FastDeploy/examples/vision/detection/yolov5/sophgo

English | 简体中文

YOLOv5 SOPHGO Deployment Example

Supporting Model List

For YOLOv5 v6.0 model deployment, please refer to YOLOv5 and Pretrained model based on COCO.

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.
• Convert ONNX model to bmodel. Please refer to 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

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 project. For the installing process, please refer to TPU-MLIR Document.

1. Installation

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

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.

Other Documents

• Cpp Deployment