更换文档检测模型
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paddle_detection/deploy/EXPORT_ONNX_MODEL_en.md
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paddle_detection/deploy/EXPORT_ONNX_MODEL_en.md
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# PaddleDetection Model Export as ONNX Format Tutorial
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PaddleDetection Model support is saved in ONNX format and the list of current test support is as follows
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| Model | OP Version | NOTE |
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| :---- | :----- | :--- |
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| YOLOv3 | 11 | Only batch=1 inferring is supported. Model export needs fixed shape |
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| 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 |
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| PP-YOLOv2 | 11 | Only batch=1 inferring is supported. MatrixNMS will be converted to NMS with slightly different precision; Model export needs fixed shape |
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| PP-YOLO Tiny | 11 | Only batch=1 inferring is supported. Model export needs fixed shape |
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| PP-YOLOE | 11 | Only batch=1 inferring is supported. Model export needs fixed shape |
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| PP-PicoDet | 11 | Only batch=1 inferring is supported. Model export needs fixed shape |
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| FCOS | 11 |Only batch=1 inferring is supported |
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| PAFNet | 11 |- |
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| TTFNet | 11 |-|
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| SSD | 11 |Only batch=1 inferring is supported |
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| PP-TinyPose | 11 | - |
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| Faster RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7|
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| Mask RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7|
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| Cascade RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7|
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| Cascade Mask RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7|
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The function of saving ONNX is provided by [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX). If there is feedback on related problems during conversion, Communicate with engineers in Paddle2ONNX's Github project via [ISSUE](https://github.com/PaddlePaddle/Paddle2ONNX/issues).
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## Export Tutorial
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### Step 1. Export the Paddle deployment model
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Export procedure reference document[Tutorial on PaddleDetection deployment model export](./EXPORT_MODEL_en.md), for example:
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- Models except RCNN series, take YOLOv3 as example
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```
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cd PaddleDetection
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python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml \
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-o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams \
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TestReader.inputs_def.image_shape=[3,608,608] \
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--output_dir inference_model
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```
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The derived models were saved in `inference_model/yolov3_darknet53_270e_coco/`, with the structure as follows
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```
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yolov3_darknet
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├── infer_cfg.yml # Model configuration file information
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├── model.pdiparams # Static diagram model parameters
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├── model.pdiparams.info # Parameter Information is not required
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└── model.pdmodel # Static diagram model file
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```
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> check`TestReader.inputs_def.image_shape`, For YOLO series models, specify this parameter when exporting; otherwise, the conversion fails
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- RCNN series models, take Faster RCNN as example
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The conditional block needs to be removed in RCNN series when export ONNX model. Add `export_onnx=True` in command line
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```
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cd PaddleDetection
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python tools/export_model.py -c configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.yml \
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-o weights=https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_1x_coco.pdparams \
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export_onnx=True \
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--output_dir inference_model
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```
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The derived models were saved in `inference_model/faster_rcnn_r50_fpn_1x_coco/`, with the structure as follows
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```
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faster_rcnn_r50_fpn_1x_coco
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├── infer_cfg.yml # Model configuration file information
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├── model.pdiparams # Static diagram model parameters
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├── model.pdiparams.info # Parameter Information is not required
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└── model.pdmodel # Static diagram model file
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```
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### Step 2. Convert the deployment model to ONNX format
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Install Paddle2ONNX (version 0.9.7 or higher)
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```
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pip install paddle2onnx
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```
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Use the following command to convert
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```
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# YOLOv3
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paddle2onnx --model_dir inference_model/yolov3_darknet53_270e_coco \
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--model_filename model.pdmodel \
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--params_filename model.pdiparams \
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--opset_version 11 \
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--save_file yolov3.onnx
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# Faster RCNN
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paddle2onnx --model_dir inference_model/faster_rcnn_r50_fpn_1x_coco \
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--model_filename model.pdmodel \
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--params_filename model.pdiparams \
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--opset_version 16 \
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--save_file faster_rcnn.onnx
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```
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The transformed model is under the current path`yolov3.onnx` and `faster_rcnn.onnx`
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### Step 3. Inference with onnxruntime
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Install onnxruntime
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```
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pip install onnxruntime
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```
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Inference code examples are in [deploy/third_engine/onnx](./third_engine/onnx)
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Use the following commands for inference：
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```
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# YOLOv3
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python deploy/third_engine/onnx/infer.py
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--infer_cfg inference_model/yolov3_darknet53_270e_coco/infer_cfg.yml \
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--onnx_file yolov3.onnx \
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--image_file demo/000000014439.jpg
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# Faster RCNN
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python deploy/third_engine/onnx/infer.py
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--infer_cfg inference_model/faster_rcnn_r50_fpn_1x_coco/infer_cfg.yml \
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--onnx_file faster_rcnn.onnx \
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--image_file demo/000000014439.jpg
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```
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