mirror of
https://github.com/RapidAI/RapidOCR.git
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74 lines
2.8 KiB
Markdown
Executable File
74 lines
2.8 KiB
Markdown
Executable File
### 目录说明
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- `onnxruntime_infer`: 基于ONNXRuntime推理引擎推理
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- `openvino_infer`: 基于OpenVINO推理引擎推理
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### TODO
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- [ ] 模型转INT8，正在尝试
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- [ ] OpenVINO推理方向分类模型有误，已经提了[issue](https://github.com/openvinotoolkit/openvino/issues/11501)
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- [x] 其他推理模型代码整理
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- [x] 模型转换代码整理
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### 关于OpenVINO
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- OpenVINO可以直接推理IR、ONNX和PaddlePaddle模型，具体如下(图来源:[link](https://docs.openvino.ai/latest/openvino_docs_OV_UG_OV_Runtime_User_Guide.html#doxid-openvino-docs-o-v-u-g-o-v-runtime-user-guide))：
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<div align="center">
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<img src="../assets/BASIC_FLOW_IE_C.svg">
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</div>
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- 和ONNXRuntime同时推理同一个ONNX模型，OpenVINO推理速度更快
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- 但是从对比来看，OpenVINO占用内存更大，其原因是拿空间换的时间
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- 当指定`input_shape`在一个区间范围时，推理时内存占用会减少一些
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- 示例命令:
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```bash
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mo --input_model models/ch_PP-OCRv2_det_infer.onnx \
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--output_dir models/IR/static \
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--input_shape "[1,3,960:1200,800]"
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```
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### OpenVINO与ONNXRuntime性能对比
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- 推理设备：`Windows 64位 Intel(R) Core(TM) i5-4210M CPU @ 2.60GHz 2.59 GHz`
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- [测试图像宽高](https://drive.google.com/file/d/1iJcGvOVIdUlyOS52bBdvO8uzx8QORo5M/view?usp=sharing): `12119x810`
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| 测试模型 | 推理框架 | 占用内存(3次平均) | 推理时间(3次平均) |
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| ------------------------------------ | -------------------- | ----------------- | ----------------- |
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| `ch_PP-OCRv2_det_infer.onnx` | `ONNXRuntime=1.10.0` | 0.8G | 5.354s |
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| `ch_PP-OCRv2_det_infer.onnx` | `openvino=2022.1.0` | 3.225G | 2.53s |
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| `ch_PP-OCRv2_det_infer.xml` FP32 动态图 | `openvino=2022.1.0` | 3.175G | 2.0455s |
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### OpenVINO与ONNXRuntime推理代码写法对比
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NOTE: 以`ch_ppocr_mobile_v2_det`中推理代码为例子
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- ONNXRuntime
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```python
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import onnxruntime
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# 声明
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sess_opt = onnxruntime.SessionOptions()
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sess_opt.log_severity_level = 4
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sess_opt.enable_cpu_mem_arena = False
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self.session = onnxruntime.InferenceSession(det_model_path, sess_opt)
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self.input_name = self.session.get_inputs()[0].name
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self.output_name = self.session.get_outputs()[0].name
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# 推理
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preds = self.session.run([self.output_name],
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{self.input_name: img})
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```
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- OpenVINO
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```python
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from openvino.runtime import Core
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# 初始化
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ie = Core()
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model_onnx = ie.read_model(det_model_path)
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compile_model = ie.compile_model(model=model_onnx,
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device_name='CPU')
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self.vino_session = compile_model.create_infer_request()
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# 推理
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self.vino_session.infer(inputs=[img])
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vino_preds = self.vino_session.get_output_tensor().data
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```
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