mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-08 18:11:00 +08:00
1135d33dd7
* 第一次提交 * 补充一处漏翻译 * deleted: docs/en/quantize.md * Update one translation * Update en version * Update one translation in code * Standardize one writing * Standardize one writing * Update some en version * Fix a grammer problem * Update en version for api/vision result * Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop * Checkout the link in README in vision_results/ to the en documents * Modify a title * Add link to serving/docs/ * Finish translation of demo.md * Update english version of serving/docs/ * Update title of readme * Update some links * Modify a title * Update some links * Update en version of java android README * Modify some titles * Modify some titles * Modify some titles * modify article to document * update some english version of documents in examples * Add english version of documents in examples/visions * Sync to current branch * Add english version of documents in examples
77 lines
2.8 KiB
Markdown
77 lines
2.8 KiB
Markdown
English | [简体中文](README_CN.md)
|
|
# YOLOv5 SOPHGO Deployment Example
|
|
|
|
## Supporting Model List
|
|
|
|
For YOLOv5 v6.0 model deployment, please refer to [YOLOv5](https://github.com/ultralytics/yolov5/tree/v6.0) and [Pretrained model based on COCO](https://github.com/ultralytics/yolov5/releases/tag/v6.0).
|
|
|
|
## Preparing YOLOv5 Model Deployment and Conversion
|
|
|
|
Before deploying SOPHGO-TPU model, you need to first convert Paddle model to bmodel. Specific steps are as follows:
|
|
- Download the pre-trained ONNX model. Please refer to [YOLOv5 Ready-to-deploy Model](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/vision/detection/yolov5).
|
|
- Convert ONNX model to bmodel. Please refer to [TPU-MLIR](https://github.com/sophgo/tpu-mlir).
|
|
|
|
## Model conversion example
|
|
|
|
Here we take YOLOv5s as an example to show you how to convert ONNX model to SOPHGO-TPU model.
|
|
|
|
## Download YOLOv5s Model
|
|
|
|
### Download ONNX YOLOv5s Static Map Model
|
|
```shell
|
|
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
|
|
|
|
```
|
|
### Export bmodel Model
|
|
|
|
Here we take BM1684x bmodel as an example. You need to download [TPU-MLIR](https://github.com/sophgo/tpu-mlir) project. For the installing process, please refer to [TPU-MLIR Document](https://github.com/sophgo/tpu-mlir/blob/master/README.md).
|
|
### 1. Installation
|
|
``` shell
|
|
docker pull sophgo/tpuc_dev:latest
|
|
|
|
# myname1234 is just an example, you can customize your own name.
|
|
docker run --privileged --name myname1234 -v $PWD:/workspace -it sophgo/tpuc_dev:latest
|
|
|
|
source ./envsetup.sh
|
|
./build.sh
|
|
```
|
|
|
|
### 2. Convert ONNX model to bmodel
|
|
``` shell
|
|
mkdir YOLOv5s && cd YOLOv5s
|
|
|
|
# Put the test image in this file, and put the yolov5s.onnx into this folder.
|
|
cp -rf ${REGRESSION_PATH}/dataset/COCO2017 .
|
|
cp -rf ${REGRESSION_PATH}/image .
|
|
# Put in the onnx model file yolov5s.onnx
|
|
|
|
mkdir workspace && cd workspace
|
|
|
|
# Convert ONNX model to mlir model, the parameter --output_names can be viewed via NETRON.
|
|
model_transform.py \
|
|
--model_name yolov5s \
|
|
--model_def ../yolov5s.onnx \
|
|
--input_shapes [[1,3,640,640]] \
|
|
--mean 0.0,0.0,0.0 \
|
|
--scale 0.0039216,0.0039216,0.0039216 \
|
|
--keep_aspect_ratio \
|
|
--pixel_format rgb \
|
|
--output_names output,350,498,646 \
|
|
--test_input ../image/dog.jpg \
|
|
--test_result yolov5s_top_outputs.npz \
|
|
--mlir yolov5s.mlir
|
|
|
|
# Convert mlir model to BM1684x F32 bmodel.
|
|
model_deploy.py \
|
|
--mlir yolov5s.mlir \
|
|
--quantize F32 \
|
|
--chip bm1684x \
|
|
--test_input yolov5s_in_f32.npz \
|
|
--test_reference yolov5s_top_outputs.npz \
|
|
--model yolov5s_1684x_f32.bmodel
|
|
```
|
|
The final bmodel, yolov5s_1684x_f32.bmodel, can run on BM1684x. If you want to further accelerate the model, you can convert ONNX model to INT8 bmodel. For details, please refer to [TPU-MLIR Document](https://github.com/sophgo/tpu-mlir/blob/master/README.md).
|
|
|
|
## Other Documents
|
|
- [Cpp Deployment](./cpp)
|