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PaddleDetection Model Export as ONNX Format Tutorial

PaddleDetection Model support is saved in ONNX format and the list of current test support is as follows

Model OP Version NOTE
YOLOv3 11 Only batch=1 inferring is supported. Model export needs fixed shape
PP-YOLO 11 Only batch=1 inferring is supported. A MatrixNMS will be converted to an NMS with slightly different precision; Model export needs fixed shape
PP-YOLOv2 11 Only batch=1 inferring is supported. MatrixNMS will be converted to NMS with slightly different precision; Model export needs fixed shape
PP-YOLO Tiny 11 Only batch=1 inferring is supported. Model export needs fixed shape
PP-YOLOE 11 Only batch=1 inferring is supported. Model export needs fixed shape
PP-PicoDet 11 Only batch=1 inferring is supported. Model export needs fixed shape
FCOS 11 Only batch=1 inferring is supported
PAFNet 11 -
TTFNet 11 -
SSD 11 Only batch=1 inferring is supported
PP-TinyPose 11 -
Faster RCNN 16 Only batch=1 inferring is supported, require paddle2onnx>=0.9.7
Mask RCNN 16 Only batch=1 inferring is supported, require paddle2onnx>=0.9.7
Cascade RCNN 16 Only batch=1 inferring is supported, require paddle2onnx>=0.9.7
Cascade Mask RCNN 16 Only batch=1 inferring is supported, require paddle2onnx>=0.9.7

The function of saving ONNX is provided by Paddle2ONNX. If there is feedback on related problems during conversion, Communicate with engineers in Paddle2ONNX's Github project via ISSUE.

Export Tutorial

Step 1. Export the Paddle deployment model

Export procedure reference documentTutorial on PaddleDetection deployment model export, for example:

  • Models except RCNN series, take YOLOv3 as example
cd PaddleDetection
python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml \
                             -o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams \
                             TestReader.inputs_def.image_shape=[3,608,608] \
                             --output_dir inference_model

The derived models were saved in inference_model/yolov3_darknet53_270e_coco/, with the structure as follows

yolov3_darknet
  ├── infer_cfg.yml          # Model configuration file information
  ├── model.pdiparams        # Static diagram model parameters
  ├── model.pdiparams.info   # Parameter Information is not required
  └── model.pdmodel          # Static diagram model file

checkTestReader.inputs_def.image_shape, For YOLO series models, specify this parameter when exporting; otherwise, the conversion fails

  • RCNN series models, take Faster RCNN as example

The conditional block needs to be removed in RCNN series when export ONNX model. Add export_onnx=True in command line

cd PaddleDetection
python tools/export_model.py -c configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.yml \
                             -o weights=https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_1x_coco.pdparams \
                             export_onnx=True \
                             --output_dir inference_model

The derived models were saved in inference_model/faster_rcnn_r50_fpn_1x_coco/, with the structure as follows

faster_rcnn_r50_fpn_1x_coco
  ├── infer_cfg.yml          # Model configuration file information
  ├── model.pdiparams        # Static diagram model parameters
  ├── model.pdiparams.info   # Parameter Information is not required
  └── model.pdmodel          # Static diagram model file

Step 2. Convert the deployment model to ONNX format

Install Paddle2ONNX (version 0.9.7 or higher)

pip install paddle2onnx

Use the following command to convert

# YOLOv3
paddle2onnx --model_dir inference_model/yolov3_darknet53_270e_coco \
            --model_filename model.pdmodel \
            --params_filename model.pdiparams \
            --opset_version 11 \
            --save_file yolov3.onnx

# Faster RCNN
paddle2onnx --model_dir inference_model/faster_rcnn_r50_fpn_1x_coco \
            --model_filename model.pdmodel \
            --params_filename model.pdiparams \
            --opset_version 16 \
            --save_file faster_rcnn.onnx

The transformed model is under the current pathyolov3.onnx and faster_rcnn.onnx

Step 3. Inference with onnxruntime

Install onnxruntime

pip install onnxruntime

Inference code examples are in deploy/third_engine/onnx

Use the following commands for inference：

# YOLOv3
python deploy/third_engine/onnx/infer.py
            --infer_cfg inference_model/yolov3_darknet53_270e_coco/infer_cfg.yml \
            --onnx_file yolov3.onnx \
            --image_file demo/000000014439.jpg

# Faster RCNN
python deploy/third_engine/onnx/infer.py
            --infer_cfg inference_model/faster_rcnn_r50_fpn_1x_coco/infer_cfg.yml \
            --onnx_file faster_rcnn.onnx \
            --image_file demo/000000014439.jpg