Description of all arguments
|
|
|
|
- `onnx_path`: The path of the `ONNX` model to convert.
|
|
- `output_path`: The converted `PPLNN` algorithm path in json format.
|
|
- `device`: The device of the model during conversion.
|
|
- `opt-shapes`: Optimal shapes for PPLNN optimization. The shape of each tensor should be wrap with "\[\]" or "()" and the shapes of tensors should be separated by ",".
|
|
- `--log-level`: To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
|
|
|
|
## onnx2tensorrt
|
|
|
|
This tool can be used to convert ONNX to TensorRT engine.
|
|
|
|
### Usage
|
|
|
|
```bash
|
|
python tools/onnx2tensorrt.py \
|
|
${DEPLOY_CFG} \
|
|
${ONNX_PATH} \
|
|
${OUTPUT} \
|
|
--device-id 0 \
|
|
--log-level INFO
|
|
```
|
|
|
|
### Description of all arguments
|
|
|
|
- `deploy_cfg` : The path of the deploy config file in MMDeploy codebase.
|
|
- `onnx_path` : The ONNX model path to convert.
|
|
- `output` : The path of output TensorRT engine.
|
|
- `--device-id` : The device index, default to `0`.
|
|
- `--calib-file` : The calibration data used to calibrate engine to int8.
|
|
- `--log-level` : To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
|
|
|
|
## onnx2ncnn
|
|
|
|
This tool helps to convert an `ONNX` model to an `ncnn` model.
|
|
|
|
### Usage
|
|
|
|
```bash
|
|
python tools/onnx2ncnn.py \
|
|
${ONNX_PATH} \
|
|
${NCNN_PARAM} \
|
|
${NCNN_BIN} \
|
|
--log-level INFO
|
|
```
|
|
|
|
### Description of all arguments
|
|
|
|
- `onnx_path` : The path of the `ONNX` model to convert from.
|
|
- `output_param` : The converted `ncnn` param path.
|
|
- `output_bin` : The converted `ncnn` bin path.
|
|
- `--log-level` : To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
|