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* Add Huawei Ascend NPU deploy through PaddleLite CANN * Add NNAdapter interface for paddlelite * Modify Huawei Ascend Cmake * Update way for compiling Huawei Ascend NPU deployment * remove UseLiteBackend in UseCANN * Support compile python whlee * Change names of nnadapter API * Add nnadapter pybind and remove useless API * Support Python deployment on Huawei Ascend NPU * Add models suppor for ascend * Add PPOCR rec reszie for ascend * fix conflict for ascend * Rename CANN to Ascend * Rename CANN to Ascend * Improve ascend * fix ascend bug * improve ascend docs * improve ascend docs * improve ascend docs * Improve Ascend * Improve Ascend * Move ascend python demo * Imporve ascend * Improve ascend * Improve ascend * Improve ascend * Improve ascend * Imporve ascend * Imporve ascend * Improve ascend * acc eval script * acc eval * remove acc_eval from branch huawei * Add detection and segmentation examples for Ascend deployment * Add detection and segmentation examples for Ascend deployment * Add PPOCR example for ascend deploy * Imporve paddle lite compiliation * Add FlyCV doc * Add FlyCV doc * Add FlyCV doc * Imporve Ascend docs * Imporve Ascend docs
67 lines
2.2 KiB
Markdown
67 lines
2.2 KiB
Markdown
[简体中文](../../cn/faq/boost_cv_by_flycv.md) | English
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# Accelerate end-to-end inference performance using FlyCV
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[FlyCV](https://github.com/PaddlePaddle/FlyCV) is a high performance computer image processing library, providing better performance than other image processing libraries, especially in the ARM architecture.
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FastDeploy is now integrated with FlyCV, allowing users to use FlyCV on supported hardware platforms to accelerate model end-to-end inference performance.
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## Supported OS and Architectures
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| OS | Architectures |
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| :-----------| :-------- |
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| Android | armeabi-v7a, arm64-v8a |
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| Linux | aarch64, armhf, x86_64|
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## Usage
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To use FlyCV, you first need to turn on the FlyCV compile option at compile time, and then add a new line of code to turn it on.
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This article uses Linux as an example to show how to enable the FlyCV compile option, and then add a new line of code to use FlyCV during deployment.
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You can turn on the FlyCV compile option when compiling the FastDeploy library as follows.
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```bash
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# When compiling C++ libraries
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-DENABLE_VISION=ON
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# When compiling Python libraries
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export ENABLE_FLYCV=ON
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```
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You can enable FlyCV by adding a new line of code to the deployment code as follows.
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```bash
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# C++ code
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fastdeploy::vision::EnableFlyCV();
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# Other..(e.g. With Huawei Ascend)
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fastdeploy::RuntimeOption option;
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option.UseAscend();
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...
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# Python code
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fastdeploy.vision.enable_flycv()
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# Other..(e.g. With Huawei Ascend)
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runtime_option = build_option()
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option.use_ascend()
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...
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```
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## Some Platforms FlyCV End-to-End Inference Performance
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KunPeng 920 CPU + Atlas 300I Pro.
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| Model | OpenCV E2E Performance(ms) | FlyCV E2E Performance(ms) |
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| :-----------| :-------- | :-------- |
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| ResNet50 | 2.78 | 1.63 |
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| PP-LCNetV2 | 2.50 | 1.39 |
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| YOLOv7 | 27.00 | 21.36 |
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| PP_HumanSegV2_Lite | 2.76 | 2.10 |
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Rockchip RV1126.
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| Model | OpenCV E2E Performance(ms) | FlyCV E2E Performance(ms) |
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| :-----------| :-------- | :-------- |
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| ResNet50 | 9.23 | 6.01 |
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| mobilenetv1_ssld_量化模型 | 9.23 | 6.01 |
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| yolov5s_量化模型 | 28.33 | 14.25 |
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| PP_LiteSeg_量化模型 | 132.25 | 60.31 |
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