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77 lines
2.7 KiB
Markdown
77 lines
2.7 KiB
Markdown
[English](README.md) | 简体中文
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# YOLOv5 SOPHGO部署示例
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## 支持模型列表
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YOLOv5 v6.0部署模型实现来自[YOLOv5](https://github.com/ultralytics/yolov5/tree/v6.0),和[基于COCO的预训练模型](https://github.com/ultralytics/yolov5/releases/tag/v6.0)
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## 准备YOLOv5部署模型以及转换模型
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SOPHGO-TPU部署模型前需要将Paddle模型转换成bmodel模型，具体步骤如下:
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- 下载预训练ONNX模型，请参考[YOLOv5准备部署模型](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/vision/detection/yolov5)
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- ONNX模型转换bmodel模型的过程，请参考[TPU-MLIR](https://github.com/sophgo/tpu-mlir)
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## 模型转换example
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下面以YOLOv5s为例子,教大家如何转换ONNX模型到SOPHGO-TPU模型
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## 下载YOLOv5s模型
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### 下载ONNX YOLOv5s静态图模型
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```shell
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
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```
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### 导出bmodel模型
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以转化BM1684x的bmodel模型为例子，我们需要下载[TPU-MLIR](https://github.com/sophgo/tpu-mlir)工程，安装过程具体参见[TPU-MLIR文档](https://github.com/sophgo/tpu-mlir/blob/master/README.md)。
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### 1. 安装
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``` shell
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docker pull sophgo/tpuc_dev:latest
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# myname1234是一个示例，也可以设置其他名字
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docker run --privileged --name myname1234 -v $PWD:/workspace -it sophgo/tpuc_dev:latest
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source ./envsetup.sh
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./build.sh
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```
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### 2. ONNX模型转换为bmodel模型
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``` shell
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mkdir YOLOv5s && cd YOLOv5s
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# 在该文件中放入测试图片，同时将上一步下载的yolov5s.onnx放入该文件夹中
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cp -rf ${REGRESSION_PATH}/dataset/COCO2017 .
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cp -rf ${REGRESSION_PATH}/image .
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# 放入onnx模型文件yolov5s.onnx
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mkdir workspace && cd workspace
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# 将ONNX模型转换为mlir模型，其中参数--output_names可以通过NETRON查看
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model_transform.py \
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--model_name yolov5s \
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--model_def ../yolov5s.onnx \
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--input_shapes [[1,3,640,640]] \
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--mean 0.0,0.0,0.0 \
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--scale 0.0039216,0.0039216,0.0039216 \
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--keep_aspect_ratio \
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--pixel_format rgb \
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--output_names output,350,498,646 \
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--test_input ../image/dog.jpg \
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--test_result yolov5s_top_outputs.npz \
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--mlir yolov5s.mlir
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# 将mlir模型转换为BM1684x的F32 bmodel模型
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model_deploy.py \
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--mlir yolov5s.mlir \
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--quantize F32 \
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--chip bm1684x \
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--test_input yolov5s_in_f32.npz \
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--test_reference yolov5s_top_outputs.npz \
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--model yolov5s_1684x_f32.bmodel
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```
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最终获得可以在BM1684x上能够运行的bmodel模型yolov5s_1684x_f32.bmodel。如果需要进一步对模型进行加速，可以将ONNX模型转换为INT8 bmodel，具体步骤参见[TPU-MLIR文档](https://github.com/sophgo/tpu-mlir/blob/master/README.md)。
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## 其他链接
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- [Cpp部署](./cpp)
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