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