mmdeploy/docs/zh_cn/05-supported-backends/openvino.md

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# OpenVINO 支持情况
This tutorial is based on Linux systems like Ubuntu-18.04.
## Installation
It is recommended to create a virtual environment for the project.
1. Install [OpenVINO](https://docs.openvino.ai/latest/get_started.html). It is recommended to use the installer or install using pip.
Installation example using [pip](https://pypi.org/project/openvino-dev/):
```bash
pip install openvino-dev>=2022.3.0
```
2. \*`Optional` If you want to use OpenVINO in SDK, you need install OpenVINO with [install_guides](https://docs.openvino.ai/latest/openvino_docs_install_guides_overview.html).
3. Install MMDeploy following the [instructions](../01-how-to-build/build_from_source.md).
To work with models from [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/get_started.md), you may need to install it additionally.
## Usage
Example:
```bash
python tools/deploy.py \
configs/mmdet/detection/detection_openvino_static-300x300.py \
/mmdetection_dir/mmdetection/configs/ssd/ssd300_coco.py \
/tmp/snapshots/ssd300_coco_20210803_015428-d231a06e.pth \
tests/data/tiger.jpeg \
--work-dir ../deploy_result \
--device cpu \
--log-level INFO
```
## List of supported models exportable to OpenVINO from MMDetection
The table below lists the models that are guaranteed to be exportable to OpenVINO from MMDetection.
| Model name | Config | Dynamic Shape |
| :----------------: | :-----------------------------------------------------------------------: | :-----------: |
| ATSS | `configs/atss/atss_r50_fpn_1x_coco.py` | Y |
| Cascade Mask R-CNN | `configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py` | Y |
| Cascade R-CNN | `configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py` | Y |
| Faster R-CNN | `configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py` | Y |
| FCOS | `configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_4x2_2x_coco.py` | Y |
| FoveaBox | `configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py ` | Y |
| FSAF | `configs/fsaf/fsaf_r50_fpn_1x_coco.py` | Y |
| Mask R-CNN | `configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py` | Y |
| RetinaNet | `configs/retinanet/retinanet_r50_fpn_1x_coco.py` | Y |
| SSD | `configs/ssd/ssd300_coco.py` | Y |
| YOLOv3 | `configs/yolo/yolov3_d53_mstrain-608_273e_coco.py` | Y |
| YOLOX | `configs/yolox/yolox_tiny_8x8_300e_coco.py` | Y |
| Faster R-CNN + DCN | `configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py` | Y |
| VFNet | `configs/vfnet/vfnet_r50_fpn_1x_coco.py` | Y |
Notes:
- Custom operations from OpenVINO use the domain `org.openvinotoolkit`.
- For faster work in OpenVINO in the Faster-RCNN, Mask-RCNN, Cascade-RCNN, Cascade-Mask-RCNN models
the RoiAlign operation is replaced with the [ExperimentalDetectronROIFeatureExtractor](https://docs.openvinotoolkit.org/latest/openvino_docs_ops_detection_ExperimentalDetectronROIFeatureExtractor_6.html) operation in the ONNX graph.
- Models "VFNet" and "Faster R-CNN + DCN" use the custom "DeformableConv2D" operation.
## Deployment config
With the deployment config, you can specify additional options for the Model Optimizer.
To do this, add the necessary parameters to the `backend_config.mo_options` in the fields `args` (for parameters with values) and `flags` (for flags).
Example:
```python
backend_config = dict(
mo_options=dict(
args=dict({
'--mean_values': [0, 0, 0],
'--scale_values': [255, 255, 255],
'--data_type': 'FP32',
}),
flags=['--disable_fusing'],
)
)
```
Information about the possible parameters for the Model Optimizer can be found in the [documentation](https://docs.openvino.ai/latest/openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model.html).
## Troubleshooting
- ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory
To resolve missing external dependency on Ubuntu\*, execute the following command:
```bash
sudo apt-get install libpython3.7
```